diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 00000000..e78de87f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,28 @@ +--- +name: Bug report +about: Report a bug +title: '' +labels: bug +assignees: '' + +--- + +### Please read this first + +- **Have you read the docs?**[Agents SDK docs](https://openai.github.io/openai-agents-python/) +- **Have you searched for related issues?** Others may have faced similar issues. + +### Describe the bug +A clear and concise description of what the bug is. + +### Debug information +- Agents SDK version: (e.g. `v0.0.3`) +- Python version (e.g. Python 3.10) + +### Repro steps + +Ideally provide a minimal python script that can be run to reproduce the bug. + + +### Expected behavior +A clear and concise description of what you expected to happen. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..73586eaa --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,16 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: enhancement +assignees: '' + +--- + +### Please read this first + +- **Have you read the docs?**[Agents SDK docs](https://openai.github.io/openai-agents-python/) +- **Have you searched for related issues?** Others may have had similar requests + +### Describe the feature +What is the feature you're requesting? How would it work? Please provide examples and details if possible. diff --git a/.github/ISSUE_TEMPLATE/model_provider.md b/.github/ISSUE_TEMPLATE/model_provider.md new file mode 100644 index 00000000..b56cb24e --- /dev/null +++ b/.github/ISSUE_TEMPLATE/model_provider.md @@ -0,0 +1,26 @@ +--- +name: Custom model providers +about: Questions or bugs about using non-OpenAI models +title: '' +labels: bug +assignees: '' + +--- + +### Please read this first + +- **Have you read the custom model provider docs, including the 'Common issues' section?** [Model provider docs](https://openai.github.io/openai-agents-python/models/#using-other-llm-providers) +- **Have you searched for related issues?** Others may have faced similar issues. + +### Describe the question +A clear and concise description of what the question or bug is. + +### Debug information +- Agents SDK version: (e.g. `v0.0.3`) +- Python version (e.g. Python 3.10) + +### Repro steps +Ideally provide a minimal python script that can be run to reproduce the issue. + +### Expected behavior +A clear and concise description of what you expected to happen. diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md new file mode 100644 index 00000000..6c639d72 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/question.md @@ -0,0 +1,16 @@ +--- +name: Question +about: Questions about the SDK +title: '' +labels: question +assignees: '' + +--- + +### Please read this first + +- **Have you read the docs?**[Agents SDK docs](https://openai.github.io/openai-agents-python/) +- **Have you searched for related issues?** Others may have had similar requests + +### Question +Describe your question. Provide details if available. diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md new file mode 100644 index 00000000..0fdeab1e --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md @@ -0,0 +1,18 @@ +### Summary + + + +### Test plan + + + +### Issue number + + + +### Checks + +- [ ] I've added new tests (if relevant) +- [ ] I've added/updated the relevant documentation +- [ ] I've run `make lint` and `make format` +- [ ] I've made sure tests pass diff --git a/.github/workflows/issues.yml b/.github/workflows/issues.yml new file mode 100644 index 00000000..6447f83e --- /dev/null +++ b/.github/workflows/issues.yml @@ -0,0 +1,26 @@ +name: Close inactive issues +on: + schedule: + - cron: "30 1 * * *" + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v9 + with: + days-before-issue-stale: 7 + days-before-issue-close: 3 + stale-issue-label: "stale" + stale-issue-message: "This issue is stale because it has been open for 7 days with no activity." + close-issue-message: "This issue was closed because it has been inactive for 3 days since being marked as stale." + any-of-issue-labels: 'question,needs-more-info' + days-before-pr-stale: 10 + days-before-pr-close: 7 + stale-pr-label: "stale" + stale-pr-message: "This PR is stale because it has been open for 10 days with no activity." + close-pr-message: "This PR was closed because it has been inactive for 7 days since being marked as stale." + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 6dce5c81..edd0d898 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -5,8 +5,10 @@ on: branches: - main pull_request: - branches: - - main + # All PRs, including stacked PRs + +env: + UV_FROZEN: "1" jobs: lint: @@ -50,8 +52,8 @@ jobs: enable-cache: true - name: Install dependencies run: make sync - - name: Run tests - run: make tests + - name: Run tests with coverage + run: make coverage build-docs: runs-on: ubuntu-latest diff --git a/.gitignore b/.gitignore index 9da95ba3..2e9b9237 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ # Byte-compiled / optimized / DLL files __pycache__/ +**/__pycache__/ *.py[cod] *$py.class @@ -134,10 +135,11 @@ dmypy.json cython_debug/ # PyCharm -#.idea/ +.idea/ # Ruff stuff: .ruff_cache/ # PyPI configuration file .pypirc +.aider* diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..9b388533 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,7 @@ +{ + "python.testing.pytestArgs": [ + "tests" + ], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true +} \ No newline at end of file diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 00000000..ff37db32 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,69 @@ +Welcome to the OpenAI Agents SDK repository. This file contains the main points for new contributors. + +## Repository overview + +- **Source code**: `src/agents/` contains the implementation. +- **Tests**: `tests/` with a short guide in `tests/README.md`. +- **Examples**: under `examples/`. +- **Documentation**: markdown pages live in `docs/` with `mkdocs.yml` controlling the site. +- **Utilities**: developer commands are defined in the `Makefile`. +- **PR template**: `.github/PULL_REQUEST_TEMPLATE/pull_request_template.md` describes the information every PR must include. + +## Local workflow + +1. Format, lint and type‑check your changes: + + ```bash + make format + make lint + make mypy + ``` + +2. Run the tests: + + ```bash + make tests + ``` + + To run a single test, use `uv run pytest -s -k `. + +3. Build the documentation (optional but recommended for docs changes): + + ```bash + make build-docs + ``` + + Coverage can be generated with `make coverage`. + +## Snapshot tests + +Some tests rely on inline snapshots. See `tests/README.md` for details on updating them: + +```bash +make snapshots-fix # update existing snapshots +make snapshots-create # create new snapshots +``` + +Run `make tests` again after updating snapshots to ensure they pass. + +## Style notes + +- Write comments as full sentences and end them with a period. + +## Pull request expectations + +PRs should use the template located at `.github/PULL_REQUEST_TEMPLATE/pull_request_template.md`. Provide a summary, test plan and issue number if applicable, then check that: + +- New tests are added when needed. +- Documentation is updated. +- `make lint` and `make format` have been run. +- The full test suite passes. + +Commit messages should be concise and written in the imperative mood. Small, focused commits are preferred. + +## What reviewers look for + +- Tests covering new behaviour. +- Consistent style: code formatted with `ruff format`, imports sorted, and type hints passing `mypy`. +- Clear documentation for any public API changes. +- Clean history and a helpful PR description. diff --git a/Makefile b/Makefile index 7dd9bbdf..5c6aba42 100644 --- a/Makefile +++ b/Makefile @@ -5,6 +5,7 @@ sync: .PHONY: format format: uv run ruff format + uv run ruff check --fix .PHONY: lint lint: @@ -18,15 +19,34 @@ mypy: tests: uv run pytest +.PHONY: coverage +coverage: + + uv run coverage run -m pytest + uv run coverage xml -o coverage.xml + uv run coverage report -m --fail-under=95 + +.PHONY: snapshots-fix +snapshots-fix: + uv run pytest --inline-snapshot=fix + +.PHONY: snapshots-create +snapshots-create: + uv run pytest --inline-snapshot=create + .PHONY: old_version_tests old_version_tests: UV_PROJECT_ENVIRONMENT=.venv_39 uv run --python 3.9 -m pytest - UV_PROJECT_ENVIRONMENT=.venv_39 uv run --python 3.9 -m mypy . .PHONY: build-docs build-docs: uv run mkdocs build +.PHONY: build-full-docs +build-full-docs: + uv run docs/scripts/translate_docs.py + uv run mkdocs build + .PHONY: serve-docs serve-docs: uv run mkdocs serve @@ -34,4 +54,6 @@ serve-docs: .PHONY: deploy-docs deploy-docs: uv run mkdocs gh-deploy --force --verbose + + diff --git a/README.md b/README.md index c27e6d82..7dcd97b3 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,15 @@ # OpenAI Agents SDK -The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. +The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs. Image of the Agents Tracing UI ### Core concepts: -1. [**Agents**](docs/agents.md): LLMs configured with instructions, tools, guardrails, and handoffs -2. [**Handoffs**](docs/handoffs.md): Allow agents to transfer control to other agents for specific tasks -3. [**Guardrails**](docs/guardrails.md): Configurable safety checks for input and output validation -4. [**Tracing**](docs/tracing.md): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows +1. [**Agents**](https://openai.github.io/openai-agents-python/agents): LLMs configured with instructions, tools, guardrails, and handoffs +2. [**Handoffs**](https://openai.github.io/openai-agents-python/handoffs/): A specialized tool call used by the Agents SDK for transferring control between agents +3. [**Guardrails**](https://openai.github.io/openai-agents-python/guardrails/): Configurable safety checks for input and output validation +4. [**Tracing**](https://openai.github.io/openai-agents-python/tracing/): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows Explore the [examples](examples) directory to see the SDK in action, and read our [documentation](https://openai.github.io/openai-agents-python/) for more details. @@ -28,6 +28,8 @@ source env/bin/activate pip install openai-agents ``` +For voice support, install with the optional `voice` group: `pip install 'openai-agents[voice]'`. + ## Hello world example ```python @@ -45,9 +47,11 @@ print(result.final_output) (_If running this, ensure you set the `OPENAI_API_KEY` environment variable_) +(_For Jupyter notebook users, see [hello_world_jupyter.py](examples/basic/hello_world_jupyter.py)_) + ## Handoffs example -```py +```python from agents import Agent, Runner import asyncio @@ -114,9 +118,9 @@ When you call `Runner.run()`, we run a loop until we get a final output. 1. We call the LLM, using the model and settings on the agent, and the message history. 2. The LLM returns a response, which may include tool calls. -3. If the response has a final output (see below for the more on this), we return it and end the loop. +3. If the response has a final output (see below for more on this), we return it and end the loop. 4. If the response has a handoff, we set the agent to the new agent and go back to step 1. -5. We process the tool calls (if any) and append the tool responses messsages. Then we go to step 1. +5. We process the tool calls (if any) and append the tool responses messages. Then we go to step 1. There is a `max_turns` parameter that you can use to limit the number of times the loop executes. @@ -138,7 +142,7 @@ The Agents SDK is designed to be highly flexible, allowing you to model a wide r ## Tracing -The Agents SDK includes built-in tracing, making it easy to track and debug the behavior of your agents. Tracing is extensible by design, supporting custom spans and a wide variety of external destinations, including [Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents), [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk), and [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk). See [Tracing](http://openai.github.io/openai-agents-python/tracing.md) for more details. +The Agents SDK automatically traces your agent runs, making it easy to track and debug the behavior of your agents. Tracing is extensible by design, supporting custom spans and a wide variety of external destinations, including [Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents), [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk), [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk), [Scorecard](https://docs.scorecard.io/docs/documentation/features/tracing#openai-agents-sdk-integration), and [Keywords AI](https://docs.keywordsai.co/integration/development-frameworks/openai-agent). For more details about how to customize or disable tracing, see [Tracing](http://openai.github.io/openai-agents-python/tracing), which also includes a larger list of [external tracing processors](http://openai.github.io/openai-agents-python/tracing/#external-tracing-processors-list). ## Development (only needed if you need to edit the SDK/examples) diff --git a/docs/agents.md b/docs/agents.md index 9b6264b5..39d4afd5 100644 --- a/docs/agents.md +++ b/docs/agents.md @@ -13,6 +13,7 @@ The most common properties of an agent you'll configure are: ```python from agents import Agent, ModelSettings, function_tool +@function_tool def get_weather(city: str) -> str: return f"The weather in {city} is sunny" @@ -20,7 +21,7 @@ agent = Agent( name="Haiku agent", instructions="Always respond in haiku form", model="o3-mini", - tools=[function_tool(get_weather)], + tools=[get_weather], ) ``` @@ -31,11 +32,11 @@ Agents are generic on their `context` type. Context is a dependency-injection to ```python @dataclass class UserContext: - uid: str - is_pro_user: bool + uid: str + is_pro_user: bool - async def fetch_purchases() -> list[Purchase]: - return ... + async def fetch_purchases() -> list[Purchase]: + return ... agent = Agent[UserContext]( ..., @@ -129,3 +130,18 @@ robot_agent = pirate_agent.clone( instructions="Write like a robot", ) ``` + +## Forcing tool use + +Supplying a list of tools doesn't always mean the LLM will use a tool. You can force tool use by setting [`ModelSettings.tool_choice`][agents.model_settings.ModelSettings.tool_choice]. Valid values are: + +1. `auto`, which allows the LLM to decide whether or not to use a tool. +2. `required`, which requires the LLM to use a tool (but it can intelligently decide which tool). +3. `none`, which requires the LLM to _not_ use a tool. +4. Setting a specific string e.g. `my_tool`, which requires the LLM to use that specific tool. + +!!! note + + To prevent infinite loops, the framework automatically resets `tool_choice` to "auto" after a tool call. This behavior is configurable via [`agent.reset_tool_choice`][agents.agent.Agent.reset_tool_choice]. The infinite loop is because tool results are sent to the LLM, which then generates another tool call because of `tool_choice`, ad infinitum. + + If you want the Agent to completely stop after a tool call (rather than continuing with auto mode), you can set [`Agent.tool_use_behavior="stop_on_first_tool"`] which will directly use the tool output as the final response without further LLM processing. diff --git a/docs/assets/images/graph.png b/docs/assets/images/graph.png new file mode 100644 index 00000000..13e2d6eb Binary files /dev/null and b/docs/assets/images/graph.png differ diff --git a/docs/assets/images/mcp-tracing.jpg b/docs/assets/images/mcp-tracing.jpg new file mode 100644 index 00000000..cefeb66b Binary files /dev/null and b/docs/assets/images/mcp-tracing.jpg differ diff --git a/docs/config.md b/docs/config.md index 198d7b7e..bfaf90e8 100644 --- a/docs/config.md +++ b/docs/config.md @@ -10,14 +10,14 @@ from agents import set_default_openai_key set_default_openai_key("sk-...") ``` -Alternatively, you can also configure an OpenAI client to be used. By default, the SDK creates an `AsyncOpenAI` instance, using the API key from the environment variable or the default key set above. You can chnage this by using the [set_default_openai_client()][agents.set_default_openai_client] function. +Alternatively, you can also configure an OpenAI client to be used. By default, the SDK creates an `AsyncOpenAI` instance, using the API key from the environment variable or the default key set above. You can change this by using the [set_default_openai_client()][agents.set_default_openai_client] function. ```python from openai import AsyncOpenAI from agents import set_default_openai_client custom_client = AsyncOpenAI(base_url="...", api_key="...") -set_default_openai_client(client) +set_default_openai_client(custom_client) ``` Finally, you can also customize the OpenAI API that is used. By default, we use the OpenAI Responses API. You can override this to use the Chat Completions API by using the [set_default_openai_api()][agents.set_default_openai_api] function. @@ -63,7 +63,7 @@ Alternatively, you can customize the logs by adding handlers, filters, formatter ```python import logging -logger = logging.getLogger("openai.agents") # or openai.agents.tracing for the Tracing logger +logger = logging.getLogger("openai.agents") # or openai.agents.tracing for the Tracing logger # To make all logs show up logger.setLevel(logging.DEBUG) diff --git a/docs/context.md b/docs/context.md index 5dcacebe..4176ec51 100644 --- a/docs/context.md +++ b/docs/context.md @@ -36,18 +36,19 @@ class UserInfo: # (1)! name: str uid: int +@function_tool async def fetch_user_age(wrapper: RunContextWrapper[UserInfo]) -> str: # (2)! return f"User {wrapper.context.name} is 47 years old" async def main(): - user_info = UserInfo(name="John", uid=123) # (3)! + user_info = UserInfo(name="John", uid=123) - agent = Agent[UserInfo]( # (4)! + agent = Agent[UserInfo]( # (3)! name="Assistant", - tools=[function_tool(fetch_user_age)], + tools=[fetch_user_age], ) - result = await Runner.run( + result = await Runner.run( # (4)! starting_agent=agent, input="What is the age of the user?", context=user_info, diff --git a/docs/examples.md b/docs/examples.md new file mode 100644 index 00000000..30d60282 --- /dev/null +++ b/docs/examples.md @@ -0,0 +1,42 @@ +# Examples + +Check out a variety of sample implementations of the SDK in the examples section of the [repo](https://github.com/openai/openai-agents-python/tree/main/examples). The examples are organized into several categories that demonstrate different patterns and capabilities. + + +## Categories + +- **[agent_patterns](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns):** + Examples in this category illustrate common agent design patterns, such as + + - Deterministic workflows + - Agents as tools + - Parallel agent execution + +- **[basic](https://github.com/openai/openai-agents-python/tree/main/examples/basic):** + These examples showcase foundational capabilities of the SDK, such as + + - Dynamic system prompts + - Streaming outputs + - Lifecycle events + +- **[tool examples](https://github.com/openai/openai-agents-python/tree/main/examples/tools):** + Learn how to implement OAI hosted tools such as web search and file search, + and integrate them into your agents. + +- **[model providers](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers):** + Explore how to use non-OpenAI models with the SDK. + +- **[handoffs](https://github.com/openai/openai-agents-python/tree/main/examples/handoffs):** + See practical examples of agent handoffs. + +- **[mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp):** + Learn how to build agents with MCP. + +- **[customer_service](https://github.com/openai/openai-agents-python/tree/main/examples/customer_service)** and **[research_bot](https://github.com/openai/openai-agents-python/tree/main/examples/research_bot):** + Two more built-out examples that illustrate real-world applications + + - **customer_service**: Example customer service system for an airline. + - **research_bot**: Simple deep research clone. + +- **[voice](https://github.com/openai/openai-agents-python/tree/main/examples/voice):** + See examples of voice agents, using our TTS and STT models. diff --git a/docs/guardrails.md b/docs/guardrails.md index 2b7369c3..2f0be0f2 100644 --- a/docs/guardrails.md +++ b/docs/guardrails.md @@ -21,7 +21,7 @@ Input guardrails run in 3 steps: ## Output guardrails -Output guardrailas run in 3 steps: +Output guardrails run in 3 steps: 1. First, the guardrail receives the same input passed to the agent. 2. Next, the guardrail function runs to produce a [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput], which is then wrapped in an [`OutputGuardrailResult`][agents.guardrail.OutputGuardrailResult] @@ -29,11 +29,11 @@ Output guardrailas run in 3 steps: !!! Note - Output guardrails are intended to run on the final agent input, so an agent's guardrails only run if the agent is the *last* agent. Similar to the input guardrails, we do this because guardrails tend to be related to the actual Agent - you'd run different guardrails for different agents, so colocating the code is useful for readability. + Output guardrails are intended to run on the final agent output, so an agent's guardrails only run if the agent is the *last* agent. Similar to the input guardrails, we do this because guardrails tend to be related to the actual Agent - you'd run different guardrails for different agents, so colocating the code is useful for readability. ## Tripwires -If the input or output fails the guardrail, the Guardrail can signal this with a tripwire. As soon as we see a guardail that has triggered the tripwires, we immediately raise a `{Input,Output}GuardrailTripwireTriggered` exception and halt the Agent execution. +If the input or output fails the guardrail, the Guardrail can signal this with a tripwire. As soon as we see a guardrail that has triggered the tripwires, we immediately raise a `{Input,Output}GuardrailTripwireTriggered` exception and halt the Agent execution. ## Implementing a guardrail @@ -111,8 +111,8 @@ class MessageOutput(BaseModel): # (1)! response: str class MathOutput(BaseModel): # (2)! - is_math: bool reasoning: str + is_math: bool guardrail_agent = Agent( name="Guardrail check", diff --git a/docs/index.md b/docs/index.md index ba757c1a..8aef6574 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,12 +1,12 @@ # OpenAI Agents SDK -The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) enables you to build agentic AI apps in a lightweight, easy to use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, [Swarm](https://github.com/openai/swarm/tree/main). The Agents SDK has a very small set of primitives: +The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, [Swarm](https://github.com/openai/swarm/tree/main). The Agents SDK has a very small set of primitives: - **Agents**, which are LLMs equipped with instructions and tools - **Handoffs**, which allow agents to delegate to other agents for specific tasks - **Guardrails**, which enable the inputs to agents to be validated -In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real world applications without a steep learning curve. In addition, the SDK comes with built-in **tracing** that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application. +In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real-world applications without a steep learning curve. In addition, the SDK comes with built-in **tracing** that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application. ## Why use the Agents SDK diff --git a/docs/ja/agents.md b/docs/ja/agents.md new file mode 100644 index 00000000..828b3635 --- /dev/null +++ b/docs/ja/agents.md @@ -0,0 +1,151 @@ +--- +search: + exclude: true +--- +# エージェント + +エージェントはアプリの主要な構成ブロックです。エージェントは、大規模言語モデル ( LLM ) に instructions と tools を設定したものです。 + +## 基本設定 + +エージェントで最も一般的に設定するプロパティは次のとおりです。 + +- `instructions`: 開発者メッセージまたは system prompt とも呼ばれます。 +- `model`: 使用する LLM と、temperature や top_p などのモデル調整パラメーターを指定する任意の `model_settings`。 +- `tools`: エージェントがタスクを達成するために利用できるツール。 + +```python +from agents import Agent, ModelSettings, function_tool + +@function_tool +def get_weather(city: str) -> str: + return f"The weather in {city} is sunny" + +agent = Agent( + name="Haiku agent", + instructions="Always respond in haiku form", + model="o3-mini", + tools=[get_weather], +) +``` + +## コンテキスト + +エージェントはその `context` 型について汎用的です。コンテキストは依存性注入の手段で、`Runner.run()` に渡すオブジェクトです。これはすべてのエージェント、ツール、ハンドオフなどに渡され、エージェント実行時の依存関係や状態をまとめて保持します。任意の Python オブジェクトをコンテキストとして渡せます。 + +```python +@dataclass +class UserContext: + uid: str + is_pro_user: bool + + async def fetch_purchases() -> list[Purchase]: + return ... + +agent = Agent[UserContext]( + ..., +) +``` + +## 出力タイプ + +デフォルトでは、エージェントはプレーンテキスト ( つまり `str` ) を出力します。特定の型で出力させたい場合は `output_type` パラメーターを使用します。一般的には [Pydantic](https://docs.pydantic.dev/) オブジェクトを利用しますが、Pydantic の [TypeAdapter](https://docs.pydantic.dev/latest/api/type_adapter/) でラップ可能な型であれば何でも対応します。たとえば dataclass、list、TypedDict などです。 + +```python +from pydantic import BaseModel +from agents import Agent + + +class CalendarEvent(BaseModel): + name: str + date: str + participants: list[str] + +agent = Agent( + name="Calendar extractor", + instructions="Extract calendar events from text", + output_type=CalendarEvent, +) +``` + +!!! note + + `output_type` を渡すと、モデルは通常のプレーンテキスト応答の代わりに [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) を使用するよう指示されます。 + +## ハンドオフ + +ハンドオフは、エージェントが委譲できるサブエージェントです。ハンドオフのリストを渡しておくと、エージェントは必要に応じてそれらに処理を委譲できます。これにより、単一のタスクに特化したモジュール式エージェントを編成できる強力なパターンが実現します。詳細は [handoffs](handoffs.md) ドキュメントをご覧ください。 + +```python +from agents import Agent + +booking_agent = Agent(...) +refund_agent = Agent(...) + +triage_agent = Agent( + name="Triage agent", + instructions=( + "Help the user with their questions." + "If they ask about booking, handoff to the booking agent." + "If they ask about refunds, handoff to the refund agent." + ), + handoffs=[booking_agent, refund_agent], +) +``` + +## 動的 instructions + +通常はエージェント作成時に instructions を指定しますが、関数を介して動的に instructions を提供することもできます。その関数はエージェントとコンテキストを受け取り、プロンプトを返す必要があります。同期関数と `async` 関数の両方に対応しています。 + +```python +def dynamic_instructions( + context: RunContextWrapper[UserContext], agent: Agent[UserContext] +) -> str: + return f"The user's name is {context.context.name}. Help them with their questions." + + +agent = Agent[UserContext]( + name="Triage agent", + instructions=dynamic_instructions, +) +``` + +## ライフサイクルイベント (hooks) + +場合によっては、エージェントのライフサイクルを観察したいことがあります。たとえば、イベントをログに記録したり、特定のイベント発生時にデータを事前取得したりする場合です。`hooks` プロパティを使ってエージェントのライフサイクルにフックできます。[`AgentHooks`][agents.lifecycle.AgentHooks] クラスをサブクラス化し、関心のあるメソッドをオーバーライドしてください。 + +## ガードレール + +ガードレールを使うと、エージェントの実行と並行してユーザー入力に対するチェックやバリデーションを実行できます。たとえば、ユーザーの入力内容が関連しているかをスクリーニングできます。詳細は [guardrails](guardrails.md) ドキュメントをご覧ください。 + +## エージェントの複製 + +`clone()` メソッドを使用すると、エージェントを複製し、必要に応じて任意のプロパティを変更できます。 + +```python +pirate_agent = Agent( + name="Pirate", + instructions="Write like a pirate", + model="o3-mini", +) + +robot_agent = pirate_agent.clone( + name="Robot", + instructions="Write like a robot", +) +``` + +## ツール使用の強制 + +ツールの一覧を渡しても、LLM が必ずツールを使用するとは限りません。[`ModelSettings.tool_choice`][agents.model_settings.ModelSettings.tool_choice] を設定することでツール使用を強制できます。有効な値は次のとおりです。 + +1. `auto` — ツールを使用するかどうかを LLM が判断します。 +2. `required` — LLM にツール使用を必須化します ( ただし使用するツールは自動選択 )。 +3. `none` — LLM にツールを使用しないことを要求します。 +4. 特定の文字列 ( 例: `my_tool` ) — その特定のツールを LLM に使用させます。 + +!!! note + + 無限ループを防ぐため、フレームワークはツール呼び出し後に `tool_choice` を自動的に "auto" にリセットします。この動作は [`agent.reset_tool_choice`][agents.agent.Agent.reset_tool_choice] で設定できます。無限ループが起こる理由は、ツールの結果が LLM に送られ、`tool_choice` により再びツール呼び出しが生成される、という流れが繰り返されるからです。 + + ツール呼び出し後にエージェントを完全に停止させたい場合 ( auto モードで続行させたくない場合 ) は、[`Agent.tool_use_behavior="stop_on_first_tool"`] を設定してください。これにより、ツールの出力を LL M の追加処理なしにそのまま最終応答として返します。 \ No newline at end of file diff --git a/docs/ja/config.md b/docs/ja/config.md new file mode 100644 index 00000000..bf76b9fb --- /dev/null +++ b/docs/ja/config.md @@ -0,0 +1,98 @@ +--- +search: + exclude: true +--- +# SDK の設定 + +## API キーとクライアント + +デフォルトでは、 SDK はインポートされた時点で LLM リクエストとトレーシングに使用する `OPENAI_API_KEY` 環境変数を探します。アプリ起動前にこの環境変数を設定できない場合は、 [set_default_openai_key()][agents.set_default_openai_key] 関数を利用してキーを設定できます。 + +```python +from agents import set_default_openai_key + +set_default_openai_key("sk-...") +``` + +また、使用する OpenAI クライアントを構成することも可能です。デフォルトでは、 SDK は環境変数または上記で設定したデフォルトキーを用いて `AsyncOpenAI` インスタンスを作成します。これを変更するには、 [set_default_openai_client()][agents.set_default_openai_client] 関数を使用します。 + +```python +from openai import AsyncOpenAI +from agents import set_default_openai_client + +custom_client = AsyncOpenAI(base_url="...", api_key="...") +set_default_openai_client(custom_client) +``` + +さらに、使用する OpenAI API をカスタマイズすることもできます。既定では OpenAI Responses API を利用します。これを Chat Completions API に変更するには、 [set_default_openai_api()][agents.set_default_openai_api] 関数を使用してください。 + +```python +from agents import set_default_openai_api + +set_default_openai_api("chat_completions") +``` + +## トレーシング + +トレーシングはデフォルトで有効になっています。前述の OpenAI API キー（環境変数または設定したデフォルトキー）が自動的に使用されます。トレーシングで使用する API キーを個別に設定したい場合は、 [`set_tracing_export_api_key`][agents.set_tracing_export_api_key] 関数を利用してください。 + +```python +from agents import set_tracing_export_api_key + +set_tracing_export_api_key("sk-...") +``` + +トレーシングを完全に無効化するには、 [`set_tracing_disabled()`][agents.set_tracing_disabled] 関数を呼び出します。 + +```python +from agents import set_tracing_disabled + +set_tracing_disabled(True) +``` + +## デバッグログ + + SDK にはハンドラーが設定されていない Python ロガーが 2 つあります。デフォルトでは、警告とエラーは `stdout` に出力されますが、それ以外のログは抑制されます。 + +詳細なログを有効にするには、 [`enable_verbose_stdout_logging()`][agents.enable_verbose_stdout_logging] 関数を使用します。 + +```python +from agents import enable_verbose_stdout_logging + +enable_verbose_stdout_logging() +``` + +必要に応じて、ハンドラー、フィルター、フォーマッターなどを追加してログをカスタマイズすることも可能です。詳しくは [Python ロギングガイド](https://docs.python.org/3/howto/logging.html) を参照してください。 + +```python +import logging + +logger = logging.getLogger("openai.agents") # or openai.agents.tracing for the Tracing logger + +# To make all logs show up +logger.setLevel(logging.DEBUG) +# To make info and above show up +logger.setLevel(logging.INFO) +# To make warning and above show up +logger.setLevel(logging.WARNING) +# etc + +# You can customize this as needed, but this will output to `stderr` by default +logger.addHandler(logging.StreamHandler()) +``` + +### ログに含まれる機微情報 + +特定のログには機微情報（たとえば ユーザー データ）が含まれる場合があります。この情報が記録されるのを防ぎたい場合は、次の環境変数を設定してください。 + +LLM の入力および出力のログを無効にする: + +```bash +export OPENAI_AGENTS_DONT_LOG_MODEL_DATA=1 +``` + +ツールの入力および出力のログを無効にする: + +```bash +export OPENAI_AGENTS_DONT_LOG_TOOL_DATA=1 +``` \ No newline at end of file diff --git a/docs/ja/context.md b/docs/ja/context.md new file mode 100644 index 00000000..72c0938c --- /dev/null +++ b/docs/ja/context.md @@ -0,0 +1,81 @@ +--- +search: + exclude: true +--- +# コンテキスト管理 + +コンテキストという言葉には複数の意味があります。ここでは主に 2 つのコンテキストについて説明します。 + +1. コード内でローカルに利用できるコンテキスト: ツール関数の実行時や `on_handoff` などのコールバック、ライフサイクルフックで必要となるデータや依存関係です。 +2. LLM が参照できるコンテキスト: LLM がレスポンスを生成する際に見えるデータです。 + +## ローカルコンテキスト + +ローカルコンテキストは [`RunContextWrapper`][agents.run_context.RunContextWrapper] クラスと、その中の [`context`][agents.run_context.RunContextWrapper.context] プロパティで表現されます。仕組みは次のとおりです。 + +1. 任意の Python オブジェクトを作成します。一般的なパターンとして dataclass や Pydantic オブジェクトを使用します。 +2. そのオブジェクトを各種 run メソッド（例: `Runner.run(..., **context=whatever** )`）に渡します。 +3. すべてのツール呼び出しやライフサイクルフックには、ラッパーオブジェクト `RunContextWrapper[T]` が渡されます。ここで `T` はコンテキストオブジェクトの型で、`wrapper.context` からアクセスできます。 + +**最重要ポイント**: あるエージェントの実行において、エージェント・ツール関数・ライフサイクルフックなどはすべて同じ _型_ のコンテキストを使用しなければなりません。 + +コンテキストでは次のような用途が考えられます。 + +- 実行に関するデータ（例: ユーザー名 / uid やその他のユーザー情報） +- 依存オブジェクト（例: ロガー、データフェッチャーなど） +- ヘルパー関数 + +!!! danger "Note" + + コンテキストオブジェクトは LLM には送信されません。あくまでローカルのオブジェクトであり、読み書きやメソッド呼び出しが可能です。 + +```python +import asyncio +from dataclasses import dataclass + +from agents import Agent, RunContextWrapper, Runner, function_tool + +@dataclass +class UserInfo: # (1)! + name: str + uid: int + +@function_tool +async def fetch_user_age(wrapper: RunContextWrapper[UserInfo]) -> str: # (2)! + return f"User {wrapper.context.name} is 47 years old" + +async def main(): + user_info = UserInfo(name="John", uid=123) + + agent = Agent[UserInfo]( # (3)! + name="Assistant", + tools=[fetch_user_age], + ) + + result = await Runner.run( # (4)! + starting_agent=agent, + input="What is the age of the user?", + context=user_info, + ) + + print(result.final_output) # (5)! + # The user John is 47 years old. + +if __name__ == "__main__": + asyncio.run(main()) +``` + +1. これがコンテキストオブジェクトです。ここでは dataclass を使っていますが、任意の型を使用できます。 +2. これはツールです。`RunContextWrapper[UserInfo]` を受け取り、実装内でコンテキストを参照しています。 +3. エージェントにジェネリック `UserInfo` を付与することで、型チェッカーが誤りを検出できます（たとえば別のコンテキスト型を受け取るツールを渡した場合など）。 +4. `run` 関数にコンテキストを渡します。 +5. エージェントはツールを正しく呼び出し、年齢を取得します。 + +## エージェント / LLM コンテキスト + +LLM が呼び出されるとき、LLM が参照できるデータは会話履歴に含まれるものだけです。したがって、新しいデータを LLM に渡したい場合は、そのデータを履歴に含める形で提供する必要があります。方法はいくつかあります。 + +1. Agent の `instructions` に追加する。いわゆる「system prompt」や「developer message」と呼ばれるものです。システムプロンプトは静的な文字列でも、コンテキストを受け取って文字列を返す動的な関数でも構いません。ユーザー名や現在の日付など、常に有用な情報を渡す際によく使われます。 +2. `Runner.run` 呼び出し時の `input` に追加する。`instructions` と似ていますが、[chain of command](https://cdn.openai.com/spec/model-spec-2024-05-08.html#follow-the-chain-of-command) の下位レイヤーにメッセージを配置できます。 +3. 関数ツール経由で公開する。オンデマンドで取得するコンテキストに適しており、LLM が必要に応じてツールを呼び出してデータを取得します。 +4. retrieval や web search を使う。これらは特別なツールで、ファイルやデータベースから関連データを取得する（retrieval）、もしくは Web から取得する（web search）ことができます。レスポンスを関連コンテキストで「グラウンディング」するのに有効です。 \ No newline at end of file diff --git a/docs/ja/examples.md b/docs/ja/examples.md new file mode 100644 index 00000000..00f634ec --- /dev/null +++ b/docs/ja/examples.md @@ -0,0 +1,45 @@ +--- +search: + exclude: true +--- +# コード例 + +リポジトリの [examples セクション](https://github.com/openai/openai-agents-python/tree/main/examples) には、 SDK のさまざまなサンプル実装が用意されています。これらの例は、異なるパターンや機能を示す複数のカテゴリーに整理されています。 + + +## カテゴリー + +- **[agent_patterns](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns):** + このカテゴリーの例では、一般的なエージェント設計パターンを紹介しています。 + + - 決定論的ワークフロー + - ツールとしてのエージェント + - エージェントの並列実行 + +- **[basic](https://github.com/openai/openai-agents-python/tree/main/examples/basic):** + SDK の基礎的な機能を示す例です。 + + - 動的なシステムプロンプト + - ストリーミング出力 + - ライフサイクルイベント + +- **[tool examples](https://github.com/openai/openai-agents-python/tree/main/examples/tools):** + Web 検索やファイル検索など、 OpenAI がホストするツールの実装方法と、それらをエージェントに統合する方法を学べます。 + +- **[model providers](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers):** + OpenAI 以外のモデルを SDK で利用する方法を探ります。 + +- **[handoffs](https://github.com/openai/openai-agents-python/tree/main/examples/handoffs):** + エージェントのハンドオフを実践的に示す例です。 + +- **[mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp):** + MCP を使ったエージェントの構築方法を学べます。 + +- **[customer_service](https://github.com/openai/openai-agents-python/tree/main/examples/customer_service)** と **[research_bot](https://github.com/openai/openai-agents-python/tree/main/examples/research_bot):** + より実践的なユースケースを示す、拡張された 2 つの例です。 + + - **customer_service**: 航空会社向けカスタマーサービスシステムの例 + - **research_bot**: シンプルなディープリサーチクローン + +- **[voice](https://github.com/openai/openai-agents-python/tree/main/examples/voice):** + TTS と STT モデルを用いた音声エージェントの例をご覧ください。 \ No newline at end of file diff --git a/docs/ja/guardrails.md b/docs/ja/guardrails.md new file mode 100644 index 00000000..e7b02a6e --- /dev/null +++ b/docs/ja/guardrails.md @@ -0,0 +1,158 @@ +--- +search: + exclude: true +--- +# ガードレール + +ガードレールは エージェント と _並列_ に実行され、 ユーザー入力 のチェックとバリデーションを行います。たとえば、顧客からのリクエストを支援するために非常に賢い (そのため遅く / 高価な) モデルを使うエージェントがあるとします。悪意のある ユーザー がモデルに数学の宿題を手伝わせようとするのは避けたいですよね。その場合、 高速 / 低コスト のモデルでガードレールを実行できます。ガードレールが悪意のある利用を検知した場合、即座にエラーを送出して高価なモデルの実行を停止し、時間と費用を節約できます。 + +ガードレールには 2 種類あります。 + +1. Input ガードレールは最初の ユーザー入力 に対して実行されます +2. Output ガードレールは最終的なエージェント出力に対して実行されます + +## Input ガードレール + +Input ガードレールは 3 つのステップで実行されます。 + +1. まず、ガードレールはエージェントに渡されたものと同じ入力を受け取ります。 +2. 次に、ガードレール関数が実行され [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を生成し、それが [`InputGuardrailResult`][agents.guardrail.InputGuardrailResult] でラップされます。 +3. 最後に [`.tripwire_triggered`][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] が true かどうかを確認します。true の場合、[`InputGuardrailTripwireTriggered`][agents.exceptions.InputGuardrailTripwireTriggered] 例外が送出されるので、 ユーザー への適切な応答や例外処理を行えます。 + +!!! Note + + Input ガードレールは ユーザー入力 に対して実行されることを想定しているため、エージェントのガードレールが実行されるのはそのエージェントが *最初* のエージェントである場合だけです。「なぜ `guardrails` プロパティがエージェントにあり、 `Runner.run` に渡さないのか？」と思うかもしれません。ガードレールは実際の エージェント に密接に関連する場合が多く、エージェントごとに異なるガードレールを実行するため、コードを同じ場所に置くことで可読性が向上するからです。 + +## Output ガードレール + +Output ガードレールは 3 つのステップで実行されます。 + +1. まず、ガードレールはエージェントに渡されたものと同じ入力を受け取ります。 +2. 次に、ガードレール関数が実行され [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を生成し、それが [`OutputGuardrailResult`][agents.guardrail.OutputGuardrailResult] でラップされます。 +3. 最後に [`.tripwire_triggered`][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] が true かどうかを確認します。true の場合、[`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered] 例外が送出されるので、 ユーザー への適切な応答や例外処理を行えます。 + +!!! Note + + Output ガードレールは最終的なエージェント出力に対して実行されることを想定しているため、エージェントのガードレールが実行されるのはそのエージェントが *最後* のエージェントである場合だけです。Input ガードレール同様、ガードレールは実際の エージェント に密接に関連するため、コードを同じ場所に置くことで可読性が向上します。 + +## トリップワイヤ + +入力または出力がガードレールに失敗した場合、ガードレールはトリップワイヤを用いてそれを通知できます。ガードレールがトリップワイヤを発火したことを検知すると、ただちに `{Input,Output}GuardrailTripwireTriggered` 例外を送出してエージェントの実行を停止します。 + +## ガードレールの実装 + +入力を受け取り、[`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を返す関数を用意する必要があります。次の例では、内部で エージェント を実行してこれを行います。 + +```python +from pydantic import BaseModel +from agents import ( + Agent, + GuardrailFunctionOutput, + InputGuardrailTripwireTriggered, + RunContextWrapper, + Runner, + TResponseInputItem, + input_guardrail, +) + +class MathHomeworkOutput(BaseModel): + is_math_homework: bool + reasoning: str + +guardrail_agent = Agent( # (1)! + name="Guardrail check", + instructions="Check if the user is asking you to do their math homework.", + output_type=MathHomeworkOutput, +) + + +@input_guardrail +async def math_guardrail( # (2)! + ctx: RunContextWrapper[None], agent: Agent, input: str | list[TResponseInputItem] +) -> GuardrailFunctionOutput: + result = await Runner.run(guardrail_agent, input, context=ctx.context) + + return GuardrailFunctionOutput( + output_info=result.final_output, # (3)! + tripwire_triggered=result.final_output.is_math_homework, + ) + + +agent = Agent( # (4)! + name="Customer support agent", + instructions="You are a customer support agent. You help customers with their questions.", + input_guardrails=[math_guardrail], +) + +async def main(): + # This should trip the guardrail + try: + await Runner.run(agent, "Hello, can you help me solve for x: 2x + 3 = 11?") + print("Guardrail didn't trip - this is unexpected") + + except InputGuardrailTripwireTriggered: + print("Math homework guardrail tripped") +``` + +1. この エージェント をガードレール関数内で使用します。 +2. これはエージェントの入力 / コンテキストを受け取り、結果を返すガードレール関数です。 +3. ガードレール結果に追加情報を含めることができます。 +4. これはワークフローを定義する実際のエージェントです。 + +Output ガードレールも同様です。 + +```python +from pydantic import BaseModel +from agents import ( + Agent, + GuardrailFunctionOutput, + OutputGuardrailTripwireTriggered, + RunContextWrapper, + Runner, + output_guardrail, +) +class MessageOutput(BaseModel): # (1)! + response: str + +class MathOutput(BaseModel): # (2)! + reasoning: str + is_math: bool + +guardrail_agent = Agent( + name="Guardrail check", + instructions="Check if the output includes any math.", + output_type=MathOutput, +) + +@output_guardrail +async def math_guardrail( # (3)! + ctx: RunContextWrapper, agent: Agent, output: MessageOutput +) -> GuardrailFunctionOutput: + result = await Runner.run(guardrail_agent, output.response, context=ctx.context) + + return GuardrailFunctionOutput( + output_info=result.final_output, + tripwire_triggered=result.final_output.is_math, + ) + +agent = Agent( # (4)! + name="Customer support agent", + instructions="You are a customer support agent. You help customers with their questions.", + output_guardrails=[math_guardrail], + output_type=MessageOutput, +) + +async def main(): + # This should trip the guardrail + try: + await Runner.run(agent, "Hello, can you help me solve for x: 2x + 3 = 11?") + print("Guardrail didn't trip - this is unexpected") + + except OutputGuardrailTripwireTriggered: + print("Math output guardrail tripped") +``` + +1. これは実際のエージェントの出力型です。 +2. これはガードレールの出力型です。 +3. これはエージェントの出力を受け取り、結果を返すガードレール関数です。 +4. これはワークフローを定義する実際のエージェントです。 \ No newline at end of file diff --git a/docs/ja/handoffs.md b/docs/ja/handoffs.md new file mode 100644 index 00000000..c0e99556 --- /dev/null +++ b/docs/ja/handoffs.md @@ -0,0 +1,117 @@ +--- +search: + exclude: true +--- +# ハンドオフ + +ハンドオフを使用すると、エージェント がタスクを別の エージェント に委譲できます。これは、複数の エージェント がそれぞれ異なる分野を専門とするシナリオで特に便利です。たとえばカスタマーサポートアプリでは、注文状況、返金、 FAQ などのタスクを個別に担当する エージェント を用意できます。 + +ハンドオフは LLM からはツールとして認識されます。そのため、`Refund Agent` という エージェント へのハンドオフであれば、ツール名は `transfer_to_refund_agent` になります。 + +## ハンドオフの作成 + +すべての エージェント には [`handoffs`][agents.agent.Agent.handoffs] パラメーターがあり、直接 `Agent` を渡すことも、ハンドオフをカスタマイズする `Handoff` オブジェクトを渡すこともできます。 + +Agents SDK が提供する [`handoff()`][agents.handoffs.handoff] 関数を使ってハンドオフを作成できます。この関数では、引き継ぎ先の エージェント を指定し、オーバーライドや入力フィルターをオプションで設定できます。 + +### 基本的な使い方 + +シンプルなハンドオフを作成する例を示します。 + +```python +from agents import Agent, handoff + +billing_agent = Agent(name="Billing agent") +refund_agent = Agent(name="Refund agent") + +# (1)! +triage_agent = Agent(name="Triage agent", handoffs=[billing_agent, handoff(refund_agent)]) +``` + +1. `billing_agent` のように エージェント を直接指定することも、`handoff()` 関数を使用することもできます。 + +### `handoff()` 関数によるハンドオフのカスタマイズ + +[`handoff()`][agents.handoffs.handoff] 関数を使うと、ハンドオフを細かくカスタマイズできます。 + +- `agent`: ここで指定した エージェント に処理が引き渡されます。 +- `tool_name_override`: デフォルトでは `Handoff.default_tool_name()` が使用され、`transfer_to_` という名前になります。これを上書きできます。 +- `tool_description_override`: `Handoff.default_tool_description()` が返すデフォルトのツール説明を上書きします。 +- `on_handoff`: ハンドオフ実行時に呼び出されるコールバック関数です。ハンドオフが呼ばれたタイミングでデータ取得を開始するなどに便利です。この関数は エージェント のコンテキストを受け取り、オプションで LLM が生成した入力も受け取れます。渡されるデータは `input_type` パラメーターで制御します。 +- `input_type`: ハンドオフが受け取る入力の型（任意）。 +- `input_filter`: 次の エージェント が受け取る入力をフィルタリングできます。詳細は後述します。 + +```python +from agents import Agent, handoff, RunContextWrapper + +def on_handoff(ctx: RunContextWrapper[None]): + print("Handoff called") + +agent = Agent(name="My agent") + +handoff_obj = handoff( + agent=agent, + on_handoff=on_handoff, + tool_name_override="custom_handoff_tool", + tool_description_override="Custom description", +) +``` + +## ハンドオフ入力 + +場合によっては、 LLM がハンドオフを呼び出す際に追加のデータを渡してほしいことがあります。たとえば「Escalation エージェント」へのハンドオフでは、ログ用に理由を渡してもらいたいかもしれません。 + +```python +from pydantic import BaseModel + +from agents import Agent, handoff, RunContextWrapper + +class EscalationData(BaseModel): + reason: str + +async def on_handoff(ctx: RunContextWrapper[None], input_data: EscalationData): + print(f"Escalation agent called with reason: {input_data.reason}") + +agent = Agent(name="Escalation agent") + +handoff_obj = handoff( + agent=agent, + on_handoff=on_handoff, + input_type=EscalationData, +) +``` + +## 入力フィルター + +ハンドオフが発生すると、新しい エージェント が会話を引き継ぎ、これまでの会話履歴全体を閲覧できる状態になります。これを変更したい場合は [`input_filter`][agents.handoffs.Handoff.input_filter] を設定してください。入力フィルターは、[`HandoffInputData`][agents.handoffs.HandoffInputData] として渡される既存の入力を受け取り、新しい `HandoffInputData` を返す関数です。 + +よくあるパターン（たとえば履歴からすべてのツール呼び出しを削除するなど）は [`agents.extensions.handoff_filters`][] に実装済みです。 + +```python +from agents import Agent, handoff +from agents.extensions import handoff_filters + +agent = Agent(name="FAQ agent") + +handoff_obj = handoff( + agent=agent, + input_filter=handoff_filters.remove_all_tools, # (1)! +) +``` + +1. これにより `FAQ agent` が呼ばれた際に、履歴からすべてのツール呼び出しが自動で削除されます。 + +## 推奨プロンプト + +LLM がハンドオフを正しく理解できるよう、エージェント にハンドオフに関する情報を含めることを推奨します。事前に用意したプレフィックス [`agents.extensions.handoff_prompt.RECOMMENDED_PROMPT_PREFIX`][] を利用するか、[`agents.extensions.handoff_prompt.prompt_with_handoff_instructions`][] を呼び出してプロンプトに推奨情報を自動で追加できます。 + +```python +from agents import Agent +from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX + +billing_agent = Agent( + name="Billing agent", + instructions=f"""{RECOMMENDED_PROMPT_PREFIX} + .""", +) +``` \ No newline at end of file diff --git a/docs/ja/index.md b/docs/ja/index.md new file mode 100644 index 00000000..39692a16 --- /dev/null +++ b/docs/ja/index.md @@ -0,0 +1,56 @@ +--- +search: + exclude: true +--- +# OpenAI Agents SDK + +[OpenAI Agents SDK](https://github.com/openai/openai-agents-python) は、抽象化をほとんど排した軽量で使いやすいパッケージにより、エージェントベースの AI アプリを構築できるようにします。これは、以前のエージェント向け実験プロジェクトである [Swarm](https://github.com/openai/swarm/tree/main) をプロダクションレベルへとアップグレードしたものです。Agents SDK にはごく少数の基本コンポーネントがあります。 + +- **エージェント**: instructions と tools を備えた LLM +- **ハンドオフ**: エージェントが特定タスクを他のエージェントへ委任するしくみ +- **ガードレール**: エージェントへの入力を検証する機能 + +Python と組み合わせることで、これらのコンポーネントはツールとエージェント間の複雑な関係を表現でき、学習コストを抑えつつ実際のアプリケーションを構築できます。さらに SDK には、エージェントフローを可視化・デバッグできる **トレーシング** が標準搭載されており、評価やファインチューニングにも活用可能です。 + +## Agents SDK を使用する理由 + +SDK には 2 つの設計原則があります。 + +1. 使う価値のある十分な機能を備えつつ、学習が早いようコンポーネント数を絞る。 +2. すぐに使い始められる初期設定で動作しつつ、挙動を細かくカスタマイズできる。 + +主な機能は次のとおりです。 + +- エージェントループ: ツール呼び出し、結果を LLM に送信、LLM が完了するまでのループを自動で処理。 +- Python ファースト: 新しい抽象化を学ばずに、言語標準機能でエージェントをオーケストレーション。 +- ハンドオフ: 複数エージェント間の協調と委譲を実現する強力な機能。 +- ガードレール: エージェントと並列で入力バリデーションを実行し、失敗時に早期終了。 +- 関数ツール: 任意の Python 関数をツール化し、自動スキーマ生成と Pydantic での検証を提供。 +- トレーシング: フローの可視化・デバッグ・モニタリングに加え、OpenAI の評価・ファインチューニング・蒸留ツールを利用可能。 + +## インストール + +```bash +pip install openai-agents +``` + +## Hello World の例 + +```python +from agents import Agent, Runner + +agent = Agent(name="Assistant", instructions="You are a helpful assistant") + +result = Runner.run_sync(agent, "Write a haiku about recursion in programming.") +print(result.final_output) + +# Code within the code, +# Functions calling themselves, +# Infinite loop's dance. +``` + +(_これを実行する場合は、`OPENAI_API_KEY` 環境変数を設定してください_) + +```bash +export OPENAI_API_KEY=sk-... +``` \ No newline at end of file diff --git a/docs/ja/mcp.md b/docs/ja/mcp.md new file mode 100644 index 00000000..7cdaa57e --- /dev/null +++ b/docs/ja/mcp.md @@ -0,0 +1,64 @@ +--- +search: + exclude: true +--- +# Model context protocol (MCP) + +[Model context protocol](https://modelcontextprotocol.io/introduction)（通称 MCP）は、 LLM にツールとコンテキストを提供するための仕組みです。MCP のドキュメントでは次のように説明されています。 + +> MCP は、アプリケーションが LLM にコンテキストを提供する方法を標準化するオープンプロトコルです。MCP は AI アプリケーションにとっての USB‑C ポートのようなものと考えてください。USB‑C が各種デバイスを周辺機器と接続するための標準化された方法を提供するのと同様に、MCP は AI モデルをさまざまなデータソースやツールと接続するための標準化された方法を提供します。 + +Agents SDK は MCP をサポートしており、これにより幅広い MCP サーバーをエージェントにツールとして追加できます。 + +## MCP サーバー + +現在、MCP 仕様では使用するトランスポート方式に基づき 2 種類のサーバーが定義されています。 + +1. **stdio** サーバー: アプリケーションのサブプロセスとして実行されます。ローカルで動かすイメージです。 +2. **HTTP over SSE** サーバー: リモートで動作し、 URL 経由で接続します。 + +これらのサーバーへは [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] と [`MCPServerSse`][agents.mcp.server.MCPServerSse] クラスを使用して接続できます。 + +たとえば、[公式 MCP filesystem サーバー](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem)を利用する場合は次のようになります。 + +```python +async with MCPServerStdio( + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + } +) as server: + tools = await server.list_tools() +``` + +## MCP サーバーの利用 + +MCP サーバーはエージェントに追加できます。Agents SDK はエージェント実行時に毎回 MCP サーバーへ `list_tools()` を呼び出し、 LLM に MCP サーバーのツールを認識させます。LLM が MCP サーバーのツールを呼び出すと、SDK はそのサーバーへ `call_tool()` を実行します。 + +```python + +agent=Agent( + name="Assistant", + instructions="Use the tools to achieve the task", + mcp_servers=[mcp_server_1, mcp_server_2] +) +``` + +## キャッシュ + +エージェントが実行されるたびに、MCP サーバーへ `list_tools()` が呼び出されます。サーバーがリモートの場合は特にレイテンシが発生します。ツール一覧を自動でキャッシュしたい場合は、[`MCPServerStdio`][agents.mcp.server.MCPServerStdio] と [`MCPServerSse`][agents.mcp.server.MCPServerSse] の両方に `cache_tools_list=True` を渡してください。ツール一覧が変更されないと確信できる場合のみ使用してください。 + +キャッシュを無効化したい場合は、サーバーで `invalidate_tools_cache()` を呼び出します。 + +## エンドツーエンドのコード例 + +完全な動作例は [examples/mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp) をご覧ください。 + +## トレーシング + +[トレーシング](./tracing.md) は MCP の操作を自動的にキャプチャします。具体的には次の内容が含まれます。 + +1. ツール一覧取得のための MCP サーバー呼び出し +2. 関数呼び出しに関する MCP 情報 + +![MCP Tracing Screenshot](../assets/images/mcp-tracing.jpg) \ No newline at end of file diff --git a/docs/ja/models.md b/docs/ja/models.md new file mode 100644 index 00000000..5a76d60e --- /dev/null +++ b/docs/ja/models.md @@ -0,0 +1,106 @@ +# モデル + +Agents SDK には、OpenAI モデルの 2 種類のサポートが標準で用意されています。 + +- **推奨**: [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] は、新しい [Responses API](https://platform.openai.com/docs/api-reference/responses) を使って OpenAI API を呼び出します。 +- [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] は、[Chat Completions API](https://platform.openai.com/docs/api-reference/chat) を使って OpenAI API を呼び出します。 + +## モデルの組み合わせ + +1 つのワークフロー内で、各エージェントごとに異なるモデルを使いたい場合があります。たとえば、トリアージには小型で高速なモデルを使い、複雑なタスクにはより大きく高性能なモデルを使うことができます。[`Agent`][agents.Agent] を設定する際、以下のいずれかの方法で特定のモデルを選択できます。 + +1. OpenAI モデル名を直接渡す。 +2. 任意のモデル名と、その名前を Model インスタンスにマッピングできる [`ModelProvider`][agents.models.interface.ModelProvider] を渡す。 +3. [`Model`][agents.models.interface.Model] 実装を直接指定する。 + +!!!note + + SDK は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] と [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] の両方の形状をサポートしていますが、各ワークフローで 1 つのモデル形状のみを使うことを推奨します。なぜなら、2 つの形状はサポートする機能やツールが異なるためです。ワークフローでモデル形状を組み合わせて使う場合は、利用するすべての機能が両方で利用可能かご確認ください。 + +```python +from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel +import asyncio + +spanish_agent = Agent( + name="Spanish agent", + instructions="You only speak Spanish.", + model="o3-mini", # (1)! +) + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model=OpenAIChatCompletionsModel( # (2)! + model="gpt-4o", + openai_client=AsyncOpenAI() + ), +) + +triage_agent = Agent( + name="Triage agent", + instructions="Handoff to the appropriate agent based on the language of the request.", + handoffs=[spanish_agent, english_agent], + model="gpt-3.5-turbo", +) + +async def main(): + result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") + print(result.final_output) +``` + +1. OpenAI モデル名を直接設定します。 +2. [`Model`][agents.models.interface.Model] 実装を指定します。 + +エージェントで使用するモデルをさらに細かく設定したい場合は、[`ModelSettings`][agents.models.interface.ModelSettings] を渡すことができます。これにより、temperature などのオプションのモデル設定パラメーターを指定できます。 + +```python +from agents import Agent, ModelSettings + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4o", + model_settings=ModelSettings(temperature=0.1), +) +``` + +## 他の LLM プロバイダーの利用 + +他の LLM プロバイダーは、3 つの方法で利用できます（[こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/) に code examples があります）。 + +1. [`set_default_openai_client`][agents.set_default_openai_client] は、`AsyncOpenAI` のインスタンスを LLM クライアントとしてグローバルに利用したい場合に便利です。これは、LLM プロバイダーが OpenAI 互換の API エンドポイントを持ち、`base_url` と `api_key` を設定できる場合に使います。[examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py) に設定例があります。 +2. [`ModelProvider`][agents.models.interface.ModelProvider] は `Runner.run` レベルで利用します。これにより、「この実行のすべてのエージェントでカスタムモデルプロバイダーを使う」と指定できます。[examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py) に設定例があります。 +3. [`Agent.model`][agents.agent.Agent.model] で、特定のエージェントインスタンスにモデルを指定できます。これにより、エージェントごとに異なるプロバイダーを組み合わせて使うことができます。[examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py) に設定例があります。 + +`platform.openai.com` の API キーがない場合は、`set_tracing_disabled()` でトレーシングを無効にするか、[別のトレーシングプロセッサー](tracing.md) を設定することを推奨します。 + +!!! note + + これらの code examples では Chat Completions API/モデルを使っています。なぜなら、ほとんどの LLM プロバイダーはまだ Responses API をサポートしていないためです。もし LLM プロバイダーが Responses API をサポートしている場合は、Responses の利用を推奨します。 + +## 他の LLM プロバイダー利用時のよくある問題 + +### Tracing クライアントの 401 エラー + +トレーシングに関連するエラーが発生した場合、これはトレースが OpenAI サーバーにアップロードされるため、OpenAI API キーがないことが原因です。解決方法は 3 つあります。 + +1. トレーシングを完全に無効化する: [`set_tracing_disabled(True)`][agents.set_tracing_disabled]。 +2. トレーシング用の OpenAI キーを設定する: [`set_tracing_export_api_key(...)`][agents.set_tracing_export_api_key]。この API キーはトレースのアップロードのみに使われ、[platform.openai.com](https://platform.openai.com/) のものが必要です。 +3. OpenAI 以外のトレースプロセッサーを使う。[トレーシングのドキュメント](tracing.md#custom-tracing-processors) をご覧ください。 + +### Responses API サポート + +SDK はデフォルトで Responses API を使いますが、ほとんどの他の LLM プロバイダーはまだ対応していません。そのため、404 エラーなどが発生する場合があります。解決方法は 2 つあります。 + +1. [`set_default_openai_api("chat_completions")`][agents.set_default_openai_api] を呼び出します。これは、環境変数で `OPENAI_API_KEY` と `OPENAI_BASE_URL` を設定している場合に有効です。 +2. [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] を使います。[こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/) に code examples があります。 + +### structured outputs サポート + +一部のモデルプロバイダーは [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) をサポートしていません。その場合、次のようなエラーが発生することがあります。 + +``` +BadRequestError: Error code: 400 - {'error': {'message': "'response_format.type' : value is not one of the allowed values ['text','json_object']", 'type': 'invalid_request_error'}} +``` + +これは一部のモデルプロバイダーの制限で、JSON 出力には対応していても、出力に使う `json_schema` を指定できない場合があります。現在この問題の修正に取り組んでいますが、JSON schema 出力をサポートしているプロバイダーの利用を推奨します。そうでない場合、不正な JSON によりアプリが頻繁に動作しなくなる可能性があります。 \ No newline at end of file diff --git a/docs/ja/models/index.md b/docs/ja/models/index.md new file mode 100644 index 00000000..a40ae38f --- /dev/null +++ b/docs/ja/models/index.md @@ -0,0 +1,116 @@ +--- +search: + exclude: true +--- +# モデル + +Agents SDK には、標準で 2 種類の OpenAI モデルサポートが含まれています。 + +- **推奨**: [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] — 新しい [Responses API](https://platform.openai.com/docs/api-reference/responses) を利用して OpenAI API を呼び出します。 +- [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] — [Chat Completions API](https://platform.openai.com/docs/api-reference/chat) を利用して OpenAI API を呼び出します。 + +## モデルの組み合わせ + +1 つのワークフロー内で、エージェントごとに異なるモデルを使用したい場合があります。たとえば、振り分けには小さく高速なモデルを、複雑なタスクには大きく高性能なモデルを使う、といった使い分けです。[`Agent`][agents.Agent] を設定する際は、以下のいずれかで特定のモデルを指定できます。 + +1. OpenAI モデル名を直接渡す +2. 任意のモデル名と、それを `Model` インスタンスへマッピングできる [`ModelProvider`][agents.models.interface.ModelProvider] を渡す +3. [`Model`][agents.models.interface.Model] 実装を直接渡す + +!!!note + SDK は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] と [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] の両方の形に対応していますが、ワークフローごとに 1 つのモデル形を使用することを推奨します。2 つの形ではサポートする機能・ツールが異なるためです。どうしても混在させる場合は、利用するすべての機能が両方で利用可能であることを確認してください。 + +```python +from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel +import asyncio + +spanish_agent = Agent( + name="Spanish agent", + instructions="You only speak Spanish.", + model="o3-mini", # (1)! +) + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model=OpenAIChatCompletionsModel( # (2)! + model="gpt-4o", + openai_client=AsyncOpenAI() + ), +) + +triage_agent = Agent( + name="Triage agent", + instructions="Handoff to the appropriate agent based on the language of the request.", + handoffs=[spanish_agent, english_agent], + model="gpt-3.5-turbo", +) + +async def main(): + result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") + print(result.final_output) +``` + +1. OpenAI モデル名を直接指定 +2. [`Model`][agents.models.interface.Model] 実装を提供 + +エージェントで使用するモデルをさらに細かく設定したい場合は、`temperature` などのオプションを指定できる [`ModelSettings`][agents.models.interface.ModelSettings] を渡します。 + +```python +from agents import Agent, ModelSettings + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4o", + model_settings=ModelSettings(temperature=0.1), +) +``` + +## 他の LLM プロバイダーの利用 + +他の LLM プロバイダーは 3 通りの方法で利用できます（コード例は [こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/)）。 + +1. [`set_default_openai_client`][agents.set_default_openai_client] + OpenAI 互換の API エンドポイントを持つ場合に、`AsyncOpenAI` インスタンスをグローバルに LLM クライアントとして設定できます。`base_url` と `api_key` を設定するケースです。設定例は [examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py)。 + +2. [`ModelProvider`][agents.models.interface.ModelProvider] + `Runner.run` レベルで「この実行中のすべてのエージェントにカスタムモデルプロバイダーを使う」と宣言できます。設定例は [examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py)。 + +3. [`Agent.model`][agents.agent.Agent.model] + 特定の Agent インスタンスにモデルを指定できます。エージェントごとに異なるプロバイダーを組み合わせられます。設定例は [examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py)。多くのモデルを簡単に使う方法として [LiteLLM 連携](./litellm.md) があります。 + +`platform.openai.com` の API キーを持たない場合は、`set_tracing_disabled()` でトレーシングを無効化するか、[別のトレーシングプロセッサー](../tracing.md) を設定することを推奨します。 + +!!! note + これらの例では Chat Completions API/モデルを使用しています。多くの LLM プロバイダーがまだ Responses API をサポートしていないためです。もしプロバイダーが Responses API をサポートしている場合は、Responses の使用を推奨します。 + +## 他の LLM プロバイダーでよくある問題 + +### Tracing クライアントの 401 エラー + +トレースは OpenAI サーバーへアップロードされるため、OpenAI API キーがない場合にエラーになります。解決策は次の 3 つです。 + +1. トレーシングを完全に無効化する: [`set_tracing_disabled(True)`][agents.set_tracing_disabled] +2. トレーシング用の OpenAI キーを設定する: [`set_tracing_export_api_key(...)`][agents.set_tracing_export_api_key] + このキーはトレースのアップロードにのみ使用され、[platform.openai.com](https://platform.openai.com/) のものが必要です。 +3. OpenAI 以外のトレースプロセッサーを使う。詳しくは [tracing ドキュメント](../tracing.md#custom-tracing-processors) を参照してください。 + +### Responses API サポート + +SDK は既定で Responses API を使用しますが、多くの LLM プロバイダーはまだ対応していません。そのため 404 などのエラーが発生する場合があります。対処方法は 2 つです。 + +1. [`set_default_openai_api("chat_completions")`][agents.set_default_openai_api] を呼び出す + 環境変数 `OPENAI_API_KEY` と `OPENAI_BASE_URL` を設定している場合に機能します。 +2. [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] を使用する + コード例は [こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/) にあります。 + +### structured outputs のサポート + +一部のモデルプロバイダーは [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) をサポートしていません。その場合、次のようなエラーが発生することがあります。 + +``` +BadRequestError: Error code: 400 - {'error': {'message': "'response_format.type' : value is not one of the allowed values ['text','json_object']", 'type': 'invalid_request_error'}} +``` + +これは一部プロバイダーの制限で、JSON 出力はサポートしていても `json_schema` を指定できません。現在修正に取り組んでいますが、JSON スキーマ出力をサポートしているプロバイダーを利用することを推奨します。そうでない場合、不正な JSON によりアプリが頻繁に壊れる可能性があります。 \ No newline at end of file diff --git a/docs/ja/models/litellm.md b/docs/ja/models/litellm.md new file mode 100644 index 00000000..651d7a51 --- /dev/null +++ b/docs/ja/models/litellm.md @@ -0,0 +1,77 @@ +--- +search: + exclude: true +--- +# LiteLLM 経由でのモデル利用 + +!!! note + + LiteLLM との統合は現在ベータ版です。特に小規模なモデルプロバイダーでは問題が発生する可能性があります。問題を見つけた場合は、[GitHub Issues](https://github.com/openai/openai-agents-python/issues) からご報告ください。迅速に対応いたします。 + +[LiteLLM](https://docs.litellm.ai/docs/) は、1 つのインターフェースで 100 以上のモデルを利用できるライブラリです。Agents SDK では LiteLLM との統合により、任意の AI モデルを使用できます。 + +## セットアップ + +`litellm` がインストールされていることを確認してください。オプションの `litellm` 依存関係グループをインストールすることで対応できます。 + +```bash +pip install "openai-agents[litellm]" +``` + +インストール後、任意のエージェントで [`LitellmModel`][agents.extensions.models.litellm_model.LitellmModel] を利用できます。 + +## 例 + +以下は動作する完全なサンプルです。実行するとモデル名と API キーの入力を求められます。例えば次のように入力できます。 + +- `openai/gpt-4.1` をモデル名に、OpenAI API キーを入力 +- `anthropic/claude-3-5-sonnet-20240620` をモデル名に、Anthropic API キーを入力 +- その他 + +LiteLLM でサポートされているモデルの全リストは、[litellm providers docs](https://docs.litellm.ai/docs/providers) を参照してください。 + +```python +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents.extensions.models.litellm_model import LitellmModel + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(model: str, api_key: str): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=LitellmModel(model=model, api_key=api_key), + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + # First try to get model/api key from args + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=False) + parser.add_argument("--api-key", type=str, required=False) + args = parser.parse_args() + + model = args.model + if not model: + model = input("Enter a model name for Litellm: ") + + api_key = args.api_key + if not api_key: + api_key = input("Enter an API key for Litellm: ") + + asyncio.run(main(model, api_key)) +``` \ No newline at end of file diff --git a/docs/ja/multi_agent.md b/docs/ja/multi_agent.md new file mode 100644 index 00000000..a179fed3 --- /dev/null +++ b/docs/ja/multi_agent.md @@ -0,0 +1,41 @@ +--- +search: + exclude: true +--- +# 複数エージェントのオーケストレーション + +オーケストレーションとは、アプリ内でエージェントがどのように流れるかを指します。どのエージェントが、どの順序で実行され、その後どう決定するかを制御します。エージェントをオーケストレーションする主な方法は次の 2 つです。 + +1. LLM に判断させる: LLM の知能を活用し、計画・推論を行い、その結果に基づいて次のステップを決定します。 +2. コードでオーケストレーションする: コード側でエージェントの流れを定義します。 + +これらのパターンは組み合わせて使用できます。それぞれにトレードオフがあり、以下で説明します。 + +## LLM によるオーケストレーション + +エージェントとは、 instructions、ツール、ハンドオフを備えた LLM です。オープンエンドなタスクが与えられた場合、 LLM はタスクをどのように進めるかを自律的に計画し、ツールを使ってアクションやデータ取得を行い、ハンドオフでサブエージェントへタスクを委譲できます。たとえば、リサーチエージェントには次のようなツールを装備できます。 + +- Web 検索でオンライン情報を取得する +- ファイル検索で独自データや接続を調べる +- コンピュータ操作でコンピュータ上のアクションを実行する +- コード実行でデータ分析を行う +- 計画立案やレポート作成などに長けた専門エージェントへのハンドオフ + +このパターンはタスクがオープンエンドで、 LLM の知能に頼りたい場合に最適です。重要な戦術は次のとおりです。 + +1. 良いプロンプトに投資する。利用可能なツール、使い方、守るべきパラメーターを明確に示します。 +2. アプリを監視し、改善を繰り返す。問題が起きた箇所を特定し、プロンプトを改善します。 +3. エージェントに内省と改善を許可する。たとえばループで実行し自己批評させたり、エラーメッセージを渡して修正させたりします。 +4. 何でもこなす汎用エージェントより、特定タスクに特化したエージェントを用意します。 +5. [evals](https://platform.openai.com/docs/guides/evals) に投資する。これによりエージェントを訓練し、タスク性能を向上できます。 + +## コードによるオーケストレーション + +LLM によるオーケストレーションは強力ですが、コードでオーケストレーションすると速度・コスト・性能の面でより決定的かつ予測可能になります。よく使われるパターンは次のとおりです。 + +- [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) を使って、コード側で検査できる 適切な形式のデータ を生成する。たとえばエージェントにタスクをいくつかのカテゴリーに分類させ、そのカテゴリーに応じて次のエージェントを選択します。 +- あるエージェントの出力を次のエージェントの入力に変換して複数エージェントをチェーンする。ブログ記事執筆を「リサーチ → アウトライン作成 → 記事執筆 → 批評 → 改善」という一連のステップに分解できます。 +- タスクを実行するエージェントを `while` ループで回し、評価とフィードバックを行うエージェントと組み合わせ、評価者が基準を満たしたと判断するまで繰り返します。 +- `asyncio.gather` など Python の基本コンポーネントを用いて複数エージェントを並列実行する。互いに依存しない複数タスクがある場合に高速化できます。 + +[`examples/agent_patterns`](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns) には多数のコード例があります。 \ No newline at end of file diff --git a/tests/docs/quickstart.md b/docs/ja/quickstart.md similarity index 59% rename from tests/docs/quickstart.md rename to docs/ja/quickstart.md index 19051f49..30c3219d 100644 --- a/tests/docs/quickstart.md +++ b/docs/ja/quickstart.md @@ -1,8 +1,12 @@ -# Quickstart +--- +search: + exclude: true +--- +# クイックスタート -## Create a project and virtual environment +## プロジェクトと仮想環境の作成 -You'll only need to do this once. +これは一度だけ行えば十分です。 ```bash mkdir my_project @@ -10,31 +14,31 @@ cd my_project python -m venv .venv ``` -### Activate the virtual environment +### 仮想環境の有効化 -Do this every time you start a new terminal session. +新しいターミナルセッションを開始するたびに実行してください。 ```bash source .venv/bin/activate ``` -### Install the Agents SDK +### Agents SDK のインストール ```bash pip install openai-agents # or `uv add openai-agents`, etc ``` -### Set an OpenAI API key +### OpenAI API キーの設定 -If you don't have one, follow [these instructions](https://platform.openai.com/docs/quickstart#create-and-export-an-api-key) to create an OpenAI API key. +まだお持ちでない場合は、[こちらの手順](https://platform.openai.com/docs/quickstart#create-and-export-an-api-key)に従って OpenAI API キーを作成してください。 ```bash export OPENAI_API_KEY=sk-... ``` -## Create your first agent +## 最初のエージェントの作成 -Agents are defined with instructions, a name, and optional config (such as `model_config`) +エージェントは instructions 、名前、`model_config` などのオプション設定で定義します。 ```python from agents import Agent @@ -45,9 +49,9 @@ agent = Agent( ) ``` -## Add a few more agents +## さらにエージェントを追加 -Additional agents can be defined in the same way. `handoff_descriptions` provide additional context for determining handoff routing +追加のエージェントも同様の方法で定義できます。`handoff_descriptions` はハンドオフのルーティングを判断するための追加コンテキストを提供します。 ```python from agents import Agent @@ -65,9 +69,9 @@ math_tutor_agent = Agent( ) ``` -## Define your handoffs +## ハンドオフの定義 -On each agent, you can define an inventory of outgoing handoff options that the agent can choose from to decide how to make progress on their task. +各エージェントに対して、タスクを進める際に選択できるハンドオフ先の一覧を定義できます。 ```python triage_agent = Agent( @@ -77,9 +81,9 @@ triage_agent = Agent( ) ``` -## Run the agent orchestration +## エージェントオーケストレーションの実行 -Let's check that the workflow runs and the triage agent correctly routes between the two specialist agents. +ワークフローが実行され、トリアージエージェントが 2 つの専門エージェント間で正しくルーティングすることを確認しましょう。 ```python from agents import Runner @@ -89,9 +93,9 @@ async def main(): print(result.final_output) ``` -## Add a guardrail +## ガードレールの追加 -You can define custom guardrails to run on the input or output. +入力または出力に対して実行されるカスタムガードレールを定義できます。 ```python from agents import GuardrailFunctionOutput, Agent, Runner @@ -116,12 +120,12 @@ async def homework_guardrail(ctx, agent, input_data): ) ``` -## Put it all together +## すべてをまとめる -Let's put it all together and run the entire workflow, using handoffs and the input guardrail. +ハンドオフと入力ガードレールを組み合わせて、ワークフロー全体を実行してみましょう。 ```python -from agents import Agent, InputGuardrail,GuardrailFunctionOutput, Runner +from agents import Agent, InputGuardrail, GuardrailFunctionOutput, Runner from pydantic import BaseModel import asyncio @@ -166,6 +170,9 @@ triage_agent = Agent( ) async def main(): + result = await Runner.run(triage_agent, "who was the first president of the united states?") + print(result.final_output) + result = await Runner.run(triage_agent, "what is life") print(result.final_output) @@ -173,14 +180,14 @@ if __name__ == "__main__": asyncio.run(main()) ``` -## View your traces +## トレースの表示 -To review what happened during your agent run, navigate to the [Trace viewer in the OpenAI Dashboard](https://platform.openai.com/traces) to view traces of your agent runs. +エージェントの実行内容を確認するには、[OpenAI ダッシュボードの Trace viewer](https://platform.openai.com/traces) に移動してトレースを閲覧してください。 -## Next steps +## 次のステップ -Learn how to build more complex agentic flows: +より複雑なエージェントフローの構築方法を学びましょう。 -- Learn about how to configure [Agents](agents.md). -- Learn about [running agents](running_agents.md). -- Learn about [tools](tools.md), [guardrails](guardrails.md) and [models](models.md). +- [エージェント](agents.md) の設定方法を学ぶ。 +- [エージェントの実行](running_agents.md) について学ぶ。 +- [ツール](tools.md)、[ガードレール](guardrails.md)、[モデル](models/index.md) について学ぶ。 \ No newline at end of file diff --git a/docs/ja/results.md b/docs/ja/results.md new file mode 100644 index 00000000..cedd9240 --- /dev/null +++ b/docs/ja/results.md @@ -0,0 +1,56 @@ +--- +search: + exclude: true +--- +# 結果 + +`Runner.run` メソッドを呼び出すと、以下のいずれかが返されます。 + +- `run` または `run_sync` を呼び出した場合は [`RunResult`][agents.result.RunResult] +- `run_streamed` を呼び出した場合は [`RunResultStreaming`][agents.result.RunResultStreaming] + +これらはどちらも [`RunResultBase`][agents.result.RunResultBase] を継承しており、ほとんどの有用な情報はここに格納されています。 + +## 最終出力 + +[`final_output`][agents.result.RunResultBase.final_output] プロパティには、最後に実行されたエージェントの最終出力が格納されます。内容は以下のいずれかです。 + +- `output_type` が定義されていない場合は `str` +- `output_type` が定義されている場合は `last_agent.output_type` 型のオブジェクト + +!!! note + + `final_output` の型は `Any` です。ハンドオフが発生する可能性があるため、静的に型付けできません。ハンドオフが発生すると、どのエージェントでも最後になり得るため、可能性のある出力型を静的に特定できないのです。 + +## 次のターンへの入力 + +[`result.to_input_list()`][agents.result.RunResultBase.to_input_list] を使用すると、エージェント実行中に生成されたアイテムを元の入力に連結した入力リストへ変換できます。これにより、あるエージェント実行の出力を別の実行へ渡したり、ループで実行して毎回新しいユーザー入力を追加したりすることが容易になります。 + +## 最後のエージェント + +[`last_agent`][agents.result.RunResultBase.last_agent] プロパティには、最後に実行されたエージェントが格納されています。アプリケーションによっては、次回ユーザーが入力する際にこれが役立つことがよくあります。例えば、フロントラインのトリアージ エージェントが言語専用のエージェントにハンドオフする場合、最後のエージェントを保存しておき、ユーザーが次にメッセージを送ったときに再利用できます。 + +## 新しいアイテム + +[`new_items`][agents.result.RunResultBase.new_items] プロパティには、実行中に生成された新しいアイテムが含まれます。これらのアイテムは [`RunItem`][agents.items.RunItem] です。RunItem は、 LLM が生成した raw アイテムをラップします。 + +- [`MessageOutputItem`][agents.items.MessageOutputItem] — LLM からのメッセージを示します。 raw アイテムは生成されたメッセージです。 +- [`HandoffCallItem`][agents.items.HandoffCallItem] — LLM がハンドオフ ツールを呼び出したことを示します。 raw アイテムは LLM からのツール呼び出しアイテムです。 +- [`HandoffOutputItem`][agents.items.HandoffOutputItem] — ハンドオフが発生したことを示します。 raw アイテムはハンドオフ ツール呼び出しに対するツール応答です。また、アイテムから送信元 / 送信先エージェントにもアクセスできます。 +- [`ToolCallItem`][agents.items.ToolCallItem] — LLM がツールを呼び出したことを示します。 +- [`ToolCallOutputItem`][agents.items.ToolCallOutputItem] — ツールが呼び出されたことを示します。 raw アイテムはツール応答です。また、アイテムからツール出力にもアクセスできます。 +- [`ReasoningItem`][agents.items.ReasoningItem] — LLM からの推論アイテムを示します。 raw アイテムは生成された推論内容です。 + +## その他の情報 + +### ガードレール結果 + +[`input_guardrail_results`][agents.result.RunResultBase.input_guardrail_results] と [`output_guardrail_results`][agents.result.RunResultBase.output_guardrail_results] プロパティには、ガードレールの結果が存在する場合に格納されます。ガードレール結果には、ログや保存を行いたい有用な情報が含まれることがあるため、これらを参照できるようにしています。 + +### raw レスポンス + +[`raw_responses`][agents.result.RunResultBase.raw_responses] プロパティには、 LLM が生成した [`ModelResponse`][agents.items.ModelResponse] が格納されます。 + +### 元の入力 + +[`input`][agents.result.RunResultBase.input] プロパティには、`run` メソッドに渡した元の入力が格納されます。ほとんどの場合は必要ありませんが、必要に応じて参照できるように用意されています。 \ No newline at end of file diff --git a/docs/ja/running_agents.md b/docs/ja/running_agents.md new file mode 100644 index 00000000..83d5bec6 --- /dev/null +++ b/docs/ja/running_agents.md @@ -0,0 +1,99 @@ +--- +search: + exclude: true +--- +# エージェントの実行 + +`Runner` クラス [`Runner`][agents.run.Runner] を使用して エージェント を実行できます。方法は 3 つあります。 + +1. 非同期で実行し、[`RunResult`][agents.result.RunResult] を返す [`Runner.run()`][agents.run.Runner.run] +2. 同期メソッドで、内部的には `.run()` を呼び出す [`Runner.run_sync()`][agents.run.Runner.run_sync] +3. 非同期で実行し、[`RunResultStreaming`][agents.result.RunResultStreaming] を返す [`Runner.run_streamed()`][agents.run.Runner.run_streamed] + LLM をストリーミングモードで呼び出し、受信したイベントを逐次 ストリーミング します。 + +```python +from agents import Agent, Runner + +async def main(): + agent = Agent(name="Assistant", instructions="You are a helpful assistant") + + result = await Runner.run(agent, "Write a haiku about recursion in programming.") + print(result.final_output) + # Code within the code, + # Functions calling themselves, + # Infinite loop's dance. +``` + +詳細は [結果ガイド](results.md) を参照してください。 + +## エージェントループ + +`Runner` の run メソッドを使用する際は、開始 エージェント と入力を渡します。入力は文字列（ユーザー メッセージと見なされます）または入力項目のリスト（OpenAI Responses API の項目）です。 + +Runner は以下のループを実行します。 + +1. 現在の エージェント と現在の入力で LLM を呼び出します。 +2. LLM が出力を生成します。 + 1. `final_output` が返された場合、ループを終了して結果を返します。 + 2. ハンドオフ が発生した場合、現在の エージェント と入力を更新し、ループを再実行します。 + 3. ツール呼び出し がある場合、それらを実行し、結果を追加してループを再実行します。 +3. 指定した `max_turns` を超えた場合、[`MaxTurnsExceeded`][agents.exceptions.MaxTurnsExceeded] 例外を送出します。 + +!!! note + LLM の出力が「最終出力」と見なされる条件は、望ましい型のテキスト出力であり、ツール呼び出しがないことです。 + +## ストリーミング + +ストリーミング を使用すると、LLM の実行中に ストリーミング イベントを受け取れます。ストリーム完了後、[`RunResultStreaming`][agents.result.RunResultStreaming] には実行に関する完全な情報（新しく生成されたすべての出力を含む）が格納されます。`.stream_events()` を呼び出して ストリーミング イベントを取得できます。詳しくは [ストリーミングガイド](streaming.md) をご覧ください。 + +## Run 設定 + +`run_config` パラメーターにより、エージェント実行のグローバル設定を行えます。 + +- [`model`][agents.run.RunConfig.model]: 各 Agent の `model` 設定に関わらず使用するグローバル LLM モデルを指定します。 +- [`model_provider`][agents.run.RunConfig.model_provider]: モデル名を解決する モデルプロバイダー。デフォルトは OpenAI です。 +- [`model_settings`][agents.run.RunConfig.model_settings]: エージェント固有設定を上書きします。例としてグローバル `temperature` や `top_p` の設定など。 +- [`input_guardrails`][agents.run.RunConfig.input_guardrails], [`output_guardrails`][agents.run.RunConfig.output_guardrails]: すべての実行に適用する入力 / 出力 ガードレール のリスト。 +- [`handoff_input_filter`][agents.run.RunConfig.handoff_input_filter]: ハンドオフ に入力フィルターが設定されていない場合に適用されるグローバル入力フィルター。新しい エージェント へ送信される入力を編集できます。詳細は [`Handoff.input_filter`][agents.handoffs.Handoff.input_filter] を参照してください。 +- [`tracing_disabled`][agents.run.RunConfig.tracing_disabled]: 実行全体の [トレーシング](tracing.md) を無効化します。 +- [`trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data]: トレースに LLM やツール呼び出しの入出力など、機微なデータを含めるかどうかを設定します。 +- [`workflow_name`][agents.run.RunConfig.workflow_name], [`trace_id`][agents.run.RunConfig.trace_id], [`group_id`][agents.run.RunConfig.group_id]: 実行のトレーシング ワークフロー名、トレース ID、トレース グループ ID を設定します。少なくとも `workflow_name` の設定を推奨します。`group_id` を設定すると、複数の実行にまたがるトレースをリンクできます。 +- [`trace_metadata`][agents.run.RunConfig.trace_metadata]: すべてのトレースに付与するメタデータ。 + +## 会話 / チャットスレッド + +いずれかの run メソッドを呼び出すと、1 つ以上の エージェント が実行され（つまり 1 つ以上の LLM 呼び出しが行われ）、チャット会話の 1 つの論理ターンを表します。例: + +1. ユーザーターン: ユーザー がテキストを入力 +2. Runner 実行: 最初の エージェント が LLM を呼び出し、ツールを実行し、2 番目の エージェント へハンドオフ。2 番目の エージェント がさらにツールを実行し、最終出力を生成。 + +エージェント実行の終了時に、ユーザー に何を表示するかは自由です。たとえば、エージェント が生成したすべての新しい項目を表示する、または最終出力のみを表示する等です。いずれの場合でも、ユーザー がフォローアップ質問をしたら、再度 run メソッドを呼び出せます。 + +次ターンの入力は、基底クラス [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] を使用して取得できます。 + +```python +async def main(): + agent = Agent(name="Assistant", instructions="Reply very concisely.") + + with trace(workflow_name="Conversation", group_id=thread_id): + # First turn + result = await Runner.run(agent, "What city is the Golden Gate Bridge in?") + print(result.final_output) + # San Francisco + + # Second turn + new_input = result.to_input_list() + [{"role": "user", "content": "What state is it in?"}] + result = await Runner.run(agent, new_input) + print(result.final_output) + # California +``` + +## 例外 + +特定の状況で SDK は例外を送出します。完全な一覧は [`agents.exceptions`][] にあります。概要は以下のとおりです。 + +- [`AgentsException`][agents.exceptions.AgentsException]: SDK が送出するすべての例外の基底クラス +- [`MaxTurnsExceeded`][agents.exceptions.MaxTurnsExceeded]: 実行が `max_turns` を超えた場合に送出 +- [`ModelBehaviorError`][agents.exceptions.ModelBehaviorError]: モデルが不正な出力（例: JSON 形式違反、存在しないツールの呼び出しなど）を生成した場合に送出 +- [`UserError`][agents.exceptions.UserError]: SDK の使用方法に誤りがある場合に送出 +- [`InputGuardrailTripwireTriggered`][agents.exceptions.InputGuardrailTripwireTriggered], [`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered]: [ガードレール](guardrails.md) が発火した場合に送出 \ No newline at end of file diff --git a/tests/docs/streaming.md b/docs/ja/streaming.md similarity index 51% rename from tests/docs/streaming.md rename to docs/ja/streaming.md index b2c7c095..a8a46cca 100644 --- a/tests/docs/streaming.md +++ b/docs/ja/streaming.md @@ -1,14 +1,18 @@ -# Streaming +--- +search: + exclude: true +--- +# ストリーミング -Streaming lets you subscribe to updates of the agent run as it proceeds. This can be useful for showing the end-user progress updates and partial responses. +ストリーミングを使用すると、 エージェント の実行が進行するにつれて発生する更新を購読できます。これにより、エンド ユーザーに進捗状況や部分的な応答を表示するのに役立ちます。 -To stream, you can call [`Runner.run_streamed()`][agents.run.Runner.run_streamed], which will give you a [`RunResultStreaming`][agents.result.RunResultStreaming]. Calling `result.stream_events()` gives you an async stream of [`StreamEvent`][agents.stream_events.StreamEvent] objects, which are described below. +ストリーミングを行うには、 [`Runner.run_streamed()`][agents.run.Runner.run_streamed] を呼び出します。これにより [`RunResultStreaming`][agents.result.RunResultStreaming] が返されます。続いて `result.stream_events()` を呼び出すと、後述する [`StreamEvent`][agents.stream_events.StreamEvent] オブジェクトの非同期ストリームを取得できます。 -## Raw response events +## raw response イベント -[`RawResponsesStreamEvent`][agents.stream_events.RawResponsesStreamEvent] are raw events passed directly from the LLM. They are in OpenAI Responses API format, which means each event has a type (like `response.created`, `response.output_text.delta`, etc) and data. These events are useful if you want to stream response messages to the user as soon as they are generated. +[`RawResponsesStreamEvent`][agents.stream_events.RawResponsesStreamEvent] は、 LLM から直接渡される raw なイベントです。これらは OpenAI Responses API 形式であり、各イベントには `response.created` や `response.output_text.delta` などの type とデータが含まれます。生成されたメッセージを即座にユーザーへストリーミングしたい場合に便利です。 -For example, this will output the text generated by the LLM token-by-token. +たとえば、以下のコードは LLM が生成したテキストをトークンごとに出力します。 ```python import asyncio @@ -31,11 +35,11 @@ if __name__ == "__main__": asyncio.run(main()) ``` -## Run item events and agent events +## Run item イベントと エージェント イベント -[`RunItemStreamEvent`][agents.stream_events.RunItemStreamEvent]s are higher level events. They inform you when an item has been fully generated. This allows you to push progress updates at the level of "message generated", "tool ran", etc, instead of each token. Similarly, [`AgentUpdatedStreamEvent`][agents.stream_events.AgentUpdatedStreamEvent] gives you updates when the current agent changes (e.g. as the result of a handoff). +[`RunItemStreamEvent`][agents.stream_events.RunItemStreamEvent] は、より高レベルなイベントです。アイテムが完全に生成されたタイミングを通知するため、トークン単位ではなく「メッセージが生成された」「ツールが実行された」といったレベルで進捗をプッシュできます。同様に、 [`AgentUpdatedStreamEvent`][agents.stream_events.AgentUpdatedStreamEvent] はハンドオフなどで現在の エージェント が変わった際に更新を提供します。 -For example, this will ignore raw events and stream updates to the user. +たとえば、以下のコードは raw イベントを無視し、ユーザーへ更新のみをストリーミングします。 ```python import asyncio @@ -84,4 +88,4 @@ async def main(): if __name__ == "__main__": asyncio.run(main()) -``` +``` \ No newline at end of file diff --git a/docs/ja/tools.md b/docs/ja/tools.md new file mode 100644 index 00000000..7ab15e47 --- /dev/null +++ b/docs/ja/tools.md @@ -0,0 +1,295 @@ +--- +search: + exclude: true +--- +# ツール + +ツールはエージェントがアクションを実行できるようにします。たとえばデータの取得、コードの実行、外部 API の呼び出し、さらにはコンピュータ操作などです。Agents SDK には次の 3 種類のツールがあります。 + +- ホストツール: これらは LLM サーバー上で AI モデルと一緒に実行されます。OpenAI は retrieval、Web 検索、コンピュータ操作をホストツールとして提供しています。 +- 関数呼び出し: 任意の Python 関数をツールとして利用できます。 +- ツールとしてのエージェント: ハンドオフせずに、エージェントから他のエージェントを呼び出すことができます。 + +## ホストツール + +OpenAI は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] を使用する際に、いくつかの組み込みツールを提供しています。 + +- [`WebSearchTool`][agents.tool.WebSearchTool] はエージェントに Web 検索を行わせます。 +- [`FileSearchTool`][agents.tool.FileSearchTool] は OpenAI ベクトルストアから情報を取得します。 +- [`ComputerTool`][agents.tool.ComputerTool] はコンピュータ操作タスクを自動化します。 + +```python +from agents import Agent, FileSearchTool, Runner, WebSearchTool + +agent = Agent( + name="Assistant", + tools=[ + WebSearchTool(), + FileSearchTool( + max_num_results=3, + vector_store_ids=["VECTOR_STORE_ID"], + ), + ], +) + +async def main(): + result = await Runner.run(agent, "Which coffee shop should I go to, taking into account my preferences and the weather today in SF?") + print(result.final_output) +``` + +## 関数ツール + +任意の Python 関数をツールとして使用できます。Agents SDK が自動的に設定を行います。 + +- ツールの名前は Python 関数の名前になります（任意で名前を指定することも可能です） +- ツールの説明は関数の docstring から取得されます（任意で説明を指定することも可能です） +- 関数の引数から自動的に入力スキーマを生成します +- 各入力の説明は、無効化しない限り docstring から取得されます + +Python の `inspect` モジュールを使用して関数シグネチャを抽出し、[`griffe`](https://mkdocstrings.github.io/griffe/) で docstring を解析し、`pydantic` でスキーマを作成します。 + +```python +import json + +from typing_extensions import TypedDict, Any + +from agents import Agent, FunctionTool, RunContextWrapper, function_tool + + +class Location(TypedDict): + lat: float + long: float + +@function_tool # (1)! +async def fetch_weather(location: Location) -> str: + # (2)! + """Fetch the weather for a given location. + + Args: + location: The location to fetch the weather for. + """ + # In real life, we'd fetch the weather from a weather API + return "sunny" + + +@function_tool(name_override="fetch_data") # (3)! +def read_file(ctx: RunContextWrapper[Any], path: str, directory: str | None = None) -> str: + """Read the contents of a file. + + Args: + path: The path to the file to read. + directory: The directory to read the file from. + """ + # In real life, we'd read the file from the file system + return "" + + +agent = Agent( + name="Assistant", + tools=[fetch_weather, read_file], # (4)! +) + +for tool in agent.tools: + if isinstance(tool, FunctionTool): + print(tool.name) + print(tool.description) + print(json.dumps(tool.params_json_schema, indent=2)) + print() + +``` + +1. 関数の引数には任意の Python 型を使用でき、同期・非同期どちらの関数も利用できます。 +2. docstring が存在する場合、ツールと引数の説明を取得します。 +3. 関数はオプションで `context` を受け取れます（最初の引数である必要があります）。ツール名、説明、docstring のスタイルなどを上書き設定することも可能です。 +4. デコレートされた関数をツールのリストに渡してください。 + +??? note "展開して出力を確認" + + ``` + fetch_weather + Fetch the weather for a given location. + { + "$defs": { + "Location": { + "properties": { + "lat": { + "title": "Lat", + "type": "number" + }, + "long": { + "title": "Long", + "type": "number" + } + }, + "required": [ + "lat", + "long" + ], + "title": "Location", + "type": "object" + } + }, + "properties": { + "location": { + "$ref": "#/$defs/Location", + "description": "The location to fetch the weather for." + } + }, + "required": [ + "location" + ], + "title": "fetch_weather_args", + "type": "object" + } + + fetch_data + Read the contents of a file. + { + "properties": { + "path": { + "description": "The path to the file to read.", + "title": "Path", + "type": "string" + }, + "directory": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The directory to read the file from.", + "title": "Directory" + } + }, + "required": [ + "path" + ], + "title": "fetch_data_args", + "type": "object" + } + ``` + +### カスタム関数ツール + +Python 関数をそのままツールにしたくない場合は、[`FunctionTool`][agents.tool.FunctionTool] を直接作成できます。次を指定する必要があります。 + +- `name` +- `description` +- `params_json_schema`（引数の JSON スキーマ） +- `on_invoke_tool`（context と引数の JSON 文字列を受け取り、ツールの出力を文字列で返す async 関数） + +```python +from typing import Any + +from pydantic import BaseModel + +from agents import RunContextWrapper, FunctionTool + + + +def do_some_work(data: str) -> str: + return "done" + + +class FunctionArgs(BaseModel): + username: str + age: int + + +async def run_function(ctx: RunContextWrapper[Any], args: str) -> str: + parsed = FunctionArgs.model_validate_json(args) + return do_some_work(data=f"{parsed.username} is {parsed.age} years old") + + +tool = FunctionTool( + name="process_user", + description="Processes extracted user data", + params_json_schema=FunctionArgs.model_json_schema(), + on_invoke_tool=run_function, +) +``` + +### 引数と docstring の自動解析 + +前述のとおり、関数シグネチャを自動解析してツールのスキーマを生成し、docstring を解析してツールおよび個別引数の説明を抽出します。主な注意点は次のとおりです。 + +1. シグネチャ解析は `inspect` モジュールで行います。型アノテーションを用いて引数の型を認識し、Pydantic モデルを動的に構築して全体のスキーマを表現します。Python の基本型、Pydantic モデル、TypedDict などほとんどの型をサポートします。 +2. `griffe` を使用して docstring を解析します。対応する docstring 形式は `google`、`sphinx`、`numpy` です。形式は自動検出を試みますが、`function_tool` 呼び出し時に明示的に指定することもできます。`use_docstring_info` を `False` に設定すると docstring 解析を無効化できます。 + +スキーマ抽出のコードは [`agents.function_schema`][] にあります。 + +## ツールとしてのエージェント + +一部のワークフローでは、ハンドオフせずに中央のエージェントが複数の専門エージェントをオーケストレーションしたい場合があります。そのような場合、エージェントをツールとしてモデル化できます。 + +```python +from agents import Agent, Runner +import asyncio + +spanish_agent = Agent( + name="Spanish agent", + instructions="You translate the user's message to Spanish", +) + +french_agent = Agent( + name="French agent", + instructions="You translate the user's message to French", +) + +orchestrator_agent = Agent( + name="orchestrator_agent", + instructions=( + "You are a translation agent. You use the tools given to you to translate." + "If asked for multiple translations, you call the relevant tools." + ), + tools=[ + spanish_agent.as_tool( + tool_name="translate_to_spanish", + tool_description="Translate the user's message to Spanish", + ), + french_agent.as_tool( + tool_name="translate_to_french", + tool_description="Translate the user's message to French", + ), + ], +) + +async def main(): + result = await Runner.run(orchestrator_agent, input="Say 'Hello, how are you?' in Spanish.") + print(result.final_output) +``` + +### ツールエージェントのカスタマイズ + +`agent.as_tool` 関数はエージェントを簡単にツール化するためのヘルパーです。ただし、すべての設定に対応しているわけではありません（例: `max_turns` は設定不可）。高度なユースケースでは、ツール実装内で `Runner.run` を直接使用してください。 + +```python +@function_tool +async def run_my_agent() -> str: + """A tool that runs the agent with custom configs". + + agent = Agent(name="My agent", instructions="...") + + result = await Runner.run( + agent, + input="...", + max_turns=5, + run_config=... + ) + + return str(result.final_output) +``` + +## 関数ツールでのエラー処理 + +`@function_tool` で関数ツールを作成する際、`failure_error_function` を渡せます。これはツール呼び出しが失敗した場合に LLM へ返すエラーレスポンスを生成する関数です。 + +- 何も指定しない場合、`default_tool_error_function` が実行され、LLM にエラー発生を伝えます。 +- 独自のエラー関数を渡した場合はそちらが実行され、そのレスポンスが LLM へ送信されます。 +- 明示的に `None` を渡すと、ツール呼び出し時のエラーは再送出されます。モデルが無効な JSON を生成した場合は `ModelBehaviorError`、コードがクラッシュした場合は `UserError` などになります。 + +`FunctionTool` オブジェクトを手動で作成する場合は、`on_invoke_tool` 関数内でエラーを処理する必要があります。 \ No newline at end of file diff --git a/docs/ja/tracing.md b/docs/ja/tracing.md new file mode 100644 index 00000000..0e0d0e77 --- /dev/null +++ b/docs/ja/tracing.md @@ -0,0 +1,122 @@ +--- +search: + exclude: true +--- +# トレーシング + +Agents SDK にはビルトインのトレーシング機能があり、エージェントの実行中に発生するイベント―― LLM 生成、ツール呼び出し、ハンドオフ、ガードレール、さらにカスタムイベントまで――を網羅的に記録します。開発時と本番環境の両方で [Traces dashboard](https://platform.openai.com/traces) を使用すると、ワークフローをデバッグ・可視化・モニタリングできます。 + +!!!note + + トレーシングはデフォルトで有効です。無効化する方法は次の 2 つです: + + 1. 環境変数 `OPENAI_AGENTS_DISABLE_TRACING=1` を設定してグローバルに無効化する + 2. 単一の実行に対しては [`agents.run.RunConfig.tracing_disabled`][] を `True` に設定する + +***OpenAI の API を Zero Data Retention (ZDR) ポリシーで利用している組織では、トレーシングを利用できません。*** + +## トレースとスパン + +- **トレース** は 1 度のワークフロー全体を表します。複数のスパンで構成され、次のプロパティを持ちます: + - `workflow_name`: 論理的なワークフローまたはアプリ名。例: 「Code generation」や「Customer service」 + - `trace_id`: トレースを一意に識別する ID。指定しない場合は自動生成されます。形式は `trace_<32_alphanumeric>` である必要があります。 + - `group_id`: オプションのグループ ID。会話内の複数トレースを関連付けます。たとえばチャットスレッド ID など。 + - `disabled`: `True` の場合、このトレースは記録されません。 + - `metadata`: トレースに付随する任意のメタデータ。 +- **スパン** は開始時刻と終了時刻を持つ個々の処理を表します。スパンは以下を保持します: + - `started_at` と `ended_at` タイムスタンプ + - 所属トレースを示す `trace_id` + - 親スパンを指す `parent_id` (存在する場合) + - スパンに関する情報を格納する `span_data`。たとえば `AgentSpanData` にはエージェント情報が、`GenerationSpanData` には LLM 生成情報が含まれます。 + +## デフォルトのトレーシング + +デフォルトで SDK は以下をトレースします: + +- `Runner.{run, run_sync, run_streamed}()` 全体を `trace()` でラップ +- エージェントが実行されるたびに `agent_span()` でラップ +- LLM 生成を `generation_span()` でラップ +- 関数ツール呼び出しを `function_span()` でラップ +- ガードレールを `guardrail_span()` でラップ +- ハンドオフを `handoff_span()` でラップ +- 音声入力 (speech‑to‑text) を `transcription_span()` でラップ +- 音声出力 (text‑to‑speech) を `speech_span()` でラップ +- 関連する音声スパンは `speech_group_span()` の下にネストされる場合があります + +トレース名はデフォルトで「Agent trace」です。`trace` を使用して指定したり、[`RunConfig`][agents.run.RunConfig] で名前やその他のプロパティを設定できます。 + +さらに [カスタムトレーシングプロセッサー](#custom-tracing-processors) を設定して、トレースを別の送信先に出力（置き換えまたは追加）することも可能です。 + +## 上位レベルのトレース + +複数回の `run()` 呼び出しを 1 つのトレースにまとめたい場合があります。その場合、コード全体を `trace()` でラップします。 + +```python +from agents import Agent, Runner, trace + +async def main(): + agent = Agent(name="Joke generator", instructions="Tell funny jokes.") + + with trace("Joke workflow"): # (1)! + first_result = await Runner.run(agent, "Tell me a joke") + second_result = await Runner.run(agent, f"Rate this joke: {first_result.final_output}") + print(f"Joke: {first_result.final_output}") + print(f"Rating: {second_result.final_output}") +``` + +1. `with trace()` で 2 つの `Runner.run` 呼び出しをラップしているため、それぞれが個別のトレースを作成せず、全体で 1 つのトレースになります。 + +## トレースの作成 + +[`trace()`][agents.tracing.trace] 関数を使ってトレースを作成できます。開始と終了が必要で、方法は 2 つあります。 + +1. **推奨**: `with trace(...) as my_trace` のようにコンテキストマネージャーとして使用する。開始と終了が自動で行われます。 +2. [`trace.start()`][agents.tracing.Trace.start] と [`trace.finish()`][agents.tracing.Trace.finish] を手動で呼び出す。 + +現在のトレースは Python の [`contextvar`](https://docs.python.org/3/library/contextvars.html) で管理されているため、並行処理でも自動で機能します。手動で開始／終了する場合は `start()`／`finish()` に `mark_as_current` と `reset_current` を渡して現在のトレースを更新してください。 + +## スパンの作成 + +各種 [`*_span()`][agents.tracing.create] メソッドでスパンを作成できます。一般的には手動で作成する必要はありません。カスタム情報を追跡するための [`custom_span()`][agents.tracing.custom_span] も利用できます。 + +スパンは自動的に現在のトレースの一部となり、最も近い現在のスパンの下にネストされます。これも Python の [`contextvar`](https://docs.python.org/3/library/contextvars.html) で管理されています。 + +## 機密データ + +一部のスパンでは機密データが収集される可能性があります。 + +`generation_span()` には LLM の入力と出力、`function_span()` には関数呼び出しの入力と出力が保存されます。これらに機密データが含まれる場合、[`RunConfig.trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data] を使用して記録を無効化できます。 + +同様に、音声スパンにはデフォルトで base64 エンコードされた PCM 音声データが含まれます。[`VoicePipelineConfig.trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data] を設定して音声データの記録を無効化できます。 + +## カスタムトレーシングプロセッサー + +トレーシングの高レベル構成は次のとおりです。 + +- 初期化時にグローバルな [`TraceProvider`][agents.tracing.setup.TraceProvider] を作成し、トレースを生成。 +- `TraceProvider` は [`BatchTraceProcessor`][agents.tracing.processors.BatchTraceProcessor] を用いてスパン／トレースをバッチ送信し、[`BackendSpanExporter`][agents.tracing.processors.BackendSpanExporter] が OpenAI バックエンドへバッチでエクスポートします。 + +デフォルト設定を変更して別のバックエンドへ送信したり、エクスポーターの挙動を修正するには次の 2 通りがあります。 + +1. [`add_trace_processor()`][agents.tracing.add_trace_processor] + 既定の送信に加え、**追加** のトレースプロセッサーを登録できます。これにより OpenAI バックエンドへの送信に加えて独自処理が可能です。 +2. [`set_trace_processors()`][agents.tracing.set_trace_processors] + 既定のプロセッサーを置き換え、**独自** のトレースプロセッサーだけを使用します。OpenAI バックエンドへ送信する場合は、その機能を持つ `TracingProcessor` を含める必要があります。 + +## 外部トレーシングプロセッサー一覧 + +- [Weights & Biases](https://weave-docs.wandb.ai/guides/integrations/openai_agents) +- [Arize‑Phoenix](https://docs.arize.com/phoenix/tracing/integrations-tracing/openai-agents-sdk) +- [MLflow (self‑hosted/OSS](https://mlflow.org/docs/latest/tracing/integrations/openai-agent) +- [MLflow (Databricks hosted](https://docs.databricks.com/aws/en/mlflow/mlflow-tracing#-automatic-tracing) +- [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk) +- [Pydantic Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents) +- [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk) +- [Scorecard](https://docs.scorecard.io/docs/documentation/features/tracing#openai-agents-sdk-integration) +- [Keywords AI](https://docs.keywordsai.co/integration/development-frameworks/openai-agent) +- [LangSmith](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_openai_agents_sdk) +- [Maxim AI](https://www.getmaxim.ai/docs/observe/integrations/openai-agents-sdk) +- [Comet Opik](https://www.comet.com/docs/opik/tracing/integrations/openai_agents) +- [Langfuse](https://langfuse.com/docs/integrations/openaiagentssdk/openai-agents) +- [Langtrace](https://docs.langtrace.ai/supported-integrations/llm-frameworks/openai-agents-sdk) +- [Okahu‑Monocle](https://github.com/monocle2ai/monocle) \ No newline at end of file diff --git a/docs/ja/visualization.md b/docs/ja/visualization.md new file mode 100644 index 00000000..9093bb65 --- /dev/null +++ b/docs/ja/visualization.md @@ -0,0 +1,87 @@ +--- +search: + exclude: true +--- +# エージェントの可視化 + +エージェントの可視化を使用すると、 ** Graphviz ** を用いてエージェントとその関係を構造化されたグラフィカル表現として生成できます。これは、アプリケーション内でエージェント、ツール、handoffs がどのように相互作用するかを理解するのに役立ちます。 + +## インストール + +オプションの `viz` 依存関係グループをインストールします: + +```bash +pip install "openai-agents[viz]" +``` + +## グラフの生成 + +`draw_graph` 関数を使用してエージェントの可視化を生成できます。この関数は有向グラフを作成し、以下のように表現します。 + +- **エージェント** は黄色のボックスで表されます。 +- **ツール** は緑色の楕円で表されます。 +- **handoffs** はエージェント間の有向エッジで示されます。 + +### 使用例 + +```python +from agents import Agent, function_tool +from agents.extensions.visualization import draw_graph + +@function_tool +def get_weather(city: str) -> str: + return f"The weather in {city} is sunny." + +spanish_agent = Agent( + name="Spanish agent", + instructions="You only speak Spanish.", +) + +english_agent = Agent( + name="English agent", + instructions="You only speak English", +) + +triage_agent = Agent( + name="Triage agent", + instructions="Handoff to the appropriate agent based on the language of the request.", + handoffs=[spanish_agent, english_agent], + tools=[get_weather], +) + +draw_graph(triage_agent) +``` + +![Agent Graph](../assets/images/graph.png) + +これにより、 **triage agent** の構造と、それがサブエージェントやツールとどのようにつながっているかを視覚的に表すグラフが生成されます。 + +## 可視化の理解 + +生成されたグラフには次の要素が含まれます。 + +- エントリーポイントを示す **start node** (`__start__`) +- 黄色の塗りつぶしを持つ **矩形** のエージェント +- 緑色の塗りつぶしを持つ **楕円** のツール +- 相互作用を示す有向エッジ + - エージェント間の handoffs には **実線の矢印** + - ツール呼び出しには **破線の矢印** +- 実行が終了する位置を示す **end node** (`__end__`) + +## グラフのカスタマイズ + +### グラフの表示 +デフォルトでは、`draw_graph` はグラフをインラインで表示します。別ウィンドウでグラフを表示するには、次のように記述します。 + +```python +draw_graph(triage_agent).view() +``` + +### グラフの保存 +デフォルトでは、`draw_graph` はグラフをインラインで表示します。ファイルとして保存するには、ファイル名を指定します: + +```python +draw_graph(triage_agent, filename="agent_graph") +``` + +これにより、作業ディレクトリに `agent_graph.png` が生成されます。 diff --git a/docs/ja/voice/pipeline.md b/docs/ja/voice/pipeline.md new file mode 100644 index 00000000..d52fb77f --- /dev/null +++ b/docs/ja/voice/pipeline.md @@ -0,0 +1,81 @@ +--- +search: + exclude: true +--- +# パイプラインと ワークフロー + +[`VoicePipeline`][agents.voice.pipeline.VoicePipeline] は、エージェント的なワークフローを音声アプリに簡単に変換できるクラスです。ワークフローを渡すと、パイプラインが入力音声の文字起こし、音声終了の検知、適切なタイミングでのワークフロー呼び出し、そしてワークフロー出力を音声へ変換する処理を担当します。 + +```mermaid +graph LR + %% Input + A["🎤 Audio Input"] + + %% Voice Pipeline + subgraph Voice_Pipeline [Voice Pipeline] + direction TB + B["Transcribe (speech-to-text)"] + C["Your Code"]:::highlight + D["Text-to-speech"] + B --> C --> D + end + + %% Output + E["🎧 Audio Output"] + + %% Flow + A --> Voice_Pipeline + Voice_Pipeline --> E + + %% Custom styling + classDef highlight fill:#ffcc66,stroke:#333,stroke-width:1px,font-weight:700; + +``` + +## パイプラインの設定 + +パイプラインを作成する際に、以下を設定できます。 + +1. [`workflow`][agents.voice.workflow.VoiceWorkflowBase] ‐ 新しい音声が文字起こしされるたびに実行されるコード +2. 使用する [`speech-to-text`][agents.voice.model.STTModel] および [`text-to-speech`][agents.voice.model.TTSModel] モデル +3. [`config`][agents.voice.pipeline_config.VoicePipelineConfig] ‐ 以下のような内容を設定可能 + - モデルプロバイダー。モデル名をモデルにマッピングします + - トレーシング。トレーシングの無効化、音声ファイルのアップロード可否、ワークフロー名、トレース ID など + - TTS と STT モデルの設定。プロンプト、言語、使用するデータ型など + +## パイプラインの実行 + +パイプラインは [`run()`][agents.voice.pipeline.VoicePipeline.run] メソッドで実行できます。音声入力は次の 2 形式で渡せます。 + +1. [`AudioInput`][agents.voice.input.AudioInput] + 完全な音声トランスクリプトがある場合に使用し、その結果だけを生成したいときに便利です。話者の発話終了を検知する必要がないケース、たとえば録音済み音声やプッシュトゥートーク型アプリのようにユーザーが話し終えたタイミングが明確な場合に向いています。 +2. [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput] + ユーザーの発話終了検知が必要な場合に使用します。検出された音声チャンクを順次プッシュでき、音声パイプラインが「アクティビティ検知」と呼ばれるプロセスを通じて適切なタイミングでエージェント ワークフローを自動的に実行します。 + +## 結果 + +音声パイプライン実行の結果は [`StreamedAudioResult`][agents.voice.result.StreamedAudioResult] です。これは発生したイベントをストリーミングで受け取れるオブジェクトです。いくつかの [`VoiceStreamEvent`][agents.voice.events.VoiceStreamEvent] があり、主なものは次のとおりです。 + +1. [`VoiceStreamEventAudio`][agents.voice.events.VoiceStreamEventAudio] ‐ 音声チャンクを含みます +2. [`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle] ‐ ターンの開始や終了などのライフサイクルイベントを通知します +3. [`VoiceStreamEventError`][agents.voice.events.VoiceStreamEventError] ‐ エラーイベントです + +```python + +result = await pipeline.run(input) + +async for event in result.stream(): + if event.type == "voice_stream_event_audio": + # play audio + elif event.type == "voice_stream_event_lifecycle": + # lifecycle + elif event.type == "voice_stream_event_error" + # error + ... +``` + +## ベストプラクティス + +### 割り込み + +Agents SDK は現在 [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput] に対して、組み込みの割り込み処理をサポートしていません。そのため、検出された各ターンごとにワークフローが個別に実行されます。アプリケーション内で割り込みを処理したい場合は、[`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle] イベントを監視できます。`turn_started` は新しいターンが文字起こしされ、処理が開始されたことを示します。`turn_ended` は該当ターンのすべての音声が送信された後にトリガーされます。たとえば、モデルがターンを開始した際にスピーカーのマイクをミュートし、そのターンに関連する音声をすべて送信し終えた後にアンミュートするといった制御に、これらのイベントを利用できます。 \ No newline at end of file diff --git a/docs/ja/voice/quickstart.md b/docs/ja/voice/quickstart.md new file mode 100644 index 00000000..291b9882 --- /dev/null +++ b/docs/ja/voice/quickstart.md @@ -0,0 +1,198 @@ +--- +search: + exclude: true +--- +# クイックスタート + +## 前提条件 + +まずは [クイックスタート手順](../quickstart.md) に従って Agents SDK をセットアップし、仮想環境を作成してください。その後、SDK の音声関連のオプション依存関係をインストールします: + +```bash +pip install 'openai-agents[voice]' +``` + +## コンセプト + +押さえておくべき主な概念は [`VoicePipeline`][agents.voice.pipeline.VoicePipeline] です。これは次の 3 ステップから成るプロセスです。 + +1. speech-to-text モデルを実行して音声をテキストに変換します。 +2. 通常はエージェント的ワークフローであるあなたのコードを実行し、結果を生成します。 +3. text-to-speech モデルを実行して結果のテキストを再び音声に変換します。 + +```mermaid +graph LR + %% Input + A["🎤 Audio Input"] + + %% Voice Pipeline + subgraph Voice_Pipeline [Voice Pipeline] + direction TB + B["Transcribe (speech-to-text)"] + C["Your Code"]:::highlight + D["Text-to-speech"] + B --> C --> D + end + + %% Output + E["🎧 Audio Output"] + + %% Flow + A --> Voice_Pipeline + Voice_Pipeline --> E + + %% Custom styling + classDef highlight fill:#ffcc66,stroke:#333,stroke-width:1px,font-weight:700; + +``` + +## エージェント + +まず、いくつかの エージェント をセットアップしましょう。この SDK でエージェントを構築したことがあれば、見覚えがあるはずです。ここでは複数の エージェント、ハンドオフ、そしてツールを用意します。 + +```python +import asyncio +import random + +from agents import ( + Agent, + function_tool, +) +from agents.extensions.handoff_prompt import prompt_with_handoff_instructions + + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city.""" + print(f"[debug] get_weather called with city: {city}") + choices = ["sunny", "cloudy", "rainy", "snowy"] + return f"The weather in {city} is {random.choice(choices)}." + + +spanish_agent = Agent( + name="Spanish", + handoff_description="A spanish speaking agent.", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. Speak in Spanish.", + ), + model="gpt-4o-mini", +) + +agent = Agent( + name="Assistant", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the spanish agent.", + ), + model="gpt-4o-mini", + handoffs=[spanish_agent], + tools=[get_weather], +) +``` + +## 音声パイプライン + +[`SingleAgentVoiceWorkflow`][agents.voice.workflow.SingleAgentVoiceWorkflow] をワークフローとして、シンプルな音声パイプラインを構築します。 + +```python +from agents.voice import SingleAgentVoiceWorkflow, VoicePipeline +pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent)) +``` + +## パイプラインの実行 + +```python +import numpy as np +import sounddevice as sd +from agents.voice import AudioInput + +# For simplicity, we'll just create 3 seconds of silence +# In reality, you'd get microphone data +buffer = np.zeros(24000 * 3, dtype=np.int16) +audio_input = AudioInput(buffer=buffer) + +result = await pipeline.run(audio_input) + +# Create an audio player using `sounddevice` +player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16) +player.start() + +# Play the audio stream as it comes in +async for event in result.stream(): + if event.type == "voice_stream_event_audio": + player.write(event.data) + +``` + +## まとめて実行 + +```python +import asyncio +import random + +import numpy as np +import sounddevice as sd + +from agents import ( + Agent, + function_tool, + set_tracing_disabled, +) +from agents.voice import ( + AudioInput, + SingleAgentVoiceWorkflow, + VoicePipeline, +) +from agents.extensions.handoff_prompt import prompt_with_handoff_instructions + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city.""" + print(f"[debug] get_weather called with city: {city}") + choices = ["sunny", "cloudy", "rainy", "snowy"] + return f"The weather in {city} is {random.choice(choices)}." + + +spanish_agent = Agent( + name="Spanish", + handoff_description="A spanish speaking agent.", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. Speak in Spanish.", + ), + model="gpt-4o-mini", +) + +agent = Agent( + name="Assistant", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the spanish agent.", + ), + model="gpt-4o-mini", + handoffs=[spanish_agent], + tools=[get_weather], +) + + +async def main(): + pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent)) + buffer = np.zeros(24000 * 3, dtype=np.int16) + audio_input = AudioInput(buffer=buffer) + + result = await pipeline.run(audio_input) + + # Create an audio player using `sounddevice` + player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16) + player.start() + + # Play the audio stream as it comes in + async for event in result.stream(): + if event.type == "voice_stream_event_audio": + player.write(event.data) + + +if __name__ == "__main__": + asyncio.run(main()) +``` + +この例を実行すると、エージェントがあなたに話しかけます。実際にエージェントと会話できるデモは、[examples/voice/static](https://github.com/openai/openai-agents-python/tree/main/examples/voice/static) をご覧ください。 \ No newline at end of file diff --git a/docs/ja/voice/tracing.md b/docs/ja/voice/tracing.md new file mode 100644 index 00000000..21f4788f --- /dev/null +++ b/docs/ja/voice/tracing.md @@ -0,0 +1,18 @@ +--- +search: + exclude: true +--- +# トレーシング + +[エージェントのトレーシング](../tracing.md) と同様に、音声パイプラインも自動的にトレーシングされます。 + +基本的なトレーシング情報については上記のドキュメントを参照してください。さらに、[`VoicePipelineConfig`][agents.voice.pipeline_config.VoicePipelineConfig] でパイプラインのトレーシング設定を行えます。 + +主なトレーシング関連フィールドは次のとおりです。 + +- [`tracing_disabled`][agents.voice.pipeline_config.VoicePipelineConfig.tracing_disabled]：トレーシングを無効にするかどうかを制御します。デフォルトではトレーシングは有効です。 +- [`trace_include_sensitive_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_data]：トレースに音声テキストなどの機微なデータを含めるかどうかを制御します。これは音声パイプライン専用であり、Workflow 内部で発生する処理には影響しません。 +- [`trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data]：トレースに音声データを含めるかどうかを制御します。 +- [`workflow_name`][agents.voice.pipeline_config.VoicePipelineConfig.workflow_name]：トレース Workflow の名前です。 +- [`group_id`][agents.voice.pipeline_config.VoicePipelineConfig.group_id]：複数のトレースを関連付けるための `group_id` です。 +- [`trace_metadata`][agents.voice.pipeline_config.VoicePipelineConfig.tracing_disabled]：トレースに追加するメタデータです。 \ No newline at end of file diff --git a/docs/mcp.md b/docs/mcp.md new file mode 100644 index 00000000..e279a25e --- /dev/null +++ b/docs/mcp.md @@ -0,0 +1,60 @@ +# Model context protocol (MCP) + +The [Model context protocol](https://modelcontextprotocol.io/introduction) (aka MCP) is a way to provide tools and context to the LLM. From the MCP docs: + +> MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools. + +The Agents SDK has support for MCP. This enables you to use a wide range of MCP servers to provide tools to your Agents. + +## MCP servers + +Currently, the MCP spec defines two kinds of servers, based on the transport mechanism they use: + +1. **stdio** servers run as a subprocess of your application. You can think of them as running "locally". +2. **HTTP over SSE** servers run remotely. You connect to them via a URL. + +You can use the [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse] classes to connect to these servers. + +For example, this is how you'd use the [official MCP filesystem server](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem). + +```python +async with MCPServerStdio( + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + } +) as server: + tools = await server.list_tools() +``` + +## Using MCP servers + +MCP servers can be added to Agents. The Agents SDK will call `list_tools()` on the MCP servers each time the Agent is run. This makes the LLM aware of the MCP server's tools. When the LLM calls a tool from an MCP server, the SDK calls `call_tool()` on that server. + +```python + +agent=Agent( + name="Assistant", + instructions="Use the tools to achieve the task", + mcp_servers=[mcp_server_1, mcp_server_2] +) +``` + +## Caching + +Every time an Agent runs, it calls `list_tools()` on the MCP server. This can be a latency hit, especially if the server is a remote server. To automatically cache the list of tools, you can pass `cache_tools_list=True` to both [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse]. You should only do this if you're certain the tool list will not change. + +If you want to invalidate the cache, you can call `invalidate_tools_cache()` on the servers. + +## End-to-end examples + +View complete working examples at [examples/mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp). + +## Tracing + +[Tracing](./tracing.md) automatically captures MCP operations, including: + +1. Calls to the MCP server to list tools +2. MCP-related info on function calls + +![MCP Tracing Screenshot](./assets/images/mcp-tracing.jpg) diff --git a/docs/models.md b/docs/models.md deleted file mode 100644 index 7d2ff1ff..00000000 --- a/docs/models.md +++ /dev/null @@ -1,73 +0,0 @@ -# Models - -The Agents SDK comes with out of the box support for OpenAI models in two flavors: - -- **Recommended**: the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel], which calls OpenAI APIs using the new [Responses API](https://platform.openai.com/docs/api-reference/responses). -- The [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel], which calls OpenAI APIs using the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). - -## Mixing and matching models - -Within a single workflow, you may want to use different models for each agent. For example, you could use a smaller, faster model for triage, while using a larger, more capable model for complex tasks. When configuring an [`Agent`][agents.Agent], you can select a specific model by either: - -1. Passing the name of an OpenAI model. -2. Passing any model name + a [`ModelProvider`][agents.models.interface.ModelProvider] that can map that name to a Model instance. -3. Directly providing a [`Model`][agents.models.interface.Model] implementation. - -!!!note - - While our SDK supports both the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] and the[`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] shapes, we recommend using a single model shape for each workflow because the two shapes support a different set of features and tools. If your workflow requires mixing and matching model shapes, make sure that all the features you're using are available on both. - -```python -from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel -import asyncio - -spanish_agent = Agent( - name="Spanish agent", - instructions="You only speak Spanish.", - model="o3-mini", # (1)! -) - -english_agent = Agent( - name="English agent", - instructions="You only speak English", - model=OpenAIChatCompletionsModel( # (2)! - model="gpt-4o", - openai_client=AsyncOpenAI() - ), -) - -triage_agent = Agent( - name="Triage agent", - instructions="Handoff to the appropriate agent based on the language of the request.", - handoffs=[spanish_agent, english_agent], - model="gpt-3.5-turbo", -) - -async def main(): - result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") - print(result.final_output) -``` - -1. Sets the the name of an OpenAI model directly. -2. Provides a [`Model`][agents.models.interface.Model] implementation. - -## Using other LLM providers - -Many providers also support the OpenAI API format, which means you can pass a `base_url` to the existing OpenAI model implementations and use them easily. `ModelSettings` is used to configure tuning parameters (e.g., temperature, top_p) for the model you select. - -```python -external_client = AsyncOpenAI( - api_key="EXTERNAL_API_KEY", - base_url="https://api.external.com/v1/", -) - -spanish_agent = Agent( - name="Spanish agent", - instructions="You only speak Spanish.", - model=OpenAIChatCompletionsModel( - model="EXTERNAL_MODEL_NAME", - openai_client=external_client, - ), - model_settings=ModelSettings(temperature=0.5), -) -``` diff --git a/docs/models/index.md b/docs/models/index.md new file mode 100644 index 00000000..1c89d778 --- /dev/null +++ b/docs/models/index.md @@ -0,0 +1,131 @@ +# Models + +The Agents SDK comes with out-of-the-box support for OpenAI models in two flavors: + +- **Recommended**: the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel], which calls OpenAI APIs using the new [Responses API](https://platform.openai.com/docs/api-reference/responses). +- The [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel], which calls OpenAI APIs using the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). + +## Non-OpenAI models + +You can use most other non-OpenAI models via the [LiteLLM integration](./litellm.md). First, install the litellm dependency group: + +```bash +pip install "openai-agents[litellm]" +``` + +Then, use any of the [supported models](https://docs.litellm.ai/docs/providers) with the `litellm/` prefix: + +```python +claude_agent = Agent(model="litellm/anthropic/claude-3-5-sonnet-20240620", ...) +gemini_agent = Agent(model="litellm/gemini/gemini-2.5-flash-preview-04-17", ...) +``` + +### Other ways to use non-OpenAI models + +You can integrate other LLM providers in 3 more ways (examples [here](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/)): + +1. [`set_default_openai_client`][agents.set_default_openai_client] is useful in cases where you want to globally use an instance of `AsyncOpenAI` as the LLM client. This is for cases where the LLM provider has an OpenAI compatible API endpoint, and you can set the `base_url` and `api_key`. See a configurable example in [examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py). +2. [`ModelProvider`][agents.models.interface.ModelProvider] is at the `Runner.run` level. This lets you say "use a custom model provider for all agents in this run". See a configurable example in [examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py). +3. [`Agent.model`][agents.agent.Agent.model] lets you specify the model on a specific Agent instance. This enables you to mix and match different providers for different agents. See a configurable example in [examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py). An easy way to use most available models is via the [LiteLLM integration](./litellm.md). + +In cases where you do not have an API key from `platform.openai.com`, we recommend disabling tracing via `set_tracing_disabled()`, or setting up a [different tracing processor](../tracing.md). + +!!! note + + In these examples, we use the Chat Completions API/model, because most LLM providers don't yet support the Responses API. If your LLM provider does support it, we recommend using Responses. + +## Mixing and matching models + +Within a single workflow, you may want to use different models for each agent. For example, you could use a smaller, faster model for triage, while using a larger, more capable model for complex tasks. When configuring an [`Agent`][agents.Agent], you can select a specific model by either: + +1. Passing the name of a model. +2. Passing any model name + a [`ModelProvider`][agents.models.interface.ModelProvider] that can map that name to a Model instance. +3. Directly providing a [`Model`][agents.models.interface.Model] implementation. + +!!!note + + While our SDK supports both the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] and the [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] shapes, we recommend using a single model shape for each workflow because the two shapes support a different set of features and tools. If your workflow requires mixing and matching model shapes, make sure that all the features you're using are available on both. + +```python +from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel +import asyncio + +spanish_agent = Agent( + name="Spanish agent", + instructions="You only speak Spanish.", + model="o3-mini", # (1)! +) + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model=OpenAIChatCompletionsModel( # (2)! + model="gpt-4o", + openai_client=AsyncOpenAI() + ), +) + +triage_agent = Agent( + name="Triage agent", + instructions="Handoff to the appropriate agent based on the language of the request.", + handoffs=[spanish_agent, english_agent], + model="gpt-3.5-turbo", +) + +async def main(): + result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") + print(result.final_output) +``` + +1. Sets the name of an OpenAI model directly. +2. Provides a [`Model`][agents.models.interface.Model] implementation. + +When you want to further configure the model used for an agent, you can pass [`ModelSettings`][agents.models.interface.ModelSettings], which provides optional model configuration parameters such as temperature. + +```python +from agents import Agent, ModelSettings + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4o", + model_settings=ModelSettings(temperature=0.1), +) +``` + +## Common issues with using other LLM providers + +### Tracing client error 401 + +If you get errors related to tracing, this is because traces are uploaded to OpenAI servers, and you don't have an OpenAI API key. You have three options to resolve this: + +1. Disable tracing entirely: [`set_tracing_disabled(True)`][agents.set_tracing_disabled]. +2. Set an OpenAI key for tracing: [`set_tracing_export_api_key(...)`][agents.set_tracing_export_api_key]. This API key will only be used for uploading traces, and must be from [platform.openai.com](https://platform.openai.com/). +3. Use a non-OpenAI trace processor. See the [tracing docs](../tracing.md#custom-tracing-processors). + +### Responses API support + +The SDK uses the Responses API by default, but most other LLM providers don't yet support it. You may see 404s or similar issues as a result. To resolve, you have two options: + +1. Call [`set_default_openai_api("chat_completions")`][agents.set_default_openai_api]. This works if you are setting `OPENAI_API_KEY` and `OPENAI_BASE_URL` via environment vars. +2. Use [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel]. There are examples [here](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/). + +### Structured outputs support + +Some model providers don't have support for [structured outputs](https://platform.openai.com/docs/guides/structured-outputs). This sometimes results in an error that looks something like this: + +``` + +BadRequestError: Error code: 400 - {'error': {'message': "'response_format.type' : value is not one of the allowed values ['text','json_object']", 'type': 'invalid_request_error'}} + +``` + +This is a shortcoming of some model providers - they support JSON outputs, but don't allow you to specify the `json_schema` to use for the output. We are working on a fix for this, but we suggest relying on providers that do have support for JSON schema output, because otherwise your app will often break because of malformed JSON. + +## Mixing models across providers + +You need to be aware of feature differences between model providers, or you may run into errors. For example, OpenAI supports structured outputs, multimodal input, and hosted file search and web search, but many other providers don't support these features. Be aware of these limitations: + +- Don't send unsupported `tools` to providers that don't understand them +- Filter out multimodal inputs before calling models that are text-only +- Be aware that providers that don't support structured JSON outputs will occasionally produce invalid JSON. diff --git a/docs/models/litellm.md b/docs/models/litellm.md new file mode 100644 index 00000000..90572a28 --- /dev/null +++ b/docs/models/litellm.md @@ -0,0 +1,73 @@ +# Using any model via LiteLLM + +!!! note + + The LiteLLM integration is in beta. You may run into issues with some model providers, especially smaller ones. Please report any issues via [Github issues](https://github.com/openai/openai-agents-python/issues) and we'll fix quickly. + +[LiteLLM](https://docs.litellm.ai/docs/) is a library that allows you to use 100+ models via a single interface. We've added a LiteLLM integration to allow you to use any AI model in the Agents SDK. + +## Setup + +You'll need to ensure `litellm` is available. You can do this by installing the optional `litellm` dependency group: + +```bash +pip install "openai-agents[litellm]" +``` + +Once done, you can use [`LitellmModel`][agents.extensions.models.litellm_model.LitellmModel] in any agent. + +## Example + +This is a fully working example. When you run it, you'll be prompted for a model name and API key. For example, you could enter: + +- `openai/gpt-4.1` for the model, and your OpenAI API key +- `anthropic/claude-3-5-sonnet-20240620` for the model, and your Anthropic API key +- etc + +For a full list of models supported in LiteLLM, see the [litellm providers docs](https://docs.litellm.ai/docs/providers). + +```python +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents.extensions.models.litellm_model import LitellmModel + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(model: str, api_key: str): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=LitellmModel(model=model, api_key=api_key), + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + # First try to get model/api key from args + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=False) + parser.add_argument("--api-key", type=str, required=False) + args = parser.parse_args() + + model = args.model + if not model: + model = input("Enter a model name for Litellm: ") + + api_key = args.api_key + if not api_key: + api_key = input("Enter an API key for Litellm: ") + + asyncio.run(main(model, api_key)) +``` diff --git a/docs/multi_agent.md b/docs/multi_agent.md index c1182492..aa1b6bc0 100644 --- a/docs/multi_agent.md +++ b/docs/multi_agent.md @@ -27,11 +27,11 @@ This pattern is great when the task is open-ended and you want to rely on the in ## Orchestrating via code -While orchestrating via LLM is powerful, orchestrating via LLM makes tasks more deterministic and predictable, in terms of speed, cost and performance. Common patterns here are: +While orchestrating via LLM is powerful, orchestrating via code makes tasks more deterministic and predictable, in terms of speed, cost and performance. Common patterns here are: - Using [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) to generate well formed data that you can inspect with your code. For example, you might ask an agent to classify the task into a few categories, and then pick the next agent based on the category. - Chaining multiple agents by transforming the output of one into the input of the next. You can decompose a task like writing a blog post into a series of steps - do research, write an outline, write the blog post, critique it, and then improve it. - Running the agent that performs the task in a `while` loop with an agent that evaluates and provides feedback, until the evaluator says the output passes certain criteria. - Running multiple agents in parallel, e.g. via Python primitives like `asyncio.gather`. This is useful for speed when you have multiple tasks that don't depend on each other. -We have a number of examples in [`examples/agent_patterns`](https://github.com/openai/openai-agents-python/examples/agent_patterns). +We have a number of examples in [`examples/agent_patterns`](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns). diff --git a/docs/quickstart.md b/docs/quickstart.md index 19051f49..213d16e5 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -121,7 +121,7 @@ async def homework_guardrail(ctx, agent, input_data): Let's put it all together and run the entire workflow, using handoffs and the input guardrail. ```python -from agents import Agent, InputGuardrail,GuardrailFunctionOutput, Runner +from agents import Agent, InputGuardrail, GuardrailFunctionOutput, Runner from pydantic import BaseModel import asyncio @@ -166,6 +166,9 @@ triage_agent = Agent( ) async def main(): + result = await Runner.run(triage_agent, "who was the first president of the united states?") + print(result.final_output) + result = await Runner.run(triage_agent, "what is life") print(result.final_output) @@ -183,4 +186,4 @@ Learn how to build more complex agentic flows: - Learn about how to configure [Agents](agents.md). - Learn about [running agents](running_agents.md). -- Learn about [tools](tools.md), [guardrails](guardrails.md) and [models](models.md). +- Learn about [tools](tools.md), [guardrails](guardrails.md) and [models](models/index.md). diff --git a/docs/ref/extensions/litellm.md b/docs/ref/extensions/litellm.md new file mode 100644 index 00000000..7bd67fde --- /dev/null +++ b/docs/ref/extensions/litellm.md @@ -0,0 +1,3 @@ +# `LiteLLM Models` + +::: agents.extensions.models.litellm_model diff --git a/docs/ref/mcp/server.md b/docs/ref/mcp/server.md new file mode 100644 index 00000000..e58efab2 --- /dev/null +++ b/docs/ref/mcp/server.md @@ -0,0 +1,3 @@ +# `MCP Servers` + +::: agents.mcp.server diff --git a/docs/ref/mcp/util.md b/docs/ref/mcp/util.md new file mode 100644 index 00000000..b3f7db25 --- /dev/null +++ b/docs/ref/mcp/util.md @@ -0,0 +1,3 @@ +# `MCP Util` + +::: agents.mcp.util diff --git a/docs/ref/voice/events.md b/docs/ref/voice/events.md new file mode 100644 index 00000000..71e88e3e --- /dev/null +++ b/docs/ref/voice/events.md @@ -0,0 +1,3 @@ +# `Events` + +::: agents.voice.events diff --git a/docs/ref/voice/exceptions.md b/docs/ref/voice/exceptions.md new file mode 100644 index 00000000..61f6ca89 --- /dev/null +++ b/docs/ref/voice/exceptions.md @@ -0,0 +1,3 @@ +# `Exceptions` + +::: agents.voice.exceptions diff --git a/docs/ref/voice/input.md b/docs/ref/voice/input.md new file mode 100644 index 00000000..b61d2f5b --- /dev/null +++ b/docs/ref/voice/input.md @@ -0,0 +1,3 @@ +# `Input` + +::: agents.voice.input diff --git a/docs/ref/voice/model.md b/docs/ref/voice/model.md new file mode 100644 index 00000000..212d3ded --- /dev/null +++ b/docs/ref/voice/model.md @@ -0,0 +1,3 @@ +# `Model` + +::: agents.voice.model diff --git a/docs/ref/voice/models/openai_provider.md b/docs/ref/voice/models/openai_provider.md new file mode 100644 index 00000000..f8a40889 --- /dev/null +++ b/docs/ref/voice/models/openai_provider.md @@ -0,0 +1,3 @@ +# `OpenAIVoiceModelProvider` + +::: agents.voice.models.openai_model_provider diff --git a/docs/ref/voice/models/openai_stt.md b/docs/ref/voice/models/openai_stt.md new file mode 100644 index 00000000..eeeb6411 --- /dev/null +++ b/docs/ref/voice/models/openai_stt.md @@ -0,0 +1,3 @@ +# `OpenAI STT` + +::: agents.voice.models.openai_stt diff --git a/docs/ref/voice/models/openai_tts.md b/docs/ref/voice/models/openai_tts.md new file mode 100644 index 00000000..920c3242 --- /dev/null +++ b/docs/ref/voice/models/openai_tts.md @@ -0,0 +1,3 @@ +# `OpenAI TTS` + +::: agents.voice.models.openai_tts diff --git a/docs/ref/voice/pipeline.md b/docs/ref/voice/pipeline.md new file mode 100644 index 00000000..7a1ec69c --- /dev/null +++ b/docs/ref/voice/pipeline.md @@ -0,0 +1,3 @@ +# `Pipeline` + +::: agents.voice.pipeline diff --git a/docs/ref/voice/pipeline_config.md b/docs/ref/voice/pipeline_config.md new file mode 100644 index 00000000..0bc0467c --- /dev/null +++ b/docs/ref/voice/pipeline_config.md @@ -0,0 +1,3 @@ +# `Pipeline Config` + +::: agents.voice.pipeline_config diff --git a/docs/ref/voice/result.md b/docs/ref/voice/result.md new file mode 100644 index 00000000..60d985a1 --- /dev/null +++ b/docs/ref/voice/result.md @@ -0,0 +1,3 @@ +# `Result` + +::: agents.voice.result diff --git a/docs/ref/voice/utils.md b/docs/ref/voice/utils.md new file mode 100644 index 00000000..c13efc6a --- /dev/null +++ b/docs/ref/voice/utils.md @@ -0,0 +1,3 @@ +# `Utils` + +::: agents.voice.utils diff --git a/docs/ref/voice/workflow.md b/docs/ref/voice/workflow.md new file mode 100644 index 00000000..a5ae128e --- /dev/null +++ b/docs/ref/voice/workflow.md @@ -0,0 +1,3 @@ +# `Workflow` + +::: agents.voice.workflow diff --git a/docs/results.md b/docs/results.md index d1864fa8..52408d4a 100644 --- a/docs/results.md +++ b/docs/results.md @@ -32,7 +32,7 @@ The [`new_items`][agents.result.RunResultBase.new_items] property contains the n - [`MessageOutputItem`][agents.items.MessageOutputItem] indicates a message from the LLM. The raw item is the message generated. - [`HandoffCallItem`][agents.items.HandoffCallItem] indicates that the LLM called the handoff tool. The raw item is the tool call item from the LLM. -- [`HandoffOutputItem`][agents.items.HandoffOutputItem] indicates that a handoff occured. The raw item is the tool response to the handoff tool call. You can also access the source/target agents from the item. +- [`HandoffOutputItem`][agents.items.HandoffOutputItem] indicates that a handoff occurred. The raw item is the tool response to the handoff tool call. You can also access the source/target agents from the item. - [`ToolCallItem`][agents.items.ToolCallItem] indicates that the LLM invoked a tool. - [`ToolCallOutputItem`][agents.items.ToolCallOutputItem] indicates that a tool was called. The raw item is the tool response. You can also access the tool output from the item. - [`ReasoningItem`][agents.items.ReasoningItem] indicates a reasoning item from the LLM. The raw item is the reasoning generated. diff --git a/docs/running_agents.md b/docs/running_agents.md index a2f137cf..f631cf46 100644 --- a/docs/running_agents.md +++ b/docs/running_agents.md @@ -53,7 +53,7 @@ The `run_config` parameter lets you configure some global settings for the agent - [`handoff_input_filter`][agents.run.RunConfig.handoff_input_filter]: A global input filter to apply to all handoffs, if the handoff doesn't already have one. The input filter allows you to edit the inputs that are sent to the new agent. See the documentation in [`Handoff.input_filter`][agents.handoffs.Handoff.input_filter] for more details. - [`tracing_disabled`][agents.run.RunConfig.tracing_disabled]: Allows you to disable [tracing](tracing.md) for the entire run. - [`trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data]: Configures whether traces will include potentially sensitive data, such as LLM and tool call inputs/outputs. -- [`workflow_name`][agents.run.RunConfig.workflow_name], [`trace_id`][agents.run.RunConfig.trace_id], [`group_id`][agents.run.RunConfig.group_id]: Sets the tracing workflow name, trace ID and trace group ID for the run. We recommend at least setting `workflow_name`. The session ID is an optional field that lets you link traces across multiple runs. +- [`workflow_name`][agents.run.RunConfig.workflow_name], [`trace_id`][agents.run.RunConfig.trace_id], [`group_id`][agents.run.RunConfig.group_id]: Sets the tracing workflow name, trace ID and trace group ID for the run. We recommend at least setting `workflow_name`. The group ID is an optional field that lets you link traces across multiple runs. - [`trace_metadata`][agents.run.RunConfig.trace_metadata]: Metadata to include on all traces. ## Conversations/chat threads @@ -78,7 +78,7 @@ async def main(): # San Francisco # Second turn - new_input = output.to_input_list() + [{"role": "user", "content": "What state is it in?"}] + new_input = result.to_input_list() + [{"role": "user", "content": "What state is it in?"}] result = await Runner.run(agent, new_input) print(result.final_output) # California diff --git a/docs/scripts/translate_docs.py b/docs/scripts/translate_docs.py new file mode 100644 index 00000000..b2e8b44f --- /dev/null +++ b/docs/scripts/translate_docs.py @@ -0,0 +1,288 @@ +# ruff: noqa +import os +from openai import OpenAI +from concurrent.futures import ThreadPoolExecutor + +# import logging +# logging.basicConfig(level=logging.INFO) +# logging.getLogger("openai").setLevel(logging.DEBUG) + +OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "o3") + +ENABLE_CODE_SNIPPET_EXCLUSION = True +# gpt-4.5 needed this for better quality +ENABLE_SMALL_CHUNK_TRANSLATION = False + +SEARCH_EXCLUSION = """--- +search: + exclude: true +--- +""" + + +# Define the source and target directories +source_dir = "docs" +languages = { + "ja": "Japanese", + # Add more languages here, e.g., "fr": "French" +} + +# Initialize OpenAI client +openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + +# Define dictionaries for translation control +do_not_translate = [ + "OpenAI", + "Agents SDK", + "Hello World", + "Model context protocol", + "MCP", + "structured outputs", + "Chain-of-Thought", + "Chat Completions", + "Computer-Using Agent", + "Code Interpreter", + "Function Calling", + "LLM", + "Operator", + "Playground", + "Realtime API", + "Sora", + # Add more terms here +] + +eng_to_non_eng_mapping = { + "ja": { + "agents": "エージェント", + "computer use": "コンピュータ操作", + "OAI hosted tools": "OpenAI がホストするツール", + "well formed data": "適切な形式のデータ", + "guardrail": "ガードレール", + "handoffs": "ハンドオフ", + "function tools": "関数ツール", + "tracing": "トレーシング", + "code examples": "コード例", + "vector store": "ベクトルストア", + "deep research": "ディープリサーチ", + "category": "カテゴリー", + "user": "ユーザー", + "parameter": "パラメーター", + "processor": "プロセッサー", + "server": "サーバー", + "web search": "Web 検索", + "file search": "ファイル検索", + "streaming": "ストリーミング", + "system prompt": "システムプロンプト", + "Python first": "Python ファースト", + # Add more Japanese mappings here + }, + # Add more languages here +} +eng_to_non_eng_instructions = { + "common": [ + "* The term 'examples' must be code examples when the page mentions the code examples in the repo, it can be translated as either 'code examples' or 'sample code'.", + "* The term 'primitives' can be translated as basic components.", + "* When the terms 'instructions' and 'tools' are mentioned as API parameter names, they must be kept as is.", + "* The terms 'temperature', 'top_p', 'max_tokens', 'presence_penalty', 'frequency_penalty' as parameter names must be kept as is.", + ], + "ja": [ + "* The term 'result' in the Runner guide context must be translated like 'execution results'", + "* The term 'raw' in 'raw response events' must be kept as is", + "* You must consistently use polite wording such as です/ます rather than である/なのだ.", + # Add more Japanese mappings here + ], + # Add more languages here +} + + +def built_instructions(target_language: str, lang_code: str) -> str: + do_not_translate_terms = "\n".join(do_not_translate) + specific_terms = "\n".join( + [f"* {k} -> {v}" for k, v in eng_to_non_eng_mapping.get(lang_code, {}).items()] + ) + specific_instructions = "\n".join( + eng_to_non_eng_instructions.get("common", []) + + eng_to_non_eng_instructions.get(lang_code, []) + ) + return f"""You are an expert technical translator. + +Your task: translate the markdown passed as a user input from English into {target_language}. +The inputs are the official OpenAI Agents SDK framework documentation, and your translation outputs'll be used for serving the official {target_language} version of them. Thus, accuracy, clarity, and fidelity to the original are critical. + +############################ +## OUTPUT REQUIREMENTS ## +############################ +You must return **only** the translated markdown. Do not include any commentary, metadata, or explanations. The original markdown structure must be strictly preserved. + +######################### +## GENERAL RULES ## +######################### +- Be professional and polite. +- Keep the tone **natural** and concise. +- Do not omit any content. If a segment should stay in English, copy it verbatim. +- Do not change the markdown data structure, including the indentations. +- Section titles starting with # or ## must be a noun form rather than a sentence. +- Section titles must be translated except for the Do-Not-Translate list. +- Keep all placeholders such as `CODE_BLOCK_*` and `CODE_LINE_PREFIX` unchanged. +- Convert asset paths: `./assets/…` → `../assets/…`. + *Example:* `![img](./assets/pic.png)` → `![img](../assets/pic.png)` +- Treat the **Do‑Not‑Translate list** and **Term‑Specific list** as case‑insensitive; preserve the original casing you see. +- Skip translation for: + - Inline code surrounded by single back‑ticks ( `like_this` ). + - Fenced code blocks delimited by ``` or ~~~, including all comments inside them. + - Link URLs inside `[label](URL)` – translate the label, never the URL. + +######################### +## LANGUAGE‑SPECIFIC ## +######################### +*(applies only when {target_language} = Japanese)* +- Insert a half‑width space before and after all alphanumeric terms. +- Add a half‑width space just outside markdown emphasis markers: ` **太字** ` (good) vs `** 太字 **` (bad). + +######################### +## DO NOT TRANSLATE ## +######################### +When replacing the following terms, do not have extra spaces before/after them: +{do_not_translate_terms} + +######################### +## TERM‑SPECIFIC ## +######################### +Translate these terms exactly as provided (no extra spaces): +{specific_terms} + +######################### +## EXTRA GUIDELINES ## +######################### +{specific_instructions} + +######################### +## IF UNSURE ## +######################### +If you are uncertain about a term, leave the original English term in parentheses after your translation. + +######################### +## WORKFLOW ## +######################### + +Follow the following workflow to translate the given markdown text data: + +1. Read the input markdown text given by the user. +2. Translate the markdown file into {target_language}, carefully following the requirements above. +3. Perform a self-review to evaluate the quality of the translation, focusing on naturalness, accuracy, and consistency in detail. +4. If improvements are necessary, refine the content without changing the original meaning. +5. Continue improving the translation until you are fully satisfied with the result. +6. Once the final output is ready, return **only** the translated markdown text. No extra commentary. +""" + + +# Function to translate and save files +def translate_file(file_path: str, target_path: str, lang_code: str) -> None: + print(f"Translating {file_path} into a different language: {lang_code}") + with open(file_path, encoding="utf-8") as f: + content = f.read() + + # Split content into lines + lines: list[str] = content.splitlines() + chunks: list[str] = [] + current_chunk: list[str] = [] + + # Split content into chunks of up to 120 lines, ensuring splits occur before section titles + in_code_block = False + code_blocks: list[str] = [] + code_block_chunks: list[str] = [] + for line in lines: + if ( + ENABLE_SMALL_CHUNK_TRANSLATION is True + and len(current_chunk) >= 120 # required for gpt-4.5 + and not in_code_block + and line.startswith("#") + ): + chunks.append("\n".join(current_chunk)) + current_chunk = [] + if ENABLE_CODE_SNIPPET_EXCLUSION is True and line.strip().startswith("```"): + code_block_chunks.append(line) + if in_code_block is True: + code_blocks.append("\n".join(code_block_chunks)) + current_chunk.append(f"CODE_BLOCK_{(len(code_blocks) - 1):02}") + code_block_chunks.clear() + in_code_block = not in_code_block + continue + if in_code_block is True: + code_block_chunks.append(line) + else: + current_chunk.append(line) + if current_chunk: + chunks.append("\n".join(current_chunk)) + + # Translate each chunk separately and combine results + translated_content: list[str] = [] + for chunk in chunks: + instructions = built_instructions(languages[lang_code], lang_code) + if OPENAI_MODEL.startswith("o"): + response = openai_client.responses.create( + model=OPENAI_MODEL, + instructions=instructions, + input=chunk, + ) + translated_content.append(response.output_text) + else: + response = openai_client.responses.create( + model=OPENAI_MODEL, + instructions=instructions, + input=chunk, + temperature=0.0, + ) + translated_content.append(response.output_text) + + translated_text = "\n".join(translated_content) + for idx, code_block in enumerate(code_blocks): + translated_text = translated_text.replace(f"CODE_BLOCK_{idx:02}", code_block) + + # FIXME: enable mkdocs search plugin to seamlessly work with i18n plugin + translated_text = SEARCH_EXCLUSION + translated_text + # Save the combined translated content + with open(target_path, "w", encoding="utf-8") as f: + f.write(translated_text) + + +def translate_single_source_file(file_path: str) -> None: + relative_path = os.path.relpath(file_path, source_dir) + if "ref/" in relative_path or not file_path.endswith(".md"): + return + + for lang_code in languages: + target_dir = os.path.join(source_dir, lang_code) + target_path = os.path.join(target_dir, relative_path) + + # Ensure the target directory exists + os.makedirs(os.path.dirname(target_path), exist_ok=True) + + # Translate and save the file + translate_file(file_path, target_path, lang_code) + + +def main(): + # Traverse the source directory + for root, _, file_names in os.walk(source_dir): + # Skip the target directories + if any(lang in root for lang in languages): + continue + # Increasing this will make the translation faster; you can decide considering the model's capacity + concurrency = 6 + with ThreadPoolExecutor(max_workers=concurrency) as executor: + futures = [] + for file_name in file_names: + filepath = os.path.join(root, file_name) + futures.append(executor.submit(translate_single_source_file, filepath)) + if len(futures) >= concurrency: + for future in futures: + future.result() + futures.clear() + + print("Translation completed.") + + +if __name__ == "__main__": + # translate_single_source_file("docs/index.md") + main() diff --git a/docs/tools.md b/docs/tools.md index f7a88691..5fe2eced 100644 --- a/docs/tools.md +++ b/docs/tools.md @@ -259,6 +259,27 @@ async def main(): print(result.final_output) ``` +### Customizing tool-agents + +The `agent.as_tool` function is a convenience method to make it easy to turn an agent into a tool. It doesn't support all configuration though; for example, you can't set `max_turns`. For advanced use cases, use `Runner.run` directly in your tool implementation: + +```python +@function_tool +async def run_my_agent() -> str: + """A tool that runs the agent with custom configs". + + agent = Agent(name="My agent", instructions="...") + + result = await Runner.run( + agent, + input="...", + max_turns=5, + run_config=... + ) + + return str(result.final_output) +``` + ## Handling errors in function tools When you create a function tool via `@function_tool`, you can pass a `failure_error_function`. This is a function that provides an error response to the LLM in case the tool call crashes. diff --git a/docs/tracing.md b/docs/tracing.md index fbf2ae41..4a9c1bd9 100644 --- a/docs/tracing.md +++ b/docs/tracing.md @@ -9,6 +9,8 @@ The Agents SDK includes built-in tracing, collecting a comprehensive record of e 1. You can globally disable tracing by setting the env var `OPENAI_AGENTS_DISABLE_TRACING=1` 2. You can disable tracing for a single run by setting [`agents.run.RunConfig.tracing_disabled`][] to `True` +***For organizations operating under a Zero Data Retention (ZDR) policy using OpenAI's APIs, tracing is unavailable.*** + ## Traces and spans - **Traces** represent a single end-to-end operation of a "workflow". They're composed of Spans. Traces have the following properties: @@ -16,7 +18,7 @@ The Agents SDK includes built-in tracing, collecting a comprehensive record of e - `trace_id`: A unique ID for the trace. Automatically generated if you don't pass one. Must have the format `trace_<32_alphanumeric>`. - `group_id`: Optional group ID, to link multiple traces from the same conversation. For example, you might use a chat thread ID. - `disabled`: If True, the trace will not be recorded. - - `metadata`: Optiona metadata for the trace. + - `metadata`: Optional metadata for the trace. - **Spans** represent operations that have a start and end time. Spans have: - `started_at` and `ended_at` timestamps. - `trace_id`, to represent the trace they belong to @@ -33,6 +35,9 @@ By default, the SDK traces the following: - Function tool calls are each wrapped in `function_span()` - Guardrails are wrapped in `guardrail_span()` - Handoffs are wrapped in `handoff_span()` +- Audio inputs (speech-to-text) are wrapped in a `transcription_span()` +- Audio outputs (text-to-speech) are wrapped in a `speech_span()` +- Related audio spans may be parented under a `speech_group_span()` By default, the trace is named "Agent trace". You can set this name if you use `trace`, or you can can configure the name and other properties with the [`RunConfig`][agents.run.RunConfig]. @@ -50,7 +55,7 @@ async def main(): with trace("Joke workflow"): # (1)! first_result = await Runner.run(agent, "Tell me a joke") - second_result = await Runner.run(agent, f"Rate this joke: {first_output.final_output}") + second_result = await Runner.run(agent, f"Rate this joke: {first_result.final_output}") print(f"Joke: {first_result.final_output}") print(f"Rating: {second_result.final_output}") ``` @@ -74,7 +79,11 @@ Spans are automatically part of the current trace, and are nested under the near ## Sensitive data -Some spans track potentially sensitive data. For example, the `generation_span()` stores the inputs/outputs of the LLM generation, and `function_span()` stores the inputs/outputs of function calls. These may contain sensitive data, so you can disable capturing that data via [`RunConfig.trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data]. +Certain spans may capture potentially sensitive data. + +The `generation_span()` stores the inputs/outputs of the LLM generation, and `function_span()` stores the inputs/outputs of function calls. These may contain sensitive data, so you can disable capturing that data via [`RunConfig.trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data]. + +Similarly, Audio spans include base64-encoded PCM data for input and output audio by default. You can disable capturing this audio data by configuring [`VoicePipelineConfig.trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data]. ## Custom tracing processors @@ -88,8 +97,22 @@ To customize this default setup, to send traces to alternative or additional bac 1. [`add_trace_processor()`][agents.tracing.add_trace_processor] lets you add an **additional** trace processor that will receive traces and spans as they are ready. This lets you do your own processing in addition to sending traces to OpenAI's backend. 2. [`set_trace_processors()`][agents.tracing.set_trace_processors] lets you **replace** the default processors with your own trace processors. This means traces will not be sent to the OpenAI backend unless you include a `TracingProcessor` that does so. -External trace processors include: +## External tracing processors list +- [Weights & Biases](https://weave-docs.wandb.ai/guides/integrations/openai_agents) +- [Arize-Phoenix](https://docs.arize.com/phoenix/tracing/integrations-tracing/openai-agents-sdk) +- [Future AGI](https://docs.futureagi.com/future-agi/products/observability/auto-instrumentation/openai_agents) +- [MLflow (self-hosted/OSS](https://mlflow.org/docs/latest/tracing/integrations/openai-agent) +- [MLflow (Databricks hosted](https://docs.databricks.com/aws/en/mlflow/mlflow-tracing#-automatic-tracing) - [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk) - [Pydantic Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents) - [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk) +- [Scorecard](https://docs.scorecard.io/docs/documentation/features/tracing#openai-agents-sdk-integration) +- [Keywords AI](https://docs.keywordsai.co/integration/development-frameworks/openai-agent) +- [LangSmith](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_openai_agents_sdk) +- [Maxim AI](https://www.getmaxim.ai/docs/observe/integrations/openai-agents-sdk) +- [Comet Opik](https://www.comet.com/docs/opik/tracing/integrations/openai_agents) +- [Langfuse](https://langfuse.com/docs/integrations/openaiagentssdk/openai-agents) +- [Langtrace](https://docs.langtrace.ai/supported-integrations/llm-frameworks/openai-agents-sdk) +- [Okahu-Monocle](https://github.com/monocle2ai/monocle) +- [Galileo](https://v2docs.galileo.ai/integrations/openai-agent-integration#openai-agent-integration) diff --git a/docs/visualization.md b/docs/visualization.md new file mode 100644 index 00000000..409803f7 --- /dev/null +++ b/docs/visualization.md @@ -0,0 +1,84 @@ +# Agent Visualization + +Agent visualization allows you to generate a structured graphical representation of agents and their relationships using **Graphviz**. This is useful for understanding how agents, tools, and handoffs interact within an application. + +## Installation + +Install the optional `viz` dependency group: + +```bash +pip install "openai-agents[viz]" +``` + +## Generating a Graph + +You can generate an agent visualization using the `draw_graph` function. This function creates a directed graph where: + +- **Agents** are represented as yellow boxes. +- **Tools** are represented as green ellipses. +- **Handoffs** are directed edges from one agent to another. + +### Example Usage + +```python +from agents import Agent, function_tool +from agents.extensions.visualization import draw_graph + +@function_tool +def get_weather(city: str) -> str: + return f"The weather in {city} is sunny." + +spanish_agent = Agent( + name="Spanish agent", + instructions="You only speak Spanish.", +) + +english_agent = Agent( + name="English agent", + instructions="You only speak English", +) + +triage_agent = Agent( + name="Triage agent", + instructions="Handoff to the appropriate agent based on the language of the request.", + handoffs=[spanish_agent, english_agent], + tools=[get_weather], +) + +draw_graph(triage_agent) +``` + +![Agent Graph](./assets/images/graph.png) + +This generates a graph that visually represents the structure of the **triage agent** and its connections to sub-agents and tools. + + +## Understanding the Visualization + +The generated graph includes: + +- A **start node** (`__start__`) indicating the entry point. +- Agents represented as **rectangles** with yellow fill. +- Tools represented as **ellipses** with green fill. +- Directed edges indicating interactions: + - **Solid arrows** for agent-to-agent handoffs. + - **Dotted arrows** for tool invocations. +- An **end node** (`__end__`) indicating where execution terminates. + +## Customizing the Graph + +### Showing the Graph +By default, `draw_graph` displays the graph inline. To show the graph in a separate window, write the following: + +```python +draw_graph(triage_agent).view() +``` + +### Saving the Graph +By default, `draw_graph` displays the graph inline. To save it as a file, specify a filename: + +```python +draw_graph(triage_agent, filename="agent_graph") +``` + +This will generate `agent_graph.png` in the working directory. diff --git a/docs/voice/pipeline.md b/docs/voice/pipeline.md new file mode 100644 index 00000000..8cf5dafe --- /dev/null +++ b/docs/voice/pipeline.md @@ -0,0 +1,75 @@ +# Pipelines and workflows + +[`VoicePipeline`][agents.voice.pipeline.VoicePipeline] is a class that makes it easy to turn your agentic workflows into a voice app. You pass in a workflow to run, and the pipeline takes care of transcribing input audio, detecting when the audio ends, calling your workflow at the right time, and turning the workflow output back into audio. + +```mermaid +graph LR + %% Input + A["🎤 Audio Input"] + + %% Voice Pipeline + subgraph Voice_Pipeline [Voice Pipeline] + direction TB + B["Transcribe (speech-to-text)"] + C["Your Code"]:::highlight + D["Text-to-speech"] + B --> C --> D + end + + %% Output + E["🎧 Audio Output"] + + %% Flow + A --> Voice_Pipeline + Voice_Pipeline --> E + + %% Custom styling + classDef highlight fill:#ffcc66,stroke:#333,stroke-width:1px,font-weight:700; + +``` + +## Configuring a pipeline + +When you create a pipeline, you can set a few things: + +1. The [`workflow`][agents.voice.workflow.VoiceWorkflowBase], which is the code that runs each time new audio is transcribed. +2. The [`speech-to-text`][agents.voice.model.STTModel] and [`text-to-speech`][agents.voice.model.TTSModel] models used +3. The [`config`][agents.voice.pipeline_config.VoicePipelineConfig], which lets you configure things like: + - A model provider, which can map model names to models + - Tracing, including whether to disable tracing, whether audio files are uploaded, the workflow name, trace IDs etc. + - Settings on the TTS and STT models, like the prompt, language and data types used. + +## Running a pipeline + +You can run a pipeline via the [`run()`][agents.voice.pipeline.VoicePipeline.run] method, which lets you pass in audio input in two forms: + +1. [`AudioInput`][agents.voice.input.AudioInput] is used when you have a full audio transcript, and just want to produce a result for it. This is useful in cases where you don't need to detect when a speaker is done speaking; for example, when you have pre-recorded audio or in push-to-talk apps where it's clear when the user is done speaking. +2. [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput] is used when you might need to detect when a user is done speaking. It allows you to push audio chunks as they are detected, and the voice pipeline will automatically run the agent workflow at the right time, via a process called "activity detection". + +## Results + +The result of a voice pipeline run is a [`StreamedAudioResult`][agents.voice.result.StreamedAudioResult]. This is an object that lets you stream events as they occur. There are a few kinds of [`VoiceStreamEvent`][agents.voice.events.VoiceStreamEvent], including: + +1. [`VoiceStreamEventAudio`][agents.voice.events.VoiceStreamEventAudio], which contains a chunk of audio. +2. [`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle], which informs you of lifecycle events like a turn starting or ending. +3. [`VoiceStreamEventError`][agents.voice.events.VoiceStreamEventError], is an error event. + +```python + +result = await pipeline.run(input) + +async for event in result.stream(): + if event.type == "voice_stream_event_audio": + # play audio + elif event.type == "voice_stream_event_lifecycle": + # lifecycle + elif event.type == "voice_stream_event_error" + # error + ... +``` + +## Best practices + +### Interruptions + +The Agents SDK currently does not support any built-in interruptions support for [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput]. Instead for every detected turn it will trigger a separate run of your workflow. If you want to handle interruptions inside your application you can listen to the [`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle] events. `turn_started` will indicate that a new turn was transcribed and processing is beginning. `turn_ended` will trigger after all the audio was dispatched for a respective turn. You could use these events to mute the microphone of the speaker when the model starts a turn and unmute it after you flushed all the related audio for a turn. diff --git a/docs/voice/quickstart.md b/docs/voice/quickstart.md new file mode 100644 index 00000000..896ffe83 --- /dev/null +++ b/docs/voice/quickstart.md @@ -0,0 +1,194 @@ +# Quickstart + +## Prerequisites + +Make sure you've followed the base [quickstart instructions](../quickstart.md) for the Agents SDK, and set up a virtual environment. Then, install the optional voice dependencies from the SDK: + +```bash +pip install 'openai-agents[voice]' +``` + +## Concepts + +The main concept to know about is a [`VoicePipeline`][agents.voice.pipeline.VoicePipeline], which is a 3 step process: + +1. Run a speech-to-text model to turn audio into text. +2. Run your code, which is usually an agentic workflow, to produce a result. +3. Run a text-to-speech model to turn the result text back into audio. + +```mermaid +graph LR + %% Input + A["🎤 Audio Input"] + + %% Voice Pipeline + subgraph Voice_Pipeline [Voice Pipeline] + direction TB + B["Transcribe (speech-to-text)"] + C["Your Code"]:::highlight + D["Text-to-speech"] + B --> C --> D + end + + %% Output + E["🎧 Audio Output"] + + %% Flow + A --> Voice_Pipeline + Voice_Pipeline --> E + + %% Custom styling + classDef highlight fill:#ffcc66,stroke:#333,stroke-width:1px,font-weight:700; + +``` + +## Agents + +First, let's set up some Agents. This should feel familiar to you if you've built any agents with this SDK. We'll have a couple of Agents, a handoff, and a tool. + +```python +import asyncio +import random + +from agents import ( + Agent, + function_tool, +) +from agents.extensions.handoff_prompt import prompt_with_handoff_instructions + + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city.""" + print(f"[debug] get_weather called with city: {city}") + choices = ["sunny", "cloudy", "rainy", "snowy"] + return f"The weather in {city} is {random.choice(choices)}." + + +spanish_agent = Agent( + name="Spanish", + handoff_description="A spanish speaking agent.", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. Speak in Spanish.", + ), + model="gpt-4o-mini", +) + +agent = Agent( + name="Assistant", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the spanish agent.", + ), + model="gpt-4o-mini", + handoffs=[spanish_agent], + tools=[get_weather], +) +``` + +## Voice pipeline + +We'll set up a simple voice pipeline, using [`SingleAgentVoiceWorkflow`][agents.voice.workflow.SingleAgentVoiceWorkflow] as the workflow. + +```python +from agents.voice import SingleAgentVoiceWorkflow, VoicePipeline +pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent)) +``` + +## Run the pipeline + +```python +import numpy as np +import sounddevice as sd +from agents.voice import AudioInput + +# For simplicity, we'll just create 3 seconds of silence +# In reality, you'd get microphone data +buffer = np.zeros(24000 * 3, dtype=np.int16) +audio_input = AudioInput(buffer=buffer) + +result = await pipeline.run(audio_input) + +# Create an audio player using `sounddevice` +player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16) +player.start() + +# Play the audio stream as it comes in +async for event in result.stream(): + if event.type == "voice_stream_event_audio": + player.write(event.data) + +``` + +## Put it all together + +```python +import asyncio +import random + +import numpy as np +import sounddevice as sd + +from agents import ( + Agent, + function_tool, + set_tracing_disabled, +) +from agents.voice import ( + AudioInput, + SingleAgentVoiceWorkflow, + VoicePipeline, +) +from agents.extensions.handoff_prompt import prompt_with_handoff_instructions + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city.""" + print(f"[debug] get_weather called with city: {city}") + choices = ["sunny", "cloudy", "rainy", "snowy"] + return f"The weather in {city} is {random.choice(choices)}." + + +spanish_agent = Agent( + name="Spanish", + handoff_description="A spanish speaking agent.", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. Speak in Spanish.", + ), + model="gpt-4o-mini", +) + +agent = Agent( + name="Assistant", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the spanish agent.", + ), + model="gpt-4o-mini", + handoffs=[spanish_agent], + tools=[get_weather], +) + + +async def main(): + pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent)) + buffer = np.zeros(24000 * 3, dtype=np.int16) + audio_input = AudioInput(buffer=buffer) + + result = await pipeline.run(audio_input) + + # Create an audio player using `sounddevice` + player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16) + player.start() + + # Play the audio stream as it comes in + async for event in result.stream(): + if event.type == "voice_stream_event_audio": + player.write(event.data) + + +if __name__ == "__main__": + asyncio.run(main()) +``` + +If you run this example, the agent will speak to you! Check out the example in [examples/voice/static](https://github.com/openai/openai-agents-python/tree/main/examples/voice/static) to see a demo where you can speak to the agent yourself. diff --git a/docs/voice/tracing.md b/docs/voice/tracing.md new file mode 100644 index 00000000..311a9ba8 --- /dev/null +++ b/docs/voice/tracing.md @@ -0,0 +1,14 @@ +# Tracing + +Just like the way [agents are traced](../tracing.md), voice pipelines are also automatically traced. + +You can read the tracing doc above for basic tracing information, but you can additionally configure tracing of a pipeline via [`VoicePipelineConfig`][agents.voice.pipeline_config.VoicePipelineConfig]. + +Key tracing related fields are: + +- [`tracing_disabled`][agents.voice.pipeline_config.VoicePipelineConfig.tracing_disabled]: controls whether tracing is disabled. By default, tracing is enabled. +- [`trace_include_sensitive_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_data]: controls whether traces include potentially sensitive data, like audio transcripts. This is specifically for the voice pipeline, and not for anything that goes on inside your Workflow. +- [`trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data]: controls whether traces include audio data. +- [`workflow_name`][agents.voice.pipeline_config.VoicePipelineConfig.workflow_name]: The name of the trace workflow. +- [`group_id`][agents.voice.pipeline_config.VoicePipelineConfig.group_id]: The `group_id` of the trace, which lets you link multiple traces. +- [`trace_metadata`][agents.voice.pipeline_config.VoicePipelineConfig.tracing_disabled]: Additional metadata to include with the trace. diff --git a/examples/agent_patterns/README.md b/examples/agent_patterns/README.md index 4599b001..96b48920 100644 --- a/examples/agent_patterns/README.md +++ b/examples/agent_patterns/README.md @@ -51,4 +51,4 @@ You can definitely do this without any special Agents SDK features by using para This is really useful for latency: for example, you might have a very fast model that runs the guardrail and a slow model that runs the actual agent. You wouldn't want to wait for the slow model to finish, so guardrails let you quickly reject invalid inputs. -See the [`guardrails.py`](./guardrails.py) file for an example of this. +See the [`input_guardrails.py`](./input_guardrails.py) and [`output_guardrails.py`](./output_guardrails.py) files for examples. diff --git a/examples/agent_patterns/forcing_tool_use.py b/examples/agent_patterns/forcing_tool_use.py new file mode 100644 index 00000000..3f4e35ae --- /dev/null +++ b/examples/agent_patterns/forcing_tool_use.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +import asyncio +from typing import Any, Literal + +from pydantic import BaseModel + +from agents import ( + Agent, + FunctionToolResult, + ModelSettings, + RunContextWrapper, + Runner, + ToolsToFinalOutputFunction, + ToolsToFinalOutputResult, + function_tool, +) + +""" +This example shows how to force the agent to use a tool. It uses `ModelSettings(tool_choice="required")` +to force the agent to use any tool. + +You can run it with 3 options: +1. `default`: The default behavior, which is to send the tool output to the LLM. In this case, + `tool_choice` is not set, because otherwise it would result in an infinite loop - the LLM would + call the tool, the tool would run and send the results to the LLM, and that would repeat + (because the model is forced to use a tool every time.) +2. `first_tool_result`: The first tool result is used as the final output. +3. `custom`: A custom tool use behavior function is used. The custom function receives all the tool + results, and chooses to use the first tool result to generate the final output. + +Usage: +python examples/agent_patterns/forcing_tool_use.py -t default +python examples/agent_patterns/forcing_tool_use.py -t first_tool +python examples/agent_patterns/forcing_tool_use.py -t custom +""" + + +class Weather(BaseModel): + city: str + temperature_range: str + conditions: str + + +@function_tool +def get_weather(city: str) -> Weather: + print("[debug] get_weather called") + return Weather(city=city, temperature_range="14-20C", conditions="Sunny with wind") + + +async def custom_tool_use_behavior( + context: RunContextWrapper[Any], results: list[FunctionToolResult] +) -> ToolsToFinalOutputResult: + weather: Weather = results[0].output + return ToolsToFinalOutputResult( + is_final_output=True, final_output=f"{weather.city} is {weather.conditions}." + ) + + +async def main(tool_use_behavior: Literal["default", "first_tool", "custom"] = "default"): + if tool_use_behavior == "default": + behavior: Literal["run_llm_again", "stop_on_first_tool"] | ToolsToFinalOutputFunction = ( + "run_llm_again" + ) + elif tool_use_behavior == "first_tool": + behavior = "stop_on_first_tool" + elif tool_use_behavior == "custom": + behavior = custom_tool_use_behavior + + agent = Agent( + name="Weather agent", + instructions="You are a helpful agent.", + tools=[get_weather], + tool_use_behavior=behavior, + model_settings=ModelSettings( + tool_choice="required" if tool_use_behavior != "default" else None + ), + ) + + result = await Runner.run(agent, input="What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "-t", + "--tool-use-behavior", + type=str, + required=True, + choices=["default", "first_tool", "custom"], + help="The behavior to use for tool use. Default will cause tool outputs to be sent to the model. " + "first_tool_result will cause the first tool result to be used as the final output. " + "custom will use a custom tool use behavior function.", + ) + args = parser.parse_args() + asyncio.run(main(args.tool_use_behavior)) diff --git a/examples/agent_patterns/input_guardrails.py b/examples/agent_patterns/input_guardrails.py index 62591886..15453551 100644 --- a/examples/agent_patterns/input_guardrails.py +++ b/examples/agent_patterns/input_guardrails.py @@ -30,8 +30,8 @@ ### 1. An agent-based guardrail that is triggered if the user is asking to do math homework class MathHomeworkOutput(BaseModel): - is_math_homework: bool reasoning: str + is_math_homework: bool guardrail_agent = Agent( @@ -53,7 +53,7 @@ async def math_guardrail( return GuardrailFunctionOutput( output_info=final_output, - tripwire_triggered=not final_output.is_math_homework, + tripwire_triggered=final_output.is_math_homework, ) diff --git a/examples/agent_patterns/llm_as_a_judge.py b/examples/agent_patterns/llm_as_a_judge.py index d13a67cb..5a46cc3e 100644 --- a/examples/agent_patterns/llm_as_a_judge.py +++ b/examples/agent_patterns/llm_as_a_judge.py @@ -23,8 +23,8 @@ @dataclass class EvaluationFeedback: - score: Literal["pass", "needs_improvement", "fail"] feedback: str + score: Literal["pass", "needs_improvement", "fail"] evaluator = Agent[None]( diff --git a/examples/agent_patterns/streaming_guardrails.py b/examples/agent_patterns/streaming_guardrails.py new file mode 100644 index 00000000..f4db2869 --- /dev/null +++ b/examples/agent_patterns/streaming_guardrails.py @@ -0,0 +1,93 @@ +from __future__ import annotations + +import asyncio + +from openai.types.responses import ResponseTextDeltaEvent +from pydantic import BaseModel, Field + +from agents import Agent, Runner + +""" +This example shows how to use guardrails as the model is streaming. Output guardrails run after the +final output has been generated; this example runs guardails every N tokens, allowing for early +termination if bad output is detected. + +The expected output is that you'll see a bunch of tokens stream in, then the guardrail will trigger +and stop the streaming. +""" + + +agent = Agent( + name="Assistant", + instructions=( + "You are a helpful assistant. You ALWAYS write long responses, making sure to be verbose " + "and detailed." + ), +) + + +class GuardrailOutput(BaseModel): + reasoning: str = Field( + description="Reasoning about whether the response could be understood by a ten year old." + ) + is_readable_by_ten_year_old: bool = Field( + description="Whether the response is understandable by a ten year old." + ) + + +guardrail_agent = Agent( + name="Checker", + instructions=( + "You will be given a question and a response. Your goal is to judge whether the response " + "is simple enough to be understood by a ten year old." + ), + output_type=GuardrailOutput, + model="gpt-4o-mini", +) + + +async def check_guardrail(text: str) -> GuardrailOutput: + result = await Runner.run(guardrail_agent, text) + return result.final_output_as(GuardrailOutput) + + +async def main(): + question = "What is a black hole, and how does it behave?" + result = Runner.run_streamed(agent, question) + current_text = "" + + # We will check the guardrail every N characters + next_guardrail_check_len = 300 + guardrail_task = None + + async for event in result.stream_events(): + if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent): + print(event.data.delta, end="", flush=True) + current_text += event.data.delta + + # Check if it's time to run the guardrail check + # Note that we don't run the guardrail check if there's already a task running. An + # alternate implementation is to have N guardrails running, or cancel the previous + # one. + if len(current_text) >= next_guardrail_check_len and not guardrail_task: + print("Running guardrail check") + guardrail_task = asyncio.create_task(check_guardrail(current_text)) + next_guardrail_check_len += 300 + + # Every iteration of the loop, check if the guardrail has been triggered + if guardrail_task and guardrail_task.done(): + guardrail_result = guardrail_task.result() + if not guardrail_result.is_readable_by_ten_year_old: + print("\n\n================\n\n") + print(f"Guardrail triggered. Reasoning:\n{guardrail_result.reasoning}") + break + + # Do one final check on the final output + guardrail_result = await check_guardrail(current_text) + if not guardrail_result.is_readable_by_ten_year_old: + print("\n\n================\n\n") + print(f"Guardrail triggered. Reasoning:\n{guardrail_result.reasoning}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/basic/agent_lifecycle_example.py b/examples/basic/agent_lifecycle_example.py index bc0bbe43..29bb18c9 100644 --- a/examples/basic/agent_lifecycle_example.py +++ b/examples/basic/agent_lifecycle_example.py @@ -74,7 +74,7 @@ class FinalResult(BaseModel): start_agent = Agent( name="Start Agent", - instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multipler agent.", + instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multiply agent.", tools=[random_number], output_type=FinalResult, handoffs=[multiply_agent], diff --git a/examples/basic/hello_world_jupyter.py b/examples/basic/hello_world_jupyter.py new file mode 100644 index 00000000..c929a7c6 --- /dev/null +++ b/examples/basic/hello_world_jupyter.py @@ -0,0 +1,11 @@ +from agents import Agent, Runner + +agent = Agent(name="Assistant", instructions="You are a helpful assistant") + +# Intended for Jupyter notebooks where there's an existing event loop +result = await Runner.run(agent, "Write a haiku about recursion in programming.") # type: ignore[top-level-await] # noqa: F704 +print(result.final_output) + +# Code within code loops, +# Infinite mirrors reflect— +# Logic folds on self. diff --git a/examples/basic/lifecycle_example.py b/examples/basic/lifecycle_example.py index 9b365106..285bfecd 100644 --- a/examples/basic/lifecycle_example.py +++ b/examples/basic/lifecycle_example.py @@ -79,7 +79,7 @@ class FinalResult(BaseModel): start_agent = Agent( name="Start Agent", - instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multipler agent.", + instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multiplier agent.", tools=[random_number], output_type=FinalResult, handoffs=[multiply_agent], diff --git a/examples/basic/local_image.py b/examples/basic/local_image.py new file mode 100644 index 00000000..d4a784ba --- /dev/null +++ b/examples/basic/local_image.py @@ -0,0 +1,48 @@ +import asyncio +import base64 +import os + +from agents import Agent, Runner + +FILEPATH = os.path.join(os.path.dirname(__file__), "media/image_bison.jpg") + + +def image_to_base64(image_path): + with open(image_path, "rb") as image_file: + encoded_string = base64.b64encode(image_file.read()).decode("utf-8") + return encoded_string + + +async def main(): + # Print base64-encoded image + b64_image = image_to_base64(FILEPATH) + + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + ) + + result = await Runner.run( + agent, + [ + { + "role": "user", + "content": [ + { + "type": "input_image", + "detail": "auto", + "image_url": f"data:image/jpeg;base64,{b64_image}", + } + ], + }, + { + "role": "user", + "content": "What do you see in this image?", + }, + ], + ) + print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/basic/media/image_bison.jpg b/examples/basic/media/image_bison.jpg new file mode 100644 index 00000000..b113c91f Binary files /dev/null and b/examples/basic/media/image_bison.jpg differ diff --git a/examples/basic/non_strict_output_type.py b/examples/basic/non_strict_output_type.py new file mode 100644 index 00000000..49fcc4e2 --- /dev/null +++ b/examples/basic/non_strict_output_type.py @@ -0,0 +1,81 @@ +import asyncio +import json +from dataclasses import dataclass +from typing import Any + +from agents import Agent, AgentOutputSchema, AgentOutputSchemaBase, Runner + +"""This example demonstrates how to use an output type that is not in strict mode. Strict mode +allows us to guarantee valid JSON output, but some schemas are not strict-compatible. + +In this example, we define an output type that is not strict-compatible, and then we run the +agent with strict_json_schema=False. + +We also demonstrate a custom output type. + +To understand which schemas are strict-compatible, see: +https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#supported-schemas +""" + + +@dataclass +class OutputType: + jokes: dict[int, str] + """A list of jokes, indexed by joke number.""" + + +class CustomOutputSchema(AgentOutputSchemaBase): + """A demonstration of a custom output schema.""" + + def is_plain_text(self) -> bool: + return False + + def name(self) -> str: + return "CustomOutputSchema" + + def json_schema(self) -> dict[str, Any]: + return { + "type": "object", + "properties": {"jokes": {"type": "object", "properties": {"joke": {"type": "string"}}}}, + } + + def is_strict_json_schema(self) -> bool: + return False + + def validate_json(self, json_str: str) -> Any: + json_obj = json.loads(json_str) + # Just for demonstration, we'll return a list. + return list(json_obj["jokes"].values()) + + +async def main(): + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + output_type=OutputType, + ) + + input = "Tell me 3 short jokes." + + # First, let's try with a strict output type. This should raise an exception. + try: + result = await Runner.run(agent, input) + raise AssertionError("Should have raised an exception") + except Exception as e: + print(f"Error (expected): {e}") + + # Now let's try again with a non-strict output type. This should work. + # In some cases, it will raise an error - the schema isn't strict, so the model may + # produce an invalid JSON object. + agent.output_type = AgentOutputSchema(OutputType, strict_json_schema=False) + result = await Runner.run(agent, input) + print(result.final_output) + + # Finally, let's try a custom output type. + agent.output_type = CustomOutputSchema() + result = await Runner.run(agent, input) + print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/basic/previous_response_id.py b/examples/basic/previous_response_id.py new file mode 100644 index 00000000..b00bf3aa --- /dev/null +++ b/examples/basic/previous_response_id.py @@ -0,0 +1,66 @@ +import asyncio + +from agents import Agent, Runner + +"""This demonstrates usage of the `previous_response_id` parameter to continue a conversation. +The second run passes the previous response ID to the model, which allows it to continue the +conversation without re-sending the previous messages. + +Notes: +1. This only applies to the OpenAI Responses API. Other models will ignore this parameter. +2. Responses are only stored for 30 days as of this writing, so in production you should +store the response ID along with an expiration date; if the response is no longer valid, +you'll need to re-send the previous conversation history. +""" + + +async def main(): + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant. be VERY concise.", + ) + + result = await Runner.run(agent, "What is the largest country in South America?") + print(result.final_output) + # Brazil + + result = await Runner.run( + agent, + "What is the capital of that country?", + previous_response_id=result.last_response_id, + ) + print(result.final_output) + # Brasilia + + +async def main_stream(): + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant. be VERY concise.", + ) + + result = Runner.run_streamed(agent, "What is the largest country in South America?") + + async for event in result.stream_events(): + if event.type == "raw_response_event" and event.data.type == "response.output_text.delta": + print(event.data.delta, end="", flush=True) + + print() + + result = Runner.run_streamed( + agent, + "What is the capital of that country?", + previous_response_id=result.last_response_id, + ) + + async for event in result.stream_events(): + if event.type == "raw_response_event" and event.data.type == "response.output_text.delta": + print(event.data.delta, end="", flush=True) + + +if __name__ == "__main__": + is_stream = input("Run in stream mode? (y/n): ") + if is_stream == "y": + asyncio.run(main_stream()) + else: + asyncio.run(main()) diff --git a/examples/basic/remote_image.py b/examples/basic/remote_image.py new file mode 100644 index 00000000..948a22d9 --- /dev/null +++ b/examples/basic/remote_image.py @@ -0,0 +1,31 @@ +import asyncio + +from agents import Agent, Runner + +URL = "https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg" + + +async def main(): + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + ) + + result = await Runner.run( + agent, + [ + { + "role": "user", + "content": [{"type": "input_image", "detail": "auto", "image_url": URL}], + }, + { + "role": "user", + "content": "What do you see in this image?", + }, + ], + ) + print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/basic/tools.py b/examples/basic/tools.py new file mode 100644 index 00000000..8936065a --- /dev/null +++ b/examples/basic/tools.py @@ -0,0 +1,34 @@ +import asyncio + +from pydantic import BaseModel + +from agents import Agent, Runner, function_tool + + +class Weather(BaseModel): + city: str + temperature_range: str + conditions: str + + +@function_tool +def get_weather(city: str) -> Weather: + print("[debug] get_weather called") + return Weather(city=city, temperature_range="14-20C", conditions="Sunny with wind.") + + +agent = Agent( + name="Hello world", + instructions="You are a helpful agent.", + tools=[get_weather], +) + + +async def main(): + result = await Runner.run(agent, input="What's the weather in Tokyo?") + print(result.final_output) + # The weather in Tokyo is sunny. + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/financial_research_agent/README.md b/examples/financial_research_agent/README.md new file mode 100644 index 00000000..756ade6e --- /dev/null +++ b/examples/financial_research_agent/README.md @@ -0,0 +1,38 @@ +# Financial Research Agent Example + +This example shows how you might compose a richer financial research agent using the Agents SDK. The pattern is similar to the `research_bot` example, but with more specialized sub‑agents and a verification step. + +The flow is: + +1. **Planning**: A planner agent turns the end user’s request into a list of search terms relevant to financial analysis – recent news, earnings calls, corporate filings, industry commentary, etc. +2. **Search**: A search agent uses the built‑in `WebSearchTool` to retrieve terse summaries for each search term. (You could also add `FileSearchTool` if you have indexed PDFs or 10‑Ks.) +3. **Sub‑analysts**: Additional agents (e.g. a fundamentals analyst and a risk analyst) are exposed as tools so the writer can call them inline and incorporate their outputs. +4. **Writing**: A senior writer agent brings together the search snippets and any sub‑analyst summaries into a long‑form markdown report plus a short executive summary. +5. **Verification**: A final verifier agent audits the report for obvious inconsistencies or missing sourcing. + +You can run the example with: + +```bash +python -m examples.financial_research_agent.main +``` + +and enter a query like: + +``` +Write up an analysis of Apple Inc.'s most recent quarter. +``` + +### Starter prompt + +The writer agent is seeded with instructions similar to: + +``` +You are a senior financial analyst. You will be provided with the original query +and a set of raw search summaries. Your job is to synthesize these into a +long‑form markdown report (at least several paragraphs) with a short executive +summary. You also have access to tools like `fundamentals_analysis` and +`risk_analysis` to get short specialist write‑ups if you want to incorporate them. +Add a few follow‑up questions for further research. +``` + +You can tweak these prompts and sub‑agents to suit your own data sources and preferred report structure. diff --git a/tests/examples/research_bot/agents/__init__.py b/examples/financial_research_agent/__init__.py similarity index 100% rename from tests/examples/research_bot/agents/__init__.py rename to examples/financial_research_agent/__init__.py diff --git a/tests/src/agents/extensions/__init__.py b/examples/financial_research_agent/agents/__init__.py similarity index 100% rename from tests/src/agents/extensions/__init__.py rename to examples/financial_research_agent/agents/__init__.py diff --git a/examples/financial_research_agent/agents/financials_agent.py b/examples/financial_research_agent/agents/financials_agent.py new file mode 100644 index 00000000..953531f2 --- /dev/null +++ b/examples/financial_research_agent/agents/financials_agent.py @@ -0,0 +1,23 @@ +from pydantic import BaseModel + +from agents import Agent + +# A sub‑agent focused on analyzing a company's fundamentals. +FINANCIALS_PROMPT = ( + "You are a financial analyst focused on company fundamentals such as revenue, " + "profit, margins and growth trajectory. Given a collection of web (and optional file) " + "search results about a company, write a concise analysis of its recent financial " + "performance. Pull out key metrics or quotes. Keep it under 2 paragraphs." +) + + +class AnalysisSummary(BaseModel): + summary: str + """Short text summary for this aspect of the analysis.""" + + +financials_agent = Agent( + name="FundamentalsAnalystAgent", + instructions=FINANCIALS_PROMPT, + output_type=AnalysisSummary, +) diff --git a/examples/financial_research_agent/agents/planner_agent.py b/examples/financial_research_agent/agents/planner_agent.py new file mode 100644 index 00000000..14aaa0b1 --- /dev/null +++ b/examples/financial_research_agent/agents/planner_agent.py @@ -0,0 +1,35 @@ +from pydantic import BaseModel + +from agents import Agent + +# Generate a plan of searches to ground the financial analysis. +# For a given financial question or company, we want to search for +# recent news, official filings, analyst commentary, and other +# relevant background. +PROMPT = ( + "You are a financial research planner. Given a request for financial analysis, " + "produce a set of web searches to gather the context needed. Aim for recent " + "headlines, earnings calls or 10‑K snippets, analyst commentary, and industry background. " + "Output between 5 and 15 search terms to query for." +) + + +class FinancialSearchItem(BaseModel): + reason: str + """Your reasoning for why this search is relevant.""" + + query: str + """The search term to feed into a web (or file) search.""" + + +class FinancialSearchPlan(BaseModel): + searches: list[FinancialSearchItem] + """A list of searches to perform.""" + + +planner_agent = Agent( + name="FinancialPlannerAgent", + instructions=PROMPT, + model="o3-mini", + output_type=FinancialSearchPlan, +) diff --git a/examples/financial_research_agent/agents/risk_agent.py b/examples/financial_research_agent/agents/risk_agent.py new file mode 100644 index 00000000..e24deb4e --- /dev/null +++ b/examples/financial_research_agent/agents/risk_agent.py @@ -0,0 +1,22 @@ +from pydantic import BaseModel + +from agents import Agent + +# A sub‑agent specializing in identifying risk factors or concerns. +RISK_PROMPT = ( + "You are a risk analyst looking for potential red flags in a company's outlook. " + "Given background research, produce a short analysis of risks such as competitive threats, " + "regulatory issues, supply chain problems, or slowing growth. Keep it under 2 paragraphs." +) + + +class AnalysisSummary(BaseModel): + summary: str + """Short text summary for this aspect of the analysis.""" + + +risk_agent = Agent( + name="RiskAnalystAgent", + instructions=RISK_PROMPT, + output_type=AnalysisSummary, +) diff --git a/examples/financial_research_agent/agents/search_agent.py b/examples/financial_research_agent/agents/search_agent.py new file mode 100644 index 00000000..4ef2522d --- /dev/null +++ b/examples/financial_research_agent/agents/search_agent.py @@ -0,0 +1,18 @@ +from agents import Agent, WebSearchTool +from agents.model_settings import ModelSettings + +# Given a search term, use web search to pull back a brief summary. +# Summaries should be concise but capture the main financial points. +INSTRUCTIONS = ( + "You are a research assistant specializing in financial topics. " + "Given a search term, use web search to retrieve up‑to‑date context and " + "produce a short summary of at most 300 words. Focus on key numbers, events, " + "or quotes that will be useful to a financial analyst." +) + +search_agent = Agent( + name="FinancialSearchAgent", + instructions=INSTRUCTIONS, + tools=[WebSearchTool()], + model_settings=ModelSettings(tool_choice="required"), +) diff --git a/examples/financial_research_agent/agents/verifier_agent.py b/examples/financial_research_agent/agents/verifier_agent.py new file mode 100644 index 00000000..9ae660ef --- /dev/null +++ b/examples/financial_research_agent/agents/verifier_agent.py @@ -0,0 +1,27 @@ +from pydantic import BaseModel + +from agents import Agent + +# Agent to sanity‑check a synthesized report for consistency and recall. +# This can be used to flag potential gaps or obvious mistakes. +VERIFIER_PROMPT = ( + "You are a meticulous auditor. You have been handed a financial analysis report. " + "Your job is to verify the report is internally consistent, clearly sourced, and makes " + "no unsupported claims. Point out any issues or uncertainties." +) + + +class VerificationResult(BaseModel): + verified: bool + """Whether the report seems coherent and plausible.""" + + issues: str + """If not verified, describe the main issues or concerns.""" + + +verifier_agent = Agent( + name="VerificationAgent", + instructions=VERIFIER_PROMPT, + model="gpt-4o", + output_type=VerificationResult, +) diff --git a/examples/financial_research_agent/agents/writer_agent.py b/examples/financial_research_agent/agents/writer_agent.py new file mode 100644 index 00000000..0f561006 --- /dev/null +++ b/examples/financial_research_agent/agents/writer_agent.py @@ -0,0 +1,34 @@ +from pydantic import BaseModel + +from agents import Agent + +# Writer agent brings together the raw search results and optionally calls out +# to sub‑analyst tools for specialized commentary, then returns a cohesive markdown report. +WRITER_PROMPT = ( + "You are a senior financial analyst. You will be provided with the original query and " + "a set of raw search summaries. Your task is to synthesize these into a long‑form markdown " + "report (at least several paragraphs) including a short executive summary and follow‑up " + "questions. If needed, you can call the available analysis tools (e.g. fundamentals_analysis, " + "risk_analysis) to get short specialist write‑ups to incorporate." +) + + +class FinancialReportData(BaseModel): + short_summary: str + """A short 2‑3 sentence executive summary.""" + + markdown_report: str + """The full markdown report.""" + + follow_up_questions: list[str] + """Suggested follow‑up questions for further research.""" + + +# Note: We will attach handoffs to specialist analyst agents at runtime in the manager. +# This shows how an agent can use handoffs to delegate to specialized subagents. +writer_agent = Agent( + name="FinancialWriterAgent", + instructions=WRITER_PROMPT, + model="gpt-4.5-preview-2025-02-27", + output_type=FinancialReportData, +) diff --git a/examples/financial_research_agent/main.py b/examples/financial_research_agent/main.py new file mode 100644 index 00000000..b5b6cfdf --- /dev/null +++ b/examples/financial_research_agent/main.py @@ -0,0 +1,17 @@ +import asyncio + +from .manager import FinancialResearchManager + + +# Entrypoint for the financial bot example. +# Run this as `python -m examples.financial_research_agent.main` and enter a +# financial research query, for example: +# "Write up an analysis of Apple Inc.'s most recent quarter." +async def main() -> None: + query = input("Enter a financial research query: ") + mgr = FinancialResearchManager() + await mgr.run(query) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/financial_research_agent/manager.py b/examples/financial_research_agent/manager.py new file mode 100644 index 00000000..58ec11bf --- /dev/null +++ b/examples/financial_research_agent/manager.py @@ -0,0 +1,135 @@ +from __future__ import annotations + +import asyncio +import time +from collections.abc import Sequence + +from rich.console import Console + +from agents import Runner, RunResult, custom_span, gen_trace_id, trace + +from .agents.financials_agent import financials_agent +from .agents.planner_agent import FinancialSearchItem, FinancialSearchPlan, planner_agent +from .agents.risk_agent import risk_agent +from .agents.search_agent import search_agent +from .agents.verifier_agent import VerificationResult, verifier_agent +from .agents.writer_agent import FinancialReportData, writer_agent +from .printer import Printer + + +async def _summary_extractor(run_result: RunResult) -> str: + """Custom output extractor for sub‑agents that return an AnalysisSummary.""" + # The financial/risk analyst agents emit an AnalysisSummary with a `summary` field. + # We want the tool call to return just that summary text so the writer can drop it inline. + return str(run_result.final_output.summary) + + +class FinancialResearchManager: + """ + Orchestrates the full flow: planning, searching, sub‑analysis, writing, and verification. + """ + + def __init__(self) -> None: + self.console = Console() + self.printer = Printer(self.console) + + async def run(self, query: str) -> None: + trace_id = gen_trace_id() + with trace("Financial research trace", trace_id=trace_id): + self.printer.update_item( + "trace_id", + f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}", + is_done=True, + hide_checkmark=True, + ) + self.printer.update_item("start", "Starting financial research...", is_done=True) + search_plan = await self._plan_searches(query) + search_results = await self._perform_searches(search_plan) + report = await self._write_report(query, search_results) + verification = await self._verify_report(report) + + final_report = f"Report summary\n\n{report.short_summary}" + self.printer.update_item("final_report", final_report, is_done=True) + + self.printer.end() + + # Print to stdout + print("\n\n=====REPORT=====\n\n") + print(f"Report:\n{report.markdown_report}") + print("\n\n=====FOLLOW UP QUESTIONS=====\n\n") + print("\n".join(report.follow_up_questions)) + print("\n\n=====VERIFICATION=====\n\n") + print(verification) + + async def _plan_searches(self, query: str) -> FinancialSearchPlan: + self.printer.update_item("planning", "Planning searches...") + result = await Runner.run(planner_agent, f"Query: {query}") + self.printer.update_item( + "planning", + f"Will perform {len(result.final_output.searches)} searches", + is_done=True, + ) + return result.final_output_as(FinancialSearchPlan) + + async def _perform_searches(self, search_plan: FinancialSearchPlan) -> Sequence[str]: + with custom_span("Search the web"): + self.printer.update_item("searching", "Searching...") + tasks = [asyncio.create_task(self._search(item)) for item in search_plan.searches] + results: list[str] = [] + num_completed = 0 + for task in asyncio.as_completed(tasks): + result = await task + if result is not None: + results.append(result) + num_completed += 1 + self.printer.update_item( + "searching", f"Searching... {num_completed}/{len(tasks)} completed" + ) + self.printer.mark_item_done("searching") + return results + + async def _search(self, item: FinancialSearchItem) -> str | None: + input_data = f"Search term: {item.query}\nReason: {item.reason}" + try: + result = await Runner.run(search_agent, input_data) + return str(result.final_output) + except Exception: + return None + + async def _write_report(self, query: str, search_results: Sequence[str]) -> FinancialReportData: + # Expose the specialist analysts as tools so the writer can invoke them inline + # and still produce the final FinancialReportData output. + fundamentals_tool = financials_agent.as_tool( + tool_name="fundamentals_analysis", + tool_description="Use to get a short write‑up of key financial metrics", + custom_output_extractor=_summary_extractor, + ) + risk_tool = risk_agent.as_tool( + tool_name="risk_analysis", + tool_description="Use to get a short write‑up of potential red flags", + custom_output_extractor=_summary_extractor, + ) + writer_with_tools = writer_agent.clone(tools=[fundamentals_tool, risk_tool]) + self.printer.update_item("writing", "Thinking about report...") + input_data = f"Original query: {query}\nSummarized search results: {search_results}" + result = Runner.run_streamed(writer_with_tools, input_data) + update_messages = [ + "Planning report structure...", + "Writing sections...", + "Finalizing report...", + ] + last_update = time.time() + next_message = 0 + async for _ in result.stream_events(): + if time.time() - last_update > 5 and next_message < len(update_messages): + self.printer.update_item("writing", update_messages[next_message]) + next_message += 1 + last_update = time.time() + self.printer.mark_item_done("writing") + return result.final_output_as(FinancialReportData) + + async def _verify_report(self, report: FinancialReportData) -> VerificationResult: + self.printer.update_item("verifying", "Verifying report...") + result = await Runner.run(verifier_agent, report.markdown_report) + self.printer.mark_item_done("verifying") + return result.final_output_as(VerificationResult) diff --git a/tests/examples/research_bot/printer.py b/examples/financial_research_agent/printer.py similarity index 86% rename from tests/examples/research_bot/printer.py rename to examples/financial_research_agent/printer.py index e820c753..4c1a4944 100644 --- a/tests/examples/research_bot/printer.py +++ b/examples/financial_research_agent/printer.py @@ -6,7 +6,12 @@ class Printer: - def __init__(self, console: Console): + """ + Simple wrapper to stream status updates. Used by the financial bot + manager as it orchestrates planning, search and writing. + """ + + def __init__(self, console: Console) -> None: self.live = Live(console=console) self.items: dict[str, tuple[str, bool]] = {} self.hide_done_ids: set[str] = set() diff --git a/examples/handoffs/message_filter.py b/examples/handoffs/message_filter.py index 9dd56ef7..b7fed6c1 100644 --- a/examples/handoffs/message_filter.py +++ b/examples/handoffs/message_filter.py @@ -60,9 +60,9 @@ async def main(): print("Step 1 done") - # 2. Ask it to square a number + # 2. Ask it to generate a number result = await Runner.run( - second_agent, + first_agent, input=result.to_input_list() + [{"content": "Can you generate a random number between 0 and 100?", "role": "user"}], ) diff --git a/examples/handoffs/message_filter_streaming.py b/examples/handoffs/message_filter_streaming.py index 8d1b4208..63cb1de3 100644 --- a/examples/handoffs/message_filter_streaming.py +++ b/examples/handoffs/message_filter_streaming.py @@ -60,9 +60,9 @@ async def main(): print("Step 1 done") - # 2. Ask it to square a number + # 2. Ask it to generate a number result = await Runner.run( - second_agent, + first_agent, input=result.to_input_list() + [{"content": "Can you generate a random number between 0 and 100?", "role": "user"}], ) diff --git a/tests/src/agents/models/__init__.py b/examples/hosted_mcp/__init__.py similarity index 100% rename from tests/src/agents/models/__init__.py rename to examples/hosted_mcp/__init__.py diff --git a/examples/hosted_mcp/approvals.py b/examples/hosted_mcp/approvals.py new file mode 100644 index 00000000..2cabb3ee --- /dev/null +++ b/examples/hosted_mcp/approvals.py @@ -0,0 +1,61 @@ +import argparse +import asyncio + +from agents import ( + Agent, + HostedMCPTool, + MCPToolApprovalFunctionResult, + MCPToolApprovalRequest, + Runner, +) + +"""This example demonstrates how to use the hosted MCP support in the OpenAI Responses API, with +approval callbacks.""" + + +def approval_callback(request: MCPToolApprovalRequest) -> MCPToolApprovalFunctionResult: + answer = input(f"Approve running the tool `{request.data.name}`? (y/n) ") + result: MCPToolApprovalFunctionResult = {"approve": answer == "y"} + if not result["approve"]: + result["reason"] = "User denied" + return result + + +async def main(verbose: bool, stream: bool): + agent = Agent( + name="Assistant", + tools=[ + HostedMCPTool( + tool_config={ + "type": "mcp", + "server_label": "gitmcp", + "server_url": "https://gitmcp.io/openai/codex", + "require_approval": "always", + }, + on_approval_request=approval_callback, + ) + ], + ) + + if stream: + result = Runner.run_streamed(agent, "Which language is this repo written in?") + async for event in result.stream_events(): + if event.type == "run_item_stream_event": + print(f"Got event of type {event.item.__class__.__name__}") + print(f"Done streaming; final result: {result.final_output}") + else: + res = await Runner.run(agent, "Which language is this repo written in?") + print(res.final_output) + + if verbose: + for item in result.new_items: + print(item) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--verbose", action="store_true", default=False) + parser.add_argument("--stream", action="store_true", default=False) + args = parser.parse_args() + + asyncio.run(main(args.verbose, args.stream)) diff --git a/examples/hosted_mcp/simple.py b/examples/hosted_mcp/simple.py new file mode 100644 index 00000000..508c3a7a --- /dev/null +++ b/examples/hosted_mcp/simple.py @@ -0,0 +1,47 @@ +import argparse +import asyncio + +from agents import Agent, HostedMCPTool, Runner + +"""This example demonstrates how to use the hosted MCP support in the OpenAI Responses API, with +approvals not required for any tools. You should only use this for trusted MCP servers.""" + + +async def main(verbose: bool, stream: bool): + agent = Agent( + name="Assistant", + tools=[ + HostedMCPTool( + tool_config={ + "type": "mcp", + "server_label": "gitmcp", + "server_url": "https://gitmcp.io/openai/codex", + "require_approval": "never", + } + ) + ], + ) + + if stream: + result = Runner.run_streamed(agent, "Which language is this repo written in?") + async for event in result.stream_events(): + if event.type == "run_item_stream_event": + print(f"Got event of type {event.item.__class__.__name__}") + print(f"Done streaming; final result: {result.final_output}") + else: + res = await Runner.run(agent, "Which language is this repo written in?") + print(res.final_output) + # The repository is primarily written in multiple languages, including Rust and TypeScript... + + if verbose: + for item in result.new_items: + print(item) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--verbose", action="store_true", default=False) + parser.add_argument("--stream", action="store_true", default=False) + args = parser.parse_args() + + asyncio.run(main(args.verbose, args.stream)) diff --git a/examples/mcp/filesystem_example/README.md b/examples/mcp/filesystem_example/README.md new file mode 100644 index 00000000..4ed6ac46 --- /dev/null +++ b/examples/mcp/filesystem_example/README.md @@ -0,0 +1,26 @@ +# MCP Filesystem Example + +This example uses the [filesystem MCP server](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem), running locally via `npx`. + +Run it via: + +``` +uv run python examples/mcp/filesystem_example/main.py +``` + +## Details + +The example uses the `MCPServerStdio` class from `agents.mcp`, with the command: + +```bash +npx -y "@modelcontextprotocol/server-filesystem" +``` + +It's only given access to the `sample_files` directory adjacent to the example, which contains some sample data. + +Under the hood: + +1. The server is spun up in a subprocess, and exposes a bunch of tools like `list_directory()`, `read_file()`, etc. +2. We add the server instance to the Agent via `mcp_agents`. +3. Each time the agent runs, we call out to the MCP server to fetch the list of tools via `server.list_tools()`. +4. If the LLM chooses to use an MCP tool, we call the MCP server to run the tool via `server.run_tool()`. diff --git a/examples/mcp/filesystem_example/main.py b/examples/mcp/filesystem_example/main.py new file mode 100644 index 00000000..92c2b2db --- /dev/null +++ b/examples/mcp/filesystem_example/main.py @@ -0,0 +1,57 @@ +import asyncio +import os +import shutil + +from agents import Agent, Runner, gen_trace_id, trace +from agents.mcp import MCPServer, MCPServerStdio + + +async def run(mcp_server: MCPServer): + agent = Agent( + name="Assistant", + instructions="Use the tools to read the filesystem and answer questions based on those files.", + mcp_servers=[mcp_server], + ) + + # List the files it can read + message = "Read the files and list them." + print(f"Running: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + # Ask about books + message = "What is my #1 favorite book?" + print(f"\n\nRunning: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + # Ask a question that reads then reasons. + message = "Look at my favorite songs. Suggest one new song that I might like." + print(f"\n\nRunning: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + +async def main(): + current_dir = os.path.dirname(os.path.abspath(__file__)) + samples_dir = os.path.join(current_dir, "sample_files") + + async with MCPServerStdio( + name="Filesystem Server, via npx", + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + }, + ) as server: + trace_id = gen_trace_id() + with trace(workflow_name="MCP Filesystem Example", trace_id=trace_id): + print(f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}\n") + await run(server) + + +if __name__ == "__main__": + # Let's make sure the user has npx installed + if not shutil.which("npx"): + raise RuntimeError("npx is not installed. Please install it with `npm install -g npx`.") + + asyncio.run(main()) diff --git a/examples/mcp/filesystem_example/sample_files/favorite_books.txt b/examples/mcp/filesystem_example/sample_files/favorite_books.txt new file mode 100644 index 00000000..c55f457e --- /dev/null +++ b/examples/mcp/filesystem_example/sample_files/favorite_books.txt @@ -0,0 +1,20 @@ +1. To Kill a Mockingbird – Harper Lee +2. Pride and Prejudice – Jane Austen +3. 1984 – George Orwell +4. The Hobbit – J.R.R. Tolkien +5. Harry Potter and the Sorcerer’s Stone – J.K. Rowling +6. The Great Gatsby – F. Scott Fitzgerald +7. Charlotte’s Web – E.B. White +8. Anne of Green Gables – Lucy Maud Montgomery +9. The Alchemist – Paulo Coelho +10. Little Women – Louisa May Alcott +11. The Catcher in the Rye – J.D. Salinger +12. Animal Farm – George Orwell +13. The Chronicles of Narnia: The Lion, the Witch, and the Wardrobe – C.S. Lewis +14. The Book Thief – Markus Zusak +15. A Wrinkle in Time – Madeleine L’Engle +16. The Secret Garden – Frances Hodgson Burnett +17. Moby-Dick – Herman Melville +18. Fahrenheit 451 – Ray Bradbury +19. Jane Eyre – Charlotte Brontë +20. The Little Prince – Antoine de Saint-Exupéry \ No newline at end of file diff --git a/examples/mcp/filesystem_example/sample_files/favorite_cities.txt b/examples/mcp/filesystem_example/sample_files/favorite_cities.txt new file mode 100644 index 00000000..1d3354f2 --- /dev/null +++ b/examples/mcp/filesystem_example/sample_files/favorite_cities.txt @@ -0,0 +1,4 @@ +- In the summer, I love visiting London. +- In the winter, Tokyo is great. +- In the spring, San Francisco. +- In the fall, New York is the best. \ No newline at end of file diff --git a/examples/mcp/filesystem_example/sample_files/favorite_songs.txt b/examples/mcp/filesystem_example/sample_files/favorite_songs.txt new file mode 100644 index 00000000..d659bb58 --- /dev/null +++ b/examples/mcp/filesystem_example/sample_files/favorite_songs.txt @@ -0,0 +1,10 @@ +1. "Here Comes the Sun" – The Beatles +2. "Imagine" – John Lennon +3. "Bohemian Rhapsody" – Queen +4. "Shake It Off" – Taylor Swift +5. "Billie Jean" – Michael Jackson +6. "Uptown Funk" – Mark Ronson ft. Bruno Mars +7. "Don’t Stop Believin’" – Journey +8. "Dancing Queen" – ABBA +9. "Happy" – Pharrell Williams +10. "Wonderwall" – Oasis diff --git a/examples/mcp/git_example/README.md b/examples/mcp/git_example/README.md new file mode 100644 index 00000000..6a809afa --- /dev/null +++ b/examples/mcp/git_example/README.md @@ -0,0 +1,26 @@ +# MCP Git Example + +This example uses the [git MCP server](https://github.com/modelcontextprotocol/servers/tree/main/src/git), running locally via `uvx`. + +Run it via: + +``` +uv run python examples/mcp/git_example/main.py +``` + +## Details + +The example uses the `MCPServerStdio` class from `agents.mcp`, with the command: + +```bash +uvx mcp-server-git +``` + +Prior to running the agent, the user is prompted to provide a local directory path to their git repo. Using that, the Agent can invoke Git MCP tools like `git_log` to inspect the git commit log. + +Under the hood: + +1. The server is spun up in a subprocess, and exposes a bunch of tools like `git_log()` +2. We add the server instance to the Agent via `mcp_agents`. +3. Each time the agent runs, we call out to the MCP server to fetch the list of tools via `server.list_tools()`. The result is cached. +4. If the LLM chooses to use an MCP tool, we call the MCP server to run the tool via `server.run_tool()`. diff --git a/examples/mcp/git_example/main.py b/examples/mcp/git_example/main.py new file mode 100644 index 00000000..ab229e85 --- /dev/null +++ b/examples/mcp/git_example/main.py @@ -0,0 +1,44 @@ +import asyncio +import shutil + +from agents import Agent, Runner, trace +from agents.mcp import MCPServer, MCPServerStdio + + +async def run(mcp_server: MCPServer, directory_path: str): + agent = Agent( + name="Assistant", + instructions=f"Answer questions about the git repository at {directory_path}, use that for repo_path", + mcp_servers=[mcp_server], + ) + + message = "Who's the most frequent contributor?" + print("\n" + "-" * 40) + print(f"Running: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + message = "Summarize the last change in the repository." + print("\n" + "-" * 40) + print(f"Running: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + +async def main(): + # Ask the user for the directory path + directory_path = input("Please enter the path to the git repository: ") + + async with MCPServerStdio( + cache_tools_list=True, # Cache the tools list, for demonstration + params={"command": "uvx", "args": ["mcp-server-git"]}, + ) as server: + with trace(workflow_name="MCP Git Example"): + await run(server, directory_path) + + +if __name__ == "__main__": + if not shutil.which("uvx"): + raise RuntimeError("uvx is not installed. Please install it with `pip install uvx`.") + + asyncio.run(main()) diff --git a/examples/mcp/sse_example/README.md b/examples/mcp/sse_example/README.md new file mode 100644 index 00000000..9a667d31 --- /dev/null +++ b/examples/mcp/sse_example/README.md @@ -0,0 +1,13 @@ +# MCP SSE Example + +This example uses a local SSE server in [server.py](server.py). + +Run the example via: + +``` +uv run python examples/mcp/sse_example/main.py +``` + +## Details + +The example uses the `MCPServerSse` class from `agents.mcp`. The server runs in a sub-process at `https://localhost:8000/sse`. diff --git a/examples/mcp/sse_example/main.py b/examples/mcp/sse_example/main.py new file mode 100644 index 00000000..7c1137d2 --- /dev/null +++ b/examples/mcp/sse_example/main.py @@ -0,0 +1,83 @@ +import asyncio +import os +import shutil +import subprocess +import time +from typing import Any + +from agents import Agent, Runner, gen_trace_id, trace +from agents.mcp import MCPServer, MCPServerSse +from agents.model_settings import ModelSettings + + +async def run(mcp_server: MCPServer): + agent = Agent( + name="Assistant", + instructions="Use the tools to answer the questions.", + mcp_servers=[mcp_server], + model_settings=ModelSettings(tool_choice="required"), + ) + + # Use the `add` tool to add two numbers + message = "Add these numbers: 7 and 22." + print(f"Running: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + # Run the `get_weather` tool + message = "What's the weather in Tokyo?" + print(f"\n\nRunning: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + # Run the `get_secret_word` tool + message = "What's the secret word?" + print(f"\n\nRunning: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + +async def main(): + async with MCPServerSse( + name="SSE Python Server", + params={ + "url": "http://localhost:8000/sse", + }, + ) as server: + trace_id = gen_trace_id() + with trace(workflow_name="SSE Example", trace_id=trace_id): + print(f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}\n") + await run(server) + + +if __name__ == "__main__": + # Let's make sure the user has uv installed + if not shutil.which("uv"): + raise RuntimeError( + "uv is not installed. Please install it: https://docs.astral.sh/uv/getting-started/installation/" + ) + + # We'll run the SSE server in a subprocess. Usually this would be a remote server, but for this + # demo, we'll run it locally at http://localhost:8000/sse + process: subprocess.Popen[Any] | None = None + try: + this_dir = os.path.dirname(os.path.abspath(__file__)) + server_file = os.path.join(this_dir, "server.py") + + print("Starting SSE server at http://localhost:8000/sse ...") + + # Run `uv run server.py` to start the SSE server + process = subprocess.Popen(["uv", "run", server_file]) + # Give it 3 seconds to start + time.sleep(3) + + print("SSE server started. Running example...\n\n") + except Exception as e: + print(f"Error starting SSE server: {e}") + exit(1) + + try: + asyncio.run(main()) + finally: + if process: + process.terminate() diff --git a/examples/mcp/sse_example/server.py b/examples/mcp/sse_example/server.py new file mode 100644 index 00000000..df364aa3 --- /dev/null +++ b/examples/mcp/sse_example/server.py @@ -0,0 +1,33 @@ +import random + +import requests +from mcp.server.fastmcp import FastMCP + +# Create server +mcp = FastMCP("Echo Server") + + +@mcp.tool() +def add(a: int, b: int) -> int: + """Add two numbers""" + print(f"[debug-server] add({a}, {b})") + return a + b + + +@mcp.tool() +def get_secret_word() -> str: + print("[debug-server] get_secret_word()") + return random.choice(["apple", "banana", "cherry"]) + + +@mcp.tool() +def get_current_weather(city: str) -> str: + print(f"[debug-server] get_current_weather({city})") + + endpoint = "https://wttr.in" + response = requests.get(f"{endpoint}/{city}") + return response.text + + +if __name__ == "__main__": + mcp.run(transport="sse") diff --git a/examples/mcp/streamablehttp_example/README.md b/examples/mcp/streamablehttp_example/README.md new file mode 100644 index 00000000..a07fe19b --- /dev/null +++ b/examples/mcp/streamablehttp_example/README.md @@ -0,0 +1,13 @@ +# MCP Streamable HTTP Example + +This example uses a local Streamable HTTP server in [server.py](server.py). + +Run the example via: + +``` +uv run python examples/mcp/streamablehttp_example/main.py +``` + +## Details + +The example uses the `MCPServerStreamableHttp` class from `agents.mcp`. The server runs in a sub-process at `https://localhost:8000/mcp`. diff --git a/examples/mcp/streamablehttp_example/main.py b/examples/mcp/streamablehttp_example/main.py new file mode 100644 index 00000000..cc95e798 --- /dev/null +++ b/examples/mcp/streamablehttp_example/main.py @@ -0,0 +1,83 @@ +import asyncio +import os +import shutil +import subprocess +import time +from typing import Any + +from agents import Agent, Runner, gen_trace_id, trace +from agents.mcp import MCPServer, MCPServerStreamableHttp +from agents.model_settings import ModelSettings + + +async def run(mcp_server: MCPServer): + agent = Agent( + name="Assistant", + instructions="Use the tools to answer the questions.", + mcp_servers=[mcp_server], + model_settings=ModelSettings(tool_choice="required"), + ) + + # Use the `add` tool to add two numbers + message = "Add these numbers: 7 and 22." + print(f"Running: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + # Run the `get_weather` tool + message = "What's the weather in Tokyo?" + print(f"\n\nRunning: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + # Run the `get_secret_word` tool + message = "What's the secret word?" + print(f"\n\nRunning: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + +async def main(): + async with MCPServerStreamableHttp( + name="Streamable HTTP Python Server", + params={ + "url": "http://localhost:8000/mcp", + }, + ) as server: + trace_id = gen_trace_id() + with trace(workflow_name="Streamable HTTP Example", trace_id=trace_id): + print(f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}\n") + await run(server) + + +if __name__ == "__main__": + # Let's make sure the user has uv installed + if not shutil.which("uv"): + raise RuntimeError( + "uv is not installed. Please install it: https://docs.astral.sh/uv/getting-started/installation/" + ) + + # We'll run the Streamable HTTP server in a subprocess. Usually this would be a remote server, but for this + # demo, we'll run it locally at http://localhost:8000/mcp + process: subprocess.Popen[Any] | None = None + try: + this_dir = os.path.dirname(os.path.abspath(__file__)) + server_file = os.path.join(this_dir, "server.py") + + print("Starting Streamable HTTP server at http://localhost:8000/mcp ...") + + # Run `uv run server.py` to start the Streamable HTTP server + process = subprocess.Popen(["uv", "run", server_file]) + # Give it 3 seconds to start + time.sleep(3) + + print("Streamable HTTP server started. Running example...\n\n") + except Exception as e: + print(f"Error starting Streamable HTTP server: {e}") + exit(1) + + try: + asyncio.run(main()) + finally: + if process: + process.terminate() diff --git a/examples/mcp/streamablehttp_example/server.py b/examples/mcp/streamablehttp_example/server.py new file mode 100644 index 00000000..d8f83965 --- /dev/null +++ b/examples/mcp/streamablehttp_example/server.py @@ -0,0 +1,33 @@ +import random + +import requests +from mcp.server.fastmcp import FastMCP + +# Create server +mcp = FastMCP("Echo Server") + + +@mcp.tool() +def add(a: int, b: int) -> int: + """Add two numbers""" + print(f"[debug-server] add({a}, {b})") + return a + b + + +@mcp.tool() +def get_secret_word() -> str: + print("[debug-server] get_secret_word()") + return random.choice(["apple", "banana", "cherry"]) + + +@mcp.tool() +def get_current_weather(city: str) -> str: + print(f"[debug-server] get_current_weather({city})") + + endpoint = "https://wttr.in" + response = requests.get(f"{endpoint}/{city}") + return response.text + + +if __name__ == "__main__": + mcp.run(transport="streamable-http") diff --git a/examples/model_providers/README.md b/examples/model_providers/README.md new file mode 100644 index 00000000..f9330c24 --- /dev/null +++ b/examples/model_providers/README.md @@ -0,0 +1,19 @@ +# Custom LLM providers + +The examples in this directory demonstrate how you might use a non-OpenAI LLM provider. To run them, first set a base URL, API key and model. + +```bash +export EXAMPLE_BASE_URL="..." +export EXAMPLE_API_KEY="..." +export EXAMPLE_MODEL_NAME"..." +``` + +Then run the examples, e.g.: + +``` +python examples/model_providers/custom_example_provider.py + +Loops within themselves, +Function calls its own being, +Depth without ending. +``` diff --git a/examples/model_providers/custom_example_agent.py b/examples/model_providers/custom_example_agent.py new file mode 100644 index 00000000..f10865c4 --- /dev/null +++ b/examples/model_providers/custom_example_agent.py @@ -0,0 +1,55 @@ +import asyncio +import os + +from openai import AsyncOpenAI + +from agents import Agent, OpenAIChatCompletionsModel, Runner, function_tool, set_tracing_disabled + +BASE_URL = os.getenv("EXAMPLE_BASE_URL") or "" +API_KEY = os.getenv("EXAMPLE_API_KEY") or "" +MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "" + +if not BASE_URL or not API_KEY or not MODEL_NAME: + raise ValueError( + "Please set EXAMPLE_BASE_URL, EXAMPLE_API_KEY, EXAMPLE_MODEL_NAME via env var or code." + ) + +"""This example uses a custom provider for a specific agent. Steps: +1. Create a custom OpenAI client. +2. Create a `Model` that uses the custom client. +3. Set the `model` on the Agent. + +Note that in this example, we disable tracing under the assumption that you don't have an API key +from platform.openai.com. If you do have one, you can either set the `OPENAI_API_KEY` env var +or call set_tracing_export_api_key() to set a tracing specific key. +""" +client = AsyncOpenAI(base_url=BASE_URL, api_key=API_KEY) +set_tracing_disabled(disabled=True) + +# An alternate approach that would also work: +# PROVIDER = OpenAIProvider(openai_client=client) +# agent = Agent(..., model="some-custom-model") +# Runner.run(agent, ..., run_config=RunConfig(model_provider=PROVIDER)) + + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(): + # This agent will use the custom LLM provider + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=OpenAIChatCompletionsModel(model=MODEL_NAME, openai_client=client), + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/model_providers/custom_example_global.py b/examples/model_providers/custom_example_global.py new file mode 100644 index 00000000..ae9756d3 --- /dev/null +++ b/examples/model_providers/custom_example_global.py @@ -0,0 +1,63 @@ +import asyncio +import os + +from openai import AsyncOpenAI + +from agents import ( + Agent, + Runner, + function_tool, + set_default_openai_api, + set_default_openai_client, + set_tracing_disabled, +) + +BASE_URL = os.getenv("EXAMPLE_BASE_URL") or "" +API_KEY = os.getenv("EXAMPLE_API_KEY") or "" +MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "" + +if not BASE_URL or not API_KEY or not MODEL_NAME: + raise ValueError( + "Please set EXAMPLE_BASE_URL, EXAMPLE_API_KEY, EXAMPLE_MODEL_NAME via env var or code." + ) + + +"""This example uses a custom provider for all requests by default. We do three things: +1. Create a custom client. +2. Set it as the default OpenAI client, and don't use it for tracing. +3. Set the default API as Chat Completions, as most LLM providers don't yet support Responses API. + +Note that in this example, we disable tracing under the assumption that you don't have an API key +from platform.openai.com. If you do have one, you can either set the `OPENAI_API_KEY` env var +or call set_tracing_export_api_key() to set a tracing specific key. +""" + +client = AsyncOpenAI( + base_url=BASE_URL, + api_key=API_KEY, +) +set_default_openai_client(client=client, use_for_tracing=False) +set_default_openai_api("chat_completions") +set_tracing_disabled(disabled=True) + + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=MODEL_NAME, + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/model_providers/custom_example_provider.py b/examples/model_providers/custom_example_provider.py new file mode 100644 index 00000000..4e590198 --- /dev/null +++ b/examples/model_providers/custom_example_provider.py @@ -0,0 +1,77 @@ +from __future__ import annotations + +import asyncio +import os + +from openai import AsyncOpenAI + +from agents import ( + Agent, + Model, + ModelProvider, + OpenAIChatCompletionsModel, + RunConfig, + Runner, + function_tool, + set_tracing_disabled, +) + +BASE_URL = os.getenv("EXAMPLE_BASE_URL") or "" +API_KEY = os.getenv("EXAMPLE_API_KEY") or "" +MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "" + +if not BASE_URL or not API_KEY or not MODEL_NAME: + raise ValueError( + "Please set EXAMPLE_BASE_URL, EXAMPLE_API_KEY, EXAMPLE_MODEL_NAME via env var or code." + ) + + +"""This example uses a custom provider for some calls to Runner.run(), and direct calls to OpenAI for +others. Steps: +1. Create a custom OpenAI client. +2. Create a ModelProvider that uses the custom client. +3. Use the ModelProvider in calls to Runner.run(), only when we want to use the custom LLM provider. + +Note that in this example, we disable tracing under the assumption that you don't have an API key +from platform.openai.com. If you do have one, you can either set the `OPENAI_API_KEY` env var +or call set_tracing_export_api_key() to set a tracing specific key. +""" +client = AsyncOpenAI(base_url=BASE_URL, api_key=API_KEY) +set_tracing_disabled(disabled=True) + + +class CustomModelProvider(ModelProvider): + def get_model(self, model_name: str | None) -> Model: + return OpenAIChatCompletionsModel(model=model_name or MODEL_NAME, openai_client=client) + + +CUSTOM_MODEL_PROVIDER = CustomModelProvider() + + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(): + agent = Agent(name="Assistant", instructions="You only respond in haikus.", tools=[get_weather]) + + # This will use the custom model provider + result = await Runner.run( + agent, + "What's the weather in Tokyo?", + run_config=RunConfig(model_provider=CUSTOM_MODEL_PROVIDER), + ) + print(result.final_output) + + # If you uncomment this, it will use OpenAI directly, not the custom provider + # result = await Runner.run( + # agent, + # "What's the weather in Tokyo?", + # ) + # print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/model_providers/litellm_auto.py b/examples/model_providers/litellm_auto.py new file mode 100644 index 00000000..12b1e891 --- /dev/null +++ b/examples/model_providers/litellm_auto.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled + +"""This example uses the built-in support for LiteLLM. To use this, ensure you have the +ANTHROPIC_API_KEY environment variable set. +""" + +set_tracing_disabled(disabled=True) + + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + # We prefix with litellm/ to tell the Runner to use the LitellmModel + model="litellm/anthropic/claude-3-5-sonnet-20240620", + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + import os + + if os.getenv("ANTHROPIC_API_KEY") is None: + raise ValueError( + "ANTHROPIC_API_KEY is not set. Please set it the environment variable and try again." + ) + + asyncio.run(main()) diff --git a/examples/model_providers/litellm_provider.py b/examples/model_providers/litellm_provider.py new file mode 100644 index 00000000..4a1a696f --- /dev/null +++ b/examples/model_providers/litellm_provider.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents.extensions.models.litellm_model import LitellmModel + +"""This example uses the LitellmModel directly, to hit any model provider. +You can run it like this: +uv run examples/model_providers/litellm_provider.py --model anthropic/claude-3-5-sonnet-20240620 +or +uv run examples/model_providers/litellm_provider.py --model gemini/gemini-2.0-flash + +Find more providers here: https://docs.litellm.ai/docs/providers +""" + +set_tracing_disabled(disabled=True) + + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(model: str, api_key: str): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=LitellmModel(model=model, api_key=api_key), + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + # First try to get model/api key from args + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=False) + parser.add_argument("--api-key", type=str, required=False) + args = parser.parse_args() + + model = args.model + if not model: + model = input("Enter a model name for Litellm: ") + + api_key = args.api_key + if not api_key: + api_key = input("Enter an API key for Litellm: ") + + asyncio.run(main(model, api_key)) diff --git a/examples/research_bot/README.md b/examples/research_bot/README.md index 4060983c..49fb3570 100644 --- a/examples/research_bot/README.md +++ b/examples/research_bot/README.md @@ -21,5 +21,5 @@ If you're building your own research bot, some ideas to add to this are: 1. Retrieval: Add support for fetching relevant information from a vector store. You could use the File Search tool for this. 2. Image and file upload: Allow users to attach PDFs or other files, as baseline context for the research. -3. More planning and thinking: Models often produce better results given more time to think. Improve the planning process to come up with a better plan, and add an evaluation step so that the model can choose to improve it's results, search for more stuff, etc. +3. More planning and thinking: Models often produce better results given more time to think. Improve the planning process to come up with a better plan, and add an evaluation step so that the model can choose to improve its results, search for more stuff, etc. 4. Code execution: Allow running code, which is useful for data analysis. diff --git a/examples/research_bot/agents/__pycache__/__init__.cpython-313.pyc b/examples/research_bot/agents/__pycache__/__init__.cpython-313.pyc deleted file mode 100644 index a094b5a5..00000000 Binary files a/examples/research_bot/agents/__pycache__/__init__.cpython-313.pyc and /dev/null differ diff --git a/examples/research_bot/agents/__pycache__/base_agent.cpython-313.pyc b/examples/research_bot/agents/__pycache__/base_agent.cpython-313.pyc deleted file mode 100644 index f33d4188..00000000 Binary files a/examples/research_bot/agents/__pycache__/base_agent.cpython-313.pyc and /dev/null differ diff --git a/examples/research_bot/agents/__pycache__/planner_agent.cpython-313.pyc b/examples/research_bot/agents/__pycache__/planner_agent.cpython-313.pyc deleted file mode 100644 index b836aacc..00000000 Binary files a/examples/research_bot/agents/__pycache__/planner_agent.cpython-313.pyc and /dev/null differ diff --git a/examples/research_bot/agents/__pycache__/research_manager_agent.cpython-313.pyc b/examples/research_bot/agents/__pycache__/research_manager_agent.cpython-313.pyc deleted file mode 100644 index edc3f5ff..00000000 Binary files a/examples/research_bot/agents/__pycache__/research_manager_agent.cpython-313.pyc and /dev/null differ diff --git a/examples/research_bot/agents/__pycache__/search_agent.cpython-313.pyc b/examples/research_bot/agents/__pycache__/search_agent.cpython-313.pyc deleted file mode 100644 index b3281242..00000000 Binary files a/examples/research_bot/agents/__pycache__/search_agent.cpython-313.pyc and /dev/null differ diff --git a/examples/research_bot/agents/__pycache__/summarization_agent.cpython-313.pyc b/examples/research_bot/agents/__pycache__/summarization_agent.cpython-313.pyc deleted file mode 100644 index b809d7c5..00000000 Binary files a/examples/research_bot/agents/__pycache__/summarization_agent.cpython-313.pyc and /dev/null differ diff --git a/examples/research_bot/agents/__pycache__/writer_agent.cpython-313.pyc b/examples/research_bot/agents/__pycache__/writer_agent.cpython-313.pyc deleted file mode 100644 index be550b1e..00000000 Binary files a/examples/research_bot/agents/__pycache__/writer_agent.cpython-313.pyc and /dev/null differ diff --git a/examples/research_bot/agents/search_agent.py b/examples/research_bot/agents/search_agent.py index 72cbc8e1..61f91701 100644 --- a/examples/research_bot/agents/search_agent.py +++ b/examples/research_bot/agents/search_agent.py @@ -2,11 +2,11 @@ from agents.model_settings import ModelSettings INSTRUCTIONS = ( - "You are a research assistant. Given a search term, you search the web for that term and" - "produce a concise summary of the results. The summary must 2-3 paragraphs and less than 300" - "words. Capture the main points. Write succintly, no need to have complete sentences or good" - "grammar. This will be consumed by someone synthesizing a report, so its vital you capture the" - "essence and ignore any fluff. Do not include any additional commentary other than the summary" + "You are a research assistant. Given a search term, you search the web for that term and " + "produce a concise summary of the results. The summary must be 2-3 paragraphs and less than 300 " + "words. Capture the main points. Write succinctly, no need to have complete sentences or good " + "grammar. This will be consumed by someone synthesizing a report, so its vital you capture the " + "essence and ignore any fluff. Do not include any additional commentary other than the summary " "itself." ) diff --git a/examples/research_bot/manager.py b/examples/research_bot/manager.py index 47306f14..dab68569 100644 --- a/examples/research_bot/manager.py +++ b/examples/research_bot/manager.py @@ -23,7 +23,7 @@ async def run(self, query: str) -> None: with trace("Research trace", trace_id=trace_id): self.printer.update_item( "trace_id", - f"View trace: https://platform.openai.com/traces/{trace_id}", + f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}", is_done=True, hide_checkmark=True, ) diff --git a/examples/research_bot/sample_outputs/product_recs.txt b/examples/research_bot/sample_outputs/product_recs.txt index 78865f23..fd14d533 100644 --- a/examples/research_bot/sample_outputs/product_recs.txt +++ b/examples/research_bot/sample_outputs/product_recs.txt @@ -3,7 +3,7 @@ $ uv run python -m examples.research_bot.main What would you like to research? Best surfboards for beginners. I can catch my own waves, but previously used an 11ft board. What should I look for, what are my options? Various budget ranges. -View trace: https://platform.openai.com/traces/trace_... +View trace: https://platform.openai.com/traces/trace?trace_id=trace_... Starting research... ✅ Will perform 15 searches ✅ Searching... 15/15 completed diff --git a/examples/research_bot/sample_outputs/vacation.txt b/examples/research_bot/sample_outputs/vacation.txt index b2649981..491c0005 100644 --- a/examples/research_bot/sample_outputs/vacation.txt +++ b/examples/research_bot/sample_outputs/vacation.txt @@ -2,7 +2,7 @@ $ uv run python -m examples.research_bot.main What would you like to research? Caribbean vacation spots in April, optimizing for surfing, hiking and water sports -View trace: https://platform.openai.com/traces/trace_.... +View trace: https://platform.openai.com/traces/trace?trace_id=trace_.... Starting research... ✅ Will perform 15 searches ✅ Searching... 15/15 completed diff --git a/examples/tools/code_interpreter.py b/examples/tools/code_interpreter.py new file mode 100644 index 00000000..a5843ce3 --- /dev/null +++ b/examples/tools/code_interpreter.py @@ -0,0 +1,34 @@ +import asyncio + +from agents import Agent, CodeInterpreterTool, Runner, trace + + +async def main(): + agent = Agent( + name="Code interpreter", + instructions="You love doing math.", + tools=[ + CodeInterpreterTool( + tool_config={"type": "code_interpreter", "container": {"type": "auto"}}, + ) + ], + ) + + with trace("Code interpreter example"): + print("Solving math problem...") + result = Runner.run_streamed(agent, "What is the square root of273 * 312821 plus 1782?") + async for event in result.stream_events(): + if ( + event.type == "run_item_stream_event" + and event.item.type == "tool_call_item" + and event.item.raw_item.type == "code_interpreter_call" + ): + print(f"Code interpreter code:\n```\n{event.item.raw_item.code}\n```\n") + elif event.type == "run_item_stream_event": + print(f"Other event: {event.item.type}") + + print(f"Final output: {result.final_output}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/tools/computer_use.py b/examples/tools/computer_use.py index ae339552..0c17cf95 100644 --- a/examples/tools/computer_use.py +++ b/examples/tools/computer_use.py @@ -1,6 +1,5 @@ import asyncio import base64 -import logging from typing import Literal, Union from playwright.async_api import Browser, Page, Playwright, async_playwright @@ -16,8 +15,10 @@ trace, ) -logging.getLogger("openai.agents").setLevel(logging.DEBUG) -logging.getLogger("openai.agents").addHandler(logging.StreamHandler()) +# Uncomment to see very verbose logs +# import logging +# logging.getLogger("openai.agents").setLevel(logging.DEBUG) +# logging.getLogger("openai.agents").addHandler(logging.StreamHandler()) async def main(): @@ -147,9 +148,11 @@ async def move(self, x: int, y: int) -> None: await self.page.mouse.move(x, y) async def keypress(self, keys: list[str]) -> None: - for key in keys: - mapped_key = CUA_KEY_TO_PLAYWRIGHT_KEY.get(key.lower(), key) - await self.page.keyboard.press(mapped_key) + mapped_keys = [CUA_KEY_TO_PLAYWRIGHT_KEY.get(key.lower(), key) for key in keys] + for key in mapped_keys: + await self.page.keyboard.down(key) + for key in reversed(mapped_keys): + await self.page.keyboard.up(key) async def drag(self, path: list[tuple[int, int]]) -> None: if not path: diff --git a/examples/tools/image_generator.py b/examples/tools/image_generator.py new file mode 100644 index 00000000..fd6fcc6b --- /dev/null +++ b/examples/tools/image_generator.py @@ -0,0 +1,54 @@ +import asyncio +import base64 +import os +import subprocess +import sys +import tempfile + +from agents import Agent, ImageGenerationTool, Runner, trace + + +def open_file(path: str) -> None: + if sys.platform.startswith("darwin"): + subprocess.run(["open", path], check=False) # macOS + elif os.name == "nt": # Windows + os.astartfile(path) # type: ignore + elif os.name == "posix": + subprocess.run(["xdg-open", path], check=False) # Linux/Unix + else: + print(f"Don't know how to open files on this platform: {sys.platform}") + + +async def main(): + agent = Agent( + name="Image generator", + instructions="You are a helpful agent.", + tools=[ + ImageGenerationTool( + tool_config={"type": "image_generation", "quality": "low"}, + ) + ], + ) + + with trace("Image generation example"): + print("Generating image, this may take a while...") + result = await Runner.run( + agent, "Create an image of a frog eating a pizza, comic book style." + ) + print(result.final_output) + for item in result.new_items: + if ( + item.type == "tool_call_item" + and item.raw_item.type == "image_generation_call" + and (img_result := item.raw_item.result) + ): + with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp: + tmp.write(base64.b64decode(img_result)) + temp_path = tmp.name + + # Open the image + open_file(temp_path) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/voice/__init__.py b/examples/voice/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/voice/static/README.md b/examples/voice/static/README.md new file mode 100644 index 00000000..74dc114b --- /dev/null +++ b/examples/voice/static/README.md @@ -0,0 +1,26 @@ +# Static voice demo + +This demo operates by capturing a recording, then running a voice pipeline on it. + +Run via: + +``` +python -m examples.voice.static.main +``` + +## How it works + +1. We create a `VoicePipeline`, setup with a custom workflow. The workflow runs an Agent, but it also has some custom responses if you say the secret word. +2. When you speak, audio is forwarded to the voice pipeline. When you stop speaking, the agent runs. +3. The pipeline is run with the audio, which causes it to: + 1. Transcribe the audio + 2. Feed the transcription to the workflow, which runs the agent. + 3. Stream the output of the agent to a text-to-speech model. +4. Play the audio. + +Some suggested examples to try: + +- Tell me a joke (_the assistant tells you a joke_) +- What's the weather in Tokyo? (_will call the `get_weather` tool and then speak_) +- Hola, como estas? (_will handoff to the spanish agent_) +- Tell me about dogs. (_will respond with the hardcoded "you guessed the secret word" message_) diff --git a/examples/voice/static/__init__.py b/examples/voice/static/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/voice/static/main.py b/examples/voice/static/main.py new file mode 100644 index 00000000..1b9e2024 --- /dev/null +++ b/examples/voice/static/main.py @@ -0,0 +1,88 @@ +import asyncio +import random + +import numpy as np + +from agents import Agent, function_tool +from agents.extensions.handoff_prompt import prompt_with_handoff_instructions +from agents.voice import ( + AudioInput, + SingleAgentVoiceWorkflow, + SingleAgentWorkflowCallbacks, + VoicePipeline, +) + +from .util import AudioPlayer, record_audio + +""" +This is a simple example that uses a recorded audio buffer. Run it via: +`python -m examples.voice.static.main` + +1. You can record an audio clip in the terminal. +2. The pipeline automatically transcribes the audio. +3. The agent workflow is a simple one that starts at the Assistant agent. +4. The output of the agent is streamed to the audio player. + +Try examples like: +- Tell me a joke (will respond with a joke) +- What's the weather in Tokyo? (will call the `get_weather` tool and then speak) +- Hola, como estas? (will handoff to the spanish agent) +""" + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city.""" + print(f"[debug] get_weather called with city: {city}") + choices = ["sunny", "cloudy", "rainy", "snowy"] + return f"The weather in {city} is {random.choice(choices)}." + + +spanish_agent = Agent( + name="Spanish", + handoff_description="A spanish speaking agent.", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. Speak in Spanish.", + ), + model="gpt-4o-mini", +) + +agent = Agent( + name="Assistant", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the spanish agent.", + ), + model="gpt-4o-mini", + handoffs=[spanish_agent], + tools=[get_weather], +) + + +class WorkflowCallbacks(SingleAgentWorkflowCallbacks): + def on_run(self, workflow: SingleAgentVoiceWorkflow, transcription: str) -> None: + print(f"[debug] on_run called with transcription: {transcription}") + + +async def main(): + pipeline = VoicePipeline( + workflow=SingleAgentVoiceWorkflow(agent, callbacks=WorkflowCallbacks()) + ) + + audio_input = AudioInput(buffer=record_audio()) + + result = await pipeline.run(audio_input) + + with AudioPlayer() as player: + async for event in result.stream(): + if event.type == "voice_stream_event_audio": + player.add_audio(event.data) + print("Received audio") + elif event.type == "voice_stream_event_lifecycle": + print(f"Received lifecycle event: {event.event}") + + # Add 1 second of silence to the end of the stream to avoid cutting off the last audio. + player.add_audio(np.zeros(24000 * 1, dtype=np.int16)) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/voice/static/util.py b/examples/voice/static/util.py new file mode 100644 index 00000000..a5806f41 --- /dev/null +++ b/examples/voice/static/util.py @@ -0,0 +1,69 @@ +import curses +import time + +import numpy as np +import numpy.typing as npt +import sounddevice as sd + + +def _record_audio(screen: curses.window) -> npt.NDArray[np.float32]: + screen.nodelay(True) # Non-blocking input + screen.clear() + screen.addstr( + "Press to start recording. Press again to stop recording.\n" + ) + screen.refresh() + + recording = False + audio_buffer: list[npt.NDArray[np.float32]] = [] + + def _audio_callback(indata, frames, time_info, status): + if status: + screen.addstr(f"Status: {status}\n") + screen.refresh() + if recording: + audio_buffer.append(indata.copy()) + + # Open the audio stream with the callback. + with sd.InputStream(samplerate=24000, channels=1, dtype=np.float32, callback=_audio_callback): + while True: + key = screen.getch() + if key == ord(" "): + recording = not recording + if recording: + screen.addstr("Recording started...\n") + else: + screen.addstr("Recording stopped.\n") + break + screen.refresh() + time.sleep(0.01) + + # Combine recorded audio chunks. + if audio_buffer: + audio_data = np.concatenate(audio_buffer, axis=0) + else: + audio_data = np.empty((0,), dtype=np.float32) + + return audio_data + + +def record_audio(): + # Using curses to record audio in a way that: + # - doesn't require accessibility permissions on macos + # - doesn't block the terminal + audio_data = curses.wrapper(_record_audio) + return audio_data + + +class AudioPlayer: + def __enter__(self): + self.stream = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16) + self.stream.start() + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.stream.stop() # wait for the stream to finish + self.stream.close() + + def add_audio(self, audio_data: npt.NDArray[np.int16]): + self.stream.write(audio_data) diff --git a/examples/voice/streamed/README.md b/examples/voice/streamed/README.md new file mode 100644 index 00000000..ab0ffedb --- /dev/null +++ b/examples/voice/streamed/README.md @@ -0,0 +1,25 @@ +# Streamed voice demo + +This is an interactive demo, where you can talk to an Agent conversationally. It uses the voice pipeline's built in turn detection feature, so if you stop speaking the Agent responds. + +Run via: + +``` +python -m examples.voice.streamed.main +``` + +## How it works + +1. We create a `VoicePipeline`, setup with a `SingleAgentVoiceWorkflow`. This is a workflow that starts at an Assistant agent, has tools and handoffs. +2. Audio input is captured from the terminal. +3. The pipeline is run with the recorded audio, which causes it to: + 1. Transcribe the audio + 2. Feed the transcription to the workflow, which runs the agent. + 3. Stream the output of the agent to a text-to-speech model. +4. Play the audio. + +Some suggested examples to try: + +- Tell me a joke (_the assistant tells you a joke_) +- What's the weather in Tokyo? (_will call the `get_weather` tool and then speak_) +- Hola, como estas? (_will handoff to the spanish agent_) diff --git a/examples/voice/streamed/__init__.py b/examples/voice/streamed/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/voice/streamed/main.py b/examples/voice/streamed/main.py new file mode 100644 index 00000000..95e93791 --- /dev/null +++ b/examples/voice/streamed/main.py @@ -0,0 +1,233 @@ +from __future__ import annotations + +import asyncio +from typing import TYPE_CHECKING + +import numpy as np +import sounddevice as sd +from textual import events +from textual.app import App, ComposeResult +from textual.containers import Container +from textual.reactive import reactive +from textual.widgets import Button, RichLog, Static +from typing_extensions import override + +from agents.voice import StreamedAudioInput, VoicePipeline + +# Import MyWorkflow class - handle both module and package use cases +if TYPE_CHECKING: + # For type checking, use the relative import + from .my_workflow import MyWorkflow +else: + # At runtime, try both import styles + try: + # Try relative import first (when used as a package) + from .my_workflow import MyWorkflow + except ImportError: + # Fall back to direct import (when run as a script) + from my_workflow import MyWorkflow + +CHUNK_LENGTH_S = 0.05 # 100ms +SAMPLE_RATE = 24000 +FORMAT = np.int16 +CHANNELS = 1 + + +class Header(Static): + """A header widget.""" + + session_id = reactive("") + + @override + def render(self) -> str: + return "Speak to the agent. When you stop speaking, it will respond." + + +class AudioStatusIndicator(Static): + """A widget that shows the current audio recording status.""" + + is_recording = reactive(False) + + @override + def render(self) -> str: + status = ( + "🔴 Recording... (Press K to stop)" + if self.is_recording + else "⚪ Press K to start recording (Q to quit)" + ) + return status + + +class RealtimeApp(App[None]): + CSS = """ + Screen { + background: #1a1b26; /* Dark blue-grey background */ + } + + Container { + border: double rgb(91, 164, 91); + } + + Horizontal { + width: 100%; + } + + #input-container { + height: 5; /* Explicit height for input container */ + margin: 1 1; + padding: 1 2; + } + + Input { + width: 80%; + height: 3; /* Explicit height for input */ + } + + Button { + width: 20%; + height: 3; /* Explicit height for button */ + } + + #bottom-pane { + width: 100%; + height: 82%; /* Reduced to make room for session display */ + border: round rgb(205, 133, 63); + content-align: center middle; + } + + #status-indicator { + height: 3; + content-align: center middle; + background: #2a2b36; + border: solid rgb(91, 164, 91); + margin: 1 1; + } + + #session-display { + height: 3; + content-align: center middle; + background: #2a2b36; + border: solid rgb(91, 164, 91); + margin: 1 1; + } + + Static { + color: white; + } + """ + + should_send_audio: asyncio.Event + audio_player: sd.OutputStream + last_audio_item_id: str | None + connected: asyncio.Event + + def __init__(self) -> None: + super().__init__() + self.last_audio_item_id = None + self.should_send_audio = asyncio.Event() + self.connected = asyncio.Event() + self.pipeline = VoicePipeline( + workflow=MyWorkflow(secret_word="dog", on_start=self._on_transcription) + ) + self._audio_input = StreamedAudioInput() + self.audio_player = sd.OutputStream( + samplerate=SAMPLE_RATE, + channels=CHANNELS, + dtype=FORMAT, + ) + + def _on_transcription(self, transcription: str) -> None: + try: + self.query_one("#bottom-pane", RichLog).write(f"Transcription: {transcription}") + except Exception: + pass + + @override + def compose(self) -> ComposeResult: + """Create child widgets for the app.""" + with Container(): + yield Header(id="session-display") + yield AudioStatusIndicator(id="status-indicator") + yield RichLog(id="bottom-pane", wrap=True, highlight=True, markup=True) + + async def on_mount(self) -> None: + self.run_worker(self.start_voice_pipeline()) + self.run_worker(self.send_mic_audio()) + + async def start_voice_pipeline(self) -> None: + try: + self.audio_player.start() + self.result = await self.pipeline.run(self._audio_input) + + async for event in self.result.stream(): + bottom_pane = self.query_one("#bottom-pane", RichLog) + if event.type == "voice_stream_event_audio": + self.audio_player.write(event.data) + bottom_pane.write( + f"Received audio: {len(event.data) if event.data is not None else '0'} bytes" + ) + elif event.type == "voice_stream_event_lifecycle": + bottom_pane.write(f"Lifecycle event: {event.event}") + except Exception as e: + bottom_pane = self.query_one("#bottom-pane", RichLog) + bottom_pane.write(f"Error: {e}") + finally: + self.audio_player.close() + + async def send_mic_audio(self) -> None: + device_info = sd.query_devices() + print(device_info) + + read_size = int(SAMPLE_RATE * 0.02) + + stream = sd.InputStream( + channels=CHANNELS, + samplerate=SAMPLE_RATE, + dtype="int16", + ) + stream.start() + + status_indicator = self.query_one(AudioStatusIndicator) + + try: + while True: + if stream.read_available < read_size: + await asyncio.sleep(0) + continue + + await self.should_send_audio.wait() + status_indicator.is_recording = True + + data, _ = stream.read(read_size) + + await self._audio_input.add_audio(data) + await asyncio.sleep(0) + except KeyboardInterrupt: + pass + finally: + stream.stop() + stream.close() + + async def on_key(self, event: events.Key) -> None: + """Handle key press events.""" + if event.key == "enter": + self.query_one(Button).press() + return + + if event.key == "q": + self.exit() + return + + if event.key == "k": + status_indicator = self.query_one(AudioStatusIndicator) + if status_indicator.is_recording: + self.should_send_audio.clear() + status_indicator.is_recording = False + else: + self.should_send_audio.set() + status_indicator.is_recording = True + + +if __name__ == "__main__": + app = RealtimeApp() + app.run() diff --git a/examples/voice/streamed/my_workflow.py b/examples/voice/streamed/my_workflow.py new file mode 100644 index 00000000..3cb804b0 --- /dev/null +++ b/examples/voice/streamed/my_workflow.py @@ -0,0 +1,81 @@ +import random +from collections.abc import AsyncIterator +from typing import Callable + +from agents import Agent, Runner, TResponseInputItem, function_tool +from agents.extensions.handoff_prompt import prompt_with_handoff_instructions +from agents.voice import VoiceWorkflowBase, VoiceWorkflowHelper + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city.""" + print(f"[debug] get_weather called with city: {city}") + choices = ["sunny", "cloudy", "rainy", "snowy"] + return f"The weather in {city} is {random.choice(choices)}." + + +spanish_agent = Agent( + name="Spanish", + handoff_description="A spanish speaking agent.", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. Speak in Spanish.", + ), + model="gpt-4o-mini", +) + +agent = Agent( + name="Assistant", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the spanish agent.", + ), + model="gpt-4o-mini", + handoffs=[spanish_agent], + tools=[get_weather], +) + + +class MyWorkflow(VoiceWorkflowBase): + def __init__(self, secret_word: str, on_start: Callable[[str], None]): + """ + Args: + secret_word: The secret word to guess. + on_start: A callback that is called when the workflow starts. The transcription + is passed in as an argument. + """ + self._input_history: list[TResponseInputItem] = [] + self._current_agent = agent + self._secret_word = secret_word.lower() + self._on_start = on_start + + async def run(self, transcription: str) -> AsyncIterator[str]: + self._on_start(transcription) + + # Add the transcription to the input history + self._input_history.append( + { + "role": "user", + "content": transcription, + } + ) + + # If the user guessed the secret word, do alternate logic + if self._secret_word in transcription.lower(): + yield "You guessed the secret word!" + self._input_history.append( + { + "role": "assistant", + "content": "You guessed the secret word!", + } + ) + return + + # Otherwise, run the agent + result = Runner.run_streamed(self._current_agent, self._input_history) + + async for chunk in VoiceWorkflowHelper.stream_text_from(result): + yield chunk + + # Update the input history and current agent + self._input_history = result.to_input_list() + self._current_agent = result.last_agent diff --git a/mkdocs.yml b/mkdocs.yml index 398fb74a..ad719670 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,121 +1,199 @@ site_name: OpenAI Agents SDK theme: - name: material - features: - # Allows copying code blocks - - content.code.copy - # Allows selecting code blocks - - content.code.select - # Shows the current path in the sidebar - - navigation.path - # Shows sections in the sidebar - - navigation.sections - # Shows sections expanded by default - - navigation.expand - # Enables annotations in code blocks - - content.code.annotate - palette: - primary: black - logo: assets/logo.svg - favicon: images/favicon-platform.svg -nav: - - Intro: index.md - - Quickstart: quickstart.md - - Documentation: - - agents.md - - running_agents.md - - results.md - - streaming.md - - tools.md - - handoffs.md - - tracing.md - - context.md - - guardrails.md - - multi_agent.md - - models.md - - config.md - - API Reference: - - Agents: - - ref/index.md - - ref/agent.md - - ref/run.md - - ref/tool.md - - ref/result.md - - ref/stream_events.md - - ref/handoffs.md - - ref/lifecycle.md - - ref/items.md - - ref/run_context.md - - ref/usage.md - - ref/exceptions.md - - ref/guardrail.md - - ref/model_settings.md - - ref/agent_output.md - - ref/function_schema.md - - ref/models/interface.md - - ref/models/openai_chatcompletions.md - - ref/models/openai_responses.md - - Tracing: - - ref/tracing/index.md - - ref/tracing/create.md - - ref/tracing/traces.md - - ref/tracing/spans.md - - ref/tracing/processor_interface.md - - ref/tracing/processors.md - - ref/tracing/scope.md - - ref/tracing/setup.md - - ref/tracing/span_data.md - - ref/tracing/util.md - - Extensions: - - ref/extensions/handoff_filters.md - - ref/extensions/handoff_prompt.md + name: material + features: + # Allows copying code blocks + - content.code.copy + # Allows selecting code blocks + - content.code.select + # Shows the current path in the sidebar + - navigation.path + # Shows sections in the sidebar + - navigation.sections + # Shows sections expanded by default + - navigation.expand + # Enables annotations in code blocks + - content.code.annotate + palette: + primary: black + logo: assets/logo.svg + favicon: images/favicon-platform.svg + +repo_name: openai-agents-python +repo_url: https://github.com/openai/openai-agents-python plugins: - - search - - mkdocstrings: - handlers: - python: - paths: ["src/agents"] - selection: - docstring_style: google - options: - # Shows links to other members in signatures - signature_crossrefs: true - # Orders members by source order, rather than alphabetical - members_order: source - # Puts the signature on a separate line from the member name - separate_signature: true - # Shows type annotations in signatures - show_signature_annotations: true - # Makes the font sizes nicer - heading_level: 3 + - search + - mkdocstrings: + handlers: + python: + paths: ["src/agents"] + selection: + docstring_style: google + options: + # Shows links to other members in signatures + signature_crossrefs: true + # Orders members by source order, rather than alphabetical + members_order: source + # Puts the signature on a separate line from the member name + separate_signature: true + # Shows type annotations in signatures + show_signature_annotations: true + # Makes the font sizes nicer + heading_level: 3 + # Show inherited members + inherited_members: true + - i18n: + docs_structure: folder + languages: + - locale: en + default: true + name: English + build: true + nav: + - Intro: index.md + - Quickstart: quickstart.md + - Examples: examples.md + - Documentation: + - agents.md + - running_agents.md + - results.md + - streaming.md + - tools.md + - mcp.md + - handoffs.md + - tracing.md + - context.md + - guardrails.md + - multi_agent.md + - Models: + - models/index.md + - models/litellm.md + - config.md + - visualization.md + - Voice agents: + - voice/quickstart.md + - voice/pipeline.md + - voice/tracing.md + - API Reference: + - Agents: + - ref/index.md + - ref/agent.md + - ref/run.md + - ref/tool.md + - ref/result.md + - ref/stream_events.md + - ref/handoffs.md + - ref/lifecycle.md + - ref/items.md + - ref/run_context.md + - ref/usage.md + - ref/exceptions.md + - ref/guardrail.md + - ref/model_settings.md + - ref/agent_output.md + - ref/function_schema.md + - ref/models/interface.md + - ref/models/openai_chatcompletions.md + - ref/models/openai_responses.md + - ref/mcp/server.md + - ref/mcp/util.md + - Tracing: + - ref/tracing/index.md + - ref/tracing/create.md + - ref/tracing/traces.md + - ref/tracing/spans.md + - ref/tracing/processor_interface.md + - ref/tracing/processors.md + - ref/tracing/scope.md + - ref/tracing/setup.md + - ref/tracing/span_data.md + - ref/tracing/util.md + - Voice: + - ref/voice/pipeline.md + - ref/voice/workflow.md + - ref/voice/input.md + - ref/voice/result.md + - ref/voice/pipeline_config.md + - ref/voice/events.md + - ref/voice/exceptions.md + - ref/voice/model.md + - ref/voice/utils.md + - ref/voice/models/openai_provider.md + - ref/voice/models/openai_stt.md + - ref/voice/models/openai_tts.md + - Extensions: + - ref/extensions/handoff_filters.md + - ref/extensions/handoff_prompt.md + - ref/extensions/litellm.md + + - locale: ja + name: 日本語 + build: true + nav: + - はじめに: index.md + - クイックスタート: quickstart.md + - コード例: examples.md + - ドキュメント: + - agents.md + - running_agents.md + - results.md + - streaming.md + - tools.md + - mcp.md + - handoffs.md + - tracing.md + - context.md + - guardrails.md + - multi_agent.md + - モデル: + - models/index.md + - models/litellm.md + - config.md + - visualization.md + - 音声エージェント: + - voice/quickstart.md + - voice/pipeline.md + - voice/tracing.md extra: - # Remove material generation message in footer - generator: false + # Remove material generation message in footer + generator: false + language: en + alternate: + - name: English + link: /openai-agents-python/ + lang: en + - name: 日本語 + link: /openai-agents-python/ja/ + lang: ja markdown_extensions: - - admonition - - pymdownx.details - - pymdownx.superfences - - attr_list - - md_in_html - - pymdownx.highlight: - anchor_linenums: true - line_spans: __span - pygments_lang_class: true - - pymdownx.inlinehilite - - pymdownx.snippets - - pymdownx.superfences + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + - admonition + - pymdownx.details + - attr_list + - md_in_html + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - pymdownx.inlinehilite + - pymdownx.snippets + - pymdownx.superfences validation: - omitted_files: warn - absolute_links: warn - unrecognized_links: warn - anchors: warn + omitted_files: warn + absolute_links: warn + unrecognized_links: warn + anchors: warn extra_css: - - stylesheets/extra.css + - stylesheets/extra.css watch: - - "src/agents" + - "src/agents" diff --git a/pyproject.toml b/pyproject.toml index 9c18d5f6..38a2f2b6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,20 +1,19 @@ [project] name = "openai-agents" -version = "0.0.2" +version = "0.0.16" description = "OpenAI Agents SDK" readme = "README.md" requires-python = ">=3.9" license = "MIT" -authors = [ - { name = "OpenAI", email = "support@openai.com" }, -] +authors = [{ name = "OpenAI", email = "support@openai.com" }] dependencies = [ - "openai>=1.66.0", + "openai>=1.81.0", "pydantic>=2.10, <3", "griffe>=1.5.6, <2", "typing-extensions>=4.12.2, <5", "requests>=2.0, <3", "types-requests>=2.0, <3", + "mcp>=1.8.0, <2; python_version >= '3.10'", ] classifiers = [ "Typing :: Typed", @@ -27,13 +26,18 @@ classifiers = [ "Intended Audience :: Developers", "Operating System :: OS Independent", "Topic :: Software Development :: Libraries :: Python Modules", - "License :: OSI Approved :: MIT License" + "License :: OSI Approved :: MIT License", ] [project.urls] Homepage = "https://github.com/openai/openai-agents-python" Repository = "https://github.com/openai/openai-agents-python" +[project.optional-dependencies] +voice = ["numpy>=2.2.0, <3; python_version>='3.10'", "websockets>=15.0, <16"] +viz = ["graphviz>=0.17"] +litellm = ["litellm>=1.67.4.post1, <2"] + [dependency-groups] dev = [ "mypy", @@ -41,13 +45,25 @@ dev = [ "pytest", "pytest-asyncio", "pytest-mock>=3.14.0", - "rich", + "rich>=13.1.0, <14", "mkdocs>=1.6.0", "mkdocs-material>=9.6.0", "mkdocstrings[python]>=0.28.0", + "mkdocs-static-i18n", "coverage>=7.6.12", "playwright==1.50.0", + "inline-snapshot>=0.20.7", + "pynput", + "types-pynput", + "sounddevice", + "textual", + "websockets", + "graphviz", + "mkdocs-static-i18n>=1.3.0", + "eval-type-backport>=0.2.2", + "fastapi >= 0.110.0, <1", ] + [tool.uv.workspace] members = ["agents"] @@ -73,8 +89,8 @@ select = [ "F", # pyflakes "I", # isort "B", # flake8-bugbear - "C4", # flake8-comprehensions - "UP", # pyupgrade + "C4", # flake8-comprehensions + "UP", # pyupgrade ] isort = { combine-as-imports = true, known-first-party = ["agents"] } @@ -90,11 +106,12 @@ disallow_incomplete_defs = false disallow_untyped_defs = false disallow_untyped_calls = false +[[tool.mypy.overrides]] +module = "sounddevice.*" +ignore_missing_imports = true + [tool.coverage.run] -source = [ - "tests", - "src/agents", -] +source = ["tests", "src/agents"] [tool.coverage.report] show_missing = true @@ -108,7 +125,7 @@ exclude_also = [ ] [tool.pytest.ini_options] -asyncio_mode = "auto" +asyncio_mode = "auto" asyncio_default_fixture_loop_scope = "session" filterwarnings = [ # This is a warning that is expected to happen: we have an async filter that raises an exception @@ -116,4 +133,7 @@ filterwarnings = [ ] markers = [ "allow_call_model_methods: mark test as allowing calls to real model implementations", -] \ No newline at end of file +] + +[tool.inline-snapshot] +format-command = "ruff format --stdin-filename {filename}" diff --git a/src/agents/__init__.py b/src/agents/__init__.py index 69c500ab..58949157 100644 --- a/src/agents/__init__.py +++ b/src/agents/__init__.py @@ -5,8 +5,8 @@ from openai import AsyncOpenAI from . import _config -from .agent import Agent -from .agent_output import AgentOutputSchema +from .agent import Agent, ToolsToFinalOutputFunction, ToolsToFinalOutputResult +from .agent_output import AgentOutputSchema, AgentOutputSchemaBase from .computer import AsyncComputer, Button, Computer, Environment from .exceptions import ( AgentsException, @@ -54,9 +54,19 @@ StreamEvent, ) from .tool import ( + CodeInterpreterTool, ComputerTool, FileSearchTool, FunctionTool, + FunctionToolResult, + HostedMCPTool, + ImageGenerationTool, + LocalShellCommandRequest, + LocalShellExecutor, + LocalShellTool, + MCPToolApprovalFunction, + MCPToolApprovalFunctionResult, + MCPToolApprovalRequest, Tool, WebSearchTool, default_tool_error_function, @@ -69,10 +79,15 @@ GenerationSpanData, GuardrailSpanData, HandoffSpanData, + MCPListToolsSpanData, Span, SpanData, SpanError, + SpeechGroupSpanData, + SpeechSpanData, Trace, + TracingProcessor, + TranscriptionSpanData, add_trace_processor, agent_span, custom_span, @@ -84,21 +99,32 @@ get_current_trace, guardrail_span, handoff_span, + mcp_tools_span, set_trace_processors, set_tracing_disabled, set_tracing_export_api_key, + speech_group_span, + speech_span, trace, + transcription_span, ) from .usage import Usage +from .version import __version__ -def set_default_openai_key(key: str) -> None: - """Set the default OpenAI API key to use for LLM requests and tracing. This is only necessary if - the OPENAI_API_KEY environment variable is not already set. +def set_default_openai_key(key: str, use_for_tracing: bool = True) -> None: + """Set the default OpenAI API key to use for LLM requests (and optionally tracing(). This is + only necessary if the OPENAI_API_KEY environment variable is not already set. If provided, this key will be used instead of the OPENAI_API_KEY environment variable. + + Args: + key: The OpenAI key to use. + use_for_tracing: Whether to also use this key to send traces to OpenAI. Defaults to True + If False, you'll either need to set the OPENAI_API_KEY environment variable or call + set_tracing_export_api_key() with the API key you want to use for tracing. """ - _config.set_default_openai_key(key) + _config.set_default_openai_key(key, use_for_tracing) def set_default_openai_client(client: AsyncOpenAI, use_for_tracing: bool = True) -> None: @@ -123,14 +149,15 @@ def set_default_openai_api(api: Literal["chat_completions", "responses"]) -> Non def enable_verbose_stdout_logging(): """Enables verbose logging to stdout. This is useful for debugging.""" - for name in ["openai.agents", "openai.agents.tracing"]: - logger = logging.getLogger(name) - logger.setLevel(logging.DEBUG) - logger.addHandler(logging.StreamHandler(sys.stdout)) + logger = logging.getLogger("openai.agents") + logger.setLevel(logging.DEBUG) + logger.addHandler(logging.StreamHandler(sys.stdout)) __all__ = [ "Agent", + "ToolsToFinalOutputFunction", + "ToolsToFinalOutputResult", "Runner", "Model", "ModelProvider", @@ -140,6 +167,7 @@ def enable_verbose_stdout_logging(): "OpenAIProvider", "OpenAIResponsesModel", "AgentOutputSchema", + "AgentOutputSchemaBase", "Computer", "AsyncComputer", "Environment", @@ -184,10 +212,20 @@ def enable_verbose_stdout_logging(): "AgentUpdatedStreamEvent", "StreamEvent", "FunctionTool", + "FunctionToolResult", "ComputerTool", "FileSearchTool", + "CodeInterpreterTool", + "ImageGenerationTool", + "LocalShellCommandRequest", + "LocalShellExecutor", + "LocalShellTool", "Tool", "WebSearchTool", + "HostedMCPTool", + "MCPToolApprovalFunction", + "MCPToolApprovalRequest", + "MCPToolApprovalFunctionResult", "function_tool", "Usage", "add_trace_processor", @@ -201,8 +239,13 @@ def enable_verbose_stdout_logging(): "handoff_span", "set_trace_processors", "set_tracing_disabled", + "speech_group_span", + "transcription_span", + "speech_span", + "mcp_tools_span", "trace", "Trace", + "TracingProcessor", "SpanError", "Span", "SpanData", @@ -212,6 +255,10 @@ def enable_verbose_stdout_logging(): "GenerationSpanData", "GuardrailSpanData", "HandoffSpanData", + "SpeechGroupSpanData", + "SpeechSpanData", + "MCPListToolsSpanData", + "TranscriptionSpanData", "set_default_openai_key", "set_default_openai_client", "set_default_openai_api", @@ -220,4 +267,5 @@ def enable_verbose_stdout_logging(): "gen_trace_id", "gen_span_id", "default_tool_error_function", + "__version__", ] diff --git a/src/agents/_config.py b/src/agents/_config.py index 55ded64d..304cfb83 100644 --- a/src/agents/_config.py +++ b/src/agents/_config.py @@ -5,15 +5,18 @@ from .tracing import set_tracing_export_api_key -def set_default_openai_key(key: str) -> None: - set_tracing_export_api_key(key) +def set_default_openai_key(key: str, use_for_tracing: bool) -> None: _openai_shared.set_default_openai_key(key) + if use_for_tracing: + set_tracing_export_api_key(key) + def set_default_openai_client(client: AsyncOpenAI, use_for_tracing: bool) -> None: + _openai_shared.set_default_openai_client(client) + if use_for_tracing: set_tracing_export_api_key(client.api_key) - _openai_shared.set_default_openai_client(client) def set_default_openai_api(api: Literal["chat_completions", "responses"]) -> None: diff --git a/src/agents/_run_impl.py b/src/agents/_run_impl.py index 112819c8..2cfa270e 100644 --- a/src/agents/_run_impl.py +++ b/src/agents/_run_impl.py @@ -1,8 +1,11 @@ from __future__ import annotations import asyncio -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any +import dataclasses +import inspect +from collections.abc import Awaitable +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any, cast from openai.types.responses import ( ResponseComputerToolCall, @@ -11,6 +14,9 @@ ResponseFunctionWebSearch, ResponseOutputMessage, ) +from openai.types.responses.response_code_interpreter_tool_call import ( + ResponseCodeInterpreterToolCall, +) from openai.types.responses.response_computer_tool_call import ( ActionClick, ActionDoubleClick, @@ -22,12 +28,17 @@ ActionType, ActionWait, ) -from openai.types.responses.response_input_param import ComputerCallOutput -from openai.types.responses.response_output_item import Reasoning +from openai.types.responses.response_input_param import ComputerCallOutput, McpApprovalResponse +from openai.types.responses.response_output_item import ( + ImageGenerationCall, + LocalShellCall, + McpApprovalRequest, + McpListTools, +) +from openai.types.responses.response_reasoning_item import ResponseReasoningItem -from . import _utils -from .agent import Agent -from .agent_output import AgentOutputSchema +from .agent import Agent, ToolsToFinalOutputResult +from .agent_output import AgentOutputSchemaBase from .computer import AsyncComputer, Computer from .exceptions import AgentsException, ModelBehaviorError, UserError from .guardrail import InputGuardrail, InputGuardrailResult, OutputGuardrail, OutputGuardrailResult @@ -36,6 +47,9 @@ HandoffCallItem, HandoffOutputItem, ItemHelpers, + MCPApprovalRequestItem, + MCPApprovalResponseItem, + MCPListToolsItem, MessageOutputItem, ModelResponse, ReasoningItem, @@ -46,10 +60,20 @@ ) from .lifecycle import RunHooks from .logger import logger +from .model_settings import ModelSettings from .models.interface import ModelTracing from .run_context import RunContextWrapper, TContext from .stream_events import RunItemStreamEvent, StreamEvent -from .tool import ComputerTool, FunctionTool +from .tool import ( + ComputerTool, + FunctionTool, + FunctionToolResult, + HostedMCPTool, + LocalShellCommandRequest, + LocalShellTool, + MCPToolApprovalRequest, + Tool, +) from .tracing import ( SpanError, Trace, @@ -59,6 +83,7 @@ handoff_span, trace, ) +from .util import _coro, _error_tracing if TYPE_CHECKING: from .run import RunConfig @@ -70,6 +95,25 @@ class QueueCompleteSentinel: QUEUE_COMPLETE_SENTINEL = QueueCompleteSentinel() +_NOT_FINAL_OUTPUT = ToolsToFinalOutputResult(is_final_output=False, final_output=None) + + +@dataclass +class AgentToolUseTracker: + agent_to_tools: list[tuple[Agent, list[str]]] = field(default_factory=list) + """Tuple of (agent, list of tools used). Can't use a dict because agents aren't hashable.""" + + def add_tool_use(self, agent: Agent[Any], tool_names: list[str]) -> None: + existing_data = next((item for item in self.agent_to_tools if item[0] == agent), None) + if existing_data: + existing_data[1].extend(tool_names) + else: + self.agent_to_tools.append((agent, tool_names)) + + def has_used_tools(self, agent: Agent[Any]) -> bool: + existing_data = next((item for item in self.agent_to_tools if item[0] == agent), None) + return existing_data is not None and len(existing_data[1]) > 0 + @dataclass class ToolRunHandoff: @@ -89,14 +133,29 @@ class ToolRunComputerAction: computer_tool: ComputerTool +@dataclass +class ToolRunMCPApprovalRequest: + request_item: McpApprovalRequest + mcp_tool: HostedMCPTool + + +@dataclass +class ToolRunLocalShellCall: + tool_call: LocalShellCall + local_shell_tool: LocalShellTool + + @dataclass class ProcessedResponse: new_items: list[RunItem] handoffs: list[ToolRunHandoff] functions: list[ToolRunFunction] computer_actions: list[ToolRunComputerAction] + local_shell_calls: list[ToolRunLocalShellCall] + tools_used: list[str] # Names of all tools used, including hosted tools + mcp_approval_requests: list[ToolRunMCPApprovalRequest] # Only requests with callbacks - def has_tools_to_run(self) -> bool: + def has_tools_or_approvals_to_run(self) -> bool: # Handoffs, functions and computer actions need local processing # Hosted tools have already run, so there's nothing to do. return any( @@ -104,6 +163,8 @@ def has_tools_to_run(self) -> bool: self.handoffs, self.functions, self.computer_actions, + self.local_shell_calls, + self.mcp_approval_requests, ] ) @@ -167,11 +228,11 @@ async def execute_tools_and_side_effects( agent: Agent[TContext], # The original input to the Runner original_input: str | list[TResponseInputItem], - # Eveything generated by Runner since the original input, but before the current step + # Everything generated by Runner since the original input, but before the current step pre_step_items: list[RunItem], new_response: ModelResponse, processed_response: ProcessedResponse, - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, @@ -199,10 +260,19 @@ async def execute_tools_and_side_effects( config=run_config, ), ) - new_step_items.extend(function_results) + new_step_items.extend([result.run_item for result in function_results]) new_step_items.extend(computer_results) - # Second, check if there are any handoffs + # Next, run the MCP approval requests + if processed_response.mcp_approval_requests: + approval_results = await cls.execute_mcp_approval_requests( + agent=agent, + approval_requests=processed_response.mcp_approval_requests, + context_wrapper=context_wrapper, + ) + new_step_items.extend(approval_results) + + # Next, check if there are any handoffs if run_handoffs := processed_response.handoffs: return await cls.execute_handoffs( agent=agent, @@ -216,6 +286,36 @@ async def execute_tools_and_side_effects( run_config=run_config, ) + # Next, we'll check if the tool use should result in a final output + check_tool_use = await cls._check_for_final_output_from_tools( + agent=agent, + tool_results=function_results, + context_wrapper=context_wrapper, + config=run_config, + ) + + if check_tool_use.is_final_output: + # If the output type is str, then let's just stringify it + if not agent.output_type or agent.output_type is str: + check_tool_use.final_output = str(check_tool_use.final_output) + + if check_tool_use.final_output is None: + logger.error( + "Model returned a final output of None. Not raising an error because we assume" + "you know what you're doing." + ) + + return await cls.execute_final_output( + agent=agent, + original_input=original_input, + new_response=new_response, + pre_step_items=pre_step_items, + new_step_items=new_step_items, + final_output=check_tool_use.final_output, + hooks=hooks, + context_wrapper=context_wrapper, + ) + # Now we can check if the model also produced a final output message_items = [item for item in new_step_items if isinstance(item, MessageOutputItem)] @@ -241,7 +341,7 @@ async def execute_tools_and_side_effects( ) elif ( not output_schema or output_schema.is_plain_text() - ) and not processed_response.has_tools_to_run(): + ) and not processed_response.has_tools_or_approvals_to_run(): return await cls.execute_final_output( agent=agent, original_input=original_input, @@ -262,13 +362,26 @@ async def execute_tools_and_side_effects( next_step=NextStepRunAgain(), ) + @classmethod + def maybe_reset_tool_choice( + cls, agent: Agent[Any], tool_use_tracker: AgentToolUseTracker, model_settings: ModelSettings + ) -> ModelSettings: + """Resets tool choice to None if the agent has used tools and the agent's reset_tool_choice + flag is True.""" + + if agent.reset_tool_choice is True and tool_use_tracker.has_used_tools(agent): + return dataclasses.replace(model_settings, tool_choice=None) + + return model_settings + @classmethod def process_model_response( cls, *, agent: Agent[Any], + all_tools: list[Tool], response: ModelResponse, - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], ) -> ProcessedResponse: items: list[RunItem] = [] @@ -276,24 +389,37 @@ def process_model_response( run_handoffs = [] functions = [] computer_actions = [] - + local_shell_calls = [] + mcp_approval_requests = [] + tools_used: list[str] = [] handoff_map = {handoff.tool_name: handoff for handoff in handoffs} - function_map = {tool.name: tool for tool in agent.tools if isinstance(tool, FunctionTool)} - computer_tool = next((tool for tool in agent.tools if isinstance(tool, ComputerTool)), None) + function_map = {tool.name: tool for tool in all_tools if isinstance(tool, FunctionTool)} + computer_tool = next((tool for tool in all_tools if isinstance(tool, ComputerTool)), None) + local_shell_tool = next( + (tool for tool in all_tools if isinstance(tool, LocalShellTool)), None + ) + hosted_mcp_server_map = { + tool.tool_config["server_label"]: tool + for tool in all_tools + if isinstance(tool, HostedMCPTool) + } for output in response.output: if isinstance(output, ResponseOutputMessage): items.append(MessageOutputItem(raw_item=output, agent=agent)) elif isinstance(output, ResponseFileSearchToolCall): items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("file_search") elif isinstance(output, ResponseFunctionWebSearch): items.append(ToolCallItem(raw_item=output, agent=agent)) - elif isinstance(output, Reasoning): + tools_used.append("web_search") + elif isinstance(output, ResponseReasoningItem): items.append(ReasoningItem(raw_item=output, agent=agent)) elif isinstance(output, ResponseComputerToolCall): items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("computer_use") if not computer_tool: - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Computer tool not found", data={}, @@ -305,6 +431,54 @@ def process_model_response( computer_actions.append( ToolRunComputerAction(tool_call=output, computer_tool=computer_tool) ) + elif isinstance(output, McpApprovalRequest): + items.append(MCPApprovalRequestItem(raw_item=output, agent=agent)) + if output.server_label not in hosted_mcp_server_map: + _error_tracing.attach_error_to_current_span( + SpanError( + message="MCP server label not found", + data={"server_label": output.server_label}, + ) + ) + raise ModelBehaviorError(f"MCP server label {output.server_label} not found") + else: + server = hosted_mcp_server_map[output.server_label] + if server.on_approval_request: + mcp_approval_requests.append( + ToolRunMCPApprovalRequest( + request_item=output, + mcp_tool=server, + ) + ) + else: + logger.warning( + f"MCP server {output.server_label} has no on_approval_request hook" + ) + elif isinstance(output, McpListTools): + items.append(MCPListToolsItem(raw_item=output, agent=agent)) + elif isinstance(output, ImageGenerationCall): + items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("image_generation") + elif isinstance(output, ResponseCodeInterpreterToolCall): + items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("code_interpreter") + elif isinstance(output, LocalShellCall): + items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("local_shell") + if not local_shell_tool: + _error_tracing.attach_error_to_current_span( + SpanError( + message="Local shell tool not found", + data={}, + ) + ) + raise ModelBehaviorError( + "Model produced local shell call without a local shell tool." + ) + local_shell_calls.append( + ToolRunLocalShellCall(tool_call=output, local_shell_tool=local_shell_tool) + ) + elif not isinstance(output, ResponseFunctionToolCall): logger.warning(f"Unexpected output type, ignoring: {type(output)}") continue @@ -313,6 +487,8 @@ def process_model_response( if not isinstance(output, ResponseFunctionToolCall): continue + tools_used.append(output.name) + # Handoffs if output.name in handoff_map: items.append(HandoffCallItem(raw_item=output, agent=agent)) @@ -324,7 +500,7 @@ def process_model_response( # Regular function tool call else: if output.name not in function_map: - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Tool not found", data={"tool_name": output.name}, @@ -344,6 +520,9 @@ def process_model_response( handoffs=run_handoffs, functions=functions, computer_actions=computer_actions, + local_shell_calls=local_shell_calls, + tools_used=tools_used, + mcp_approval_requests=mcp_approval_requests, ) @classmethod @@ -355,10 +534,10 @@ async def execute_function_tool_calls( hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], config: RunConfig, - ) -> list[RunItem]: + ) -> list[FunctionToolResult]: async def run_single_tool( func_tool: FunctionTool, tool_call: ResponseFunctionToolCall - ) -> str: + ) -> Any: with function_span(func_tool.name) as span_fn: if config.trace_include_sensitive_data: span_fn.span_data.input = tool_call.arguments @@ -368,7 +547,7 @@ async def run_single_tool( ( agent.hooks.on_tool_start(context_wrapper, agent, func_tool) if agent.hooks - else _utils.noop_coroutine() + else _coro.noop_coroutine() ), func_tool.on_invoke_tool(context_wrapper, tool_call.arguments), ) @@ -378,11 +557,11 @@ async def run_single_tool( ( agent.hooks.on_tool_end(context_wrapper, agent, func_tool, result) if agent.hooks - else _utils.noop_coroutine() + else _coro.noop_coroutine() ), ) except Exception as e: - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Error running tool", data={"tool_name": func_tool.name, "error": str(e)}, @@ -404,14 +583,42 @@ async def run_single_tool( results = await asyncio.gather(*tasks) return [ - ToolCallOutputItem( - output=str(result), - raw_item=ItemHelpers.tool_call_output_item(tool_run.tool_call, str(result)), - agent=agent, + FunctionToolResult( + tool=tool_run.function_tool, + output=result, + run_item=ToolCallOutputItem( + output=result, + raw_item=ItemHelpers.tool_call_output_item(tool_run.tool_call, str(result)), + agent=agent, + ), ) for tool_run, result in zip(tool_runs, results) ] + @classmethod + async def execute_local_shell_calls( + cls, + *, + agent: Agent[TContext], + calls: list[ToolRunLocalShellCall], + context_wrapper: RunContextWrapper[TContext], + hooks: RunHooks[TContext], + config: RunConfig, + ) -> list[RunItem]: + results: list[RunItem] = [] + # Need to run these serially, because each call can affect the local shell state + for call in calls: + results.append( + await LocalShellAction.execute( + agent=agent, + call=call, + hooks=hooks, + context_wrapper=context_wrapper, + config=config, + ) + ) + return results + @classmethod async def execute_computer_actions( cls, @@ -452,7 +659,8 @@ async def execute_handoffs( run_config: RunConfig, ) -> SingleStepResult: # If there is more than one handoff, add tool responses that reject those handoffs - if len(run_handoffs) > 1: + multiple_handoffs = len(run_handoffs) > 1 + if multiple_handoffs: output_message = "Multiple handoffs detected, ignoring this one." new_step_items.extend( [ @@ -474,6 +682,16 @@ async def execute_handoffs( context_wrapper, actual_handoff.tool_call.arguments ) span_handoff.span_data.to_agent = new_agent.name + if multiple_handoffs: + requested_agents = [handoff.handoff.agent_name for handoff in run_handoffs] + span_handoff.set_error( + SpanError( + message="Multiple handoffs requested", + data={ + "requested_agents": requested_agents, + }, + ) + ) # Append a tool output item for the handoff new_step_items.append( @@ -502,7 +720,7 @@ async def execute_handoffs( source=agent, ) if agent.hooks - else _utils.noop_coroutine() + else _coro.noop_coroutine() ), ) @@ -520,7 +738,7 @@ async def execute_handoffs( new_items=tuple(new_step_items), ) if not callable(input_filter): - _utils.attach_error_to_span( + _error_tracing.attach_error_to_span( span_handoff, SpanError( message="Invalid input filter", @@ -530,7 +748,7 @@ async def execute_handoffs( raise UserError(f"Invalid input filter: {input_filter}") filtered = input_filter(handoff_input_data) if not isinstance(filtered, HandoffInputData): - _utils.attach_error_to_span( + _error_tracing.attach_error_to_span( span_handoff, SpanError( message="Invalid input filter result", @@ -555,6 +773,40 @@ async def execute_handoffs( next_step=NextStepHandoff(new_agent), ) + @classmethod + async def execute_mcp_approval_requests( + cls, + *, + agent: Agent[TContext], + approval_requests: list[ToolRunMCPApprovalRequest], + context_wrapper: RunContextWrapper[TContext], + ) -> list[RunItem]: + async def run_single_approval(approval_request: ToolRunMCPApprovalRequest) -> RunItem: + callback = approval_request.mcp_tool.on_approval_request + assert callback is not None, "Callback is required for MCP approval requests" + maybe_awaitable_result = callback( + MCPToolApprovalRequest(context_wrapper, approval_request.request_item) + ) + if inspect.isawaitable(maybe_awaitable_result): + result = await maybe_awaitable_result + else: + result = maybe_awaitable_result + reason = result.get("reason", None) + raw_item: McpApprovalResponse = { + "approval_request_id": approval_request.request_item.id, + "approve": result["approve"], + "type": "mcp_approval_response", + } + if not result["approve"] and reason: + raw_item["reason"] = reason + return MCPApprovalResponseItem( + raw_item=raw_item, + agent=agent, + ) + + tasks = [run_single_approval(approval_request) for approval_request in approval_requests] + return await asyncio.gather(*tasks) + @classmethod async def execute_final_output( cls, @@ -591,7 +843,7 @@ async def run_final_output_hooks( hooks.on_agent_end(context_wrapper, agent, final_output), agent.hooks.on_end(context_wrapper, agent, final_output) if agent.hooks - else _utils.noop_coroutine(), + else _coro.noop_coroutine(), ) @classmethod @@ -639,6 +891,11 @@ def stream_step_result_to_queue( event = RunItemStreamEvent(item=item, name="tool_output") elif isinstance(item, ReasoningItem): event = RunItemStreamEvent(item=item, name="reasoning_item_created") + elif isinstance(item, MCPApprovalRequestItem): + event = RunItemStreamEvent(item=item, name="mcp_approval_requested") + elif isinstance(item, MCPListToolsItem): + event = RunItemStreamEvent(item=item, name="mcp_list_tools") + else: logger.warning(f"Unexpected item type: {type(item)}") event = None @@ -646,6 +903,47 @@ def stream_step_result_to_queue( if event: queue.put_nowait(event) + @classmethod + async def _check_for_final_output_from_tools( + cls, + *, + agent: Agent[TContext], + tool_results: list[FunctionToolResult], + context_wrapper: RunContextWrapper[TContext], + config: RunConfig, + ) -> ToolsToFinalOutputResult: + """Returns (i, final_output).""" + if not tool_results: + return _NOT_FINAL_OUTPUT + + if agent.tool_use_behavior == "run_llm_again": + return _NOT_FINAL_OUTPUT + elif agent.tool_use_behavior == "stop_on_first_tool": + return ToolsToFinalOutputResult( + is_final_output=True, final_output=tool_results[0].output + ) + elif isinstance(agent.tool_use_behavior, dict): + names = agent.tool_use_behavior.get("stop_at_tool_names", []) + for tool_result in tool_results: + if tool_result.tool.name in names: + return ToolsToFinalOutputResult( + is_final_output=True, final_output=tool_result.output + ) + return ToolsToFinalOutputResult(is_final_output=False, final_output=None) + elif callable(agent.tool_use_behavior): + if inspect.iscoroutinefunction(agent.tool_use_behavior): + return await cast( + Awaitable[ToolsToFinalOutputResult], + agent.tool_use_behavior(context_wrapper, tool_results), + ) + else: + return cast( + ToolsToFinalOutputResult, agent.tool_use_behavior(context_wrapper, tool_results) + ) + + logger.error(f"Invalid tool_use_behavior: {agent.tool_use_behavior}") + raise UserError(f"Invalid tool_use_behavior: {agent.tool_use_behavior}") + class TraceCtxManager: """Creates a trace only if there is no current trace, and manages the trace lifecycle.""" @@ -706,7 +1004,7 @@ async def execute( ( agent.hooks.on_tool_start(context_wrapper, agent, action.computer_tool) if agent.hooks - else _utils.noop_coroutine() + else _coro.noop_coroutine() ), output_func, ) @@ -716,7 +1014,7 @@ async def execute( ( agent.hooks.on_tool_end(context_wrapper, agent, action.computer_tool, output) if agent.hooks - else _utils.noop_coroutine() + else _coro.noop_coroutine() ), ) @@ -790,3 +1088,54 @@ async def _get_screenshot_async( await computer.wait() return await computer.screenshot() + + +class LocalShellAction: + @classmethod + async def execute( + cls, + *, + agent: Agent[TContext], + call: ToolRunLocalShellCall, + hooks: RunHooks[TContext], + context_wrapper: RunContextWrapper[TContext], + config: RunConfig, + ) -> RunItem: + await asyncio.gather( + hooks.on_tool_start(context_wrapper, agent, call.local_shell_tool), + ( + agent.hooks.on_tool_start(context_wrapper, agent, call.local_shell_tool) + if agent.hooks + else _coro.noop_coroutine() + ), + ) + + request = LocalShellCommandRequest( + ctx_wrapper=context_wrapper, + data=call.tool_call, + ) + output = call.local_shell_tool.executor(request) + if inspect.isawaitable(output): + result = await output + else: + result = output + + await asyncio.gather( + hooks.on_tool_end(context_wrapper, agent, call.local_shell_tool, result), + ( + agent.hooks.on_tool_end(context_wrapper, agent, call.local_shell_tool, result) + if agent.hooks + else _coro.noop_coroutine() + ), + ) + + return ToolCallOutputItem( + agent=agent, + output=output, + raw_item={ + "type": "local_shell_call_output", + "id": call.tool_call.call_id, + "output": result, + # "id": "out" + call.tool_call.id, # TODO remove this, it should be optional + }, + ) diff --git a/src/agents/_utils.py b/src/agents/_utils.py deleted file mode 100644 index 2a0293a6..00000000 --- a/src/agents/_utils.py +++ /dev/null @@ -1,61 +0,0 @@ -from __future__ import annotations - -import re -from collections.abc import Awaitable -from typing import Any, Literal, Union - -from pydantic import TypeAdapter, ValidationError -from typing_extensions import TypeVar - -from .exceptions import ModelBehaviorError -from .logger import logger -from .tracing import Span, SpanError, get_current_span - -T = TypeVar("T") - -MaybeAwaitable = Union[Awaitable[T], T] - - -def transform_string_function_style(name: str) -> str: - # Replace spaces with underscores - name = name.replace(" ", "_") - - # Replace non-alphanumeric characters with underscores - name = re.sub(r"[^a-zA-Z0-9]", "_", name) - - return name.lower() - - -def validate_json(json_str: str, type_adapter: TypeAdapter[T], partial: bool) -> T: - partial_setting: bool | Literal["off", "on", "trailing-strings"] = ( - "trailing-strings" if partial else False - ) - try: - validated = type_adapter.validate_json(json_str, experimental_allow_partial=partial_setting) - return validated - except ValidationError as e: - attach_error_to_current_span( - SpanError( - message="Invalid JSON provided", - data={}, - ) - ) - raise ModelBehaviorError( - f"Invalid JSON when parsing {json_str} for {type_adapter}; {e}" - ) from e - - -def attach_error_to_span(span: Span[Any], error: SpanError) -> None: - span.set_error(error) - - -def attach_error_to_current_span(error: SpanError) -> None: - span = get_current_span() - if span: - attach_error_to_span(span, error) - else: - logger.warning(f"No span to add error {error} to") - - -async def noop_coroutine() -> None: - pass diff --git a/src/agents/agent.py b/src/agents/agent.py index 61c0a896..e22f579f 100644 --- a/src/agents/agent.py +++ b/src/agents/agent.py @@ -4,31 +4,72 @@ import inspect from collections.abc import Awaitable from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any, Callable, Generic, cast +from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, cast -from . import _utils -from ._utils import MaybeAwaitable +from typing_extensions import NotRequired, TypeAlias, TypedDict + +from .agent_output import AgentOutputSchemaBase from .guardrail import InputGuardrail, OutputGuardrail from .handoffs import Handoff from .items import ItemHelpers from .logger import logger +from .mcp import MCPUtil from .model_settings import ModelSettings from .models.interface import Model from .run_context import RunContextWrapper, TContext -from .tool import Tool, function_tool +from .tool import FunctionToolResult, Tool, function_tool +from .util import _transforms +from .util._types import MaybeAwaitable if TYPE_CHECKING: from .lifecycle import AgentHooks + from .mcp import MCPServer from .result import RunResult +@dataclass +class ToolsToFinalOutputResult: + is_final_output: bool + """Whether this is the final output. If False, the LLM will run again and receive the tool call + output. + """ + + final_output: Any | None = None + """The final output. Can be None if `is_final_output` is False, otherwise must match the + `output_type` of the agent. + """ + + +ToolsToFinalOutputFunction: TypeAlias = Callable[ + [RunContextWrapper[TContext], list[FunctionToolResult]], + MaybeAwaitable[ToolsToFinalOutputResult], +] +"""A function that takes a run context and a list of tool results, and returns a +`ToolsToFinalOutputResult`. +""" + + +class StopAtTools(TypedDict): + stop_at_tool_names: list[str] + """A list of tool names, any of which will stop the agent from running further.""" + + +class MCPConfig(TypedDict): + """Configuration for MCP servers.""" + + convert_schemas_to_strict: NotRequired[bool] + """If True, we will attempt to convert the MCP schemas to strict-mode schemas. This is a + best-effort conversion, so some schemas may not be convertible. Defaults to False. + """ + + @dataclass class Agent(Generic[TContext]): """An agent is an AI model configured with instructions, tools, guardrails, handoffs and more. We strongly recommend passing `instructions`, which is the "system prompt" for the agent. In - addition, you can pass `description`, which is a human-readable description of the agent, used - when the agent is used inside tools/handoffs. + addition, you can pass `handoff_description`, which is a human-readable description of the + agent, used when the agent is used inside tools/handoffs. Agents are generic on the context type. The context is a (mutable) object you create. It is passed to tool functions, handoffs, guardrails, etc. @@ -68,7 +109,7 @@ class Agent(Generic[TContext]): """The model implementation to use when invoking the LLM. By default, if not set, the agent will use the default model configured in - `model_settings.DEFAULT_MODEL`. + `openai_provider.DEFAULT_MODEL` (currently "gpt-4o"). """ model_settings: ModelSettings = field(default_factory=ModelSettings) @@ -78,6 +119,19 @@ class Agent(Generic[TContext]): tools: list[Tool] = field(default_factory=list) """A list of tools that the agent can use.""" + mcp_servers: list[MCPServer] = field(default_factory=list) + """A list of [Model Context Protocol](https://modelcontextprotocol.io/) servers that + the agent can use. Every time the agent runs, it will include tools from these servers in the + list of available tools. + + NOTE: You are expected to manage the lifecycle of these servers. Specifically, you must call + `server.connect()` before passing it to the agent, and `server.cleanup()` when the server is no + longer needed. + """ + + mcp_config: MCPConfig = field(default_factory=lambda: MCPConfig()) + """Configuration for MCP servers.""" + input_guardrails: list[InputGuardrail[TContext]] = field(default_factory=list) """A list of checks that run in parallel to the agent's execution, before generating a response. Runs only if the agent is the first agent in the chain. @@ -88,13 +142,42 @@ class Agent(Generic[TContext]): Runs only if the agent produces a final output. """ - output_type: type[Any] | None = None - """The type of the output object. If not provided, the output will be `str`.""" + output_type: type[Any] | AgentOutputSchemaBase | None = None + """The type of the output object. If not provided, the output will be `str`. In most cases, + you should pass a regular Python type (e.g. a dataclass, Pydantic model, TypedDict, etc). + You can customize this in two ways: + 1. If you want non-strict schemas, pass `AgentOutputSchema(MyClass, strict_json_schema=False)`. + 2. If you want to use a custom JSON schema (i.e. without using the SDK's automatic schema) + creation, subclass and pass an `AgentOutputSchemaBase` subclass. + """ hooks: AgentHooks[TContext] | None = None """A class that receives callbacks on various lifecycle events for this agent. """ + tool_use_behavior: ( + Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools | ToolsToFinalOutputFunction + ) = "run_llm_again" + """This lets you configure how tool use is handled. + - "run_llm_again": The default behavior. Tools are run, and then the LLM receives the results + and gets to respond. + - "stop_on_first_tool": The output of the first tool call is used as the final output. This + means that the LLM does not process the result of the tool call. + - A list of tool names: The agent will stop running if any of the tools in the list are called. + The final output will be the output of the first matching tool call. The LLM does not + process the result of the tool call. + - A function: If you pass a function, it will be called with the run context and the list of + tool results. It must return a `ToolToFinalOutputResult`, which determines whether the tool + calls result in a final output. + + NOTE: This configuration is specific to FunctionTools. Hosted tools, such as file search, + web search, etc are always processed by the LLM. + """ + + reset_tool_choice: bool = True + """Whether to reset the tool choice to the default value after a tool has been called. Defaults + to True. This ensures that the agent doesn't enter an infinite loop of tool usage.""" + def clone(self, **kwargs: Any) -> Agent[TContext]: """Make a copy of the agent, with the given arguments changed. For example, you could do: ``` @@ -126,7 +209,7 @@ def as_tool( """ @function_tool( - name_override=tool_name or _utils.transform_string_function_style(self.name), + name_override=tool_name or _transforms.transform_string_function_style(self.name), description_override=tool_description or "", ) async def run_agent(context: RunContextWrapper, input: str) -> str: @@ -157,3 +240,13 @@ async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> s logger.error(f"Instructions must be a string or a function, got {self.instructions}") return None + + async def get_mcp_tools(self) -> list[Tool]: + """Fetches the available tools from the MCP servers.""" + convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False) + return await MCPUtil.get_all_function_tools(self.mcp_servers, convert_schemas_to_strict) + + async def get_all_tools(self) -> list[Tool]: + """All agent tools, including MCP tools and function tools.""" + mcp_tools = await self.get_mcp_tools() + return mcp_tools + self.tools diff --git a/src/agents/agent_output.py b/src/agents/agent_output.py index 8140d8c6..066bbd83 100644 --- a/src/agents/agent_output.py +++ b/src/agents/agent_output.py @@ -1,19 +1,58 @@ +import abc from dataclasses import dataclass from typing import Any from pydantic import BaseModel, TypeAdapter from typing_extensions import TypedDict, get_args, get_origin -from . import _utils from .exceptions import ModelBehaviorError, UserError from .strict_schema import ensure_strict_json_schema from .tracing import SpanError +from .util import _error_tracing, _json _WRAPPER_DICT_KEY = "response" +class AgentOutputSchemaBase(abc.ABC): + """An object that captures the JSON schema of the output, as well as validating/parsing JSON + produced by the LLM into the output type. + """ + + @abc.abstractmethod + def is_plain_text(self) -> bool: + """Whether the output type is plain text (versus a JSON object).""" + pass + + @abc.abstractmethod + def name(self) -> str: + """The name of the output type.""" + pass + + @abc.abstractmethod + def json_schema(self) -> dict[str, Any]: + """Returns the JSON schema of the output. Will only be called if the output type is not + plain text. + """ + pass + + @abc.abstractmethod + def is_strict_json_schema(self) -> bool: + """Whether the JSON schema is in strict mode. Strict mode constrains the JSON schema + features, but guarantees valis JSON. See here for details: + https://platform.openai.com/docs/guides/structured-outputs#supported-schemas + """ + pass + + @abc.abstractmethod + def validate_json(self, json_str: str) -> Any: + """Validate a JSON string against the output type. You must return the validated object, + or raise a `ModelBehaviorError` if the JSON is invalid. + """ + pass + + @dataclass(init=False) -class AgentOutputSchema: +class AgentOutputSchema(AgentOutputSchemaBase): """An object that captures the JSON schema of the output, as well as validating/parsing JSON produced by the LLM into the output type. """ @@ -32,7 +71,7 @@ class AgentOutputSchema: _output_schema: dict[str, Any] """The JSON schema of the output.""" - strict_json_schema: bool + _strict_json_schema: bool """Whether the JSON schema is in strict mode. We **strongly** recommend setting this to True, as it increases the likelihood of correct JSON input. """ @@ -45,7 +84,7 @@ def __init__(self, output_type: type[Any], strict_json_schema: bool = True): setting this to True, as it increases the likelihood of correct JSON input. """ self.output_type = output_type - self.strict_json_schema = strict_json_schema + self._strict_json_schema = strict_json_schema if output_type is None or output_type is str: self._is_wrapped = False @@ -70,27 +109,38 @@ def __init__(self, output_type: type[Any], strict_json_schema: bool = True): self._type_adapter = TypeAdapter(output_type) self._output_schema = self._type_adapter.json_schema() - if self.strict_json_schema: - self._output_schema = ensure_strict_json_schema(self._output_schema) + if self._strict_json_schema: + try: + self._output_schema = ensure_strict_json_schema(self._output_schema) + except UserError as e: + raise UserError( + "Strict JSON schema is enabled, but the output type is not valid. " + "Either make the output type strict, or pass output_schema_strict=False to " + "your Agent()" + ) from e def is_plain_text(self) -> bool: """Whether the output type is plain text (versus a JSON object).""" return self.output_type is None or self.output_type is str + def is_strict_json_schema(self) -> bool: + """Whether the JSON schema is in strict mode.""" + return self._strict_json_schema + def json_schema(self) -> dict[str, Any]: """The JSON schema of the output type.""" if self.is_plain_text(): raise UserError("Output type is plain text, so no JSON schema is available") return self._output_schema - def validate_json(self, json_str: str, partial: bool = False) -> Any: + def validate_json(self, json_str: str) -> Any: """Validate a JSON string against the output type. Returns the validated object, or raises a `ModelBehaviorError` if the JSON is invalid. """ - validated = _utils.validate_json(json_str, self._type_adapter, partial) + validated = _json.validate_json(json_str, self._type_adapter, partial=False) if self._is_wrapped: if not isinstance(validated, dict): - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Invalid JSON", data={"details": f"Expected a dict, got {type(validated)}"}, @@ -101,7 +151,7 @@ def validate_json(self, json_str: str, partial: bool = False) -> Any: ) if _WRAPPER_DICT_KEY not in validated: - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Invalid JSON", data={"details": f"Could not find key {_WRAPPER_DICT_KEY} in JSON"}, @@ -113,7 +163,7 @@ def validate_json(self, json_str: str, partial: bool = False) -> Any: return validated[_WRAPPER_DICT_KEY] return validated - def output_type_name(self) -> str: + def name(self) -> str: """The name of the output type.""" return _type_to_str(self.output_type) @@ -138,7 +188,7 @@ def _type_to_str(t: type[Any]) -> str: # It's a simple type like `str`, `int`, etc. return t.__name__ elif args: - args_str = ', '.join(_type_to_str(arg) for arg in args) + args_str = ", ".join(_type_to_str(arg) for arg in args) return f"{origin.__name__}[{args_str}]" else: return str(t) diff --git a/src/agents/extensions/models/__init__.py b/src/agents/extensions/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/agents/extensions/models/litellm_model.py b/src/agents/extensions/models/litellm_model.py new file mode 100644 index 00000000..49e2d42d --- /dev/null +++ b/src/agents/extensions/models/litellm_model.py @@ -0,0 +1,394 @@ +from __future__ import annotations + +import json +import time +from collections.abc import AsyncIterator +from typing import Any, Literal, cast, overload + +import litellm.types +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails + +from agents.exceptions import ModelBehaviorError + +try: + import litellm +except ImportError as _e: + raise ImportError( + "`litellm` is required to use the LitellmModel. You can install it via the optional " + "dependency group: `pip install 'openai-agents[litellm]'`." + ) from _e + +from openai import NOT_GIVEN, AsyncStream, NotGiven +from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageToolCall +from openai.types.chat.chat_completion_message import ( + Annotation, + AnnotationURLCitation, + ChatCompletionMessage, +) +from openai.types.chat.chat_completion_message_tool_call import Function +from openai.types.responses import Response + +from ... import _debug +from ...agent_output import AgentOutputSchemaBase +from ...handoffs import Handoff +from ...items import ModelResponse, TResponseInputItem, TResponseStreamEvent +from ...logger import logger +from ...model_settings import ModelSettings +from ...models.chatcmpl_converter import Converter +from ...models.chatcmpl_helpers import HEADERS +from ...models.chatcmpl_stream_handler import ChatCmplStreamHandler +from ...models.fake_id import FAKE_RESPONSES_ID +from ...models.interface import Model, ModelTracing +from ...tool import Tool +from ...tracing import generation_span +from ...tracing.span_data import GenerationSpanData +from ...tracing.spans import Span +from ...usage import Usage + + +class LitellmModel(Model): + """This class enables using any model via LiteLLM. LiteLLM allows you to acess OpenAPI, + Anthropic, Gemini, Mistral, and many other models. + See supported models here: [litellm models](https://docs.litellm.ai/docs/providers). + """ + + def __init__( + self, + model: str, + base_url: str | None = None, + api_key: str | None = None, + ): + self.model = model + self.base_url = base_url + self.api_key = api_key + + async def get_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + previous_response_id: str | None, + ) -> ModelResponse: + with generation_span( + model=str(self.model), + model_config=model_settings.to_json_dict() + | {"base_url": str(self.base_url or ""), "model_impl": "litellm"}, + disabled=tracing.is_disabled(), + ) as span_generation: + response = await self._fetch_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + span_generation, + tracing, + stream=False, + ) + + assert isinstance(response.choices[0], litellm.types.utils.Choices) + + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Received model response") + else: + logger.debug( + f"LLM resp:\n{json.dumps(response.choices[0].message.model_dump(), indent=2)}\n" + ) + + if hasattr(response, "usage"): + response_usage = response.usage + usage = ( + Usage( + requests=1, + input_tokens=response_usage.prompt_tokens, + output_tokens=response_usage.completion_tokens, + total_tokens=response_usage.total_tokens, + input_tokens_details=InputTokensDetails( + cached_tokens=getattr( + response_usage.prompt_tokens_details, "cached_tokens", 0 + ) or 0 + ), + output_tokens_details=OutputTokensDetails( + reasoning_tokens=getattr( + response_usage.completion_tokens_details, "reasoning_tokens", 0 + ) or 0 + ), + ) + if response.usage + else Usage() + ) + else: + usage = Usage() + logger.warning("No usage information returned from Litellm") + + if tracing.include_data(): + span_generation.span_data.output = [response.choices[0].message.model_dump()] + span_generation.span_data.usage = { + "input_tokens": usage.input_tokens, + "output_tokens": usage.output_tokens, + } + + items = Converter.message_to_output_items( + LitellmConverter.convert_message_to_openai(response.choices[0].message) + ) + + return ModelResponse( + output=items, + usage=usage, + response_id=None, + ) + + async def stream_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + *, + previous_response_id: str | None, + ) -> AsyncIterator[TResponseStreamEvent]: + with generation_span( + model=str(self.model), + model_config=model_settings.to_json_dict() + | {"base_url": str(self.base_url or ""), "model_impl": "litellm"}, + disabled=tracing.is_disabled(), + ) as span_generation: + response, stream = await self._fetch_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + span_generation, + tracing, + stream=True, + ) + + final_response: Response | None = None + async for chunk in ChatCmplStreamHandler.handle_stream(response, stream): + yield chunk + + if chunk.type == "response.completed": + final_response = chunk.response + + if tracing.include_data() and final_response: + span_generation.span_data.output = [final_response.model_dump()] + + if final_response and final_response.usage: + span_generation.span_data.usage = { + "input_tokens": final_response.usage.input_tokens, + "output_tokens": final_response.usage.output_tokens, + } + + @overload + async def _fetch_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + span: Span[GenerationSpanData], + tracing: ModelTracing, + stream: Literal[True], + ) -> tuple[Response, AsyncStream[ChatCompletionChunk]]: ... + + @overload + async def _fetch_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + span: Span[GenerationSpanData], + tracing: ModelTracing, + stream: Literal[False], + ) -> litellm.types.utils.ModelResponse: ... + + async def _fetch_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + span: Span[GenerationSpanData], + tracing: ModelTracing, + stream: bool = False, + ) -> litellm.types.utils.ModelResponse | tuple[Response, AsyncStream[ChatCompletionChunk]]: + converted_messages = Converter.items_to_messages(input) + + if system_instructions: + converted_messages.insert( + 0, + { + "content": system_instructions, + "role": "system", + }, + ) + if tracing.include_data(): + span.span_data.input = converted_messages + + parallel_tool_calls = ( + True + if model_settings.parallel_tool_calls and tools and len(tools) > 0 + else False + if model_settings.parallel_tool_calls is False + else None + ) + tool_choice = Converter.convert_tool_choice(model_settings.tool_choice) + response_format = Converter.convert_response_format(output_schema) + + converted_tools = [Converter.tool_to_openai(tool) for tool in tools] if tools else [] + + for handoff in handoffs: + converted_tools.append(Converter.convert_handoff_tool(handoff)) + + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Calling LLM") + else: + logger.debug( + f"Calling Litellm model: {self.model}\n" + f"{json.dumps(converted_messages, indent=2)}\n" + f"Tools:\n{json.dumps(converted_tools, indent=2)}\n" + f"Stream: {stream}\n" + f"Tool choice: {tool_choice}\n" + f"Response format: {response_format}\n" + ) + + reasoning_effort = model_settings.reasoning.effort if model_settings.reasoning else None + + stream_options = None + if stream and model_settings.include_usage is not None: + stream_options = {"include_usage": model_settings.include_usage} + + extra_kwargs = {} + if model_settings.extra_query: + extra_kwargs["extra_query"] = model_settings.extra_query + if model_settings.metadata: + extra_kwargs["metadata"] = model_settings.metadata + if model_settings.extra_body and isinstance(model_settings.extra_body, dict): + extra_kwargs.update(model_settings.extra_body) + + ret = await litellm.acompletion( + model=self.model, + messages=converted_messages, + tools=converted_tools or None, + temperature=model_settings.temperature, + top_p=model_settings.top_p, + frequency_penalty=model_settings.frequency_penalty, + presence_penalty=model_settings.presence_penalty, + max_tokens=model_settings.max_tokens, + tool_choice=self._remove_not_given(tool_choice), + response_format=self._remove_not_given(response_format), + parallel_tool_calls=parallel_tool_calls, + stream=stream, + stream_options=stream_options, + reasoning_effort=reasoning_effort, + extra_headers={**HEADERS, **(model_settings.extra_headers or {})}, + api_key=self.api_key, + base_url=self.base_url, + **extra_kwargs, + ) + + if isinstance(ret, litellm.types.utils.ModelResponse): + return ret + + response = Response( + id=FAKE_RESPONSES_ID, + created_at=time.time(), + model=self.model, + object="response", + output=[], + tool_choice=cast(Literal["auto", "required", "none"], tool_choice) + if tool_choice != NOT_GIVEN + else "auto", + top_p=model_settings.top_p, + temperature=model_settings.temperature, + tools=[], + parallel_tool_calls=parallel_tool_calls or False, + reasoning=model_settings.reasoning, + ) + return response, ret + + def _remove_not_given(self, value: Any) -> Any: + if isinstance(value, NotGiven): + return None + return value + + +class LitellmConverter: + @classmethod + def convert_message_to_openai( + cls, message: litellm.types.utils.Message + ) -> ChatCompletionMessage: + if message.role != "assistant": + raise ModelBehaviorError(f"Unsupported role: {message.role}") + + tool_calls = ( + [LitellmConverter.convert_tool_call_to_openai(tool) for tool in message.tool_calls] + if message.tool_calls + else None + ) + + provider_specific_fields = message.get("provider_specific_fields", None) + refusal = ( + provider_specific_fields.get("refusal", None) if provider_specific_fields else None + ) + + return ChatCompletionMessage( + content=message.content, + refusal=refusal, + role="assistant", + annotations=cls.convert_annotations_to_openai(message), + audio=message.get("audio", None), # litellm deletes audio if not present + tool_calls=tool_calls, + ) + + @classmethod + def convert_annotations_to_openai( + cls, message: litellm.types.utils.Message + ) -> list[Annotation] | None: + annotations: list[litellm.types.llms.openai.ChatCompletionAnnotation] | None = message.get( + "annotations", None + ) + if not annotations: + return None + + return [ + Annotation( + type="url_citation", + url_citation=AnnotationURLCitation( + start_index=annotation["url_citation"]["start_index"], + end_index=annotation["url_citation"]["end_index"], + url=annotation["url_citation"]["url"], + title=annotation["url_citation"]["title"], + ), + ) + for annotation in annotations + ] + + @classmethod + def convert_tool_call_to_openai( + cls, tool_call: litellm.types.utils.ChatCompletionMessageToolCall + ) -> ChatCompletionMessageToolCall: + return ChatCompletionMessageToolCall( + id=tool_call.id, + type="function", + function=Function( + name=tool_call.function.name or "", arguments=tool_call.function.arguments + ), + ) diff --git a/src/agents/extensions/models/litellm_provider.py b/src/agents/extensions/models/litellm_provider.py new file mode 100644 index 00000000..5a2dc166 --- /dev/null +++ b/src/agents/extensions/models/litellm_provider.py @@ -0,0 +1,21 @@ +from ...models.interface import Model, ModelProvider +from .litellm_model import LitellmModel + +DEFAULT_MODEL: str = "gpt-4.1" + + +class LitellmProvider(ModelProvider): + """A ModelProvider that uses LiteLLM to route to any model provider. You can use it via: + ```python + Runner.run(agent, input, run_config=RunConfig(model_provider=LitellmProvider())) + ``` + See supported models here: [litellm models](https://docs.litellm.ai/docs/providers). + + NOTE: API keys must be set via environment variables. If you're using models that require + additional configuration (e.g. Azure API base or version), those must also be set via the + environment variables that LiteLLM expects. If you have more advanced needs, we recommend + copy-pasting this class and making any modifications you need. + """ + + def get_model(self, model_name: str | None) -> Model: + return LitellmModel(model_name or DEFAULT_MODEL) diff --git a/src/agents/extensions/visualization.py b/src/agents/extensions/visualization.py new file mode 100644 index 00000000..888e262c --- /dev/null +++ b/src/agents/extensions/visualization.py @@ -0,0 +1,137 @@ +from typing import Optional + +import graphviz # type: ignore + +from agents import Agent +from agents.handoffs import Handoff +from agents.tool import Tool + + +def get_main_graph(agent: Agent) -> str: + """ + Generates the main graph structure in DOT format for the given agent. + + Args: + agent (Agent): The agent for which the graph is to be generated. + + Returns: + str: The DOT format string representing the graph. + """ + parts = [ + """ + digraph G { + graph [splines=true]; + node [fontname="Arial"]; + edge [penwidth=1.5]; + """ + ] + parts.append(get_all_nodes(agent)) + parts.append(get_all_edges(agent)) + parts.append("}") + return "".join(parts) + + +def get_all_nodes(agent: Agent, parent: Optional[Agent] = None) -> str: + """ + Recursively generates the nodes for the given agent and its handoffs in DOT format. + + Args: + agent (Agent): The agent for which the nodes are to be generated. + + Returns: + str: The DOT format string representing the nodes. + """ + parts = [] + + # Start and end the graph + parts.append( + '"__start__" [label="__start__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" + '"__end__" [label="__end__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" + ) + # Ensure parent agent node is colored + if not parent: + parts.append( + f'"{agent.name}" [label="{agent.name}", shape=box, style=filled, ' + "fillcolor=lightyellow, width=1.5, height=0.8];" + ) + + for tool in agent.tools: + parts.append( + f'"{tool.name}" [label="{tool.name}", shape=ellipse, style=filled, ' + f"fillcolor=lightgreen, width=0.5, height=0.3];" + ) + + for handoff in agent.handoffs: + if isinstance(handoff, Handoff): + parts.append( + f'"{handoff.agent_name}" [label="{handoff.agent_name}", ' + f"shape=box, style=filled, style=rounded, " + f"fillcolor=lightyellow, width=1.5, height=0.8];" + ) + if isinstance(handoff, Agent): + parts.append( + f'"{handoff.name}" [label="{handoff.name}", ' + f"shape=box, style=filled, style=rounded, " + f"fillcolor=lightyellow, width=1.5, height=0.8];" + ) + parts.append(get_all_nodes(handoff)) + + return "".join(parts) + + +def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: + """ + Recursively generates the edges for the given agent and its handoffs in DOT format. + + Args: + agent (Agent): The agent for which the edges are to be generated. + parent (Agent, optional): The parent agent. Defaults to None. + + Returns: + str: The DOT format string representing the edges. + """ + parts = [] + + if not parent: + parts.append(f'"__start__" -> "{agent.name}";') + + for tool in agent.tools: + parts.append(f""" + "{agent.name}" -> "{tool.name}" [style=dotted, penwidth=1.5]; + "{tool.name}" -> "{agent.name}" [style=dotted, penwidth=1.5];""") + + for handoff in agent.handoffs: + if isinstance(handoff, Handoff): + parts.append(f""" + "{agent.name}" -> "{handoff.agent_name}";""") + if isinstance(handoff, Agent): + parts.append(f""" + "{agent.name}" -> "{handoff.name}";""") + parts.append(get_all_edges(handoff, agent)) + + if not agent.handoffs and not isinstance(agent, Tool): # type: ignore + parts.append(f'"{agent.name}" -> "__end__";') + + return "".join(parts) + + +def draw_graph(agent: Agent, filename: Optional[str] = None) -> graphviz.Source: + """ + Draws the graph for the given agent and optionally saves it as a PNG file. + + Args: + agent (Agent): The agent for which the graph is to be drawn. + filename (str): The name of the file to save the graph as a PNG. + + Returns: + graphviz.Source: The graphviz Source object representing the graph. + """ + dot_code = get_main_graph(agent) + graph = graphviz.Source(dot_code) + + if filename: + graph.render(filename, format="png", cleanup=True) + + return graph diff --git a/src/agents/function_schema.py b/src/agents/function_schema.py index a4b57672..0e586896 100644 --- a/src/agents/function_schema.py +++ b/src/agents/function_schema.py @@ -33,6 +33,9 @@ class FuncSchema: """The signature of the function.""" takes_context: bool = False """Whether the function takes a RunContextWrapper argument (must be the first argument).""" + strict_json_schema: bool = True + """Whether the JSON schema is in strict mode. We **strongly** recommend setting this to True, + as it increases the likelihood of correct JSON input.""" def to_call_args(self, data: BaseModel) -> tuple[list[Any], dict[str, Any]]: """ @@ -128,7 +131,7 @@ def _detect_docstring_style(doc: str) -> DocstringStyle: @contextlib.contextmanager def _suppress_griffe_logging(): - # Supresses warnings about missing annotations for params + # Suppresses warnings about missing annotations for params logger = logging.getLogger("griffe") previous_level = logger.getEffectiveLevel() logger.setLevel(logging.ERROR) @@ -337,4 +340,5 @@ def function_schema( params_json_schema=json_schema, signature=sig, takes_context=takes_context, + strict_json_schema=strict_json_schema, ) diff --git a/src/agents/guardrail.py b/src/agents/guardrail.py index fcae0b8a..a96f0f7d 100644 --- a/src/agents/guardrail.py +++ b/src/agents/guardrail.py @@ -7,10 +7,10 @@ from typing_extensions import TypeVar -from ._utils import MaybeAwaitable from .exceptions import UserError from .items import TResponseInputItem from .run_context import RunContextWrapper, TContext +from .util._types import MaybeAwaitable if TYPE_CHECKING: from .agent import Agent @@ -86,7 +86,7 @@ class InputGuardrail(Generic[TContext]): [RunContextWrapper[TContext], Agent[Any], str | list[TResponseInputItem]], MaybeAwaitable[GuardrailFunctionOutput], ] - """A function that receives the the agent input and the context, and returns a + """A function that receives the agent input and the context, and returns a `GuardrailResult`. The result marks whether the tripwire was triggered, and can optionally include information about the guardrail's output. """ diff --git a/src/agents/handoffs.py b/src/agents/handoffs.py index ac157401..686191f3 100644 --- a/src/agents/handoffs.py +++ b/src/agents/handoffs.py @@ -8,12 +8,12 @@ from pydantic import TypeAdapter from typing_extensions import TypeAlias, TypeVar -from . import _utils from .exceptions import ModelBehaviorError, UserError from .items import RunItem, TResponseInputItem from .run_context import RunContextWrapper, TContext from .strict_schema import ensure_strict_json_schema from .tracing.spans import SpanError +from .util import _error_tracing, _json, _transforms if TYPE_CHECKING: from .agent import Agent @@ -104,7 +104,7 @@ def get_transfer_message(self, agent: Agent[Any]) -> str: @classmethod def default_tool_name(cls, agent: Agent[Any]) -> str: - return _utils.transform_string_function_style(f"transfer_to_{agent.name}") + return _transforms.transform_string_function_style(f"transfer_to_{agent.name}") @classmethod def default_tool_description(cls, agent: Agent[Any]) -> str: @@ -192,7 +192,7 @@ async def _invoke_handoff( ) -> Agent[Any]: if input_type is not None and type_adapter is not None: if input_json is None: - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Handoff function expected non-null input, but got None", data={"details": "input_json is None"}, @@ -200,7 +200,7 @@ async def _invoke_handoff( ) raise ModelBehaviorError("Handoff function expected non-null input, but got None") - validated_input = _utils.validate_json( + validated_input = _json.validate_json( json_str=input_json, type_adapter=type_adapter, partial=False, diff --git a/src/agents/items.py b/src/agents/items.py index bbaf49d8..64797ad2 100644 --- a/src/agents/items.py +++ b/src/agents/items.py @@ -18,8 +18,23 @@ ResponseOutputText, ResponseStreamEvent, ) -from openai.types.responses.response_input_item_param import ComputerCallOutput, FunctionCallOutput -from openai.types.responses.response_output_item import Reasoning +from openai.types.responses.response_code_interpreter_tool_call import ( + ResponseCodeInterpreterToolCall, +) +from openai.types.responses.response_input_item_param import ( + ComputerCallOutput, + FunctionCallOutput, + LocalShellCallOutput, + McpApprovalResponse, +) +from openai.types.responses.response_output_item import ( + ImageGenerationCall, + LocalShellCall, + McpApprovalRequest, + McpCall, + McpListTools, +) +from openai.types.responses.response_reasoning_item import ResponseReasoningItem from pydantic import BaseModel from typing_extensions import TypeAlias @@ -108,6 +123,10 @@ class HandoffOutputItem(RunItemBase[TResponseInputItem]): ResponseComputerToolCall, ResponseFileSearchToolCall, ResponseFunctionWebSearch, + McpCall, + ResponseCodeInterpreterToolCall, + ImageGenerationCall, + LocalShellCall, ] """A type that represents a tool call item.""" @@ -123,28 +142,62 @@ class ToolCallItem(RunItemBase[ToolCallItemTypes]): @dataclass -class ToolCallOutputItem(RunItemBase[Union[FunctionCallOutput, ComputerCallOutput]]): +class ToolCallOutputItem( + RunItemBase[Union[FunctionCallOutput, ComputerCallOutput, LocalShellCallOutput]] +): """Represents the output of a tool call.""" - raw_item: FunctionCallOutput | ComputerCallOutput + raw_item: FunctionCallOutput | ComputerCallOutput | LocalShellCallOutput """The raw item from the model.""" - output: str - """The output of the tool call.""" + output: Any + """The output of the tool call. This is whatever the tool call returned; the `raw_item` + contains a string representation of the output. + """ type: Literal["tool_call_output_item"] = "tool_call_output_item" @dataclass -class ReasoningItem(RunItemBase[Reasoning]): +class ReasoningItem(RunItemBase[ResponseReasoningItem]): """Represents a reasoning item.""" - raw_item: Reasoning + raw_item: ResponseReasoningItem """The raw reasoning item.""" type: Literal["reasoning_item"] = "reasoning_item" +@dataclass +class MCPListToolsItem(RunItemBase[McpListTools]): + """Represents a call to an MCP server to list tools.""" + + raw_item: McpListTools + """The raw MCP list tools call.""" + + type: Literal["mcp_list_tools_item"] = "mcp_list_tools_item" + + +@dataclass +class MCPApprovalRequestItem(RunItemBase[McpApprovalRequest]): + """Represents a request for MCP approval.""" + + raw_item: McpApprovalRequest + """The raw MCP approval request.""" + + type: Literal["mcp_approval_request_item"] = "mcp_approval_request_item" + + +@dataclass +class MCPApprovalResponseItem(RunItemBase[McpApprovalResponse]): + """Represents a response to an MCP approval request.""" + + raw_item: McpApprovalResponse + """The raw MCP approval response.""" + + type: Literal["mcp_approval_response_item"] = "mcp_approval_response_item" + + RunItem: TypeAlias = Union[ MessageOutputItem, HandoffCallItem, @@ -152,6 +205,9 @@ class ReasoningItem(RunItemBase[Reasoning]): ToolCallItem, ToolCallOutputItem, ReasoningItem, + MCPListToolsItem, + MCPApprovalRequestItem, + MCPApprovalResponseItem, ] """An item generated by an agent.""" @@ -164,9 +220,11 @@ class ModelResponse: usage: Usage """The usage information for the response.""" - referenceable_id: str | None + response_id: str | None """An ID for the response which can be used to refer to the response in subsequent calls to the model. Not supported by all model providers. + If using OpenAI models via the Responses API, this is the `response_id` parameter, and it can + be passed to `Runner.run`. """ def to_input_items(self) -> list[TResponseInputItem]: diff --git a/src/agents/mcp/__init__.py b/src/agents/mcp/__init__.py new file mode 100644 index 00000000..d4eb8fa6 --- /dev/null +++ b/src/agents/mcp/__init__.py @@ -0,0 +1,25 @@ +try: + from .server import ( + MCPServer, + MCPServerSse, + MCPServerSseParams, + MCPServerStdio, + MCPServerStdioParams, + MCPServerStreamableHttp, + MCPServerStreamableHttpParams, + ) +except ImportError: + pass + +from .util import MCPUtil + +__all__ = [ + "MCPServer", + "MCPServerSse", + "MCPServerSseParams", + "MCPServerStdio", + "MCPServerStdioParams", + "MCPServerStreamableHttp", + "MCPServerStreamableHttpParams", + "MCPUtil", +] diff --git a/src/agents/mcp/server.py b/src/agents/mcp/server.py new file mode 100644 index 00000000..414b517a --- /dev/null +++ b/src/agents/mcp/server.py @@ -0,0 +1,412 @@ +from __future__ import annotations + +import abc +import asyncio +from contextlib import AbstractAsyncContextManager, AsyncExitStack +from datetime import timedelta +from pathlib import Path +from typing import Any, Literal + +from anyio.streams.memory import MemoryObjectReceiveStream, MemoryObjectSendStream +from mcp import ClientSession, StdioServerParameters, Tool as MCPTool, stdio_client +from mcp.client.sse import sse_client +from mcp.client.streamable_http import GetSessionIdCallback, streamablehttp_client +from mcp.shared.message import SessionMessage +from mcp.types import CallToolResult, InitializeResult +from typing_extensions import NotRequired, TypedDict + +from ..exceptions import UserError +from ..logger import logger + + +class MCPServer(abc.ABC): + """Base class for Model Context Protocol servers.""" + + @abc.abstractmethod + async def connect(self): + """Connect to the server. For example, this might mean spawning a subprocess or + opening a network connection. The server is expected to remain connected until + `cleanup()` is called. + """ + pass + + @property + @abc.abstractmethod + def name(self) -> str: + """A readable name for the server.""" + pass + + @abc.abstractmethod + async def cleanup(self): + """Cleanup the server. For example, this might mean closing a subprocess or + closing a network connection. + """ + pass + + @abc.abstractmethod + async def list_tools(self) -> list[MCPTool]: + """List the tools available on the server.""" + pass + + @abc.abstractmethod + async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> CallToolResult: + """Invoke a tool on the server.""" + pass + + +class _MCPServerWithClientSession(MCPServer, abc.ABC): + """Base class for MCP servers that use a `ClientSession` to communicate with the server.""" + + def __init__(self, cache_tools_list: bool, client_session_timeout_seconds: float | None): + """ + Args: + cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be + cached and only fetched from the server once. If `False`, the tools list will be + fetched from the server on each call to `list_tools()`. The cache can be invalidated + by calling `invalidate_tools_cache()`. You should set this to `True` if you know the + server will not change its tools list, because it can drastically improve latency + (by avoiding a round-trip to the server every time). + + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + """ + self.session: ClientSession | None = None + self.exit_stack: AsyncExitStack = AsyncExitStack() + self._cleanup_lock: asyncio.Lock = asyncio.Lock() + self.cache_tools_list = cache_tools_list + self.server_initialize_result: InitializeResult | None = None + + self.client_session_timeout_seconds = client_session_timeout_seconds + + # The cache is always dirty at startup, so that we fetch tools at least once + self._cache_dirty = True + self._tools_list: list[MCPTool] | None = None + + @abc.abstractmethod + def create_streams( + self, + ) -> AbstractAsyncContextManager[ + tuple[ + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None + ] + ]: + """Create the streams for the server.""" + pass + + async def __aenter__(self): + await self.connect() + return self + + async def __aexit__(self, exc_type, exc_value, traceback): + await self.cleanup() + + def invalidate_tools_cache(self): + """Invalidate the tools cache.""" + self._cache_dirty = True + + async def connect(self): + """Connect to the server.""" + try: + transport = await self.exit_stack.enter_async_context(self.create_streams()) + # streamablehttp_client returns (read, write, get_session_id) + # sse_client returns (read, write) + + read, write, *_ = transport + + session = await self.exit_stack.enter_async_context( + ClientSession( + read, + write, + timedelta(seconds=self.client_session_timeout_seconds) + if self.client_session_timeout_seconds + else None, + ) + ) + server_result = await session.initialize() + self.server_initialize_result = server_result + self.session = session + except Exception as e: + logger.error(f"Error initializing MCP server: {e}") + await self.cleanup() + raise + + async def list_tools(self) -> list[MCPTool]: + """List the tools available on the server.""" + if not self.session: + raise UserError("Server not initialized. Make sure you call `connect()` first.") + + # Return from cache if caching is enabled, we have tools, and the cache is not dirty + if self.cache_tools_list and not self._cache_dirty and self._tools_list: + return self._tools_list + + # Reset the cache dirty to False + self._cache_dirty = False + + # Fetch the tools from the server + self._tools_list = (await self.session.list_tools()).tools + return self._tools_list + + async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> CallToolResult: + """Invoke a tool on the server.""" + if not self.session: + raise UserError("Server not initialized. Make sure you call `connect()` first.") + + return await self.session.call_tool(tool_name, arguments) + + async def cleanup(self): + """Cleanup the server.""" + async with self._cleanup_lock: + try: + await self.exit_stack.aclose() + except Exception as e: + logger.error(f"Error cleaning up server: {e}") + finally: + self.session = None + + +class MCPServerStdioParams(TypedDict): + """Mirrors `mcp.client.stdio.StdioServerParameters`, but lets you pass params without another + import. + """ + + command: str + """The executable to run to start the server. For example, `python` or `node`.""" + + args: NotRequired[list[str]] + """Command line args to pass to the `command` executable. For example, `['foo.py']` or + `['server.js', '--port', '8080']`.""" + + env: NotRequired[dict[str, str]] + """The environment variables to set for the server. .""" + + cwd: NotRequired[str | Path] + """The working directory to use when spawning the process.""" + + encoding: NotRequired[str] + """The text encoding used when sending/receiving messages to the server. Defaults to `utf-8`.""" + + encoding_error_handler: NotRequired[Literal["strict", "ignore", "replace"]] + """The text encoding error handler. Defaults to `strict`. + + See https://docs.python.org/3/library/codecs.html#codec-base-classes for + explanations of possible values. + """ + + +class MCPServerStdio(_MCPServerWithClientSession): + """MCP server implementation that uses the stdio transport. See the [spec] + (https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#stdio) for + details. + """ + + def __init__( + self, + params: MCPServerStdioParams, + cache_tools_list: bool = False, + name: str | None = None, + client_session_timeout_seconds: float | None = 5, + ): + """Create a new MCP server based on the stdio transport. + + Args: + params: The params that configure the server. This includes the command to run to + start the server, the args to pass to the command, the environment variables to + set for the server, the working directory to use when spawning the process, and + the text encoding used when sending/receiving messages to the server. + cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be + cached and only fetched from the server once. If `False`, the tools list will be + fetched from the server on each call to `list_tools()`. The cache can be + invalidated by calling `invalidate_tools_cache()`. You should set this to `True` + if you know the server will not change its tools list, because it can drastically + improve latency (by avoiding a round-trip to the server every time). + name: A readable name for the server. If not provided, we'll create one from the + command. + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + """ + super().__init__(cache_tools_list, client_session_timeout_seconds) + + self.params = StdioServerParameters( + command=params["command"], + args=params.get("args", []), + env=params.get("env"), + cwd=params.get("cwd"), + encoding=params.get("encoding", "utf-8"), + encoding_error_handler=params.get("encoding_error_handler", "strict"), + ) + + self._name = name or f"stdio: {self.params.command}" + + def create_streams( + self, + ) -> AbstractAsyncContextManager[ + tuple[ + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None + ] + ]: + """Create the streams for the server.""" + return stdio_client(self.params) + + @property + def name(self) -> str: + """A readable name for the server.""" + return self._name + + +class MCPServerSseParams(TypedDict): + """Mirrors the params in`mcp.client.sse.sse_client`.""" + + url: str + """The URL of the server.""" + + headers: NotRequired[dict[str, str]] + """The headers to send to the server.""" + + timeout: NotRequired[float] + """The timeout for the HTTP request. Defaults to 5 seconds.""" + + sse_read_timeout: NotRequired[float] + """The timeout for the SSE connection, in seconds. Defaults to 5 minutes.""" + + +class MCPServerSse(_MCPServerWithClientSession): + """MCP server implementation that uses the HTTP with SSE transport. See the [spec] + (https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#http-with-sse) + for details. + """ + + def __init__( + self, + params: MCPServerSseParams, + cache_tools_list: bool = False, + name: str | None = None, + client_session_timeout_seconds: float | None = 5, + ): + """Create a new MCP server based on the HTTP with SSE transport. + + Args: + params: The params that configure the server. This includes the URL of the server, + the headers to send to the server, the timeout for the HTTP request, and the + timeout for the SSE connection. + + cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be + cached and only fetched from the server once. If `False`, the tools list will be + fetched from the server on each call to `list_tools()`. The cache can be + invalidated by calling `invalidate_tools_cache()`. You should set this to `True` + if you know the server will not change its tools list, because it can drastically + improve latency (by avoiding a round-trip to the server every time). + + name: A readable name for the server. If not provided, we'll create one from the + URL. + + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + """ + super().__init__(cache_tools_list, client_session_timeout_seconds) + + self.params = params + self._name = name or f"sse: {self.params['url']}" + + def create_streams( + self, + ) -> AbstractAsyncContextManager[ + tuple[ + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None + ] + ]: + """Create the streams for the server.""" + return sse_client( + url=self.params["url"], + headers=self.params.get("headers", None), + timeout=self.params.get("timeout", 5), + sse_read_timeout=self.params.get("sse_read_timeout", 60 * 5), + ) + + @property + def name(self) -> str: + """A readable name for the server.""" + return self._name + + +class MCPServerStreamableHttpParams(TypedDict): + """Mirrors the params in`mcp.client.streamable_http.streamablehttp_client`.""" + + url: str + """The URL of the server.""" + + headers: NotRequired[dict[str, str]] + """The headers to send to the server.""" + + timeout: NotRequired[timedelta] + """The timeout for the HTTP request. Defaults to 5 seconds.""" + + sse_read_timeout: NotRequired[timedelta] + """The timeout for the SSE connection, in seconds. Defaults to 5 minutes.""" + + terminate_on_close: NotRequired[bool] + """Terminate on close""" + + +class MCPServerStreamableHttp(_MCPServerWithClientSession): + """MCP server implementation that uses the Streamable HTTP transport. See the [spec] + (https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http) + for details. + """ + + def __init__( + self, + params: MCPServerStreamableHttpParams, + cache_tools_list: bool = False, + name: str | None = None, + client_session_timeout_seconds: float | None = 5, + ): + """Create a new MCP server based on the Streamable HTTP transport. + + Args: + params: The params that configure the server. This includes the URL of the server, + the headers to send to the server, the timeout for the HTTP request, and the + timeout for the Streamable HTTP connection and whether we need to + terminate on close. + + cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be + cached and only fetched from the server once. If `False`, the tools list will be + fetched from the server on each call to `list_tools()`. The cache can be + invalidated by calling `invalidate_tools_cache()`. You should set this to `True` + if you know the server will not change its tools list, because it can drastically + improve latency (by avoiding a round-trip to the server every time). + + name: A readable name for the server. If not provided, we'll create one from the + URL. + + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + """ + super().__init__(cache_tools_list, client_session_timeout_seconds) + + self.params = params + self._name = name or f"streamable_http: {self.params['url']}" + + def create_streams( + self, + ) -> AbstractAsyncContextManager[ + tuple[ + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None + ] + ]: + """Create the streams for the server.""" + return streamablehttp_client( + url=self.params["url"], + headers=self.params.get("headers", None), + timeout=self.params.get("timeout", timedelta(seconds=30)), + sse_read_timeout=self.params.get("sse_read_timeout", timedelta(seconds=60 * 5)), + terminate_on_close=self.params.get("terminate_on_close", True) + ) + + @property + def name(self) -> str: + """A readable name for the server.""" + return self._name diff --git a/src/agents/mcp/util.py b/src/agents/mcp/util.py new file mode 100644 index 00000000..bbfe1885 --- /dev/null +++ b/src/agents/mcp/util.py @@ -0,0 +1,136 @@ +import functools +import json +from typing import TYPE_CHECKING, Any + +from agents.strict_schema import ensure_strict_json_schema + +from .. import _debug +from ..exceptions import AgentsException, ModelBehaviorError, UserError +from ..logger import logger +from ..run_context import RunContextWrapper +from ..tool import FunctionTool, Tool +from ..tracing import FunctionSpanData, get_current_span, mcp_tools_span + +if TYPE_CHECKING: + from mcp.types import Tool as MCPTool + + from .server import MCPServer + + +class MCPUtil: + """Set of utilities for interop between MCP and Agents SDK tools.""" + + @classmethod + async def get_all_function_tools( + cls, servers: list["MCPServer"], convert_schemas_to_strict: bool + ) -> list[Tool]: + """Get all function tools from a list of MCP servers.""" + tools = [] + tool_names: set[str] = set() + for server in servers: + server_tools = await cls.get_function_tools(server, convert_schemas_to_strict) + server_tool_names = {tool.name for tool in server_tools} + if len(server_tool_names & tool_names) > 0: + raise UserError( + f"Duplicate tool names found across MCP servers: " + f"{server_tool_names & tool_names}" + ) + tool_names.update(server_tool_names) + tools.extend(server_tools) + + return tools + + @classmethod + async def get_function_tools( + cls, server: "MCPServer", convert_schemas_to_strict: bool + ) -> list[Tool]: + """Get all function tools from a single MCP server.""" + + with mcp_tools_span(server=server.name) as span: + tools = await server.list_tools() + span.span_data.result = [tool.name for tool in tools] + + return [cls.to_function_tool(tool, server, convert_schemas_to_strict) for tool in tools] + + @classmethod + def to_function_tool( + cls, tool: "MCPTool", server: "MCPServer", convert_schemas_to_strict: bool + ) -> FunctionTool: + """Convert an MCP tool to an Agents SDK function tool.""" + invoke_func = functools.partial(cls.invoke_mcp_tool, server, tool) + schema, is_strict = tool.inputSchema, False + + # MCP spec doesn't require the inputSchema to have `properties`, but OpenAI spec does. + if "properties" not in schema: + schema["properties"] = {} + + if convert_schemas_to_strict: + try: + schema = ensure_strict_json_schema(schema) + is_strict = True + except Exception as e: + logger.info(f"Error converting MCP schema to strict mode: {e}") + + return FunctionTool( + name=tool.name, + description=tool.description or "", + params_json_schema=schema, + on_invoke_tool=invoke_func, + strict_json_schema=is_strict, + ) + + @classmethod + async def invoke_mcp_tool( + cls, server: "MCPServer", tool: "MCPTool", context: RunContextWrapper[Any], input_json: str + ) -> str: + """Invoke an MCP tool and return the result as a string.""" + try: + json_data: dict[str, Any] = json.loads(input_json) if input_json else {} + except Exception as e: + if _debug.DONT_LOG_TOOL_DATA: + logger.debug(f"Invalid JSON input for tool {tool.name}") + else: + logger.debug(f"Invalid JSON input for tool {tool.name}: {input_json}") + raise ModelBehaviorError( + f"Invalid JSON input for tool {tool.name}: {input_json}" + ) from e + + if _debug.DONT_LOG_TOOL_DATA: + logger.debug(f"Invoking MCP tool {tool.name}") + else: + logger.debug(f"Invoking MCP tool {tool.name} with input {input_json}") + + try: + result = await server.call_tool(tool.name, json_data) + except Exception as e: + logger.error(f"Error invoking MCP tool {tool.name}: {e}") + raise AgentsException(f"Error invoking MCP tool {tool.name}: {e}") from e + + if _debug.DONT_LOG_TOOL_DATA: + logger.debug(f"MCP tool {tool.name} completed.") + else: + logger.debug(f"MCP tool {tool.name} returned {result}") + + # The MCP tool result is a list of content items, whereas OpenAI tool outputs are a single + # string. We'll try to convert. + if len(result.content) == 1: + tool_output = result.content[0].model_dump_json() + elif len(result.content) > 1: + tool_output = json.dumps([item.model_dump() for item in result.content]) + else: + logger.error(f"Errored MCP tool result: {result}") + tool_output = "Error running tool." + + current_span = get_current_span() + if current_span: + if isinstance(current_span.span_data, FunctionSpanData): + current_span.span_data.output = tool_output + current_span.span_data.mcp_data = { + "server": server.name, + } + else: + logger.warning( + f"Current span is not a FunctionSpanData, skipping tool output: {current_span}" + ) + + return tool_output diff --git a/src/agents/model_settings.py b/src/agents/model_settings.py index 78cf9a83..7b016c98 100644 --- a/src/agents/model_settings.py +++ b/src/agents/model_settings.py @@ -1,7 +1,12 @@ from __future__ import annotations -from dataclasses import dataclass -from typing import Literal +import dataclasses +from dataclasses import dataclass, fields, replace +from typing import Any, Literal + +from openai._types import Body, Headers, Query +from openai.types.shared import Reasoning +from pydantic import BaseModel @dataclass @@ -10,26 +15,86 @@ class ModelSettings: This class holds optional model configuration parameters (e.g. temperature, top_p, penalties, truncation, etc.). + + Not all models/providers support all of these parameters, so please check the API documentation + for the specific model and provider you are using. """ + temperature: float | None = None + """The temperature to use when calling the model.""" + top_p: float | None = None + """The top_p to use when calling the model.""" + frequency_penalty: float | None = None + """The frequency penalty to use when calling the model.""" + presence_penalty: float | None = None + """The presence penalty to use when calling the model.""" + tool_choice: Literal["auto", "required", "none"] | str | None = None - parallel_tool_calls: bool | None = False + """The tool choice to use when calling the model.""" + + parallel_tool_calls: bool | None = None + """Whether to use parallel tool calls when calling the model. + Defaults to False if not provided.""" + truncation: Literal["auto", "disabled"] | None = None + """The truncation strategy to use when calling the model.""" + + max_tokens: int | None = None + """The maximum number of output tokens to generate.""" + + reasoning: Reasoning | None = None + """Configuration options for + [reasoning models](https://platform.openai.com/docs/guides/reasoning). + """ + + metadata: dict[str, str] | None = None + """Metadata to include with the model response call.""" + + store: bool | None = None + """Whether to store the generated model response for later retrieval. + Defaults to True if not provided.""" + + include_usage: bool | None = None + """Whether to include usage chunk. + Defaults to True if not provided.""" + + extra_query: Query | None = None + """Additional query fields to provide with the request. + Defaults to None if not provided.""" + + extra_body: Body | None = None + """Additional body fields to provide with the request. + Defaults to None if not provided.""" + + extra_headers: Headers | None = None + """Additional headers to provide with the request. + Defaults to None if not provided.""" def resolve(self, override: ModelSettings | None) -> ModelSettings: """Produce a new ModelSettings by overlaying any non-None values from the override on top of this instance.""" if override is None: return self - return ModelSettings( - temperature=override.temperature or self.temperature, - top_p=override.top_p or self.top_p, - frequency_penalty=override.frequency_penalty or self.frequency_penalty, - presence_penalty=override.presence_penalty or self.presence_penalty, - tool_choice=override.tool_choice or self.tool_choice, - parallel_tool_calls=override.parallel_tool_calls or self.parallel_tool_calls, - truncation=override.truncation or self.truncation, - ) + + changes = { + field.name: getattr(override, field.name) + for field in fields(self) + if getattr(override, field.name) is not None + } + return replace(self, **changes) + + def to_json_dict(self) -> dict[str, Any]: + dataclass_dict = dataclasses.asdict(self) + + json_dict: dict[str, Any] = {} + + for field_name, value in dataclass_dict.items(): + if isinstance(value, BaseModel): + json_dict[field_name] = value.model_dump(mode="json") + else: + json_dict[field_name] = value + + return json_dict diff --git a/src/agents/models/chatcmpl_converter.py b/src/agents/models/chatcmpl_converter.py new file mode 100644 index 00000000..1d599e8c --- /dev/null +++ b/src/agents/models/chatcmpl_converter.py @@ -0,0 +1,466 @@ +from __future__ import annotations + +import json +from collections.abc import Iterable +from typing import Any, Literal, cast + +from openai import NOT_GIVEN, NotGiven +from openai.types.chat import ( + ChatCompletionAssistantMessageParam, + ChatCompletionContentPartImageParam, + ChatCompletionContentPartParam, + ChatCompletionContentPartTextParam, + ChatCompletionDeveloperMessageParam, + ChatCompletionMessage, + ChatCompletionMessageParam, + ChatCompletionMessageToolCallParam, + ChatCompletionSystemMessageParam, + ChatCompletionToolChoiceOptionParam, + ChatCompletionToolMessageParam, + ChatCompletionUserMessageParam, +) +from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam +from openai.types.chat.completion_create_params import ResponseFormat +from openai.types.responses import ( + EasyInputMessageParam, + ResponseFileSearchToolCallParam, + ResponseFunctionToolCall, + ResponseFunctionToolCallParam, + ResponseInputContentParam, + ResponseInputImageParam, + ResponseInputTextParam, + ResponseOutputMessage, + ResponseOutputMessageParam, + ResponseOutputRefusal, + ResponseOutputText, +) +from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message + +from ..agent_output import AgentOutputSchemaBase +from ..exceptions import AgentsException, UserError +from ..handoffs import Handoff +from ..items import TResponseInputItem, TResponseOutputItem +from ..tool import FunctionTool, Tool +from .fake_id import FAKE_RESPONSES_ID + + +class Converter: + @classmethod + def convert_tool_choice( + cls, tool_choice: Literal["auto", "required", "none"] | str | None + ) -> ChatCompletionToolChoiceOptionParam | NotGiven: + if tool_choice is None: + return NOT_GIVEN + elif tool_choice == "auto": + return "auto" + elif tool_choice == "required": + return "required" + elif tool_choice == "none": + return "none" + else: + return { + "type": "function", + "function": { + "name": tool_choice, + }, + } + + @classmethod + def convert_response_format( + cls, final_output_schema: AgentOutputSchemaBase | None + ) -> ResponseFormat | NotGiven: + if not final_output_schema or final_output_schema.is_plain_text(): + return NOT_GIVEN + + return { + "type": "json_schema", + "json_schema": { + "name": "final_output", + "strict": final_output_schema.is_strict_json_schema(), + "schema": final_output_schema.json_schema(), + }, + } + + @classmethod + def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]: + items: list[TResponseOutputItem] = [] + + message_item = ResponseOutputMessage( + id=FAKE_RESPONSES_ID, + content=[], + role="assistant", + type="message", + status="completed", + ) + if message.content: + message_item.content.append( + ResponseOutputText(text=message.content, type="output_text", annotations=[]) + ) + if message.refusal: + message_item.content.append( + ResponseOutputRefusal(refusal=message.refusal, type="refusal") + ) + if message.audio: + raise AgentsException("Audio is not currently supported") + + if message_item.content: + items.append(message_item) + + if message.tool_calls: + for tool_call in message.tool_calls: + items.append( + ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + call_id=tool_call.id, + arguments=tool_call.function.arguments, + name=tool_call.function.name, + type="function_call", + ) + ) + + return items + + @classmethod + def maybe_easy_input_message(cls, item: Any) -> EasyInputMessageParam | None: + if not isinstance(item, dict): + return None + + keys = item.keys() + # EasyInputMessageParam only has these two keys + if keys != {"content", "role"}: + return None + + role = item.get("role", None) + if role not in ("user", "assistant", "system", "developer"): + return None + + if "content" not in item: + return None + + return cast(EasyInputMessageParam, item) + + @classmethod + def maybe_input_message(cls, item: Any) -> Message | None: + if ( + isinstance(item, dict) + and item.get("type") == "message" + and item.get("role") + in ( + "user", + "system", + "developer", + ) + ): + return cast(Message, item) + + return None + + @classmethod + def maybe_file_search_call(cls, item: Any) -> ResponseFileSearchToolCallParam | None: + if isinstance(item, dict) and item.get("type") == "file_search_call": + return cast(ResponseFileSearchToolCallParam, item) + return None + + @classmethod + def maybe_function_tool_call(cls, item: Any) -> ResponseFunctionToolCallParam | None: + if isinstance(item, dict) and item.get("type") == "function_call": + return cast(ResponseFunctionToolCallParam, item) + return None + + @classmethod + def maybe_function_tool_call_output( + cls, + item: Any, + ) -> FunctionCallOutput | None: + if isinstance(item, dict) and item.get("type") == "function_call_output": + return cast(FunctionCallOutput, item) + return None + + @classmethod + def maybe_item_reference(cls, item: Any) -> ItemReference | None: + if isinstance(item, dict) and item.get("type") == "item_reference": + return cast(ItemReference, item) + return None + + @classmethod + def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam | None: + # ResponseOutputMessage is only used for messages with role assistant + if ( + isinstance(item, dict) + and item.get("type") == "message" + and item.get("role") == "assistant" + ): + return cast(ResponseOutputMessageParam, item) + return None + + @classmethod + def extract_text_content( + cls, content: str | Iterable[ResponseInputContentParam] + ) -> str | list[ChatCompletionContentPartTextParam]: + all_content = cls.extract_all_content(content) + if isinstance(all_content, str): + return all_content + out: list[ChatCompletionContentPartTextParam] = [] + for c in all_content: + if c.get("type") == "text": + out.append(cast(ChatCompletionContentPartTextParam, c)) + return out + + @classmethod + def extract_all_content( + cls, content: str | Iterable[ResponseInputContentParam] + ) -> str | list[ChatCompletionContentPartParam]: + if isinstance(content, str): + return content + out: list[ChatCompletionContentPartParam] = [] + + for c in content: + if isinstance(c, dict) and c.get("type") == "input_text": + casted_text_param = cast(ResponseInputTextParam, c) + out.append( + ChatCompletionContentPartTextParam( + type="text", + text=casted_text_param["text"], + ) + ) + elif isinstance(c, dict) and c.get("type") == "input_image": + casted_image_param = cast(ResponseInputImageParam, c) + if "image_url" not in casted_image_param or not casted_image_param["image_url"]: + raise UserError( + f"Only image URLs are supported for input_image {casted_image_param}" + ) + out.append( + ChatCompletionContentPartImageParam( + type="image_url", + image_url={ + "url": casted_image_param["image_url"], + "detail": casted_image_param.get("detail", "auto"), + }, + ) + ) + elif isinstance(c, dict) and c.get("type") == "input_file": + raise UserError(f"File uploads are not supported for chat completions {c}") + else: + raise UserError(f"Unknown content: {c}") + return out + + @classmethod + def items_to_messages( + cls, + items: str | Iterable[TResponseInputItem], + ) -> list[ChatCompletionMessageParam]: + """ + Convert a sequence of 'Item' objects into a list of ChatCompletionMessageParam. + + Rules: + - EasyInputMessage or InputMessage (role=user) => ChatCompletionUserMessageParam + - EasyInputMessage or InputMessage (role=system) => ChatCompletionSystemMessageParam + - EasyInputMessage or InputMessage (role=developer) => ChatCompletionDeveloperMessageParam + - InputMessage (role=assistant) => Start or flush a ChatCompletionAssistantMessageParam + - response_output_message => Also produces/flushes a ChatCompletionAssistantMessageParam + - tool calls get attached to the *current* assistant message, or create one if none. + - tool outputs => ChatCompletionToolMessageParam + """ + + if isinstance(items, str): + return [ + ChatCompletionUserMessageParam( + role="user", + content=items, + ) + ] + + result: list[ChatCompletionMessageParam] = [] + current_assistant_msg: ChatCompletionAssistantMessageParam | None = None + + def flush_assistant_message() -> None: + nonlocal current_assistant_msg + if current_assistant_msg is not None: + # The API doesn't support empty arrays for tool_calls + if not current_assistant_msg.get("tool_calls"): + del current_assistant_msg["tool_calls"] + result.append(current_assistant_msg) + current_assistant_msg = None + + def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: + nonlocal current_assistant_msg + if current_assistant_msg is None: + current_assistant_msg = ChatCompletionAssistantMessageParam(role="assistant") + current_assistant_msg["tool_calls"] = [] + return current_assistant_msg + + for item in items: + # 1) Check easy input message + if easy_msg := cls.maybe_easy_input_message(item): + role = easy_msg["role"] + content = easy_msg["content"] + + if role == "user": + flush_assistant_message() + msg_user: ChatCompletionUserMessageParam = { + "role": "user", + "content": cls.extract_all_content(content), + } + result.append(msg_user) + elif role == "system": + flush_assistant_message() + msg_system: ChatCompletionSystemMessageParam = { + "role": "system", + "content": cls.extract_text_content(content), + } + result.append(msg_system) + elif role == "developer": + flush_assistant_message() + msg_developer: ChatCompletionDeveloperMessageParam = { + "role": "developer", + "content": cls.extract_text_content(content), + } + result.append(msg_developer) + elif role == "assistant": + flush_assistant_message() + msg_assistant: ChatCompletionAssistantMessageParam = { + "role": "assistant", + "content": cls.extract_text_content(content), + } + result.append(msg_assistant) + else: + raise UserError(f"Unexpected role in easy_input_message: {role}") + + # 2) Check input message + elif in_msg := cls.maybe_input_message(item): + role = in_msg["role"] + content = in_msg["content"] + flush_assistant_message() + + if role == "user": + msg_user = { + "role": "user", + "content": cls.extract_all_content(content), + } + result.append(msg_user) + elif role == "system": + msg_system = { + "role": "system", + "content": cls.extract_text_content(content), + } + result.append(msg_system) + elif role == "developer": + msg_developer = { + "role": "developer", + "content": cls.extract_text_content(content), + } + result.append(msg_developer) + else: + raise UserError(f"Unexpected role in input_message: {role}") + + # 3) response output message => assistant + elif resp_msg := cls.maybe_response_output_message(item): + flush_assistant_message() + new_asst = ChatCompletionAssistantMessageParam(role="assistant") + contents = resp_msg["content"] + + text_segments = [] + for c in contents: + if c["type"] == "output_text": + text_segments.append(c["text"]) + elif c["type"] == "refusal": + new_asst["refusal"] = c["refusal"] + elif c["type"] == "output_audio": + # Can't handle this, b/c chat completions expects an ID which we dont have + raise UserError( + f"Only audio IDs are supported for chat completions, but got: {c}" + ) + else: + raise UserError(f"Unknown content type in ResponseOutputMessage: {c}") + + if text_segments: + combined = "\n".join(text_segments) + new_asst["content"] = combined + + new_asst["tool_calls"] = [] + current_assistant_msg = new_asst + + # 4) function/file-search calls => attach to assistant + elif file_search := cls.maybe_file_search_call(item): + asst = ensure_assistant_message() + tool_calls = list(asst.get("tool_calls", [])) + new_tool_call = ChatCompletionMessageToolCallParam( + id=file_search["id"], + type="function", + function={ + "name": "file_search_call", + "arguments": json.dumps( + { + "queries": file_search.get("queries", []), + "status": file_search.get("status"), + } + ), + }, + ) + tool_calls.append(new_tool_call) + asst["tool_calls"] = tool_calls + + elif func_call := cls.maybe_function_tool_call(item): + asst = ensure_assistant_message() + tool_calls = list(asst.get("tool_calls", [])) + arguments = func_call["arguments"] if func_call["arguments"] else "{}" + new_tool_call = ChatCompletionMessageToolCallParam( + id=func_call["call_id"], + type="function", + function={ + "name": func_call["name"], + "arguments": arguments, + }, + ) + tool_calls.append(new_tool_call) + asst["tool_calls"] = tool_calls + # 5) function call output => tool message + elif func_output := cls.maybe_function_tool_call_output(item): + flush_assistant_message() + msg: ChatCompletionToolMessageParam = { + "role": "tool", + "tool_call_id": func_output["call_id"], + "content": func_output["output"], + } + result.append(msg) + + # 6) item reference => handle or raise + elif item_ref := cls.maybe_item_reference(item): + raise UserError( + f"Encountered an item_reference, which is not supported: {item_ref}" + ) + + # 7) If we haven't recognized it => fail or ignore + else: + raise UserError(f"Unhandled item type or structure: {item}") + + flush_assistant_message() + return result + + @classmethod + def tool_to_openai(cls, tool: Tool) -> ChatCompletionToolParam: + if isinstance(tool, FunctionTool): + return { + "type": "function", + "function": { + "name": tool.name, + "description": tool.description or "", + "parameters": tool.params_json_schema, + }, + } + + raise UserError( + f"Hosted tools are not supported with the ChatCompletions API. Got tool type: " + f"{type(tool)}, tool: {tool}" + ) + + @classmethod + def convert_handoff_tool(cls, handoff: Handoff[Any]) -> ChatCompletionToolParam: + return { + "type": "function", + "function": { + "name": handoff.tool_name, + "description": handoff.tool_description, + "parameters": handoff.input_json_schema, + }, + } diff --git a/src/agents/models/chatcmpl_helpers.py b/src/agents/models/chatcmpl_helpers.py new file mode 100644 index 00000000..0cee21ec --- /dev/null +++ b/src/agents/models/chatcmpl_helpers.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from openai import AsyncOpenAI + +from ..model_settings import ModelSettings +from ..version import __version__ + +_USER_AGENT = f"Agents/Python {__version__}" +HEADERS = {"User-Agent": _USER_AGENT} + + +class ChatCmplHelpers: + @classmethod + def is_openai(cls, client: AsyncOpenAI): + return str(client.base_url).startswith("https://api.openai.com") + + @classmethod + def get_store_param(cls, client: AsyncOpenAI, model_settings: ModelSettings) -> bool | None: + # Match the behavior of Responses where store is True when not given + default_store = True if cls.is_openai(client) else None + return model_settings.store if model_settings.store is not None else default_store + + @classmethod + def get_stream_options_param( + cls, client: AsyncOpenAI, model_settings: ModelSettings, stream: bool + ) -> dict[str, bool] | None: + if not stream: + return None + + default_include_usage = True if cls.is_openai(client) else None + include_usage = ( + model_settings.include_usage + if model_settings.include_usage is not None + else default_include_usage + ) + stream_options = {"include_usage": include_usage} if include_usage is not None else None + return stream_options diff --git a/src/agents/models/chatcmpl_stream_handler.py b/src/agents/models/chatcmpl_stream_handler.py new file mode 100644 index 00000000..d18f5912 --- /dev/null +++ b/src/agents/models/chatcmpl_stream_handler.py @@ -0,0 +1,316 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator +from dataclasses import dataclass, field + +from openai import AsyncStream +from openai.types.chat import ChatCompletionChunk +from openai.types.completion_usage import CompletionUsage +from openai.types.responses import ( + Response, + ResponseCompletedEvent, + ResponseContentPartAddedEvent, + ResponseContentPartDoneEvent, + ResponseCreatedEvent, + ResponseFunctionCallArgumentsDeltaEvent, + ResponseFunctionToolCall, + ResponseOutputItem, + ResponseOutputItemAddedEvent, + ResponseOutputItemDoneEvent, + ResponseOutputMessage, + ResponseOutputRefusal, + ResponseOutputText, + ResponseRefusalDeltaEvent, + ResponseTextDeltaEvent, + ResponseUsage, +) +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails + +from ..items import TResponseStreamEvent +from .fake_id import FAKE_RESPONSES_ID + + +@dataclass +class StreamingState: + started: bool = False + text_content_index_and_output: tuple[int, ResponseOutputText] | None = None + refusal_content_index_and_output: tuple[int, ResponseOutputRefusal] | None = None + function_calls: dict[int, ResponseFunctionToolCall] = field(default_factory=dict) + + +class SequenceNumber: + def __init__(self): + self._sequence_number = 0 + + def get_and_increment(self) -> int: + num = self._sequence_number + self._sequence_number += 1 + return num + + +class ChatCmplStreamHandler: + @classmethod + async def handle_stream( + cls, + response: Response, + stream: AsyncStream[ChatCompletionChunk], + ) -> AsyncIterator[TResponseStreamEvent]: + usage: CompletionUsage | None = None + state = StreamingState() + sequence_number = SequenceNumber() + async for chunk in stream: + if not state.started: + state.started = True + yield ResponseCreatedEvent( + response=response, + type="response.created", + sequence_number=sequence_number.get_and_increment(), + ) + + # This is always set by the OpenAI API, but not by others e.g. LiteLLM + usage = chunk.usage if hasattr(chunk, "usage") else None + + if not chunk.choices or not chunk.choices[0].delta: + continue + + delta = chunk.choices[0].delta + + # Handle text + if delta.content: + if not state.text_content_index_and_output: + # Initialize a content tracker for streaming text + state.text_content_index_and_output = ( + 0 if not state.refusal_content_index_and_output else 1, + ResponseOutputText( + text="", + type="output_text", + annotations=[], + ), + ) + # Start a new assistant message stream + assistant_item = ResponseOutputMessage( + id=FAKE_RESPONSES_ID, + content=[], + role="assistant", + type="message", + status="in_progress", + ) + # Notify consumers of the start of a new output message + first content part + yield ResponseOutputItemAddedEvent( + item=assistant_item, + output_index=0, + type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), + ) + yield ResponseContentPartAddedEvent( + content_index=state.text_content_index_and_output[0], + item_id=FAKE_RESPONSES_ID, + output_index=0, + part=ResponseOutputText( + text="", + type="output_text", + annotations=[], + ), + type="response.content_part.added", + sequence_number=sequence_number.get_and_increment(), + ) + # Emit the delta for this segment of content + yield ResponseTextDeltaEvent( + content_index=state.text_content_index_and_output[0], + delta=delta.content, + item_id=FAKE_RESPONSES_ID, + output_index=0, + type="response.output_text.delta", + sequence_number=sequence_number.get_and_increment(), + ) + # Accumulate the text into the response part + state.text_content_index_and_output[1].text += delta.content + + # Handle refusals (model declines to answer) + # This is always set by the OpenAI API, but not by others e.g. LiteLLM + if hasattr(delta, "refusal") and delta.refusal: + if not state.refusal_content_index_and_output: + # Initialize a content tracker for streaming refusal text + state.refusal_content_index_and_output = ( + 0 if not state.text_content_index_and_output else 1, + ResponseOutputRefusal(refusal="", type="refusal"), + ) + # Start a new assistant message if one doesn't exist yet (in-progress) + assistant_item = ResponseOutputMessage( + id=FAKE_RESPONSES_ID, + content=[], + role="assistant", + type="message", + status="in_progress", + ) + # Notify downstream that assistant message + first content part are starting + yield ResponseOutputItemAddedEvent( + item=assistant_item, + output_index=0, + type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), + ) + yield ResponseContentPartAddedEvent( + content_index=state.refusal_content_index_and_output[0], + item_id=FAKE_RESPONSES_ID, + output_index=0, + part=ResponseOutputText( + text="", + type="output_text", + annotations=[], + ), + type="response.content_part.added", + sequence_number=sequence_number.get_and_increment(), + ) + # Emit the delta for this segment of refusal + yield ResponseRefusalDeltaEvent( + content_index=state.refusal_content_index_and_output[0], + delta=delta.refusal, + item_id=FAKE_RESPONSES_ID, + output_index=0, + type="response.refusal.delta", + sequence_number=sequence_number.get_and_increment(), + ) + # Accumulate the refusal string in the output part + state.refusal_content_index_and_output[1].refusal += delta.refusal + + # Handle tool calls + # Because we don't know the name of the function until the end of the stream, we'll + # save everything and yield events at the end + if delta.tool_calls: + for tc_delta in delta.tool_calls: + if tc_delta.index not in state.function_calls: + state.function_calls[tc_delta.index] = ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + arguments="", + name="", + type="function_call", + call_id="", + ) + tc_function = tc_delta.function + + state.function_calls[tc_delta.index].arguments += ( + tc_function.arguments if tc_function else "" + ) or "" + state.function_calls[tc_delta.index].name += ( + tc_function.name if tc_function else "" + ) or "" + state.function_calls[tc_delta.index].call_id += tc_delta.id or "" + + function_call_starting_index = 0 + if state.text_content_index_and_output: + function_call_starting_index += 1 + # Send end event for this content part + yield ResponseContentPartDoneEvent( + content_index=state.text_content_index_and_output[0], + item_id=FAKE_RESPONSES_ID, + output_index=0, + part=state.text_content_index_and_output[1], + type="response.content_part.done", + sequence_number=sequence_number.get_and_increment(), + ) + + if state.refusal_content_index_and_output: + function_call_starting_index += 1 + # Send end event for this content part + yield ResponseContentPartDoneEvent( + content_index=state.refusal_content_index_and_output[0], + item_id=FAKE_RESPONSES_ID, + output_index=0, + part=state.refusal_content_index_and_output[1], + type="response.content_part.done", + sequence_number=sequence_number.get_and_increment(), + ) + + # Actually send events for the function calls + for function_call in state.function_calls.values(): + # First, a ResponseOutputItemAdded for the function call + yield ResponseOutputItemAddedEvent( + item=ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + call_id=function_call.call_id, + arguments=function_call.arguments, + name=function_call.name, + type="function_call", + ), + output_index=function_call_starting_index, + type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), + ) + # Then, yield the args + yield ResponseFunctionCallArgumentsDeltaEvent( + delta=function_call.arguments, + item_id=FAKE_RESPONSES_ID, + output_index=function_call_starting_index, + type="response.function_call_arguments.delta", + sequence_number=sequence_number.get_and_increment(), + ) + # Finally, the ResponseOutputItemDone + yield ResponseOutputItemDoneEvent( + item=ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + call_id=function_call.call_id, + arguments=function_call.arguments, + name=function_call.name, + type="function_call", + ), + output_index=function_call_starting_index, + type="response.output_item.done", + sequence_number=sequence_number.get_and_increment(), + ) + + # Finally, send the Response completed event + outputs: list[ResponseOutputItem] = [] + if state.text_content_index_and_output or state.refusal_content_index_and_output: + assistant_msg = ResponseOutputMessage( + id=FAKE_RESPONSES_ID, + content=[], + role="assistant", + type="message", + status="completed", + ) + if state.text_content_index_and_output: + assistant_msg.content.append(state.text_content_index_and_output[1]) + if state.refusal_content_index_and_output: + assistant_msg.content.append(state.refusal_content_index_and_output[1]) + outputs.append(assistant_msg) + + # send a ResponseOutputItemDone for the assistant message + yield ResponseOutputItemDoneEvent( + item=assistant_msg, + output_index=0, + type="response.output_item.done", + sequence_number=sequence_number.get_and_increment(), + ) + + for function_call in state.function_calls.values(): + outputs.append(function_call) + + final_response = response.model_copy() + final_response.output = outputs + final_response.usage = ( + ResponseUsage( + input_tokens=usage.prompt_tokens, + output_tokens=usage.completion_tokens, + total_tokens=usage.total_tokens, + output_tokens_details=OutputTokensDetails( + reasoning_tokens=usage.completion_tokens_details.reasoning_tokens + if usage.completion_tokens_details + and usage.completion_tokens_details.reasoning_tokens + else 0 + ), + input_tokens_details=InputTokensDetails( + cached_tokens=usage.prompt_tokens_details.cached_tokens + if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens + else 0 + ), + ) + if usage + else None + ) + + yield ResponseCompletedEvent( + response=final_response, + type="response.completed", + sequence_number=sequence_number.get_and_increment(), + ) diff --git a/src/agents/models/interface.py b/src/agents/models/interface.py index e9a8700c..3a79e564 100644 --- a/src/agents/models/interface.py +++ b/src/agents/models/interface.py @@ -5,7 +5,7 @@ from collections.abc import AsyncIterator from typing import TYPE_CHECKING -from ..agent_output import AgentOutputSchema +from ..agent_output import AgentOutputSchemaBase from ..handoffs import Handoff from ..items import ModelResponse, TResponseInputItem, TResponseStreamEvent from ..tool import Tool @@ -41,9 +41,11 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, + *, + previous_response_id: str | None, ) -> ModelResponse: """Get a response from the model. @@ -55,6 +57,8 @@ async def get_response( output_schema: The output schema to use. handoffs: The handoffs available to the model. tracing: Tracing configuration. + previous_response_id: the ID of the previous response. Generally not used by the model, + except for the OpenAI Responses API. Returns: The full model response. @@ -68,9 +72,11 @@ def stream_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, + *, + previous_response_id: str | None, ) -> AsyncIterator[TResponseStreamEvent]: """Stream a response from the model. @@ -82,6 +88,8 @@ def stream_response( output_schema: The output schema to use. handoffs: The handoffs available to the model. tracing: Tracing configuration. + previous_response_id: the ID of the previous response. Generally not used by the model, + except for the OpenAI Responses API. Returns: An iterator of response stream events, in OpenAI Responses format. diff --git a/src/agents/models/multi_provider.py b/src/agents/models/multi_provider.py new file mode 100644 index 00000000..d075ac9b --- /dev/null +++ b/src/agents/models/multi_provider.py @@ -0,0 +1,144 @@ +from __future__ import annotations + +from openai import AsyncOpenAI + +from ..exceptions import UserError +from .interface import Model, ModelProvider +from .openai_provider import OpenAIProvider + + +class MultiProviderMap: + """A map of model name prefixes to ModelProviders.""" + + def __init__(self): + self._mapping: dict[str, ModelProvider] = {} + + def has_prefix(self, prefix: str) -> bool: + """Returns True if the given prefix is in the mapping.""" + return prefix in self._mapping + + def get_mapping(self) -> dict[str, ModelProvider]: + """Returns a copy of the current prefix -> ModelProvider mapping.""" + return self._mapping.copy() + + def set_mapping(self, mapping: dict[str, ModelProvider]): + """Overwrites the current mapping with a new one.""" + self._mapping = mapping + + def get_provider(self, prefix: str) -> ModelProvider | None: + """Returns the ModelProvider for the given prefix. + + Args: + prefix: The prefix of the model name e.g. "openai" or "my_prefix". + """ + return self._mapping.get(prefix) + + def add_provider(self, prefix: str, provider: ModelProvider): + """Adds a new prefix -> ModelProvider mapping. + + Args: + prefix: The prefix of the model name e.g. "openai" or "my_prefix". + provider: The ModelProvider to use for the given prefix. + """ + self._mapping[prefix] = provider + + def remove_provider(self, prefix: str): + """Removes the mapping for the given prefix. + + Args: + prefix: The prefix of the model name e.g. "openai" or "my_prefix". + """ + del self._mapping[prefix] + + +class MultiProvider(ModelProvider): + """This ModelProvider maps to a Model based on the prefix of the model name. By default, the + mapping is: + - "openai/" prefix or no prefix -> OpenAIProvider. e.g. "openai/gpt-4.1", "gpt-4.1" + - "litellm/" prefix -> LitellmProvider. e.g. "litellm/openai/gpt-4.1" + + You can override or customize this mapping. + """ + + def __init__( + self, + *, + provider_map: MultiProviderMap | None = None, + openai_api_key: str | None = None, + openai_base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + openai_organization: str | None = None, + openai_project: str | None = None, + openai_use_responses: bool | None = None, + ) -> None: + """Create a new OpenAI provider. + + Args: + provider_map: A MultiProviderMap that maps prefixes to ModelProviders. If not provided, + we will use a default mapping. See the documentation for this class to see the + default mapping. + openai_api_key: The API key to use for the OpenAI provider. If not provided, we will use + the default API key. + openai_base_url: The base URL to use for the OpenAI provider. If not provided, we will + use the default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + openai_organization: The organization to use for the OpenAI provider. + openai_project: The project to use for the OpenAI provider. + openai_use_responses: Whether to use the OpenAI responses API. + """ + self.provider_map = provider_map + self.openai_provider = OpenAIProvider( + api_key=openai_api_key, + base_url=openai_base_url, + openai_client=openai_client, + organization=openai_organization, + project=openai_project, + use_responses=openai_use_responses, + ) + + self._fallback_providers: dict[str, ModelProvider] = {} + + def _get_prefix_and_model_name(self, model_name: str | None) -> tuple[str | None, str | None]: + if model_name is None: + return None, None + elif "/" in model_name: + prefix, model_name = model_name.split("/", 1) + return prefix, model_name + else: + return None, model_name + + def _create_fallback_provider(self, prefix: str) -> ModelProvider: + if prefix == "litellm": + from ..extensions.models.litellm_provider import LitellmProvider + + return LitellmProvider() + else: + raise UserError(f"Unknown prefix: {prefix}") + + def _get_fallback_provider(self, prefix: str | None) -> ModelProvider: + if prefix is None or prefix == "openai": + return self.openai_provider + elif prefix in self._fallback_providers: + return self._fallback_providers[prefix] + else: + self._fallback_providers[prefix] = self._create_fallback_provider(prefix) + return self._fallback_providers[prefix] + + def get_model(self, model_name: str | None) -> Model: + """Returns a Model based on the model name. The model name can have a prefix, ending with + a "/", which will be used to look up the ModelProvider. If there is no prefix, we will use + the OpenAI provider. + + Args: + model_name: The name of the model to get. + + Returns: + A Model. + """ + prefix, model_name = self._get_prefix_and_model_name(model_name) + + if prefix and self.provider_map and (provider := self.provider_map.get_provider(prefix)): + return provider.get_model(model_name) + else: + return self._get_fallback_provider(prefix).get_model(model_name) diff --git a/src/agents/models/openai_chatcompletions.py b/src/agents/models/openai_chatcompletions.py index a7340d05..4465ff2f 100644 --- a/src/agents/models/openai_chatcompletions.py +++ b/src/agents/models/openai_chatcompletions.py @@ -1,71 +1,29 @@ from __future__ import annotations -import dataclasses import json import time -from collections.abc import AsyncIterator, Iterable -from dataclasses import dataclass, field +from collections.abc import AsyncIterator from typing import TYPE_CHECKING, Any, Literal, cast, overload -from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream, NotGiven +from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream from openai.types import ChatModel -from openai.types.chat import ( - ChatCompletion, - ChatCompletionAssistantMessageParam, - ChatCompletionChunk, - ChatCompletionContentPartImageParam, - ChatCompletionContentPartParam, - ChatCompletionContentPartTextParam, - ChatCompletionDeveloperMessageParam, - ChatCompletionMessage, - ChatCompletionMessageParam, - ChatCompletionMessageToolCallParam, - ChatCompletionSystemMessageParam, - ChatCompletionToolChoiceOptionParam, - ChatCompletionToolMessageParam, - ChatCompletionUserMessageParam, -) -from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam -from openai.types.chat.completion_create_params import ResponseFormat -from openai.types.completion_usage import CompletionUsage -from openai.types.responses import ( - EasyInputMessageParam, - Response, - ResponseCompletedEvent, - ResponseContentPartAddedEvent, - ResponseContentPartDoneEvent, - ResponseCreatedEvent, - ResponseFileSearchToolCallParam, - ResponseFunctionCallArgumentsDeltaEvent, - ResponseFunctionToolCall, - ResponseFunctionToolCallParam, - ResponseInputContentParam, - ResponseInputImageParam, - ResponseInputTextParam, - ResponseOutputItem, - ResponseOutputItemAddedEvent, - ResponseOutputItemDoneEvent, - ResponseOutputMessage, - ResponseOutputMessageParam, - ResponseOutputRefusal, - ResponseOutputText, - ResponseRefusalDeltaEvent, - ResponseTextDeltaEvent, -) -from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message +from openai.types.chat import ChatCompletion, ChatCompletionChunk +from openai.types.responses import Response +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails from .. import _debug -from ..agent_output import AgentOutputSchema -from ..exceptions import AgentsException, UserError +from ..agent_output import AgentOutputSchemaBase from ..handoffs import Handoff -from ..items import ModelResponse, TResponseInputItem, TResponseOutputItem, TResponseStreamEvent +from ..items import ModelResponse, TResponseInputItem, TResponseStreamEvent from ..logger import logger -from ..tool import FunctionTool, Tool +from ..tool import Tool from ..tracing import generation_span from ..tracing.span_data import GenerationSpanData from ..tracing.spans import Span from ..usage import Usage -from ..version import __version__ +from .chatcmpl_converter import Converter +from .chatcmpl_helpers import HEADERS, ChatCmplHelpers +from .chatcmpl_stream_handler import ChatCmplStreamHandler from .fake_id import FAKE_RESPONSES_ID from .interface import Model, ModelTracing @@ -73,18 +31,6 @@ from ..model_settings import ModelSettings -_USER_AGENT = f"Agents/Python {__version__}" -_HEADERS = {"User-Agent": _USER_AGENT} - - -@dataclass -class _StreamingState: - started: bool = False - text_content_index_and_output: tuple[int, ResponseOutputText] | None = None - refusal_content_index_and_output: tuple[int, ResponseOutputRefusal] | None = None - function_calls: dict[int, ResponseFunctionToolCall] = field(default_factory=dict) - - class OpenAIChatCompletionsModel(Model): def __init__( self, @@ -103,14 +49,14 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, + previous_response_id: str | None, ) -> ModelResponse: with generation_span( model=str(self.model), - model_config=dataclasses.asdict(model_settings) - | {"base_url": str(self._client.base_url)}, + model_config=model_settings.to_json_dict() | {"base_url": str(self._client.base_url)}, disabled=tracing.is_disabled(), ) as span_generation: response = await self._fetch_response( @@ -138,6 +84,18 @@ async def get_response( input_tokens=response.usage.prompt_tokens, output_tokens=response.usage.completion_tokens, total_tokens=response.usage.total_tokens, + input_tokens_details=InputTokensDetails( + cached_tokens=getattr( + response.usage.prompt_tokens_details, "cached_tokens", 0 + ) + or 0, + ), + output_tokens_details=OutputTokensDetails( + reasoning_tokens=getattr( + response.usage.completion_tokens_details, "reasoning_tokens", 0 + ) + or 0, + ), ) if response.usage else Usage() @@ -149,12 +107,12 @@ async def get_response( "output_tokens": usage.output_tokens, } - items = _Converter.message_to_output_items(response.choices[0].message) + items = Converter.message_to_output_items(response.choices[0].message) return ModelResponse( output=items, usage=usage, - referenceable_id=None, + response_id=None, ) async def stream_response( @@ -163,17 +121,18 @@ async def stream_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, + *, + previous_response_id: str | None, ) -> AsyncIterator[TResponseStreamEvent]: """ Yields a partial message as it is generated, as well as the usage information. """ with generation_span( model=str(self.model), - model_config=dataclasses.asdict(model_settings) - | {"base_url": str(self._client.base_url)}, + model_config=model_settings.to_json_dict() | {"base_url": str(self._client.base_url)}, disabled=tracing.is_disabled(), ) as span_generation: response, stream = await self._fetch_response( @@ -188,236 +147,20 @@ async def stream_response( stream=True, ) - usage: CompletionUsage | None = None - state = _StreamingState() - - async for chunk in stream: - if not state.started: - state.started = True - yield ResponseCreatedEvent( - response=response, - type="response.created", - ) - - # The usage is only available in the last chunk - usage = chunk.usage - - if not chunk.choices or not chunk.choices[0].delta: - continue - - delta = chunk.choices[0].delta - - # Handle text - if delta.content: - if not state.text_content_index_and_output: - # Initialize a content tracker for streaming text - state.text_content_index_and_output = ( - 0 if not state.refusal_content_index_and_output else 1, - ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - ) - # Start a new assistant message stream - assistant_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="in_progress", - ) - # Notify consumers of the start of a new output message + first content part - yield ResponseOutputItemAddedEvent( - item=assistant_item, - output_index=0, - type="response.output_item.added", - ) - yield ResponseContentPartAddedEvent( - content_index=state.text_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - type="response.content_part.added", - ) - # Emit the delta for this segment of content - yield ResponseTextDeltaEvent( - content_index=state.text_content_index_and_output[0], - delta=delta.content, - item_id=FAKE_RESPONSES_ID, - output_index=0, - type="response.output_text.delta", - ) - # Accumulate the text into the response part - state.text_content_index_and_output[1].text += delta.content - - # Handle refusals (model declines to answer) - if delta.refusal: - if not state.refusal_content_index_and_output: - # Initialize a content tracker for streaming refusal text - state.refusal_content_index_and_output = ( - 0 if not state.text_content_index_and_output else 1, - ResponseOutputRefusal(refusal="", type="refusal"), - ) - # Start a new assistant message if one doesn't exist yet (in-progress) - assistant_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="in_progress", - ) - # Notify downstream that assistant message + first content part are starting - yield ResponseOutputItemAddedEvent( - item=assistant_item, - output_index=0, - type="response.output_item.added", - ) - yield ResponseContentPartAddedEvent( - content_index=state.refusal_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - type="response.content_part.added", - ) - # Emit the delta for this segment of refusal - yield ResponseRefusalDeltaEvent( - content_index=state.refusal_content_index_and_output[0], - delta=delta.refusal, - item_id=FAKE_RESPONSES_ID, - output_index=0, - type="response.refusal.delta", - ) - # Accumulate the refusal string in the output part - state.refusal_content_index_and_output[1].refusal += delta.refusal - - # Handle tool calls - # Because we don't know the name of the function until the end of the stream, we'll - # save everything and yield events at the end - if delta.tool_calls: - for tc_delta in delta.tool_calls: - if tc_delta.index not in state.function_calls: - state.function_calls[tc_delta.index] = ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - arguments="", - name="", - type="function_call", - call_id="", - ) - tc_function = tc_delta.function - - state.function_calls[tc_delta.index].arguments += ( - tc_function.arguments if tc_function else "" - ) or "" - state.function_calls[tc_delta.index].name += ( - tc_function.name if tc_function else "" - ) or "" - state.function_calls[tc_delta.index].call_id += tc_delta.id or "" - - function_call_starting_index = 0 - if state.text_content_index_and_output: - function_call_starting_index += 1 - # Send end event for this content part - yield ResponseContentPartDoneEvent( - content_index=state.text_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=state.text_content_index_and_output[1], - type="response.content_part.done", - ) - - if state.refusal_content_index_and_output: - function_call_starting_index += 1 - # Send end event for this content part - yield ResponseContentPartDoneEvent( - content_index=state.refusal_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=state.refusal_content_index_and_output[1], - type="response.content_part.done", - ) - - # Actually send events for the function calls - for function_call in state.function_calls.values(): - # First, a ResponseOutputItemAdded for the function call - yield ResponseOutputItemAddedEvent( - item=ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=function_call.call_id, - arguments=function_call.arguments, - name=function_call.name, - type="function_call", - ), - output_index=function_call_starting_index, - type="response.output_item.added", - ) - # Then, yield the args - yield ResponseFunctionCallArgumentsDeltaEvent( - delta=function_call.arguments, - item_id=FAKE_RESPONSES_ID, - output_index=function_call_starting_index, - type="response.function_call_arguments.delta", - ) - # Finally, the ResponseOutputItemDone - yield ResponseOutputItemDoneEvent( - item=ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=function_call.call_id, - arguments=function_call.arguments, - name=function_call.name, - type="function_call", - ), - output_index=function_call_starting_index, - type="response.output_item.done", - ) - - # Finally, send the Response completed event - outputs: list[ResponseOutputItem] = [] - if state.text_content_index_and_output or state.refusal_content_index_and_output: - assistant_msg = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="completed", - ) - if state.text_content_index_and_output: - assistant_msg.content.append(state.text_content_index_and_output[1]) - if state.refusal_content_index_and_output: - assistant_msg.content.append(state.refusal_content_index_and_output[1]) - outputs.append(assistant_msg) + final_response: Response | None = None + async for chunk in ChatCmplStreamHandler.handle_stream(response, stream): + yield chunk - # send a ResponseOutputItemDone for the assistant message - yield ResponseOutputItemDoneEvent( - item=assistant_msg, - output_index=0, - type="response.output_item.done", - ) + if chunk.type == "response.completed": + final_response = chunk.response - for function_call in state.function_calls.values(): - outputs.append(function_call) - - final_response = response.model_copy(update={"output": outputs, "usage": usage}) - - yield ResponseCompletedEvent( - response=final_response, - type="response.completed", - ) - if tracing.include_data(): + if tracing.include_data() and final_response: span_generation.span_data.output = [final_response.model_dump()] - if usage: + if final_response and final_response.usage: span_generation.span_data.usage = { - "input_tokens": usage.prompt_tokens, - "output_tokens": usage.completion_tokens, + "input_tokens": final_response.usage.input_tokens, + "output_tokens": final_response.usage.output_tokens, } @overload @@ -427,7 +170,7 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], span: Span[GenerationSpanData], tracing: ModelTracing, @@ -441,7 +184,7 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], span: Span[GenerationSpanData], tracing: ModelTracing, @@ -454,13 +197,13 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], span: Span[GenerationSpanData], tracing: ModelTracing, stream: bool = False, ) -> ChatCompletion | tuple[Response, AsyncStream[ChatCompletionChunk]]: - converted_messages = _Converter.items_to_messages(input) + converted_messages = Converter.items_to_messages(input) if system_instructions: converted_messages.insert( @@ -474,15 +217,19 @@ async def _fetch_response( span.span_data.input = converted_messages parallel_tool_calls = ( - True if model_settings.parallel_tool_calls and tools and len(tools) > 0 else NOT_GIVEN + True + if model_settings.parallel_tool_calls and tools and len(tools) > 0 + else False + if model_settings.parallel_tool_calls is False + else NOT_GIVEN ) - tool_choice = _Converter.convert_tool_choice(model_settings.tool_choice) - response_format = _Converter.convert_response_format(output_schema) + tool_choice = Converter.convert_tool_choice(model_settings.tool_choice) + response_format = Converter.convert_response_format(output_schema) - converted_tools = [ToolConverter.to_openai(tool) for tool in tools] if tools else [] + converted_tools = [Converter.tool_to_openai(tool) for tool in tools] if tools else [] for handoff in handoffs: - converted_tools.append(ToolConverter.convert_handoff_tool(handoff)) + converted_tools.append(Converter.convert_handoff_tool(handoff)) if _debug.DONT_LOG_MODEL_DATA: logger.debug("Calling LLM") @@ -495,6 +242,13 @@ async def _fetch_response( f"Response format: {response_format}\n" ) + reasoning_effort = model_settings.reasoning.effort if model_settings.reasoning else None + store = ChatCmplHelpers.get_store_param(self._get_client(), model_settings) + + stream_options = ChatCmplHelpers.get_stream_options_param( + self._get_client(), model_settings, stream=stream + ) + ret = await self._get_client().chat.completions.create( model=self.model, messages=converted_messages, @@ -503,12 +257,18 @@ async def _fetch_response( top_p=self._non_null_or_not_given(model_settings.top_p), frequency_penalty=self._non_null_or_not_given(model_settings.frequency_penalty), presence_penalty=self._non_null_or_not_given(model_settings.presence_penalty), + max_tokens=self._non_null_or_not_given(model_settings.max_tokens), tool_choice=tool_choice, response_format=response_format, parallel_tool_calls=parallel_tool_calls, stream=stream, - stream_options={"include_usage": True} if stream else NOT_GIVEN, - extra_headers=_HEADERS, + stream_options=self._non_null_or_not_given(stream_options), + store=self._non_null_or_not_given(store), + reasoning_effort=self._non_null_or_not_given(reasoning_effort), + extra_headers={**HEADERS, **(model_settings.extra_headers or {})}, + extra_query=model_settings.extra_query, + extra_body=model_settings.extra_body, + metadata=self._non_null_or_not_given(model_settings.metadata), ) if isinstance(ret, ChatCompletion): @@ -527,6 +287,7 @@ async def _fetch_response( temperature=model_settings.temperature, tools=[], parallel_tool_calls=parallel_tool_calls or False, + reasoning=model_settings.reasoning, ) return response, ret @@ -534,419 +295,3 @@ def _get_client(self) -> AsyncOpenAI: if self._client is None: self._client = AsyncOpenAI() return self._client - - -class _Converter: - @classmethod - def convert_tool_choice( - cls, tool_choice: Literal["auto", "required", "none"] | str | None - ) -> ChatCompletionToolChoiceOptionParam | NotGiven: - if tool_choice is None: - return NOT_GIVEN - elif tool_choice == "auto": - return "auto" - elif tool_choice == "required": - return "required" - elif tool_choice == "none": - return "none" - else: - return { - "type": "function", - "function": { - "name": tool_choice, - }, - } - - @classmethod - def convert_response_format( - cls, final_output_schema: AgentOutputSchema | None - ) -> ResponseFormat | NotGiven: - if not final_output_schema or final_output_schema.is_plain_text(): - return NOT_GIVEN - - return { - "type": "json_schema", - "json_schema": { - "name": "final_output", - "strict": final_output_schema.strict_json_schema, - "schema": final_output_schema.json_schema(), - }, - } - - @classmethod - def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]: - items: list[TResponseOutputItem] = [] - - message_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="completed", - ) - if message.content: - message_item.content.append( - ResponseOutputText(text=message.content, type="output_text", annotations=[]) - ) - if message.refusal: - message_item.content.append( - ResponseOutputRefusal(refusal=message.refusal, type="refusal") - ) - if message.audio: - raise AgentsException("Audio is not currently supported") - - if message_item.content: - items.append(message_item) - - if message.tool_calls: - for tool_call in message.tool_calls: - items.append( - ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=tool_call.id, - arguments=tool_call.function.arguments, - name=tool_call.function.name, - type="function_call", - ) - ) - - return items - - @classmethod - def maybe_easy_input_message(cls, item: Any) -> EasyInputMessageParam | None: - if not isinstance(item, dict): - return None - - keys = item.keys() - # EasyInputMessageParam only has these two keys - if keys != {"content", "role"}: - return None - - role = item.get("role", None) - if role not in ("user", "assistant", "system", "developer"): - return None - - if "content" not in item: - return None - - return cast(EasyInputMessageParam, item) - - @classmethod - def maybe_input_message(cls, item: Any) -> Message | None: - if ( - isinstance(item, dict) - and item.get("type") == "message" - and item.get("role") - in ( - "user", - "system", - "developer", - ) - ): - return cast(Message, item) - - return None - - @classmethod - def maybe_file_search_call(cls, item: Any) -> ResponseFileSearchToolCallParam | None: - if isinstance(item, dict) and item.get("type") == "file_search_call": - return cast(ResponseFileSearchToolCallParam, item) - return None - - @classmethod - def maybe_function_tool_call(cls, item: Any) -> ResponseFunctionToolCallParam | None: - if isinstance(item, dict) and item.get("type") == "function_call": - return cast(ResponseFunctionToolCallParam, item) - return None - - @classmethod - def maybe_function_tool_call_output( - cls, - item: Any, - ) -> FunctionCallOutput | None: - if isinstance(item, dict) and item.get("type") == "function_call_output": - return cast(FunctionCallOutput, item) - return None - - @classmethod - def maybe_item_reference(cls, item: Any) -> ItemReference | None: - if isinstance(item, dict) and item.get("type") == "item_reference": - return cast(ItemReference, item) - return None - - @classmethod - def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam | None: - # ResponseOutputMessage is only used for messages with role assistant - if ( - isinstance(item, dict) - and item.get("type") == "message" - and item.get("role") == "assistant" - ): - return cast(ResponseOutputMessageParam, item) - return None - - @classmethod - def extract_text_content( - cls, content: str | Iterable[ResponseInputContentParam] - ) -> str | list[ChatCompletionContentPartTextParam]: - all_content = cls.extract_all_content(content) - if isinstance(all_content, str): - return all_content - out: list[ChatCompletionContentPartTextParam] = [] - for c in all_content: - if c.get("type") == "text": - out.append(cast(ChatCompletionContentPartTextParam, c)) - return out - - @classmethod - def extract_all_content( - cls, content: str | Iterable[ResponseInputContentParam] - ) -> str | list[ChatCompletionContentPartParam]: - if isinstance(content, str): - return content - out: list[ChatCompletionContentPartParam] = [] - - for c in content: - if isinstance(c, dict) and c.get("type") == "input_text": - casted_text_param = cast(ResponseInputTextParam, c) - out.append( - ChatCompletionContentPartTextParam( - type="text", - text=casted_text_param["text"], - ) - ) - elif isinstance(c, dict) and c.get("type") == "input_image": - casted_image_param = cast(ResponseInputImageParam, c) - if "image_url" not in casted_image_param or not casted_image_param["image_url"]: - raise UserError( - f"Only image URLs are supported for input_image {casted_image_param}" - ) - out.append( - ChatCompletionContentPartImageParam( - type="image_url", - image_url={ - "url": casted_image_param["image_url"], - "detail": casted_image_param["detail"], - }, - ) - ) - elif isinstance(c, dict) and c.get("type") == "input_file": - raise UserError(f"File uploads are not supported for chat completions {c}") - else: - raise UserError(f"Unknonw content: {c}") - return out - - @classmethod - def items_to_messages( - cls, - items: str | Iterable[TResponseInputItem], - ) -> list[ChatCompletionMessageParam]: - """ - Convert a sequence of 'Item' objects into a list of ChatCompletionMessageParam. - - Rules: - - EasyInputMessage or InputMessage (role=user) => ChatCompletionUserMessageParam - - EasyInputMessage or InputMessage (role=system) => ChatCompletionSystemMessageParam - - EasyInputMessage or InputMessage (role=developer) => ChatCompletionDeveloperMessageParam - - InputMessage (role=assistant) => Start or flush a ChatCompletionAssistantMessageParam - - response_output_message => Also produces/flushes a ChatCompletionAssistantMessageParam - - tool calls get attached to the *current* assistant message, or create one if none. - - tool outputs => ChatCompletionToolMessageParam - """ - - if isinstance(items, str): - return [ - ChatCompletionUserMessageParam( - role="user", - content=items, - ) - ] - - result: list[ChatCompletionMessageParam] = [] - current_assistant_msg: ChatCompletionAssistantMessageParam | None = None - - def flush_assistant_message() -> None: - nonlocal current_assistant_msg - if current_assistant_msg is not None: - # The API doesn't support empty arrays for tool_calls - if not current_assistant_msg.get("tool_calls"): - del current_assistant_msg["tool_calls"] - result.append(current_assistant_msg) - current_assistant_msg = None - - def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: - nonlocal current_assistant_msg - if current_assistant_msg is None: - current_assistant_msg = ChatCompletionAssistantMessageParam(role="assistant") - current_assistant_msg["tool_calls"] = [] - return current_assistant_msg - - for item in items: - # 1) Check easy input message - if easy_msg := cls.maybe_easy_input_message(item): - role = easy_msg["role"] - content = easy_msg["content"] - - if role == "user": - flush_assistant_message() - msg_user: ChatCompletionUserMessageParam = { - "role": "user", - "content": cls.extract_all_content(content), - } - result.append(msg_user) - elif role == "system": - flush_assistant_message() - msg_system: ChatCompletionSystemMessageParam = { - "role": "system", - "content": cls.extract_text_content(content), - } - result.append(msg_system) - elif role == "developer": - flush_assistant_message() - msg_developer: ChatCompletionDeveloperMessageParam = { - "role": "developer", - "content": cls.extract_text_content(content), - } - result.append(msg_developer) - else: - raise UserError(f"Unexpected role in easy_input_message: {role}") - - # 2) Check input message - elif in_msg := cls.maybe_input_message(item): - role = in_msg["role"] - content = in_msg["content"] - flush_assistant_message() - - if role == "user": - msg_user = { - "role": "user", - "content": cls.extract_all_content(content), - } - result.append(msg_user) - elif role == "system": - msg_system = { - "role": "system", - "content": cls.extract_text_content(content), - } - result.append(msg_system) - elif role == "developer": - msg_developer = { - "role": "developer", - "content": cls.extract_text_content(content), - } - result.append(msg_developer) - else: - raise UserError(f"Unexpected role in input_message: {role}") - - # 3) response output message => assistant - elif resp_msg := cls.maybe_response_output_message(item): - flush_assistant_message() - new_asst = ChatCompletionAssistantMessageParam(role="assistant") - contents = resp_msg["content"] - - text_segments = [] - for c in contents: - if c["type"] == "output_text": - text_segments.append(c["text"]) - elif c["type"] == "refusal": - new_asst["refusal"] = c["refusal"] - elif c["type"] == "output_audio": - # Can't handle this, b/c chat completions expects an ID which we dont have - raise UserError( - f"Only audio IDs are supported for chat completions, but got: {c}" - ) - else: - raise UserError(f"Unknown content type in ResponseOutputMessage: {c}") - - if text_segments: - combined = "\n".join(text_segments) - new_asst["content"] = combined - - new_asst["tool_calls"] = [] - current_assistant_msg = new_asst - - # 4) function/file-search calls => attach to assistant - elif file_search := cls.maybe_file_search_call(item): - asst = ensure_assistant_message() - tool_calls = list(asst.get("tool_calls", [])) - new_tool_call = ChatCompletionMessageToolCallParam( - id=file_search["id"], - type="function", - function={ - "name": "file_search_call", - "arguments": json.dumps( - { - "queries": file_search.get("queries", []), - "status": file_search.get("status"), - } - ), - }, - ) - tool_calls.append(new_tool_call) - asst["tool_calls"] = tool_calls - - elif func_call := cls.maybe_function_tool_call(item): - asst = ensure_assistant_message() - tool_calls = list(asst.get("tool_calls", [])) - new_tool_call = ChatCompletionMessageToolCallParam( - id=func_call["call_id"], - type="function", - function={ - "name": func_call["name"], - "arguments": func_call["arguments"], - }, - ) - tool_calls.append(new_tool_call) - asst["tool_calls"] = tool_calls - # 5) function call output => tool message - elif func_output := cls.maybe_function_tool_call_output(item): - flush_assistant_message() - msg: ChatCompletionToolMessageParam = { - "role": "tool", - "tool_call_id": func_output["call_id"], - "content": func_output["output"], - } - result.append(msg) - - # 6) item reference => handle or raise - elif item_ref := cls.maybe_item_reference(item): - raise UserError( - f"Encountered an item_reference, which is not supported: {item_ref}" - ) - - # 7) If we haven't recognized it => fail or ignore - else: - raise UserError(f"Unhandled item type or structure: {item}") - - flush_assistant_message() - return result - - -class ToolConverter: - @classmethod - def to_openai(cls, tool: Tool) -> ChatCompletionToolParam: - if isinstance(tool, FunctionTool): - return { - "type": "function", - "function": { - "name": tool.name, - "description": tool.description or "", - "parameters": tool.params_json_schema, - }, - } - - raise UserError( - f"Hosted tools are not supported with the ChatCompletions API. FGot tool type: " - f"{type(tool)}, tool: {tool}" - ) - - @classmethod - def convert_handoff_tool(cls, handoff: Handoff[Any]) -> ChatCompletionToolParam: - return { - "type": "function", - "function": { - "name": handoff.tool_name, - "description": handoff.tool_description, - "parameters": handoff.input_json_schema, - }, - } diff --git a/src/agents/models/openai_provider.py b/src/agents/models/openai_provider.py index 51946638..e7e922ab 100644 --- a/src/agents/models/openai_provider.py +++ b/src/agents/models/openai_provider.py @@ -34,32 +34,58 @@ def __init__( project: str | None = None, use_responses: bool | None = None, ) -> None: + """Create a new OpenAI provider. + + Args: + api_key: The API key to use for the OpenAI client. If not provided, we will use the + default API key. + base_url: The base URL to use for the OpenAI client. If not provided, we will use the + default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + organization: The organization to use for the OpenAI client. + project: The project to use for the OpenAI client. + use_responses: Whether to use the OpenAI responses API. + """ if openai_client is not None: assert api_key is None and base_url is None, ( "Don't provide api_key or base_url if you provide openai_client" ) - self._client = openai_client + self._client: AsyncOpenAI | None = openai_client else: - self._client = _openai_shared.get_default_openai_client() or AsyncOpenAI( - api_key=api_key or _openai_shared.get_default_openai_key(), - base_url=base_url, - organization=organization, - project=project, - http_client=shared_http_client(), - ) + self._client = None + self._stored_api_key = api_key + self._stored_base_url = base_url + self._stored_organization = organization + self._stored_project = project - self._is_openai_model = self._client.base_url.host.startswith("api.openai.com") if use_responses is not None: self._use_responses = use_responses else: self._use_responses = _openai_shared.get_use_responses_by_default() + # We lazy load the client in case you never actually use OpenAIProvider(). Otherwise + # AsyncOpenAI() raises an error if you don't have an API key set. + def _get_client(self) -> AsyncOpenAI: + if self._client is None: + self._client = _openai_shared.get_default_openai_client() or AsyncOpenAI( + api_key=self._stored_api_key or _openai_shared.get_default_openai_key(), + base_url=self._stored_base_url, + organization=self._stored_organization, + project=self._stored_project, + http_client=shared_http_client(), + ) + + return self._client + def get_model(self, model_name: str | None) -> Model: if model_name is None: model_name = DEFAULT_MODEL + client = self._get_client() + return ( - OpenAIResponsesModel(model=model_name, openai_client=self._client) + OpenAIResponsesModel(model=model_name, openai_client=client) if self._use_responses - else OpenAIChatCompletionsModel(model=model_name, openai_client=self._client) + else OpenAIChatCompletionsModel(model=model_name, openai_client=client) ) diff --git a/src/agents/models/openai_responses.py b/src/agents/models/openai_responses.py index a10d7b98..86c8e69c 100644 --- a/src/agents/models/openai_responses.py +++ b/src/agents/models/openai_responses.py @@ -5,11 +5,12 @@ from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Literal, overload -from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream, NotGiven +from openai import NOT_GIVEN, APIStatusError, AsyncOpenAI, AsyncStream, NotGiven from openai.types import ChatModel from openai.types.responses import ( Response, ResponseCompletedEvent, + ResponseIncludable, ResponseStreamEvent, ResponseTextConfigParam, ToolParam, @@ -18,12 +19,22 @@ ) from .. import _debug -from ..agent_output import AgentOutputSchema +from ..agent_output import AgentOutputSchemaBase from ..exceptions import UserError from ..handoffs import Handoff from ..items import ItemHelpers, ModelResponse, TResponseInputItem from ..logger import logger -from ..tool import ComputerTool, FileSearchTool, FunctionTool, Tool, WebSearchTool +from ..tool import ( + CodeInterpreterTool, + ComputerTool, + FileSearchTool, + FunctionTool, + HostedMCPTool, + ImageGenerationTool, + LocalShellTool, + Tool, + WebSearchTool, +) from ..tracing import SpanError, response_span from ..usage import Usage from ..version import __version__ @@ -36,13 +47,6 @@ _USER_AGENT = f"Agents/Python {__version__}" _HEADERS = {"User-Agent": _USER_AGENT} -# From the Responses API -IncludeLiteral = Literal[ - "file_search_call.results", - "message.input_image.image_url", - "computer_call_output.output.image_url", -] - class OpenAIResponsesModel(Model): """ @@ -66,9 +70,10 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, + previous_response_id: str | None, ) -> ModelResponse: with response_span(disabled=tracing.is_disabled()) as span_response: try: @@ -79,11 +84,12 @@ async def get_response( tools, output_schema, handoffs, + previous_response_id, stream=False, ) if _debug.DONT_LOG_MODEL_DATA: - logger.debug("LLM responsed") + logger.debug("LLM responded") else: logger.debug( "LLM resp:\n" @@ -96,6 +102,8 @@ async def get_response( input_tokens=response.usage.input_tokens, output_tokens=response.usage.output_tokens, total_tokens=response.usage.total_tokens, + input_tokens_details=response.usage.input_tokens_details, + output_tokens_details=response.usage.output_tokens_details, ) if response.usage else Usage() @@ -113,13 +121,14 @@ async def get_response( }, ) ) - logger.error(f"Error getting response: {e}") + request_id = e.request_id if isinstance(e, APIStatusError) else None + logger.error(f"Error getting response: {e}. (request_id: {request_id})") raise return ModelResponse( output=response.output, usage=usage, - referenceable_id=response.id, + response_id=response.id, ) async def stream_response( @@ -128,9 +137,10 @@ async def stream_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, + previous_response_id: str | None, ) -> AsyncIterator[ResponseStreamEvent]: """ Yields a partial message as it is generated, as well as the usage information. @@ -144,6 +154,7 @@ async def stream_response( tools, output_schema, handoffs, + previous_response_id, stream=True, ) @@ -177,8 +188,9 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], + previous_response_id: str | None, stream: Literal[True], ) -> AsyncStream[ResponseStreamEvent]: ... @@ -189,8 +201,9 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], + previous_response_id: str | None, stream: Literal[False], ) -> Response: ... @@ -200,14 +213,19 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], + previous_response_id: str | None, stream: Literal[True] | Literal[False] = False, ) -> Response | AsyncStream[ResponseStreamEvent]: list_input = ItemHelpers.input_to_new_input_list(input) parallel_tool_calls = ( - True if model_settings.parallel_tool_calls and tools and len(tools) > 0 else NOT_GIVEN + True + if model_settings.parallel_tool_calls and tools and len(tools) > 0 + else False + if model_settings.parallel_tool_calls is False + else NOT_GIVEN ) tool_choice = Converter.convert_tool_choice(model_settings.tool_choice) @@ -224,9 +242,11 @@ async def _fetch_response( f"Stream: {stream}\n" f"Tool choice: {tool_choice}\n" f"Response format: {response_format}\n" + f"Previous response id: {previous_response_id}\n" ) return await self._client.responses.create( + previous_response_id=self._non_null_or_not_given(previous_response_id), instructions=self._non_null_or_not_given(system_instructions), model=self.model, input=list_input, @@ -235,11 +255,17 @@ async def _fetch_response( temperature=self._non_null_or_not_given(model_settings.temperature), top_p=self._non_null_or_not_given(model_settings.top_p), truncation=self._non_null_or_not_given(model_settings.truncation), + max_output_tokens=self._non_null_or_not_given(model_settings.max_tokens), tool_choice=tool_choice, parallel_tool_calls=parallel_tool_calls, stream=stream, - extra_headers=_HEADERS, + extra_headers={**_HEADERS, **(model_settings.extra_headers or {})}, + extra_query=model_settings.extra_query, + extra_body=model_settings.extra_body, text=response_format, + store=self._non_null_or_not_given(model_settings.store), + reasoning=self._non_null_or_not_given(model_settings.reasoning), + metadata=self._non_null_or_not_given(model_settings.metadata), ) def _get_client(self) -> AsyncOpenAI: @@ -251,7 +277,7 @@ def _get_client(self) -> AsyncOpenAI: @dataclass class ConvertedTools: tools: list[ToolParam] - includes: list[IncludeLiteral] + includes: list[ResponseIncludable] class Converter: @@ -279,6 +305,18 @@ def convert_tool_choice( return { "type": "computer_use_preview", } + elif tool_choice == "image_generation": + return { + "type": "image_generation", + } + elif tool_choice == "code_interpreter": + return { + "type": "code_interpreter", + } + elif tool_choice == "mcp": + return { + "type": "mcp", + } else: return { "type": "function", @@ -287,7 +325,7 @@ def convert_tool_choice( @classmethod def get_response_format( - cls, output_schema: AgentOutputSchema | None + cls, output_schema: AgentOutputSchemaBase | None ) -> ResponseTextConfigParam | NotGiven: if output_schema is None or output_schema.is_plain_text(): return NOT_GIVEN @@ -297,7 +335,7 @@ def get_response_format( "type": "json_schema", "name": "final_output", "schema": output_schema.json_schema(), - "strict": output_schema.strict_json_schema, + "strict": output_schema.is_strict_json_schema(), } } @@ -308,7 +346,7 @@ def convert_tools( handoffs: list[Handoff[Any]], ) -> ConvertedTools: converted_tools: list[ToolParam] = [] - includes: list[IncludeLiteral] = [] + includes: list[ResponseIncludable] = [] computer_tools = [tool for tool in tools if isinstance(tool, ComputerTool)] if len(computer_tools) > 1: @@ -326,7 +364,7 @@ def convert_tools( return ConvertedTools(tools=converted_tools, includes=includes) @classmethod - def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, IncludeLiteral | None]: + def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, ResponseIncludable | None]: """Returns converted tool and includes""" if isinstance(tool, FunctionTool): @@ -337,7 +375,7 @@ def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, IncludeLiteral | None]: "type": "function", "description": tool.description, } - includes: IncludeLiteral | None = None + includes: ResponseIncludable | None = None elif isinstance(tool, WebSearchTool): ws: WebSearchToolParam = { "type": "web_search_preview", @@ -361,13 +399,26 @@ def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, IncludeLiteral | None]: includes = "file_search_call.results" if tool.include_search_results else None elif isinstance(tool, ComputerTool): converted_tool = { - "type": "computer-preview", + "type": "computer_use_preview", "environment": tool.computer.environment, "display_width": tool.computer.dimensions[0], "display_height": tool.computer.dimensions[1], } includes = None - + elif isinstance(tool, HostedMCPTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, ImageGenerationTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, CodeInterpreterTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, LocalShellTool): + converted_tool = { + "type": "local_shell", + } + includes = None else: raise UserError(f"Unknown tool type: {type(tool)}, tool") diff --git a/tests/examples/research_bot/__init__.py b/src/agents/py.typed similarity index 100% rename from tests/examples/research_bot/__init__.py rename to src/agents/py.typed diff --git a/src/agents/result.py b/src/agents/result.py index 56838273..243db155 100644 --- a/src/agents/result.py +++ b/src/agents/result.py @@ -10,13 +10,15 @@ from ._run_impl import QueueCompleteSentinel from .agent import Agent -from .agent_output import AgentOutputSchema +from .agent_output import AgentOutputSchemaBase from .exceptions import InputGuardrailTripwireTriggered, MaxTurnsExceeded from .guardrail import InputGuardrailResult, OutputGuardrailResult from .items import ItemHelpers, ModelResponse, RunItem, TResponseInputItem from .logger import logger +from .run_context import RunContextWrapper from .stream_events import StreamEvent from .tracing import Trace +from .util._pretty_print import pretty_print_result, pretty_print_run_result_streaming if TYPE_CHECKING: from ._run_impl import QueueCompleteSentinel @@ -49,6 +51,9 @@ class RunResultBase(abc.ABC): output_guardrail_results: list[OutputGuardrailResult] """Guardrail results for the final output of the agent.""" + context_wrapper: RunContextWrapper[Any] + """The context wrapper for the agent run.""" + @property @abc.abstractmethod def last_agent(self) -> Agent[Any]: @@ -79,6 +84,14 @@ def to_input_list(self) -> list[TResponseInputItem]: return original_items + new_items + @property + def last_response_id(self) -> str | None: + """Convenience method to get the response ID of the last model response.""" + if not self.raw_responses: + return None + + return self.raw_responses[-1].response_id + @dataclass class RunResult(RunResultBase): @@ -89,6 +102,9 @@ def last_agent(self) -> Agent[Any]: """The last agent that was run.""" return self._last_agent + def __str__(self) -> str: + return pretty_print_result(self) + @dataclass class RunResultStreaming(RunResultBase): @@ -112,9 +128,9 @@ class RunResultStreaming(RunResultBase): final_output: Any """The final output of the agent. This is None until the agent has finished running.""" - _current_agent_output_schema: AgentOutputSchema | None = field(repr=False) + _current_agent_output_schema: AgentOutputSchemaBase | None = field(repr=False) - _trace: Trace | None = field(repr=False) + trace: Trace | None = field(repr=False) is_complete: bool = False """Whether the agent has finished running.""" @@ -140,6 +156,18 @@ def last_agent(self) -> Agent[Any]: """ return self.current_agent + def cancel(self) -> None: + """Cancels the streaming run, stopping all background tasks and marking the run as + complete.""" + self._cleanup_tasks() # Cancel all running tasks + self.is_complete = True # Mark the run as complete to stop event streaming + + # Optionally, clear the event queue to prevent processing stale events + while not self._event_queue.empty(): + self._event_queue.get_nowait() + while not self._input_guardrail_queue.empty(): + self._input_guardrail_queue.get_nowait() + async def stream_events(self) -> AsyncIterator[StreamEvent]: """Stream deltas for new items as they are generated. We're using the types from the OpenAI Responses API, so these are semantic events: each event has a `type` field that @@ -173,9 +201,6 @@ async def stream_events(self) -> AsyncIterator[StreamEvent]: yield item self._event_queue.task_done() - if self._trace: - self._trace.finish(reset_current=True) - self._cleanup_tasks() if self._stored_exception: @@ -216,5 +241,6 @@ def _cleanup_tasks(self): if self._output_guardrails_task and not self._output_guardrails_task.done(): self._output_guardrails_task.cancel() - self._output_guardrails_task.cancel() - self._output_guardrails_task.cancel() + + def __str__(self) -> str: + return pretty_print_run_result_streaming(self) diff --git a/src/agents/run.py b/src/agents/run.py index dfff7e38..b196c3bf 100644 --- a/src/agents/run.py +++ b/src/agents/run.py @@ -7,8 +7,8 @@ from openai.types.responses import ResponseCompletedEvent -from . import Model, _utils from ._run_impl import ( + AgentToolUseTracker, NextStepFinalOutput, NextStepHandoff, NextStepRunAgain, @@ -19,7 +19,7 @@ get_model_tracing_impl, ) from .agent import Agent -from .agent_output import AgentOutputSchema +from .agent_output import AgentOutputSchema, AgentOutputSchemaBase from .exceptions import ( AgentsException, InputGuardrailTripwireTriggered, @@ -33,14 +33,16 @@ from .lifecycle import RunHooks from .logger import logger from .model_settings import ModelSettings -from .models.interface import ModelProvider -from .models.openai_provider import OpenAIProvider +from .models.interface import Model, ModelProvider +from .models.multi_provider import MultiProvider from .result import RunResult, RunResultStreaming from .run_context import RunContextWrapper, TContext from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent +from .tool import Tool from .tracing import Span, SpanError, agent_span, get_current_trace, trace from .tracing.span_data import AgentSpanData from .usage import Usage +from .util import _coro, _error_tracing DEFAULT_MAX_TURNS = 10 @@ -54,7 +56,7 @@ class RunConfig: agent. The model_provider passed in below must be able to resolve this model name. """ - model_provider: ModelProvider = field(default_factory=OpenAIProvider) + model_provider: ModelProvider = field(default_factory=MultiProvider) """The model provider to use when looking up string model names. Defaults to OpenAI.""" model_settings: ModelSettings | None = None @@ -115,6 +117,7 @@ async def run( max_turns: int = DEFAULT_MAX_TURNS, hooks: RunHooks[TContext] | None = None, run_config: RunConfig | None = None, + previous_response_id: str | None = None, ) -> RunResult: """Run a workflow starting at the given agent. The agent will run in a loop until a final output is generated. The loop runs like so: @@ -139,6 +142,8 @@ async def run( AI invocation (including any tool calls that might occur). hooks: An object that receives callbacks on various lifecycle events. run_config: Global settings for the entire agent run. + previous_response_id: The ID of the previous response, if using OpenAI models via the + Responses API, this allows you to skip passing in input from the previous turn. Returns: A run result containing all the inputs, guardrail results and the output of the last @@ -149,6 +154,8 @@ async def run( if run_config is None: run_config = RunConfig() + tool_use_tracker = AgentToolUseTracker() + with TraceCtxManager( workflow_name=run_config.workflow_name, trace_id=run_config.trace_id, @@ -177,23 +184,24 @@ async def run( # agent changes, or if the agent loop ends. if current_span is None: handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)] - tool_names = [t.name for t in current_agent.tools] if output_schema := cls._get_output_schema(current_agent): - output_type_name = output_schema.output_type_name() + output_type_name = output_schema.name() else: output_type_name = "str" current_span = agent_span( name=current_agent.name, handoffs=handoff_names, - tools=tool_names, output_type=output_type_name, ) current_span.start(mark_as_current=True) + all_tools = await cls._get_all_tools(current_agent) + current_span.span_data.tools = [t.name for t in all_tools] + current_turn += 1 if current_turn > max_turns: - _utils.attach_error_to_span( + _error_tracing.attach_error_to_span( current_span, SpanError( message="Max turns exceeded", @@ -217,23 +225,29 @@ async def run( ), cls._run_single_turn( agent=current_agent, + all_tools=all_tools, original_input=original_input, generated_items=generated_items, hooks=hooks, context_wrapper=context_wrapper, run_config=run_config, should_run_agent_start_hooks=should_run_agent_start_hooks, + tool_use_tracker=tool_use_tracker, + previous_response_id=previous_response_id, ), ) else: turn_result = await cls._run_single_turn( agent=current_agent, + all_tools=all_tools, original_input=original_input, generated_items=generated_items, hooks=hooks, context_wrapper=context_wrapper, run_config=run_config, should_run_agent_start_hooks=should_run_agent_start_hooks, + tool_use_tracker=tool_use_tracker, + previous_response_id=previous_response_id, ) should_run_agent_start_hooks = False @@ -256,6 +270,7 @@ async def run( _last_agent=current_agent, input_guardrail_results=input_guardrail_results, output_guardrail_results=output_guardrail_results, + context_wrapper=context_wrapper, ) elif isinstance(turn_result.next_step, NextStepHandoff): current_agent = cast(Agent[TContext], turn_result.next_step.new_agent) @@ -282,6 +297,7 @@ def run_sync( max_turns: int = DEFAULT_MAX_TURNS, hooks: RunHooks[TContext] | None = None, run_config: RunConfig | None = None, + previous_response_id: str | None = None, ) -> RunResult: """Run a workflow synchronously, starting at the given agent. Note that this just wraps the `run` method, so it will not work if there's already an event loop (e.g. inside an async @@ -310,6 +326,8 @@ def run_sync( AI invocation (including any tool calls that might occur). hooks: An object that receives callbacks on various lifecycle events. run_config: Global settings for the entire agent run. + previous_response_id: The ID of the previous response, if using OpenAI models via the + Responses API, this allows you to skip passing in input from the previous turn. Returns: A run result containing all the inputs, guardrail results and the output of the last @@ -323,6 +341,7 @@ def run_sync( max_turns=max_turns, hooks=hooks, run_config=run_config, + previous_response_id=previous_response_id, ) ) @@ -335,6 +354,7 @@ def run_streamed( max_turns: int = DEFAULT_MAX_TURNS, hooks: RunHooks[TContext] | None = None, run_config: RunConfig | None = None, + previous_response_id: str | None = None, ) -> RunResultStreaming: """Run a workflow starting at the given agent in streaming mode. The returned result object contains a method you can use to stream semantic events as they are generated. @@ -361,7 +381,8 @@ def run_streamed( AI invocation (including any tool calls that might occur). hooks: An object that receives callbacks on various lifecycle events. run_config: Global settings for the entire agent run. - + previous_response_id: The ID of the previous response, if using OpenAI models via the + Responses API, this allows you to skip passing in input from the previous turn. Returns: A result object that contains data about the run, as well as a method to stream events. """ @@ -384,10 +405,6 @@ def run_streamed( disabled=run_config.tracing_disabled, ) ) - # Need to start the trace here, because the current trace contextvar is captured at - # asyncio.create_task time - if new_trace: - new_trace.start(mark_as_current=True) output_schema = cls._get_output_schema(starting_agent) context_wrapper: RunContextWrapper[TContext] = RunContextWrapper( @@ -406,7 +423,8 @@ def run_streamed( input_guardrail_results=[], output_guardrail_results=[], _current_agent_output_schema=output_schema, - _trace=new_trace, + trace=new_trace, + context_wrapper=context_wrapper, ) # Kick off the actual agent loop in the background and return the streamed result object. @@ -419,6 +437,7 @@ def run_streamed( hooks=hooks, context_wrapper=context_wrapper, run_config=run_config, + previous_response_id=previous_response_id, ) ) return streamed_result @@ -447,7 +466,7 @@ async def _run_input_guardrails_with_queue( for done in asyncio.as_completed(guardrail_tasks): result = await done if result.output.tripwire_triggered: - _utils.attach_error_to_span( + _error_tracing.attach_error_to_span( parent_span, SpanError( message="Guardrail tripwire triggered", @@ -476,11 +495,16 @@ async def _run_streamed_impl( hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, + previous_response_id: str | None, ): + if streamed_result.trace: + streamed_result.trace.start(mark_as_current=True) + current_span: Span[AgentSpanData] | None = None current_agent = starting_agent current_turn = 0 should_run_agent_start_hooks = True + tool_use_tracker = AgentToolUseTracker() streamed_result._event_queue.put_nowait(AgentUpdatedStreamEvent(new_agent=current_agent)) @@ -493,25 +517,26 @@ async def _run_streamed_impl( # agent changes, or if the agent loop ends. if current_span is None: handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)] - tool_names = [t.name for t in current_agent.tools] if output_schema := cls._get_output_schema(current_agent): - output_type_name = output_schema.output_type_name() + output_type_name = output_schema.name() else: output_type_name = "str" current_span = agent_span( name=current_agent.name, handoffs=handoff_names, - tools=tool_names, output_type=output_type_name, ) current_span.start(mark_as_current=True) + all_tools = await cls._get_all_tools(current_agent) + tool_names = [t.name for t in all_tools] + current_span.span_data.tools = tool_names current_turn += 1 streamed_result.current_turn = current_turn if current_turn > max_turns: - _utils.attach_error_to_span( + _error_tracing.attach_error_to_span( current_span, SpanError( message="Max turns exceeded", @@ -541,6 +566,9 @@ async def _run_streamed_impl( context_wrapper, run_config, should_run_agent_start_hooks, + tool_use_tracker, + all_tools, + previous_response_id, ) should_run_agent_start_hooks = False @@ -583,7 +611,7 @@ async def _run_streamed_impl( pass except Exception as e: if current_span: - _utils.attach_error_to_span( + _error_tracing.attach_error_to_span( current_span, SpanError( message="Error in agent run", @@ -598,6 +626,8 @@ async def _run_streamed_impl( finally: if current_span: current_span.finish(reset_current=True) + if streamed_result.trace: + streamed_result.trace.finish(reset_current=True) @classmethod async def _run_single_turn_streamed( @@ -608,6 +638,9 @@ async def _run_single_turn_streamed( context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, should_run_agent_start_hooks: bool, + tool_use_tracker: AgentToolUseTracker, + all_tools: list[Tool], + previous_response_id: str | None, ) -> SingleStepResult: if should_run_agent_start_hooks: await asyncio.gather( @@ -615,7 +648,7 @@ async def _run_single_turn_streamed( ( agent.hooks.on_start(context_wrapper, agent) if agent.hooks - else _utils.noop_coroutine() + else _coro.noop_coroutine() ), ) @@ -627,9 +660,10 @@ async def _run_single_turn_streamed( system_prompt = await agent.get_system_prompt(context_wrapper) handoffs = cls._get_handoffs(agent) - model = cls._get_model(agent, run_config) model_settings = agent.model_settings.resolve(run_config.model_settings) + model_settings = RunImpl.maybe_reset_tool_choice(agent, tool_use_tracker, model_settings) + final_response: ModelResponse | None = None input = ItemHelpers.input_to_new_input_list(streamed_result.input) @@ -640,12 +674,13 @@ async def _run_single_turn_streamed( system_prompt, input, model_settings, - agent.tools, + all_tools, output_schema, handoffs, get_model_tracing_impl( run_config.tracing_disabled, run_config.trace_include_sensitive_data ), + previous_response_id=previous_response_id, ): if isinstance(event, ResponseCompletedEvent): usage = ( @@ -654,6 +689,8 @@ async def _run_single_turn_streamed( input_tokens=event.response.usage.input_tokens, output_tokens=event.response.usage.output_tokens, total_tokens=event.response.usage.total_tokens, + input_tokens_details=event.response.usage.input_tokens_details, + output_tokens_details=event.response.usage.output_tokens_details, ) if event.response.usage else Usage() @@ -661,8 +698,9 @@ async def _run_single_turn_streamed( final_response = ModelResponse( output=event.response.output, usage=usage, - referenceable_id=event.response.id, + response_id=event.response.id, ) + context_wrapper.usage.add(usage) streamed_result._event_queue.put_nowait(RawResponsesStreamEvent(data=event)) @@ -677,10 +715,12 @@ async def _run_single_turn_streamed( pre_step_items=streamed_result.new_items, new_response=final_response, output_schema=output_schema, + all_tools=all_tools, handoffs=handoffs, hooks=hooks, context_wrapper=context_wrapper, run_config=run_config, + tool_use_tracker=tool_use_tracker, ) RunImpl.stream_step_result_to_queue(single_step_result, streamed_result._event_queue) @@ -691,12 +731,15 @@ async def _run_single_turn( cls, *, agent: Agent[TContext], + all_tools: list[Tool], original_input: str | list[TResponseInputItem], generated_items: list[RunItem], hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, should_run_agent_start_hooks: bool, + tool_use_tracker: AgentToolUseTracker, + previous_response_id: str | None, ) -> SingleStepResult: # Ensure we run the hooks before anything else if should_run_agent_start_hooks: @@ -705,7 +748,7 @@ async def _run_single_turn( ( agent.hooks.on_start(context_wrapper, agent) if agent.hooks - else _utils.noop_coroutine() + else _coro.noop_coroutine() ), ) @@ -721,9 +764,12 @@ async def _run_single_turn( system_prompt, input, output_schema, + all_tools, handoffs, context_wrapper, run_config, + tool_use_tracker, + previous_response_id, ) return await cls._get_single_step_result_from_response( @@ -732,10 +778,12 @@ async def _run_single_turn( pre_step_items=generated_items, new_response=new_response, output_schema=output_schema, + all_tools=all_tools, handoffs=handoffs, hooks=hooks, context_wrapper=context_wrapper, run_config=run_config, + tool_use_tracker=tool_use_tracker, ) @classmethod @@ -743,21 +791,27 @@ async def _get_single_step_result_from_response( cls, *, agent: Agent[TContext], + all_tools: list[Tool], original_input: str | list[TResponseInputItem], pre_step_items: list[RunItem], new_response: ModelResponse, - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, + tool_use_tracker: AgentToolUseTracker, ) -> SingleStepResult: processed_response = RunImpl.process_model_response( agent=agent, + all_tools=all_tools, response=new_response, output_schema=output_schema, handoffs=handoffs, ) + + tool_use_tracker.add_tool_use(agent, processed_response.tools_used) + return await RunImpl.execute_tools_and_side_effects( agent=agent, original_input=original_input, @@ -796,7 +850,7 @@ async def _run_input_guardrails( # Cancel all guardrail tasks if a tripwire is triggered. for t in guardrail_tasks: t.cancel() - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Guardrail tripwire triggered", data={"guardrail": result.guardrail.get_name()}, @@ -834,7 +888,7 @@ async def _run_output_guardrails( # Cancel all guardrail tasks if a tripwire is triggered. for t in guardrail_tasks: t.cancel() - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Guardrail tripwire triggered", data={"guardrail": result.guardrail.get_name()}, @@ -852,23 +906,29 @@ async def _get_new_response( agent: Agent[TContext], system_prompt: str | None, input: list[TResponseInputItem], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, + all_tools: list[Tool], handoffs: list[Handoff], context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, + tool_use_tracker: AgentToolUseTracker, + previous_response_id: str | None, ) -> ModelResponse: model = cls._get_model(agent, run_config) model_settings = agent.model_settings.resolve(run_config.model_settings) + model_settings = RunImpl.maybe_reset_tool_choice(agent, tool_use_tracker, model_settings) + new_response = await model.get_response( system_instructions=system_prompt, input=input, model_settings=model_settings, - tools=agent.tools, + tools=all_tools, output_schema=output_schema, handoffs=handoffs, tracing=get_model_tracing_impl( run_config.tracing_disabled, run_config.trace_include_sensitive_data ), + previous_response_id=previous_response_id, ) context_wrapper.usage.add(new_response.usage) @@ -876,9 +936,11 @@ async def _get_new_response( return new_response @classmethod - def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchema | None: + def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchemaBase | None: if agent.output_type is None or agent.output_type is str: return None + elif isinstance(agent.output_type, AgentOutputSchemaBase): + return agent.output_type return AgentOutputSchema(agent.output_type) @@ -892,6 +954,10 @@ def _get_handoffs(cls, agent: Agent[Any]) -> list[Handoff]: handoffs.append(handoff(handoff_item)) return handoffs + @classmethod + async def _get_all_tools(cls, agent: Agent[Any]) -> list[Tool]: + return await agent.get_all_tools() + @classmethod def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model: if isinstance(run_config.model, Model): diff --git a/src/agents/stream_events.py b/src/agents/stream_events.py index bd37d11f..111d0b95 100644 --- a/src/agents/stream_events.py +++ b/src/agents/stream_events.py @@ -35,6 +35,8 @@ class RunItemStreamEvent: "tool_called", "tool_output", "reasoning_item_created", + "mcp_approval_requested", + "mcp_list_tools", ] """The name of the event.""" diff --git a/src/agents/strict_schema.py b/src/agents/strict_schema.py index 910ad85f..3f37660a 100644 --- a/src/agents/strict_schema.py +++ b/src/agents/strict_schema.py @@ -54,7 +54,7 @@ def _ensure_strict_json_schema( elif ( typ == "object" and "additionalProperties" in json_schema - and json_schema["additionalProperties"] is True + and json_schema["additionalProperties"] ): raise UserError( "additionalProperties should not be set for object types. This could be because " diff --git a/src/agents/tool.py b/src/agents/tool.py index 75872680..fd5a21c8 100644 --- a/src/agents/tool.py +++ b/src/agents/tool.py @@ -7,18 +7,22 @@ from typing import Any, Callable, Literal, Union, overload from openai.types.responses.file_search_tool_param import Filters, RankingOptions +from openai.types.responses.response_output_item import LocalShellCall, McpApprovalRequest +from openai.types.responses.tool_param import CodeInterpreter, ImageGeneration, Mcp from openai.types.responses.web_search_tool_param import UserLocation from pydantic import ValidationError -from typing_extensions import Concatenate, ParamSpec +from typing_extensions import Concatenate, NotRequired, ParamSpec, TypedDict -from . import _debug, _utils -from ._utils import MaybeAwaitable +from . import _debug from .computer import AsyncComputer, Computer from .exceptions import ModelBehaviorError from .function_schema import DocstringStyle, function_schema +from .items import RunItem from .logger import logger from .run_context import RunContextWrapper from .tracing import SpanError +from .util import _error_tracing +from .util._types import MaybeAwaitable ToolParams = ParamSpec("ToolParams") @@ -28,6 +32,18 @@ ToolFunction = Union[ToolFunctionWithoutContext[ToolParams], ToolFunctionWithContext[ToolParams]] +@dataclass +class FunctionToolResult: + tool: FunctionTool + """The tool that was run.""" + + output: Any + """The output of the tool.""" + + run_item: RunItem + """The run item that was produced as a result of the tool call.""" + + @dataclass class FunctionTool: """A tool that wraps a function. In most cases, you should use the `function_tool` helpers to @@ -43,15 +59,15 @@ class FunctionTool: params_json_schema: dict[str, Any] """The JSON schema for the tool's parameters.""" - on_invoke_tool: Callable[[RunContextWrapper[Any], str], Awaitable[str]] + on_invoke_tool: Callable[[RunContextWrapper[Any], str], Awaitable[Any]] """A function that invokes the tool with the given context and parameters. The params passed are: 1. The tool run context. 2. The arguments from the LLM, as a JSON string. - You must return a string representation of the tool output. In case of errors, you can either - raise an Exception (which will cause the run to fail) or return a string error message (which - will be sent back to the LLM). + You must return a string representation of the tool output, or something we can call `str()` on. + In case of errors, you can either raise an Exception (which will cause the run to fail) or + return a string error message (which will be sent back to the LLM). """ strict_json_schema: bool = True @@ -116,7 +132,115 @@ def name(self): return "computer_use_preview" -Tool = Union[FunctionTool, FileSearchTool, WebSearchTool, ComputerTool] +@dataclass +class MCPToolApprovalRequest: + """A request to approve a tool call.""" + + ctx_wrapper: RunContextWrapper[Any] + """The run context.""" + + data: McpApprovalRequest + """The data from the MCP tool approval request.""" + + +class MCPToolApprovalFunctionResult(TypedDict): + """The result of an MCP tool approval function.""" + + approve: bool + """Whether to approve the tool call.""" + + reason: NotRequired[str] + """An optional reason, if rejected.""" + + +MCPToolApprovalFunction = Callable[ + [MCPToolApprovalRequest], MaybeAwaitable[MCPToolApprovalFunctionResult] +] +"""A function that approves or rejects a tool call.""" + + +@dataclass +class HostedMCPTool: + """A tool that allows the LLM to use a remote MCP server. The LLM will automatically list and + call tools, without requiring a a round trip back to your code. + If you want to run MCP servers locally via stdio, in a VPC or other non-publicly-accessible + environment, or you just prefer to run tool calls locally, then you can instead use the servers + in `agents.mcp` and pass `Agent(mcp_servers=[...])` to the agent.""" + + tool_config: Mcp + """The MCP tool config, which includes the server URL and other settings.""" + + on_approval_request: MCPToolApprovalFunction | None = None + """An optional function that will be called if approval is requested for an MCP tool. If not + provided, you will need to manually add approvals/rejections to the input and call + `Runner.run(...)` again.""" + + @property + def name(self): + return "hosted_mcp" + + +@dataclass +class CodeInterpreterTool: + """A tool that allows the LLM to execute code in a sandboxed environment.""" + + tool_config: CodeInterpreter + """The tool config, which includes the container and other settings.""" + + @property + def name(self): + return "code_interpreter" + + +@dataclass +class ImageGenerationTool: + """A tool that allows the LLM to generate images.""" + + tool_config: ImageGeneration + """The tool config, which image generation settings.""" + + @property + def name(self): + return "image_generation" + + +@dataclass +class LocalShellCommandRequest: + """A request to execute a command on a shell.""" + + ctx_wrapper: RunContextWrapper[Any] + """The run context.""" + + data: LocalShellCall + """The data from the local shell tool call.""" + + +LocalShellExecutor = Callable[[LocalShellCommandRequest], MaybeAwaitable[str]] +"""A function that executes a command on a shell.""" + + +@dataclass +class LocalShellTool: + """A tool that allows the LLM to execute commands on a shell.""" + + executor: LocalShellExecutor + """A function that executes a command on a shell.""" + + @property + def name(self): + return "local_shell" + + +Tool = Union[ + FunctionTool, + FileSearchTool, + WebSearchTool, + ComputerTool, + HostedMCPTool, + LocalShellTool, + ImageGenerationTool, + CodeInterpreterTool, +] """A tool that can be used in an agent.""" @@ -137,6 +261,7 @@ def function_tool( docstring_style: DocstringStyle | None = None, use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = None, + strict_mode: bool = True, ) -> FunctionTool: """Overload for usage as @function_tool (no parentheses).""" ... @@ -150,6 +275,7 @@ def function_tool( docstring_style: DocstringStyle | None = None, use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = None, + strict_mode: bool = True, ) -> Callable[[ToolFunction[...]], FunctionTool]: """Overload for usage as @function_tool(...).""" ... @@ -163,6 +289,7 @@ def function_tool( docstring_style: DocstringStyle | None = None, use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = default_tool_error_function, + strict_mode: bool = True, ) -> FunctionTool | Callable[[ToolFunction[...]], FunctionTool]: """ Decorator to create a FunctionTool from a function. By default, we will: @@ -186,6 +313,11 @@ def function_tool( failure_error_function: If provided, use this function to generate an error message when the tool call fails. The error message is sent to the LLM. If you pass None, then no error message will be sent and instead an Exception will be raised. + strict_mode: Whether to enable strict mode for the tool's JSON schema. We *strongly* + recommend setting this to True, as it increases the likelihood of correct JSON input. + If False, it allows non-strict JSON schemas. For example, if a parameter has a default + value, it will be optional, additional properties are allowed, etc. See here for more: + https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#supported-schemas """ def _create_function_tool(the_func: ToolFunction[...]) -> FunctionTool: @@ -195,9 +327,10 @@ def _create_function_tool(the_func: ToolFunction[...]) -> FunctionTool: description_override=description_override, docstring_style=docstring_style, use_docstring_info=use_docstring_info, + strict_json_schema=strict_mode, ) - async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> str: + async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> Any: try: json_data: dict[str, Any] = json.loads(input) if input else {} except Exception as e: @@ -244,9 +377,9 @@ async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> str: else: logger.debug(f"Tool {schema.name} returned {result}") - return str(result) + return result - async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> str: + async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> Any: try: return await _on_invoke_tool_impl(ctx, input) except Exception as e: @@ -257,7 +390,7 @@ async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> str: if inspect.isawaitable(result): return await result - _utils.attach_error_to_current_span( + _error_tracing.attach_error_to_current_span( SpanError( message="Error running tool (non-fatal)", data={ @@ -273,6 +406,7 @@ async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> str: description=schema.description or "", params_json_schema=schema.params_json_schema, on_invoke_tool=_on_invoke_tool, + strict_json_schema=strict_mode, ) # If func is actually a callable, we were used as @function_tool with no parentheses diff --git a/src/agents/tracing/__init__.py b/src/agents/tracing/__init__.py index 8e802018..9df94426 100644 --- a/src/agents/tracing/__init__.py +++ b/src/agents/tracing/__init__.py @@ -9,8 +9,12 @@ get_current_trace, guardrail_span, handoff_span, + mcp_tools_span, response_span, + speech_group_span, + speech_span, trace, + transcription_span, ) from .processor_interface import TracingProcessor from .processors import default_exporter, default_processor @@ -22,8 +26,12 @@ GenerationSpanData, GuardrailSpanData, HandoffSpanData, + MCPListToolsSpanData, ResponseSpanData, SpanData, + SpeechGroupSpanData, + SpeechSpanData, + TranscriptionSpanData, ) from .spans import Span, SpanError from .traces import Trace @@ -53,10 +61,18 @@ "GenerationSpanData", "GuardrailSpanData", "HandoffSpanData", + "MCPListToolsSpanData", "ResponseSpanData", + "SpeechGroupSpanData", + "SpeechSpanData", + "TranscriptionSpanData", "TracingProcessor", "gen_trace_id", "gen_span_id", + "speech_group_span", + "speech_span", + "transcription_span", + "mcp_tools_span", ] diff --git a/src/agents/tracing/create.py b/src/agents/tracing/create.py index 8d7fc493..b6fe4610 100644 --- a/src/agents/tracing/create.py +++ b/src/agents/tracing/create.py @@ -3,7 +3,7 @@ from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any -from .logger import logger +from ..logger import logger from .setup import GLOBAL_TRACE_PROVIDER from .span_data import ( AgentSpanData, @@ -12,7 +12,11 @@ GenerationSpanData, GuardrailSpanData, HandoffSpanData, + MCPListToolsSpanData, ResponseSpanData, + SpeechGroupSpanData, + SpeechSpanData, + TranscriptionSpanData, ) from .spans import Span from .traces import Trace @@ -181,7 +185,11 @@ def generation_span( """ return GLOBAL_TRACE_PROVIDER.create_span( span_data=GenerationSpanData( - input=input, output=output, model=model, model_config=model_config, usage=usage + input=input, + output=output, + model=model, + model_config=model_config, + usage=usage, ), span_id=span_id, parent=parent, @@ -304,3 +312,144 @@ def guardrail_span( parent=parent, disabled=disabled, ) + + +def transcription_span( + model: str | None = None, + input: str | None = None, + input_format: str | None = "pcm", + output: str | None = None, + model_config: Mapping[str, Any] | None = None, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, +) -> Span[TranscriptionSpanData]: + """Create a new transcription span. The span will not be started automatically, you should + either do `with transcription_span() ...` or call `span.start()` + `span.finish()` manually. + + Args: + model: The name of the model used for the speech-to-text. + input: The audio input of the speech-to-text transcription, as a base64 encoded string of + audio bytes. + input_format: The format of the audio input (defaults to "pcm"). + output: The output of the speech-to-text transcription. + model_config: The model configuration (hyperparameters) used. + span_id: The ID of the span. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are + correctly formatted. + parent: The parent span or trace. If not provided, we will automatically use the current + trace/span as the parent. + disabled: If True, we will return a Span but the Span will not be recorded. + + Returns: + The newly created speech-to-text span. + """ + return GLOBAL_TRACE_PROVIDER.create_span( + span_data=TranscriptionSpanData( + input=input, + input_format=input_format, + output=output, + model=model, + model_config=model_config, + ), + span_id=span_id, + parent=parent, + disabled=disabled, + ) + + +def speech_span( + model: str | None = None, + input: str | None = None, + output: str | None = None, + output_format: str | None = "pcm", + model_config: Mapping[str, Any] | None = None, + first_content_at: str | None = None, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, +) -> Span[SpeechSpanData]: + """Create a new speech span. The span will not be started automatically, you should either do + `with speech_span() ...` or call `span.start()` + `span.finish()` manually. + + Args: + model: The name of the model used for the text-to-speech. + input: The text input of the text-to-speech. + output: The audio output of the text-to-speech as base64 encoded string of PCM audio bytes. + output_format: The format of the audio output (defaults to "pcm"). + model_config: The model configuration (hyperparameters) used. + first_content_at: The time of the first byte of the audio output. + span_id: The ID of the span. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are + correctly formatted. + parent: The parent span or trace. If not provided, we will automatically use the current + trace/span as the parent. + disabled: If True, we will return a Span but the Span will not be recorded. + """ + return GLOBAL_TRACE_PROVIDER.create_span( + span_data=SpeechSpanData( + model=model, + input=input, + output=output, + output_format=output_format, + model_config=model_config, + first_content_at=first_content_at, + ), + span_id=span_id, + parent=parent, + disabled=disabled, + ) + + +def speech_group_span( + input: str | None = None, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, +) -> Span[SpeechGroupSpanData]: + """Create a new speech group span. The span will not be started automatically, you should + either do `with speech_group_span() ...` or call `span.start()` + `span.finish()` manually. + + Args: + input: The input text used for the speech request. + span_id: The ID of the span. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are + correctly formatted. + parent: The parent span or trace. If not provided, we will automatically use the current + trace/span as the parent. + disabled: If True, we will return a Span but the Span will not be recorded. + """ + return GLOBAL_TRACE_PROVIDER.create_span( + span_data=SpeechGroupSpanData(input=input), + span_id=span_id, + parent=parent, + disabled=disabled, + ) + + +def mcp_tools_span( + server: str | None = None, + result: list[str] | None = None, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, +) -> Span[MCPListToolsSpanData]: + """Create a new MCP list tools span. The span will not be started automatically, you should + either do `with mcp_tools_span() ...` or call `span.start()` + `span.finish()` manually. + + Args: + server: The name of the MCP server. + result: The result of the MCP list tools call. + span_id: The ID of the span. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are + correctly formatted. + parent: The parent span or trace. If not provided, we will automatically use the current + trace/span as the parent. + disabled: If True, we will return a Span but the Span will not be recorded. + """ + return GLOBAL_TRACE_PROVIDER.create_span( + span_data=MCPListToolsSpanData(server=server, result=result), + span_id=span_id, + parent=parent, + disabled=disabled, + ) diff --git a/src/agents/tracing/processors.py b/src/agents/tracing/processors.py index 282bc23c..2913b11a 100644 --- a/src/agents/tracing/processors.py +++ b/src/agents/tracing/processors.py @@ -5,11 +5,12 @@ import random import threading import time +from functools import cached_property from typing import Any import httpx -from .logger import logger +from ..logger import logger from .processor_interface import TracingExporter, TracingProcessor from .spans import Span from .traces import Trace @@ -40,7 +41,7 @@ def __init__( """ Args: api_key: The API key for the "Authorization" header. Defaults to - `os.environ["OPENAI_TRACE_API_KEY"]` if not provided. + `os.environ["OPENAI_API_KEY"]` if not provided. organization: The OpenAI organization to use. Defaults to `os.environ["OPENAI_ORG_ID"]` if not provided. project: The OpenAI project to use. Defaults to @@ -50,9 +51,9 @@ def __init__( base_delay: Base delay (in seconds) for the first backoff. max_delay: Maximum delay (in seconds) for backoff growth. """ - self.api_key = api_key or os.environ.get("OPENAI_API_KEY") - self.organization = organization or os.environ.get("OPENAI_ORG_ID") - self.project = project or os.environ.get("OPENAI_PROJECT_ID") + self._api_key = api_key + self._organization = organization + self._project = project self.endpoint = endpoint self.max_retries = max_retries self.base_delay = base_delay @@ -68,8 +69,22 @@ def set_api_key(self, api_key: str): api_key: The OpenAI API key to use. This is the same key used by the OpenAI Python client. """ + # We're specifically setting the underlying cached property as well + self._api_key = api_key self.api_key = api_key + @cached_property + def api_key(self): + return self._api_key or os.environ.get("OPENAI_API_KEY") + + @cached_property + def organization(self): + return self._organization or os.environ.get("OPENAI_ORG_ID") + + @cached_property + def project(self): + return self._project or os.environ.get("OPENAI_PROJECT_ID") + def export(self, items: list[Trace | Span[Any]]) -> None: if not items: return @@ -78,9 +93,6 @@ def export(self, items: list[Trace | Span[Any]]) -> None: logger.warning("OPENAI_API_KEY is not set, skipping trace export") return - traces: list[dict[str, Any]] = [] - spans: list[dict[str, Any]] = [] - data = [item.export() for item in items if item.export()] payload = {"data": data} @@ -90,6 +102,12 @@ def export(self, items: list[Trace | Span[Any]]) -> None: "OpenAI-Beta": "traces=v1", } + if self.organization: + headers["OpenAI-Organization"] = self.organization + + if self.project: + headers["OpenAI-Project"] = self.project + # Exponential backoff loop attempt = 0 delay = self.base_delay @@ -100,23 +118,27 @@ def export(self, items: list[Trace | Span[Any]]) -> None: # If the response is successful, break out of the loop if response.status_code < 300: - logger.debug(f"Exported {len(traces)} traces, {len(spans)} spans") + logger.debug(f"Exported {len(items)} items") return # If the response is a client error (4xx), we wont retry if 400 <= response.status_code < 500: - logger.error(f"Tracing client error {response.status_code}: {response.text}") + logger.error( + f"[non-fatal] Tracing client error {response.status_code}: {response.text}" + ) return # For 5xx or other unexpected codes, treat it as transient and retry - logger.warning(f"Server error {response.status_code}, retrying.") + logger.warning( + f"[non-fatal] Tracing: server error {response.status_code}, retrying." + ) except httpx.RequestError as exc: # Network or other I/O error, we'll retry - logger.warning(f"Request failed: {exc}") + logger.warning(f"[non-fatal] Tracing: request failed: {exc}") # If we reach here, we need to retry or give up if attempt >= self.max_retries: - logger.error("Max retries reached, giving up on this batch.") + logger.error("[non-fatal] Tracing: max retries reached, giving up on this batch.") return # Exponential backoff + jitter @@ -166,7 +188,6 @@ def __init__( # Track when we next *must* perform a scheduled export self._next_export_time = time.time() + self._schedule_delay - self._shutdown_event = threading.Event() self._worker_thread = threading.Thread(target=self._run, daemon=True) self._worker_thread.start() diff --git a/src/agents/tracing/scope.py b/src/agents/tracing/scope.py index 9ccd9f87..1d31c1bd 100644 --- a/src/agents/tracing/scope.py +++ b/src/agents/tracing/scope.py @@ -2,7 +2,7 @@ import contextvars from typing import TYPE_CHECKING, Any -from .logger import logger +from ..logger import logger if TYPE_CHECKING: from .spans import Span @@ -18,6 +18,10 @@ class Scope: + """ + Manages the current span and trace in the context. + """ + @classmethod def get_current_span(cls) -> "Span[Any] | None": return _current_span.get() diff --git a/src/agents/tracing/setup.py b/src/agents/tracing/setup.py index bc340c9f..9e27d210 100644 --- a/src/agents/tracing/setup.py +++ b/src/agents/tracing/setup.py @@ -4,8 +4,8 @@ import threading from typing import Any +from ..logger import logger from . import util -from .logger import logger from .processor_interface import TracingProcessor from .scope import Scope from .spans import NoOpSpan, Span, SpanImpl, TSpanData @@ -201,6 +201,9 @@ def create_span( ) def shutdown(self) -> None: + if self._disabled: + return + try: logger.debug("Shutting down trace provider") self._multi_processor.shutdown() diff --git a/src/agents/tracing/span_data.py b/src/agents/tracing/span_data.py index 5e5d38cb..cb3e8491 100644 --- a/src/agents/tracing/span_data.py +++ b/src/agents/tracing/span_data.py @@ -9,17 +9,28 @@ class SpanData(abc.ABC): + """ + Represents span data in the trace. + """ + @abc.abstractmethod def export(self) -> dict[str, Any]: + """Export the span data as a dictionary.""" pass @property @abc.abstractmethod def type(self) -> str: + """Return the type of the span.""" pass class AgentSpanData(SpanData): + """ + Represents an Agent Span in the trace. + Includes name, handoffs, tools, and output type. + """ + __slots__ = ("name", "handoffs", "tools", "output_type") def __init__( @@ -49,12 +60,24 @@ def export(self) -> dict[str, Any]: class FunctionSpanData(SpanData): - __slots__ = ("name", "input", "output") + """ + Represents a Function Span in the trace. + Includes input, output and MCP data (if applicable). + """ - def __init__(self, name: str, input: str | None, output: str | None): + __slots__ = ("name", "input", "output", "mcp_data") + + def __init__( + self, + name: str, + input: str | None, + output: Any | None, + mcp_data: dict[str, Any] | None = None, + ): self.name = name self.input = input self.output = output + self.mcp_data = mcp_data @property def type(self) -> str: @@ -65,11 +88,17 @@ def export(self) -> dict[str, Any]: "type": self.type, "name": self.name, "input": self.input, - "output": self.output, + "output": str(self.output) if self.output else None, + "mcp_data": self.mcp_data, } class GenerationSpanData(SpanData): + """ + Represents a Generation Span in the trace. + Includes input, output, model, model configuration, and usage. + """ + __slots__ = ( "input", "output", @@ -108,6 +137,11 @@ def export(self) -> dict[str, Any]: class ResponseSpanData(SpanData): + """ + Represents a Response Span in the trace. + Includes response and input. + """ + __slots__ = ("response", "input") def __init__( @@ -132,6 +166,11 @@ def export(self) -> dict[str, Any]: class HandoffSpanData(SpanData): + """ + Represents a Handoff Span in the trace. + Includes source and destination agents. + """ + __slots__ = ("from_agent", "to_agent") def __init__(self, from_agent: str | None, to_agent: str | None): @@ -151,6 +190,11 @@ def export(self) -> dict[str, Any]: class CustomSpanData(SpanData): + """ + Represents a Custom Span in the trace. + Includes name and data property bag. + """ + __slots__ = ("name", "data") def __init__(self, name: str, data: dict[str, Any]): @@ -170,6 +214,11 @@ def export(self) -> dict[str, Any]: class GuardrailSpanData(SpanData): + """ + Represents a Guardrail Span in the trace. + Includes name and triggered status. + """ + __slots__ = ("name", "triggered") def __init__(self, name: str, triggered: bool = False): @@ -186,3 +235,140 @@ def export(self) -> dict[str, Any]: "name": self.name, "triggered": self.triggered, } + + +class TranscriptionSpanData(SpanData): + """ + Represents a Transcription Span in the trace. + Includes input, output, model, and model configuration. + """ + + __slots__ = ( + "input", + "output", + "model", + "model_config", + ) + + def __init__( + self, + input: str | None = None, + input_format: str | None = "pcm", + output: str | None = None, + model: str | None = None, + model_config: Mapping[str, Any] | None = None, + ): + self.input = input + self.input_format = input_format + self.output = output + self.model = model + self.model_config = model_config + + @property + def type(self) -> str: + return "transcription" + + def export(self) -> dict[str, Any]: + return { + "type": self.type, + "input": { + "data": self.input or "", + "format": self.input_format, + }, + "output": self.output, + "model": self.model, + "model_config": self.model_config, + } + + +class SpeechSpanData(SpanData): + """ + Represents a Speech Span in the trace. + Includes input, output, model, model configuration, and first content timestamp. + """ + + __slots__ = ("input", "output", "model", "model_config", "first_content_at") + + def __init__( + self, + input: str | None = None, + output: str | None = None, + output_format: str | None = "pcm", + model: str | None = None, + model_config: Mapping[str, Any] | None = None, + first_content_at: str | None = None, + ): + self.input = input + self.output = output + self.output_format = output_format + self.model = model + self.model_config = model_config + self.first_content_at = first_content_at + + @property + def type(self) -> str: + return "speech" + + def export(self) -> dict[str, Any]: + return { + "type": self.type, + "input": self.input, + "output": { + "data": self.output or "", + "format": self.output_format, + }, + "model": self.model, + "model_config": self.model_config, + "first_content_at": self.first_content_at, + } + + +class SpeechGroupSpanData(SpanData): + """ + Represents a Speech Group Span in the trace. + """ + + __slots__ = "input" + + def __init__( + self, + input: str | None = None, + ): + self.input = input + + @property + def type(self) -> str: + return "speech_group" + + def export(self) -> dict[str, Any]: + return { + "type": self.type, + "input": self.input, + } + + +class MCPListToolsSpanData(SpanData): + """ + Represents an MCP List Tools Span in the trace. + Includes server and result. + """ + + __slots__ = ( + "server", + "result", + ) + + def __init__(self, server: str | None = None, result: list[str] | None = None): + self.server = server + self.result = result + + @property + def type(self) -> str: + return "mcp_tools" + + def export(self) -> dict[str, Any]: + return { + "type": self.type, + "server": self.server, + "result": self.result, + } diff --git a/src/agents/tracing/spans.py b/src/agents/tracing/spans.py index d682a9a0..ee933e73 100644 --- a/src/agents/tracing/spans.py +++ b/src/agents/tracing/spans.py @@ -6,8 +6,8 @@ from typing_extensions import TypedDict +from ..logger import logger from . import util -from .logger import logger from .processor_interface import TracingProcessor from .scope import Scope from .span_data import SpanData diff --git a/src/agents/tracing/traces.py b/src/agents/tracing/traces.py index bf3b43df..53d06284 100644 --- a/src/agents/tracing/traces.py +++ b/src/agents/tracing/traces.py @@ -4,8 +4,8 @@ import contextvars from typing import Any +from ..logger import logger from . import util -from .logger import logger from .processor_interface import TracingProcessor from .scope import Scope diff --git a/src/agents/tracing/util.py b/src/agents/tracing/util.py index 3e5cad90..f546b4e5 100644 --- a/src/agents/tracing/util.py +++ b/src/agents/tracing/util.py @@ -15,3 +15,8 @@ def gen_trace_id() -> str: def gen_span_id() -> str: """Generates a new span ID.""" return f"span_{uuid.uuid4().hex[:24]}" + + +def gen_group_id() -> str: + """Generates a new group ID.""" + return f"group_{uuid.uuid4().hex[:24]}" diff --git a/src/agents/usage.py b/src/agents/usage.py index 23d989b4..843f6293 100644 --- a/src/agents/usage.py +++ b/src/agents/usage.py @@ -1,4 +1,6 @@ -from dataclasses import dataclass +from dataclasses import dataclass, field + +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails @dataclass @@ -9,9 +11,18 @@ class Usage: input_tokens: int = 0 """Total input tokens sent, across all requests.""" + input_tokens_details: InputTokensDetails = field( + default_factory=lambda: InputTokensDetails(cached_tokens=0) + ) + """Details about the input tokens, matching responses API usage details.""" output_tokens: int = 0 """Total output tokens received, across all requests.""" + output_tokens_details: OutputTokensDetails = field( + default_factory=lambda: OutputTokensDetails(reasoning_tokens=0) + ) + """Details about the output tokens, matching responses API usage details.""" + total_tokens: int = 0 """Total tokens sent and received, across all requests.""" @@ -20,3 +31,12 @@ def add(self, other: "Usage") -> None: self.input_tokens += other.input_tokens if other.input_tokens else 0 self.output_tokens += other.output_tokens if other.output_tokens else 0 self.total_tokens += other.total_tokens if other.total_tokens else 0 + self.input_tokens_details = InputTokensDetails( + cached_tokens=self.input_tokens_details.cached_tokens + + other.input_tokens_details.cached_tokens + ) + + self.output_tokens_details = OutputTokensDetails( + reasoning_tokens=self.output_tokens_details.reasoning_tokens + + other.output_tokens_details.reasoning_tokens + ) diff --git a/src/agents/util/__init__.py b/src/agents/util/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/agents/util/_coro.py b/src/agents/util/_coro.py new file mode 100644 index 00000000..647ab86a --- /dev/null +++ b/src/agents/util/_coro.py @@ -0,0 +1,2 @@ +async def noop_coroutine() -> None: + pass diff --git a/src/agents/util/_error_tracing.py b/src/agents/util/_error_tracing.py new file mode 100644 index 00000000..09dbb1de --- /dev/null +++ b/src/agents/util/_error_tracing.py @@ -0,0 +1,16 @@ +from typing import Any + +from ..logger import logger +from ..tracing import Span, SpanError, get_current_span + + +def attach_error_to_span(span: Span[Any], error: SpanError) -> None: + span.set_error(error) + + +def attach_error_to_current_span(error: SpanError) -> None: + span = get_current_span() + if span: + attach_error_to_span(span, error) + else: + logger.warning(f"No span to add error {error} to") diff --git a/src/agents/util/_json.py b/src/agents/util/_json.py new file mode 100644 index 00000000..1e081f68 --- /dev/null +++ b/src/agents/util/_json.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +from typing import Literal + +from pydantic import TypeAdapter, ValidationError +from typing_extensions import TypeVar + +from ..exceptions import ModelBehaviorError +from ..tracing import SpanError +from ._error_tracing import attach_error_to_current_span + +T = TypeVar("T") + + +def validate_json(json_str: str, type_adapter: TypeAdapter[T], partial: bool) -> T: + partial_setting: bool | Literal["off", "on", "trailing-strings"] = ( + "trailing-strings" if partial else False + ) + try: + validated = type_adapter.validate_json(json_str, experimental_allow_partial=partial_setting) + return validated + except ValidationError as e: + attach_error_to_current_span( + SpanError( + message="Invalid JSON provided", + data={}, + ) + ) + raise ModelBehaviorError( + f"Invalid JSON when parsing {json_str} for {type_adapter}; {e}" + ) from e diff --git a/src/agents/util/_pretty_print.py b/src/agents/util/_pretty_print.py new file mode 100644 index 00000000..afd3e2b1 --- /dev/null +++ b/src/agents/util/_pretty_print.py @@ -0,0 +1,56 @@ +from typing import TYPE_CHECKING + +from pydantic import BaseModel + +if TYPE_CHECKING: + from ..result import RunResult, RunResultBase, RunResultStreaming + + +def _indent(text: str, indent_level: int) -> str: + indent_string = " " * indent_level + return "\n".join(f"{indent_string}{line}" for line in text.splitlines()) + + +def _final_output_str(result: "RunResultBase") -> str: + if result.final_output is None: + return "None" + elif isinstance(result.final_output, str): + return result.final_output + elif isinstance(result.final_output, BaseModel): + return result.final_output.model_dump_json(indent=2) + else: + return str(result.final_output) + + +def pretty_print_result(result: "RunResult") -> str: + output = "RunResult:" + output += f'\n- Last agent: Agent(name="{result.last_agent.name}", ...)' + output += ( + f"\n- Final output ({type(result.final_output).__name__}):\n" + f"{_indent(_final_output_str(result), 2)}" + ) + output += f"\n- {len(result.new_items)} new item(s)" + output += f"\n- {len(result.raw_responses)} raw response(s)" + output += f"\n- {len(result.input_guardrail_results)} input guardrail result(s)" + output += f"\n- {len(result.output_guardrail_results)} output guardrail result(s)" + output += "\n(See `RunResult` for more details)" + + return output + + +def pretty_print_run_result_streaming(result: "RunResultStreaming") -> str: + output = "RunResultStreaming:" + output += f'\n- Current agent: Agent(name="{result.current_agent.name}", ...)' + output += f"\n- Current turn: {result.current_turn}" + output += f"\n- Max turns: {result.max_turns}" + output += f"\n- Is complete: {result.is_complete}" + output += ( + f"\n- Final output ({type(result.final_output).__name__}):\n" + f"{_indent(_final_output_str(result), 2)}" + ) + output += f"\n- {len(result.new_items)} new item(s)" + output += f"\n- {len(result.raw_responses)} raw response(s)" + output += f"\n- {len(result.input_guardrail_results)} input guardrail result(s)" + output += f"\n- {len(result.output_guardrail_results)} output guardrail result(s)" + output += "\n(See `RunResultStreaming` for more details)" + return output diff --git a/src/agents/util/_transforms.py b/src/agents/util/_transforms.py new file mode 100644 index 00000000..b303074d --- /dev/null +++ b/src/agents/util/_transforms.py @@ -0,0 +1,11 @@ +import re + + +def transform_string_function_style(name: str) -> str: + # Replace spaces with underscores + name = name.replace(" ", "_") + + # Replace non-alphanumeric characters with underscores + name = re.sub(r"[^a-zA-Z0-9]", "_", name) + + return name.lower() diff --git a/src/agents/util/_types.py b/src/agents/util/_types.py new file mode 100644 index 00000000..8571a694 --- /dev/null +++ b/src/agents/util/_types.py @@ -0,0 +1,7 @@ +from collections.abc import Awaitable +from typing import Union + +from typing_extensions import TypeVar + +T = TypeVar("T") +MaybeAwaitable = Union[Awaitable[T], T] diff --git a/src/agents/version.py b/src/agents/version.py index a0b7e9be..9b22499e 100644 --- a/src/agents/version.py +++ b/src/agents/version.py @@ -1,7 +1,7 @@ import importlib.metadata try: - __version__ = importlib.metadata.version("agents") + __version__ = importlib.metadata.version("openai-agents") except importlib.metadata.PackageNotFoundError: # Fallback if running from source without being installed __version__ = "0.0.0" diff --git a/src/agents/voice/__init__.py b/src/agents/voice/__init__.py new file mode 100644 index 00000000..e11ee446 --- /dev/null +++ b/src/agents/voice/__init__.py @@ -0,0 +1,53 @@ +from .events import VoiceStreamEvent, VoiceStreamEventAudio, VoiceStreamEventLifecycle +from .exceptions import STTWebsocketConnectionError +from .input import AudioInput, StreamedAudioInput +from .model import ( + StreamedTranscriptionSession, + STTModel, + STTModelSettings, + TTSModel, + TTSModelSettings, + TTSVoice, + VoiceModelProvider, +) +from .models.openai_model_provider import OpenAIVoiceModelProvider +from .models.openai_stt import OpenAISTTModel, OpenAISTTTranscriptionSession +from .models.openai_tts import OpenAITTSModel +from .pipeline import VoicePipeline +from .pipeline_config import VoicePipelineConfig +from .result import StreamedAudioResult +from .utils import get_sentence_based_splitter +from .workflow import ( + SingleAgentVoiceWorkflow, + SingleAgentWorkflowCallbacks, + VoiceWorkflowBase, + VoiceWorkflowHelper, +) + +__all__ = [ + "AudioInput", + "StreamedAudioInput", + "STTModel", + "STTModelSettings", + "TTSModel", + "TTSModelSettings", + "TTSVoice", + "VoiceModelProvider", + "StreamedAudioResult", + "SingleAgentVoiceWorkflow", + "OpenAIVoiceModelProvider", + "OpenAISTTModel", + "OpenAITTSModel", + "VoiceStreamEventAudio", + "VoiceStreamEventLifecycle", + "VoiceStreamEvent", + "VoicePipeline", + "VoicePipelineConfig", + "get_sentence_based_splitter", + "VoiceWorkflowHelper", + "VoiceWorkflowBase", + "SingleAgentWorkflowCallbacks", + "StreamedTranscriptionSession", + "OpenAISTTTranscriptionSession", + "STTWebsocketConnectionError", +] diff --git a/src/agents/voice/events.py b/src/agents/voice/events.py new file mode 100644 index 00000000..bdcd0815 --- /dev/null +++ b/src/agents/voice/events.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal, Union + +from typing_extensions import TypeAlias + +from .imports import np, npt + + +@dataclass +class VoiceStreamEventAudio: + """Streaming event from the VoicePipeline""" + + data: npt.NDArray[np.int16 | np.float32] | None + """The audio data.""" + + type: Literal["voice_stream_event_audio"] = "voice_stream_event_audio" + """The type of event.""" + + +@dataclass +class VoiceStreamEventLifecycle: + """Streaming event from the VoicePipeline""" + + event: Literal["turn_started", "turn_ended", "session_ended"] + """The event that occurred.""" + + type: Literal["voice_stream_event_lifecycle"] = "voice_stream_event_lifecycle" + """The type of event.""" + + +@dataclass +class VoiceStreamEventError: + """Streaming event from the VoicePipeline""" + + error: Exception + """The error that occurred.""" + + type: Literal["voice_stream_event_error"] = "voice_stream_event_error" + """The type of event.""" + + +VoiceStreamEvent: TypeAlias = Union[ + VoiceStreamEventAudio, VoiceStreamEventLifecycle, VoiceStreamEventError +] +"""An event from the `VoicePipeline`, streamed via `StreamedAudioResult.stream()`.""" diff --git a/src/agents/voice/exceptions.py b/src/agents/voice/exceptions.py new file mode 100644 index 00000000..97dccac8 --- /dev/null +++ b/src/agents/voice/exceptions.py @@ -0,0 +1,8 @@ +from ..exceptions import AgentsException + + +class STTWebsocketConnectionError(AgentsException): + """Exception raised when the STT websocket connection fails.""" + + def __init__(self, message: str): + self.message = message diff --git a/src/agents/voice/imports.py b/src/agents/voice/imports.py new file mode 100644 index 00000000..b1c09508 --- /dev/null +++ b/src/agents/voice/imports.py @@ -0,0 +1,11 @@ +try: + import numpy as np + import numpy.typing as npt + import websockets +except ImportError as _e: + raise ImportError( + "`numpy` + `websockets` are required to use voice. You can install them via the optional " + "dependency group: `pip install 'openai-agents[voice]'`." + ) from _e + +__all__ = ["np", "npt", "websockets"] diff --git a/src/agents/voice/input.py b/src/agents/voice/input.py new file mode 100644 index 00000000..8613d27a --- /dev/null +++ b/src/agents/voice/input.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +import asyncio +import base64 +import io +import wave +from dataclasses import dataclass + +from ..exceptions import UserError +from .imports import np, npt + +DEFAULT_SAMPLE_RATE = 24000 + + +def _buffer_to_audio_file( + buffer: npt.NDArray[np.int16 | np.float32], + frame_rate: int = DEFAULT_SAMPLE_RATE, + sample_width: int = 2, + channels: int = 1, +) -> tuple[str, io.BytesIO, str]: + if buffer.dtype == np.float32: + # convert to int16 + buffer = np.clip(buffer, -1.0, 1.0) + buffer = (buffer * 32767).astype(np.int16) + elif buffer.dtype != np.int16: + raise UserError("Buffer must be a numpy array of int16 or float32") + + audio_file = io.BytesIO() + with wave.open(audio_file, "w") as wav_file: + wav_file.setnchannels(channels) + wav_file.setsampwidth(sample_width) + wav_file.setframerate(frame_rate) + wav_file.writeframes(buffer.tobytes()) + audio_file.seek(0) + + # (filename, bytes, content_type) + return ("audio.wav", audio_file, "audio/wav") + + +@dataclass +class AudioInput: + """Static audio to be used as input for the VoicePipeline.""" + + buffer: npt.NDArray[np.int16 | np.float32] + """ + A buffer containing the audio data for the agent. Must be a numpy array of int16 or float32. + """ + + frame_rate: int = DEFAULT_SAMPLE_RATE + """The sample rate of the audio data. Defaults to 24000.""" + + sample_width: int = 2 + """The sample width of the audio data. Defaults to 2.""" + + channels: int = 1 + """The number of channels in the audio data. Defaults to 1.""" + + def to_audio_file(self) -> tuple[str, io.BytesIO, str]: + """Returns a tuple of (filename, bytes, content_type)""" + return _buffer_to_audio_file(self.buffer, self.frame_rate, self.sample_width, self.channels) + + def to_base64(self) -> str: + """Returns the audio data as a base64 encoded string.""" + if self.buffer.dtype == np.float32: + # convert to int16 + self.buffer = np.clip(self.buffer, -1.0, 1.0) + self.buffer = (self.buffer * 32767).astype(np.int16) + elif self.buffer.dtype != np.int16: + raise UserError("Buffer must be a numpy array of int16 or float32") + + return base64.b64encode(self.buffer.tobytes()).decode("utf-8") + + +class StreamedAudioInput: + """Audio input represented as a stream of audio data. You can pass this to the `VoicePipeline` + and then push audio data into the queue using the `add_audio` method. + """ + + def __init__(self): + self.queue: asyncio.Queue[npt.NDArray[np.int16 | np.float32]] = asyncio.Queue() + + async def add_audio(self, audio: npt.NDArray[np.int16 | np.float32]): + """Adds more audio data to the stream. + + Args: + audio: The audio data to add. Must be a numpy array of int16 or float32. + """ + await self.queue.put(audio) diff --git a/src/agents/voice/model.py b/src/agents/voice/model.py new file mode 100644 index 00000000..c36a4de7 --- /dev/null +++ b/src/agents/voice/model.py @@ -0,0 +1,192 @@ +from __future__ import annotations + +import abc +from collections.abc import AsyncIterator +from dataclasses import dataclass +from typing import Any, Callable, Literal + +from .imports import np, npt +from .input import AudioInput, StreamedAudioInput +from .utils import get_sentence_based_splitter + +DEFAULT_TTS_INSTRUCTIONS = ( + "You will receive partial sentences. Do not complete the sentence, just read out the text." +) +DEFAULT_TTS_BUFFER_SIZE = 120 + +TTSVoice = Literal["alloy", "ash", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer"] +"""Exportable type for the TTSModelSettings voice enum""" + +@dataclass +class TTSModelSettings: + """Settings for a TTS model.""" + voice: TTSVoice | None = None + """ + The voice to use for the TTS model. If not provided, the default voice for the respective model + will be used. + """ + + buffer_size: int = 120 + """The minimal size of the chunks of audio data that are being streamed out.""" + + dtype: npt.DTypeLike = np.int16 + """The data type for the audio data to be returned in.""" + + transform_data: ( + Callable[[npt.NDArray[np.int16 | np.float32]], npt.NDArray[np.int16 | np.float32]] | None + ) = None + """ + A function to transform the data from the TTS model. This is useful if you want the resulting + audio stream to have the data in a specific shape already. + """ + + instructions: str = ( + "You will receive partial sentences. Do not complete the sentence just read out the text." + ) + """ + The instructions to use for the TTS model. This is useful if you want to control the tone of the + audio output. + """ + + text_splitter: Callable[[str], tuple[str, str]] = get_sentence_based_splitter() + """ + A function to split the text into chunks. This is useful if you want to split the text into + chunks before sending it to the TTS model rather than waiting for the whole text to be + processed. + """ + + speed: float | None = None + """The speed with which the TTS model will read the text. Between 0.25 and 4.0.""" + + +class TTSModel(abc.ABC): + """A text-to-speech model that can convert text into audio output.""" + + @property + @abc.abstractmethod + def model_name(self) -> str: + """The name of the TTS model.""" + pass + + @abc.abstractmethod + def run(self, text: str, settings: TTSModelSettings) -> AsyncIterator[bytes]: + """Given a text string, produces a stream of audio bytes, in PCM format. + + Args: + text: The text to convert to audio. + + Returns: + An async iterator of audio bytes, in PCM format. + """ + pass + + +class StreamedTranscriptionSession(abc.ABC): + """A streamed transcription of audio input.""" + + @abc.abstractmethod + def transcribe_turns(self) -> AsyncIterator[str]: + """Yields a stream of text transcriptions. Each transcription is a turn in the conversation. + + This method is expected to return only after `close()` is called. + """ + pass + + @abc.abstractmethod + async def close(self) -> None: + """Closes the session.""" + pass + + +@dataclass +class STTModelSettings: + """Settings for a speech-to-text model.""" + + prompt: str | None = None + """Instructions for the model to follow.""" + + language: str | None = None + """The language of the audio input.""" + + temperature: float | None = None + """The temperature of the model.""" + + turn_detection: dict[str, Any] | None = None + """The turn detection settings for the model when using streamed audio input.""" + + +class STTModel(abc.ABC): + """A speech-to-text model that can convert audio input into text.""" + + @property + @abc.abstractmethod + def model_name(self) -> str: + """The name of the STT model.""" + pass + + @abc.abstractmethod + async def transcribe( + self, + input: AudioInput, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ) -> str: + """Given an audio input, produces a text transcription. + + Args: + input: The audio input to transcribe. + settings: The settings to use for the transcription. + trace_include_sensitive_data: Whether to include sensitive data in traces. + trace_include_sensitive_audio_data: Whether to include sensitive audio data in traces. + + Returns: + The text transcription of the audio input. + """ + pass + + @abc.abstractmethod + async def create_session( + self, + input: StreamedAudioInput, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ) -> StreamedTranscriptionSession: + """Creates a new transcription session, which you can push audio to, and receive a stream + of text transcriptions. + + Args: + input: The audio input to transcribe. + settings: The settings to use for the transcription. + trace_include_sensitive_data: Whether to include sensitive data in traces. + trace_include_sensitive_audio_data: Whether to include sensitive audio data in traces. + + Returns: + A new transcription session. + """ + pass + + +class VoiceModelProvider(abc.ABC): + """The base interface for a voice model provider. + + A model provider is responsible for creating speech-to-text and text-to-speech models, given a + name. + """ + + @abc.abstractmethod + def get_stt_model(self, model_name: str | None) -> STTModel: + """Get a speech-to-text model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The speech-to-text model. + """ + pass + + @abc.abstractmethod + def get_tts_model(self, model_name: str | None) -> TTSModel: + """Get a text-to-speech model by name.""" diff --git a/src/agents/voice/models/__init__.py b/src/agents/voice/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/agents/voice/models/openai_model_provider.py b/src/agents/voice/models/openai_model_provider.py new file mode 100644 index 00000000..094df4cc --- /dev/null +++ b/src/agents/voice/models/openai_model_provider.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +import httpx +from openai import AsyncOpenAI, DefaultAsyncHttpxClient + +from ...models import _openai_shared +from ..model import STTModel, TTSModel, VoiceModelProvider +from .openai_stt import OpenAISTTModel +from .openai_tts import OpenAITTSModel + +_http_client: httpx.AsyncClient | None = None + + +# If we create a new httpx client for each request, that would mean no sharing of connection pools, +# which would mean worse latency and resource usage. So, we share the client across requests. +def shared_http_client() -> httpx.AsyncClient: + global _http_client + if _http_client is None: + _http_client = DefaultAsyncHttpxClient() + return _http_client + + +DEFAULT_STT_MODEL = "gpt-4o-transcribe" +DEFAULT_TTS_MODEL = "gpt-4o-mini-tts" + + +class OpenAIVoiceModelProvider(VoiceModelProvider): + """A voice model provider that uses OpenAI models.""" + + def __init__( + self, + *, + api_key: str | None = None, + base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + organization: str | None = None, + project: str | None = None, + ) -> None: + """Create a new OpenAI voice model provider. + + Args: + api_key: The API key to use for the OpenAI client. If not provided, we will use the + default API key. + base_url: The base URL to use for the OpenAI client. If not provided, we will use the + default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + organization: The organization to use for the OpenAI client. + project: The project to use for the OpenAI client. + """ + if openai_client is not None: + assert api_key is None and base_url is None, ( + "Don't provide api_key or base_url if you provide openai_client" + ) + self._client: AsyncOpenAI | None = openai_client + else: + self._client = None + self._stored_api_key = api_key + self._stored_base_url = base_url + self._stored_organization = organization + self._stored_project = project + + # We lazy load the client in case you never actually use OpenAIProvider(). Otherwise + # AsyncOpenAI() raises an error if you don't have an API key set. + def _get_client(self) -> AsyncOpenAI: + if self._client is None: + self._client = _openai_shared.get_default_openai_client() or AsyncOpenAI( + api_key=self._stored_api_key or _openai_shared.get_default_openai_key(), + base_url=self._stored_base_url, + organization=self._stored_organization, + project=self._stored_project, + http_client=shared_http_client(), + ) + + return self._client + + def get_stt_model(self, model_name: str | None) -> STTModel: + """Get a speech-to-text model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The speech-to-text model. + """ + return OpenAISTTModel(model_name or DEFAULT_STT_MODEL, self._get_client()) + + def get_tts_model(self, model_name: str | None) -> TTSModel: + """Get a text-to-speech model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The text-to-speech model. + """ + return OpenAITTSModel(model_name or DEFAULT_TTS_MODEL, self._get_client()) diff --git a/src/agents/voice/models/openai_stt.py b/src/agents/voice/models/openai_stt.py new file mode 100644 index 00000000..1ae4ea14 --- /dev/null +++ b/src/agents/voice/models/openai_stt.py @@ -0,0 +1,456 @@ +from __future__ import annotations + +import asyncio +import base64 +import json +import time +from collections.abc import AsyncIterator +from dataclasses import dataclass +from typing import Any, cast + +from openai import AsyncOpenAI + +from ... import _debug +from ...exceptions import AgentsException +from ...logger import logger +from ...tracing import Span, SpanError, TranscriptionSpanData, transcription_span +from ..exceptions import STTWebsocketConnectionError +from ..imports import np, npt, websockets +from ..input import AudioInput, StreamedAudioInput +from ..model import StreamedTranscriptionSession, STTModel, STTModelSettings + +EVENT_INACTIVITY_TIMEOUT = 1000 # Timeout for inactivity in event processing +SESSION_CREATION_TIMEOUT = 10 # Timeout waiting for session.created event +SESSION_UPDATE_TIMEOUT = 10 # Timeout waiting for session.updated event + +DEFAULT_TURN_DETECTION = {"type": "semantic_vad"} + + +@dataclass +class ErrorSentinel: + error: Exception + + +class SessionCompleteSentinel: + pass + + +class WebsocketDoneSentinel: + pass + + +def _audio_to_base64(audio_data: list[npt.NDArray[np.int16 | np.float32]]) -> str: + concatenated_audio = np.concatenate(audio_data) + if concatenated_audio.dtype == np.float32: + # convert to int16 + concatenated_audio = np.clip(concatenated_audio, -1.0, 1.0) + concatenated_audio = (concatenated_audio * 32767).astype(np.int16) + audio_bytes = concatenated_audio.tobytes() + return base64.b64encode(audio_bytes).decode("utf-8") + + +async def _wait_for_event( + event_queue: asyncio.Queue[dict[str, Any]], expected_types: list[str], timeout: float +): + """ + Wait for an event from event_queue whose type is in expected_types within the specified timeout. + """ + start_time = time.time() + while True: + remaining = timeout - (time.time() - start_time) + if remaining <= 0: + raise TimeoutError(f"Timeout waiting for event(s): {expected_types}") + evt = await asyncio.wait_for(event_queue.get(), timeout=remaining) + evt_type = evt.get("type", "") + if evt_type in expected_types: + return evt + elif evt_type == "error": + raise Exception(f"Error event: {evt.get('error')}") + + +class OpenAISTTTranscriptionSession(StreamedTranscriptionSession): + """A transcription session for OpenAI's STT model.""" + + def __init__( + self, + input: StreamedAudioInput, + client: AsyncOpenAI, + model: str, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ): + self.connected: bool = False + self._client = client + self._model = model + self._settings = settings + self._turn_detection = settings.turn_detection or DEFAULT_TURN_DETECTION + self._trace_include_sensitive_data = trace_include_sensitive_data + self._trace_include_sensitive_audio_data = trace_include_sensitive_audio_data + + self._input_queue: asyncio.Queue[npt.NDArray[np.int16 | np.float32]] = input.queue + self._output_queue: asyncio.Queue[str | ErrorSentinel | SessionCompleteSentinel] = ( + asyncio.Queue() + ) + self._websocket: websockets.ClientConnection | None = None + self._event_queue: asyncio.Queue[dict[str, Any] | WebsocketDoneSentinel] = asyncio.Queue() + self._state_queue: asyncio.Queue[dict[str, Any]] = asyncio.Queue() + self._turn_audio_buffer: list[npt.NDArray[np.int16 | np.float32]] = [] + self._tracing_span: Span[TranscriptionSpanData] | None = None + + # tasks + self._listener_task: asyncio.Task[Any] | None = None + self._process_events_task: asyncio.Task[Any] | None = None + self._stream_audio_task: asyncio.Task[Any] | None = None + self._connection_task: asyncio.Task[Any] | None = None + self._stored_exception: Exception | None = None + + def _start_turn(self) -> None: + self._tracing_span = transcription_span( + model=self._model, + model_config={ + "temperature": self._settings.temperature, + "language": self._settings.language, + "prompt": self._settings.prompt, + "turn_detection": self._turn_detection, + }, + ) + self._tracing_span.start() + + def _end_turn(self, _transcript: str) -> None: + if len(_transcript) < 1: + return + + if self._tracing_span: + if self._trace_include_sensitive_audio_data: + self._tracing_span.span_data.input = _audio_to_base64(self._turn_audio_buffer) + + self._tracing_span.span_data.input_format = "pcm" + + if self._trace_include_sensitive_data: + self._tracing_span.span_data.output = _transcript + + self._tracing_span.finish() + self._turn_audio_buffer = [] + self._tracing_span = None + + async def _event_listener(self) -> None: + assert self._websocket is not None, "Websocket not initialized" + + async for message in self._websocket: + try: + event = json.loads(message) + + if event.get("type") == "error": + raise STTWebsocketConnectionError(f"Error event: {event.get('error')}") + + if event.get("type") in [ + "session.updated", + "transcription_session.updated", + "session.created", + "transcription_session.created", + ]: + await self._state_queue.put(event) + + await self._event_queue.put(event) + except Exception as e: + await self._output_queue.put(ErrorSentinel(e)) + raise STTWebsocketConnectionError("Error parsing events") from e + await self._event_queue.put(WebsocketDoneSentinel()) + + async def _configure_session(self) -> None: + assert self._websocket is not None, "Websocket not initialized" + await self._websocket.send( + json.dumps( + { + "type": "transcription_session.update", + "session": { + "input_audio_format": "pcm16", + "input_audio_transcription": {"model": self._model}, + "turn_detection": self._turn_detection, + }, + } + ) + ) + + async def _setup_connection(self, ws: websockets.ClientConnection) -> None: + self._websocket = ws + self._listener_task = asyncio.create_task(self._event_listener()) + + try: + event = await _wait_for_event( + self._state_queue, + ["session.created", "transcription_session.created"], + SESSION_CREATION_TIMEOUT, + ) + except TimeoutError as e: + wrapped_err = STTWebsocketConnectionError( + "Timeout waiting for transcription_session.created event" + ) + await self._output_queue.put(ErrorSentinel(wrapped_err)) + raise wrapped_err from e + except Exception as e: + await self._output_queue.put(ErrorSentinel(e)) + raise e + + await self._configure_session() + + try: + event = await _wait_for_event( + self._state_queue, + ["session.updated", "transcription_session.updated"], + SESSION_UPDATE_TIMEOUT, + ) + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Session updated") + else: + logger.debug(f"Session updated: {event}") + except TimeoutError as e: + wrapped_err = STTWebsocketConnectionError( + "Timeout waiting for transcription_session.updated event" + ) + await self._output_queue.put(ErrorSentinel(wrapped_err)) + raise wrapped_err from e + except Exception as e: + await self._output_queue.put(ErrorSentinel(e)) + raise + + async def _handle_events(self) -> None: + while True: + try: + event = await asyncio.wait_for( + self._event_queue.get(), timeout=EVENT_INACTIVITY_TIMEOUT + ) + if isinstance(event, WebsocketDoneSentinel): + # processed all events and websocket is done + break + + event_type = event.get("type", "unknown") + if event_type == "conversation.item.input_audio_transcription.completed": + transcript = cast(str, event.get("transcript", "")) + if len(transcript) > 0: + self._end_turn(transcript) + self._start_turn() + await self._output_queue.put(transcript) + await asyncio.sleep(0) # yield control + except asyncio.TimeoutError: + # No new events for a while. Assume the session is done. + break + except Exception as e: + await self._output_queue.put(ErrorSentinel(e)) + raise e + await self._output_queue.put(SessionCompleteSentinel()) + + async def _stream_audio( + self, audio_queue: asyncio.Queue[npt.NDArray[np.int16 | np.float32]] + ) -> None: + assert self._websocket is not None, "Websocket not initialized" + self._start_turn() + while True: + buffer = await audio_queue.get() + if buffer is None: + break + + self._turn_audio_buffer.append(buffer) + try: + await self._websocket.send( + json.dumps( + { + "type": "input_audio_buffer.append", + "audio": base64.b64encode(buffer.tobytes()).decode("utf-8"), + } + ) + ) + except websockets.ConnectionClosed: + break + except Exception as e: + await self._output_queue.put(ErrorSentinel(e)) + raise e + + await asyncio.sleep(0) # yield control + + async def _process_websocket_connection(self) -> None: + try: + async with websockets.connect( + "wss://api.openai.com/v1/realtime?intent=transcription", + additional_headers={ + "Authorization": f"Bearer {self._client.api_key}", + "OpenAI-Beta": "realtime=v1", + "OpenAI-Log-Session": "1", + }, + ) as ws: + await self._setup_connection(ws) + self._process_events_task = asyncio.create_task(self._handle_events()) + self._stream_audio_task = asyncio.create_task(self._stream_audio(self._input_queue)) + self.connected = True + if self._listener_task: + await self._listener_task + else: + logger.error("Listener task not initialized") + raise AgentsException("Listener task not initialized") + except Exception as e: + await self._output_queue.put(ErrorSentinel(e)) + raise e + + def _check_errors(self) -> None: + if self._connection_task and self._connection_task.done(): + exc = self._connection_task.exception() + if exc and isinstance(exc, Exception): + self._stored_exception = exc + + if self._process_events_task and self._process_events_task.done(): + exc = self._process_events_task.exception() + if exc and isinstance(exc, Exception): + self._stored_exception = exc + + if self._stream_audio_task and self._stream_audio_task.done(): + exc = self._stream_audio_task.exception() + if exc and isinstance(exc, Exception): + self._stored_exception = exc + + if self._listener_task and self._listener_task.done(): + exc = self._listener_task.exception() + if exc and isinstance(exc, Exception): + self._stored_exception = exc + + def _cleanup_tasks(self) -> None: + if self._listener_task and not self._listener_task.done(): + self._listener_task.cancel() + + if self._process_events_task and not self._process_events_task.done(): + self._process_events_task.cancel() + + if self._stream_audio_task and not self._stream_audio_task.done(): + self._stream_audio_task.cancel() + + if self._connection_task and not self._connection_task.done(): + self._connection_task.cancel() + + async def transcribe_turns(self) -> AsyncIterator[str]: + self._connection_task = asyncio.create_task(self._process_websocket_connection()) + + while True: + try: + turn = await self._output_queue.get() + except asyncio.CancelledError: + break + + if ( + turn is None + or isinstance(turn, ErrorSentinel) + or isinstance(turn, SessionCompleteSentinel) + ): + self._output_queue.task_done() + break + yield turn + self._output_queue.task_done() + + if self._tracing_span: + self._end_turn("") + + if self._websocket: + await self._websocket.close() + + self._check_errors() + if self._stored_exception: + raise self._stored_exception + + async def close(self) -> None: + if self._websocket: + await self._websocket.close() + + self._cleanup_tasks() + + +class OpenAISTTModel(STTModel): + """A speech-to-text model for OpenAI.""" + + def __init__( + self, + model: str, + openai_client: AsyncOpenAI, + ): + """Create a new OpenAI speech-to-text model. + + Args: + model: The name of the model to use. + openai_client: The OpenAI client to use. + """ + self.model = model + self._client = openai_client + + @property + def model_name(self) -> str: + return self.model + + def _non_null_or_not_given(self, value: Any) -> Any: + return value if value is not None else None # NOT_GIVEN + + async def transcribe( + self, + input: AudioInput, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ) -> str: + """Transcribe an audio input. + + Args: + input: The audio input to transcribe. + settings: The settings to use for the transcription. + + Returns: + The transcribed text. + """ + with transcription_span( + model=self.model, + input=input.to_base64() if trace_include_sensitive_audio_data else "", + input_format="pcm", + model_config={ + "temperature": self._non_null_or_not_given(settings.temperature), + "language": self._non_null_or_not_given(settings.language), + "prompt": self._non_null_or_not_given(settings.prompt), + }, + ) as span: + try: + response = await self._client.audio.transcriptions.create( + model=self.model, + file=input.to_audio_file(), + prompt=self._non_null_or_not_given(settings.prompt), + language=self._non_null_or_not_given(settings.language), + temperature=self._non_null_or_not_given(settings.temperature), + ) + if trace_include_sensitive_data: + span.span_data.output = response.text + return response.text + except Exception as e: + span.span_data.output = "" + span.set_error(SpanError(message=str(e), data={})) + raise e + + async def create_session( + self, + input: StreamedAudioInput, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ) -> StreamedTranscriptionSession: + """Create a new transcription session. + + Args: + input: The audio input to transcribe. + settings: The settings to use for the transcription. + trace_include_sensitive_data: Whether to include sensitive data in traces. + trace_include_sensitive_audio_data: Whether to include sensitive audio data in traces. + + Returns: + A new transcription session. + """ + return OpenAISTTTranscriptionSession( + input, + self._client, + self.model, + settings, + trace_include_sensitive_data, + trace_include_sensitive_audio_data, + ) diff --git a/src/agents/voice/models/openai_tts.py b/src/agents/voice/models/openai_tts.py new file mode 100644 index 00000000..3b7dcf15 --- /dev/null +++ b/src/agents/voice/models/openai_tts.py @@ -0,0 +1,54 @@ +from collections.abc import AsyncIterator +from typing import Literal + +from openai import AsyncOpenAI + +from ..model import TTSModel, TTSModelSettings + +DEFAULT_VOICE: Literal["ash"] = "ash" + + +class OpenAITTSModel(TTSModel): + """A text-to-speech model for OpenAI.""" + + def __init__( + self, + model: str, + openai_client: AsyncOpenAI, + ): + """Create a new OpenAI text-to-speech model. + + Args: + model: The name of the model to use. + openai_client: The OpenAI client to use. + """ + self.model = model + self._client = openai_client + + @property + def model_name(self) -> str: + return self.model + + async def run(self, text: str, settings: TTSModelSettings) -> AsyncIterator[bytes]: + """Run the text-to-speech model. + + Args: + text: The text to convert to speech. + settings: The settings to use for the text-to-speech model. + + Returns: + An iterator of audio chunks. + """ + response = self._client.audio.speech.with_streaming_response.create( + model=self.model, + voice=settings.voice or DEFAULT_VOICE, + input=text, + response_format="pcm", + extra_body={ + "instructions": settings.instructions, + }, + ) + + async with response as stream: + async for chunk in stream.iter_bytes(chunk_size=1024): + yield chunk diff --git a/src/agents/voice/pipeline.py b/src/agents/voice/pipeline.py new file mode 100644 index 00000000..d1dac57c --- /dev/null +++ b/src/agents/voice/pipeline.py @@ -0,0 +1,151 @@ +from __future__ import annotations + +import asyncio + +from .._run_impl import TraceCtxManager +from ..exceptions import UserError +from ..logger import logger +from .input import AudioInput, StreamedAudioInput +from .model import STTModel, TTSModel +from .pipeline_config import VoicePipelineConfig +from .result import StreamedAudioResult +from .workflow import VoiceWorkflowBase + + +class VoicePipeline: + """An opinionated voice agent pipeline. It works in three steps: + 1. Transcribe audio input into text. + 2. Run the provided `workflow`, which produces a sequence of text responses. + 3. Convert the text responses into streaming audio output. + """ + + def __init__( + self, + *, + workflow: VoiceWorkflowBase, + stt_model: STTModel | str | None = None, + tts_model: TTSModel | str | None = None, + config: VoicePipelineConfig | None = None, + ): + """Create a new voice pipeline. + + Args: + workflow: The workflow to run. See `VoiceWorkflowBase`. + stt_model: The speech-to-text model to use. If not provided, a default OpenAI + model will be used. + tts_model: The text-to-speech model to use. If not provided, a default OpenAI + model will be used. + config: The pipeline configuration. If not provided, a default configuration will be + used. + """ + self.workflow = workflow + self.stt_model = stt_model if isinstance(stt_model, STTModel) else None + self.tts_model = tts_model if isinstance(tts_model, TTSModel) else None + self._stt_model_name = stt_model if isinstance(stt_model, str) else None + self._tts_model_name = tts_model if isinstance(tts_model, str) else None + self.config = config or VoicePipelineConfig() + + async def run(self, audio_input: AudioInput | StreamedAudioInput) -> StreamedAudioResult: + """Run the voice pipeline. + + Args: + audio_input: The audio input to process. This can either be an `AudioInput` instance, + which is a single static buffer, or a `StreamedAudioInput` instance, which is a + stream of audio data that you can append to. + + Returns: + A `StreamedAudioResult` instance. You can use this object to stream audio events and + play them out. + """ + if isinstance(audio_input, AudioInput): + return await self._run_single_turn(audio_input) + elif isinstance(audio_input, StreamedAudioInput): + return await self._run_multi_turn(audio_input) + else: + raise UserError(f"Unsupported audio input type: {type(audio_input)}") + + def _get_tts_model(self) -> TTSModel: + if not self.tts_model: + self.tts_model = self.config.model_provider.get_tts_model(self._tts_model_name) + return self.tts_model + + def _get_stt_model(self) -> STTModel: + if not self.stt_model: + self.stt_model = self.config.model_provider.get_stt_model(self._stt_model_name) + return self.stt_model + + async def _process_audio_input(self, audio_input: AudioInput) -> str: + model = self._get_stt_model() + return await model.transcribe( + audio_input, + self.config.stt_settings, + self.config.trace_include_sensitive_data, + self.config.trace_include_sensitive_audio_data, + ) + + async def _run_single_turn(self, audio_input: AudioInput) -> StreamedAudioResult: + # Since this is single turn, we can use the TraceCtxManager to manage starting/ending the + # trace + with TraceCtxManager( + workflow_name=self.config.workflow_name or "Voice Agent", + trace_id=None, # Automatically generated + group_id=self.config.group_id, + metadata=self.config.trace_metadata, + disabled=self.config.tracing_disabled, + ): + input_text = await self._process_audio_input(audio_input) + + output = StreamedAudioResult( + self._get_tts_model(), self.config.tts_settings, self.config + ) + + async def stream_events(): + try: + async for text_event in self.workflow.run(input_text): + await output._add_text(text_event) + await output._turn_done() + await output._done() + except Exception as e: + logger.error(f"Error processing single turn: {e}") + await output._add_error(e) + raise e + + output._set_task(asyncio.create_task(stream_events())) + return output + + async def _run_multi_turn(self, audio_input: StreamedAudioInput) -> StreamedAudioResult: + with TraceCtxManager( + workflow_name=self.config.workflow_name or "Voice Agent", + trace_id=None, + group_id=self.config.group_id, + metadata=self.config.trace_metadata, + disabled=self.config.tracing_disabled, + ): + output = StreamedAudioResult( + self._get_tts_model(), self.config.tts_settings, self.config + ) + + transcription_session = await self._get_stt_model().create_session( + audio_input, + self.config.stt_settings, + self.config.trace_include_sensitive_data, + self.config.trace_include_sensitive_audio_data, + ) + + async def process_turns(): + try: + async for input_text in transcription_session.transcribe_turns(): + result = self.workflow.run(input_text) + async for text_event in result: + await output._add_text(text_event) + await output._turn_done() + except Exception as e: + logger.error(f"Error processing turns: {e}") + await output._add_error(e) + raise e + finally: + await transcription_session.close() + await output._done() + + output._set_task(asyncio.create_task(process_turns())) + return output diff --git a/src/agents/voice/pipeline_config.py b/src/agents/voice/pipeline_config.py new file mode 100644 index 00000000..a4871612 --- /dev/null +++ b/src/agents/voice/pipeline_config.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from ..tracing.util import gen_group_id +from .model import STTModelSettings, TTSModelSettings, VoiceModelProvider +from .models.openai_model_provider import OpenAIVoiceModelProvider + + +@dataclass +class VoicePipelineConfig: + """Configuration for a `VoicePipeline`.""" + + model_provider: VoiceModelProvider = field(default_factory=OpenAIVoiceModelProvider) + """The voice model provider to use for the pipeline. Defaults to OpenAI.""" + + tracing_disabled: bool = False + """Whether to disable tracing of the pipeline. Defaults to `False`.""" + + trace_include_sensitive_data: bool = True + """Whether to include sensitive data in traces. Defaults to `True`. This is specifically for the + voice pipeline, and not for anything that goes on inside your Workflow.""" + + trace_include_sensitive_audio_data: bool = True + """Whether to include audio data in traces. Defaults to `True`.""" + + workflow_name: str = "Voice Agent" + """The name of the workflow to use for tracing. Defaults to `Voice Agent`.""" + + group_id: str = field(default_factory=gen_group_id) + """ + A grouping identifier to use for tracing, to link multiple traces from the same conversation + or process. If not provided, we will create a random group ID. + """ + + trace_metadata: dict[str, Any] | None = None + """ + An optional dictionary of additional metadata to include with the trace. + """ + + stt_settings: STTModelSettings = field(default_factory=STTModelSettings) + """The settings to use for the STT model.""" + + tts_settings: TTSModelSettings = field(default_factory=TTSModelSettings) + """The settings to use for the TTS model.""" diff --git a/src/agents/voice/result.py b/src/agents/voice/result.py new file mode 100644 index 00000000..fea79902 --- /dev/null +++ b/src/agents/voice/result.py @@ -0,0 +1,287 @@ +from __future__ import annotations + +import asyncio +import base64 +from collections.abc import AsyncIterator +from typing import Any + +from ..exceptions import UserError +from ..logger import logger +from ..tracing import Span, SpeechGroupSpanData, speech_group_span, speech_span +from ..tracing.util import time_iso +from .events import ( + VoiceStreamEvent, + VoiceStreamEventAudio, + VoiceStreamEventError, + VoiceStreamEventLifecycle, +) +from .imports import np, npt +from .model import TTSModel, TTSModelSettings +from .pipeline_config import VoicePipelineConfig + + +def _audio_to_base64(audio_data: list[bytes]) -> str: + joined_audio_data = b"".join(audio_data) + return base64.b64encode(joined_audio_data).decode("utf-8") + + +class StreamedAudioResult: + """The output of a `VoicePipeline`. Streams events and audio data as they're generated.""" + + def __init__( + self, + tts_model: TTSModel, + tts_settings: TTSModelSettings, + voice_pipeline_config: VoicePipelineConfig, + ): + """Create a new `StreamedAudioResult` instance. + + Args: + tts_model: The TTS model to use. + tts_settings: The TTS settings to use. + voice_pipeline_config: The voice pipeline config to use. + """ + self.tts_model = tts_model + self.tts_settings = tts_settings + self.total_output_text = "" + self.instructions = tts_settings.instructions + self.text_generation_task: asyncio.Task[Any] | None = None + + self._voice_pipeline_config = voice_pipeline_config + self._text_buffer = "" + self._turn_text_buffer = "" + self._queue: asyncio.Queue[VoiceStreamEvent] = asyncio.Queue() + self._tasks: list[asyncio.Task[Any]] = [] + self._ordered_tasks: list[ + asyncio.Queue[VoiceStreamEvent | None] + ] = [] # New: list to hold local queues for each text segment + self._dispatcher_task: asyncio.Task[Any] | None = ( + None # Task to dispatch audio chunks in order + ) + + self._done_processing = False + self._buffer_size = tts_settings.buffer_size + self._started_processing_turn = False + self._first_byte_received = False + self._generation_start_time: str | None = None + self._completed_session = False + self._stored_exception: BaseException | None = None + self._tracing_span: Span[SpeechGroupSpanData] | None = None + + async def _start_turn(self): + if self._started_processing_turn: + return + + self._tracing_span = speech_group_span() + self._tracing_span.start() + self._started_processing_turn = True + self._first_byte_received = False + self._generation_start_time = time_iso() + await self._queue.put(VoiceStreamEventLifecycle(event="turn_started")) + + def _set_task(self, task: asyncio.Task[Any]): + self.text_generation_task = task + + async def _add_error(self, error: Exception): + await self._queue.put(VoiceStreamEventError(error)) + + def _transform_audio_buffer( + self, buffer: list[bytes], output_dtype: npt.DTypeLike + ) -> npt.NDArray[np.int16 | np.float32]: + np_array = np.frombuffer(b"".join(buffer), dtype=np.int16) + + if output_dtype == np.int16: + return np_array + elif output_dtype == np.float32: + return (np_array.astype(np.float32) / 32767.0).reshape(-1, 1) + else: + raise UserError("Invalid output dtype") + + async def _stream_audio( + self, + text: str, + local_queue: asyncio.Queue[VoiceStreamEvent | None], + finish_turn: bool = False, + ): + with speech_span( + model=self.tts_model.model_name, + input=text if self._voice_pipeline_config.trace_include_sensitive_data else "", + model_config={ + "voice": self.tts_settings.voice, + "instructions": self.instructions, + "speed": self.tts_settings.speed, + }, + output_format="pcm", + parent=self._tracing_span, + ) as tts_span: + try: + first_byte_received = False + buffer: list[bytes] = [] + full_audio_data: list[bytes] = [] + + async for chunk in self.tts_model.run(text, self.tts_settings): + if not first_byte_received: + first_byte_received = True + tts_span.span_data.first_content_at = time_iso() + + if chunk: + buffer.append(chunk) + full_audio_data.append(chunk) + if len(buffer) >= self._buffer_size: + audio_np = self._transform_audio_buffer(buffer, self.tts_settings.dtype) + if self.tts_settings.transform_data: + audio_np = self.tts_settings.transform_data(audio_np) + await local_queue.put( + VoiceStreamEventAudio(data=audio_np) + ) # Use local queue + buffer = [] + if buffer: + audio_np = self._transform_audio_buffer(buffer, self.tts_settings.dtype) + if self.tts_settings.transform_data: + audio_np = self.tts_settings.transform_data(audio_np) + await local_queue.put(VoiceStreamEventAudio(data=audio_np)) # Use local queue + + if self._voice_pipeline_config.trace_include_sensitive_audio_data: + tts_span.span_data.output = _audio_to_base64(full_audio_data) + else: + tts_span.span_data.output = "" + + if finish_turn: + await local_queue.put(VoiceStreamEventLifecycle(event="turn_ended")) + else: + await local_queue.put(None) # Signal completion for this segment + except Exception as e: + tts_span.set_error( + { + "message": str(e), + "data": { + "text": text + if self._voice_pipeline_config.trace_include_sensitive_data + else "", + }, + } + ) + logger.error(f"Error streaming audio: {e}") + + # Signal completion for whole session because of error + await local_queue.put(VoiceStreamEventLifecycle(event="session_ended")) + raise e + + async def _add_text(self, text: str): + await self._start_turn() + + self._text_buffer += text + self.total_output_text += text + self._turn_text_buffer += text + + combined_sentences, self._text_buffer = self.tts_settings.text_splitter(self._text_buffer) + + if len(combined_sentences) >= 20: + local_queue: asyncio.Queue[VoiceStreamEvent | None] = asyncio.Queue() + self._ordered_tasks.append(local_queue) + self._tasks.append( + asyncio.create_task(self._stream_audio(combined_sentences, local_queue)) + ) + if self._dispatcher_task is None: + self._dispatcher_task = asyncio.create_task(self._dispatch_audio()) + + async def _turn_done(self): + if self._text_buffer: + local_queue: asyncio.Queue[VoiceStreamEvent | None] = asyncio.Queue() + self._ordered_tasks.append(local_queue) # Append the local queue for the final segment + self._tasks.append( + asyncio.create_task( + self._stream_audio(self._text_buffer, local_queue, finish_turn=True) + ) + ) + self._text_buffer = "" + self._done_processing = True + if self._dispatcher_task is None: + self._dispatcher_task = asyncio.create_task(self._dispatch_audio()) + await asyncio.gather(*self._tasks) + + def _finish_turn(self): + if self._tracing_span: + if self._voice_pipeline_config.trace_include_sensitive_data: + self._tracing_span.span_data.input = self._turn_text_buffer + else: + self._tracing_span.span_data.input = "" + + self._tracing_span.finish() + self._tracing_span = None + self._turn_text_buffer = "" + self._started_processing_turn = False + + async def _done(self): + self._completed_session = True + await self._wait_for_completion() + + async def _dispatch_audio(self): + # Dispatch audio chunks from each segment in the order they were added + while True: + if len(self._ordered_tasks) == 0: + if self._completed_session: + break + await asyncio.sleep(0) + continue + local_queue = self._ordered_tasks.pop(0) + while True: + chunk = await local_queue.get() + if chunk is None: + break + await self._queue.put(chunk) + if isinstance(chunk, VoiceStreamEventLifecycle): + local_queue.task_done() + if chunk.event == "turn_ended": + self._finish_turn() + break + await self._queue.put(VoiceStreamEventLifecycle(event="session_ended")) + + async def _wait_for_completion(self): + tasks: list[asyncio.Task[Any]] = self._tasks + if self._dispatcher_task is not None: + tasks.append(self._dispatcher_task) + await asyncio.gather(*tasks) + + def _cleanup_tasks(self): + self._finish_turn() + + for task in self._tasks: + if not task.done(): + task.cancel() + + if self._dispatcher_task and not self._dispatcher_task.done(): + self._dispatcher_task.cancel() + + if self.text_generation_task and not self.text_generation_task.done(): + self.text_generation_task.cancel() + + def _check_errors(self): + for task in self._tasks: + if task.done(): + if task.exception(): + self._stored_exception = task.exception() + break + + async def stream(self) -> AsyncIterator[VoiceStreamEvent]: + """Stream the events and audio data as they're generated.""" + while True: + try: + event = await self._queue.get() + except asyncio.CancelledError: + break + if isinstance(event, VoiceStreamEventError): + self._stored_exception = event.error + logger.error(f"Error processing output: {event.error}") + break + if event is None: + break + yield event + if event.type == "voice_stream_event_lifecycle" and event.event == "session_ended": + break + + self._check_errors() + self._cleanup_tasks() + + if self._stored_exception: + raise self._stored_exception diff --git a/src/agents/voice/utils.py b/src/agents/voice/utils.py new file mode 100644 index 00000000..1535bd0d --- /dev/null +++ b/src/agents/voice/utils.py @@ -0,0 +1,37 @@ +import re +from typing import Callable + + +def get_sentence_based_splitter( + min_sentence_length: int = 20, +) -> Callable[[str], tuple[str, str]]: + """Returns a function that splits text into chunks based on sentence boundaries. + + Args: + min_sentence_length: The minimum length of a sentence to be included in a chunk. + + Returns: + A function that splits text into chunks based on sentence boundaries. + """ + + def sentence_based_text_splitter(text_buffer: str) -> tuple[str, str]: + """ + A function to split the text into chunks. This is useful if you want to split the text into + chunks before sending it to the TTS model rather than waiting for the whole text to be + processed. + + Args: + text_buffer: The text to split. + + Returns: + A tuple of the text to process and the remaining text buffer. + """ + sentences = re.split(r"(?<=[.!?])\s+", text_buffer.strip()) + if len(sentences) >= 1: + combined_sentences = " ".join(sentences[:-1]) + if len(combined_sentences) >= min_sentence_length: + remaining_text_buffer = sentences[-1] + return combined_sentences, remaining_text_buffer + return "", text_buffer + + return sentence_based_text_splitter diff --git a/src/agents/voice/workflow.py b/src/agents/voice/workflow.py new file mode 100644 index 00000000..c706ec41 --- /dev/null +++ b/src/agents/voice/workflow.py @@ -0,0 +1,93 @@ +from __future__ import annotations + +import abc +from collections.abc import AsyncIterator +from typing import Any + +from ..agent import Agent +from ..items import TResponseInputItem +from ..result import RunResultStreaming +from ..run import Runner + + +class VoiceWorkflowBase(abc.ABC): + """ + A base class for a voice workflow. You must implement the `run` method. A "workflow" is any + code you want, that receives a transcription and yields text that will be turned into speech + by a text-to-speech model. + In most cases, you'll create `Agent`s and use `Runner.run_streamed()` to run them, returning + some or all of the text events from the stream. You can use the `VoiceWorkflowHelper` class to + help with extracting text events from the stream. + If you have a simple workflow that has a single starting agent and no custom logic, you can + use `SingleAgentVoiceWorkflow` directly. + """ + + @abc.abstractmethod + def run(self, transcription: str) -> AsyncIterator[str]: + """ + Run the voice workflow. You will receive an input transcription, and must yield text that + will be spoken to the user. You can run whatever logic you want here. In most cases, the + final logic will involve calling `Runner.run_streamed()` and yielding any text events from + the stream. + """ + pass + + +class VoiceWorkflowHelper: + @classmethod + async def stream_text_from(cls, result: RunResultStreaming) -> AsyncIterator[str]: + """Wraps a `RunResultStreaming` object and yields text events from the stream.""" + async for event in result.stream_events(): + if ( + event.type == "raw_response_event" + and event.data.type == "response.output_text.delta" + ): + yield event.data.delta + + +class SingleAgentWorkflowCallbacks: + def on_run(self, workflow: SingleAgentVoiceWorkflow, transcription: str) -> None: + """Called when the workflow is run.""" + pass + + +class SingleAgentVoiceWorkflow(VoiceWorkflowBase): + """A simple voice workflow that runs a single agent. Each transcription and result is added to + the input history. + For more complex workflows (e.g. multiple Runner calls, custom message history, custom logic, + custom configs), subclass `VoiceWorkflowBase` and implement your own logic. + """ + + def __init__(self, agent: Agent[Any], callbacks: SingleAgentWorkflowCallbacks | None = None): + """Create a new single agent voice workflow. + + Args: + agent: The agent to run. + callbacks: Optional callbacks to call during the workflow. + """ + self._input_history: list[TResponseInputItem] = [] + self._current_agent = agent + self._callbacks = callbacks + + async def run(self, transcription: str) -> AsyncIterator[str]: + if self._callbacks: + self._callbacks.on_run(self, transcription) + + # Add the transcription to the input history + self._input_history.append( + { + "role": "user", + "content": transcription, + } + ) + + # Run the agent + result = Runner.run_streamed(self._current_agent, self._input_history) + + # Stream the text from the result + async for chunk in VoiceWorkflowHelper.stream_text_from(result): + yield chunk + + # Update the input history and current agent + self._input_history = result.to_input_list() + self._current_agent = result.last_agent diff --git a/tests/LICENSE b/tests/LICENSE deleted file mode 100644 index e5ad2c5a..00000000 --- a/tests/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2025 OpenAI - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/tests/Makefile b/tests/Makefile deleted file mode 100644 index 7dd9bbdf..00000000 --- a/tests/Makefile +++ /dev/null @@ -1,37 +0,0 @@ -.PHONY: sync -sync: - uv sync --all-extras --all-packages --group dev - -.PHONY: format -format: - uv run ruff format - -.PHONY: lint -lint: - uv run ruff check - -.PHONY: mypy -mypy: - uv run mypy . - -.PHONY: tests -tests: - uv run pytest - -.PHONY: old_version_tests -old_version_tests: - UV_PROJECT_ENVIRONMENT=.venv_39 uv run --python 3.9 -m pytest - UV_PROJECT_ENVIRONMENT=.venv_39 uv run --python 3.9 -m mypy . - -.PHONY: build-docs -build-docs: - uv run mkdocs build - -.PHONY: serve-docs -serve-docs: - uv run mkdocs serve - -.PHONY: deploy-docs -deploy-docs: - uv run mkdocs gh-deploy --force --verbose - diff --git a/tests/README.md b/tests/README.md index 8acd13cb..d68e067e 100644 --- a/tests/README.md +++ b/tests/README.md @@ -1,174 +1,25 @@ -# OpenAI Agents SDK +# Tests -The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. +Before running any tests, make sure you have `uv` installed (and ideally run `make sync` after). -Image of the Agents Tracing UI +## Running tests -### Core concepts: - -1. [**Agents**](docs/agents.md): LLMs configured with instructions, tools, guardrails, and handoffs -2. [**Handoffs**](docs/handoffs.md): Allow agents to transfer control to other agents for specific tasks -3. [**Guardrails**](docs/guardrails.md): Configurable safety checks for input and output validation -4. [**Tracing**](docs/tracing.md): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows - -Explore the [examples](examples) directory to see the SDK in action. - -## Get started - -1. Set up your Python environment - -``` -python -m venv env -source env/bin/activate -``` - -2. Install Agents SDK - -``` -pip install openai-agents -``` - -## Hello world example - -```python -from agents import Agent, Runner - -agent = Agent(name="Assistant", instructions="You are a helpful assistant") - -result = Runner.run_sync(agent, "Write a haiku about recursion in programming.") -print(result.final_output) - -# Code within the code, -# Functions calling themselves, -# Infinite loop's dance. -``` - -(_If running this, ensure you set the `OPENAI_API_KEY` environment variable_) - -## Handoffs example - -```py -from agents import Agent, Runner -import asyncio - -spanish_agent = Agent( - name="Spanish agent", - instructions="You only speak Spanish.", -) - -english_agent = Agent( - name="English agent", - instructions="You only speak English", -) - -triage_agent = Agent( - name="Triage agent", - instructions="Handoff to the appropriate agent based on the language of the request.", - handoffs=[spanish_agent, english_agent], -) - - -async def main(): - result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") - print(result.final_output) - # ¡Hola! Estoy bien, gracias por preguntar. ¿Y tú, cómo estás? - - -if __name__ == "__main__": - asyncio.run(main()) ``` - -## Functions example - -```python -import asyncio - -from agents import Agent, Runner, function_tool - - -@function_tool -def get_weather(city: str) -> str: - return f"The weather in {city} is sunny." - - -agent = Agent( - name="Hello world", - instructions="You are a helpful agent.", - tools=[get_weather], -) - - -async def main(): - result = await Runner.run(agent, input="What's the weather in Tokyo?") - print(result.final_output) - # The weather in Tokyo is sunny. - - -if __name__ == "__main__": - asyncio.run(main()) +make tests ``` -## The agent loop - -When you call `Runner.run()`, we run a loop until we get a final output. - -1. We call the LLM, using the model and settings on the agent, and the message history. -2. The LLM returns a response, which may include tool calls. -3. If the response has a final output (see below for the more on this), we return it and end the loop. -4. If the response has a handoff, we set the agent to the new agent and go back to step 1. -5. We process the tool calls (if any) and append the tool responses messsages. Then we go to step 1. - -There is a `max_turns` parameter that you can use to limit the number of times the loop executes. - -### Final output +## Snapshots -Final output is the last thing the agent produces in the loop. +We use [inline-snapshots](https://15r10nk.github.io/inline-snapshot/latest/) for some tests. If your code adds new snapshot tests or breaks existing ones, you can fix/create them. After fixing/creating snapshots, run `make tests` again to verify the tests pass. -1. If you set an `output_type` on the agent, the final output is when the LLM returns something of that type. We use [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) for this. -2. If there's no `output_type` (i.e. plain text responses), then the first LLM response without any tool calls or handoffs is considered as the final output. +### Fixing snapshots -As a result, the mental model for the agent loop is: - -1. If the current agent has an `output_type`, the loop runs until the agent produces structured output matching that type. -2. If the current agent does not have an `output_type`, the loop runs until the current agent produces a message without any tool calls/handoffs. - -## Common agent patterns - -The Agents SDK is designed to be highly flexible, allowing you to model a wide range of LLM workflows including deterministic flows, iterative loops, and more. See examples in [`examples/agent_patterns`](examples/agent_patterns). - -## Tracing - -The Agents SDK includes built-in tracing, making it easy to track and debug the behavior of your agents. Tracing is extensible by design, supporting custom spans and a wide variety of external destinations, including [Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents), [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk), and [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk). See [Tracing](http://openai.github.io/openai-agents-python/tracing.md) for more details. - -## Development (only needed if you need to edit the SDK/examples) - -0. Ensure you have [`uv`](https://docs.astral.sh/uv/) installed. - -```bash -uv --version ``` - -1. Install dependencies - -```bash -make sync +make snapshots-fix ``` -2. (After making changes) lint/test +### Creating snapshots ``` -make tests # run tests -make mypy # run typechecker -make lint # run linter +make snapshots-update ``` - -## Acknowledgements - -We'd like to acknowledge the excellent work of the open-source community, especially: - -- [Pydantic](https://docs.pydantic.dev/latest/) (data validation) and [PydanticAI](https://ai.pydantic.dev/) (advanced agent framework) -- [MkDocs](https://github.com/squidfunk/mkdocs-material) -- [Griffe](https://github.com/mkdocstrings/griffe) -- [uv](https://github.com/astral-sh/uv) and [ruff](https://github.com/astral-sh/ruff) - -We're committed to continuing to build the Agents SDK as an open source framework so others in the community can expand on our approach. diff --git a/tests/docs/agents.md b/tests/docs/agents.md deleted file mode 100644 index 9b6264b5..00000000 --- a/tests/docs/agents.md +++ /dev/null @@ -1,131 +0,0 @@ -# Agents - -Agents are the core building block in your apps. An agent is a large language model (LLM), configured with instructions and tools. - -## Basic configuration - -The most common properties of an agent you'll configure are: - -- `instructions`: also known as a developer message or system prompt. -- `model`: which LLM to use, and optional `model_settings` to configure model tuning parameters like temperature, top_p, etc. -- `tools`: Tools that the agent can use to achieve its tasks. - -```python -from agents import Agent, ModelSettings, function_tool - -def get_weather(city: str) -> str: - return f"The weather in {city} is sunny" - -agent = Agent( - name="Haiku agent", - instructions="Always respond in haiku form", - model="o3-mini", - tools=[function_tool(get_weather)], -) -``` - -## Context - -Agents are generic on their `context` type. Context is a dependency-injection tool: it's an object you create and pass to `Runner.run()`, that is passed to every agent, tool, handoff etc, and it serves as a grab bag of dependencies and state for the agent run. You can provide any Python object as the context. - -```python -@dataclass -class UserContext: - uid: str - is_pro_user: bool - - async def fetch_purchases() -> list[Purchase]: - return ... - -agent = Agent[UserContext]( - ..., -) -``` - -## Output types - -By default, agents produce plain text (i.e. `str`) outputs. If you want the agent to produce a particular type of output, you can use the `output_type` parameter. A common choice is to use [Pydantic](https://docs.pydantic.dev/) objects, but we support any type that can be wrapped in a Pydantic [TypeAdapter](https://docs.pydantic.dev/latest/api/type_adapter/) - dataclasses, lists, TypedDict, etc. - -```python -from pydantic import BaseModel -from agents import Agent - - -class CalendarEvent(BaseModel): - name: str - date: str - participants: list[str] - -agent = Agent( - name="Calendar extractor", - instructions="Extract calendar events from text", - output_type=CalendarEvent, -) -``` - -!!! note - - When you pass an `output_type`, that tells the model to use [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) instead of regular plain text responses. - -## Handoffs - -Handoffs are sub-agents that the agent can delegate to. You provide a list of handoffs, and the agent can choose to delegate to them if relevant. This is a powerful pattern that allows orchestrating modular, specialized agents that excel at a single task. Read more in the [handoffs](handoffs.md) documentation. - -```python -from agents import Agent - -booking_agent = Agent(...) -refund_agent = Agent(...) - -triage_agent = Agent( - name="Triage agent", - instructions=( - "Help the user with their questions." - "If they ask about booking, handoff to the booking agent." - "If they ask about refunds, handoff to the refund agent." - ), - handoffs=[booking_agent, refund_agent], -) -``` - -## Dynamic instructions - -In most cases, you can provide instructions when you create the agent. However, you can also provide dynamic instructions via a function. The function will receive the agent and context, and must return the prompt. Both regular and `async` functions are accepted. - -```python -def dynamic_instructions( - context: RunContextWrapper[UserContext], agent: Agent[UserContext] -) -> str: - return f"The user's name is {context.context.name}. Help them with their questions." - - -agent = Agent[UserContext]( - name="Triage agent", - instructions=dynamic_instructions, -) -``` - -## Lifecycle events (hooks) - -Sometimes, you want to observe the lifecycle of an agent. For example, you may want to log events, or pre-fetch data when certain events occur. You can hook into the agent lifecycle with the `hooks` property. Subclass the [`AgentHooks`][agents.lifecycle.AgentHooks] class, and override the methods you're interested in. - -## Guardrails - -Guardrails allow you to run checks/validations on user input, in parallel to the agent running. For example, you could screen the user's input for relevance. Read more in the [guardrails](guardrails.md) documentation. - -## Cloning/copying agents - -By using the `clone()` method on an agent, you can duplicate an Agent, and optionally change any properties you like. - -```python -pirate_agent = Agent( - name="Pirate", - instructions="Write like a pirate", - model="o3-mini", -) - -robot_agent = pirate_agent.clone( - name="Robot", - instructions="Write like a robot", -) -``` diff --git a/tests/docs/assets/images/favicon-platform.svg b/tests/docs/assets/images/favicon-platform.svg deleted file mode 100644 index 91ef0aea..00000000 --- a/tests/docs/assets/images/favicon-platform.svg +++ /dev/null @@ -1,16 +0,0 @@ - - - - - - - - - - - - - - - - diff --git a/tests/docs/assets/images/orchestration.png b/tests/docs/assets/images/orchestration.png deleted file mode 100644 index 621a833b..00000000 Binary files a/tests/docs/assets/images/orchestration.png and /dev/null differ diff --git a/tests/docs/assets/logo.svg b/tests/docs/assets/logo.svg deleted file mode 100644 index ba36fc2a..00000000 --- a/tests/docs/assets/logo.svg +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - - - - - - - diff --git a/tests/docs/config.md b/tests/docs/config.md deleted file mode 100644 index 198d7b7e..00000000 --- a/tests/docs/config.md +++ /dev/null @@ -1,94 +0,0 @@ -# Configuring the SDK - -## API keys and clients - -By default, the SDK looks for the `OPENAI_API_KEY` environment variable for LLM requests and tracing, as soon as it is imported. If you are unable to set that environment variable before your app starts, you can use the [set_default_openai_key()][agents.set_default_openai_key] function to set the key. - -```python -from agents import set_default_openai_key - -set_default_openai_key("sk-...") -``` - -Alternatively, you can also configure an OpenAI client to be used. By default, the SDK creates an `AsyncOpenAI` instance, using the API key from the environment variable or the default key set above. You can chnage this by using the [set_default_openai_client()][agents.set_default_openai_client] function. - -```python -from openai import AsyncOpenAI -from agents import set_default_openai_client - -custom_client = AsyncOpenAI(base_url="...", api_key="...") -set_default_openai_client(client) -``` - -Finally, you can also customize the OpenAI API that is used. By default, we use the OpenAI Responses API. You can override this to use the Chat Completions API by using the [set_default_openai_api()][agents.set_default_openai_api] function. - -```python -from agents import set_default_openai_api - -set_default_openai_api("chat_completions") -``` - -## Tracing - -Tracing is enabled by default. It uses the OpenAI API keys from the section above by default (i.e. the environment variable or the default key you set). You can specifically set the API key used for tracing by using the [`set_tracing_export_api_key`][agents.set_tracing_export_api_key] function. - -```python -from agents import set_tracing_export_api_key - -set_tracing_export_api_key("sk-...") -``` - -You can also disable tracing entirely by using the [`set_tracing_disabled()`][agents.set_tracing_disabled] function. - -```python -from agents import set_tracing_disabled - -set_tracing_disabled(True) -``` - -## Debug logging - -The SDK has two Python loggers without any handlers set. By default, this means that warnings and errors are sent to `stdout`, but other logs are suppressed. - -To enable verbose logging, use the [`enable_verbose_stdout_logging()`][agents.enable_verbose_stdout_logging] function. - -```python -from agents import enable_verbose_stdout_logging - -enable_verbose_stdout_logging() -``` - -Alternatively, you can customize the logs by adding handlers, filters, formatters, etc. You can read more in the [Python logging guide](https://docs.python.org/3/howto/logging.html). - -```python -import logging - -logger = logging.getLogger("openai.agents") # or openai.agents.tracing for the Tracing logger - -# To make all logs show up -logger.setLevel(logging.DEBUG) -# To make info and above show up -logger.setLevel(logging.INFO) -# To make warning and above show up -logger.setLevel(logging.WARNING) -# etc - -# You can customize this as needed, but this will output to `stderr` by default -logger.addHandler(logging.StreamHandler()) -``` - -### Sensitive data in logs - -Certain logs may contain sensitive data (for example, user data). If you want to disable this data from being logged, set the following environment variables. - -To disable logging LLM inputs and outputs: - -```bash -export OPENAI_AGENTS_DONT_LOG_MODEL_DATA=1 -``` - -To disable logging tool inputs and outputs: - -```bash -export OPENAI_AGENTS_DONT_LOG_TOOL_DATA=1 -``` diff --git a/tests/docs/context.md b/tests/docs/context.md deleted file mode 100644 index 5dcacebe..00000000 --- a/tests/docs/context.md +++ /dev/null @@ -1,76 +0,0 @@ -# Context management - -Context is an overloaded term. There are two main classes of context you might care about: - -1. Context available locally to your code: this is data and dependencies you might need when tool functions run, during callbacks like `on_handoff`, in lifecycle hooks, etc. -2. Context available to LLMs: this is data the LLM sees when generating a response. - -## Local context - -This is represented via the [`RunContextWrapper`][agents.run_context.RunContextWrapper] class and the [`context`][agents.run_context.RunContextWrapper.context] property within it. The way this works is: - -1. You create any Python object you want. A common pattern is to use a dataclass or a Pydantic object. -2. You pass that object to the various run methods (e.g. `Runner.run(..., **context=whatever**))`. -3. All your tool calls, lifecycle hooks etc will be passed a wrapper object, `RunContextWrapper[T]`, where `T` represents your context object type which you can access via `wrapper.context`. - -The **most important** thing to be aware of: every agent, tool function, lifecycle etc for a given agent run must use the same _type_ of context. - -You can use the context for things like: - -- Contextual data for your run (e.g. things like a username/uid or other information about the user) -- Dependencies (e.g. logger objects, data fetchers, etc) -- Helper functions - -!!! danger "Note" - - The context object is **not** sent to the LLM. It is purely a local object that you can read from, write to and call methods on it. - -```python -import asyncio -from dataclasses import dataclass - -from agents import Agent, RunContextWrapper, Runner, function_tool - -@dataclass -class UserInfo: # (1)! - name: str - uid: int - -async def fetch_user_age(wrapper: RunContextWrapper[UserInfo]) -> str: # (2)! - return f"User {wrapper.context.name} is 47 years old" - -async def main(): - user_info = UserInfo(name="John", uid=123) # (3)! - - agent = Agent[UserInfo]( # (4)! - name="Assistant", - tools=[function_tool(fetch_user_age)], - ) - - result = await Runner.run( - starting_agent=agent, - input="What is the age of the user?", - context=user_info, - ) - - print(result.final_output) # (5)! - # The user John is 47 years old. - -if __name__ == "__main__": - asyncio.run(main()) -``` - -1. This is the context object. We've used a dataclass here, but you can use any type. -2. This is a tool. You can see it takes a `RunContextWrapper[UserInfo]`. The tool implementation reads from the context. -3. We mark the agent with the generic `UserInfo`, so that the typechecker can catch errors (for example, if we tried to pass a tool that took a different context type). -4. The context is passed to the `run` function. -5. The agent correctly calls the tool and gets the age. - -## Agent/LLM context - -When an LLM is called, the **only** data it can see is from the conversation history. This means that if you want to make some new data available to the LLM, you must do it in a way that makes it available in that history. There are a few ways to do this: - -1. You can add it to the Agent `instructions`. This is also known as a "system prompt" or "developer message". System prompts can be static strings, or they can be dynamic functions that receive the context and output a string. This is a common tactic for information that is always useful (for example, the user's name or the current date). -2. Add it to the `input` when calling the `Runner.run` functions. This is similar to the `instructions` tactic, but allows you to have messages that are lower in the [chain of command](https://cdn.openai.com/spec/model-spec-2024-05-08.html#follow-the-chain-of-command). -3. Expose it via function tools. This is useful for _on-demand_ context - the LLM decides when it needs some data, and can call the tool to fetch that data. -4. Use retrieval or web search. These are special tools that are able to fetch relevant data from files or databases (retrieval), or from the web (web search). This is useful for "grounding" the response in relevant contextual data. diff --git a/tests/docs/guardrails.md b/tests/docs/guardrails.md deleted file mode 100644 index 2b7369c3..00000000 --- a/tests/docs/guardrails.md +++ /dev/null @@ -1,154 +0,0 @@ -# Guardrails - -Guardrails run _in parallel_ to your agents, enabling you to do checks and validations of user input. For example, imagine you have an agent that uses a very smart (and hence slow/expensive) model to help with customer requests. You wouldn't want malicious users to ask the model to help them with their math homework. So, you can run a guardrail with a fast/cheap model. If the guardrail detects malicious usage, it can immediately raise an error, which stops the expensive model from running and saves you time/money. - -There are two kinds of guardrails: - -1. Input guardrails run on the initial user input -2. Output guardrails run on the final agent output - -## Input guardrails - -Input guardrails run in 3 steps: - -1. First, the guardrail receives the same input passed to the agent. -2. Next, the guardrail function runs to produce a [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput], which is then wrapped in an [`InputGuardrailResult`][agents.guardrail.InputGuardrailResult] -3. Finally, we check if [`.tripwire_triggered`][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] is true. If true, an [`InputGuardrailTripwireTriggered`][agents.exceptions.InputGuardrailTripwireTriggered] exception is raised, so you can appropriately respond to the user or handle the exception. - -!!! Note - - Input guardrails are intended to run on user input, so an agent's guardrails only run if the agent is the *first* agent. You might wonder, why is the `guardrails` property on the agent instead of passed to `Runner.run`? It's because guardrails tend to be related to the actual Agent - you'd run different guardrails for different agents, so colocating the code is useful for readability. - -## Output guardrails - -Output guardrailas run in 3 steps: - -1. First, the guardrail receives the same input passed to the agent. -2. Next, the guardrail function runs to produce a [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput], which is then wrapped in an [`OutputGuardrailResult`][agents.guardrail.OutputGuardrailResult] -3. Finally, we check if [`.tripwire_triggered`][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] is true. If true, an [`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered] exception is raised, so you can appropriately respond to the user or handle the exception. - -!!! Note - - Output guardrails are intended to run on the final agent input, so an agent's guardrails only run if the agent is the *last* agent. Similar to the input guardrails, we do this because guardrails tend to be related to the actual Agent - you'd run different guardrails for different agents, so colocating the code is useful for readability. - -## Tripwires - -If the input or output fails the guardrail, the Guardrail can signal this with a tripwire. As soon as we see a guardail that has triggered the tripwires, we immediately raise a `{Input,Output}GuardrailTripwireTriggered` exception and halt the Agent execution. - -## Implementing a guardrail - -You need to provide a function that receives input, and returns a [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput]. In this example, we'll do this by running an Agent under the hood. - -```python -from pydantic import BaseModel -from agents import ( - Agent, - GuardrailFunctionOutput, - InputGuardrailTripwireTriggered, - RunContextWrapper, - Runner, - TResponseInputItem, - input_guardrail, -) - -class MathHomeworkOutput(BaseModel): - is_math_homework: bool - reasoning: str - -guardrail_agent = Agent( # (1)! - name="Guardrail check", - instructions="Check if the user is asking you to do their math homework.", - output_type=MathHomeworkOutput, -) - - -@input_guardrail -async def math_guardrail( # (2)! - ctx: RunContextWrapper[None], agent: Agent, input: str | list[TResponseInputItem] -) -> GuardrailFunctionOutput: - result = await Runner.run(guardrail_agent, input, context=ctx.context) - - return GuardrailFunctionOutput( - output_info=result.final_output, # (3)! - tripwire_triggered=result.final_output.is_math_homework, - ) - - -agent = Agent( # (4)! - name="Customer support agent", - instructions="You are a customer support agent. You help customers with their questions.", - input_guardrails=[math_guardrail], -) - -async def main(): - # This should trip the guardrail - try: - await Runner.run(agent, "Hello, can you help me solve for x: 2x + 3 = 11?") - print("Guardrail didn't trip - this is unexpected") - - except InputGuardrailTripwireTriggered: - print("Math homework guardrail tripped") -``` - -1. We'll use this agent in our guardrail function. -2. This is the guardrail function that receives the agent's input/context, and returns the result. -3. We can include extra information in the guardrail result. -4. This is the actual agent that defines the workflow. - -Output guardrails are similar. - -```python -from pydantic import BaseModel -from agents import ( - Agent, - GuardrailFunctionOutput, - OutputGuardrailTripwireTriggered, - RunContextWrapper, - Runner, - output_guardrail, -) -class MessageOutput(BaseModel): # (1)! - response: str - -class MathOutput(BaseModel): # (2)! - is_math: bool - reasoning: str - -guardrail_agent = Agent( - name="Guardrail check", - instructions="Check if the output includes any math.", - output_type=MathOutput, -) - -@output_guardrail -async def math_guardrail( # (3)! - ctx: RunContextWrapper, agent: Agent, output: MessageOutput -) -> GuardrailFunctionOutput: - result = await Runner.run(guardrail_agent, output.response, context=ctx.context) - - return GuardrailFunctionOutput( - output_info=result.final_output, - tripwire_triggered=result.final_output.is_math, - ) - -agent = Agent( # (4)! - name="Customer support agent", - instructions="You are a customer support agent. You help customers with their questions.", - output_guardrails=[math_guardrail], - output_type=MessageOutput, -) - -async def main(): - # This should trip the guardrail - try: - await Runner.run(agent, "Hello, can you help me solve for x: 2x + 3 = 11?") - print("Guardrail didn't trip - this is unexpected") - - except OutputGuardrailTripwireTriggered: - print("Math output guardrail tripped") -``` - -1. This is the actual agent's output type. -2. This is the guardrail's output type. -3. This is the guardrail function that receives the agent's output, and returns the result. -4. This is the actual agent that defines the workflow. diff --git a/tests/docs/handoffs.md b/tests/docs/handoffs.md deleted file mode 100644 index 0b868c4a..00000000 --- a/tests/docs/handoffs.md +++ /dev/null @@ -1,113 +0,0 @@ -# Handoffs - -Handoffs allow an agent to delegate tasks to another agent. This is particularly useful in scenarios where different agents specialize in distinct areas. For example, a customer support app might have agents that each specifically handle tasks like order status, refunds, FAQs, etc. - -Handoffs are represented as tools to the LLM. So if there's a handoff to an agent named `Refund Agent`, the tool would be called `transfer_to_refund_agent`. - -## Creating a handoff - -All agents have a [`handoffs`][agents.agent.Agent.handoffs] param, which can either take an `Agent` directly, or a `Handoff` object that customizes the Handoff. - -You can create a handoff using the [`handoff()`][agents.handoffs.handoff] function provided by the Agents SDK. This function allows you to specify the agent to hand off to, along with optional overrides and input filters. - -### Basic Usage - -Here's how you can create a simple handoff: - -```python -from agents import Agent, handoff - -billing_agent = Agent(name="Billing agent") -refund_agent = Agent(name="Refund agent") - -# (1)! -triage_agent = Agent(name="Triage agent", handoffs=[billing_agent, handoff(refund_agent)]) -``` - -1. You can use the agent directly (as in `billing_agent`), or you can use the `handoff()` function. - -### Customizing handoffs via the `handoff()` function - -The [`handoff()`][agents.handoffs.handoff] function lets you customize things. - -- `agent`: This is the agent to which things will be handed off. -- `tool_name_override`: By default, the `Handoff.default_tool_name()` function is used, which resolves to `transfer_to_`. You can override this. -- `tool_description_override`: Override the default tool description from `Handoff.default_tool_description()` -- `on_handoff`: A callback function executed when the handoff is invoked. This is useful for things like kicking off some data fetching as soon as you know a handoff is being invoked. This function receives the agent context, and can optionally also receive LLM generated input. The input data is controlled by the `input_type` param. -- `input_type`: The type of input expected by the handoff (optional). -- `input_filter`: This lets you filter the input received by the next agent. See below for more. - -```python -from agents import Agent, handoff, RunContextWrapper - -def on_handoff(ctx: RunContextWrapper[None]): - print("Handoff called") - -agent = Agent(name="My agent") - -handoff_obj = handoff( - agent=agent, - on_handoff=on_handoff, - tool_name_override="custom_handoff_tool", - tool_description_override="Custom description", -) -``` - -## Handoff inputs - -In certain situations, you want the LLM to provide some data when it calls a handoff. For example, imagine a handoff to an "Escalation agent". You might want a reason to be provided, so you can log it. - -```python -from pydantic import BaseModel - -from agents import Agent, handoff, RunContextWrapper - -class EscalationData(BaseModel): - reason: str - -async def on_handoff(ctx: RunContextWrapper[None], input_data: EscalationData): - print(f"Escalation agent called with reason: {input_data.reason}") - -agent = Agent(name="Escalation agent") - -handoff_obj = handoff( - agent=agent, - on_handoff=on_handoff, - input_type=EscalationData, -) -``` - -## Input filters - -When a handoff occurs, it's as though the new agent takes over the conversation, and gets to see the entire previous conversation history. If you want to change this, you can set an [`input_filter`][agents.handoffs.Handoff.input_filter]. An input filter is a function that receives the existing input via a [`HandoffInputData`][agents.handoffs.HandoffInputData], and must return a new `HandoffInputData`. - -There are some common patterns (for example removing all tool calls from the history), which are implemented for you in [`agents.extensions.handoff_filters`][] - -```python -from agents import Agent, handoff -from agents.extensions import handoff_filters - -agent = Agent(name="FAQ agent") - -handoff_obj = handoff( - agent=agent, - input_filter=handoff_filters.remove_all_tools, # (1)! -) -``` - -1. This will automatically remove all tools from the history when `FAQ agent` is called. - -## Recommended prompts - -To make sure that LLMs understand handoffs properly, we recommend including information about handoffs in your agents. We have a suggested prefix in [`agents.extensions.handoff_prompt.RECOMMENDED_PROMPT_PREFIX`][], or you can call [`agents.extensions.handoff_prompt.prompt_with_handoff_instructions`][] to automatically add recommended data to your prompts. - -```python -from agents import Agent -from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX - -billing_agent = Agent( - name="Billing agent", - instructions=f"""{RECOMMENDED_PROMPT_PREFIX} - .""", -) -``` diff --git a/tests/docs/index.md b/tests/docs/index.md deleted file mode 100644 index 28c68708..00000000 --- a/tests/docs/index.md +++ /dev/null @@ -1,52 +0,0 @@ -# OpenAI Agents SDK - -The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy to use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, [Swarm](https://github.com/openai/swarm/tree/main). The Agents SDK has a very small set of primitives: - -- **Agents**, which are LLMs equipped with instructions and tools -- **Handoffs**, which allow agents to delegate to other agents for specific tasks -- **Guardrails**, which enable the inputs to agents to be validated - -In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real world applications without a steep learning curve. In addition, the SDK comes with built-in **tracing** that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application. - -## Why use the Agents SDK - -The SDK has two driving design principles: - -1. Enough features to be worth using, but few enough primitives to make it quick to learn. -2. Works great out of the box, but you can customize exactly what happens. - -Here are the main features of the SDK: - -- Agent loop: Built-in agent loop that handles calling tools, sending results to the LLM, and looping until the LLM is done. -- Python-first: Use built-in language features to orchestrate and chain agents, rather than needing to learn new abstractions. -- Handoffs: A powerful feature to coordinate and delegate between multiple agents. -- Guardrails: Run input validations and checks in parallel to your agents, breaking early if the checks fail. -- Function tools: Turn any Python function into a tool, with automatic schema generation and Pydantic-powered validation. -- Tracing: Built-in tracing that lets you visualize, debug and monitor your workflows, as well as use the OpenAI suite of evaluation, fine-tuning and distillation tools. - -## Installation - -```bash -pip install openai-agents -``` - -## Hello world example - -```python -from agents import Agent, Runner - -agent = Agent(name="Assistant", instructions="You are a helpful assistant") - -result = Runner.run_sync(agent, "Write a haiku about recursion in programming.") -print(result.final_output) - -# Code within the code, -# Functions calling themselves, -# Infinite loop's dance. -``` - -(_If running this, ensure you set the `OPENAI_API_KEY` environment variable_) - -```bash -export OPENAI_API_KEY=sk-... -``` diff --git a/tests/docs/models.md b/tests/docs/models.md deleted file mode 100644 index 7d2ff1ff..00000000 --- a/tests/docs/models.md +++ /dev/null @@ -1,73 +0,0 @@ -# Models - -The Agents SDK comes with out of the box support for OpenAI models in two flavors: - -- **Recommended**: the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel], which calls OpenAI APIs using the new [Responses API](https://platform.openai.com/docs/api-reference/responses). -- The [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel], which calls OpenAI APIs using the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). - -## Mixing and matching models - -Within a single workflow, you may want to use different models for each agent. For example, you could use a smaller, faster model for triage, while using a larger, more capable model for complex tasks. When configuring an [`Agent`][agents.Agent], you can select a specific model by either: - -1. Passing the name of an OpenAI model. -2. Passing any model name + a [`ModelProvider`][agents.models.interface.ModelProvider] that can map that name to a Model instance. -3. Directly providing a [`Model`][agents.models.interface.Model] implementation. - -!!!note - - While our SDK supports both the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] and the[`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] shapes, we recommend using a single model shape for each workflow because the two shapes support a different set of features and tools. If your workflow requires mixing and matching model shapes, make sure that all the features you're using are available on both. - -```python -from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel -import asyncio - -spanish_agent = Agent( - name="Spanish agent", - instructions="You only speak Spanish.", - model="o3-mini", # (1)! -) - -english_agent = Agent( - name="English agent", - instructions="You only speak English", - model=OpenAIChatCompletionsModel( # (2)! - model="gpt-4o", - openai_client=AsyncOpenAI() - ), -) - -triage_agent = Agent( - name="Triage agent", - instructions="Handoff to the appropriate agent based on the language of the request.", - handoffs=[spanish_agent, english_agent], - model="gpt-3.5-turbo", -) - -async def main(): - result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") - print(result.final_output) -``` - -1. Sets the the name of an OpenAI model directly. -2. Provides a [`Model`][agents.models.interface.Model] implementation. - -## Using other LLM providers - -Many providers also support the OpenAI API format, which means you can pass a `base_url` to the existing OpenAI model implementations and use them easily. `ModelSettings` is used to configure tuning parameters (e.g., temperature, top_p) for the model you select. - -```python -external_client = AsyncOpenAI( - api_key="EXTERNAL_API_KEY", - base_url="https://api.external.com/v1/", -) - -spanish_agent = Agent( - name="Spanish agent", - instructions="You only speak Spanish.", - model=OpenAIChatCompletionsModel( - model="EXTERNAL_MODEL_NAME", - openai_client=external_client, - ), - model_settings=ModelSettings(temperature=0.5), -) -``` diff --git a/tests/docs/multi_agent.md b/tests/docs/multi_agent.md deleted file mode 100644 index c1182492..00000000 --- a/tests/docs/multi_agent.md +++ /dev/null @@ -1,37 +0,0 @@ -# Orchestrating multiple agents - -Orchestration refers to the flow of agents in your app. Which agents run, in what order, and how do they decide what happens next? There are two main ways to orchestrate agents: - -1. Allowing the LLM to make decisions: this uses the intelligence of an LLM to plan, reason, and decide on what steps to take based on that. -2. Orchestrating via code: determining the flow of agents via your code. - -You can mix and match these patterns. Each has their own tradeoffs, described below. - -## Orchestrating via LLM - -An agent is an LLM equipped with instructions, tools and handoffs. This means that given an open-ended task, the LLM can autonomously plan how it will tackle the task, using tools to take actions and acquire data, and using handoffs to delegate tasks to sub-agents. For example, a research agent could be equipped with tools like: - -- Web search to find information online -- File search and retrieval to search through proprietary data and connections -- Computer use to take actions on a computer -- Code execution to do data analysis -- Handoffs to specialized agents that are great at planning, report writing and more. - -This pattern is great when the task is open-ended and you want to rely on the intelligence of an LLM. The most important tactics here are: - -1. Invest in good prompts. Make it clear what tools are available, how to use them, and what parameters it must operate within. -2. Monitor your app and iterate on it. See where things go wrong, and iterate on your prompts. -3. Allow the agent to introspect and improve. For example, run it in a loop, and let it critique itself; or, provide error messages and let it improve. -4. Have specialized agents that excel in one task, rather than having a general purpose agent that is expected to be good at anything. -5. Invest in [evals](https://platform.openai.com/docs/guides/evals). This lets you train your agents to improve and get better at tasks. - -## Orchestrating via code - -While orchestrating via LLM is powerful, orchestrating via LLM makes tasks more deterministic and predictable, in terms of speed, cost and performance. Common patterns here are: - -- Using [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) to generate well formed data that you can inspect with your code. For example, you might ask an agent to classify the task into a few categories, and then pick the next agent based on the category. -- Chaining multiple agents by transforming the output of one into the input of the next. You can decompose a task like writing a blog post into a series of steps - do research, write an outline, write the blog post, critique it, and then improve it. -- Running the agent that performs the task in a `while` loop with an agent that evaluates and provides feedback, until the evaluator says the output passes certain criteria. -- Running multiple agents in parallel, e.g. via Python primitives like `asyncio.gather`. This is useful for speed when you have multiple tasks that don't depend on each other. - -We have a number of examples in [`examples/agent_patterns`](https://github.com/openai/openai-agents-python/examples/agent_patterns). diff --git a/tests/docs/ref/agent.md b/tests/docs/ref/agent.md deleted file mode 100644 index 9f8b10d2..00000000 --- a/tests/docs/ref/agent.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Agents` - -::: agents.agent diff --git a/tests/docs/ref/agent_output.md b/tests/docs/ref/agent_output.md deleted file mode 100644 index e453de03..00000000 --- a/tests/docs/ref/agent_output.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Agent output` - -::: agents.agent_output diff --git a/tests/docs/ref/exceptions.md b/tests/docs/ref/exceptions.md deleted file mode 100644 index 7c1a2547..00000000 --- a/tests/docs/ref/exceptions.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Exceptions` - -::: agents.exceptions diff --git a/tests/docs/ref/extensions/handoff_filters.md b/tests/docs/ref/extensions/handoff_filters.md deleted file mode 100644 index 0ffcb13c..00000000 --- a/tests/docs/ref/extensions/handoff_filters.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Handoff filters` - -::: agents.extensions.handoff_filters diff --git a/tests/docs/ref/extensions/handoff_prompt.md b/tests/docs/ref/extensions/handoff_prompt.md deleted file mode 100644 index ca800765..00000000 --- a/tests/docs/ref/extensions/handoff_prompt.md +++ /dev/null @@ -1,8 +0,0 @@ -# `Handoff prompt` - -::: agents.extensions.handoff_prompt - - options: - members: - - RECOMMENDED_PROMPT_PREFIX - - prompt_with_handoff_instructions diff --git a/tests/docs/ref/function_schema.md b/tests/docs/ref/function_schema.md deleted file mode 100644 index 06aac2a6..00000000 --- a/tests/docs/ref/function_schema.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Function schema` - -::: agents.function_schema diff --git a/tests/docs/ref/guardrail.md b/tests/docs/ref/guardrail.md deleted file mode 100644 index 17ec929c..00000000 --- a/tests/docs/ref/guardrail.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Guardrails` - -::: agents.guardrail diff --git a/tests/docs/ref/handoffs.md b/tests/docs/ref/handoffs.md deleted file mode 100644 index 717a9181..00000000 --- a/tests/docs/ref/handoffs.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Handoffs` - -::: agents.handoffs diff --git a/tests/docs/ref/index.md b/tests/docs/ref/index.md deleted file mode 100644 index 1b8439fa..00000000 --- a/tests/docs/ref/index.md +++ /dev/null @@ -1,13 +0,0 @@ -# Agents module - -::: agents - - options: - members: - - set_default_openai_key - - set_default_openai_client - - set_default_openai_api - - set_tracing_export_api_key - - set_tracing_disabled - - set_trace_processors - - enable_verbose_stdout_logging diff --git a/tests/docs/ref/items.md b/tests/docs/ref/items.md deleted file mode 100644 index 29279e15..00000000 --- a/tests/docs/ref/items.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Items` - -::: agents.items diff --git a/tests/docs/ref/lifecycle.md b/tests/docs/ref/lifecycle.md deleted file mode 100644 index 432af147..00000000 --- a/tests/docs/ref/lifecycle.md +++ /dev/null @@ -1,6 +0,0 @@ -# `Lifecycle` - -::: agents.lifecycle - - options: - show_source: false diff --git a/tests/docs/ref/model_settings.md b/tests/docs/ref/model_settings.md deleted file mode 100644 index f7f411f0..00000000 --- a/tests/docs/ref/model_settings.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Model settings` - -::: agents.model_settings diff --git a/tests/docs/ref/models/interface.md b/tests/docs/ref/models/interface.md deleted file mode 100644 index e7bd89a8..00000000 --- a/tests/docs/ref/models/interface.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Model interface` - -::: agents.models.interface diff --git a/tests/docs/ref/models/openai_chatcompletions.md b/tests/docs/ref/models/openai_chatcompletions.md deleted file mode 100644 index 76cf5633..00000000 --- a/tests/docs/ref/models/openai_chatcompletions.md +++ /dev/null @@ -1,3 +0,0 @@ -# `OpenAI Chat Completions model` - -::: agents.models.openai_chatcompletions diff --git a/tests/docs/ref/models/openai_responses.md b/tests/docs/ref/models/openai_responses.md deleted file mode 100644 index e1794bae..00000000 --- a/tests/docs/ref/models/openai_responses.md +++ /dev/null @@ -1,3 +0,0 @@ -# `OpenAI Responses model` - -::: agents.models.openai_responses diff --git a/tests/docs/ref/result.md b/tests/docs/ref/result.md deleted file mode 100644 index 3a9e4a9b..00000000 --- a/tests/docs/ref/result.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Results` - -::: agents.result diff --git a/tests/docs/ref/run.md b/tests/docs/ref/run.md deleted file mode 100644 index ddf4475f..00000000 --- a/tests/docs/ref/run.md +++ /dev/null @@ -1,8 +0,0 @@ -# `Runner` - -::: agents.run - - options: - members: - - Runner - - RunConfig diff --git a/tests/docs/ref/run_context.md b/tests/docs/ref/run_context.md deleted file mode 100644 index 49e87305..00000000 --- a/tests/docs/ref/run_context.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Run context` - -::: agents.run_context diff --git a/tests/docs/ref/stream_events.md b/tests/docs/ref/stream_events.md deleted file mode 100644 index ea484317..00000000 --- a/tests/docs/ref/stream_events.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Streaming events` - -::: agents.stream_events diff --git a/tests/docs/ref/tool.md b/tests/docs/ref/tool.md deleted file mode 100644 index 887bef75..00000000 --- a/tests/docs/ref/tool.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Tools` - -::: agents.tool diff --git a/tests/docs/ref/tracing/create.md b/tests/docs/ref/tracing/create.md deleted file mode 100644 index c983e336..00000000 --- a/tests/docs/ref/tracing/create.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Creating traces/spans` - -::: agents.tracing.create diff --git a/tests/docs/ref/tracing/index.md b/tests/docs/ref/tracing/index.md deleted file mode 100644 index 88a0fe61..00000000 --- a/tests/docs/ref/tracing/index.md +++ /dev/null @@ -1,3 +0,0 @@ -# Tracing module - -::: agents.tracing diff --git a/tests/docs/ref/tracing/processor_interface.md b/tests/docs/ref/tracing/processor_interface.md deleted file mode 100644 index 9fb04e86..00000000 --- a/tests/docs/ref/tracing/processor_interface.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Processor interface` - -::: agents.tracing.processor_interface diff --git a/tests/docs/ref/tracing/processors.md b/tests/docs/ref/tracing/processors.md deleted file mode 100644 index d7ac4af1..00000000 --- a/tests/docs/ref/tracing/processors.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Processors` - -::: agents.tracing.processors diff --git a/tests/docs/ref/tracing/scope.md b/tests/docs/ref/tracing/scope.md deleted file mode 100644 index 7b5b9fdf..00000000 --- a/tests/docs/ref/tracing/scope.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Scope` - -::: agents.tracing.scope diff --git a/tests/docs/ref/tracing/setup.md b/tests/docs/ref/tracing/setup.md deleted file mode 100644 index 1dc6a0fe..00000000 --- a/tests/docs/ref/tracing/setup.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Setup` - -::: agents.tracing.setup diff --git a/tests/docs/ref/tracing/span_data.md b/tests/docs/ref/tracing/span_data.md deleted file mode 100644 index 6ace7a88..00000000 --- a/tests/docs/ref/tracing/span_data.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Span data` - -::: agents.tracing.span_data diff --git a/tests/docs/ref/tracing/spans.md b/tests/docs/ref/tracing/spans.md deleted file mode 100644 index 9071707c..00000000 --- a/tests/docs/ref/tracing/spans.md +++ /dev/null @@ -1,9 +0,0 @@ -# `Spans` - -::: agents.tracing.spans - - options: - members: - - Span - - NoOpSpan - - SpanImpl diff --git a/tests/docs/ref/tracing/traces.md b/tests/docs/ref/tracing/traces.md deleted file mode 100644 index 0b7377f9..00000000 --- a/tests/docs/ref/tracing/traces.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Traces` - -::: agents.tracing.traces diff --git a/tests/docs/ref/tracing/util.md b/tests/docs/ref/tracing/util.md deleted file mode 100644 index 2be3d58c..00000000 --- a/tests/docs/ref/tracing/util.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Util` - -::: agents.tracing.util diff --git a/tests/docs/ref/usage.md b/tests/docs/ref/usage.md deleted file mode 100644 index b8b29db5..00000000 --- a/tests/docs/ref/usage.md +++ /dev/null @@ -1,3 +0,0 @@ -# `Usage` - -::: agents.usage diff --git a/tests/docs/results.md b/tests/docs/results.md deleted file mode 100644 index d1864fa8..00000000 --- a/tests/docs/results.md +++ /dev/null @@ -1,52 +0,0 @@ -# Results - -When you call the `Runner.run` methods, you either get a: - -- [`RunResult`][agents.result.RunResult] if you call `run` or `run_sync` -- [`RunResultStreaming`][agents.result.RunResultStreaming] if you call `run_streamed` - -Both of these inherit from [`RunResultBase`][agents.result.RunResultBase], which is where most useful information is present. - -## Final output - -The [`final_output`][agents.result.RunResultBase.final_output] property contains the final output of the last agent that ran. This is either: - -- a `str`, if the last agent didn't have an `output_type` defined -- an object of type `last_agent.output_type`, if the agent had an output type defined. - -!!! note - - `final_output` is of type `Any`. We can't statically type this, because of handoffs. If handoffs occur, that means any Agent might be the last agent, so we don't statically know the set of possible output types. - -## Inputs for the next turn - -You can use [`result.to_input_list()`][agents.result.RunResultBase.to_input_list] to turn the result into an input list that concatenates the original input you provided, to the items generated during the agent run. This makes it convenient to take the outputs of one agent run and pass them into another run, or to run it in a loop and append new user inputs each time. - -## Last agent - -The [`last_agent`][agents.result.RunResultBase.last_agent] property contains the last agent that ran. Depending on your application, this is often useful for the next time the user inputs something. For example, if you have a frontline triage agent that hands off to a language-specific agent, you can store the last agent, and re-use it the next time the user messages the agent. - -## New items - -The [`new_items`][agents.result.RunResultBase.new_items] property contains the new items generated during the run. The items are [`RunItem`][agents.items.RunItem]s. A run item wraps the raw item generated by the LLM. - -- [`MessageOutputItem`][agents.items.MessageOutputItem] indicates a message from the LLM. The raw item is the message generated. -- [`HandoffCallItem`][agents.items.HandoffCallItem] indicates that the LLM called the handoff tool. The raw item is the tool call item from the LLM. -- [`HandoffOutputItem`][agents.items.HandoffOutputItem] indicates that a handoff occured. The raw item is the tool response to the handoff tool call. You can also access the source/target agents from the item. -- [`ToolCallItem`][agents.items.ToolCallItem] indicates that the LLM invoked a tool. -- [`ToolCallOutputItem`][agents.items.ToolCallOutputItem] indicates that a tool was called. The raw item is the tool response. You can also access the tool output from the item. -- [`ReasoningItem`][agents.items.ReasoningItem] indicates a reasoning item from the LLM. The raw item is the reasoning generated. - -## Other information - -### Guardrail results - -The [`input_guardrail_results`][agents.result.RunResultBase.input_guardrail_results] and [`output_guardrail_results`][agents.result.RunResultBase.output_guardrail_results] properties contain the results of the guardrails, if any. Guardrail results can sometimes contain useful information you want to log or store, so we make these available to you. - -### Raw responses - -The [`raw_responses`][agents.result.RunResultBase.raw_responses] property contains the [`ModelResponse`][agents.items.ModelResponse]s generated by the LLM. - -### Original input - -The [`input`][agents.result.RunResultBase.input] property contains the original input you provided to the `run` method. In most cases you won't need this, but it's available in case you do. diff --git a/tests/docs/running_agents.md b/tests/docs/running_agents.md deleted file mode 100644 index a2f137cf..00000000 --- a/tests/docs/running_agents.md +++ /dev/null @@ -1,95 +0,0 @@ -# Running agents - -You can run agents via the [`Runner`][agents.run.Runner] class. You have 3 options: - -1. [`Runner.run()`][agents.run.Runner.run], which runs async and returns a [`RunResult`][agents.result.RunResult]. -2. [`Runner.run_sync()`][agents.run.Runner.run_sync], which is a sync method and just runs `.run()` under the hood. -3. [`Runner.run_streamed()`][agents.run.Runner.run_streamed], which runs async and returns a [`RunResultStreaming`][agents.result.RunResultStreaming]. It calls the LLM in streaming mode, and streams those events to you as they are received. - -```python -from agents import Agent, Runner - -async def main(): - agent = Agent(name="Assistant", instructions="You are a helpful assistant") - - result = await Runner.run(agent, "Write a haiku about recursion in programming.") - print(result.final_output) - # Code within the code, - # Functions calling themselves, - # Infinite loop's dance. -``` - -Read more in the [results guide](results.md). - -## The agent loop - -When you use the run method in `Runner`, you pass in a starting agent and input. The input can either be a string (which is considered a user message), or a list of input items, which are the items in the OpenAI Responses API. - -The runner then runs a loop: - -1. We call the LLM for the current agent, with the current input. -2. The LLM produces its output. - 1. If the LLM returns a `final_output`, the loop ends and we return the result. - 2. If the LLM does a handoff, we update the current agent and input, and re-run the loop. - 3. If the LLM produces tool calls, we run those tool calls, append the results, and re-run the loop. -3. If we exceed the `max_turns` passed, we raise a [`MaxTurnsExceeded`][agents.exceptions.MaxTurnsExceeded] exception. - -!!! note - - The rule for whether the LLM output is considered as a "final output" is that it produces text output with the desired type, and there are no tool calls. - -## Streaming - -Streaming allows you to additionally receive streaming events as the LLM runs. Once the stream is done, the [`RunResultStreaming`][agents.result.RunResultStreaming] will contain the complete information about the run, including all the new outputs produces. You can call `.stream_events()` for the streaming events. Read more in the [streaming guide](streaming.md). - -## Run config - -The `run_config` parameter lets you configure some global settings for the agent run: - -- [`model`][agents.run.RunConfig.model]: Allows setting a global LLM model to use, irrespective of what `model` each Agent has. -- [`model_provider`][agents.run.RunConfig.model_provider]: A model provider for looking up model names, which defaults to OpenAI. -- [`model_settings`][agents.run.RunConfig.model_settings]: Overrides agent-specific settings. For example, you can set a global `temperature` or `top_p`. -- [`input_guardrails`][agents.run.RunConfig.input_guardrails], [`output_guardrails`][agents.run.RunConfig.output_guardrails]: A list of input or output guardrails to include on all runs. -- [`handoff_input_filter`][agents.run.RunConfig.handoff_input_filter]: A global input filter to apply to all handoffs, if the handoff doesn't already have one. The input filter allows you to edit the inputs that are sent to the new agent. See the documentation in [`Handoff.input_filter`][agents.handoffs.Handoff.input_filter] for more details. -- [`tracing_disabled`][agents.run.RunConfig.tracing_disabled]: Allows you to disable [tracing](tracing.md) for the entire run. -- [`trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data]: Configures whether traces will include potentially sensitive data, such as LLM and tool call inputs/outputs. -- [`workflow_name`][agents.run.RunConfig.workflow_name], [`trace_id`][agents.run.RunConfig.trace_id], [`group_id`][agents.run.RunConfig.group_id]: Sets the tracing workflow name, trace ID and trace group ID for the run. We recommend at least setting `workflow_name`. The session ID is an optional field that lets you link traces across multiple runs. -- [`trace_metadata`][agents.run.RunConfig.trace_metadata]: Metadata to include on all traces. - -## Conversations/chat threads - -Calling any of the run methods can result in one or more agents running (and hence one or more LLM calls), but it represents a single logical turn in a chat conversation. For example: - -1. User turn: user enter text -2. Runner run: first agent calls LLM, runs tools, does a handoff to a second agent, second agent runs more tools, and then produces an output. - -At the end of the agent run, you can choose what to show to the user. For example, you might show the user every new item generated by the agents, or just the final output. Either way, the user might then ask a followup question, in which case you can call the run method again. - -You can use the base [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] method to get the inputs for the next turn. - -```python -async def main(): - agent = Agent(name="Assistant", instructions="Reply very concisely.") - - with trace(workflow_name="Conversation", group_id=thread_id): - # First turn - result = await Runner.run(agent, "What city is the Golden Gate Bridge in?") - print(result.final_output) - # San Francisco - - # Second turn - new_input = output.to_input_list() + [{"role": "user", "content": "What state is it in?"}] - result = await Runner.run(agent, new_input) - print(result.final_output) - # California -``` - -## Exceptions - -The SDK raises exceptions in certain cases. The full list is in [`agents.exceptions`][]. As an overview: - -- [`AgentsException`][agents.exceptions.AgentsException] is the base class for all exceptions raised in the SDK. -- [`MaxTurnsExceeded`][agents.exceptions.MaxTurnsExceeded] is raised when the run exceeds the `max_turns` passed to the run methods. -- [`ModelBehaviorError`][agents.exceptions.ModelBehaviorError] is raised when the model produces invalid outputs, e.g. malformed JSON or using non-existent tools. -- [`UserError`][agents.exceptions.UserError] is raised when you (the person writing code using the SDK) make an error using the SDK. -- [`InputGuardrailTripwireTriggered`][agents.exceptions.InputGuardrailTripwireTriggered], [`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered] is raised when a [guardrail](guardrails.md) is tripped. diff --git a/tests/docs/stylesheets/extra.css b/tests/docs/stylesheets/extra.css deleted file mode 100644 index 89cf164b..00000000 --- a/tests/docs/stylesheets/extra.css +++ /dev/null @@ -1,194 +0,0 @@ -@font-face { - font-display: swap; 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-} - -.md-clipboard.md-icon { - color: #9e9e9e; -} - -/* Reset scrollbar styling to browser default with high priority */ -.md-sidebar__scrollwrap { - scrollbar-color: auto !important; -} diff --git a/tests/docs/tools.md b/tests/docs/tools.md deleted file mode 100644 index f7a88691..00000000 --- a/tests/docs/tools.md +++ /dev/null @@ -1,270 +0,0 @@ -# Tools - -Tools let agents take actions: things like fetching data, running code, calling external APIs, and even using a computer. There are three classes of tools in the Agent SDK: - -- Hosted tools: these run on LLM servers alongside the AI models. OpenAI offers retrieval, web search and computer use as hosted tools. -- Function calling: these allow you to use any Python function as a tool. -- Agents as tools: this allows you to use an agent as a tool, allowing Agents to call other agents without handing off to them. - -## Hosted tools - -OpenAI offers a few built-in tools when using the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel]: - -- The [`WebSearchTool`][agents.tool.WebSearchTool] lets an agent search the web. -- The [`FileSearchTool`][agents.tool.FileSearchTool] allows retrieving information from your OpenAI Vector Stores. -- The [`ComputerTool`][agents.tool.ComputerTool] allows automating computer use tasks. - -```python -from agents import Agent, FileSearchTool, Runner, WebSearchTool - -agent = Agent( - name="Assistant", - tools=[ - WebSearchTool(), - FileSearchTool( - max_num_results=3, - vector_store_ids=["VECTOR_STORE_ID"], - ), - ], -) - -async def main(): - result = await Runner.run(agent, "Which coffee shop should I go to, taking into account my preferences and the weather today in SF?") - print(result.final_output) -``` - -## Function tools - -You can use any Python function as a tool. The Agents SDK will setup the tool automatically: - -- The name of the tool will be the name of the Python function (or you can provide a name) -- Tool description will be taken from the docstring of the function (or you can provide a description) -- The schema for the function inputs is automatically created from the function's arguments -- Descriptions for each input are taken from the docstring of the function, unless disabled - -We use Python's `inspect` module to extract the function signature, along with [`griffe`](https://mkdocstrings.github.io/griffe/) to parse docstrings and `pydantic` for schema creation. - -```python -import json - -from typing_extensions import TypedDict, Any - -from agents import Agent, FunctionTool, RunContextWrapper, function_tool - - -class Location(TypedDict): - lat: float - long: float - -@function_tool # (1)! -async def fetch_weather(location: Location) -> str: - # (2)! - """Fetch the weather for a given location. - - Args: - location: The location to fetch the weather for. - """ - # In real life, we'd fetch the weather from a weather API - return "sunny" - - -@function_tool(name_override="fetch_data") # (3)! -def read_file(ctx: RunContextWrapper[Any], path: str, directory: str | None = None) -> str: - """Read the contents of a file. - - Args: - path: The path to the file to read. - directory: The directory to read the file from. - """ - # In real life, we'd read the file from the file system - return "" - - -agent = Agent( - name="Assistant", - tools=[fetch_weather, read_file], # (4)! -) - -for tool in agent.tools: - if isinstance(tool, FunctionTool): - print(tool.name) - print(tool.description) - print(json.dumps(tool.params_json_schema, indent=2)) - print() - -``` - -1. You can use any Python types as arguments to your functions, and the function can be sync or async. -2. Docstrings, if present, are used to capture descriptions and argument descriptions -3. Functions can optionally take the `context` (must be the first argument). You can also set overrides, like the name of the tool, description, which docstring style to use, etc. -4. You can pass the decorated functions to the list of tools. - -??? note "Expand to see output" - - ``` - fetch_weather - Fetch the weather for a given location. - { - "$defs": { - "Location": { - "properties": { - "lat": { - "title": "Lat", - "type": "number" - }, - "long": { - "title": "Long", - "type": "number" - } - }, - "required": [ - "lat", - "long" - ], - "title": "Location", - "type": "object" - } - }, - "properties": { - "location": { - "$ref": "#/$defs/Location", - "description": "The location to fetch the weather for." - } - }, - "required": [ - "location" - ], - "title": "fetch_weather_args", - "type": "object" - } - - fetch_data - Read the contents of a file. - { - "properties": { - "path": { - "description": "The path to the file to read.", - "title": "Path", - "type": "string" - }, - "directory": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "description": "The directory to read the file from.", - "title": "Directory" - } - }, - "required": [ - "path" - ], - "title": "fetch_data_args", - "type": "object" - } - ``` - -### Custom function tools - -Sometimes, you don't want to use a Python function as a tool. You can directly create a [`FunctionTool`][agents.tool.FunctionTool] if you prefer. You'll need to provide: - -- `name` -- `description` -- `params_json_schema`, which is the JSON schema for the arguments -- `on_invoke_tool`, which is an async function that receives the context and the arguments as a JSON string, and must return the tool output as a string. - -```python -from typing import Any - -from pydantic import BaseModel - -from agents import RunContextWrapper, FunctionTool - - - -def do_some_work(data: str) -> str: - return "done" - - -class FunctionArgs(BaseModel): - username: str - age: int - - -async def run_function(ctx: RunContextWrapper[Any], args: str) -> str: - parsed = FunctionArgs.model_validate_json(args) - return do_some_work(data=f"{parsed.username} is {parsed.age} years old") - - -tool = FunctionTool( - name="process_user", - description="Processes extracted user data", - params_json_schema=FunctionArgs.model_json_schema(), - on_invoke_tool=run_function, -) -``` - -### Automatic argument and docstring parsing - -As mentioned before, we automatically parse the function signature to extract the schema for the tool, and we parse the docstring to extract descriptions for the tool and for individual arguments. Some notes on that: - -1. The signature parsing is done via the `inspect` module. We use type annotations to understand the types for the arguments, and dynamically build a Pydantic model to represent the overall schema. It supports most types, including Python primitives, Pydantic models, TypedDicts, and more. -2. We use `griffe` to parse docstrings. Supported docstring formats are `google`, `sphinx` and `numpy`. We attempt to automatically detect the docstring format, but this is best-effort and you can explicitly set it when calling `function_tool`. You can also disable docstring parsing by setting `use_docstring_info` to `False`. - -The code for the schema extraction lives in [`agents.function_schema`][]. - -## Agents as tools - -In some workflows, you may want a central agent to orchestrate a network of specialized agents, instead of handing off control. You can do this by modeling agents as tools. - -```python -from agents import Agent, Runner -import asyncio - -spanish_agent = Agent( - name="Spanish agent", - instructions="You translate the user's message to Spanish", -) - -french_agent = Agent( - name="French agent", - instructions="You translate the user's message to French", -) - -orchestrator_agent = Agent( - name="orchestrator_agent", - instructions=( - "You are a translation agent. You use the tools given to you to translate." - "If asked for multiple translations, you call the relevant tools." - ), - tools=[ - spanish_agent.as_tool( - tool_name="translate_to_spanish", - tool_description="Translate the user's message to Spanish", - ), - french_agent.as_tool( - tool_name="translate_to_french", - tool_description="Translate the user's message to French", - ), - ], -) - -async def main(): - result = await Runner.run(orchestrator_agent, input="Say 'Hello, how are you?' in Spanish.") - print(result.final_output) -``` - -## Handling errors in function tools - -When you create a function tool via `@function_tool`, you can pass a `failure_error_function`. This is a function that provides an error response to the LLM in case the tool call crashes. - -- By default (i.e. if you don't pass anything), it runs a `default_tool_error_function` which tells the LLM an error occurred. -- If you pass your own error function, it runs that instead, and sends the response to the LLM. -- If you explicitly pass `None`, then any tool call errors will be re-raised for you to handle. This could be a `ModelBehaviorError` if the model produced invalid JSON, or a `UserError` if your code crashed, etc. - -If you are manually creating a `FunctionTool` object, then you must handle errors inside the `on_invoke_tool` function. diff --git a/tests/docs/tracing.md b/tests/docs/tracing.md deleted file mode 100644 index fbf2ae41..00000000 --- a/tests/docs/tracing.md +++ /dev/null @@ -1,95 +0,0 @@ -# Tracing - -The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, handoffs, guardrails, and even custom events that occur. Using the [Traces dashboard](https://platform.openai.com/traces), you can debug, visualize, and monitor your workflows during development and in production. - -!!!note - - Tracing is enabled by default. There are two ways to disable tracing: - - 1. You can globally disable tracing by setting the env var `OPENAI_AGENTS_DISABLE_TRACING=1` - 2. You can disable tracing for a single run by setting [`agents.run.RunConfig.tracing_disabled`][] to `True` - -## Traces and spans - -- **Traces** represent a single end-to-end operation of a "workflow". They're composed of Spans. Traces have the following properties: - - `workflow_name`: This is the logical workflow or app. For example "Code generation" or "Customer service". - - `trace_id`: A unique ID for the trace. Automatically generated if you don't pass one. Must have the format `trace_<32_alphanumeric>`. - - `group_id`: Optional group ID, to link multiple traces from the same conversation. For example, you might use a chat thread ID. - - `disabled`: If True, the trace will not be recorded. - - `metadata`: Optiona metadata for the trace. -- **Spans** represent operations that have a start and end time. Spans have: - - `started_at` and `ended_at` timestamps. - - `trace_id`, to represent the trace they belong to - - `parent_id`, which points to the parent Span of this Span (if any) - - `span_data`, which is information about the Span. For example, `AgentSpanData` contains information about the Agent, `GenerationSpanData` contains information about the LLM generation, etc. - -## Default tracing - -By default, the SDK traces the following: - -- The entire `Runner.{run, run_sync, run_streamed}()` is wrapped in a `trace()`. -- Each time an agent runs, it is wrapped in `agent_span()` -- LLM generations are wrapped in `generation_span()` -- Function tool calls are each wrapped in `function_span()` -- Guardrails are wrapped in `guardrail_span()` -- Handoffs are wrapped in `handoff_span()` - -By default, the trace is named "Agent trace". You can set this name if you use `trace`, or you can can configure the name and other properties with the [`RunConfig`][agents.run.RunConfig]. - -In addition, you can set up [custom trace processors](#custom-tracing-processors) to push traces to other destinations (as a replacement, or secondary destination). - -## Higher level traces - -Sometimes, you might want multiple calls to `run()` to be part of a single trace. You can do this by wrapping the entire code in a `trace()`. - -```python -from agents import Agent, Runner, trace - -async def main(): - agent = Agent(name="Joke generator", instructions="Tell funny jokes.") - - with trace("Joke workflow"): # (1)! - first_result = await Runner.run(agent, "Tell me a joke") - second_result = await Runner.run(agent, f"Rate this joke: {first_output.final_output}") - print(f"Joke: {first_result.final_output}") - print(f"Rating: {second_result.final_output}") -``` - -1. Because the two calls to `Runner.run` are wrapped in a `with trace()`, the individual runs will be part of the overall trace rather than creating two traces. - -## Creating traces - -You can use the [`trace()`][agents.tracing.trace] function to create a trace. Traces need to be started and finished. You have two options to do so: - -1. **Recommended**: use the trace as a context manager, i.e. `with trace(...) as my_trace`. This will automatically start and end the trace at the right time. -2. You can also manually call [`trace.start()`][agents.tracing.Trace.start] and [`trace.finish()`][agents.tracing.Trace.finish]. - -The current trace is tracked via a Python [`contextvar`](https://docs.python.org/3/library/contextvars.html). This means that it works with concurrency automatically. If you manually start/end a trace, you'll need to pass `mark_as_current` and `reset_current` to `start()`/`finish()` to update the current trace. - -## Creating spans - -You can use the various [`*_span()`][agents.tracing.create] methods to create a span. In general, you don't need to manually create spans. A [`custom_span()`][agents.tracing.custom_span] function is available for tracking custom span information. - -Spans are automatically part of the current trace, and are nested under the nearest current span, which is tracked via a Python [`contextvar`](https://docs.python.org/3/library/contextvars.html). - -## Sensitive data - -Some spans track potentially sensitive data. For example, the `generation_span()` stores the inputs/outputs of the LLM generation, and `function_span()` stores the inputs/outputs of function calls. These may contain sensitive data, so you can disable capturing that data via [`RunConfig.trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data]. - -## Custom tracing processors - -The high level architecture for tracing is: - -- At initialization, we create a global [`TraceProvider`][agents.tracing.setup.TraceProvider], which is responsible for creating traces. -- We configure the `TraceProvider` with a [`BatchTraceProcessor`][agents.tracing.processors.BatchTraceProcessor] that sends traces/spans in batches to a [`BackendSpanExporter`][agents.tracing.processors.BackendSpanExporter], which exports the spans and traces to the OpenAI backend in batches. - -To customize this default setup, to send traces to alternative or additional backends or modifying exporter behavior, you have two options: - -1. [`add_trace_processor()`][agents.tracing.add_trace_processor] lets you add an **additional** trace processor that will receive traces and spans as they are ready. This lets you do your own processing in addition to sending traces to OpenAI's backend. -2. [`set_trace_processors()`][agents.tracing.set_trace_processors] lets you **replace** the default processors with your own trace processors. This means traces will not be sent to the OpenAI backend unless you include a `TracingProcessor` that does so. - -External trace processors include: - -- [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk) -- [Pydantic Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents) -- [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk) diff --git a/tests/examples/__init__.py b/tests/examples/__init__.py deleted file mode 100644 index e333a2e3..00000000 --- a/tests/examples/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -# Make the examples directory into a package to avoid top-level module name collisions. -# This is needed so that mypy treats files like examples/customer_service/main.py and -# examples/researcher_app/main.py as distinct modules rather than both named "main". diff --git a/tests/examples/agent_patterns/README.md b/tests/examples/agent_patterns/README.md deleted file mode 100644 index 4599b001..00000000 --- a/tests/examples/agent_patterns/README.md +++ /dev/null @@ -1,54 +0,0 @@ -# Common agentic patterns - -This folder contains examples of different common patterns for agents. - -## Deterministic flows - -A common tactic is to break down a task into a series of smaller steps. Each task can be performed by an agent, and the output of one agent is used as input to the next. For example, if your task was to generate a story, you could break it down into the following steps: - -1. Generate an outline -2. Generate the story -3. Generate the ending - -Each of these steps can be performed by an agent. The output of one agent is used as input to the next. - -See the [`deterministic.py`](./deterministic.py) file for an example of this. - -## Handoffs and routing - -In many situations, you have specialized sub-agents that handle specific tasks. You can use handoffs to route the task to the right agent. - -For example, you might have a frontline agent that receives a request, and then hands off to a specialized agent based on the language of the request. -See the [`routing.py`](./routing.py) file for an example of this. - -## Agents as tools - -The mental model for handoffs is that the new agent "takes over". It sees the previous conversation history, and owns the conversation from that point onwards. However, this is not the only way to use agents. You can also use agents as a tool - the tool agent goes off and runs on its own, and then returns the result to the original agent. - -For example, you could model the translation task above as tool calls instead: rather than handing over to the language-specific agent, you could call the agent as a tool, and then use the result in the next step. This enables things like translating multiple languages at once. - -See the [`agents_as_tools.py`](./agents_as_tools.py) file for an example of this. - -## LLM-as-a-judge - -LLMs can often improve the quality of their output if given feedback. A common pattern is to generate a response using a model, and then use a second model to provide feedback. You can even use a small model for the initial generation and a larger model for the feedback, to optimize cost. - -For example, you could use an LLM to generate an outline for a story, and then use a second LLM to evaluate the outline and provide feedback. You can then use the feedback to improve the outline, and repeat until the LLM is satisfied with the outline. - -See the [`llm_as_a_judge.py`](./llm_as_a_judge.py) file for an example of this. - -## Parallelization - -Running multiple agents in parallel is a common pattern. This can be useful for both latency (e.g. if you have multiple steps that don't depend on each other) and also for other reasons e.g. generating multiple responses and picking the best one. - -See the [`parallelization.py`](./parallelization.py) file for an example of this. It runs a translation agent multiple times in parallel, and then picks the best translation. - -## Guardrails - -Related to parallelization, you often want to run input guardrails to make sure the inputs to your agents are valid. For example, if you have a customer support agent, you might want to make sure that the user isn't trying to ask for help with a math problem. - -You can definitely do this without any special Agents SDK features by using parallelization, but we support a special guardrail primitive. Guardrails can have a "tripwire" - if the tripwire is triggered, the agent execution will immediately stop and a `GuardrailTripwireTriggered` exception will be raised. - -This is really useful for latency: for example, you might have a very fast model that runs the guardrail and a slow model that runs the actual agent. You wouldn't want to wait for the slow model to finish, so guardrails let you quickly reject invalid inputs. - -See the [`guardrails.py`](./guardrails.py) file for an example of this. diff --git a/tests/examples/agent_patterns/agents_as_tools.py b/tests/examples/agent_patterns/agents_as_tools.py deleted file mode 100644 index 9fd118ef..00000000 --- a/tests/examples/agent_patterns/agents_as_tools.py +++ /dev/null @@ -1,79 +0,0 @@ -import asyncio - -from agents import Agent, ItemHelpers, MessageOutputItem, Runner, trace - -""" -This example shows the agents-as-tools pattern. The frontline agent receives a user message and -then picks which agents to call, as tools. In this case, it picks from a set of translation -agents. -""" - -spanish_agent = Agent( - name="spanish_agent", - instructions="You translate the user's message to Spanish", - handoff_description="An english to spanish translator", -) - -french_agent = Agent( - name="french_agent", - instructions="You translate the user's message to French", - handoff_description="An english to french translator", -) - -italian_agent = Agent( - name="italian_agent", - instructions="You translate the user's message to Italian", - handoff_description="An english to italian translator", -) - -orchestrator_agent = Agent( - name="orchestrator_agent", - instructions=( - "You are a translation agent. You use the tools given to you to translate." - "If asked for multiple translations, you call the relevant tools in order." - "You never translate on your own, you always use the provided tools." - ), - tools=[ - spanish_agent.as_tool( - tool_name="translate_to_spanish", - tool_description="Translate the user's message to Spanish", - ), - french_agent.as_tool( - tool_name="translate_to_french", - tool_description="Translate the user's message to French", - ), - italian_agent.as_tool( - tool_name="translate_to_italian", - tool_description="Translate the user's message to Italian", - ), - ], -) - -synthesizer_agent = Agent( - name="synthesizer_agent", - instructions="You inspect translations, correct them if needed, and produce a final concatenated response.", -) - - -async def main(): - msg = input("Hi! What would you like translated, and to which languages? ") - - # Run the entire orchestration in a single trace - with trace("Orchestrator evaluator"): - orchestrator_result = await Runner.run(orchestrator_agent, msg) - - for item in orchestrator_result.new_items: - if isinstance(item, MessageOutputItem): - text = ItemHelpers.text_message_output(item) - if text: - print(f" - Translation step: {text}") - - synthesizer_result = await Runner.run( - synthesizer_agent, orchestrator_result.to_input_list() - ) - - print(f"\n\nFinal response:\n{synthesizer_result.final_output}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/agent_patterns/deterministic.py b/tests/examples/agent_patterns/deterministic.py deleted file mode 100644 index 0c163afe..00000000 --- a/tests/examples/agent_patterns/deterministic.py +++ /dev/null @@ -1,80 +0,0 @@ -import asyncio - -from pydantic import BaseModel - -from agents import Agent, Runner, trace - -""" -This example demonstrates a deterministic flow, where each step is performed by an agent. -1. The first agent generates a story outline -2. We feed the outline into the second agent -3. The second agent checks if the outline is good quality and if it is a scifi story -4. If the outline is not good quality or not a scifi story, we stop here -5. If the outline is good quality and a scifi story, we feed the outline into the third agent -6. The third agent writes the story -""" - -story_outline_agent = Agent( - name="story_outline_agent", - instructions="Generate a very short story outline based on the user's input.", -) - - -class OutlineCheckerOutput(BaseModel): - good_quality: bool - is_scifi: bool - - -outline_checker_agent = Agent( - name="outline_checker_agent", - instructions="Read the given story outline, and judge the quality. Also, determine if it is a scifi story.", - output_type=OutlineCheckerOutput, -) - -story_agent = Agent( - name="story_agent", - instructions="Write a short story based on the given outline.", - output_type=str, -) - - -async def main(): - input_prompt = input("What kind of story do you want? ") - - # Ensure the entire workflow is a single trace - with trace("Deterministic story flow"): - # 1. Generate an outline - outline_result = await Runner.run( - story_outline_agent, - input_prompt, - ) - print("Outline generated") - - # 2. Check the outline - outline_checker_result = await Runner.run( - outline_checker_agent, - outline_result.final_output, - ) - - # 3. Add a gate to stop if the outline is not good quality or not a scifi story - assert isinstance(outline_checker_result.final_output, OutlineCheckerOutput) - if not outline_checker_result.final_output.good_quality: - print("Outline is not good quality, so we stop here.") - exit(0) - - if not outline_checker_result.final_output.is_scifi: - print("Outline is not a scifi story, so we stop here.") - exit(0) - - print("Outline is good quality and a scifi story, so we continue to write the story.") - - # 4. Write the story - story_result = await Runner.run( - story_agent, - outline_result.final_output, - ) - print(f"Story: {story_result.final_output}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/agent_patterns/input_guardrails.py b/tests/examples/agent_patterns/input_guardrails.py deleted file mode 100644 index 62591886..00000000 --- a/tests/examples/agent_patterns/input_guardrails.py +++ /dev/null @@ -1,105 +0,0 @@ -from __future__ import annotations - -import asyncio - -from pydantic import BaseModel - -from agents import ( - Agent, - GuardrailFunctionOutput, - InputGuardrailTripwireTriggered, - RunContextWrapper, - Runner, - TResponseInputItem, - input_guardrail, -) - -""" -This example shows how to use guardrails. - -Guardrails are checks that run in parallel to the agent's execution. -They can be used to do things like: -- Check if input messages are off-topic -- Check that output messages don't violate any policies -- Take over control of the agent's execution if an unexpected input is detected - -In this example, we'll setup an input guardrail that trips if the user is asking to do math homework. -If the guardrail trips, we'll respond with a refusal message. -""" - - -### 1. An agent-based guardrail that is triggered if the user is asking to do math homework -class MathHomeworkOutput(BaseModel): - is_math_homework: bool - reasoning: str - - -guardrail_agent = Agent( - name="Guardrail check", - instructions="Check if the user is asking you to do their math homework.", - output_type=MathHomeworkOutput, -) - - -@input_guardrail -async def math_guardrail( - context: RunContextWrapper[None], agent: Agent, input: str | list[TResponseInputItem] -) -> GuardrailFunctionOutput: - """This is an input guardrail function, which happens to call an agent to check if the input - is a math homework question. - """ - result = await Runner.run(guardrail_agent, input, context=context.context) - final_output = result.final_output_as(MathHomeworkOutput) - - return GuardrailFunctionOutput( - output_info=final_output, - tripwire_triggered=not final_output.is_math_homework, - ) - - -### 2. The run loop - - -async def main(): - agent = Agent( - name="Customer support agent", - instructions="You are a customer support agent. You help customers with their questions.", - input_guardrails=[math_guardrail], - ) - - input_data: list[TResponseInputItem] = [] - - while True: - user_input = input("Enter a message: ") - input_data.append( - { - "role": "user", - "content": user_input, - } - ) - - try: - result = await Runner.run(agent, input_data) - print(result.final_output) - # If the guardrail didn't trigger, we use the result as the input for the next run - input_data = result.to_input_list() - except InputGuardrailTripwireTriggered: - # If the guardrail triggered, we instead add a refusal message to the input - message = "Sorry, I can't help you with your math homework." - print(message) - input_data.append( - { - "role": "assistant", - "content": message, - } - ) - - # Sample run: - # Enter a message: What's the capital of California? - # The capital of California is Sacramento. - # Enter a message: Can you help me solve for x: 2x + 5 = 11 - # Sorry, I can't help you with your math homework. - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/agent_patterns/llm_as_a_judge.py b/tests/examples/agent_patterns/llm_as_a_judge.py deleted file mode 100644 index d13a67cb..00000000 --- a/tests/examples/agent_patterns/llm_as_a_judge.py +++ /dev/null @@ -1,76 +0,0 @@ -from __future__ import annotations - -import asyncio -from dataclasses import dataclass -from typing import Literal - -from agents import Agent, ItemHelpers, Runner, TResponseInputItem, trace - -""" -This example shows the LLM as a judge pattern. The first agent generates an outline for a story. -The second agent judges the outline and provides feedback. We loop until the judge is satisfied -with the outline. -""" - -story_outline_generator = Agent( - name="story_outline_generator", - instructions=( - "You generate a very short story outline based on the user's input." - "If there is any feedback provided, use it to improve the outline." - ), -) - - -@dataclass -class EvaluationFeedback: - score: Literal["pass", "needs_improvement", "fail"] - feedback: str - - -evaluator = Agent[None]( - name="evaluator", - instructions=( - "You evaluate a story outline and decide if it's good enough." - "If it's not good enough, you provide feedback on what needs to be improved." - "Never give it a pass on the first try." - ), - output_type=EvaluationFeedback, -) - - -async def main() -> None: - msg = input("What kind of story would you like to hear? ") - input_items: list[TResponseInputItem] = [{"content": msg, "role": "user"}] - - latest_outline: str | None = None - - # We'll run the entire workflow in a single trace - with trace("LLM as a judge"): - while True: - story_outline_result = await Runner.run( - story_outline_generator, - input_items, - ) - - input_items = story_outline_result.to_input_list() - latest_outline = ItemHelpers.text_message_outputs(story_outline_result.new_items) - print("Story outline generated") - - evaluator_result = await Runner.run(evaluator, input_items) - result: EvaluationFeedback = evaluator_result.final_output - - print(f"Evaluator score: {result.score}") - - if result.score == "pass": - print("Story outline is good enough, exiting.") - break - - print("Re-running with feedback") - - input_items.append({"content": f"Feedback: {result.feedback}", "role": "user"}) - - print(f"Final story outline: {latest_outline}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/agent_patterns/output_guardrails.py b/tests/examples/agent_patterns/output_guardrails.py deleted file mode 100644 index 526a0852..00000000 --- a/tests/examples/agent_patterns/output_guardrails.py +++ /dev/null @@ -1,80 +0,0 @@ -from __future__ import annotations - -import asyncio -import json - -from pydantic import BaseModel, Field - -from agents import ( - Agent, - GuardrailFunctionOutput, - OutputGuardrailTripwireTriggered, - RunContextWrapper, - Runner, - output_guardrail, -) - -""" -This example shows how to use output guardrails. - -Output guardrails are checks that run on the final output of an agent. -They can be used to do things like: -- Check if the output contains sensitive data -- Check if the output is a valid response to the user's message - -In this example, we'll use a (contrived) example where we check if the agent's response contains -a phone number. -""" - - -# The agent's output type -class MessageOutput(BaseModel): - reasoning: str = Field(description="Thoughts on how to respond to the user's message") - response: str = Field(description="The response to the user's message") - user_name: str | None = Field(description="The name of the user who sent the message, if known") - - -@output_guardrail -async def sensitive_data_check( - context: RunContextWrapper, agent: Agent, output: MessageOutput -) -> GuardrailFunctionOutput: - phone_number_in_response = "650" in output.response - phone_number_in_reasoning = "650" in output.reasoning - - return GuardrailFunctionOutput( - output_info={ - "phone_number_in_response": phone_number_in_response, - "phone_number_in_reasoning": phone_number_in_reasoning, - }, - tripwire_triggered=phone_number_in_response or phone_number_in_reasoning, - ) - - -agent = Agent( - name="Assistant", - instructions="You are a helpful assistant.", - output_type=MessageOutput, - output_guardrails=[sensitive_data_check], -) - - -async def main(): - # This should be ok - await Runner.run(agent, "What's the capital of California?") - print("First message passed") - - # This should trip the guardrail - try: - result = await Runner.run( - agent, "My phone number is 650-123-4567. Where do you think I live?" - ) - print( - f"Guardrail didn't trip - this is unexpected. Output: {json.dumps(result.final_output.model_dump(), indent=2)}" - ) - - except OutputGuardrailTripwireTriggered as e: - print(f"Guardrail tripped. Info: {e.guardrail_result.output.output_info}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/agent_patterns/parallelization.py b/tests/examples/agent_patterns/parallelization.py deleted file mode 100644 index fe2a8ecd..00000000 --- a/tests/examples/agent_patterns/parallelization.py +++ /dev/null @@ -1,61 +0,0 @@ -import asyncio - -from agents import Agent, ItemHelpers, Runner, trace - -""" -This example shows the parallelization pattern. We run the agent three times in parallel, and pick -the best result. -""" - -spanish_agent = Agent( - name="spanish_agent", - instructions="You translate the user's message to Spanish", -) - -translation_picker = Agent( - name="translation_picker", - instructions="You pick the best Spanish translation from the given options.", -) - - -async def main(): - msg = input("Hi! Enter a message, and we'll translate it to Spanish.\n\n") - - # Ensure the entire workflow is a single trace - with trace("Parallel translation"): - res_1, res_2, res_3 = await asyncio.gather( - Runner.run( - spanish_agent, - msg, - ), - Runner.run( - spanish_agent, - msg, - ), - Runner.run( - spanish_agent, - msg, - ), - ) - - outputs = [ - ItemHelpers.text_message_outputs(res_1.new_items), - ItemHelpers.text_message_outputs(res_2.new_items), - ItemHelpers.text_message_outputs(res_3.new_items), - ] - - translations = "\n\n".join(outputs) - print(f"\n\nTranslations:\n\n{translations}") - - best_translation = await Runner.run( - translation_picker, - f"Input: {msg}\n\nTranslations:\n{translations}", - ) - - print("\n\n-----") - - print(f"Best translation: {best_translation.final_output}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/agent_patterns/routing.py b/tests/examples/agent_patterns/routing.py deleted file mode 100644 index 3dcaefa9..00000000 --- a/tests/examples/agent_patterns/routing.py +++ /dev/null @@ -1,70 +0,0 @@ -import asyncio -import uuid - -from openai.types.responses import ResponseContentPartDoneEvent, ResponseTextDeltaEvent - -from agents import Agent, RawResponsesStreamEvent, Runner, TResponseInputItem, trace - -""" -This example shows the handoffs/routing pattern. The triage agent receives the first message, and -then hands off to the appropriate agent based on the language of the request. Responses are -streamed to the user. -""" - -french_agent = Agent( - name="french_agent", - instructions="You only speak French", -) - -spanish_agent = Agent( - name="spanish_agent", - instructions="You only speak Spanish", -) - -english_agent = Agent( - name="english_agent", - instructions="You only speak English", -) - -triage_agent = Agent( - name="triage_agent", - instructions="Handoff to the appropriate agent based on the language of the request.", - handoffs=[french_agent, spanish_agent, english_agent], -) - - -async def main(): - # We'll create an ID for this conversation, so we can link each trace - conversation_id = str(uuid.uuid4().hex[:16]) - - msg = input("Hi! We speak French, Spanish and English. How can I help? ") - agent = triage_agent - inputs: list[TResponseInputItem] = [{"content": msg, "role": "user"}] - - while True: - # Each conversation turn is a single trace. Normally, each input from the user would be an - # API request to your app, and you can wrap the request in a trace() - with trace("Routing example", group_id=conversation_id): - result = Runner.run_streamed( - agent, - input=inputs, - ) - async for event in result.stream_events(): - if not isinstance(event, RawResponsesStreamEvent): - continue - data = event.data - if isinstance(data, ResponseTextDeltaEvent): - print(data.delta, end="", flush=True) - elif isinstance(data, ResponseContentPartDoneEvent): - print("\n") - - inputs = result.to_input_list() - print("\n") - - user_msg = input("Enter a message: ") - inputs.append({"content": user_msg, "role": "user"}) - agent = result.current_agent - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/basic/agent_lifecycle_example.py b/tests/examples/basic/agent_lifecycle_example.py deleted file mode 100644 index bc0bbe43..00000000 --- a/tests/examples/basic/agent_lifecycle_example.py +++ /dev/null @@ -1,112 +0,0 @@ -import asyncio -import random -from typing import Any - -from pydantic import BaseModel - -from agents import Agent, AgentHooks, RunContextWrapper, Runner, Tool, function_tool - - -class CustomAgentHooks(AgentHooks): - def __init__(self, display_name: str): - self.event_counter = 0 - self.display_name = display_name - - async def on_start(self, context: RunContextWrapper, agent: Agent) -> None: - self.event_counter += 1 - print(f"### ({self.display_name}) {self.event_counter}: Agent {agent.name} started") - - async def on_end(self, context: RunContextWrapper, agent: Agent, output: Any) -> None: - self.event_counter += 1 - print( - f"### ({self.display_name}) {self.event_counter}: Agent {agent.name} ended with output {output}" - ) - - async def on_handoff(self, context: RunContextWrapper, agent: Agent, source: Agent) -> None: - self.event_counter += 1 - print( - f"### ({self.display_name}) {self.event_counter}: Agent {source.name} handed off to {agent.name}" - ) - - async def on_tool_start(self, context: RunContextWrapper, agent: Agent, tool: Tool) -> None: - self.event_counter += 1 - print( - f"### ({self.display_name}) {self.event_counter}: Agent {agent.name} started tool {tool.name}" - ) - - async def on_tool_end( - self, context: RunContextWrapper, agent: Agent, tool: Tool, result: str - ) -> None: - self.event_counter += 1 - print( - f"### ({self.display_name}) {self.event_counter}: Agent {agent.name} ended tool {tool.name} with result {result}" - ) - - -### - - -@function_tool -def random_number(max: int) -> int: - """ - Generate a random number up to the provided maximum. - """ - return random.randint(0, max) - - -@function_tool -def multiply_by_two(x: int) -> int: - """Simple multiplication by two.""" - return x * 2 - - -class FinalResult(BaseModel): - number: int - - -multiply_agent = Agent( - name="Multiply Agent", - instructions="Multiply the number by 2 and then return the final result.", - tools=[multiply_by_two], - output_type=FinalResult, - hooks=CustomAgentHooks(display_name="Multiply Agent"), -) - -start_agent = Agent( - name="Start Agent", - instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multipler agent.", - tools=[random_number], - output_type=FinalResult, - handoffs=[multiply_agent], - hooks=CustomAgentHooks(display_name="Start Agent"), -) - - -async def main() -> None: - user_input = input("Enter a max number: ") - await Runner.run( - start_agent, - input=f"Generate a random number between 0 and {user_input}.", - ) - - print("Done!") - - -if __name__ == "__main__": - asyncio.run(main()) -""" -$ python examples/basic/agent_lifecycle_example.py - -Enter a max number: 250 -### (Start Agent) 1: Agent Start Agent started -### (Start Agent) 2: Agent Start Agent started tool random_number -### (Start Agent) 3: Agent Start Agent ended tool random_number with result 37 -### (Start Agent) 4: Agent Start Agent started -### (Start Agent) 5: Agent Start Agent handed off to Multiply Agent -### (Multiply Agent) 1: Agent Multiply Agent started -### (Multiply Agent) 2: Agent Multiply Agent started tool multiply_by_two -### (Multiply Agent) 3: Agent Multiply Agent ended tool multiply_by_two with result 74 -### (Multiply Agent) 4: Agent Multiply Agent started -### (Multiply Agent) 5: Agent Multiply Agent ended with output number=74 -Done! -""" diff --git a/tests/examples/basic/dynamic_system_prompt.py b/tests/examples/basic/dynamic_system_prompt.py deleted file mode 100644 index 7bcf90c0..00000000 --- a/tests/examples/basic/dynamic_system_prompt.py +++ /dev/null @@ -1,69 +0,0 @@ -import asyncio -import random -from typing import Literal - -from agents import Agent, RunContextWrapper, Runner - - -class CustomContext: - def __init__(self, style: Literal["haiku", "pirate", "robot"]): - self.style = style - - -def custom_instructions( - run_context: RunContextWrapper[CustomContext], agent: Agent[CustomContext] -) -> str: - context = run_context.context - if context.style == "haiku": - return "Only respond in haikus." - elif context.style == "pirate": - return "Respond as a pirate." - else: - return "Respond as a robot and say 'beep boop' a lot." - - -agent = Agent( - name="Chat agent", - instructions=custom_instructions, -) - - -async def main(): - choice: Literal["haiku", "pirate", "robot"] = random.choice(["haiku", "pirate", "robot"]) - context = CustomContext(style=choice) - print(f"Using style: {choice}\n") - - user_message = "Tell me a joke." - print(f"User: {user_message}") - result = await Runner.run(agent, user_message, context=context) - - print(f"Assistant: {result.final_output}") - - -if __name__ == "__main__": - asyncio.run(main()) - -""" -$ python examples/basic/dynamic_system_prompt.py - -Using style: haiku - -User: Tell me a joke. -Assistant: Why don't eggs tell jokes? -They might crack each other's shells, -leaving yolk on face. - -$ python examples/basic/dynamic_system_prompt.py -Using style: robot - -User: Tell me a joke. -Assistant: Beep boop! Why was the robot so bad at soccer? Beep boop... because it kept kicking up a debug! Beep boop! - -$ python examples/basic/dynamic_system_prompt.py -Using style: pirate - -User: Tell me a joke. -Assistant: Why did the pirate go to school? - -To improve his arrr-ticulation! Har har har! 🏴‍☠️ -""" diff --git a/tests/examples/basic/hello_world.py b/tests/examples/basic/hello_world.py deleted file mode 100644 index 169290d6..00000000 --- a/tests/examples/basic/hello_world.py +++ /dev/null @@ -1,20 +0,0 @@ -import asyncio - -from agents import Agent, Runner - - -async def main(): - agent = Agent( - name="Assistant", - instructions="You only respond in haikus.", - ) - - result = await Runner.run(agent, "Tell me about recursion in programming.") - print(result.final_output) - # Function calls itself, - # Looping in smaller pieces, - # Endless by design. - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/basic/lifecycle_example.py b/tests/examples/basic/lifecycle_example.py deleted file mode 100644 index 9b365106..00000000 --- a/tests/examples/basic/lifecycle_example.py +++ /dev/null @@ -1,118 +0,0 @@ -import asyncio -import random -from typing import Any - -from pydantic import BaseModel - -from agents import Agent, RunContextWrapper, RunHooks, Runner, Tool, Usage, function_tool - - -class ExampleHooks(RunHooks): - def __init__(self): - self.event_counter = 0 - - def _usage_to_str(self, usage: Usage) -> str: - return f"{usage.requests} requests, {usage.input_tokens} input tokens, {usage.output_tokens} output tokens, {usage.total_tokens} total tokens" - - async def on_agent_start(self, context: RunContextWrapper, agent: Agent) -> None: - self.event_counter += 1 - print( - f"### {self.event_counter}: Agent {agent.name} started. Usage: {self._usage_to_str(context.usage)}" - ) - - async def on_agent_end(self, context: RunContextWrapper, agent: Agent, output: Any) -> None: - self.event_counter += 1 - print( - f"### {self.event_counter}: Agent {agent.name} ended with output {output}. Usage: {self._usage_to_str(context.usage)}" - ) - - async def on_tool_start(self, context: RunContextWrapper, agent: Agent, tool: Tool) -> None: - self.event_counter += 1 - print( - f"### {self.event_counter}: Tool {tool.name} started. Usage: {self._usage_to_str(context.usage)}" - ) - - async def on_tool_end( - self, context: RunContextWrapper, agent: Agent, tool: Tool, result: str - ) -> None: - self.event_counter += 1 - print( - f"### {self.event_counter}: Tool {tool.name} ended with result {result}. Usage: {self._usage_to_str(context.usage)}" - ) - - async def on_handoff( - self, context: RunContextWrapper, from_agent: Agent, to_agent: Agent - ) -> None: - self.event_counter += 1 - print( - f"### {self.event_counter}: Handoff from {from_agent.name} to {to_agent.name}. Usage: {self._usage_to_str(context.usage)}" - ) - - -hooks = ExampleHooks() - -### - - -@function_tool -def random_number(max: int) -> int: - """Generate a random number up to the provided max.""" - return random.randint(0, max) - - -@function_tool -def multiply_by_two(x: int) -> int: - """Return x times two.""" - return x * 2 - - -class FinalResult(BaseModel): - number: int - - -multiply_agent = Agent( - name="Multiply Agent", - instructions="Multiply the number by 2 and then return the final result.", - tools=[multiply_by_two], - output_type=FinalResult, -) - -start_agent = Agent( - name="Start Agent", - instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multipler agent.", - tools=[random_number], - output_type=FinalResult, - handoffs=[multiply_agent], -) - - -async def main() -> None: - user_input = input("Enter a max number: ") - await Runner.run( - start_agent, - hooks=hooks, - input=f"Generate a random number between 0 and {user_input}.", - ) - - print("Done!") - - -if __name__ == "__main__": - asyncio.run(main()) -""" -$ python examples/basic/lifecycle_example.py - -Enter a max number: 250 -### 1: Agent Start Agent started. Usage: 0 requests, 0 input tokens, 0 output tokens, 0 total tokens -### 2: Tool random_number started. Usage: 1 requests, 148 input tokens, 15 output tokens, 163 total tokens -### 3: Tool random_number ended with result 101. Usage: 1 requests, 148 input tokens, 15 output tokens, 163 total tokens -### 4: Agent Start Agent started. Usage: 1 requests, 148 input tokens, 15 output tokens, 163 total tokens -### 5: Handoff from Start Agent to Multiply Agent. Usage: 2 requests, 323 input tokens, 30 output tokens, 353 total tokens -### 6: Agent Multiply Agent started. Usage: 2 requests, 323 input tokens, 30 output tokens, 353 total tokens -### 7: Tool multiply_by_two started. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens -### 8: Tool multiply_by_two ended with result 202. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens -### 9: Agent Multiply Agent started. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens -### 10: Agent Multiply Agent ended with output number=202. Usage: 4 requests, 714 input tokens, 63 output tokens, 777 total tokens -Done! - -""" diff --git a/tests/examples/basic/stream_items.py b/tests/examples/basic/stream_items.py deleted file mode 100644 index c1f2257a..00000000 --- a/tests/examples/basic/stream_items.py +++ /dev/null @@ -1,65 +0,0 @@ -import asyncio -import random - -from agents import Agent, ItemHelpers, Runner, function_tool - - -@function_tool -def how_many_jokes() -> int: - return random.randint(1, 10) - - -async def main(): - agent = Agent( - name="Joker", - instructions="First call the `how_many_jokes` tool, then tell that many jokes.", - tools=[how_many_jokes], - ) - - result = Runner.run_streamed( - agent, - input="Hello", - ) - print("=== Run starting ===") - async for event in result.stream_events(): - # We'll ignore the raw responses event deltas - if event.type == "raw_response_event": - continue - elif event.type == "agent_updated_stream_event": - print(f"Agent updated: {event.new_agent.name}") - continue - elif event.type == "run_item_stream_event": - if event.item.type == "tool_call_item": - print("-- Tool was called") - elif event.item.type == "tool_call_output_item": - print(f"-- Tool output: {event.item.output}") - elif event.item.type == "message_output_item": - print(f"-- Message output:\n {ItemHelpers.text_message_output(event.item)}") - else: - pass # Ignore other event types - - print("=== Run complete ===") - - -if __name__ == "__main__": - asyncio.run(main()) - - # === Run starting === - # Agent updated: Joker - # -- Tool was called - # -- Tool output: 4 - # -- Message output: - # Sure, here are four jokes for you: - - # 1. **Why don't skeletons fight each other?** - # They don't have the guts! - - # 2. **What do you call fake spaghetti?** - # An impasta! - - # 3. **Why did the scarecrow win an award?** - # Because he was outstanding in his field! - - # 4. **Why did the bicycle fall over?** - # Because it was two-tired! - # === Run complete === diff --git a/tests/examples/basic/stream_text.py b/tests/examples/basic/stream_text.py deleted file mode 100644 index a73c1fee..00000000 --- a/tests/examples/basic/stream_text.py +++ /dev/null @@ -1,21 +0,0 @@ -import asyncio - -from openai.types.responses import ResponseTextDeltaEvent - -from agents import Agent, Runner - - -async def main(): - agent = Agent( - name="Joker", - instructions="You are a helpful assistant.", - ) - - result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") - async for event in result.stream_events(): - if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent): - print(event.data.delta, end="", flush=True) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/customer_service/main.py b/tests/examples/customer_service/main.py deleted file mode 100644 index bd802e22..00000000 --- a/tests/examples/customer_service/main.py +++ /dev/null @@ -1,169 +0,0 @@ -from __future__ import annotations as _annotations - -import asyncio -import random -import uuid - -from pydantic import BaseModel - -from agents import ( - Agent, - HandoffOutputItem, - ItemHelpers, - MessageOutputItem, - RunContextWrapper, - Runner, - ToolCallItem, - ToolCallOutputItem, - TResponseInputItem, - function_tool, - handoff, - trace, -) -from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX - -### CONTEXT - - -class AirlineAgentContext(BaseModel): - passenger_name: str | None = None - confirmation_number: str | None = None - seat_number: str | None = None - flight_number: str | None = None - - -### TOOLS - - -@function_tool( - name_override="faq_lookup_tool", description_override="Lookup frequently asked questions." -) -async def faq_lookup_tool(question: str) -> str: - if "bag" in question or "baggage" in question: - return ( - "You are allowed to bring one bag on the plane. " - "It must be under 50 pounds and 22 inches x 14 inches x 9 inches." - ) - elif "seats" in question or "plane" in question: - return ( - "There are 120 seats on the plane. " - "There are 22 business class seats and 98 economy seats. " - "Exit rows are rows 4 and 16. " - "Rows 5-8 are Economy Plus, with extra legroom. " - ) - elif "wifi" in question: - return "We have free wifi on the plane, join Airline-Wifi" - return "I'm sorry, I don't know the answer to that question." - - -@function_tool -async def update_seat( - context: RunContextWrapper[AirlineAgentContext], confirmation_number: str, new_seat: str -) -> str: - """ - Update the seat for a given confirmation number. - - Args: - confirmation_number: The confirmation number for the flight. - new_seat: The new seat to update to. - """ - # Update the context based on the customer's input - context.context.confirmation_number = confirmation_number - context.context.seat_number = new_seat - # Ensure that the flight number has been set by the incoming handoff - assert context.context.flight_number is not None, "Flight number is required" - return f"Updated seat to {new_seat} for confirmation number {confirmation_number}" - - -### HOOKS - - -async def on_seat_booking_handoff(context: RunContextWrapper[AirlineAgentContext]) -> None: - flight_number = f"FLT-{random.randint(100, 999)}" - context.context.flight_number = flight_number - - -### AGENTS - -faq_agent = Agent[AirlineAgentContext]( - name="FAQ Agent", - handoff_description="A helpful agent that can answer questions about the airline.", - instructions=f"""{RECOMMENDED_PROMPT_PREFIX} - You are an FAQ agent. If you are speaking to a customer, you probably were transferred to from the triage agent. - Use the following routine to support the customer. - # Routine - 1. Identify the last question asked by the customer. - 2. Use the faq lookup tool to answer the question. Do not rely on your own knowledge. - 3. If you cannot answer the question, transfer back to the triage agent.""", - tools=[faq_lookup_tool], -) - -seat_booking_agent = Agent[AirlineAgentContext]( - name="Seat Booking Agent", - handoff_description="A helpful agent that can update a seat on a flight.", - instructions=f"""{RECOMMENDED_PROMPT_PREFIX} - You are a seat booking agent. If you are speaking to a customer, you probably were transferred to from the triage agent. - Use the following routine to support the customer. - # Routine - 1. Ask for their confirmation number. - 2. Ask the customer what their desired seat number is. - 3. Use the update seat tool to update the seat on the flight. - If the customer asks a question that is not related to the routine, transfer back to the triage agent. """, - tools=[update_seat], -) - -triage_agent = Agent[AirlineAgentContext]( - name="Triage Agent", - handoff_description="A triage agent that can delegate a customer's request to the appropriate agent.", - instructions=( - f"{RECOMMENDED_PROMPT_PREFIX} " - "You are a helpful triaging agent. You can use your tools to delegate questions to other appropriate agents." - ), - handoffs=[ - faq_agent, - handoff(agent=seat_booking_agent, on_handoff=on_seat_booking_handoff), - ], -) - -faq_agent.handoffs.append(triage_agent) -seat_booking_agent.handoffs.append(triage_agent) - - -### RUN - - -async def main(): - current_agent: Agent[AirlineAgentContext] = triage_agent - input_items: list[TResponseInputItem] = [] - context = AirlineAgentContext() - - # Normally, each input from the user would be an API request to your app, and you can wrap the request in a trace() - # Here, we'll just use a random UUID for the conversation ID - conversation_id = uuid.uuid4().hex[:16] - - while True: - user_input = input("Enter your message: ") - with trace("Customer service", group_id=conversation_id): - input_items.append({"content": user_input, "role": "user"}) - result = await Runner.run(current_agent, input_items, context=context) - - for new_item in result.new_items: - agent_name = new_item.agent.name - if isinstance(new_item, MessageOutputItem): - print(f"{agent_name}: {ItemHelpers.text_message_output(new_item)}") - elif isinstance(new_item, HandoffOutputItem): - print( - f"Handed off from {new_item.source_agent.name} to {new_item.target_agent.name}" - ) - elif isinstance(new_item, ToolCallItem): - print(f"{agent_name}: Calling a tool") - elif isinstance(new_item, ToolCallOutputItem): - print(f"{agent_name}: Tool call output: {new_item.output}") - else: - print(f"{agent_name}: Skipping item: {new_item.__class__.__name__}") - input_items = result.to_input_list() - current_agent = result.last_agent - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/handoffs/message_filter.py b/tests/examples/handoffs/message_filter.py deleted file mode 100644 index 9dd56ef7..00000000 --- a/tests/examples/handoffs/message_filter.py +++ /dev/null @@ -1,176 +0,0 @@ -from __future__ import annotations - -import json -import random - -from agents import Agent, HandoffInputData, Runner, function_tool, handoff, trace -from agents.extensions import handoff_filters - - -@function_tool -def random_number_tool(max: int) -> int: - """Return a random integer between 0 and the given maximum.""" - return random.randint(0, max) - - -def spanish_handoff_message_filter(handoff_message_data: HandoffInputData) -> HandoffInputData: - # First, we'll remove any tool-related messages from the message history - handoff_message_data = handoff_filters.remove_all_tools(handoff_message_data) - - # Second, we'll also remove the first two items from the history, just for demonstration - history = ( - tuple(handoff_message_data.input_history[2:]) - if isinstance(handoff_message_data.input_history, tuple) - else handoff_message_data.input_history - ) - - return HandoffInputData( - input_history=history, - pre_handoff_items=tuple(handoff_message_data.pre_handoff_items), - new_items=tuple(handoff_message_data.new_items), - ) - - -first_agent = Agent( - name="Assistant", - instructions="Be extremely concise.", - tools=[random_number_tool], -) - -spanish_agent = Agent( - name="Spanish Assistant", - instructions="You only speak Spanish and are extremely concise.", - handoff_description="A Spanish-speaking assistant.", -) - -second_agent = Agent( - name="Assistant", - instructions=( - "Be a helpful assistant. If the user speaks Spanish, handoff to the Spanish assistant." - ), - handoffs=[handoff(spanish_agent, input_filter=spanish_handoff_message_filter)], -) - - -async def main(): - # Trace the entire run as a single workflow - with trace(workflow_name="Message filtering"): - # 1. Send a regular message to the first agent - result = await Runner.run(first_agent, input="Hi, my name is Sora.") - - print("Step 1 done") - - # 2. Ask it to square a number - result = await Runner.run( - second_agent, - input=result.to_input_list() - + [{"content": "Can you generate a random number between 0 and 100?", "role": "user"}], - ) - - print("Step 2 done") - - # 3. Call the second agent - result = await Runner.run( - second_agent, - input=result.to_input_list() - + [ - { - "content": "I live in New York City. Whats the population of the city?", - "role": "user", - } - ], - ) - - print("Step 3 done") - - # 4. Cause a handoff to occur - result = await Runner.run( - second_agent, - input=result.to_input_list() - + [ - { - "content": "Por favor habla en español. ¿Cuál es mi nombre y dónde vivo?", - "role": "user", - } - ], - ) - - print("Step 4 done") - - print("\n===Final messages===\n") - - # 5. That should have caused spanish_handoff_message_filter to be called, which means the - # output should be missing the first two messages, and have no tool calls. - # Let's print the messages to see what happened - for message in result.to_input_list(): - print(json.dumps(message, indent=2)) - # tool_calls = message.tool_calls if isinstance(message, AssistantMessage) else None - - # print(f"{message.role}: {message.content}\n - Tool calls: {tool_calls or 'None'}") - """ - $python examples/handoffs/message_filter.py - Step 1 done - Step 2 done - Step 3 done - Step 4 done - - ===Final messages=== - - { - "content": "Can you generate a random number between 0 and 100?", - "role": "user" - } - { - "id": "...", - "content": [ - { - "annotations": [], - "text": "Sure! Here's a random number between 0 and 100: **42**.", - "type": "output_text" - } - ], - "role": "assistant", - "status": "completed", - "type": "message" - } - { - "content": "I live in New York City. Whats the population of the city?", - "role": "user" - } - { - "id": "...", - "content": [ - { - "annotations": [], - "text": "As of the most recent estimates, the population of New York City is approximately 8.6 million people. However, this number is constantly changing due to various factors such as migration and birth rates. For the latest and most accurate information, it's always a good idea to check the official data from sources like the U.S. Census Bureau.", - "type": "output_text" - } - ], - "role": "assistant", - "status": "completed", - "type": "message" - } - { - "content": "Por favor habla en espa\u00f1ol. \u00bfCu\u00e1l es mi nombre y d\u00f3nde vivo?", - "role": "user" - } - { - "id": "...", - "content": [ - { - "annotations": [], - "text": "No tengo acceso a esa informaci\u00f3n personal, solo s\u00e9 lo que me has contado: vives en Nueva York.", - "type": "output_text" - } - ], - "role": "assistant", - "status": "completed", - "type": "message" - } - """ - - -if __name__ == "__main__": - import asyncio - - asyncio.run(main()) diff --git a/tests/examples/handoffs/message_filter_streaming.py b/tests/examples/handoffs/message_filter_streaming.py deleted file mode 100644 index 8d1b4208..00000000 --- a/tests/examples/handoffs/message_filter_streaming.py +++ /dev/null @@ -1,176 +0,0 @@ -from __future__ import annotations - -import json -import random - -from agents import Agent, HandoffInputData, Runner, function_tool, handoff, trace -from agents.extensions import handoff_filters - - -@function_tool -def random_number_tool(max: int) -> int: - """Return a random integer between 0 and the given maximum.""" - return random.randint(0, max) - - -def spanish_handoff_message_filter(handoff_message_data: HandoffInputData) -> HandoffInputData: - # First, we'll remove any tool-related messages from the message history - handoff_message_data = handoff_filters.remove_all_tools(handoff_message_data) - - # Second, we'll also remove the first two items from the history, just for demonstration - history = ( - tuple(handoff_message_data.input_history[2:]) - if isinstance(handoff_message_data.input_history, tuple) - else handoff_message_data.input_history - ) - - return HandoffInputData( - input_history=history, - pre_handoff_items=tuple(handoff_message_data.pre_handoff_items), - new_items=tuple(handoff_message_data.new_items), - ) - - -first_agent = Agent( - name="Assistant", - instructions="Be extremely concise.", - tools=[random_number_tool], -) - -spanish_agent = Agent( - name="Spanish Assistant", - instructions="You only speak Spanish and are extremely concise.", - handoff_description="A Spanish-speaking assistant.", -) - -second_agent = Agent( - name="Assistant", - instructions=( - "Be a helpful assistant. If the user speaks Spanish, handoff to the Spanish assistant." - ), - handoffs=[handoff(spanish_agent, input_filter=spanish_handoff_message_filter)], -) - - -async def main(): - # Trace the entire run as a single workflow - with trace(workflow_name="Streaming message filter"): - # 1. Send a regular message to the first agent - result = await Runner.run(first_agent, input="Hi, my name is Sora.") - - print("Step 1 done") - - # 2. Ask it to square a number - result = await Runner.run( - second_agent, - input=result.to_input_list() - + [{"content": "Can you generate a random number between 0 and 100?", "role": "user"}], - ) - - print("Step 2 done") - - # 3. Call the second agent - result = await Runner.run( - second_agent, - input=result.to_input_list() - + [ - { - "content": "I live in New York City. Whats the population of the city?", - "role": "user", - } - ], - ) - - print("Step 3 done") - - # 4. Cause a handoff to occur - stream_result = Runner.run_streamed( - second_agent, - input=result.to_input_list() - + [ - { - "content": "Por favor habla en español. ¿Cuál es mi nombre y dónde vivo?", - "role": "user", - } - ], - ) - async for _ in stream_result.stream_events(): - pass - - print("Step 4 done") - - print("\n===Final messages===\n") - - # 5. That should have caused spanish_handoff_message_filter to be called, which means the - # output should be missing the first two messages, and have no tool calls. - # Let's print the messages to see what happened - for item in stream_result.to_input_list(): - print(json.dumps(item, indent=2)) - """ - $python examples/handoffs/message_filter_streaming.py - Step 1 done - Step 2 done - Step 3 done - Tu nombre y lugar de residencia no los tengo disponibles. Solo sé que mencionaste vivir en la ciudad de Nueva York. - Step 4 done - - ===Final messages=== - - { - "content": "Can you generate a random number between 0 and 100?", - "role": "user" - } - { - "id": "...", - "content": [ - { - "annotations": [], - "text": "Sure! Here's a random number between 0 and 100: **37**.", - "type": "output_text" - } - ], - "role": "assistant", - "status": "completed", - "type": "message" - } - { - "content": "I live in New York City. Whats the population of the city?", - "role": "user" - } - { - "id": "...", - "content": [ - { - "annotations": [], - "text": "As of the latest estimates, New York City's population is approximately 8.5 million people. Would you like more information about the city?", - "type": "output_text" - } - ], - "role": "assistant", - "status": "completed", - "type": "message" - } - { - "content": "Por favor habla en espa\u00f1ol. \u00bfCu\u00e1l es mi nombre y d\u00f3nde vivo?", - "role": "user" - } - { - "id": "...", - "content": [ - { - "annotations": [], - "text": "No s\u00e9 tu nombre, pero me dijiste que vives en Nueva York.", - "type": "output_text" - } - ], - "role": "assistant", - "status": "completed", - "type": "message" - } - """ - - -if __name__ == "__main__": - import asyncio - - asyncio.run(main()) diff --git a/tests/examples/research_bot/README.md b/tests/examples/research_bot/README.md deleted file mode 100644 index 4060983c..00000000 --- a/tests/examples/research_bot/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# Research bot - -This is a simple example of a multi-agent research bot. To run it: - -```bash -python -m examples.research_bot.main -``` - -## Architecture - -The flow is: - -1. User enters their research topic -2. `planner_agent` comes up with a plan to search the web for information. The plan is a list of search queries, with a search term and a reason for each query. -3. For each search item, we run a `search_agent`, which uses the Web Search tool to search for that term and summarize the results. These all run in parallel. -4. Finally, the `writer_agent` receives the search summaries, and creates a written report. - -## Suggested improvements - -If you're building your own research bot, some ideas to add to this are: - -1. Retrieval: Add support for fetching relevant information from a vector store. You could use the File Search tool for this. -2. Image and file upload: Allow users to attach PDFs or other files, as baseline context for the research. -3. More planning and thinking: Models often produce better results given more time to think. Improve the planning process to come up with a better plan, and add an evaluation step so that the model can choose to improve it's results, search for more stuff, etc. -4. Code execution: Allow running code, which is useful for data analysis. diff --git a/tests/examples/research_bot/agents/planner_agent.py b/tests/examples/research_bot/agents/planner_agent.py deleted file mode 100644 index e80a8e65..00000000 --- a/tests/examples/research_bot/agents/planner_agent.py +++ /dev/null @@ -1,29 +0,0 @@ -from pydantic import BaseModel - -from agents import Agent - -PROMPT = ( - "You are a helpful research assistant. Given a query, come up with a set of web searches " - "to perform to best answer the query. Output between 5 and 20 terms to query for." -) - - -class WebSearchItem(BaseModel): - reason: str - "Your reasoning for why this search is important to the query." - - query: str - "The search term to use for the web search." - - -class WebSearchPlan(BaseModel): - searches: list[WebSearchItem] - """A list of web searches to perform to best answer the query.""" - - -planner_agent = Agent( - name="PlannerAgent", - instructions=PROMPT, - model="gpt-4o", - output_type=WebSearchPlan, -) diff --git a/tests/examples/research_bot/agents/search_agent.py b/tests/examples/research_bot/agents/search_agent.py deleted file mode 100644 index 72cbc8e1..00000000 --- a/tests/examples/research_bot/agents/search_agent.py +++ /dev/null @@ -1,18 +0,0 @@ -from agents import Agent, WebSearchTool -from agents.model_settings import ModelSettings - -INSTRUCTIONS = ( - "You are a research assistant. Given a search term, you search the web for that term and" - "produce a concise summary of the results. The summary must 2-3 paragraphs and less than 300" - "words. Capture the main points. Write succintly, no need to have complete sentences or good" - "grammar. This will be consumed by someone synthesizing a report, so its vital you capture the" - "essence and ignore any fluff. Do not include any additional commentary other than the summary" - "itself." -) - -search_agent = Agent( - name="Search agent", - instructions=INSTRUCTIONS, - tools=[WebSearchTool()], - model_settings=ModelSettings(tool_choice="required"), -) diff --git a/tests/examples/research_bot/agents/writer_agent.py b/tests/examples/research_bot/agents/writer_agent.py deleted file mode 100644 index 7b7d01a2..00000000 --- a/tests/examples/research_bot/agents/writer_agent.py +++ /dev/null @@ -1,33 +0,0 @@ -# Agent used to synthesize a final report from the individual summaries. -from pydantic import BaseModel - -from agents import Agent - -PROMPT = ( - "You are a senior researcher tasked with writing a cohesive report for a research query. " - "You will be provided with the original query, and some initial research done by a research " - "assistant.\n" - "You should first come up with an outline for the report that describes the structure and " - "flow of the report. Then, generate the report and return that as your final output.\n" - "The final output should be in markdown format, and it should be lengthy and detailed. Aim " - "for 5-10 pages of content, at least 1000 words." -) - - -class ReportData(BaseModel): - short_summary: str - """A short 2-3 sentence summary of the findings.""" - - markdown_report: str - """The final report""" - - follow_up_questions: list[str] - """Suggested topics to research further""" - - -writer_agent = Agent( - name="WriterAgent", - instructions=PROMPT, - model="o3-mini", - output_type=ReportData, -) diff --git a/tests/examples/research_bot/main.py b/tests/examples/research_bot/main.py deleted file mode 100644 index a0fd43dc..00000000 --- a/tests/examples/research_bot/main.py +++ /dev/null @@ -1,12 +0,0 @@ -import asyncio - -from .manager import ResearchManager - - -async def main() -> None: - query = input("What would you like to research? ") - await ResearchManager().run(query) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/research_bot/manager.py b/tests/examples/research_bot/manager.py deleted file mode 100644 index 47306f14..00000000 --- a/tests/examples/research_bot/manager.py +++ /dev/null @@ -1,119 +0,0 @@ -from __future__ import annotations - -import asyncio -import time - -from rich.console import Console - -from agents import Runner, custom_span, gen_trace_id, trace - -from .agents.planner_agent import WebSearchItem, WebSearchPlan, planner_agent -from .agents.search_agent import search_agent -from .agents.writer_agent import ReportData, writer_agent -from .printer import Printer - - -class ResearchManager: - def __init__(self): - self.console = Console() - self.printer = Printer(self.console) - - async def run(self, query: str) -> None: - trace_id = gen_trace_id() - with trace("Research trace", trace_id=trace_id): - self.printer.update_item( - "trace_id", - f"View trace: https://platform.openai.com/traces/{trace_id}", - is_done=True, - hide_checkmark=True, - ) - - self.printer.update_item( - "starting", - "Starting research...", - is_done=True, - hide_checkmark=True, - ) - search_plan = await self._plan_searches(query) - search_results = await self._perform_searches(search_plan) - report = await self._write_report(query, search_results) - - final_report = f"Report summary\n\n{report.short_summary}" - self.printer.update_item("final_report", final_report, is_done=True) - - self.printer.end() - - print("\n\n=====REPORT=====\n\n") - print(f"Report: {report.markdown_report}") - print("\n\n=====FOLLOW UP QUESTIONS=====\n\n") - follow_up_questions = "\n".join(report.follow_up_questions) - print(f"Follow up questions: {follow_up_questions}") - - async def _plan_searches(self, query: str) -> WebSearchPlan: - self.printer.update_item("planning", "Planning searches...") - result = await Runner.run( - planner_agent, - f"Query: {query}", - ) - self.printer.update_item( - "planning", - f"Will perform {len(result.final_output.searches)} searches", - is_done=True, - ) - return result.final_output_as(WebSearchPlan) - - async def _perform_searches(self, search_plan: WebSearchPlan) -> list[str]: - with custom_span("Search the web"): - self.printer.update_item("searching", "Searching...") - num_completed = 0 - tasks = [asyncio.create_task(self._search(item)) for item in search_plan.searches] - results = [] - for task in asyncio.as_completed(tasks): - result = await task - if result is not None: - results.append(result) - num_completed += 1 - self.printer.update_item( - "searching", f"Searching... {num_completed}/{len(tasks)} completed" - ) - self.printer.mark_item_done("searching") - return results - - async def _search(self, item: WebSearchItem) -> str | None: - input = f"Search term: {item.query}\nReason for searching: {item.reason}" - try: - result = await Runner.run( - search_agent, - input, - ) - return str(result.final_output) - except Exception: - return None - - async def _write_report(self, query: str, search_results: list[str]) -> ReportData: - self.printer.update_item("writing", "Thinking about report...") - input = f"Original query: {query}\nSummarized search results: {search_results}" - result = Runner.run_streamed( - writer_agent, - input, - ) - update_messages = [ - "Thinking about report...", - "Planning report structure...", - "Writing outline...", - "Creating sections...", - "Cleaning up formatting...", - "Finalizing report...", - "Finishing report...", - ] - - last_update = time.time() - next_message = 0 - async for _ in result.stream_events(): - if time.time() - last_update > 5 and next_message < len(update_messages): - self.printer.update_item("writing", update_messages[next_message]) - next_message += 1 - last_update = time.time() - - self.printer.mark_item_done("writing") - return result.final_output_as(ReportData) diff --git a/tests/examples/research_bot/sample_outputs/product_recs.md b/tests/examples/research_bot/sample_outputs/product_recs.md deleted file mode 100644 index 70789eb3..00000000 --- a/tests/examples/research_bot/sample_outputs/product_recs.md +++ /dev/null @@ -1,180 +0,0 @@ -# Comprehensive Guide on Best Surfboards for Beginners: Transitioning, Features, and Budget Options - -Surfing is not only a sport but a lifestyle that hooks its enthusiasts with the allure of riding waves and connecting with nature. For beginners, selecting the right surfboard is critical to safety, learning, and performance. This comprehensive guide has been crafted to walk through the essential aspects of choosing the ideal surfboard for beginners, especially those looking to transition from an 11-foot longboard to a shorter, more dynamic board. We discuss various board types, materials, design elements, and budget ranges, providing a detailed road map for both new surfers and those in the process of progression. - ---- - -## Table of Contents - -1. [Introduction](#introduction) -2. [Board Types and Design Considerations](#board-types-and-design-considerations) -3. [Key Board Dimensions and Features](#key-board-dimensions-and-features) -4. [Materials: Soft-Top vs. Hard-Top Boards](#materials-soft-top-vs-hard-top-boards) -5. [Tips for Transitioning from Longboards to Shorter Boards](#tips-for-transitioning-from-longboards-to-shorter-boards) -6. [Budget and Pricing Options](#budget-and-pricing-options) -7. [Recommended Models and Buying Options](#recommended-models-and-buying-options) -8. [Conclusion](#conclusion) -9. [Follow-up Questions](#follow-up-questions) - ---- - -## Introduction - -Surfing is a dynamic sport that requires not only skill and technique but also the proper equipment. For beginners, the right surfboard can make the difference between a frustrating experience and one that builds confidence and enthusiasm. Many newcomers start with longboards due to their stability and ease of paddling; however, as skills develop, transitioning to a shorter board might be desirable for enhancing maneuverability and performance. This guide is designed for surfers who can already catch waves on an 11-foot board and are now considering stepping down to a more versatile option. - -The overarching goal of this document is to help beginners identify which surfboard characteristics are most important, including board length, width, thickness, volume, and materials, while also considering factors like weight distribution, buoyancy, and control. We will also take a look at board types that are particularly welcoming for beginners and discuss gradual transitioning strategies. - ---- - -## Board Types and Design Considerations - -Choosing a board involves understanding the variety of designs available. Below are the main types of surfboards that cater to beginners and transitional surfers: - -### Longboards and Mini-Mals - -Longboards, typically 8 to 11 feet in length, provide ample stability, smoother paddling, and are well-suited for wave-catching. Their generous volume and width allow beginners to build confidence when standing up and riding waves. Mini-mal or mini-malibus (often around 8 to 9 feet) are a popular bridge between the longboard and the more agile shortboard, offering both stability and moderate maneuverability, which makes them excellent for gradual progress. - -### Funboards and Hybrids - -Funboards and hybrid boards blend the benefits of longboards and shortboards. They typically range from 6’6" to 8’0" in length, with extra volume and width that help preserve stability while introducing elements of sharper turning and improved agility. Hybrids are particularly helpful for surfers transitioning from longboards, as they maintain some of the buoyancy and ease of catching waves, yet offer a taste of the performance found in smaller boards. - -### Shortboards - -Shortboards emphasize performance, maneuverability, and a more responsive ride. However, they have less volume and require stronger paddling, quicker pop-up techniques, and more refined balance. For beginners, moving to a traditional shortboard immediately can be challenging. It is generally advised to make a gradual transition, potentially starting with a funboard or hybrid before making a direct leap to a performance shortboard. - ---- - -## Key Board Dimensions and Features - -When selecting a beginner surfboard, several key dimensions and features drastically affect performance, ease of learning, and safety: - -### Length and Width - -- **Length**: Starting with an 8 to 9-foot board is ideal. Longer boards offer enhanced stability and improved paddling capabilities. Gradual downsizing is recommended if you plan to move from an 11-foot board. -- **Width**: A board with a width over 20 inches provides greater stability and facilitates balance, especially vital for beginners. - -### Thickness and Volume - -- **Thickness**: Typically around 2.5 to 3 inches. Thicker decks increase buoyancy, allowing the surfer to paddle easier while catching waves. -- **Volume**: Measured in liters, volume is critical in understanding a board's flotation capacity. Higher volumes (e.g., 60-100 liters) are essential for beginners as they make the board more forgiving and stable. Suitable volumes might vary according to the surfer’s weight and experience level. - -### Nose and Tail Shape - -- **Nose Shape**: A wide, rounded nose expands the board’s planing surface, which can help in catching waves sooner and maintaining stability as you ride. -- **Tail Design**: Square or rounded tails are generally recommended as they enhance stability and allow for controlled turns, essential during the learning phase. - -### Rocker - -- **Rocker**: This is the curvature of the board from nose to tail. For beginners, a minimal or relaxed rocker provides better stability and ease during paddling. A steeper rocker might be introduced progressively as the surfer’s skills improve. - ---- - -## Materials: Soft-Top vs. Hard-Top Boards - -The material composition of a surfboard is a crucial factor in determining its performance, durability, and safety. Beginners have two primary choices: - -### Soft-Top (Foam) Boards - -Soft-top boards are constructed almost entirely from foam. Their attributes include: - -- **Safety and Forgiveness**: The foam construction minimizes injury upon impact which is advantageous for beginners who might fall frequently. -- **Stability and Buoyancy**: These boards typically offer greater buoyancy due to their softer material and thicker construction, easing the initial learning process. -- **Maintenance**: They often require less maintenance—there is typically no need for waxing and they are more resistant to dings and scratches. - -However, as a surfer’s skills progress, a soft-top might limit maneuverability and overall performance. - -### Hard-Top Boards - -Hard-tops, in contrast, offer a more traditional surfboard feel. They generally rely on a foam core encased in resin, with two prevalent combinations: - -- **PU (Polyurethane) Core with Polyester Resin**: This combination gives a classic feel and is relatively economical; however, these boards can be heavier and, as they age, more prone to damage. -- **EPS (Expanded Polystyrene) Core with Epoxy Resin**: Lightweight and durable, EPS boards are often more buoyant and resistant to damage, although they usually carry a higher price tag and may be less forgiving. - -Deciding between soft-top and hard-top boards often depends on a beginner’s progression goals, overall comfort, and budget constraints. - ---- - -## Tips for Transitioning from Longboards to Shorter Boards - -For surfers who have mastered the basics on an 11-foot board, the transition to a shorter board requires careful consideration, patience, and incremental changes. Here are some key tips: - -### Gradual Downsizing - -Experts recommend reducing the board length gradually—by about a foot at a time—to allow the body to adjust slowly to a board with less buoyancy and more responsiveness. This process helps maintain wave-catching ability and reduces the shock of transitioning to a very different board feel. - -### Strengthening Core Skills - -Before transitioning, make sure your surfing fundamentals are solid. Focus on practicing: - -- **Steep Take-offs**: Ensure that your pop-up is swift and robust to keep pace with shorter boards that demand a rapid transition from paddling to standing. -- **Angling and Paddling Techniques**: Learn to angle your takeoffs properly to compensate for the lower buoyancy and increased maneuverability of shorter boards. - -### Experimenting with Rentals or Borrowed Boards - -If possible, try out a friend’s shorter board or rent one for a day to experience firsthand the differences in performance. This practical trial can provide valuable insights and inform your decision before making a purchase. - ---- - -## Budget and Pricing Options - -Surfboards are available across a range of prices to match different budgets. Whether you are looking for an affordable beginner board or a more expensive model that grows with your skills, it’s important to understand what features you can expect at different price points. - -### Budget-Friendly Options - -For those on a tight budget, several entry-level models offer excellent value. Examples include: - -- **Wavestorm 8' Classic Pinline Surfboard**: Priced affordably, this board is popular for its ease of use, ample volume, and forgiving nature. Despite its low cost, it delivers the stability needed to get started. -- **Liquid Shredder EZ Slider Foamie**: A smaller board catering to younger or lighter surfers, this budget option provides easy paddling and a minimal risk of injury due to its soft construction. - -### Moderate Price Range - -As you move into the intermediate range, boards typically become slightly more specialized in their design, offering features such as improved stringer systems or versatile fin setups. These are excellent for surfers who wish to continue progressing their skills without compromising stability. Many surfboard packages from retailers also bundle a board with essential accessories like board bags, leashes, and wax for additional savings. - -### Higher-End Models and Transitional Packages - -For surfers looking for durability, performance, and advanced design features, investing in an EPS/epoxy board might be ideal. Although they come at a premium, these boards are lightweight, strong, and customizable with various fin configurations. Some options include boards from brands like South Bay Board Co. and ISLE, which combine high-quality construction with beginner-friendly features that help mediate the transition from longboard to shortboard performance. - ---- - -## Recommended Models and Buying Options - -Based on extensive research and community recommendations, here are some standout models and tips on where to buy: - -### Recommended Models - -- **South Bay Board Co. 8'8" Heritage**: Combining foam and resin construction, this board is ideal for beginners who need stability and a forgiving surface. Its 86-liter volume suits both lightweight and somewhat heavier surfers. -- **Rock-It 8' Big Softy**: With a high volume and an easy paddling profile, this board is designed for beginners, offering ample buoyancy to smooth out the learning curve. -- **Wave Bandit EZ Rider Series**: Available in multiple lengths (7', 8', 9'), these boards offer versatility, with construction features that balance the stability of longboards and the agility required for shorter boards. -- **Hybrid/Funboards Like the Poacher Funboard**: Perfect for transitioning surfers, these boards blend the ease of catching waves with the capability for more dynamic maneuvers. - -### Buying Options - -- **Surf Shops and Local Retailers**: Traditional surf shops allow you to test different boards, which is ideal for assessing the board feel and condition—especially if you are considering a used board. -- **Online Retailers and Marketplaces**: Websites like Evo, Surfboards Direct, and even local online marketplaces like Craigslist and Facebook Marketplace provide options that range from new to gently used boards. Always inspect reviews and verify seller policies before purchase. -- **Package Deals and Bundles**: Many retailers offer bundled packages that include not just the board, but also essentials like a leash, wax, fins, and board bags. These packages can be more cost-effective and are great for beginners who need a complete surf kit. - ---- - -## Conclusion - -Selecting the right surfboard as a beginner is about balancing various factors: stability, buoyancy, maneuverability, and budget. - -For those who have honed the basics using an 11-foot longboard, the transition to a shorter board should be gradual. Start by focusing on boards that preserve stability—such as funboards and hybrids—before moving to the more performance-oriented shortboards. Key characteristics like board length, width, thickness, volume, and material profoundly influence your surfing experience. Soft-top boards provide a forgiving entry point, while hard-top boards, especially those with EPS cores and epoxy resin, offer benefits for more advanced progression despite the increased learning curve. - -Emphasizing fundamentals like proper pop-up technique and effective paddle work will ease the transition and ensure that the new board complements your evolving skills. Additionally, understanding the pricing spectrum—from budget-friendly models to premium options—allows you to make an informed purchase that suits both your financial and performance needs. - -With a thoughtful approach to board selection, you can enhance your learning curve, enjoy safer sessions in the water, and ultimately develop the skills necessary to master the diverse challenges surfing presents. Whether your goal is to ride gentle waves or eventually experiment with sharper turns and dynamic maneuvers, choosing the right board is your first step towards a rewarding and sustainable surfing journey. - ---- - -## Follow-up Questions - -1. What is your current budget range for a new surfboard, or are you considering buying used? -2. How frequently do you plan to surf, and in what type of wave conditions? -3. Are you interested in a board that you can grow into as your skills progress, or do you prefer one that is more specialized for certain conditions? -4. Would you be interested in additional equipment bundles (like fins, leashes, boards bags) offered by local retailers or online shops? -5. Have you had the opportunity to test ride any boards before, and what feedback did you gather from that experience? - ---- - -With this detailed guide, beginners should now have a comprehensive understanding of the surfboard market and the key factors influencing board performance, safety, and ease of progression. Happy surfing, and may you find the perfect board that rides the waves as beautifully as your passion for the sport! diff --git a/tests/examples/research_bot/sample_outputs/product_recs.txt b/tests/examples/research_bot/sample_outputs/product_recs.txt deleted file mode 100644 index 78865f23..00000000 --- a/tests/examples/research_bot/sample_outputs/product_recs.txt +++ /dev/null @@ -1,212 +0,0 @@ -# Terminal output for a product recommendation related query. See product_recs.md for final report. - -$ uv run python -m examples.research_bot.main - -What would you like to research? Best surfboards for beginners. I can catch my own waves, but previously used an 11ft board. What should I look for, what are my options? Various budget ranges. -View trace: https://platform.openai.com/traces/trace_... -Starting research... -✅ Will perform 15 searches -✅ Searching... 15/15 completed -✅ Finishing report... -✅ Report summary - -This report provides a detailed guide on selecting the best surfboards for beginners, especially for those transitioning from an 11-foot longboard to a -shorter board. It covers design considerations such as board dimensions, shape, materials, and volume, while comparing soft-top and hard-top boards. In -addition, the report discusses various budget ranges, recommended board models, buying options (both new and used), and techniques to ease the transition to -more maneuverable boards. By understanding these factors, beginner surfers can select a board that not only enhances their skills but also suits their -individual needs. - - -=====REPORT===== - - -Report: # Comprehensive Guide on Best Surfboards for Beginners: Transitioning, Features, and Budget Options - -Surfing is not only a sport but a lifestyle that hooks its enthusiasts with the allure of riding waves and connecting with nature. For beginners, selecting the right surfboard is critical to safety, learning, and performance. This comprehensive guide has been crafted to walk through the essential aspects of choosing the ideal surfboard for beginners, especially those looking to transition from an 11-foot longboard to a shorter, more dynamic board. We discuss various board types, materials, design elements, and budget ranges, providing a detailed road map for both new surfers and those in the process of progression. - ---- - -## Table of Contents - -1. [Introduction](#introduction) -2. [Board Types and Design Considerations](#board-types-and-design-considerations) -3. [Key Board Dimensions and Features](#key-board-dimensions-and-features) -4. [Materials: Soft-Top vs. Hard-Top Boards](#materials-soft-top-vs-hard-top-boards) -5. [Tips for Transitioning from Longboards to Shorter Boards](#tips-for-transitioning-from-longboards-to-shorter-boards) -6. [Budget and Pricing Options](#budget-and-pricing-options) -7. [Recommended Models and Buying Options](#recommended-models-and-buying-options) -8. [Conclusion](#conclusion) -9. [Follow-up Questions](#follow-up-questions) - ---- - -## Introduction - -Surfing is a dynamic sport that requires not only skill and technique but also the proper equipment. For beginners, the right surfboard can make the difference between a frustrating experience and one that builds confidence and enthusiasm. Many newcomers start with longboards due to their stability and ease of paddling; however, as skills develop, transitioning to a shorter board might be desirable for enhancing maneuverability and performance. This guide is designed for surfers who can already catch waves on an 11-foot board and are now considering stepping down to a more versatile option. - -The overarching goal of this document is to help beginners identify which surfboard characteristics are most important, including board length, width, thickness, volume, and materials, while also considering factors like weight distribution, buoyancy, and control. We will also take a look at board types that are particularly welcoming for beginners and discuss gradual transitioning strategies. - ---- - -## Board Types and Design Considerations - -Choosing a board involves understanding the variety of designs available. Below are the main types of surfboards that cater to beginners and transitional surfers: - -### Longboards and Mini-Mals - -Longboards, typically 8 to 11 feet in length, provide ample stability, smoother paddling, and are well-suited for wave-catching. Their generous volume and width allow beginners to build confidence when standing up and riding waves. Mini-mal or mini-malibus (often around 8 to 9 feet) are a popular bridge between the longboard and the more agile shortboard, offering both stability and moderate maneuverability, which makes them excellent for gradual progress. - -### Funboards and Hybrids - -Funboards and hybrid boards blend the benefits of longboards and shortboards. They typically range from 6’6" to 8’0" in length, with extra volume and width that help preserve stability while introducing elements of sharper turning and improved agility. Hybrids are particularly helpful for surfers transitioning from longboards, as they maintain some of the buoyancy and ease of catching waves, yet offer a taste of the performance found in smaller boards. - -### Shortboards - -Shortboards emphasize performance, maneuverability, and a more responsive ride. However, they have less volume and require stronger paddling, quicker pop-up techniques, and more refined balance. For beginners, moving to a traditional shortboard immediately can be challenging. It is generally advised to make a gradual transition, potentially starting with a funboard or hybrid before making a direct leap to a performance shortboard. - ---- - -## Key Board Dimensions and Features - -When selecting a beginner surfboard, several key dimensions and features drastically affect performance, ease of learning, and safety: - -### Length and Width - -- **Length**: Starting with an 8 to 9-foot board is ideal. Longer boards offer enhanced stability and improved paddling capabilities. Gradual downsizing is recommended if you plan to move from an 11-foot board. -- **Width**: A board with a width over 20 inches provides greater stability and facilitates balance, especially vital for beginners. - -### Thickness and Volume - -- **Thickness**: Typically around 2.5 to 3 inches. Thicker decks increase buoyancy, allowing the surfer to paddle easier while catching waves. -- **Volume**: Measured in liters, volume is critical in understanding a board's flotation capacity. Higher volumes (e.g., 60-100 liters) are essential for beginners as they make the board more forgiving and stable. Suitable volumes might vary according to the surfer’s weight and experience level. - -### Nose and Tail Shape - -- **Nose Shape**: A wide, rounded nose expands the board’s planing surface, which can help in catching waves sooner and maintaining stability as you ride. -- **Tail Design**: Square or rounded tails are generally recommended as they enhance stability and allow for controlled turns, essential during the learning phase. - -### Rocker - -- **Rocker**: This is the curvature of the board from nose to tail. For beginners, a minimal or relaxed rocker provides better stability and ease during paddling. A steeper rocker might be introduced progressively as the surfer’s skills improve. - ---- - -## Materials: Soft-Top vs. Hard-Top Boards - -The material composition of a surfboard is a crucial factor in determining its performance, durability, and safety. Beginners have two primary choices: - -### Soft-Top (Foam) Boards - -Soft-top boards are constructed almost entirely from foam. Their attributes include: - -- **Safety and Forgiveness**: The foam construction minimizes injury upon impact which is advantageous for beginners who might fall frequently. -- **Stability and Buoyancy**: These boards typically offer greater buoyancy due to their softer material and thicker construction, easing the initial learning process. -- **Maintenance**: They often require less maintenance—there is typically no need for waxing and they are more resistant to dings and scratches. - -However, as a surfer’s skills progress, a soft-top might limit maneuverability and overall performance. - -### Hard-Top Boards - -Hard-tops, in contrast, offer a more traditional surfboard feel. They generally rely on a foam core encased in resin, with two prevalent combinations: - -- **PU (Polyurethane) Core with Polyester Resin**: This combination gives a classic feel and is relatively economical; however, these boards can be heavier and, as they age, more prone to damage. -- **EPS (Expanded Polystyrene) Core with Epoxy Resin**: Lightweight and durable, EPS boards are often more buoyant and resistant to damage, although they usually carry a higher price tag and may be less forgiving. - -Deciding between soft-top and hard-top boards often depends on a beginner’s progression goals, overall comfort, and budget constraints. - ---- - -## Tips for Transitioning from Longboards to Shorter Boards - -For surfers who have mastered the basics on an 11-foot board, the transition to a shorter board requires careful consideration, patience, and incremental changes. Here are some key tips: - -### Gradual Downsizing - -Experts recommend reducing the board length gradually—by about a foot at a time—to allow the body to adjust slowly to a board with less buoyancy and more responsiveness. This process helps maintain wave-catching ability and reduces the shock of transitioning to a very different board feel. - -### Strengthening Core Skills - -Before transitioning, make sure your surfing fundamentals are solid. Focus on practicing: - -- **Steep Take-offs**: Ensure that your pop-up is swift and robust to keep pace with shorter boards that demand a rapid transition from paddling to standing. -- **Angling and Paddling Techniques**: Learn to angle your takeoffs properly to compensate for the lower buoyancy and increased maneuverability of shorter boards. - -### Experimenting with Rentals or Borrowed Boards - -If possible, try out a friend’s shorter board or rent one for a day to experience firsthand the differences in performance. This practical trial can provide valuable insights and inform your decision before making a purchase. - ---- - -## Budget and Pricing Options - -Surfboards are available across a range of prices to match different budgets. Whether you are looking for an affordable beginner board or a more expensive model that grows with your skills, it’s important to understand what features you can expect at different price points. - -### Budget-Friendly Options - -For those on a tight budget, several entry-level models offer excellent value. Examples include: - -- **Wavestorm 8' Classic Pinline Surfboard**: Priced affordably, this board is popular for its ease of use, ample volume, and forgiving nature. Despite its low cost, it delivers the stability needed to get started. -- **Liquid Shredder EZ Slider Foamie**: A smaller board catering to younger or lighter surfers, this budget option provides easy paddling and a minimal risk of injury due to its soft construction. - -### Moderate Price Range - -As you move into the intermediate range, boards typically become slightly more specialized in their design, offering features such as improved stringer systems or versatile fin setups. These are excellent for surfers who wish to continue progressing their skills without compromising stability. Many surfboard packages from retailers also bundle a board with essential accessories like board bags, leashes, and wax for additional savings. - -### Higher-End Models and Transitional Packages - -For surfers looking for durability, performance, and advanced design features, investing in an EPS/epoxy board might be ideal. Although they come at a premium, these boards are lightweight, strong, and customizable with various fin configurations. Some options include boards from brands like South Bay Board Co. and ISLE, which combine high-quality construction with beginner-friendly features that help mediate the transition from longboard to shortboard performance. - ---- - -## Recommended Models and Buying Options - -Based on extensive research and community recommendations, here are some standout models and tips on where to buy: - -### Recommended Models - -- **South Bay Board Co. 8'8" Heritage**: Combining foam and resin construction, this board is ideal for beginners who need stability and a forgiving surface. Its 86-liter volume suits both lightweight and somewhat heavier surfers. -- **Rock-It 8' Big Softy**: With a high volume and an easy paddling profile, this board is designed for beginners, offering ample buoyancy to smooth out the learning curve. -- **Wave Bandit EZ Rider Series**: Available in multiple lengths (7', 8', 9'), these boards offer versatility, with construction features that balance the stability of longboards and the agility required for shorter boards. -- **Hybrid/Funboards Like the Poacher Funboard**: Perfect for transitioning surfers, these boards blend the ease of catching waves with the capability for more dynamic maneuvers. - -### Buying Options - -- **Surf Shops and Local Retailers**: Traditional surf shops allow you to test different boards, which is ideal for assessing the board feel and condition—especially if you are considering a used board. -- **Online Retailers and Marketplaces**: Websites like Evo, Surfboards Direct, and even local online marketplaces like Craigslist and Facebook Marketplace provide options that range from new to gently used boards. Always inspect reviews and verify seller policies before purchase. -- **Package Deals and Bundles**: Many retailers offer bundled packages that include not just the board, but also essentials like a leash, wax, fins, and board bags. These packages can be more cost-effective and are great for beginners who need a complete surf kit. - ---- - -## Conclusion - -Selecting the right surfboard as a beginner is about balancing various factors: stability, buoyancy, maneuverability, and budget. - -For those who have honed the basics using an 11-foot longboard, the transition to a shorter board should be gradual. Start by focusing on boards that preserve stability—such as funboards and hybrids—before moving to the more performance-oriented shortboards. Key characteristics like board length, width, thickness, volume, and material profoundly influence your surfing experience. Soft-top boards provide a forgiving entry point, while hard-top boards, especially those with EPS cores and epoxy resin, offer benefits for more advanced progression despite the increased learning curve. - -Emphasizing fundamentals like proper pop-up technique and effective paddle work will ease the transition and ensure that the new board complements your evolving skills. Additionally, understanding the pricing spectrum—from budget-friendly models to premium options—allows you to make an informed purchase that suits both your financial and performance needs. - -With a thoughtful approach to board selection, you can enhance your learning curve, enjoy safer sessions in the water, and ultimately develop the skills necessary to master the diverse challenges surfing presents. Whether your goal is to ride gentle waves or eventually experiment with sharper turns and dynamic maneuvers, choosing the right board is your first step towards a rewarding and sustainable surfing journey. - ---- - -## Follow-up Questions - -1. What is your current budget range for a new surfboard, or are you considering buying used? -2. How frequently do you plan to surf, and in what type of wave conditions? -3. Are you interested in a board that you can grow into as your skills progress, or do you prefer one that is more specialized for certain conditions? -4. Would you be interested in additional equipment bundles (like fins, leashes, boards bags) offered by local retailers or online shops? -5. Have you had the opportunity to test ride any boards before, and what feedback did you gather from that experience? - ---- - -With this detailed guide, beginners should now have a comprehensive understanding of the surfboard market and the key factors influencing board performance, safety, and ease of progression. Happy surfing, and may you find the perfect board that rides the waves as beautifully as your passion for the sport! - - -=====FOLLOW UP QUESTIONS===== - - -Follow up questions: What is your current budget range for a new surfboard, or are you considering a used board? -What types of waves do you typically surf, and how might that affect your board choice? -Would you be interested in a transitional board that grows with your skills, or are you looking for a more specialized design? -Have you had experience with renting or borrowing boards to try different sizes before making a purchase? -Do you require additional equipment bundles (like fins, leash, or wax), or do you already have those? diff --git a/tests/examples/research_bot/sample_outputs/vacation.md b/tests/examples/research_bot/sample_outputs/vacation.md deleted file mode 100644 index 82c137af..00000000 --- a/tests/examples/research_bot/sample_outputs/vacation.md +++ /dev/null @@ -1,177 +0,0 @@ -Report: # Caribbean Adventure in April: Surfing, Hiking, and Water Sports Exploration - -The Caribbean is renowned for its crystal-clear waters, vibrant culture, and diverse outdoor activities. April is an especially attractive month for visitors: warm temperatures, clear skies, and the promise of abundant activities. This report explores the best Caribbean destinations in April, with a focus on optimizing your vacation for surfing, hiking, and water sports. - ---- - -## Table of Contents - -1. [Introduction](#introduction) -2. [Why April is the Perfect Time in the Caribbean](#why-april-is-the-perfect-time-in-the-caribbean) -3. [Surfing in the Caribbean](#surfing-in-the-caribbean) - - 3.1 [Barbados: The Tale of Two Coasts](#barbados-the-tale-of-two-coasts) - - 3.2 [Puerto Rico: Rincón and Beyond](#puerto-rico-rinc%C3%B3n-and-beyond) - - 3.3 [Dominican Republic and Other Hotspots](#dominican-republic-and-other-hotspots) -4. [Hiking Adventures Across the Caribbean](#hiking-adventures-across-the-caribbean) - - 4.1 [Trekking Through Tropical Rainforests](#trekking-through-tropical-rainforests) - - 4.2 [Volcanic Peaks and Rugged Landscapes](#volcanic-peaks-and-rugged-landscapes) -5. [Diverse Water Sports Experiences](#diverse-water-sports-experiences) - - 5.1 [Snorkeling, Diving, and Jet Skiing](#snorkeling-diving-and-jet-skiing) - - 5.2 [Kiteboarding and Windsurfing](#kiteboarding-and-windsurfing) -6. [Combining Adventures: Multi-Activity Destinations](#combining-adventures-multi-activity-destinations) -7. [Practical Advice and Travel Tips](#practical-advice-and-travel-tips) -8. [Conclusion](#conclusion) - ---- - -## Introduction - -Caribbean vacations are much more than just beach relaxation; they offer adventure, exploration, and a lively cultural tapestry waiting to be discovered. For travelers seeking an adrenaline-filled getaway, April provides optimal conditions. This report synthesizes diverse research findings and travel insights to help you create an itinerary that combines the thrill of surfing, the challenge of hiking, and the excitement of water sports. - -Whether you're standing on the edge of a powerful reef break or trekking through lush tropical landscapes, the Caribbean in April invites you to dive into nature, adventure, and culture. The following sections break down the best destinations and activities, ensuring that every aspect of your trip is meticulously planned for an unforgettable experience. - ---- - -## Why April is the Perfect Time in the Caribbean - -April stands at the crossroads of seasons in many Caribbean destinations. It marks the tail end of the dry season, ensuring: - -- **Consistent Warm Temperatures:** Average daytime highs around 29°C (84°F) foster comfortable conditions for both land and water activities. -- **Pleasant Sea Temperatures:** With sea temperatures near 26°C (79°F), swimmers, surfers, and divers are treated to inviting waters. -- **Clear Skies and Minimal Rainfall:** Crisp, blue skies make for excellent visibility during snorkeling and diving, as well as clear panoramic views while hiking. -- **Festivals and Cultural Events:** Many islands host seasonal festivals such as Barbados' Fish Festival and Antigua's Sailing Week, adding a cultural layer to your vacation. - -These factors create an ideal backdrop for balancing your outdoor pursuits, whether you’re catching epic waves, trekking rugged trails, or partaking in water sports. - ---- - -## Surfing in the Caribbean - -Surfing in the Caribbean offers diverse wave experiences, ranging from gentle, beginner-friendly rollers to powerful reef breaks that challenge even seasoned surfers. April, in particular, provides excellent conditions for those looking to ride its picturesque waves. - -### Barbados: The Tale of Two Coasts - -Barbados is a prime destination: - -- **Soup Bowl in Bathsheba:** On the east coast, the Soup Bowl is famous for its consistent, powerful waves. This spot attracts experienced surfers who appreciate its challenging right-hand reef break with steep drops, providing the kind of performance wave rarely found elsewhere. -- **Freights Bay:** On the south coast, visitors find more forgiving, gentle wave conditions. Ideal for beginners and longboarders, this spot offers the perfect balance for those still mastering their craft. - -Barbados not only excels in its surfing credentials but also complements the experience with a rich local culture and events in April, making it a well-rounded destination. - -### Puerto Rico: Rincón and Beyond - -Rincón in Puerto Rico is hailed as the Caribbean’s surfing capital: - -- **Diverse Breaks:** With spots ranging from challenging reef breaks such as Tres Palmas and Dogman's to more inviting waves at Domes and Maria's, Puerto Rico offers a spectrum for all surfing skill levels. -- **Local Culture:** Aside from its surf culture, the island boasts vibrant local food scenes, historic sites, and exciting nightlife, enriching your overall travel experience. - -In addition, Puerto Rico’s coasts often feature opportunities for hiking and other outdoor adventures, making it an attractive option for multi-activity travelers. - -### Dominican Republic and Other Hotspots - -Other islands such as the Dominican Republic, with Playa Encuentro on its north coast, provide consistent surf year-round. Highlights include: - -- **Playa Encuentro:** A hotspot known for its dependable breaks, ideal for both intermediate and advanced surfers during the cooler months of October to April. -- **Jamaica and The Bahamas:** Jamaica’s Boston Bay offers a mix of beginner and intermediate waves, and The Bahamas’ Surfer’s Beach on Eleuthera draws parallels to the legendary surf spots of Hawaii, especially during the winter months. - -These destinations not only spotlight surfing but also serve as gateways to additional outdoor activities, ensuring there's never a dull moment whether you're balancing waves with hikes or cultural exploration. - ---- - -## Hiking Adventures Across the Caribbean - -The Caribbean's topography is as varied as it is beautiful. Its network of hiking trails traverses volcanic peaks, ancient rainforests, and dramatic coastal cliffs, offering breathtaking vistas to intrepid explorers. - -### Trekking Through Tropical Rainforests - -For nature enthusiasts, the lush forests of the Caribbean present an immersive encounter with biodiversity: - -- **El Yunque National Forest, Puerto Rico:** The only tropical rainforest within the U.S. National Forest System, El Yunque is rich in endemic species such as the Puerto Rican parrot and the famous coquí frog. Trails like the El Yunque Peak Trail and La Mina Falls Trail provide both challenging hikes and scenic rewards. -- **Virgin Islands National Park, St. John:** With over 20 well-defined trails, this park offers hikes that reveal historical petroglyphs, colonial ruins, and stunning coastal views along the Reef Bay Trail. - -### Volcanic Peaks and Rugged Landscapes - -For those seeking more rugged challenges, several destinations offer unforgettable adventures: - -- **Morne Trois Pitons National Park, Dominica:** A UNESCO World Heritage Site showcasing volcanic landscapes, hot springs, the famed Boiling Lake, and lush trails that lead to hidden waterfalls. -- **Gros Piton, Saint Lucia:** The iconic hike up Gros Piton provides a moderately challenging trek that ends with panoramic views of the Caribbean Sea, a truly rewarding experience for hikers. -- **La Soufrière, St. Vincent:** This active volcano not only offers a dynamic hiking environment but also the opportunity to observe the ongoing geological transformations up close. - -Other noteworthy hiking spots include the Blue Mountains in Jamaica for coffee plantation tours and expansive views, as well as trails in Martinique around Montagne Pelée, which combine historical context with natural beauty. - ---- - -## Diverse Water Sports Experiences - -While surfing and hiking attract a broad range of adventurers, the Caribbean also scores high on other water sports. Whether you're drawn to snorkeling, jet skiing, or wind- and kiteboarding, the islands offer a plethora of aquatic activities. - -### Snorkeling, Diving, and Jet Skiing - -Caribbean waters teem with life and color, making them ideal for underwater exploration: - -- **Bonaire:** Its protected marine parks serve as a magnet for divers and snorkelers. With vibrant coral reefs and diverse marine species, Bonaire is a top destination for those who appreciate the underwater world. -- **Cayman Islands:** Unique attractions such as Stingray City provide opportunities to interact with friendly stingrays in clear, calm waters. Additionally, the Underwater Sculpture Park is an innovative blend of art and nature. -- **The Bahamas:** In places like Eleuthera, excursions often cater to families and thrill-seekers alike. Options include jet ski rentals, where groups can explore hidden beaches and pristine coves while enjoying the vibrant marine life. - -### Kiteboarding and Windsurfing - -Harnessing the steady trade winds and warm Caribbean waters, several islands have become hubs for kiteboarding and windsurfing: - -- **Aruba:** Known as "One Happy Island," Aruba’s Fisherman's Huts area provides consistent winds, perfect for enthusiasts of windsurfing and kiteboarding alike. -- **Cabarete, Dominican Republic and Silver Rock, Barbados:** Both destinations benefit from reliable trade winds, making them popular among kitesurfers. These spots often combine water sports with a lively beach culture, ensuring that the fun continues on land as well. - -Local operators provide equipment rental and lessons, ensuring that even first-time adventurers can safely and confidently enjoy these exciting sports. - ---- - -## Combining Adventures: Multi-Activity Destinations - -For travelers seeking a comprehensive vacation where surfing, hiking, and water sports converge, several Caribbean destinations offer the best of all worlds. - -- **Puerto Rico:** With its robust surf scene in Rincón, world-class hiking in El Yunque, and opportunities for snorkeling and jet skiing in San Juan Bay, Puerto Rico is a true multi-adventure destination. -- **Barbados:** In addition to the surf breaks along its coasts, Barbados offers a mix of cultural events, local cuisine, and even hiking excursions to scenic rural areas, making for a well-rounded experience. -- **Dominican Republic and Jamaica:** Both are renowned not only for their consistent surf conditions but also for expansive hiking trails and water sports. From the rugged landscapes of the Dominican Republic to Jamaica’s blend of cultural history and natural exploration, these islands allow travelers to mix and match activities seamlessly. - -Group tours and local guides further enhance these experiences, providing insider tips, safe excursions, and personalized itineraries that cater to multiple interests within one trip. - ---- - -## Practical Advice and Travel Tips - -### Weather and Timing - -- **Optimal Climate:** April offers ideal weather conditions across the Caribbean. With minimal rainfall and warm temperatures, it is a great time to schedule outdoor activities. -- **Surfing Seasons:** While April marks the end of the prime surf season in some areas (like Rincón in Puerto Rico), many destinations maintain consistent conditions during this month. - -### Booking and Costs - -- **Surfing Lessons:** Expect to pay between $40 and $110 per session depending on the location. For instance, Puerto Rico typically charges around $75 for beginner lessons, while group lessons in the Dominican Republic average approximately $95. -- **Equipment Rentals:** Pricing for jet ski, surfboard, and snorkeling equipment may vary. In the Bahamas, an hour-long jet ski tour might cost about $120 per group, whereas a similar experience might be available at a lower cost in other regions. -- **Accommodations:** Prices also vary by island. Many travelers find that even affordable stays do not skimp on amenities, allowing you to invest more in guided excursions and local experiences. - -### Cultural Considerations - -- **Festivals and Events:** Check local event calendars. Destinations like Barbados and Antigua host festivals in April that combine cultural heritage with festive outdoor activities. -- **Local Cuisine:** Incorporate food tours into your itinerary. Caribbean cuisine—with its fusion of flavors—can be as adventurous as the outdoor activities. - -### Health and Safety - -- **Staying Hydrated:** The warm temperatures demand that you stay properly hydrated. Always carry water, especially during long hikes. -- **Sun Protection:** Use sunscreen, hats, and sunglasses to protect yourself during extended periods outdoors on both land and water. -- **Local Guides:** Utilize local tour operators for both hiking and water sports. Their expertise not only enriches your experience but also ensures safety in unfamiliar terrain or water bodies. - ---- - -## Conclusion - -The Caribbean in April is a haven for adventure seekers. With its pristine beaches, diverse ecosystems, and rich cultural tapestry, it offers something for every type of traveler. Whether you're chasing the perfect wave along the shores of Barbados and Puerto Rico, trekking through the lush landscapes of El Yunque or Morne Trois Pitons, or engaging in an array of water sports from snorkeling to kiteboarding, your ideal vacation is only a booking away. - -This report has outlined the best destinations and provided practical advice to optimize your vacation for surfing, hiking, and water sports. By considering the diverse offerings—from epic surf breaks and challenging hiking trails to vibrant water sports—the Caribbean stands out as a multi-adventure destination where every day brings a new experience. - -Plan carefully, pack wisely, and get ready to explore the vibrant mosaic of landscapes and activities that make the Caribbean in April a truly unforgettable adventure. - -Happy travels! - ---- - -_References available upon request. Many insights were drawn from trusted sources including Lonely Planet, TravelPug, and various Caribbean-centric exploration sites, ensuring a well-rounded and practical guide for your vacation planning._ diff --git a/tests/examples/research_bot/sample_outputs/vacation.txt b/tests/examples/research_bot/sample_outputs/vacation.txt deleted file mode 100644 index b2649981..00000000 --- a/tests/examples/research_bot/sample_outputs/vacation.txt +++ /dev/null @@ -1,206 +0,0 @@ -# Terminal output for a vacation related query. See vacation.md for final report. - -$ uv run python -m examples.research_bot.main -What would you like to research? Caribbean vacation spots in April, optimizing for surfing, hiking and water sports -View trace: https://platform.openai.com/traces/trace_.... -Starting research... -✅ Will perform 15 searches -✅ Searching... 15/15 completed -✅ Finishing report... -✅ Report summary - -This report provides an in-depth exploration of selected Caribbean vacation spots in April that are ideal for surfing, hiking, and water sports. Covering -destinations from Barbados and Puerto Rico to the Bahamas and Jamaica, it examines favorable weather conditions, recommended surf breaks, scenic hiking -trails, and various water sports activities. Detailed destination profiles, activity highlights, and travel tips are integrated to help travelers design a -multi-adventure itinerary in the Caribbean during April. - - -=====REPORT===== - - -Report: # Caribbean Adventure in April: Surfing, Hiking, and Water Sports Exploration - -The Caribbean is renowned for its crystal-clear waters, vibrant culture, and diverse outdoor activities. April is an especially attractive month for visitors: warm temperatures, clear skies, and the promise of abundant activities. This report explores the best Caribbean destinations in April, with a focus on optimizing your vacation for surfing, hiking, and water sports. - ---- - -## Table of Contents - -1. [Introduction](#introduction) -2. [Why April is the Perfect Time in the Caribbean](#why-april-is-the-perfect-time-in-the-caribbean) -3. [Surfing in the Caribbean](#surfing-in-the-caribbean) - - 3.1 [Barbados: The Tale of Two Coasts](#barbados-the-tale-of-two-coasts) - - 3.2 [Puerto Rico: Rincón and Beyond](#puerto-rico-rinc%C3%B3n-and-beyond) - - 3.3 [Dominican Republic and Other Hotspots](#dominican-republic-and-other-hotspots) -4. [Hiking Adventures Across the Caribbean](#hiking-adventures-across-the-caribbean) - - 4.1 [Trekking Through Tropical Rainforests](#trekking-through-tropical-rainforests) - - 4.2 [Volcanic Peaks and Rugged Landscapes](#volcanic-peaks-and-rugged-landscapes) -5. [Diverse Water Sports Experiences](#diverse-water-sports-experiences) - - 5.1 [Snorkeling, Diving, and Jet Skiing](#snorkeling-diving-and-jet-skiing) - - 5.2 [Kiteboarding and Windsurfing](#kiteboarding-and-windsurfing) -6. [Combining Adventures: Multi-Activity Destinations](#combining-adventures-multi-activity-destinations) -7. [Practical Advice and Travel Tips](#practical-advice-and-travel-tips) -8. [Conclusion](#conclusion) - ---- - -## Introduction - -Caribbean vacations are much more than just beach relaxation; they offer adventure, exploration, and a lively cultural tapestry waiting to be discovered. For travelers seeking an adrenaline-filled getaway, April provides optimal conditions. This report synthesizes diverse research findings and travel insights to help you create an itinerary that combines the thrill of surfing, the challenge of hiking, and the excitement of water sports. - -Whether you're standing on the edge of a powerful reef break or trekking through lush tropical landscapes, the Caribbean in April invites you to dive into nature, adventure, and culture. The following sections break down the best destinations and activities, ensuring that every aspect of your trip is meticulously planned for an unforgettable experience. - ---- - -## Why April is the Perfect Time in the Caribbean - -April stands at the crossroads of seasons in many Caribbean destinations. It marks the tail end of the dry season, ensuring: - -- **Consistent Warm Temperatures:** Average daytime highs around 29°C (84°F) foster comfortable conditions for both land and water activities. -- **Pleasant Sea Temperatures:** With sea temperatures near 26°C (79°F), swimmers, surfers, and divers are treated to inviting waters. -- **Clear Skies and Minimal Rainfall:** Crisp, blue skies make for excellent visibility during snorkeling and diving, as well as clear panoramic views while hiking. -- **Festivals and Cultural Events:** Many islands host seasonal festivals such as Barbados' Fish Festival and Antigua's Sailing Week, adding a cultural layer to your vacation. - -These factors create an ideal backdrop for balancing your outdoor pursuits, whether you’re catching epic waves, trekking rugged trails, or partaking in water sports. - ---- - -## Surfing in the Caribbean - -Surfing in the Caribbean offers diverse wave experiences, ranging from gentle, beginner-friendly rollers to powerful reef breaks that challenge even seasoned surfers. April, in particular, provides excellent conditions for those looking to ride its picturesque waves. - -### Barbados: The Tale of Two Coasts - -Barbados is a prime destination: - -- **Soup Bowl in Bathsheba:** On the east coast, the Soup Bowl is famous for its consistent, powerful waves. This spot attracts experienced surfers who appreciate its challenging right-hand reef break with steep drops, providing the kind of performance wave rarely found elsewhere. -- **Freights Bay:** On the south coast, visitors find more forgiving, gentle wave conditions. Ideal for beginners and longboarders, this spot offers the perfect balance for those still mastering their craft. - -Barbados not only excels in its surfing credentials but also complements the experience with a rich local culture and events in April, making it a well-rounded destination. - -### Puerto Rico: Rincón and Beyond - -Rincón in Puerto Rico is hailed as the Caribbean’s surfing capital: - -- **Diverse Breaks:** With spots ranging from challenging reef breaks such as Tres Palmas and Dogman's to more inviting waves at Domes and Maria's, Puerto Rico offers a spectrum for all surfing skill levels. -- **Local Culture:** Aside from its surf culture, the island boasts vibrant local food scenes, historic sites, and exciting nightlife, enriching your overall travel experience. - -In addition, Puerto Rico’s coasts often feature opportunities for hiking and other outdoor adventures, making it an attractive option for multi-activity travelers. - -### Dominican Republic and Other Hotspots - -Other islands such as the Dominican Republic, with Playa Encuentro on its north coast, provide consistent surf year-round. Highlights include: - -- **Playa Encuentro:** A hotspot known for its dependable breaks, ideal for both intermediate and advanced surfers during the cooler months of October to April. -- **Jamaica and The Bahamas:** Jamaica’s Boston Bay offers a mix of beginner and intermediate waves, and The Bahamas’ Surfer’s Beach on Eleuthera draws parallels to the legendary surf spots of Hawaii, especially during the winter months. - -These destinations not only spotlight surfing but also serve as gateways to additional outdoor activities, ensuring there's never a dull moment whether you're balancing waves with hikes or cultural exploration. - ---- - -## Hiking Adventures Across the Caribbean - -The Caribbean's topography is as varied as it is beautiful. Its network of hiking trails traverses volcanic peaks, ancient rainforests, and dramatic coastal cliffs, offering breathtaking vistas to intrepid explorers. - -### Trekking Through Tropical Rainforests - -For nature enthusiasts, the lush forests of the Caribbean present an immersive encounter with biodiversity: - -- **El Yunque National Forest, Puerto Rico:** The only tropical rainforest within the U.S. National Forest System, El Yunque is rich in endemic species such as the Puerto Rican parrot and the famous coquí frog. Trails like the El Yunque Peak Trail and La Mina Falls Trail provide both challenging hikes and scenic rewards. -- **Virgin Islands National Park, St. John:** With over 20 well-defined trails, this park offers hikes that reveal historical petroglyphs, colonial ruins, and stunning coastal views along the Reef Bay Trail. - -### Volcanic Peaks and Rugged Landscapes - -For those seeking more rugged challenges, several destinations offer unforgettable adventures: - -- **Morne Trois Pitons National Park, Dominica:** A UNESCO World Heritage Site showcasing volcanic landscapes, hot springs, the famed Boiling Lake, and lush trails that lead to hidden waterfalls. -- **Gros Piton, Saint Lucia:** The iconic hike up Gros Piton provides a moderately challenging trek that ends with panoramic views of the Caribbean Sea, a truly rewarding experience for hikers. -- **La Soufrière, St. Vincent:** This active volcano not only offers a dynamic hiking environment but also the opportunity to observe the ongoing geological transformations up close. - -Other noteworthy hiking spots include the Blue Mountains in Jamaica for coffee plantation tours and expansive views, as well as trails in Martinique around Montagne Pelée, which combine historical context with natural beauty. - ---- - -## Diverse Water Sports Experiences - -While surfing and hiking attract a broad range of adventurers, the Caribbean also scores high on other water sports. Whether you're drawn to snorkeling, jet skiing, or wind- and kiteboarding, the islands offer a plethora of aquatic activities. - -### Snorkeling, Diving, and Jet Skiing - -Caribbean waters teem with life and color, making them ideal for underwater exploration: - -- **Bonaire:** Its protected marine parks serve as a magnet for divers and snorkelers. With vibrant coral reefs and diverse marine species, Bonaire is a top destination for those who appreciate the underwater world. -- **Cayman Islands:** Unique attractions such as Stingray City provide opportunities to interact with friendly stingrays in clear, calm waters. Additionally, the Underwater Sculpture Park is an innovative blend of art and nature. -- **The Bahamas:** In places like Eleuthera, excursions often cater to families and thrill-seekers alike. Options include jet ski rentals, where groups can explore hidden beaches and pristine coves while enjoying the vibrant marine life. - -### Kiteboarding and Windsurfing - -Harnessing the steady trade winds and warm Caribbean waters, several islands have become hubs for kiteboarding and windsurfing: - -- **Aruba:** Known as "One Happy Island," Aruba’s Fisherman's Huts area provides consistent winds, perfect for enthusiasts of windsurfing and kiteboarding alike. -- **Cabarete, Dominican Republic and Silver Rock, Barbados:** Both destinations benefit from reliable trade winds, making them popular among kitesurfers. These spots often combine water sports with a lively beach culture, ensuring that the fun continues on land as well. - -Local operators provide equipment rental and lessons, ensuring that even first-time adventurers can safely and confidently enjoy these exciting sports. - ---- - -## Combining Adventures: Multi-Activity Destinations - -For travelers seeking a comprehensive vacation where surfing, hiking, and water sports converge, several Caribbean destinations offer the best of all worlds. - -- **Puerto Rico:** With its robust surf scene in Rincón, world-class hiking in El Yunque, and opportunities for snorkeling and jet skiing in San Juan Bay, Puerto Rico is a true multi-adventure destination. -- **Barbados:** In addition to the surf breaks along its coasts, Barbados offers a mix of cultural events, local cuisine, and even hiking excursions to scenic rural areas, making for a well-rounded experience. -- **Dominican Republic and Jamaica:** Both are renowned not only for their consistent surf conditions but also for expansive hiking trails and water sports. From the rugged landscapes of the Dominican Republic to Jamaica’s blend of cultural history and natural exploration, these islands allow travelers to mix and match activities seamlessly. - -Group tours and local guides further enhance these experiences, providing insider tips, safe excursions, and personalized itineraries that cater to multiple interests within one trip. - ---- - -## Practical Advice and Travel Tips - -### Weather and Timing - -- **Optimal Climate:** April offers ideal weather conditions across the Caribbean. With minimal rainfall and warm temperatures, it is a great time to schedule outdoor activities. -- **Surfing Seasons:** While April marks the end of the prime surf season in some areas (like Rincón in Puerto Rico), many destinations maintain consistent conditions during this month. - -### Booking and Costs - -- **Surfing Lessons:** Expect to pay between $40 and $110 per session depending on the location. For instance, Puerto Rico typically charges around $75 for beginner lessons, while group lessons in the Dominican Republic average approximately $95. -- **Equipment Rentals:** Pricing for jet ski, surfboard, and snorkeling equipment may vary. In the Bahamas, an hour-long jet ski tour might cost about $120 per group, whereas a similar experience might be available at a lower cost in other regions. -- **Accommodations:** Prices also vary by island. Many travelers find that even affordable stays do not skimp on amenities, allowing you to invest more in guided excursions and local experiences. - -### Cultural Considerations - -- **Festivals and Events:** Check local event calendars. Destinations like Barbados and Antigua host festivals in April that combine cultural heritage with festive outdoor activities. -- **Local Cuisine:** Incorporate food tours into your itinerary. Caribbean cuisine—with its fusion of flavors—can be as adventurous as the outdoor activities. - -### Health and Safety - -- **Staying Hydrated:** The warm temperatures demand that you stay properly hydrated. Always carry water, especially during long hikes. -- **Sun Protection:** Use sunscreen, hats, and sunglasses to protect yourself during extended periods outdoors on both land and water. -- **Local Guides:** Utilize local tour operators for both hiking and water sports. Their expertise not only enriches your experience but also ensures safety in unfamiliar terrain or water bodies. - ---- - -## Conclusion - -The Caribbean in April is a haven for adventure seekers. With its pristine beaches, diverse ecosystems, and rich cultural tapestry, it offers something for every type of traveler. Whether you're chasing the perfect wave along the shores of Barbados and Puerto Rico, trekking through the lush landscapes of El Yunque or Morne Trois Pitons, or engaging in an array of water sports from snorkeling to kiteboarding, your ideal vacation is only a booking away. - -This report has outlined the best destinations and provided practical advice to optimize your vacation for surfing, hiking, and water sports. By considering the diverse offerings—from epic surf breaks and challenging hiking trails to vibrant water sports—the Caribbean stands out as a multi-adventure destination where every day brings a new experience. - -Plan carefully, pack wisely, and get ready to explore the vibrant mosaic of landscapes and activities that make the Caribbean in April a truly unforgettable adventure. - -Happy travels! - ---- - -*References available upon request. Many insights were drawn from trusted sources including Lonely Planet, TravelPug, and various Caribbean-centric exploration sites, ensuring a well-rounded and practical guide for your vacation planning.* - - - -=====FOLLOW UP QUESTIONS===== - - -Follow up questions: Would you like detailed profiles for any of the highlighted destinations (e.g., Puerto Rico or Barbados)? -Are you interested in more information about booking details and local tour operators in specific islands? -Do you need guidance on combining cultural events with outdoor adventures during your Caribbean vacation? \ No newline at end of file diff --git a/tests/examples/tools/computer_use.py b/tests/examples/tools/computer_use.py deleted file mode 100644 index ae339552..00000000 --- a/tests/examples/tools/computer_use.py +++ /dev/null @@ -1,165 +0,0 @@ -import asyncio -import base64 -import logging -from typing import Literal, Union - -from playwright.async_api import Browser, Page, Playwright, async_playwright - -from agents import ( - Agent, - AsyncComputer, - Button, - ComputerTool, - Environment, - ModelSettings, - Runner, - trace, -) - -logging.getLogger("openai.agents").setLevel(logging.DEBUG) -logging.getLogger("openai.agents").addHandler(logging.StreamHandler()) - - -async def main(): - async with LocalPlaywrightComputer() as computer: - with trace("Computer use example"): - agent = Agent( - name="Browser user", - instructions="You are a helpful agent.", - tools=[ComputerTool(computer)], - # Use the computer using model, and set truncation to auto because its required - model="computer-use-preview", - model_settings=ModelSettings(truncation="auto"), - ) - result = await Runner.run(agent, "Search for SF sports news and summarize.") - print(result.final_output) - - -CUA_KEY_TO_PLAYWRIGHT_KEY = { - "/": "Divide", - "\\": "Backslash", - "alt": "Alt", - "arrowdown": "ArrowDown", - "arrowleft": "ArrowLeft", - "arrowright": "ArrowRight", - "arrowup": "ArrowUp", - "backspace": "Backspace", - "capslock": "CapsLock", - "cmd": "Meta", - "ctrl": "Control", - "delete": "Delete", - "end": "End", - "enter": "Enter", - "esc": "Escape", - "home": "Home", - "insert": "Insert", - "option": "Alt", - "pagedown": "PageDown", - "pageup": "PageUp", - "shift": "Shift", - "space": " ", - "super": "Meta", - "tab": "Tab", - "win": "Meta", -} - - -class LocalPlaywrightComputer(AsyncComputer): - """A computer, implemented using a local Playwright browser.""" - - def __init__(self): - self._playwright: Union[Playwright, None] = None - self._browser: Union[Browser, None] = None - self._page: Union[Page, None] = None - - async def _get_browser_and_page(self) -> tuple[Browser, Page]: - width, height = self.dimensions - launch_args = [f"--window-size={width},{height}"] - browser = await self.playwright.chromium.launch(headless=False, args=launch_args) - page = await browser.new_page() - await page.set_viewport_size({"width": width, "height": height}) - await page.goto("https://www.bing.com") - return browser, page - - async def __aenter__(self): - # Start Playwright and call the subclass hook for getting browser/page - self._playwright = await async_playwright().start() - self._browser, self._page = await self._get_browser_and_page() - return self - - async def __aexit__(self, exc_type, exc_val, exc_tb): - if self._browser: - await self._browser.close() - if self._playwright: - await self._playwright.stop() - - @property - def playwright(self) -> Playwright: - assert self._playwright is not None - return self._playwright - - @property - def browser(self) -> Browser: - assert self._browser is not None - return self._browser - - @property - def page(self) -> Page: - assert self._page is not None - return self._page - - @property - def environment(self) -> Environment: - return "browser" - - @property - def dimensions(self) -> tuple[int, int]: - return (1024, 768) - - async def screenshot(self) -> str: - """Capture only the viewport (not full_page).""" - png_bytes = await self.page.screenshot(full_page=False) - return base64.b64encode(png_bytes).decode("utf-8") - - async def click(self, x: int, y: int, button: Button = "left") -> None: - playwright_button: Literal["left", "middle", "right"] = "left" - - # Playwright only supports left, middle, right buttons - if button in ("left", "right", "middle"): - playwright_button = button # type: ignore - - await self.page.mouse.click(x, y, button=playwright_button) - - async def double_click(self, x: int, y: int) -> None: - await self.page.mouse.dblclick(x, y) - - async def scroll(self, x: int, y: int, scroll_x: int, scroll_y: int) -> None: - await self.page.mouse.move(x, y) - await self.page.evaluate(f"window.scrollBy({scroll_x}, {scroll_y})") - - async def type(self, text: str) -> None: - await self.page.keyboard.type(text) - - async def wait(self) -> None: - await asyncio.sleep(1) - - async def move(self, x: int, y: int) -> None: - await self.page.mouse.move(x, y) - - async def keypress(self, keys: list[str]) -> None: - for key in keys: - mapped_key = CUA_KEY_TO_PLAYWRIGHT_KEY.get(key.lower(), key) - await self.page.keyboard.press(mapped_key) - - async def drag(self, path: list[tuple[int, int]]) -> None: - if not path: - return - await self.page.mouse.move(path[0][0], path[0][1]) - await self.page.mouse.down() - for px, py in path[1:]: - await self.page.mouse.move(px, py) - await self.page.mouse.up() - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/tools/file_search.py b/tests/examples/tools/file_search.py deleted file mode 100644 index 2a3d4cf1..00000000 --- a/tests/examples/tools/file_search.py +++ /dev/null @@ -1,36 +0,0 @@ -import asyncio - -from agents import Agent, FileSearchTool, Runner, trace - - -async def main(): - agent = Agent( - name="File searcher", - instructions="You are a helpful agent.", - tools=[ - FileSearchTool( - max_num_results=3, - vector_store_ids=["vs_67bf88953f748191be42b462090e53e7"], - include_search_results=True, - ) - ], - ) - - with trace("File search example"): - result = await Runner.run( - agent, "Be concise, and tell me 1 sentence about Arrakis I might not know." - ) - print(result.final_output) - """ - Arrakis, the desert planet in Frank Herbert's "Dune," was inspired by the scarcity of water - as a metaphor for oil and other finite resources. - """ - - print("\n".join([str(out) for out in result.new_items])) - """ - {"id":"...", "queries":["Arrakis"], "results":[...]} - """ - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/examples/tools/web_search.py b/tests/examples/tools/web_search.py deleted file mode 100644 index 35eeb680..00000000 --- a/tests/examples/tools/web_search.py +++ /dev/null @@ -1,23 +0,0 @@ -import asyncio - -from agents import Agent, Runner, WebSearchTool, trace - - -async def main(): - agent = Agent( - name="Web searcher", - instructions="You are a helpful agent.", - tools=[WebSearchTool(user_location={"type": "approximate", "city": "New York"})], - ) - - with trace("Web search example"): - result = await Runner.run( - agent, - "search the web for 'local sports news' and give me 1 interesting update in a sentence.", - ) - print(result.final_output) - # The New York Giants are reportedly pursuing quarterback Aaron Rodgers after his ... - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/tests/fake_model.py b/tests/fake_model.py index f2ba6229..9f0c83a2 100644 --- a/tests/fake_model.py +++ b/tests/fake_model.py @@ -1,10 +1,12 @@ from __future__ import annotations from collections.abc import AsyncIterator +from typing import Any -from openai.types.responses import Response, ResponseCompletedEvent +from openai.types.responses import Response, ResponseCompletedEvent, ResponseUsage +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails -from agents.agent_output import AgentOutputSchema +from agents.agent_output import AgentOutputSchemaBase from agents.handoffs import Handoff from agents.items import ( ModelResponse, @@ -31,6 +33,11 @@ def __init__( [initial_output] if initial_output else [] ) self.tracing_enabled = tracing_enabled + self.last_turn_args: dict[str, Any] = {} + self.hardcoded_usage: Usage | None = None + + def set_hardcoded_usage(self, usage: Usage): + self.hardcoded_usage = usage def set_next_output(self, output: list[TResponseOutputItem] | Exception): self.turn_outputs.append(output) @@ -49,10 +56,21 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, + *, + previous_response_id: str | None, ) -> ModelResponse: + self.last_turn_args = { + "system_instructions": system_instructions, + "input": input, + "model_settings": model_settings, + "tools": tools, + "output_schema": output_schema, + "previous_response_id": previous_response_id, + } + with generation_span(disabled=not self.tracing_enabled) as span: output = self.get_next_output() @@ -70,8 +88,8 @@ async def get_response( return ModelResponse( output=output, - usage=Usage(), - referenceable_id=None, + usage=self.hardcoded_usage or Usage(), + response_id=None, ) async def stream_response( @@ -80,10 +98,20 @@ async def stream_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, + *, + previous_response_id: str | None, ) -> AsyncIterator[TResponseStreamEvent]: + self.last_turn_args = { + "system_instructions": system_instructions, + "input": input, + "model_settings": model_settings, + "tools": tools, + "output_schema": output_schema, + "previous_response_id": previous_response_id, + } with generation_span(disabled=not self.tracing_enabled) as span: output = self.get_next_output() if isinstance(output, Exception): @@ -100,11 +128,16 @@ async def stream_response( yield ResponseCompletedEvent( type="response.completed", - response=get_response_obj(output), + response=get_response_obj(output, usage=self.hardcoded_usage), + sequence_number=0, ) -def get_response_obj(output: list[TResponseOutputItem], response_id: str | None = None) -> Response: +def get_response_obj( + output: list[TResponseOutputItem], + response_id: str | None = None, + usage: Usage | None = None, +) -> Response: return Response( id=response_id or "123", created_at=123, @@ -115,4 +148,11 @@ def get_response_obj(output: list[TResponseOutputItem], response_id: str | None tools=[], top_p=None, parallel_tool_calls=False, + usage=ResponseUsage( + input_tokens=usage.input_tokens if usage else 0, + output_tokens=usage.output_tokens if usage else 0, + total_tokens=usage.total_tokens if usage else 0, + input_tokens_details=InputTokensDetails(cached_tokens=0), + output_tokens_details=OutputTokensDetails(reasoning_tokens=0), + ), ) diff --git a/tests/fastapi/__init__.py b/tests/fastapi/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/fastapi/streaming_app.py b/tests/fastapi/streaming_app.py new file mode 100644 index 00000000..b93ccf3f --- /dev/null +++ b/tests/fastapi/streaming_app.py @@ -0,0 +1,30 @@ +from collections.abc import AsyncIterator + +from fastapi import FastAPI +from starlette.responses import StreamingResponse + +from agents import Agent, Runner, RunResultStreaming + +agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", +) + + +app = FastAPI() + + +@app.post("/stream") +async def stream(): + result = Runner.run_streamed(agent, input="Tell me a joke") + stream_handler = StreamHandler(result) + return StreamingResponse(stream_handler.stream_events(), media_type="application/x-ndjson") + + +class StreamHandler: + def __init__(self, result: RunResultStreaming): + self.result = result + + async def stream_events(self) -> AsyncIterator[str]: + async for event in self.result.stream_events(): + yield f"{event.type}\n\n" diff --git a/tests/fastapi/test_streaming_context.py b/tests/fastapi/test_streaming_context.py new file mode 100644 index 00000000..ee13045e --- /dev/null +++ b/tests/fastapi/test_streaming_context.py @@ -0,0 +1,29 @@ +import pytest +from httpx import ASGITransport, AsyncClient +from inline_snapshot import snapshot + +from ..fake_model import FakeModel +from ..test_responses import get_text_message +from .streaming_app import agent, app + + +@pytest.mark.asyncio +async def test_streaming_context(): + """This ensures that FastAPI streaming works. The context for this test is that the Runner + method was called in one async context, and the streaming was ended in another context, + leading to a tracing error because the context was closed in the wrong context. This test + ensures that this actually works. + """ + model = FakeModel() + agent.model = model + model.set_next_output([get_text_message("done")]) + + transport = ASGITransport(app) + async with AsyncClient(transport=transport, base_url="http://test") as ac: + async with ac.stream("POST", "/stream") as r: + assert r.status_code == 200 + body = (await r.aread()).decode("utf-8") + lines = [line for line in body.splitlines() if line] + assert lines == snapshot( + ["agent_updated_stream_event", "raw_response_event", "run_item_stream_event"] + ) diff --git a/tests/mcp/__init__.py b/tests/mcp/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/mcp/conftest.py b/tests/mcp/conftest.py new file mode 100644 index 00000000..80fd15ec --- /dev/null +++ b/tests/mcp/conftest.py @@ -0,0 +1,11 @@ +import os +import sys + + +# Skip MCP tests on Python 3.9 +def pytest_ignore_collect(collection_path, config): + if sys.version_info[:2] == (3, 9): + this_dir = os.path.dirname(__file__) + + if str(collection_path).startswith(this_dir): + return True diff --git a/tests/mcp/helpers.py b/tests/mcp/helpers.py new file mode 100644 index 00000000..8ff153c1 --- /dev/null +++ b/tests/mcp/helpers.py @@ -0,0 +1,58 @@ +import json +import shutil +from typing import Any + +from mcp import Tool as MCPTool +from mcp.types import CallToolResult, TextContent + +from agents.mcp import MCPServer + +tee = shutil.which("tee") or "" +assert tee, "tee not found" + + +# Added dummy stream classes for patching stdio_client to avoid real I/O during tests +class DummyStream: + async def send(self, msg): + pass + + async def receive(self): + raise Exception("Dummy receive not implemented") + + +class DummyStreamsContextManager: + async def __aenter__(self): + return (DummyStream(), DummyStream()) + + async def __aexit__(self, exc_type, exc_val, exc_tb): + pass + + +class FakeMCPServer(MCPServer): + def __init__(self, tools: list[MCPTool] | None = None): + self.tools: list[MCPTool] = tools or [] + self.tool_calls: list[str] = [] + self.tool_results: list[str] = [] + + def add_tool(self, name: str, input_schema: dict[str, Any]): + self.tools.append(MCPTool(name=name, inputSchema=input_schema)) + + async def connect(self): + pass + + async def cleanup(self): + pass + + async def list_tools(self): + return self.tools + + async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> CallToolResult: + self.tool_calls.append(tool_name) + self.tool_results.append(f"result_{tool_name}_{json.dumps(arguments)}") + return CallToolResult( + content=[TextContent(text=self.tool_results[-1], type="text")], + ) + + @property + def name(self) -> str: + return "fake_mcp_server" diff --git a/tests/mcp/test_caching.py b/tests/mcp/test_caching.py new file mode 100644 index 00000000..cac409e6 --- /dev/null +++ b/tests/mcp/test_caching.py @@ -0,0 +1,57 @@ +from unittest.mock import AsyncMock, patch + +import pytest +from mcp.types import ListToolsResult, Tool as MCPTool + +from agents.mcp import MCPServerStdio + +from .helpers import DummyStreamsContextManager, tee + + +@pytest.mark.asyncio +@patch("mcp.client.stdio.stdio_client", return_value=DummyStreamsContextManager()) +@patch("mcp.client.session.ClientSession.initialize", new_callable=AsyncMock, return_value=None) +@patch("mcp.client.session.ClientSession.list_tools") +async def test_server_caching_works( + mock_list_tools: AsyncMock, mock_initialize: AsyncMock, mock_stdio_client +): + """Test that if we turn caching on, the list of tools is cached and not fetched from the server + on each call to `list_tools()`. + """ + server = MCPServerStdio( + params={ + "command": tee, + }, + cache_tools_list=True, + ) + + tools = [ + MCPTool(name="tool1", inputSchema={}), + MCPTool(name="tool2", inputSchema={}), + ] + + mock_list_tools.return_value = ListToolsResult(tools=tools) + + async with server: + # Call list_tools() multiple times + tools = await server.list_tools() + assert tools == tools + + assert mock_list_tools.call_count == 1, "list_tools() should have been called once" + + # Call list_tools() again, should return the cached value + tools = await server.list_tools() + assert tools == tools + + assert mock_list_tools.call_count == 1, "list_tools() should not have been called again" + + # Invalidate the cache and call list_tools() again + server.invalidate_tools_cache() + tools = await server.list_tools() + assert tools == tools + + assert mock_list_tools.call_count == 2, "list_tools() should be called again" + + # Without invalidating the cache, calling list_tools() again should return the cached value + tools = await server.list_tools() + assert tools == tools diff --git a/tests/mcp/test_connect_disconnect.py b/tests/mcp/test_connect_disconnect.py new file mode 100644 index 00000000..b0013039 --- /dev/null +++ b/tests/mcp/test_connect_disconnect.py @@ -0,0 +1,69 @@ +from unittest.mock import AsyncMock, patch + +import pytest +from mcp.types import ListToolsResult, Tool as MCPTool + +from agents.mcp import MCPServerStdio + +from .helpers import DummyStreamsContextManager, tee + + +@pytest.mark.asyncio +@patch("mcp.client.stdio.stdio_client", return_value=DummyStreamsContextManager()) +@patch("mcp.client.session.ClientSession.initialize", new_callable=AsyncMock, return_value=None) +@patch("mcp.client.session.ClientSession.list_tools") +async def test_async_ctx_manager_works( + mock_list_tools: AsyncMock, mock_initialize: AsyncMock, mock_stdio_client +): + """Test that the async context manager works.""" + server = MCPServerStdio( + params={ + "command": tee, + }, + cache_tools_list=True, + ) + + tools = [ + MCPTool(name="tool1", inputSchema={}), + MCPTool(name="tool2", inputSchema={}), + ] + + mock_list_tools.return_value = ListToolsResult(tools=tools) + + assert server.session is None, "Server should not be connected" + + async with server: + assert server.session is not None, "Server should be connected" + + assert server.session is None, "Server should be disconnected" + + +@pytest.mark.asyncio +@patch("mcp.client.stdio.stdio_client", return_value=DummyStreamsContextManager()) +@patch("mcp.client.session.ClientSession.initialize", new_callable=AsyncMock, return_value=None) +@patch("mcp.client.session.ClientSession.list_tools") +async def test_manual_connect_disconnect_works( + mock_list_tools: AsyncMock, mock_initialize: AsyncMock, mock_stdio_client +): + """Test that the async context manager works.""" + server = MCPServerStdio( + params={ + "command": tee, + }, + cache_tools_list=True, + ) + + tools = [ + MCPTool(name="tool1", inputSchema={}), + MCPTool(name="tool2", inputSchema={}), + ] + + mock_list_tools.return_value = ListToolsResult(tools=tools) + + assert server.session is None, "Server should not be connected" + + await server.connect() + assert server.session is not None, "Server should be connected" + + await server.cleanup() + assert server.session is None, "Server should be disconnected" diff --git a/tests/mcp/test_mcp_tracing.py b/tests/mcp/test_mcp_tracing.py new file mode 100644 index 00000000..b71954b5 --- /dev/null +++ b/tests/mcp/test_mcp_tracing.py @@ -0,0 +1,198 @@ +import pytest +from inline_snapshot import snapshot + +from agents import Agent, Runner + +from ..fake_model import FakeModel +from ..test_responses import get_function_tool, get_function_tool_call, get_text_message +from ..testing_processor import SPAN_PROCESSOR_TESTING, fetch_normalized_spans +from .helpers import FakeMCPServer + + +@pytest.mark.asyncio +async def test_mcp_tracing(): + model = FakeModel() + server = FakeMCPServer() + server.add_tool("test_tool_1", {}) + agent = Agent( + name="test", + model=model, + mcp_servers=[server], + tools=[get_function_tool("non_mcp_tool", "tool_result")], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("test_tool_1", "")], + # Second turn: text message + [get_text_message("done")], + ] + ) + + # First run: should list MCP tools before first and second steps + x = Runner.run_streamed(agent, input="first_test") + async for _ in x.stream_events(): + pass + + assert x.final_output == "done" + spans = fetch_normalized_spans() + + # Should have a single tool listing, and the function span should have MCP data + assert spans == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test", + "handoffs": [], + "tools": ["test_tool_1", "non_mcp_tool"], + "output_type": "str", + }, + "children": [ + { + "type": "mcp_tools", + "data": {"server": "fake_mcp_server", "result": ["test_tool_1"]}, + }, + { + "type": "function", + "data": { + "name": "test_tool_1", + "input": "", + "output": '{"type":"text","text":"result_test_tool_1_{}","annotations":null}', # noqa: E501 + "mcp_data": {"server": "fake_mcp_server"}, + }, + }, + ], + } + ], + } + ] + ) + + server.add_tool("test_tool_2", {}) + + SPAN_PROCESSOR_TESTING.clear() + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [ + get_text_message("a_message"), + get_function_tool_call("non_mcp_tool", ""), + get_function_tool_call("test_tool_2", ""), + ], + # Second turn: text message + [get_text_message("done")], + ] + ) + + await Runner.run(agent, input="second_test") + spans = fetch_normalized_spans() + + # Should have a single tool listing, and the function span should have MCP data, and the non-mcp + # tool function span should not have MCP data + assert spans == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test", + "handoffs": [], + "tools": ["test_tool_1", "test_tool_2", "non_mcp_tool"], + "output_type": "str", + }, + "children": [ + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2"], + }, + }, + { + "type": "function", + "data": { + "name": "non_mcp_tool", + "input": "", + "output": "tool_result", + }, + }, + { + "type": "function", + "data": { + "name": "test_tool_2", + "input": "", + "output": '{"type":"text","text":"result_test_tool_2_{}","annotations":null}', # noqa: E501 + "mcp_data": {"server": "fake_mcp_server"}, + }, + }, + ], + } + ], + } + ] + ) + + SPAN_PROCESSOR_TESTING.clear() + + # Add more tools to the server + server.add_tool("test_tool_3", {}) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("test_tool_3", "")], + # Second turn: text message + [get_text_message("done")], + ] + ) + + await Runner.run(agent, input="third_test") + + spans = fetch_normalized_spans() + + # Should have a single tool listing, and the function span should have MCP data, and the non-mcp + # tool function span should not have MCP data + assert spans == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test", + "handoffs": [], + "tools": ["test_tool_1", "test_tool_2", "test_tool_3", "non_mcp_tool"], + "output_type": "str", + }, + "children": [ + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2", "test_tool_3"], + }, + }, + { + "type": "function", + "data": { + "name": "test_tool_3", + "input": "", + "output": '{"type":"text","text":"result_test_tool_3_{}","annotations":null}', # noqa: E501 + "mcp_data": {"server": "fake_mcp_server"}, + }, + }, + ], + } + ], + } + ] + ) diff --git a/tests/mcp/test_mcp_util.py b/tests/mcp/test_mcp_util.py new file mode 100644 index 00000000..74356a16 --- /dev/null +++ b/tests/mcp/test_mcp_util.py @@ -0,0 +1,291 @@ +import logging +from typing import Any + +import pytest +from inline_snapshot import snapshot +from mcp.types import Tool as MCPTool +from pydantic import BaseModel, TypeAdapter + +from agents import Agent, FunctionTool, RunContextWrapper +from agents.exceptions import AgentsException, ModelBehaviorError +from agents.mcp import MCPServer, MCPUtil + +from .helpers import FakeMCPServer + + +class Foo(BaseModel): + bar: str + baz: int + + +class Bar(BaseModel): + qux: dict[str, str] + + +Baz = TypeAdapter(dict[str, str]) + + +def _convertible_schema() -> dict[str, Any]: + schema = Foo.model_json_schema() + schema["additionalProperties"] = False + return schema + + +@pytest.mark.asyncio +async def test_get_all_function_tools(): + """Test that the get_all_function_tools function returns all function tools from a list of MCP + servers. + """ + names = ["test_tool_1", "test_tool_2", "test_tool_3", "test_tool_4", "test_tool_5"] + schemas = [ + {}, + {}, + {}, + Foo.model_json_schema(), + Bar.model_json_schema(), + ] + + server1 = FakeMCPServer() + server1.add_tool(names[0], schemas[0]) + server1.add_tool(names[1], schemas[1]) + + server2 = FakeMCPServer() + server2.add_tool(names[2], schemas[2]) + server2.add_tool(names[3], schemas[3]) + + server3 = FakeMCPServer() + server3.add_tool(names[4], schemas[4]) + + servers: list[MCPServer] = [server1, server2, server3] + tools = await MCPUtil.get_all_function_tools(servers, convert_schemas_to_strict=False) + assert len(tools) == 5 + assert all(tool.name in names for tool in tools) + + for idx, tool in enumerate(tools): + assert isinstance(tool, FunctionTool) + if schemas[idx] == {}: + assert tool.params_json_schema == snapshot({"properties": {}}) + else: + assert tool.params_json_schema == schemas[idx] + assert tool.name == names[idx] + + # Also make sure it works with strict schemas + tools = await MCPUtil.get_all_function_tools(servers, convert_schemas_to_strict=True) + assert len(tools) == 5 + assert all(tool.name in names for tool in tools) + + +@pytest.mark.asyncio +async def test_invoke_mcp_tool(): + """Test that the invoke_mcp_tool function invokes an MCP tool and returns the result.""" + server = FakeMCPServer() + server.add_tool("test_tool_1", {}) + + ctx = RunContextWrapper(context=None) + tool = MCPTool(name="test_tool_1", inputSchema={}) + + await MCPUtil.invoke_mcp_tool(server, tool, ctx, "") + # Just making sure it doesn't crash + + +@pytest.mark.asyncio +async def test_mcp_invoke_bad_json_errors(caplog: pytest.LogCaptureFixture): + caplog.set_level(logging.DEBUG) + + """Test that bad JSON input errors are logged and re-raised.""" + server = FakeMCPServer() + server.add_tool("test_tool_1", {}) + + ctx = RunContextWrapper(context=None) + tool = MCPTool(name="test_tool_1", inputSchema={}) + + with pytest.raises(ModelBehaviorError): + await MCPUtil.invoke_mcp_tool(server, tool, ctx, "not_json") + + assert "Invalid JSON input for tool test_tool_1" in caplog.text + + +class CrashingFakeMCPServer(FakeMCPServer): + async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None): + raise Exception("Crash!") + + +@pytest.mark.asyncio +async def test_mcp_invocation_crash_causes_error(caplog: pytest.LogCaptureFixture): + caplog.set_level(logging.DEBUG) + + """Test that bad JSON input errors are logged and re-raised.""" + server = CrashingFakeMCPServer() + server.add_tool("test_tool_1", {}) + + ctx = RunContextWrapper(context=None) + tool = MCPTool(name="test_tool_1", inputSchema={}) + + with pytest.raises(AgentsException): + await MCPUtil.invoke_mcp_tool(server, tool, ctx, "") + + assert "Error invoking MCP tool test_tool_1" in caplog.text + + +@pytest.mark.asyncio +async def test_agent_convert_schemas_true(): + """Test that setting convert_schemas_to_strict to True converts non-strict schemas to strict. + - 'foo' tool is already strict and remains strict. + - 'bar' tool is non-strict and becomes strict (additionalProperties set to False, etc). + """ + strict_schema = Foo.model_json_schema() + non_strict_schema = Baz.json_schema() + possible_to_convert_schema = _convertible_schema() + + server = FakeMCPServer() + server.add_tool("foo", strict_schema) + server.add_tool("bar", non_strict_schema) + server.add_tool("baz", possible_to_convert_schema) + agent = Agent( + name="test_agent", mcp_servers=[server], mcp_config={"convert_schemas_to_strict": True} + ) + tools = await agent.get_mcp_tools() + + foo_tool = next(tool for tool in tools if tool.name == "foo") + assert isinstance(foo_tool, FunctionTool) + bar_tool = next(tool for tool in tools if tool.name == "bar") + assert isinstance(bar_tool, FunctionTool) + baz_tool = next(tool for tool in tools if tool.name == "baz") + assert isinstance(baz_tool, FunctionTool) + + # Checks that additionalProperties is set to False + assert foo_tool.params_json_schema == snapshot( + { + "properties": { + "bar": {"title": "Bar", "type": "string"}, + "baz": {"title": "Baz", "type": "integer"}, + }, + "required": ["bar", "baz"], + "title": "Foo", + "type": "object", + "additionalProperties": False, + } + ) + assert foo_tool.strict_json_schema is True, "foo_tool should be strict" + + # Checks that additionalProperties is set to False + assert bar_tool.params_json_schema == snapshot( + {"type": "object", "additionalProperties": {"type": "string"}, "properties": {}} + ) + assert bar_tool.strict_json_schema is False, "bar_tool should not be strict" + + # Checks that additionalProperties is set to False + assert baz_tool.params_json_schema == snapshot( + { + "properties": { + "bar": {"title": "Bar", "type": "string"}, + "baz": {"title": "Baz", "type": "integer"}, + }, + "required": ["bar", "baz"], + "title": "Foo", + "type": "object", + "additionalProperties": False, + } + ) + assert baz_tool.strict_json_schema is True, "baz_tool should be strict" + + +@pytest.mark.asyncio +async def test_agent_convert_schemas_false(): + """Test that setting convert_schemas_to_strict to False leaves tool schemas as non-strict. + - 'foo' tool remains strict. + - 'bar' tool remains non-strict (additionalProperties remains True). + """ + strict_schema = Foo.model_json_schema() + non_strict_schema = Baz.json_schema() + possible_to_convert_schema = _convertible_schema() + + server = FakeMCPServer() + server.add_tool("foo", strict_schema) + server.add_tool("bar", non_strict_schema) + server.add_tool("baz", possible_to_convert_schema) + + agent = Agent( + name="test_agent", mcp_servers=[server], mcp_config={"convert_schemas_to_strict": False} + ) + tools = await agent.get_mcp_tools() + + foo_tool = next(tool for tool in tools if tool.name == "foo") + assert isinstance(foo_tool, FunctionTool) + bar_tool = next(tool for tool in tools if tool.name == "bar") + assert isinstance(bar_tool, FunctionTool) + baz_tool = next(tool for tool in tools if tool.name == "baz") + assert isinstance(baz_tool, FunctionTool) + + assert foo_tool.params_json_schema == strict_schema + assert foo_tool.strict_json_schema is False, "Shouldn't be converted unless specified" + + assert bar_tool.params_json_schema == snapshot( + {"type": "object", "additionalProperties": {"type": "string"}, "properties": {}} + ) + assert bar_tool.strict_json_schema is False + + assert baz_tool.params_json_schema == possible_to_convert_schema + assert baz_tool.strict_json_schema is False, "Shouldn't be converted unless specified" + + +@pytest.mark.asyncio +async def test_agent_convert_schemas_unset(): + """Test that leaving convert_schemas_to_strict unset (defaulting to False) leaves tool schemas + as non-strict. + - 'foo' tool remains strict. + - 'bar' tool remains non-strict. + """ + strict_schema = Foo.model_json_schema() + non_strict_schema = Baz.json_schema() + possible_to_convert_schema = _convertible_schema() + + server = FakeMCPServer() + server.add_tool("foo", strict_schema) + server.add_tool("bar", non_strict_schema) + server.add_tool("baz", possible_to_convert_schema) + agent = Agent(name="test_agent", mcp_servers=[server]) + tools = await agent.get_mcp_tools() + + foo_tool = next(tool for tool in tools if tool.name == "foo") + assert isinstance(foo_tool, FunctionTool) + bar_tool = next(tool for tool in tools if tool.name == "bar") + assert isinstance(bar_tool, FunctionTool) + baz_tool = next(tool for tool in tools if tool.name == "baz") + assert isinstance(baz_tool, FunctionTool) + + assert foo_tool.params_json_schema == strict_schema + assert foo_tool.strict_json_schema is False, "Shouldn't be converted unless specified" + + assert bar_tool.params_json_schema == snapshot( + {"type": "object", "additionalProperties": {"type": "string"}, "properties": {}} + ) + assert bar_tool.strict_json_schema is False + + assert baz_tool.params_json_schema == possible_to_convert_schema + assert baz_tool.strict_json_schema is False, "Shouldn't be converted unless specified" + + +@pytest.mark.asyncio +async def test_util_adds_properties(): + """The MCP spec doesn't require the inputSchema to have `properties`, so we need to add it + if it's missing. + """ + schema = { + "type": "object", + "description": "Test tool", + } + + server = FakeMCPServer() + server.add_tool("test_tool", schema) + + tools = await MCPUtil.get_all_function_tools([server], convert_schemas_to_strict=False) + tool = next(tool for tool in tools if tool.name == "test_tool") + + assert isinstance(tool, FunctionTool) + assert "properties" in tool.params_json_schema + assert tool.params_json_schema["properties"] == {} + + assert tool.params_json_schema == snapshot( + {"type": "object", "description": "Test tool", "properties": {}} + ) diff --git a/tests/mcp/test_runner_calls_mcp.py b/tests/mcp/test_runner_calls_mcp.py new file mode 100644 index 00000000..3319c097 --- /dev/null +++ b/tests/mcp/test_runner_calls_mcp.py @@ -0,0 +1,197 @@ +import json + +import pytest +from pydantic import BaseModel + +from agents import Agent, ModelBehaviorError, Runner, UserError + +from ..fake_model import FakeModel +from ..test_responses import get_function_tool_call, get_text_message +from .helpers import FakeMCPServer + + +@pytest.mark.asyncio +@pytest.mark.parametrize("streaming", [False, True]) +async def test_runner_calls_mcp_tool(streaming: bool): + """Test that the runner calls an MCP tool when the model produces a tool call.""" + server = FakeMCPServer() + server.add_tool("test_tool_1", {}) + server.add_tool("test_tool_2", {}) + server.add_tool("test_tool_3", {}) + model = FakeModel() + agent = Agent( + name="test", + model=model, + mcp_servers=[server], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("test_tool_2", "")], + # Second turn: text message + [get_text_message("done")], + ] + ) + + if streaming: + result = Runner.run_streamed(agent, input="user_message") + async for _ in result.stream_events(): + pass + else: + await Runner.run(agent, input="user_message") + + assert server.tool_calls == ["test_tool_2"] + + +@pytest.mark.asyncio +@pytest.mark.parametrize("streaming", [False, True]) +async def test_runner_asserts_when_mcp_tool_not_found(streaming: bool): + """Test that the runner asserts when an MCP tool is not found.""" + server = FakeMCPServer() + server.add_tool("test_tool_1", {}) + server.add_tool("test_tool_2", {}) + server.add_tool("test_tool_3", {}) + model = FakeModel() + agent = Agent( + name="test", + model=model, + mcp_servers=[server], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("test_tool_doesnt_exist", "")], + # Second turn: text message + [get_text_message("done")], + ] + ) + + with pytest.raises(ModelBehaviorError): + if streaming: + result = Runner.run_streamed(agent, input="user_message") + async for _ in result.stream_events(): + pass + else: + await Runner.run(agent, input="user_message") + + +@pytest.mark.asyncio +@pytest.mark.parametrize("streaming", [False, True]) +async def test_runner_works_with_multiple_mcp_servers(streaming: bool): + """Test that the runner works with multiple MCP servers.""" + server1 = FakeMCPServer() + server1.add_tool("test_tool_1", {}) + + server2 = FakeMCPServer() + server2.add_tool("test_tool_2", {}) + server2.add_tool("test_tool_3", {}) + + model = FakeModel() + agent = Agent( + name="test", + model=model, + mcp_servers=[server1, server2], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("test_tool_2", "")], + # Second turn: text message + [get_text_message("done")], + ] + ) + + if streaming: + result = Runner.run_streamed(agent, input="user_message") + async for _ in result.stream_events(): + pass + else: + await Runner.run(agent, input="user_message") + + assert server1.tool_calls == [] + assert server2.tool_calls == ["test_tool_2"] + + +@pytest.mark.asyncio +@pytest.mark.parametrize("streaming", [False, True]) +async def test_runner_errors_when_mcp_tools_clash(streaming: bool): + """Test that the runner errors when multiple servers have the same tool name.""" + server1 = FakeMCPServer() + server1.add_tool("test_tool_1", {}) + server1.add_tool("test_tool_2", {}) + + server2 = FakeMCPServer() + server2.add_tool("test_tool_2", {}) + server2.add_tool("test_tool_3", {}) + + model = FakeModel() + agent = Agent( + name="test", + model=model, + mcp_servers=[server1, server2], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("test_tool_3", "")], + # Second turn: text message + [get_text_message("done")], + ] + ) + + with pytest.raises(UserError): + if streaming: + result = Runner.run_streamed(agent, input="user_message") + async for _ in result.stream_events(): + pass + else: + await Runner.run(agent, input="user_message") + + +class Foo(BaseModel): + bar: str + baz: int + + +@pytest.mark.asyncio +@pytest.mark.parametrize("streaming", [False, True]) +async def test_runner_calls_mcp_tool_with_args(streaming: bool): + """Test that the runner calls an MCP tool when the model produces a tool call.""" + server = FakeMCPServer() + await server.connect() + server.add_tool("test_tool_1", {}) + server.add_tool("test_tool_2", Foo.model_json_schema()) + server.add_tool("test_tool_3", {}) + model = FakeModel() + agent = Agent( + name="test", + model=model, + mcp_servers=[server], + ) + + json_args = json.dumps(Foo(bar="baz", baz=1).model_dump()) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("test_tool_2", json_args)], + # Second turn: text message + [get_text_message("done")], + ] + ) + + if streaming: + result = Runner.run_streamed(agent, input="user_message") + async for _ in result.stream_events(): + pass + else: + await Runner.run(agent, input="user_message") + + assert server.tool_calls == ["test_tool_2"] + assert server.tool_results == [f"result_test_tool_2_{json_args}"] + + await server.cleanup() diff --git a/tests/mcp/test_server_errors.py b/tests/mcp/test_server_errors.py new file mode 100644 index 00000000..fbd8db17 --- /dev/null +++ b/tests/mcp/test_server_errors.py @@ -0,0 +1,42 @@ +import pytest + +from agents.exceptions import UserError +from agents.mcp.server import _MCPServerWithClientSession + + +class CrashingClientSessionServer(_MCPServerWithClientSession): + def __init__(self): + super().__init__(cache_tools_list=False, client_session_timeout_seconds=5) + self.cleanup_called = False + + def create_streams(self): + raise ValueError("Crash!") + + async def cleanup(self): + self.cleanup_called = True + await super().cleanup() + + @property + def name(self) -> str: + return "crashing_client_session_server" + + +@pytest.mark.asyncio +async def test_server_errors_cause_error_and_cleanup_called(): + server = CrashingClientSessionServer() + + with pytest.raises(ValueError): + await server.connect() + + assert server.cleanup_called + + +@pytest.mark.asyncio +async def test_not_calling_connect_causes_error(): + server = CrashingClientSessionServer() + + with pytest.raises(UserError): + await server.list_tools() + + with pytest.raises(UserError): + await server.call_tool("foo", {}) diff --git a/tests/mkdocs.yml b/tests/mkdocs.yml deleted file mode 100644 index 398fb74a..00000000 --- a/tests/mkdocs.yml +++ /dev/null @@ -1,121 +0,0 @@ -site_name: OpenAI Agents SDK -theme: - name: material - features: - # Allows copying code blocks - - content.code.copy - # Allows selecting code blocks - - content.code.select - # Shows the current path in the sidebar - - navigation.path - # Shows sections in the sidebar - - navigation.sections - # Shows sections expanded by default - - navigation.expand - # Enables annotations in code blocks - - content.code.annotate - palette: - primary: black - logo: assets/logo.svg - favicon: images/favicon-platform.svg -nav: - - Intro: index.md - - Quickstart: quickstart.md - - Documentation: - - agents.md - - running_agents.md - - results.md - - streaming.md - - tools.md - - handoffs.md - - tracing.md - - context.md - - guardrails.md - - multi_agent.md - - models.md - - config.md - - API Reference: - - Agents: - - ref/index.md - - ref/agent.md - - ref/run.md - - ref/tool.md - - ref/result.md - - ref/stream_events.md - - ref/handoffs.md - - ref/lifecycle.md - - ref/items.md - - ref/run_context.md - - ref/usage.md - - ref/exceptions.md - - ref/guardrail.md - - ref/model_settings.md - - ref/agent_output.md - - ref/function_schema.md - - ref/models/interface.md - - ref/models/openai_chatcompletions.md - - ref/models/openai_responses.md - - Tracing: - - ref/tracing/index.md - - ref/tracing/create.md - - ref/tracing/traces.md - - ref/tracing/spans.md - - ref/tracing/processor_interface.md - - ref/tracing/processors.md - - ref/tracing/scope.md - - ref/tracing/setup.md - - ref/tracing/span_data.md - - ref/tracing/util.md - - Extensions: - - ref/extensions/handoff_filters.md - - ref/extensions/handoff_prompt.md - -plugins: - - search - - mkdocstrings: - handlers: - python: - paths: ["src/agents"] - selection: - docstring_style: google - options: - # Shows links to other members in signatures - signature_crossrefs: true - # Orders members by source order, rather than alphabetical - members_order: source - # Puts the signature on a separate line from the member name - separate_signature: true - # Shows type annotations in signatures - show_signature_annotations: true - # Makes the font sizes nicer - heading_level: 3 - -extra: - # Remove material generation message in footer - generator: false - -markdown_extensions: - - admonition - - pymdownx.details - - pymdownx.superfences - - attr_list - - md_in_html - - pymdownx.highlight: - anchor_linenums: true - line_spans: __span - pygments_lang_class: true - - pymdownx.inlinehilite - - pymdownx.snippets - - pymdownx.superfences - -validation: - omitted_files: warn - absolute_links: warn - unrecognized_links: warn - anchors: warn - -extra_css: - - stylesheets/extra.css - -watch: - - "src/agents" diff --git a/tests/model_settings/test_serialization.py b/tests/model_settings/test_serialization.py new file mode 100644 index 00000000..d76a58d1 --- /dev/null +++ b/tests/model_settings/test_serialization.py @@ -0,0 +1,59 @@ +import json +from dataclasses import fields + +from openai.types.shared import Reasoning + +from agents.model_settings import ModelSettings + + +def verify_serialization(model_settings: ModelSettings) -> None: + """Verify that ModelSettings can be serialized to a JSON string.""" + json_dict = model_settings.to_json_dict() + json_string = json.dumps(json_dict) + assert json_string is not None + + +def test_basic_serialization() -> None: + """Tests whether ModelSettings can be serialized to a JSON string.""" + + # First, lets create a ModelSettings instance + model_settings = ModelSettings( + temperature=0.5, + top_p=0.9, + max_tokens=100, + ) + + # Now, lets serialize the ModelSettings instance to a JSON string + verify_serialization(model_settings) + + +def test_all_fields_serialization() -> None: + """Tests whether ModelSettings can be serialized to a JSON string.""" + + # First, lets create a ModelSettings instance + model_settings = ModelSettings( + temperature=0.5, + top_p=0.9, + frequency_penalty=0.0, + presence_penalty=0.0, + tool_choice="auto", + parallel_tool_calls=True, + truncation="auto", + max_tokens=100, + reasoning=Reasoning(), + metadata={"foo": "bar"}, + store=False, + include_usage=False, + extra_query={"foo": "bar"}, + extra_body={"foo": "bar"}, + extra_headers={"foo": "bar"}, + ) + + # Verify that every single field is set to a non-None value + for field in fields(model_settings): + assert getattr(model_settings, field.name) is not None, ( + f"You must set the {field.name} field" + ) + + # Now, lets serialize the ModelSettings instance to a JSON string + verify_serialization(model_settings) diff --git a/tests/models/__init__.py b/tests/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/models/conftest.py b/tests/models/conftest.py new file mode 100644 index 00000000..79d85d8b --- /dev/null +++ b/tests/models/conftest.py @@ -0,0 +1,11 @@ +import os +import sys + + +# Skip voice tests on Python 3.9 +def pytest_ignore_collect(collection_path, config): + if sys.version_info[:2] == (3, 9): + this_dir = os.path.dirname(__file__) + + if str(collection_path).startswith(this_dir): + return True diff --git a/tests/models/test_litellm_chatcompletions_stream.py b/tests/models/test_litellm_chatcompletions_stream.py new file mode 100644 index 00000000..06e46b39 --- /dev/null +++ b/tests/models/test_litellm_chatcompletions_stream.py @@ -0,0 +1,298 @@ +from collections.abc import AsyncIterator + +import pytest +from openai.types.chat.chat_completion_chunk import ( + ChatCompletionChunk, + Choice, + ChoiceDelta, + ChoiceDeltaToolCall, + ChoiceDeltaToolCallFunction, +) +from openai.types.completion_usage import ( + CompletionTokensDetails, + CompletionUsage, + PromptTokensDetails, +) +from openai.types.responses import ( + Response, + ResponseFunctionToolCall, + ResponseOutputMessage, + ResponseOutputRefusal, + ResponseOutputText, +) + +from agents.extensions.models.litellm_model import LitellmModel +from agents.extensions.models.litellm_provider import LitellmProvider +from agents.model_settings import ModelSettings +from agents.models.interface import ModelTracing + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_yields_events_for_text_content(monkeypatch) -> None: + """ + Validate that `stream_response` emits the correct sequence of events when + streaming a simple assistant message consisting of plain text content. + We simulate two chunks of text returned from the chat completion stream. + """ + # Create two chunks that will be emitted by the fake stream. + chunk1 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(content="He"))], + ) + # Mark last chunk with usage so stream_response knows this is final. + chunk2 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(content="llo"))], + usage=CompletionUsage( + completion_tokens=5, + prompt_tokens=7, + total_tokens=12, + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=2), + prompt_tokens_details=PromptTokensDetails(cached_tokens=6), + ), + ) + + async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: + for c in (chunk1, chunk2): + yield c + + # Patch _fetch_response to inject our fake stream + async def patched_fetch_response(self, *args, **kwargs): + # `_fetch_response` is expected to return a Response skeleton and the async stream + resp = Response( + id="resp-id", + created_at=0, + model="fake-model", + object="response", + output=[], + tool_choice="none", + tools=[], + parallel_tool_calls=False, + ) + return resp, fake_stream() + + monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response) + model = LitellmProvider().get_model("gpt-4") + output_events = [] + async for event in model.stream_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ): + output_events.append(event) + # We expect a response.created, then a response.output_item.added, content part added, + # two content delta events (for "He" and "llo"), a content part done, the assistant message + # output_item.done, and finally response.completed. + # There should be 8 events in total. + assert len(output_events) == 8 + # First event indicates creation. + assert output_events[0].type == "response.created" + # The output item added and content part added events should mark the assistant message. + assert output_events[1].type == "response.output_item.added" + assert output_events[2].type == "response.content_part.added" + # Two text delta events. + assert output_events[3].type == "response.output_text.delta" + assert output_events[3].delta == "He" + assert output_events[4].type == "response.output_text.delta" + assert output_events[4].delta == "llo" + # After streaming, the content part and item should be marked done. + assert output_events[5].type == "response.content_part.done" + assert output_events[6].type == "response.output_item.done" + # Last event indicates completion of the stream. + assert output_events[7].type == "response.completed" + # The completed response should have one output message with full text. + completed_resp = output_events[7].response + assert isinstance(completed_resp.output[0], ResponseOutputMessage) + assert isinstance(completed_resp.output[0].content[0], ResponseOutputText) + assert completed_resp.output[0].content[0].text == "Hello" + + assert completed_resp.usage, "usage should not be None" + assert completed_resp.usage.input_tokens == 7 + assert completed_resp.usage.output_tokens == 5 + assert completed_resp.usage.total_tokens == 12 + assert completed_resp.usage.input_tokens_details.cached_tokens == 6 + assert completed_resp.usage.output_tokens_details.reasoning_tokens == 2 + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_yields_events_for_refusal_content(monkeypatch) -> None: + """ + Validate that when the model streams a refusal string instead of normal content, + `stream_response` emits the appropriate sequence of events including + `response.refusal.delta` events for each chunk of the refusal message and + constructs a completed assistant message with a `ResponseOutputRefusal` part. + """ + # Simulate refusal text coming in two pieces, like content but using the `refusal` + # field on the delta rather than `content`. + chunk1 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(refusal="No"))], + ) + chunk2 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(refusal="Thanks"))], + usage=CompletionUsage(completion_tokens=2, prompt_tokens=2, total_tokens=4), + ) + + async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: + for c in (chunk1, chunk2): + yield c + + async def patched_fetch_response(self, *args, **kwargs): + resp = Response( + id="resp-id", + created_at=0, + model="fake-model", + object="response", + output=[], + tool_choice="none", + tools=[], + parallel_tool_calls=False, + ) + return resp, fake_stream() + + monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response) + model = LitellmProvider().get_model("gpt-4") + output_events = [] + async for event in model.stream_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ): + output_events.append(event) + # Expect sequence similar to text: created, output_item.added, content part added, + # two refusal delta events, content part done, output_item.done, completed. + assert len(output_events) == 8 + assert output_events[0].type == "response.created" + assert output_events[1].type == "response.output_item.added" + assert output_events[2].type == "response.content_part.added" + assert output_events[3].type == "response.refusal.delta" + assert output_events[3].delta == "No" + assert output_events[4].type == "response.refusal.delta" + assert output_events[4].delta == "Thanks" + assert output_events[5].type == "response.content_part.done" + assert output_events[6].type == "response.output_item.done" + assert output_events[7].type == "response.completed" + completed_resp = output_events[7].response + assert isinstance(completed_resp.output[0], ResponseOutputMessage) + refusal_part = completed_resp.output[0].content[0] + assert isinstance(refusal_part, ResponseOutputRefusal) + assert refusal_part.refusal == "NoThanks" + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_yields_events_for_tool_call(monkeypatch) -> None: + """ + Validate that `stream_response` emits the correct sequence of events when + the model is streaming a function/tool call instead of plain text. + The function call will be split across two chunks. + """ + # Simulate a single tool call whose ID stays constant and function name/args built over chunks. + tool_call_delta1 = ChoiceDeltaToolCall( + index=0, + id="tool-id", + function=ChoiceDeltaToolCallFunction(name="my_", arguments="arg1"), + type="function", + ) + tool_call_delta2 = ChoiceDeltaToolCall( + index=0, + id="tool-id", + function=ChoiceDeltaToolCallFunction(name="func", arguments="arg2"), + type="function", + ) + chunk1 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))], + ) + chunk2 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))], + usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2), + ) + + async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: + for c in (chunk1, chunk2): + yield c + + async def patched_fetch_response(self, *args, **kwargs): + resp = Response( + id="resp-id", + created_at=0, + model="fake-model", + object="response", + output=[], + tool_choice="none", + tools=[], + parallel_tool_calls=False, + ) + return resp, fake_stream() + + monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response) + model = LitellmProvider().get_model("gpt-4") + output_events = [] + async for event in model.stream_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ): + output_events.append(event) + # Sequence should be: response.created, then after loop we expect function call-related events: + # one response.output_item.added for function call, a response.function_call_arguments.delta, + # a response.output_item.done, and finally response.completed. + assert output_events[0].type == "response.created" + # The next three events are about the tool call. + assert output_events[1].type == "response.output_item.added" + # The added item should be a ResponseFunctionToolCall. + added_fn = output_events[1].item + assert isinstance(added_fn, ResponseFunctionToolCall) + assert added_fn.name == "my_func" # Name should be concatenation of both chunks. + assert added_fn.arguments == "arg1arg2" + assert output_events[2].type == "response.function_call_arguments.delta" + assert output_events[2].delta == "arg1arg2" + assert output_events[3].type == "response.output_item.done" + assert output_events[4].type == "response.completed" + assert output_events[2].delta == "arg1arg2" + assert output_events[3].type == "response.output_item.done" + assert output_events[4].type == "response.completed" + assert added_fn.name == "my_func" # Name should be concatenation of both chunks. + assert added_fn.arguments == "arg1arg2" + assert output_events[2].type == "response.function_call_arguments.delta" + assert output_events[2].delta == "arg1arg2" + assert output_events[3].type == "response.output_item.done" + assert output_events[4].type == "response.completed" diff --git a/tests/models/test_litellm_extra_body.py b/tests/models/test_litellm_extra_body.py new file mode 100644 index 00000000..ac56c25c --- /dev/null +++ b/tests/models/test_litellm_extra_body.py @@ -0,0 +1,45 @@ +import litellm +import pytest +from litellm.types.utils import Choices, Message, ModelResponse, Usage + +from agents.extensions.models.litellm_model import LitellmModel +from agents.model_settings import ModelSettings +from agents.models.interface import ModelTracing + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_extra_body_is_forwarded(monkeypatch): + """ + Forward `extra_body` entries into litellm.acompletion kwargs. + + This ensures that user-provided parameters (e.g. cached_content) + arrive alongside default arguments. + """ + captured: dict[str, object] = {} + + async def fake_acompletion(model, messages=None, **kwargs): + captured.update(kwargs) + msg = Message(role="assistant", content="ok") + choice = Choices(index=0, message=msg) + return ModelResponse(choices=[choice], usage=Usage(0, 0, 0)) + + monkeypatch.setattr(litellm, "acompletion", fake_acompletion) + settings = ModelSettings( + temperature=0.1, + extra_body={"cached_content": "some_cache", "foo": 123} + ) + model = LitellmModel(model="test-model") + + await model.get_response( + system_instructions=None, + input=[], + model_settings=settings, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + assert {"cached_content": "some_cache", "foo": 123}.items() <= captured.items() diff --git a/tests/models/test_map.py b/tests/models/test_map.py new file mode 100644 index 00000000..6b65fc09 --- /dev/null +++ b/tests/models/test_map.py @@ -0,0 +1,20 @@ +from agents import Agent, OpenAIResponsesModel, RunConfig, Runner +from agents.extensions.models.litellm_model import LitellmModel + + +def test_no_prefix_is_openai(): + agent = Agent(model="gpt-4o", instructions="", name="test") + model = Runner._get_model(agent, RunConfig()) + assert isinstance(model, OpenAIResponsesModel) + + +def openai_prefix_is_openai(): + agent = Agent(model="openai/gpt-4o", instructions="", name="test") + model = Runner._get_model(agent, RunConfig()) + assert isinstance(model, OpenAIResponsesModel) + + +def test_litellm_prefix_is_litellm(): + agent = Agent(model="litellm/foo/bar", instructions="", name="test") + model = Runner._get_model(agent, RunConfig()) + assert isinstance(model, LitellmModel) diff --git a/tests/pyproject.toml b/tests/pyproject.toml deleted file mode 100644 index 24e08eb7..00000000 --- a/tests/pyproject.toml +++ /dev/null @@ -1,119 +0,0 @@ -[project] -name = "openai-agents" -version = "0.0.1" -description = "OpenAI Agents SDK" -readme = "README.md" -requires-python = ">=3.9" -license = "MIT" -authors = [ - { name = "OpenAI", email = "support@openai.com" }, -] -dependencies = [ - "openai>=1.66.0", - "pydantic>=2.10, <3", - "griffe>=1.5.6, <2", - "typing-extensions>=4.12.2, <5", - "requests>=2.0, <3", - "types-requests>=2.0, <3", -] -classifiers = [ - "Typing :: Typed", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Intended Audience :: Developers", - "Operating System :: OS Independent", - "Topic :: Software Development :: Libraries :: Python Modules", - "License :: OSI Approved :: MIT License" -] - -[project.urls] -Homepage = "https://github.com/openai/openai-agents-python" -Repository = "https://github.com/openai/openai-agents-python" - -[dependency-groups] -dev = [ - "mypy", - "ruff==0.9.2", - "pytest", - "pytest-asyncio", - "pytest-mock>=3.14.0", - "rich", - "mkdocs>=1.6.0", - "mkdocs-material>=9.6.0", - "mkdocstrings[python]>=0.28.0", - "coverage>=7.6.12", - "playwright==1.50.0", -] -[tool.uv.workspace] -members = ["agents"] - -[tool.uv.sources] -agents = { workspace = true } - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.wheel] -packages = ["src/agents"] - - -[tool.ruff] -line-length = 100 -target-version = "py39" - -[tool.ruff.lint] -select = [ - "E", # pycodestyle errors - "W", # pycodestyle warnings - "F", # pyflakes - "I", # isort - "B", # flake8-bugbear - "C4", # flake8-comprehensions - "UP", # pyupgrade -] -isort = { combine-as-imports = true, known-first-party = ["agents"] } - -[tool.ruff.lint.pydocstyle] -convention = "google" - -[tool.ruff.lint.per-file-ignores] -"examples/**/*.py" = ["E501"] - -[tool.mypy] -strict = true -disallow_incomplete_defs = false -disallow_untyped_defs = false -disallow_untyped_calls = false - -[tool.coverage.run] -source = [ - "tests", - "src/agents", -] - -[tool.coverage.report] -show_missing = true -sort = "-Cover" -exclude_also = [ - # This is only executed while typechecking - "if TYPE_CHECKING:", - "@abc.abstractmethod", - "raise NotImplementedError", - "logger.debug", -] - -[tool.pytest.ini_options] -asyncio_mode = "auto" -asyncio_default_fixture_loop_scope = "session" -filterwarnings = [ - # This is a warning that is expected to happen: we have an async filter that raises an exception - "ignore:coroutine 'test_async_input_filter_fails..invalid_input_filter' was never awaited:RuntimeWarning", -] -markers = [ - "allow_call_model_methods: mark test as allowing calls to real model implementations", -] \ No newline at end of file diff --git a/tests/src/agents/__init__.py b/tests/src/agents/__init__.py deleted file mode 100644 index 69c500ab..00000000 --- a/tests/src/agents/__init__.py +++ /dev/null @@ -1,223 +0,0 @@ -import logging -import sys -from typing import Literal - -from openai import AsyncOpenAI - -from . import _config -from .agent import Agent -from .agent_output import AgentOutputSchema -from .computer import AsyncComputer, Button, Computer, Environment -from .exceptions import ( - AgentsException, - InputGuardrailTripwireTriggered, - MaxTurnsExceeded, - ModelBehaviorError, - OutputGuardrailTripwireTriggered, - UserError, -) -from .guardrail import ( - GuardrailFunctionOutput, - InputGuardrail, - InputGuardrailResult, - OutputGuardrail, - OutputGuardrailResult, - input_guardrail, - output_guardrail, -) -from .handoffs import Handoff, HandoffInputData, HandoffInputFilter, handoff -from .items import ( - HandoffCallItem, - HandoffOutputItem, - ItemHelpers, - MessageOutputItem, - ModelResponse, - ReasoningItem, - RunItem, - ToolCallItem, - ToolCallOutputItem, - TResponseInputItem, -) -from .lifecycle import AgentHooks, RunHooks -from .model_settings import ModelSettings -from .models.interface import Model, ModelProvider, ModelTracing -from .models.openai_chatcompletions import OpenAIChatCompletionsModel -from .models.openai_provider import OpenAIProvider -from .models.openai_responses import OpenAIResponsesModel -from .result import RunResult, RunResultStreaming -from .run import RunConfig, Runner -from .run_context import RunContextWrapper, TContext -from .stream_events import ( - AgentUpdatedStreamEvent, - RawResponsesStreamEvent, - RunItemStreamEvent, - StreamEvent, -) -from .tool import ( - ComputerTool, - FileSearchTool, - FunctionTool, - Tool, - WebSearchTool, - default_tool_error_function, - function_tool, -) -from .tracing import ( - AgentSpanData, - CustomSpanData, - FunctionSpanData, - GenerationSpanData, - GuardrailSpanData, - HandoffSpanData, - Span, - SpanData, - SpanError, - Trace, - add_trace_processor, - agent_span, - custom_span, - function_span, - gen_span_id, - gen_trace_id, - generation_span, - get_current_span, - get_current_trace, - guardrail_span, - handoff_span, - set_trace_processors, - set_tracing_disabled, - set_tracing_export_api_key, - trace, -) -from .usage import Usage - - -def set_default_openai_key(key: str) -> None: - """Set the default OpenAI API key to use for LLM requests and tracing. This is only necessary if - the OPENAI_API_KEY environment variable is not already set. - - If provided, this key will be used instead of the OPENAI_API_KEY environment variable. - """ - _config.set_default_openai_key(key) - - -def set_default_openai_client(client: AsyncOpenAI, use_for_tracing: bool = True) -> None: - """Set the default OpenAI client to use for LLM requests and/or tracing. If provided, this - client will be used instead of the default OpenAI client. - - Args: - client: The OpenAI client to use. - use_for_tracing: Whether to use the API key from this client for uploading traces. If False, - you'll either need to set the OPENAI_API_KEY environment variable or call - set_tracing_export_api_key() with the API key you want to use for tracing. - """ - _config.set_default_openai_client(client, use_for_tracing) - - -def set_default_openai_api(api: Literal["chat_completions", "responses"]) -> None: - """Set the default API to use for OpenAI LLM requests. By default, we will use the responses API - but you can set this to use the chat completions API instead. - """ - _config.set_default_openai_api(api) - - -def enable_verbose_stdout_logging(): - """Enables verbose logging to stdout. This is useful for debugging.""" - for name in ["openai.agents", "openai.agents.tracing"]: - logger = logging.getLogger(name) - logger.setLevel(logging.DEBUG) - logger.addHandler(logging.StreamHandler(sys.stdout)) - - -__all__ = [ - "Agent", - "Runner", - "Model", - "ModelProvider", - "ModelTracing", - "ModelSettings", - "OpenAIChatCompletionsModel", - "OpenAIProvider", - "OpenAIResponsesModel", - "AgentOutputSchema", - "Computer", - "AsyncComputer", - "Environment", - "Button", - "AgentsException", - "InputGuardrailTripwireTriggered", - "OutputGuardrailTripwireTriggered", - "MaxTurnsExceeded", - "ModelBehaviorError", - "UserError", - "InputGuardrail", - "InputGuardrailResult", - "OutputGuardrail", - "OutputGuardrailResult", - "GuardrailFunctionOutput", - "input_guardrail", - "output_guardrail", - "handoff", - "Handoff", - "HandoffInputData", - "HandoffInputFilter", - "TResponseInputItem", - "MessageOutputItem", - "ModelResponse", - "RunItem", - "HandoffCallItem", - "HandoffOutputItem", - "ToolCallItem", - "ToolCallOutputItem", - "ReasoningItem", - "ModelResponse", - "ItemHelpers", - "RunHooks", - "AgentHooks", - "RunContextWrapper", - "TContext", - "RunResult", - "RunResultStreaming", - "RunConfig", - "RawResponsesStreamEvent", - "RunItemStreamEvent", - "AgentUpdatedStreamEvent", - "StreamEvent", - "FunctionTool", - "ComputerTool", - "FileSearchTool", - "Tool", - "WebSearchTool", - "function_tool", - "Usage", - "add_trace_processor", - "agent_span", - "custom_span", - "function_span", - "generation_span", - "get_current_span", - "get_current_trace", - "guardrail_span", - "handoff_span", - "set_trace_processors", - "set_tracing_disabled", - "trace", - "Trace", - "SpanError", - "Span", - "SpanData", - "AgentSpanData", - "CustomSpanData", - "FunctionSpanData", - "GenerationSpanData", - "GuardrailSpanData", - "HandoffSpanData", - "set_default_openai_key", - "set_default_openai_client", - "set_default_openai_api", - "set_tracing_export_api_key", - "enable_verbose_stdout_logging", - "gen_trace_id", - "gen_span_id", - "default_tool_error_function", -] diff --git a/tests/src/agents/_config.py b/tests/src/agents/_config.py deleted file mode 100644 index 55ded64d..00000000 --- a/tests/src/agents/_config.py +++ /dev/null @@ -1,23 +0,0 @@ -from openai import AsyncOpenAI -from typing_extensions import Literal - -from .models import _openai_shared -from .tracing import set_tracing_export_api_key - - -def set_default_openai_key(key: str) -> None: - set_tracing_export_api_key(key) - _openai_shared.set_default_openai_key(key) - - -def set_default_openai_client(client: AsyncOpenAI, use_for_tracing: bool) -> None: - if use_for_tracing: - set_tracing_export_api_key(client.api_key) - _openai_shared.set_default_openai_client(client) - - -def set_default_openai_api(api: Literal["chat_completions", "responses"]) -> None: - if api == "chat_completions": - _openai_shared.set_use_responses_by_default(False) - else: - _openai_shared.set_use_responses_by_default(True) diff --git a/tests/src/agents/_debug.py b/tests/src/agents/_debug.py deleted file mode 100644 index 4da91be4..00000000 --- a/tests/src/agents/_debug.py +++ /dev/null @@ -1,17 +0,0 @@ -import os - - -def _debug_flag_enabled(flag: str) -> bool: - flag_value = os.getenv(flag) - return flag_value is not None and (flag_value == "1" or flag_value.lower() == "true") - - -DONT_LOG_MODEL_DATA = _debug_flag_enabled("OPENAI_AGENTS_DONT_LOG_MODEL_DATA") -"""By default we don't log LLM inputs/outputs, to prevent exposing sensitive information. Set this -flag to enable logging them. -""" - -DONT_LOG_TOOL_DATA = _debug_flag_enabled("OPENAI_AGENTS_DONT_LOG_TOOL_DATA") -"""By default we don't log tool call inputs/outputs, to prevent exposing sensitive information. Set -this flag to enable logging them. -""" diff --git a/tests/src/agents/_run_impl.py b/tests/src/agents/_run_impl.py deleted file mode 100644 index 112819c8..00000000 --- a/tests/src/agents/_run_impl.py +++ /dev/null @@ -1,792 +0,0 @@ -from __future__ import annotations - -import asyncio -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any - -from openai.types.responses import ( - ResponseComputerToolCall, - ResponseFileSearchToolCall, - ResponseFunctionToolCall, - ResponseFunctionWebSearch, - ResponseOutputMessage, -) -from openai.types.responses.response_computer_tool_call import ( - ActionClick, - ActionDoubleClick, - ActionDrag, - ActionKeypress, - ActionMove, - ActionScreenshot, - ActionScroll, - ActionType, - ActionWait, -) -from openai.types.responses.response_input_param import ComputerCallOutput -from openai.types.responses.response_output_item import Reasoning - -from . import _utils -from .agent import Agent -from .agent_output import AgentOutputSchema -from .computer import AsyncComputer, Computer -from .exceptions import AgentsException, ModelBehaviorError, UserError -from .guardrail import InputGuardrail, InputGuardrailResult, OutputGuardrail, OutputGuardrailResult -from .handoffs import Handoff, HandoffInputData -from .items import ( - HandoffCallItem, - HandoffOutputItem, - ItemHelpers, - MessageOutputItem, - ModelResponse, - ReasoningItem, - RunItem, - ToolCallItem, - ToolCallOutputItem, - TResponseInputItem, -) -from .lifecycle import RunHooks -from .logger import logger -from .models.interface import ModelTracing -from .run_context import RunContextWrapper, TContext -from .stream_events import RunItemStreamEvent, StreamEvent -from .tool import ComputerTool, FunctionTool -from .tracing import ( - SpanError, - Trace, - function_span, - get_current_trace, - guardrail_span, - handoff_span, - trace, -) - -if TYPE_CHECKING: - from .run import RunConfig - - -class QueueCompleteSentinel: - pass - - -QUEUE_COMPLETE_SENTINEL = QueueCompleteSentinel() - - -@dataclass -class ToolRunHandoff: - handoff: Handoff - tool_call: ResponseFunctionToolCall - - -@dataclass -class ToolRunFunction: - tool_call: ResponseFunctionToolCall - function_tool: FunctionTool - - -@dataclass -class ToolRunComputerAction: - tool_call: ResponseComputerToolCall - computer_tool: ComputerTool - - -@dataclass -class ProcessedResponse: - new_items: list[RunItem] - handoffs: list[ToolRunHandoff] - functions: list[ToolRunFunction] - computer_actions: list[ToolRunComputerAction] - - def has_tools_to_run(self) -> bool: - # Handoffs, functions and computer actions need local processing - # Hosted tools have already run, so there's nothing to do. - return any( - [ - self.handoffs, - self.functions, - self.computer_actions, - ] - ) - - -@dataclass -class NextStepHandoff: - new_agent: Agent[Any] - - -@dataclass -class NextStepFinalOutput: - output: Any - - -@dataclass -class NextStepRunAgain: - pass - - -@dataclass -class SingleStepResult: - original_input: str | list[TResponseInputItem] - """The input items i.e. the items before run() was called. May be mutated by handoff input - filters.""" - - model_response: ModelResponse - """The model response for the current step.""" - - pre_step_items: list[RunItem] - """Items generated before the current step.""" - - new_step_items: list[RunItem] - """Items generated during this current step.""" - - next_step: NextStepHandoff | NextStepFinalOutput | NextStepRunAgain - """The next step to take.""" - - @property - def generated_items(self) -> list[RunItem]: - """Items generated during the agent run (i.e. everything generated after - `original_input`).""" - return self.pre_step_items + self.new_step_items - - -def get_model_tracing_impl( - tracing_disabled: bool, trace_include_sensitive_data: bool -) -> ModelTracing: - if tracing_disabled: - return ModelTracing.DISABLED - elif trace_include_sensitive_data: - return ModelTracing.ENABLED - else: - return ModelTracing.ENABLED_WITHOUT_DATA - - -class RunImpl: - @classmethod - async def execute_tools_and_side_effects( - cls, - *, - agent: Agent[TContext], - # The original input to the Runner - original_input: str | list[TResponseInputItem], - # Eveything generated by Runner since the original input, but before the current step - pre_step_items: list[RunItem], - new_response: ModelResponse, - processed_response: ProcessedResponse, - output_schema: AgentOutputSchema | None, - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - run_config: RunConfig, - ) -> SingleStepResult: - # Make a copy of the generated items - pre_step_items = list(pre_step_items) - - new_step_items: list[RunItem] = [] - new_step_items.extend(processed_response.new_items) - - # First, lets run the tool calls - function tools and computer actions - function_results, computer_results = await asyncio.gather( - cls.execute_function_tool_calls( - agent=agent, - tool_runs=processed_response.functions, - hooks=hooks, - context_wrapper=context_wrapper, - config=run_config, - ), - cls.execute_computer_actions( - agent=agent, - actions=processed_response.computer_actions, - hooks=hooks, - context_wrapper=context_wrapper, - config=run_config, - ), - ) - new_step_items.extend(function_results) - new_step_items.extend(computer_results) - - # Second, check if there are any handoffs - if run_handoffs := processed_response.handoffs: - return await cls.execute_handoffs( - agent=agent, - original_input=original_input, - pre_step_items=pre_step_items, - new_step_items=new_step_items, - new_response=new_response, - run_handoffs=run_handoffs, - hooks=hooks, - context_wrapper=context_wrapper, - run_config=run_config, - ) - - # Now we can check if the model also produced a final output - message_items = [item for item in new_step_items if isinstance(item, MessageOutputItem)] - - # We'll use the last content output as the final output - potential_final_output_text = ( - ItemHelpers.extract_last_text(message_items[-1].raw_item) if message_items else None - ) - - # There are two possibilities that lead to a final output: - # 1. Structured output schema => always leads to a final output - # 2. Plain text output schema => only leads to a final output if there are no tool calls - if output_schema and not output_schema.is_plain_text() and potential_final_output_text: - final_output = output_schema.validate_json(potential_final_output_text) - return await cls.execute_final_output( - agent=agent, - original_input=original_input, - new_response=new_response, - pre_step_items=pre_step_items, - new_step_items=new_step_items, - final_output=final_output, - hooks=hooks, - context_wrapper=context_wrapper, - ) - elif ( - not output_schema or output_schema.is_plain_text() - ) and not processed_response.has_tools_to_run(): - return await cls.execute_final_output( - agent=agent, - original_input=original_input, - new_response=new_response, - pre_step_items=pre_step_items, - new_step_items=new_step_items, - final_output=potential_final_output_text or "", - hooks=hooks, - context_wrapper=context_wrapper, - ) - else: - # If there's no final output, we can just run again - return SingleStepResult( - original_input=original_input, - model_response=new_response, - pre_step_items=pre_step_items, - new_step_items=new_step_items, - next_step=NextStepRunAgain(), - ) - - @classmethod - def process_model_response( - cls, - *, - agent: Agent[Any], - response: ModelResponse, - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - ) -> ProcessedResponse: - items: list[RunItem] = [] - - run_handoffs = [] - functions = [] - computer_actions = [] - - handoff_map = {handoff.tool_name: handoff for handoff in handoffs} - function_map = {tool.name: tool for tool in agent.tools if isinstance(tool, FunctionTool)} - computer_tool = next((tool for tool in agent.tools if isinstance(tool, ComputerTool)), None) - - for output in response.output: - if isinstance(output, ResponseOutputMessage): - items.append(MessageOutputItem(raw_item=output, agent=agent)) - elif isinstance(output, ResponseFileSearchToolCall): - items.append(ToolCallItem(raw_item=output, agent=agent)) - elif isinstance(output, ResponseFunctionWebSearch): - items.append(ToolCallItem(raw_item=output, agent=agent)) - elif isinstance(output, Reasoning): - items.append(ReasoningItem(raw_item=output, agent=agent)) - elif isinstance(output, ResponseComputerToolCall): - items.append(ToolCallItem(raw_item=output, agent=agent)) - if not computer_tool: - _utils.attach_error_to_current_span( - SpanError( - message="Computer tool not found", - data={}, - ) - ) - raise ModelBehaviorError( - "Model produced computer action without a computer tool." - ) - computer_actions.append( - ToolRunComputerAction(tool_call=output, computer_tool=computer_tool) - ) - elif not isinstance(output, ResponseFunctionToolCall): - logger.warning(f"Unexpected output type, ignoring: {type(output)}") - continue - - # At this point we know it's a function tool call - if not isinstance(output, ResponseFunctionToolCall): - continue - - # Handoffs - if output.name in handoff_map: - items.append(HandoffCallItem(raw_item=output, agent=agent)) - handoff = ToolRunHandoff( - tool_call=output, - handoff=handoff_map[output.name], - ) - run_handoffs.append(handoff) - # Regular function tool call - else: - if output.name not in function_map: - _utils.attach_error_to_current_span( - SpanError( - message="Tool not found", - data={"tool_name": output.name}, - ) - ) - raise ModelBehaviorError(f"Tool {output.name} not found in agent {agent.name}") - items.append(ToolCallItem(raw_item=output, agent=agent)) - functions.append( - ToolRunFunction( - tool_call=output, - function_tool=function_map[output.name], - ) - ) - - return ProcessedResponse( - new_items=items, - handoffs=run_handoffs, - functions=functions, - computer_actions=computer_actions, - ) - - @classmethod - async def execute_function_tool_calls( - cls, - *, - agent: Agent[TContext], - tool_runs: list[ToolRunFunction], - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - config: RunConfig, - ) -> list[RunItem]: - async def run_single_tool( - func_tool: FunctionTool, tool_call: ResponseFunctionToolCall - ) -> str: - with function_span(func_tool.name) as span_fn: - if config.trace_include_sensitive_data: - span_fn.span_data.input = tool_call.arguments - try: - _, _, result = await asyncio.gather( - hooks.on_tool_start(context_wrapper, agent, func_tool), - ( - agent.hooks.on_tool_start(context_wrapper, agent, func_tool) - if agent.hooks - else _utils.noop_coroutine() - ), - func_tool.on_invoke_tool(context_wrapper, tool_call.arguments), - ) - - await asyncio.gather( - hooks.on_tool_end(context_wrapper, agent, func_tool, result), - ( - agent.hooks.on_tool_end(context_wrapper, agent, func_tool, result) - if agent.hooks - else _utils.noop_coroutine() - ), - ) - except Exception as e: - _utils.attach_error_to_current_span( - SpanError( - message="Error running tool", - data={"tool_name": func_tool.name, "error": str(e)}, - ) - ) - if isinstance(e, AgentsException): - raise e - raise UserError(f"Error running tool {func_tool.name}: {e}") from e - - if config.trace_include_sensitive_data: - span_fn.span_data.output = result - return result - - tasks = [] - for tool_run in tool_runs: - function_tool = tool_run.function_tool - tasks.append(run_single_tool(function_tool, tool_run.tool_call)) - - results = await asyncio.gather(*tasks) - - return [ - ToolCallOutputItem( - output=str(result), - raw_item=ItemHelpers.tool_call_output_item(tool_run.tool_call, str(result)), - agent=agent, - ) - for tool_run, result in zip(tool_runs, results) - ] - - @classmethod - async def execute_computer_actions( - cls, - *, - agent: Agent[TContext], - actions: list[ToolRunComputerAction], - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - config: RunConfig, - ) -> list[RunItem]: - results: list[RunItem] = [] - # Need to run these serially, because each action can affect the computer state - for action in actions: - results.append( - await ComputerAction.execute( - agent=agent, - action=action, - hooks=hooks, - context_wrapper=context_wrapper, - config=config, - ) - ) - - return results - - @classmethod - async def execute_handoffs( - cls, - *, - agent: Agent[TContext], - original_input: str | list[TResponseInputItem], - pre_step_items: list[RunItem], - new_step_items: list[RunItem], - new_response: ModelResponse, - run_handoffs: list[ToolRunHandoff], - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - run_config: RunConfig, - ) -> SingleStepResult: - # If there is more than one handoff, add tool responses that reject those handoffs - if len(run_handoffs) > 1: - output_message = "Multiple handoffs detected, ignoring this one." - new_step_items.extend( - [ - ToolCallOutputItem( - output=output_message, - raw_item=ItemHelpers.tool_call_output_item( - handoff.tool_call, output_message - ), - agent=agent, - ) - for handoff in run_handoffs[1:] - ] - ) - - actual_handoff = run_handoffs[0] - with handoff_span(from_agent=agent.name) as span_handoff: - handoff = actual_handoff.handoff - new_agent: Agent[Any] = await handoff.on_invoke_handoff( - context_wrapper, actual_handoff.tool_call.arguments - ) - span_handoff.span_data.to_agent = new_agent.name - - # Append a tool output item for the handoff - new_step_items.append( - HandoffOutputItem( - agent=agent, - raw_item=ItemHelpers.tool_call_output_item( - actual_handoff.tool_call, - handoff.get_transfer_message(new_agent), - ), - source_agent=agent, - target_agent=new_agent, - ) - ) - - # Execute handoff hooks - await asyncio.gather( - hooks.on_handoff( - context=context_wrapper, - from_agent=agent, - to_agent=new_agent, - ), - ( - agent.hooks.on_handoff( - context_wrapper, - agent=new_agent, - source=agent, - ) - if agent.hooks - else _utils.noop_coroutine() - ), - ) - - # If there's an input filter, filter the input for the next agent - input_filter = handoff.input_filter or ( - run_config.handoff_input_filter if run_config else None - ) - if input_filter: - logger.debug("Filtering inputs for handoff") - handoff_input_data = HandoffInputData( - input_history=tuple(original_input) - if isinstance(original_input, list) - else original_input, - pre_handoff_items=tuple(pre_step_items), - new_items=tuple(new_step_items), - ) - if not callable(input_filter): - _utils.attach_error_to_span( - span_handoff, - SpanError( - message="Invalid input filter", - data={"details": "not callable()"}, - ), - ) - raise UserError(f"Invalid input filter: {input_filter}") - filtered = input_filter(handoff_input_data) - if not isinstance(filtered, HandoffInputData): - _utils.attach_error_to_span( - span_handoff, - SpanError( - message="Invalid input filter result", - data={"details": "not a HandoffInputData"}, - ), - ) - raise UserError(f"Invalid input filter result: {filtered}") - - original_input = ( - filtered.input_history - if isinstance(filtered.input_history, str) - else list(filtered.input_history) - ) - pre_step_items = list(filtered.pre_handoff_items) - new_step_items = list(filtered.new_items) - - return SingleStepResult( - original_input=original_input, - model_response=new_response, - pre_step_items=pre_step_items, - new_step_items=new_step_items, - next_step=NextStepHandoff(new_agent), - ) - - @classmethod - async def execute_final_output( - cls, - *, - agent: Agent[TContext], - original_input: str | list[TResponseInputItem], - new_response: ModelResponse, - pre_step_items: list[RunItem], - new_step_items: list[RunItem], - final_output: Any, - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - ) -> SingleStepResult: - # Run the on_end hooks - await cls.run_final_output_hooks(agent, hooks, context_wrapper, final_output) - - return SingleStepResult( - original_input=original_input, - model_response=new_response, - pre_step_items=pre_step_items, - new_step_items=new_step_items, - next_step=NextStepFinalOutput(final_output), - ) - - @classmethod - async def run_final_output_hooks( - cls, - agent: Agent[TContext], - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - final_output: Any, - ): - await asyncio.gather( - hooks.on_agent_end(context_wrapper, agent, final_output), - agent.hooks.on_end(context_wrapper, agent, final_output) - if agent.hooks - else _utils.noop_coroutine(), - ) - - @classmethod - async def run_single_input_guardrail( - cls, - agent: Agent[Any], - guardrail: InputGuardrail[TContext], - input: str | list[TResponseInputItem], - context: RunContextWrapper[TContext], - ) -> InputGuardrailResult: - with guardrail_span(guardrail.get_name()) as span_guardrail: - result = await guardrail.run(agent, input, context) - span_guardrail.span_data.triggered = result.output.tripwire_triggered - return result - - @classmethod - async def run_single_output_guardrail( - cls, - guardrail: OutputGuardrail[TContext], - agent: Agent[Any], - agent_output: Any, - context: RunContextWrapper[TContext], - ) -> OutputGuardrailResult: - with guardrail_span(guardrail.get_name()) as span_guardrail: - result = await guardrail.run(agent=agent, agent_output=agent_output, context=context) - span_guardrail.span_data.triggered = result.output.tripwire_triggered - return result - - @classmethod - def stream_step_result_to_queue( - cls, - step_result: SingleStepResult, - queue: asyncio.Queue[StreamEvent | QueueCompleteSentinel], - ): - for item in step_result.new_step_items: - if isinstance(item, MessageOutputItem): - event = RunItemStreamEvent(item=item, name="message_output_created") - elif isinstance(item, HandoffCallItem): - event = RunItemStreamEvent(item=item, name="handoff_requested") - elif isinstance(item, HandoffOutputItem): - event = RunItemStreamEvent(item=item, name="handoff_occured") - elif isinstance(item, ToolCallItem): - event = RunItemStreamEvent(item=item, name="tool_called") - elif isinstance(item, ToolCallOutputItem): - event = RunItemStreamEvent(item=item, name="tool_output") - elif isinstance(item, ReasoningItem): - event = RunItemStreamEvent(item=item, name="reasoning_item_created") - else: - logger.warning(f"Unexpected item type: {type(item)}") - event = None - - if event: - queue.put_nowait(event) - - -class TraceCtxManager: - """Creates a trace only if there is no current trace, and manages the trace lifecycle.""" - - def __init__( - self, - workflow_name: str, - trace_id: str | None, - group_id: str | None, - metadata: dict[str, Any] | None, - disabled: bool, - ): - self.trace: Trace | None = None - self.workflow_name = workflow_name - self.trace_id = trace_id - self.group_id = group_id - self.metadata = metadata - self.disabled = disabled - - def __enter__(self) -> TraceCtxManager: - current_trace = get_current_trace() - if not current_trace: - self.trace = trace( - workflow_name=self.workflow_name, - trace_id=self.trace_id, - group_id=self.group_id, - metadata=self.metadata, - disabled=self.disabled, - ) - self.trace.start(mark_as_current=True) - - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - if self.trace: - self.trace.finish(reset_current=True) - - -class ComputerAction: - @classmethod - async def execute( - cls, - *, - agent: Agent[TContext], - action: ToolRunComputerAction, - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - config: RunConfig, - ) -> RunItem: - output_func = ( - cls._get_screenshot_async(action.computer_tool.computer, action.tool_call) - if isinstance(action.computer_tool.computer, AsyncComputer) - else cls._get_screenshot_sync(action.computer_tool.computer, action.tool_call) - ) - - _, _, output = await asyncio.gather( - hooks.on_tool_start(context_wrapper, agent, action.computer_tool), - ( - agent.hooks.on_tool_start(context_wrapper, agent, action.computer_tool) - if agent.hooks - else _utils.noop_coroutine() - ), - output_func, - ) - - await asyncio.gather( - hooks.on_tool_end(context_wrapper, agent, action.computer_tool, output), - ( - agent.hooks.on_tool_end(context_wrapper, agent, action.computer_tool, output) - if agent.hooks - else _utils.noop_coroutine() - ), - ) - - # TODO: don't send a screenshot every single time, use references - image_url = f"data:image/png;base64,{output}" - return ToolCallOutputItem( - agent=agent, - output=image_url, - raw_item=ComputerCallOutput( - call_id=action.tool_call.call_id, - output={ - "type": "computer_screenshot", - "image_url": image_url, - }, - type="computer_call_output", - ), - ) - - @classmethod - async def _get_screenshot_sync( - cls, - computer: Computer, - tool_call: ResponseComputerToolCall, - ) -> str: - action = tool_call.action - if isinstance(action, ActionClick): - computer.click(action.x, action.y, action.button) - elif isinstance(action, ActionDoubleClick): - computer.double_click(action.x, action.y) - elif isinstance(action, ActionDrag): - computer.drag([(p.x, p.y) for p in action.path]) - elif isinstance(action, ActionKeypress): - computer.keypress(action.keys) - elif isinstance(action, ActionMove): - computer.move(action.x, action.y) - elif isinstance(action, ActionScreenshot): - computer.screenshot() - elif isinstance(action, ActionScroll): - computer.scroll(action.x, action.y, action.scroll_x, action.scroll_y) - elif isinstance(action, ActionType): - computer.type(action.text) - elif isinstance(action, ActionWait): - computer.wait() - - return computer.screenshot() - - @classmethod - async def _get_screenshot_async( - cls, - computer: AsyncComputer, - tool_call: ResponseComputerToolCall, - ) -> str: - action = tool_call.action - if isinstance(action, ActionClick): - await computer.click(action.x, action.y, action.button) - elif isinstance(action, ActionDoubleClick): - await computer.double_click(action.x, action.y) - elif isinstance(action, ActionDrag): - await computer.drag([(p.x, p.y) for p in action.path]) - elif isinstance(action, ActionKeypress): - await computer.keypress(action.keys) - elif isinstance(action, ActionMove): - await computer.move(action.x, action.y) - elif isinstance(action, ActionScreenshot): - await computer.screenshot() - elif isinstance(action, ActionScroll): - await computer.scroll(action.x, action.y, action.scroll_x, action.scroll_y) - elif isinstance(action, ActionType): - await computer.type(action.text) - elif isinstance(action, ActionWait): - await computer.wait() - - return await computer.screenshot() diff --git a/tests/src/agents/_utils.py b/tests/src/agents/_utils.py deleted file mode 100644 index 2a0293a6..00000000 --- a/tests/src/agents/_utils.py +++ /dev/null @@ -1,61 +0,0 @@ -from __future__ import annotations - -import re -from collections.abc import Awaitable -from typing import Any, Literal, Union - -from pydantic import TypeAdapter, ValidationError -from typing_extensions import TypeVar - -from .exceptions import ModelBehaviorError -from .logger import logger -from .tracing import Span, SpanError, get_current_span - -T = TypeVar("T") - -MaybeAwaitable = Union[Awaitable[T], T] - - -def transform_string_function_style(name: str) -> str: - # Replace spaces with underscores - name = name.replace(" ", "_") - - # Replace non-alphanumeric characters with underscores - name = re.sub(r"[^a-zA-Z0-9]", "_", name) - - return name.lower() - - -def validate_json(json_str: str, type_adapter: TypeAdapter[T], partial: bool) -> T: - partial_setting: bool | Literal["off", "on", "trailing-strings"] = ( - "trailing-strings" if partial else False - ) - try: - validated = type_adapter.validate_json(json_str, experimental_allow_partial=partial_setting) - return validated - except ValidationError as e: - attach_error_to_current_span( - SpanError( - message="Invalid JSON provided", - data={}, - ) - ) - raise ModelBehaviorError( - f"Invalid JSON when parsing {json_str} for {type_adapter}; {e}" - ) from e - - -def attach_error_to_span(span: Span[Any], error: SpanError) -> None: - span.set_error(error) - - -def attach_error_to_current_span(error: SpanError) -> None: - span = get_current_span() - if span: - attach_error_to_span(span, error) - else: - logger.warning(f"No span to add error {error} to") - - -async def noop_coroutine() -> None: - pass diff --git a/tests/src/agents/agent.py b/tests/src/agents/agent.py deleted file mode 100644 index 61c0a896..00000000 --- a/tests/src/agents/agent.py +++ /dev/null @@ -1,159 +0,0 @@ -from __future__ import annotations - -import dataclasses -import inspect -from collections.abc import Awaitable -from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any, Callable, Generic, cast - -from . import _utils -from ._utils import MaybeAwaitable -from .guardrail import InputGuardrail, OutputGuardrail -from .handoffs import Handoff -from .items import ItemHelpers -from .logger import logger -from .model_settings import ModelSettings -from .models.interface import Model -from .run_context import RunContextWrapper, TContext -from .tool import Tool, function_tool - -if TYPE_CHECKING: - from .lifecycle import AgentHooks - from .result import RunResult - - -@dataclass -class Agent(Generic[TContext]): - """An agent is an AI model configured with instructions, tools, guardrails, handoffs and more. - - We strongly recommend passing `instructions`, which is the "system prompt" for the agent. In - addition, you can pass `description`, which is a human-readable description of the agent, used - when the agent is used inside tools/handoffs. - - Agents are generic on the context type. The context is a (mutable) object you create. It is - passed to tool functions, handoffs, guardrails, etc. - """ - - name: str - """The name of the agent.""" - - instructions: ( - str - | Callable[ - [RunContextWrapper[TContext], Agent[TContext]], - MaybeAwaitable[str], - ] - | None - ) = None - """The instructions for the agent. Will be used as the "system prompt" when this agent is - invoked. Describes what the agent should do, and how it responds. - - Can either be a string, or a function that dynamically generates instructions for the agent. If - you provide a function, it will be called with the context and the agent instance. It must - return a string. - """ - - handoff_description: str | None = None - """A description of the agent. This is used when the agent is used as a handoff, so that an - LLM knows what it does and when to invoke it. - """ - - handoffs: list[Agent[Any] | Handoff[TContext]] = field(default_factory=list) - """Handoffs are sub-agents that the agent can delegate to. You can provide a list of handoffs, - and the agent can choose to delegate to them if relevant. Allows for separation of concerns and - modularity. - """ - - model: str | Model | None = None - """The model implementation to use when invoking the LLM. - - By default, if not set, the agent will use the default model configured in - `model_settings.DEFAULT_MODEL`. - """ - - model_settings: ModelSettings = field(default_factory=ModelSettings) - """Configures model-specific tuning parameters (e.g. temperature, top_p). - """ - - tools: list[Tool] = field(default_factory=list) - """A list of tools that the agent can use.""" - - input_guardrails: list[InputGuardrail[TContext]] = field(default_factory=list) - """A list of checks that run in parallel to the agent's execution, before generating a - response. Runs only if the agent is the first agent in the chain. - """ - - output_guardrails: list[OutputGuardrail[TContext]] = field(default_factory=list) - """A list of checks that run on the final output of the agent, after generating a response. - Runs only if the agent produces a final output. - """ - - output_type: type[Any] | None = None - """The type of the output object. If not provided, the output will be `str`.""" - - hooks: AgentHooks[TContext] | None = None - """A class that receives callbacks on various lifecycle events for this agent. - """ - - def clone(self, **kwargs: Any) -> Agent[TContext]: - """Make a copy of the agent, with the given arguments changed. For example, you could do: - ``` - new_agent = agent.clone(instructions="New instructions") - ``` - """ - return dataclasses.replace(self, **kwargs) - - def as_tool( - self, - tool_name: str | None, - tool_description: str | None, - custom_output_extractor: Callable[[RunResult], Awaitable[str]] | None = None, - ) -> Tool: - """Transform this agent into a tool, callable by other agents. - - This is different from handoffs in two ways: - 1. In handoffs, the new agent receives the conversation history. In this tool, the new agent - receives generated input. - 2. In handoffs, the new agent takes over the conversation. In this tool, the new agent is - called as a tool, and the conversation is continued by the original agent. - - Args: - tool_name: The name of the tool. If not provided, the agent's name will be used. - tool_description: The description of the tool, which should indicate what it does and - when to use it. - custom_output_extractor: A function that extracts the output from the agent. If not - provided, the last message from the agent will be used. - """ - - @function_tool( - name_override=tool_name or _utils.transform_string_function_style(self.name), - description_override=tool_description or "", - ) - async def run_agent(context: RunContextWrapper, input: str) -> str: - from .run import Runner - - output = await Runner.run( - starting_agent=self, - input=input, - context=context.context, - ) - if custom_output_extractor: - return await custom_output_extractor(output) - - return ItemHelpers.text_message_outputs(output.new_items) - - return run_agent - - async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> str | None: - """Get the system prompt for the agent.""" - if isinstance(self.instructions, str): - return self.instructions - elif callable(self.instructions): - if inspect.iscoroutinefunction(self.instructions): - return await cast(Awaitable[str], self.instructions(run_context, self)) - else: - return cast(str, self.instructions(run_context, self)) - elif self.instructions is not None: - logger.error(f"Instructions must be a string or a function, got {self.instructions}") - - return None diff --git a/tests/src/agents/agent_output.py b/tests/src/agents/agent_output.py deleted file mode 100644 index 8140d8c6..00000000 --- a/tests/src/agents/agent_output.py +++ /dev/null @@ -1,144 +0,0 @@ -from dataclasses import dataclass -from typing import Any - -from pydantic import BaseModel, TypeAdapter -from typing_extensions import TypedDict, get_args, get_origin - -from . import _utils -from .exceptions import ModelBehaviorError, UserError -from .strict_schema import ensure_strict_json_schema -from .tracing import SpanError - -_WRAPPER_DICT_KEY = "response" - - -@dataclass(init=False) -class AgentOutputSchema: - """An object that captures the JSON schema of the output, as well as validating/parsing JSON - produced by the LLM into the output type. - """ - - output_type: type[Any] - """The type of the output.""" - - _type_adapter: TypeAdapter[Any] - """A type adapter that wraps the output type, so that we can validate JSON.""" - - _is_wrapped: bool - """Whether the output type is wrapped in a dictionary. This is generally done if the base - output type cannot be represented as a JSON Schema object. - """ - - _output_schema: dict[str, Any] - """The JSON schema of the output.""" - - strict_json_schema: bool - """Whether the JSON schema is in strict mode. We **strongly** recommend setting this to True, - as it increases the likelihood of correct JSON input. - """ - - def __init__(self, output_type: type[Any], strict_json_schema: bool = True): - """ - Args: - output_type: The type of the output. - strict_json_schema: Whether the JSON schema is in strict mode. We **strongly** recommend - setting this to True, as it increases the likelihood of correct JSON input. - """ - self.output_type = output_type - self.strict_json_schema = strict_json_schema - - if output_type is None or output_type is str: - self._is_wrapped = False - self._type_adapter = TypeAdapter(output_type) - self._output_schema = self._type_adapter.json_schema() - return - - # We should wrap for things that are not plain text, and for things that would definitely - # not be a JSON Schema object. - self._is_wrapped = not _is_subclass_of_base_model_or_dict(output_type) - - if self._is_wrapped: - OutputType = TypedDict( - "OutputType", - { - _WRAPPER_DICT_KEY: output_type, # type: ignore - }, - ) - self._type_adapter = TypeAdapter(OutputType) - self._output_schema = self._type_adapter.json_schema() - else: - self._type_adapter = TypeAdapter(output_type) - self._output_schema = self._type_adapter.json_schema() - - if self.strict_json_schema: - self._output_schema = ensure_strict_json_schema(self._output_schema) - - def is_plain_text(self) -> bool: - """Whether the output type is plain text (versus a JSON object).""" - return self.output_type is None or self.output_type is str - - def json_schema(self) -> dict[str, Any]: - """The JSON schema of the output type.""" - if self.is_plain_text(): - raise UserError("Output type is plain text, so no JSON schema is available") - return self._output_schema - - def validate_json(self, json_str: str, partial: bool = False) -> Any: - """Validate a JSON string against the output type. Returns the validated object, or raises - a `ModelBehaviorError` if the JSON is invalid. - """ - validated = _utils.validate_json(json_str, self._type_adapter, partial) - if self._is_wrapped: - if not isinstance(validated, dict): - _utils.attach_error_to_current_span( - SpanError( - message="Invalid JSON", - data={"details": f"Expected a dict, got {type(validated)}"}, - ) - ) - raise ModelBehaviorError( - f"Expected a dict, got {type(validated)} for JSON: {json_str}" - ) - - if _WRAPPER_DICT_KEY not in validated: - _utils.attach_error_to_current_span( - SpanError( - message="Invalid JSON", - data={"details": f"Could not find key {_WRAPPER_DICT_KEY} in JSON"}, - ) - ) - raise ModelBehaviorError( - f"Could not find key {_WRAPPER_DICT_KEY} in JSON: {json_str}" - ) - return validated[_WRAPPER_DICT_KEY] - return validated - - def output_type_name(self) -> str: - """The name of the output type.""" - return _type_to_str(self.output_type) - - -def _is_subclass_of_base_model_or_dict(t: Any) -> bool: - if not isinstance(t, type): - return False - - # If it's a generic alias, 'origin' will be the actual type, e.g. 'list' - origin = get_origin(t) - - allowed_types = (BaseModel, dict) - # If it's a generic alias e.g. list[str], then we should check the origin type i.e. list - return issubclass(origin or t, allowed_types) - - -def _type_to_str(t: type[Any]) -> str: - origin = get_origin(t) - args = get_args(t) - - if origin is None: - # It's a simple type like `str`, `int`, etc. - return t.__name__ - elif args: - args_str = ', '.join(_type_to_str(arg) for arg in args) - return f"{origin.__name__}[{args_str}]" - else: - return str(t) diff --git a/tests/src/agents/computer.py b/tests/src/agents/computer.py deleted file mode 100644 index 1b9224d5..00000000 --- a/tests/src/agents/computer.py +++ /dev/null @@ -1,107 +0,0 @@ -import abc -from typing import Literal - -Environment = Literal["mac", "windows", "ubuntu", "browser"] -Button = Literal["left", "right", "wheel", "back", "forward"] - - -class Computer(abc.ABC): - """A computer implemented with sync operations. The Computer interface abstracts the - operations needed to control a computer or browser.""" - - @property - @abc.abstractmethod - def environment(self) -> Environment: - pass - - @property - @abc.abstractmethod - def dimensions(self) -> tuple[int, int]: - pass - - @abc.abstractmethod - def screenshot(self) -> str: - pass - - @abc.abstractmethod - def click(self, x: int, y: int, button: Button) -> None: - pass - - @abc.abstractmethod - def double_click(self, x: int, y: int) -> None: - pass - - @abc.abstractmethod - def scroll(self, x: int, y: int, scroll_x: int, scroll_y: int) -> None: - pass - - @abc.abstractmethod - def type(self, text: str) -> None: - pass - - @abc.abstractmethod - def wait(self) -> None: - pass - - @abc.abstractmethod - def move(self, x: int, y: int) -> None: - pass - - @abc.abstractmethod - def keypress(self, keys: list[str]) -> None: - pass - - @abc.abstractmethod - def drag(self, path: list[tuple[int, int]]) -> None: - pass - - -class AsyncComputer(abc.ABC): - """A computer implemented with async operations. The Computer interface abstracts the - operations needed to control a computer or browser.""" - - @property - @abc.abstractmethod - def environment(self) -> Environment: - pass - - @property - @abc.abstractmethod - def dimensions(self) -> tuple[int, int]: - pass - - @abc.abstractmethod - async def screenshot(self) -> str: - pass - - @abc.abstractmethod - async def click(self, x: int, y: int, button: Button) -> None: - pass - - @abc.abstractmethod - async def double_click(self, x: int, y: int) -> None: - pass - - @abc.abstractmethod - async def scroll(self, x: int, y: int, scroll_x: int, scroll_y: int) -> None: - pass - - @abc.abstractmethod - async def type(self, text: str) -> None: - pass - - @abc.abstractmethod - async def wait(self) -> None: - pass - - @abc.abstractmethod - async def move(self, x: int, y: int) -> None: - pass - - @abc.abstractmethod - async def keypress(self, keys: list[str]) -> None: - pass - - @abc.abstractmethod - async def drag(self, path: list[tuple[int, int]]) -> None: - pass diff --git a/tests/src/agents/exceptions.py b/tests/src/agents/exceptions.py deleted file mode 100644 index 78898f01..00000000 --- a/tests/src/agents/exceptions.py +++ /dev/null @@ -1,63 +0,0 @@ -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from .guardrail import InputGuardrailResult, OutputGuardrailResult - - -class AgentsException(Exception): - """Base class for all exceptions in the Agents SDK.""" - - -class MaxTurnsExceeded(AgentsException): - """Exception raised when the maximum number of turns is exceeded.""" - - message: str - - def __init__(self, message: str): - self.message = message - - -class ModelBehaviorError(AgentsException): - """Exception raised when the model does something unexpected, e.g. calling a tool that doesn't - exist, or providing malformed JSON. - """ - - message: str - - def __init__(self, message: str): - self.message = message - - -class UserError(AgentsException): - """Exception raised when the user makes an error using the SDK.""" - - message: str - - def __init__(self, message: str): - self.message = message - - -class InputGuardrailTripwireTriggered(AgentsException): - """Exception raised when a guardrail tripwire is triggered.""" - - guardrail_result: "InputGuardrailResult" - """The result data of the guardrail that was triggered.""" - - def __init__(self, guardrail_result: "InputGuardrailResult"): - self.guardrail_result = guardrail_result - super().__init__( - f"Guardrail {guardrail_result.guardrail.__class__.__name__} triggered tripwire" - ) - - -class OutputGuardrailTripwireTriggered(AgentsException): - """Exception raised when a guardrail tripwire is triggered.""" - - guardrail_result: "OutputGuardrailResult" - """The result data of the guardrail that was triggered.""" - - def __init__(self, guardrail_result: "OutputGuardrailResult"): - self.guardrail_result = guardrail_result - super().__init__( - f"Guardrail {guardrail_result.guardrail.__class__.__name__} triggered tripwire" - ) diff --git a/tests/src/agents/extensions/handoff_filters.py b/tests/src/agents/extensions/handoff_filters.py deleted file mode 100644 index f4f9b8bf..00000000 --- a/tests/src/agents/extensions/handoff_filters.py +++ /dev/null @@ -1,67 +0,0 @@ -from __future__ import annotations - -from ..handoffs import HandoffInputData -from ..items import ( - HandoffCallItem, - HandoffOutputItem, - RunItem, - ToolCallItem, - ToolCallOutputItem, - TResponseInputItem, -) - -"""Contains common handoff input filters, for convenience. """ - - -def remove_all_tools(handoff_input_data: HandoffInputData) -> HandoffInputData: - """Filters out all tool items: file search, web search and function calls+output.""" - - history = handoff_input_data.input_history - new_items = handoff_input_data.new_items - - filtered_history = ( - _remove_tool_types_from_input(history) if isinstance(history, tuple) else history - ) - filtered_pre_handoff_items = _remove_tools_from_items(handoff_input_data.pre_handoff_items) - filtered_new_items = _remove_tools_from_items(new_items) - - return HandoffInputData( - input_history=filtered_history, - pre_handoff_items=filtered_pre_handoff_items, - new_items=filtered_new_items, - ) - - -def _remove_tools_from_items(items: tuple[RunItem, ...]) -> tuple[RunItem, ...]: - filtered_items = [] - for item in items: - if ( - isinstance(item, HandoffCallItem) - or isinstance(item, HandoffOutputItem) - or isinstance(item, ToolCallItem) - or isinstance(item, ToolCallOutputItem) - ): - continue - filtered_items.append(item) - return tuple(filtered_items) - - -def _remove_tool_types_from_input( - items: tuple[TResponseInputItem, ...], -) -> tuple[TResponseInputItem, ...]: - tool_types = [ - "function_call", - "function_call_output", - "computer_call", - "computer_call_output", - "file_search_call", - "web_search_call", - ] - - filtered_items: list[TResponseInputItem] = [] - for item in items: - itype = item.get("type") - if itype in tool_types: - continue - filtered_items.append(item) - return tuple(filtered_items) diff --git a/tests/src/agents/extensions/handoff_prompt.py b/tests/src/agents/extensions/handoff_prompt.py deleted file mode 100644 index cfb5ca7e..00000000 --- a/tests/src/agents/extensions/handoff_prompt.py +++ /dev/null @@ -1,19 +0,0 @@ -# A recommended prompt prefix for agents that use handoffs. We recommend including this or -# similar instructions in any agents that use handoffs. -RECOMMENDED_PROMPT_PREFIX = ( - "# System context\n" - "You are part of a multi-agent system called the Agents SDK, designed to make agent " - "coordination and execution easy. Agents uses two primary abstraction: **Agents** and " - "**Handoffs**. An agent encompasses instructions and tools and can hand off a " - "conversation to another agent when appropriate. " - "Handoffs are achieved by calling a handoff function, generally named " - "`transfer_to_`. Transfers between agents are handled seamlessly in the background;" - " do not mention or draw attention to these transfers in your conversation with the user.\n" -) - - -def prompt_with_handoff_instructions(prompt: str) -> str: - """ - Add recommended instructions to the prompt for agents that use handoffs. - """ - return f"{RECOMMENDED_PROMPT_PREFIX}\n\n{prompt}" diff --git a/tests/src/agents/function_schema.py b/tests/src/agents/function_schema.py deleted file mode 100644 index a4b57672..00000000 --- a/tests/src/agents/function_schema.py +++ /dev/null @@ -1,340 +0,0 @@ -from __future__ import annotations - -import contextlib -import inspect -import logging -import re -from dataclasses import dataclass -from typing import Any, Callable, Literal, get_args, get_origin, get_type_hints - -from griffe import Docstring, DocstringSectionKind -from pydantic import BaseModel, Field, create_model - -from .exceptions import UserError -from .run_context import RunContextWrapper -from .strict_schema import ensure_strict_json_schema - - -@dataclass -class FuncSchema: - """ - Captures the schema for a python function, in preparation for sending it to an LLM as a tool. - """ - - name: str - """The name of the function.""" - description: str | None - """The description of the function.""" - params_pydantic_model: type[BaseModel] - """A Pydantic model that represents the function's parameters.""" - params_json_schema: dict[str, Any] - """The JSON schema for the function's parameters, derived from the Pydantic model.""" - signature: inspect.Signature - """The signature of the function.""" - takes_context: bool = False - """Whether the function takes a RunContextWrapper argument (must be the first argument).""" - - def to_call_args(self, data: BaseModel) -> tuple[list[Any], dict[str, Any]]: - """ - Converts validated data from the Pydantic model into (args, kwargs), suitable for calling - the original function. - """ - positional_args: list[Any] = [] - keyword_args: dict[str, Any] = {} - seen_var_positional = False - - # Use enumerate() so we can skip the first parameter if it's context. - for idx, (name, param) in enumerate(self.signature.parameters.items()): - # If the function takes a RunContextWrapper and this is the first parameter, skip it. - if self.takes_context and idx == 0: - continue - - value = getattr(data, name, None) - if param.kind == param.VAR_POSITIONAL: - # e.g. *args: extend positional args and mark that *args is now seen - positional_args.extend(value or []) - seen_var_positional = True - elif param.kind == param.VAR_KEYWORD: - # e.g. **kwargs handling - keyword_args.update(value or {}) - elif param.kind in (param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD): - # Before *args, add to positional args. After *args, add to keyword args. - if not seen_var_positional: - positional_args.append(value) - else: - keyword_args[name] = value - else: - # For KEYWORD_ONLY parameters, always use keyword args. - keyword_args[name] = value - return positional_args, keyword_args - - -@dataclass -class FuncDocumentation: - """Contains metadata about a python function, extracted from its docstring.""" - - name: str - """The name of the function, via `__name__`.""" - description: str | None - """The description of the function, derived from the docstring.""" - param_descriptions: dict[str, str] | None - """The parameter descriptions of the function, derived from the docstring.""" - - -DocstringStyle = Literal["google", "numpy", "sphinx"] - - -# As of Feb 2025, the automatic style detection in griffe is an Insiders feature. This -# code approximates it. -def _detect_docstring_style(doc: str) -> DocstringStyle: - scores: dict[DocstringStyle, int] = {"sphinx": 0, "numpy": 0, "google": 0} - - # Sphinx style detection: look for :param, :type, :return:, and :rtype: - sphinx_patterns = [r"^:param\s", r"^:type\s", r"^:return:", r"^:rtype:"] - for pattern in sphinx_patterns: - if re.search(pattern, doc, re.MULTILINE): - scores["sphinx"] += 1 - - # Numpy style detection: look for headers like 'Parameters', 'Returns', or 'Yields' followed by - # a dashed underline - numpy_patterns = [ - r"^Parameters\s*\n\s*-{3,}", - r"^Returns\s*\n\s*-{3,}", - r"^Yields\s*\n\s*-{3,}", - ] - for pattern in numpy_patterns: - if re.search(pattern, doc, re.MULTILINE): - scores["numpy"] += 1 - - # Google style detection: look for section headers with a trailing colon - google_patterns = [r"^(Args|Arguments):", r"^(Returns):", r"^(Raises):"] - for pattern in google_patterns: - if re.search(pattern, doc, re.MULTILINE): - scores["google"] += 1 - - max_score = max(scores.values()) - if max_score == 0: - return "google" - - # Priority order: sphinx > numpy > google in case of tie - styles: list[DocstringStyle] = ["sphinx", "numpy", "google"] - - for style in styles: - if scores[style] == max_score: - return style - - return "google" - - -@contextlib.contextmanager -def _suppress_griffe_logging(): - # Supresses warnings about missing annotations for params - logger = logging.getLogger("griffe") - previous_level = logger.getEffectiveLevel() - logger.setLevel(logging.ERROR) - try: - yield - finally: - logger.setLevel(previous_level) - - -def generate_func_documentation( - func: Callable[..., Any], style: DocstringStyle | None = None -) -> FuncDocumentation: - """ - Extracts metadata from a function docstring, in preparation for sending it to an LLM as a tool. - - Args: - func: The function to extract documentation from. - style: The style of the docstring to use for parsing. If not provided, we will attempt to - auto-detect the style. - - Returns: - A FuncDocumentation object containing the function's name, description, and parameter - descriptions. - """ - name = func.__name__ - doc = inspect.getdoc(func) - if not doc: - return FuncDocumentation(name=name, description=None, param_descriptions=None) - - with _suppress_griffe_logging(): - docstring = Docstring(doc, lineno=1, parser=style or _detect_docstring_style(doc)) - parsed = docstring.parse() - - description: str | None = next( - (section.value for section in parsed if section.kind == DocstringSectionKind.text), None - ) - - param_descriptions: dict[str, str] = { - param.name: param.description - for section in parsed - if section.kind == DocstringSectionKind.parameters - for param in section.value - } - - return FuncDocumentation( - name=func.__name__, - description=description, - param_descriptions=param_descriptions or None, - ) - - -def function_schema( - func: Callable[..., Any], - docstring_style: DocstringStyle | None = None, - name_override: str | None = None, - description_override: str | None = None, - use_docstring_info: bool = True, - strict_json_schema: bool = True, -) -> FuncSchema: - """ - Given a python function, extracts a `FuncSchema` from it, capturing the name, description, - parameter descriptions, and other metadata. - - Args: - func: The function to extract the schema from. - docstring_style: The style of the docstring to use for parsing. If not provided, we will - attempt to auto-detect the style. - name_override: If provided, use this name instead of the function's `__name__`. - description_override: If provided, use this description instead of the one derived from the - docstring. - use_docstring_info: If True, uses the docstring to generate the description and parameter - descriptions. - strict_json_schema: Whether the JSON schema is in strict mode. If True, we'll ensure that - the schema adheres to the "strict" standard the OpenAI API expects. We **strongly** - recommend setting this to True, as it increases the likelihood of the LLM providing - correct JSON input. - - Returns: - A `FuncSchema` object containing the function's name, description, parameter descriptions, - and other metadata. - """ - - # 1. Grab docstring info - if use_docstring_info: - doc_info = generate_func_documentation(func, docstring_style) - param_descs = doc_info.param_descriptions or {} - else: - doc_info = None - param_descs = {} - - func_name = name_override or doc_info.name if doc_info else func.__name__ - - # 2. Inspect function signature and get type hints - sig = inspect.signature(func) - type_hints = get_type_hints(func) - params = list(sig.parameters.items()) - takes_context = False - filtered_params = [] - - if params: - first_name, first_param = params[0] - # Prefer the evaluated type hint if available - ann = type_hints.get(first_name, first_param.annotation) - if ann != inspect._empty: - origin = get_origin(ann) or ann - if origin is RunContextWrapper: - takes_context = True # Mark that the function takes context - else: - filtered_params.append((first_name, first_param)) - else: - filtered_params.append((first_name, first_param)) - - # For parameters other than the first, raise error if any use RunContextWrapper. - for name, param in params[1:]: - ann = type_hints.get(name, param.annotation) - if ann != inspect._empty: - origin = get_origin(ann) or ann - if origin is RunContextWrapper: - raise UserError( - f"RunContextWrapper param found at non-first position in function" - f" {func.__name__}" - ) - filtered_params.append((name, param)) - - # We will collect field definitions for create_model as a dict: - # field_name -> (type_annotation, default_value_or_Field(...)) - fields: dict[str, Any] = {} - - for name, param in filtered_params: - ann = type_hints.get(name, param.annotation) - default = param.default - - # If there's no type hint, assume `Any` - if ann == inspect._empty: - ann = Any - - # If a docstring param description exists, use it - field_description = param_descs.get(name, None) - - # Handle different parameter kinds - if param.kind == param.VAR_POSITIONAL: - # e.g. *args: extend positional args - if get_origin(ann) is tuple: - # e.g. def foo(*args: tuple[int, ...]) -> treat as List[int] - args_of_tuple = get_args(ann) - if len(args_of_tuple) == 2 and args_of_tuple[1] is Ellipsis: - ann = list[args_of_tuple[0]] # type: ignore - else: - ann = list[Any] - else: - # If user wrote *args: int, treat as List[int] - ann = list[ann] # type: ignore - - # Default factory to empty list - fields[name] = ( - ann, - Field(default_factory=list, description=field_description), # type: ignore - ) - - elif param.kind == param.VAR_KEYWORD: - # **kwargs handling - if get_origin(ann) is dict: - # e.g. def foo(**kwargs: dict[str, int]) - dict_args = get_args(ann) - if len(dict_args) == 2: - ann = dict[dict_args[0], dict_args[1]] # type: ignore - else: - ann = dict[str, Any] - else: - # e.g. def foo(**kwargs: int) -> Dict[str, int] - ann = dict[str, ann] # type: ignore - - fields[name] = ( - ann, - Field(default_factory=dict, description=field_description), # type: ignore - ) - - else: - # Normal parameter - if default == inspect._empty: - # Required field - fields[name] = ( - ann, - Field(..., description=field_description), - ) - else: - # Parameter with a default value - fields[name] = ( - ann, - Field(default=default, description=field_description), - ) - - # 3. Dynamically build a Pydantic model - dynamic_model = create_model(f"{func_name}_args", __base__=BaseModel, **fields) - - # 4. Build JSON schema from that model - json_schema = dynamic_model.model_json_schema() - if strict_json_schema: - json_schema = ensure_strict_json_schema(json_schema) - - # 5. Return as a FuncSchema dataclass - return FuncSchema( - name=func_name, - description=description_override or doc_info.description if doc_info else None, - params_pydantic_model=dynamic_model, - params_json_schema=json_schema, - signature=sig, - takes_context=takes_context, - ) diff --git a/tests/src/agents/guardrail.py b/tests/src/agents/guardrail.py deleted file mode 100644 index fcae0b8a..00000000 --- a/tests/src/agents/guardrail.py +++ /dev/null @@ -1,320 +0,0 @@ -from __future__ import annotations - -import inspect -from collections.abc import Awaitable -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Callable, Generic, Union, overload - -from typing_extensions import TypeVar - -from ._utils import MaybeAwaitable -from .exceptions import UserError -from .items import TResponseInputItem -from .run_context import RunContextWrapper, TContext - -if TYPE_CHECKING: - from .agent import Agent - - -@dataclass -class GuardrailFunctionOutput: - """The output of a guardrail function.""" - - output_info: Any - """ - Optional information about the guardrail's output. For example, the guardrail could include - information about the checks it performed and granular results. - """ - - tripwire_triggered: bool - """ - Whether the tripwire was triggered. If triggered, the agent's execution will be halted. - """ - - -@dataclass -class InputGuardrailResult: - """The result of a guardrail run.""" - - guardrail: InputGuardrail[Any] - """ - The guardrail that was run. - """ - - output: GuardrailFunctionOutput - """The output of the guardrail function.""" - - -@dataclass -class OutputGuardrailResult: - """The result of a guardrail run.""" - - guardrail: OutputGuardrail[Any] - """ - The guardrail that was run. - """ - - agent_output: Any - """ - The output of the agent that was checked by the guardrail. - """ - - agent: Agent[Any] - """ - The agent that was checked by the guardrail. - """ - - output: GuardrailFunctionOutput - """The output of the guardrail function.""" - - -@dataclass -class InputGuardrail(Generic[TContext]): - """Input guardrails are checks that run in parallel to the agent's execution. - They can be used to do things like: - - Check if input messages are off-topic - - Take over control of the agent's execution if an unexpected input is detected - - You can use the `@input_guardrail()` decorator to turn a function into an `InputGuardrail`, or - create an `InputGuardrail` manually. - - Guardrails return a `GuardrailResult`. If `result.tripwire_triggered` is `True`, the agent - execution will immediately stop and a `InputGuardrailTripwireTriggered` exception will be raised - """ - - guardrail_function: Callable[ - [RunContextWrapper[TContext], Agent[Any], str | list[TResponseInputItem]], - MaybeAwaitable[GuardrailFunctionOutput], - ] - """A function that receives the the agent input and the context, and returns a - `GuardrailResult`. The result marks whether the tripwire was triggered, and can optionally - include information about the guardrail's output. - """ - - name: str | None = None - """The name of the guardrail, used for tracing. If not provided, we'll use the guardrail - function's name. - """ - - def get_name(self) -> str: - if self.name: - return self.name - - return self.guardrail_function.__name__ - - async def run( - self, - agent: Agent[Any], - input: str | list[TResponseInputItem], - context: RunContextWrapper[TContext], - ) -> InputGuardrailResult: - if not callable(self.guardrail_function): - raise UserError(f"Guardrail function must be callable, got {self.guardrail_function}") - - output = self.guardrail_function(context, agent, input) - if inspect.isawaitable(output): - return InputGuardrailResult( - guardrail=self, - output=await output, - ) - - return InputGuardrailResult( - guardrail=self, - output=output, - ) - - -@dataclass -class OutputGuardrail(Generic[TContext]): - """Output guardrails are checks that run on the final output of an agent. - They can be used to do check if the output passes certain validation criteria - - You can use the `@output_guardrail()` decorator to turn a function into an `OutputGuardrail`, - or create an `OutputGuardrail` manually. - - Guardrails return a `GuardrailResult`. If `result.tripwire_triggered` is `True`, a - `OutputGuardrailTripwireTriggered` exception will be raised. - """ - - guardrail_function: Callable[ - [RunContextWrapper[TContext], Agent[Any], Any], - MaybeAwaitable[GuardrailFunctionOutput], - ] - """A function that receives the final agent, its output, and the context, and returns a - `GuardrailResult`. The result marks whether the tripwire was triggered, and can optionally - include information about the guardrail's output. - """ - - name: str | None = None - """The name of the guardrail, used for tracing. If not provided, we'll use the guardrail - function's name. - """ - - def get_name(self) -> str: - if self.name: - return self.name - - return self.guardrail_function.__name__ - - async def run( - self, context: RunContextWrapper[TContext], agent: Agent[Any], agent_output: Any - ) -> OutputGuardrailResult: - if not callable(self.guardrail_function): - raise UserError(f"Guardrail function must be callable, got {self.guardrail_function}") - - output = self.guardrail_function(context, agent, agent_output) - if inspect.isawaitable(output): - return OutputGuardrailResult( - guardrail=self, - agent=agent, - agent_output=agent_output, - output=await output, - ) - - return OutputGuardrailResult( - guardrail=self, - agent=agent, - agent_output=agent_output, - output=output, - ) - - -TContext_co = TypeVar("TContext_co", bound=Any, covariant=True) - -# For InputGuardrail -_InputGuardrailFuncSync = Callable[ - [RunContextWrapper[TContext_co], "Agent[Any]", Union[str, list[TResponseInputItem]]], - GuardrailFunctionOutput, -] -_InputGuardrailFuncAsync = Callable[ - [RunContextWrapper[TContext_co], "Agent[Any]", Union[str, list[TResponseInputItem]]], - Awaitable[GuardrailFunctionOutput], -] - - -@overload -def input_guardrail( - func: _InputGuardrailFuncSync[TContext_co], -) -> InputGuardrail[TContext_co]: ... - - -@overload -def input_guardrail( - func: _InputGuardrailFuncAsync[TContext_co], -) -> InputGuardrail[TContext_co]: ... - - -@overload -def input_guardrail( - *, - name: str | None = None, -) -> Callable[ - [_InputGuardrailFuncSync[TContext_co] | _InputGuardrailFuncAsync[TContext_co]], - InputGuardrail[TContext_co], -]: ... - - -def input_guardrail( - func: _InputGuardrailFuncSync[TContext_co] - | _InputGuardrailFuncAsync[TContext_co] - | None = None, - *, - name: str | None = None, -) -> ( - InputGuardrail[TContext_co] - | Callable[ - [_InputGuardrailFuncSync[TContext_co] | _InputGuardrailFuncAsync[TContext_co]], - InputGuardrail[TContext_co], - ] -): - """ - Decorator that transforms a sync or async function into an `InputGuardrail`. - It can be used directly (no parentheses) or with keyword args, e.g.: - - @input_guardrail - def my_sync_guardrail(...): ... - - @input_guardrail(name="guardrail_name") - async def my_async_guardrail(...): ... - """ - - def decorator( - f: _InputGuardrailFuncSync[TContext_co] | _InputGuardrailFuncAsync[TContext_co], - ) -> InputGuardrail[TContext_co]: - return InputGuardrail(guardrail_function=f, name=name) - - if func is not None: - # Decorator was used without parentheses - return decorator(func) - - # Decorator used with keyword arguments - return decorator - - -_OutputGuardrailFuncSync = Callable[ - [RunContextWrapper[TContext_co], "Agent[Any]", Any], - GuardrailFunctionOutput, -] -_OutputGuardrailFuncAsync = Callable[ - [RunContextWrapper[TContext_co], "Agent[Any]", Any], - Awaitable[GuardrailFunctionOutput], -] - - -@overload -def output_guardrail( - func: _OutputGuardrailFuncSync[TContext_co], -) -> OutputGuardrail[TContext_co]: ... - - -@overload -def output_guardrail( - func: _OutputGuardrailFuncAsync[TContext_co], -) -> OutputGuardrail[TContext_co]: ... - - -@overload -def output_guardrail( - *, - name: str | None = None, -) -> Callable[ - [_OutputGuardrailFuncSync[TContext_co] | _OutputGuardrailFuncAsync[TContext_co]], - OutputGuardrail[TContext_co], -]: ... - - -def output_guardrail( - func: _OutputGuardrailFuncSync[TContext_co] - | _OutputGuardrailFuncAsync[TContext_co] - | None = None, - *, - name: str | None = None, -) -> ( - OutputGuardrail[TContext_co] - | Callable[ - [_OutputGuardrailFuncSync[TContext_co] | _OutputGuardrailFuncAsync[TContext_co]], - OutputGuardrail[TContext_co], - ] -): - """ - Decorator that transforms a sync or async function into an `OutputGuardrail`. - It can be used directly (no parentheses) or with keyword args, e.g.: - - @output_guardrail - def my_sync_guardrail(...): ... - - @output_guardrail(name="guardrail_name") - async def my_async_guardrail(...): ... - """ - - def decorator( - f: _OutputGuardrailFuncSync[TContext_co] | _OutputGuardrailFuncAsync[TContext_co], - ) -> OutputGuardrail[TContext_co]: - return OutputGuardrail(guardrail_function=f, name=name) - - if func is not None: - # Decorator was used without parentheses - return decorator(func) - - # Decorator used with keyword arguments - return decorator diff --git a/tests/src/agents/handoffs.py b/tests/src/agents/handoffs.py deleted file mode 100644 index ac157401..00000000 --- a/tests/src/agents/handoffs.py +++ /dev/null @@ -1,236 +0,0 @@ -from __future__ import annotations - -import inspect -from collections.abc import Awaitable -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Callable, Generic, cast, overload - -from pydantic import TypeAdapter -from typing_extensions import TypeAlias, TypeVar - -from . import _utils -from .exceptions import ModelBehaviorError, UserError -from .items import RunItem, TResponseInputItem -from .run_context import RunContextWrapper, TContext -from .strict_schema import ensure_strict_json_schema -from .tracing.spans import SpanError - -if TYPE_CHECKING: - from .agent import Agent - - -# The handoff input type is the type of data passed when the agent is called via a handoff. -THandoffInput = TypeVar("THandoffInput", default=Any) - -OnHandoffWithInput = Callable[[RunContextWrapper[Any], THandoffInput], Any] -OnHandoffWithoutInput = Callable[[RunContextWrapper[Any]], Any] - - -@dataclass(frozen=True) -class HandoffInputData: - input_history: str | tuple[TResponseInputItem, ...] - """ - The input history before `Runner.run()` was called. - """ - - pre_handoff_items: tuple[RunItem, ...] - """ - The items generated before the agent turn where the handoff was invoked. - """ - - new_items: tuple[RunItem, ...] - """ - The new items generated during the current agent turn, including the item that triggered the - handoff and the tool output message representing the response from the handoff output. - """ - - -HandoffInputFilter: TypeAlias = Callable[[HandoffInputData], HandoffInputData] -"""A function that filters the input data passed to the next agent.""" - - -@dataclass -class Handoff(Generic[TContext]): - """A handoff is when an agent delegates a task to another agent. - For example, in a customer support scenario you might have a "triage agent" that determines - which agent should handle the user's request, and sub-agents that specialize in different - areas like billing, account management, etc. - """ - - tool_name: str - """The name of the tool that represents the handoff.""" - - tool_description: str - """The description of the tool that represents the handoff.""" - - input_json_schema: dict[str, Any] - """The JSON schema for the handoff input. Can be empty if the handoff does not take an input. - """ - - on_invoke_handoff: Callable[[RunContextWrapper[Any], str], Awaitable[Agent[TContext]]] - """The function that invokes the handoff. The parameters passed are: - 1. The handoff run context - 2. The arguments from the LLM, as a JSON string. Empty string if input_json_schema is empty. - - Must return an agent. - """ - - agent_name: str - """The name of the agent that is being handed off to.""" - - input_filter: HandoffInputFilter | None = None - """A function that filters the inputs that are passed to the next agent. By default, the new - agent sees the entire conversation history. In some cases, you may want to filter inputs e.g. - to remove older inputs, or remove tools from existing inputs. - - The function will receive the entire conversation history so far, including the input item - that triggered the handoff and a tool call output item representing the handoff tool's output. - - You are free to modify the input history or new items as you see fit. The next agent that - runs will receive `handoff_input_data.all_items`. - - IMPORTANT: in streaming mode, we will not stream anything as a result of this function. The - items generated before will already have been streamed. - """ - - strict_json_schema: bool = True - """Whether the input JSON schema is in strict mode. We **strongly** recommend setting this to - True, as it increases the likelihood of correct JSON input. - """ - - def get_transfer_message(self, agent: Agent[Any]) -> str: - base = f"{{'assistant': '{agent.name}'}}" - return base - - @classmethod - def default_tool_name(cls, agent: Agent[Any]) -> str: - return _utils.transform_string_function_style(f"transfer_to_{agent.name}") - - @classmethod - def default_tool_description(cls, agent: Agent[Any]) -> str: - return ( - f"Handoff to the {agent.name} agent to handle the request. " - f"{agent.handoff_description or ''}" - ) - - -@overload -def handoff( - agent: Agent[TContext], - *, - tool_name_override: str | None = None, - tool_description_override: str | None = None, - input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, -) -> Handoff[TContext]: ... - - -@overload -def handoff( - agent: Agent[TContext], - *, - on_handoff: OnHandoffWithInput[THandoffInput], - input_type: type[THandoffInput], - tool_description_override: str | None = None, - tool_name_override: str | None = None, - input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, -) -> Handoff[TContext]: ... - - -@overload -def handoff( - agent: Agent[TContext], - *, - on_handoff: OnHandoffWithoutInput, - tool_description_override: str | None = None, - tool_name_override: str | None = None, - input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, -) -> Handoff[TContext]: ... - - -def handoff( - agent: Agent[TContext], - tool_name_override: str | None = None, - tool_description_override: str | None = None, - on_handoff: OnHandoffWithInput[THandoffInput] | OnHandoffWithoutInput | None = None, - input_type: type[THandoffInput] | None = None, - input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, -) -> Handoff[TContext]: - """Create a handoff from an agent. - - Args: - agent: The agent to handoff to, or a function that returns an agent. - tool_name_override: Optional override for the name of the tool that represents the handoff. - tool_description_override: Optional override for the description of the tool that - represents the handoff. - on_handoff: A function that runs when the handoff is invoked. - input_type: the type of the input to the handoff. If provided, the input will be validated - against this type. Only relevant if you pass a function that takes an input. - input_filter: a function that filters the inputs that are passed to the next agent. - """ - assert (on_handoff and input_type) or not (on_handoff and input_type), ( - "You must provide either both on_input and input_type, or neither" - ) - type_adapter: TypeAdapter[Any] | None - if input_type is not None: - assert callable(on_handoff), "on_handoff must be callable" - sig = inspect.signature(on_handoff) - if len(sig.parameters) != 2: - raise UserError("on_handoff must take two arguments: context and input") - - type_adapter = TypeAdapter(input_type) - input_json_schema = type_adapter.json_schema() - else: - type_adapter = None - input_json_schema = {} - if on_handoff is not None: - sig = inspect.signature(on_handoff) - if len(sig.parameters) != 1: - raise UserError("on_handoff must take one argument: context") - - async def _invoke_handoff( - ctx: RunContextWrapper[Any], input_json: str | None = None - ) -> Agent[Any]: - if input_type is not None and type_adapter is not None: - if input_json is None: - _utils.attach_error_to_current_span( - SpanError( - message="Handoff function expected non-null input, but got None", - data={"details": "input_json is None"}, - ) - ) - raise ModelBehaviorError("Handoff function expected non-null input, but got None") - - validated_input = _utils.validate_json( - json_str=input_json, - type_adapter=type_adapter, - partial=False, - ) - input_func = cast(OnHandoffWithInput[THandoffInput], on_handoff) - if inspect.iscoroutinefunction(input_func): - await input_func(ctx, validated_input) - else: - input_func(ctx, validated_input) - elif on_handoff is not None: - no_input_func = cast(OnHandoffWithoutInput, on_handoff) - if inspect.iscoroutinefunction(no_input_func): - await no_input_func(ctx) - else: - no_input_func(ctx) - - return agent - - tool_name = tool_name_override or Handoff.default_tool_name(agent) - tool_description = tool_description_override or Handoff.default_tool_description(agent) - - # Always ensure the input JSON schema is in strict mode - # If there is a need, we can make this configurable in the future - input_json_schema = ensure_strict_json_schema(input_json_schema) - - return Handoff( - tool_name=tool_name, - tool_description=tool_description, - input_json_schema=input_json_schema, - on_invoke_handoff=_invoke_handoff, - input_filter=input_filter, - agent_name=agent.name, - ) diff --git a/tests/src/agents/items.py b/tests/src/agents/items.py deleted file mode 100644 index bbaf49d8..00000000 --- a/tests/src/agents/items.py +++ /dev/null @@ -1,246 +0,0 @@ -from __future__ import annotations - -import abc -import copy -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Generic, Literal, TypeVar, Union - -from openai.types.responses import ( - Response, - ResponseComputerToolCall, - ResponseFileSearchToolCall, - ResponseFunctionToolCall, - ResponseFunctionWebSearch, - ResponseInputItemParam, - ResponseOutputItem, - ResponseOutputMessage, - ResponseOutputRefusal, - ResponseOutputText, - ResponseStreamEvent, -) -from openai.types.responses.response_input_item_param import ComputerCallOutput, FunctionCallOutput -from openai.types.responses.response_output_item import Reasoning -from pydantic import BaseModel -from typing_extensions import TypeAlias - -from .exceptions import AgentsException, ModelBehaviorError -from .usage import Usage - -if TYPE_CHECKING: - from .agent import Agent - -TResponse = Response -"""A type alias for the Response type from the OpenAI SDK.""" - -TResponseInputItem = ResponseInputItemParam -"""A type alias for the ResponseInputItemParam type from the OpenAI SDK.""" - -TResponseOutputItem = ResponseOutputItem -"""A type alias for the ResponseOutputItem type from the OpenAI SDK.""" - -TResponseStreamEvent = ResponseStreamEvent -"""A type alias for the ResponseStreamEvent type from the OpenAI SDK.""" - -T = TypeVar("T", bound=Union[TResponseOutputItem, TResponseInputItem]) - - -@dataclass -class RunItemBase(Generic[T], abc.ABC): - agent: Agent[Any] - """The agent whose run caused this item to be generated.""" - - raw_item: T - """The raw Responses item from the run. This will always be a either an output item (i.e. - `openai.types.responses.ResponseOutputItem` or an input item - (i.e. `openai.types.responses.ResponseInputItemParam`). - """ - - def to_input_item(self) -> TResponseInputItem: - """Converts this item into an input item suitable for passing to the model.""" - if isinstance(self.raw_item, dict): - # We know that input items are dicts, so we can ignore the type error - return self.raw_item # type: ignore - elif isinstance(self.raw_item, BaseModel): - # All output items are Pydantic models that can be converted to input items. - return self.raw_item.model_dump(exclude_unset=True) # type: ignore - else: - raise AgentsException(f"Unexpected raw item type: {type(self.raw_item)}") - - -@dataclass -class MessageOutputItem(RunItemBase[ResponseOutputMessage]): - """Represents a message from the LLM.""" - - raw_item: ResponseOutputMessage - """The raw response output message.""" - - type: Literal["message_output_item"] = "message_output_item" - - -@dataclass -class HandoffCallItem(RunItemBase[ResponseFunctionToolCall]): - """Represents a tool call for a handoff from one agent to another.""" - - raw_item: ResponseFunctionToolCall - """The raw response function tool call that represents the handoff.""" - - type: Literal["handoff_call_item"] = "handoff_call_item" - - -@dataclass -class HandoffOutputItem(RunItemBase[TResponseInputItem]): - """Represents the output of a handoff.""" - - raw_item: TResponseInputItem - """The raw input item that represents the handoff taking place.""" - - source_agent: Agent[Any] - """The agent that made the handoff.""" - - target_agent: Agent[Any] - """The agent that is being handed off to.""" - - type: Literal["handoff_output_item"] = "handoff_output_item" - - -ToolCallItemTypes: TypeAlias = Union[ - ResponseFunctionToolCall, - ResponseComputerToolCall, - ResponseFileSearchToolCall, - ResponseFunctionWebSearch, -] -"""A type that represents a tool call item.""" - - -@dataclass -class ToolCallItem(RunItemBase[ToolCallItemTypes]): - """Represents a tool call e.g. a function call or computer action call.""" - - raw_item: ToolCallItemTypes - """The raw tool call item.""" - - type: Literal["tool_call_item"] = "tool_call_item" - - -@dataclass -class ToolCallOutputItem(RunItemBase[Union[FunctionCallOutput, ComputerCallOutput]]): - """Represents the output of a tool call.""" - - raw_item: FunctionCallOutput | ComputerCallOutput - """The raw item from the model.""" - - output: str - """The output of the tool call.""" - - type: Literal["tool_call_output_item"] = "tool_call_output_item" - - -@dataclass -class ReasoningItem(RunItemBase[Reasoning]): - """Represents a reasoning item.""" - - raw_item: Reasoning - """The raw reasoning item.""" - - type: Literal["reasoning_item"] = "reasoning_item" - - -RunItem: TypeAlias = Union[ - MessageOutputItem, - HandoffCallItem, - HandoffOutputItem, - ToolCallItem, - ToolCallOutputItem, - ReasoningItem, -] -"""An item generated by an agent.""" - - -@dataclass -class ModelResponse: - output: list[TResponseOutputItem] - """A list of outputs (messages, tool calls, etc) generated by the model""" - - usage: Usage - """The usage information for the response.""" - - referenceable_id: str | None - """An ID for the response which can be used to refer to the response in subsequent calls to the - model. Not supported by all model providers. - """ - - def to_input_items(self) -> list[TResponseInputItem]: - """Convert the output into a list of input items suitable for passing to the model.""" - # We happen to know that the shape of the Pydantic output items are the same as the - # equivalent TypedDict input items, so we can just convert each one. - # This is also tested via unit tests. - return [it.model_dump(exclude_unset=True) for it in self.output] # type: ignore - - -class ItemHelpers: - @classmethod - def extract_last_content(cls, message: TResponseOutputItem) -> str: - """Extracts the last text content or refusal from a message.""" - if not isinstance(message, ResponseOutputMessage): - return "" - - last_content = message.content[-1] - if isinstance(last_content, ResponseOutputText): - return last_content.text - elif isinstance(last_content, ResponseOutputRefusal): - return last_content.refusal - else: - raise ModelBehaviorError(f"Unexpected content type: {type(last_content)}") - - @classmethod - def extract_last_text(cls, message: TResponseOutputItem) -> str | None: - """Extracts the last text content from a message, if any. Ignores refusals.""" - if isinstance(message, ResponseOutputMessage): - last_content = message.content[-1] - if isinstance(last_content, ResponseOutputText): - return last_content.text - - return None - - @classmethod - def input_to_new_input_list( - cls, input: str | list[TResponseInputItem] - ) -> list[TResponseInputItem]: - """Converts a string or list of input items into a list of input items.""" - if isinstance(input, str): - return [ - { - "content": input, - "role": "user", - } - ] - return copy.deepcopy(input) - - @classmethod - def text_message_outputs(cls, items: list[RunItem]) -> str: - """Concatenates all the text content from a list of message output items.""" - text = "" - for item in items: - if isinstance(item, MessageOutputItem): - text += cls.text_message_output(item) - return text - - @classmethod - def text_message_output(cls, message: MessageOutputItem) -> str: - """Extracts all the text content from a single message output item.""" - text = "" - for item in message.raw_item.content: - if isinstance(item, ResponseOutputText): - text += item.text - return text - - @classmethod - def tool_call_output_item( - cls, tool_call: ResponseFunctionToolCall, output: str - ) -> FunctionCallOutput: - """Creates a tool call output item from a tool call and its output.""" - return { - "call_id": tool_call.call_id, - "output": output, - "type": "function_call_output", - } diff --git a/tests/src/agents/lifecycle.py b/tests/src/agents/lifecycle.py deleted file mode 100644 index 8643248b..00000000 --- a/tests/src/agents/lifecycle.py +++ /dev/null @@ -1,105 +0,0 @@ -from typing import Any, Generic - -from .agent import Agent -from .run_context import RunContextWrapper, TContext -from .tool import Tool - - -class RunHooks(Generic[TContext]): - """A class that receives callbacks on various lifecycle events in an agent run. Subclass and - override the methods you need. - """ - - async def on_agent_start( - self, context: RunContextWrapper[TContext], agent: Agent[TContext] - ) -> None: - """Called before the agent is invoked. Called each time the current agent changes.""" - pass - - async def on_agent_end( - self, - context: RunContextWrapper[TContext], - agent: Agent[TContext], - output: Any, - ) -> None: - """Called when the agent produces a final output.""" - pass - - async def on_handoff( - self, - context: RunContextWrapper[TContext], - from_agent: Agent[TContext], - to_agent: Agent[TContext], - ) -> None: - """Called when a handoff occurs.""" - pass - - async def on_tool_start( - self, - context: RunContextWrapper[TContext], - agent: Agent[TContext], - tool: Tool, - ) -> None: - """Called before a tool is invoked.""" - pass - - async def on_tool_end( - self, - context: RunContextWrapper[TContext], - agent: Agent[TContext], - tool: Tool, - result: str, - ) -> None: - """Called after a tool is invoked.""" - pass - - -class AgentHooks(Generic[TContext]): - """A class that receives callbacks on various lifecycle events for a specific agent. You can - set this on `agent.hooks` to receive events for that specific agent. - - Subclass and override the methods you need. - """ - - async def on_start(self, context: RunContextWrapper[TContext], agent: Agent[TContext]) -> None: - """Called before the agent is invoked. Called each time the running agent is changed to this - agent.""" - pass - - async def on_end( - self, - context: RunContextWrapper[TContext], - agent: Agent[TContext], - output: Any, - ) -> None: - """Called when the agent produces a final output.""" - pass - - async def on_handoff( - self, - context: RunContextWrapper[TContext], - agent: Agent[TContext], - source: Agent[TContext], - ) -> None: - """Called when the agent is being handed off to. The `source` is the agent that is handing - off to this agent.""" - pass - - async def on_tool_start( - self, - context: RunContextWrapper[TContext], - agent: Agent[TContext], - tool: Tool, - ) -> None: - """Called before a tool is invoked.""" - pass - - async def on_tool_end( - self, - context: RunContextWrapper[TContext], - agent: Agent[TContext], - tool: Tool, - result: str, - ) -> None: - """Called after a tool is invoked.""" - pass diff --git a/tests/src/agents/logger.py b/tests/src/agents/logger.py deleted file mode 100644 index bd81a827..00000000 --- a/tests/src/agents/logger.py +++ /dev/null @@ -1,3 +0,0 @@ -import logging - -logger = logging.getLogger("openai.agents") diff --git a/tests/src/agents/model_settings.py b/tests/src/agents/model_settings.py deleted file mode 100644 index 78cf9a83..00000000 --- a/tests/src/agents/model_settings.py +++ /dev/null @@ -1,35 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass -from typing import Literal - - -@dataclass -class ModelSettings: - """Settings to use when calling an LLM. - - This class holds optional model configuration parameters (e.g. temperature, - top_p, penalties, truncation, etc.). - """ - temperature: float | None = None - top_p: float | None = None - frequency_penalty: float | None = None - presence_penalty: float | None = None - tool_choice: Literal["auto", "required", "none"] | str | None = None - parallel_tool_calls: bool | None = False - truncation: Literal["auto", "disabled"] | None = None - - def resolve(self, override: ModelSettings | None) -> ModelSettings: - """Produce a new ModelSettings by overlaying any non-None values from the - override on top of this instance.""" - if override is None: - return self - return ModelSettings( - temperature=override.temperature or self.temperature, - top_p=override.top_p or self.top_p, - frequency_penalty=override.frequency_penalty or self.frequency_penalty, - presence_penalty=override.presence_penalty or self.presence_penalty, - tool_choice=override.tool_choice or self.tool_choice, - parallel_tool_calls=override.parallel_tool_calls or self.parallel_tool_calls, - truncation=override.truncation or self.truncation, - ) diff --git a/tests/src/agents/models/_openai_shared.py b/tests/src/agents/models/_openai_shared.py deleted file mode 100644 index 2e145018..00000000 --- a/tests/src/agents/models/_openai_shared.py +++ /dev/null @@ -1,34 +0,0 @@ -from __future__ import annotations - -from openai import AsyncOpenAI - -_default_openai_key: str | None = None -_default_openai_client: AsyncOpenAI | None = None -_use_responses_by_default: bool = True - - -def set_default_openai_key(key: str) -> None: - global _default_openai_key - _default_openai_key = key - - -def get_default_openai_key() -> str | None: - return _default_openai_key - - -def set_default_openai_client(client: AsyncOpenAI) -> None: - global _default_openai_client - _default_openai_client = client - - -def get_default_openai_client() -> AsyncOpenAI | None: - return _default_openai_client - - -def set_use_responses_by_default(use_responses: bool) -> None: - global _use_responses_by_default - _use_responses_by_default = use_responses - - -def get_use_responses_by_default() -> bool: - return _use_responses_by_default diff --git a/tests/src/agents/models/fake_id.py b/tests/src/agents/models/fake_id.py deleted file mode 100644 index 0565b0a7..00000000 --- a/tests/src/agents/models/fake_id.py +++ /dev/null @@ -1,5 +0,0 @@ -FAKE_RESPONSES_ID = "__fake_id__" -"""This is a placeholder ID used to fill in the `id` field in Responses API related objects. It's -useful when you're creating Responses objects from non-Responses APIs, e.g. the OpenAI Chat -Completions API or other LLM providers. -""" diff --git a/tests/src/agents/models/interface.py b/tests/src/agents/models/interface.py deleted file mode 100644 index e9a8700c..00000000 --- a/tests/src/agents/models/interface.py +++ /dev/null @@ -1,107 +0,0 @@ -from __future__ import annotations - -import abc -import enum -from collections.abc import AsyncIterator -from typing import TYPE_CHECKING - -from ..agent_output import AgentOutputSchema -from ..handoffs import Handoff -from ..items import ModelResponse, TResponseInputItem, TResponseStreamEvent -from ..tool import Tool - -if TYPE_CHECKING: - from ..model_settings import ModelSettings - - -class ModelTracing(enum.Enum): - DISABLED = 0 - """Tracing is disabled entirely.""" - - ENABLED = 1 - """Tracing is enabled, and all data is included.""" - - ENABLED_WITHOUT_DATA = 2 - """Tracing is enabled, but inputs/outputs are not included.""" - - def is_disabled(self) -> bool: - return self == ModelTracing.DISABLED - - def include_data(self) -> bool: - return self == ModelTracing.ENABLED - - -class Model(abc.ABC): - """The base interface for calling an LLM.""" - - @abc.abstractmethod - async def get_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - tracing: ModelTracing, - ) -> ModelResponse: - """Get a response from the model. - - Args: - system_instructions: The system instructions to use. - input: The input items to the model, in OpenAI Responses format. - model_settings: The model settings to use. - tools: The tools available to the model. - output_schema: The output schema to use. - handoffs: The handoffs available to the model. - tracing: Tracing configuration. - - Returns: - The full model response. - """ - pass - - @abc.abstractmethod - def stream_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - tracing: ModelTracing, - ) -> AsyncIterator[TResponseStreamEvent]: - """Stream a response from the model. - - Args: - system_instructions: The system instructions to use. - input: The input items to the model, in OpenAI Responses format. - model_settings: The model settings to use. - tools: The tools available to the model. - output_schema: The output schema to use. - handoffs: The handoffs available to the model. - tracing: Tracing configuration. - - Returns: - An iterator of response stream events, in OpenAI Responses format. - """ - pass - - -class ModelProvider(abc.ABC): - """The base interface for a model provider. - - Model provider is responsible for looking up Models by name. - """ - - @abc.abstractmethod - def get_model(self, model_name: str | None) -> Model: - """Get a model by name. - - Args: - model_name: The name of the model to get. - - Returns: - The model. - """ diff --git a/tests/src/agents/models/openai_chatcompletions.py b/tests/src/agents/models/openai_chatcompletions.py deleted file mode 100644 index a7340d05..00000000 --- a/tests/src/agents/models/openai_chatcompletions.py +++ /dev/null @@ -1,952 +0,0 @@ -from __future__ import annotations - -import dataclasses -import json -import time -from collections.abc import AsyncIterator, Iterable -from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any, Literal, cast, overload - -from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream, NotGiven -from openai.types import ChatModel -from openai.types.chat import ( - ChatCompletion, - ChatCompletionAssistantMessageParam, - ChatCompletionChunk, - ChatCompletionContentPartImageParam, - ChatCompletionContentPartParam, - ChatCompletionContentPartTextParam, - ChatCompletionDeveloperMessageParam, - ChatCompletionMessage, - ChatCompletionMessageParam, - ChatCompletionMessageToolCallParam, - ChatCompletionSystemMessageParam, - ChatCompletionToolChoiceOptionParam, - ChatCompletionToolMessageParam, - ChatCompletionUserMessageParam, -) -from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam -from openai.types.chat.completion_create_params import ResponseFormat -from openai.types.completion_usage import CompletionUsage -from openai.types.responses import ( - EasyInputMessageParam, - Response, - ResponseCompletedEvent, - ResponseContentPartAddedEvent, - ResponseContentPartDoneEvent, - ResponseCreatedEvent, - ResponseFileSearchToolCallParam, - ResponseFunctionCallArgumentsDeltaEvent, - ResponseFunctionToolCall, - ResponseFunctionToolCallParam, - ResponseInputContentParam, - ResponseInputImageParam, - ResponseInputTextParam, - ResponseOutputItem, - ResponseOutputItemAddedEvent, - ResponseOutputItemDoneEvent, - ResponseOutputMessage, - ResponseOutputMessageParam, - ResponseOutputRefusal, - ResponseOutputText, - ResponseRefusalDeltaEvent, - ResponseTextDeltaEvent, -) -from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message - -from .. import _debug -from ..agent_output import AgentOutputSchema -from ..exceptions import AgentsException, UserError -from ..handoffs import Handoff -from ..items import ModelResponse, TResponseInputItem, TResponseOutputItem, TResponseStreamEvent -from ..logger import logger -from ..tool import FunctionTool, Tool -from ..tracing import generation_span -from ..tracing.span_data import GenerationSpanData -from ..tracing.spans import Span -from ..usage import Usage -from ..version import __version__ -from .fake_id import FAKE_RESPONSES_ID -from .interface import Model, ModelTracing - -if TYPE_CHECKING: - from ..model_settings import ModelSettings - - -_USER_AGENT = f"Agents/Python {__version__}" -_HEADERS = {"User-Agent": _USER_AGENT} - - -@dataclass -class _StreamingState: - started: bool = False - text_content_index_and_output: tuple[int, ResponseOutputText] | None = None - refusal_content_index_and_output: tuple[int, ResponseOutputRefusal] | None = None - function_calls: dict[int, ResponseFunctionToolCall] = field(default_factory=dict) - - -class OpenAIChatCompletionsModel(Model): - def __init__( - self, - model: str | ChatModel, - openai_client: AsyncOpenAI, - ) -> None: - self.model = model - self._client = openai_client - - def _non_null_or_not_given(self, value: Any) -> Any: - return value if value is not None else NOT_GIVEN - - async def get_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - tracing: ModelTracing, - ) -> ModelResponse: - with generation_span( - model=str(self.model), - model_config=dataclasses.asdict(model_settings) - | {"base_url": str(self._client.base_url)}, - disabled=tracing.is_disabled(), - ) as span_generation: - response = await self._fetch_response( - system_instructions, - input, - model_settings, - tools, - output_schema, - handoffs, - span_generation, - tracing, - stream=False, - ) - - if _debug.DONT_LOG_MODEL_DATA: - logger.debug("Received model response") - else: - logger.debug( - f"LLM resp:\n{json.dumps(response.choices[0].message.model_dump(), indent=2)}\n" - ) - - usage = ( - Usage( - requests=1, - input_tokens=response.usage.prompt_tokens, - output_tokens=response.usage.completion_tokens, - total_tokens=response.usage.total_tokens, - ) - if response.usage - else Usage() - ) - if tracing.include_data(): - span_generation.span_data.output = [response.choices[0].message.model_dump()] - span_generation.span_data.usage = { - "input_tokens": usage.input_tokens, - "output_tokens": usage.output_tokens, - } - - items = _Converter.message_to_output_items(response.choices[0].message) - - return ModelResponse( - output=items, - usage=usage, - referenceable_id=None, - ) - - async def stream_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - tracing: ModelTracing, - ) -> AsyncIterator[TResponseStreamEvent]: - """ - Yields a partial message as it is generated, as well as the usage information. - """ - with generation_span( - model=str(self.model), - model_config=dataclasses.asdict(model_settings) - | {"base_url": str(self._client.base_url)}, - disabled=tracing.is_disabled(), - ) as span_generation: - response, stream = await self._fetch_response( - system_instructions, - input, - model_settings, - tools, - output_schema, - handoffs, - span_generation, - tracing, - stream=True, - ) - - usage: CompletionUsage | None = None - state = _StreamingState() - - async for chunk in stream: - if not state.started: - state.started = True - yield ResponseCreatedEvent( - response=response, - type="response.created", - ) - - # The usage is only available in the last chunk - usage = chunk.usage - - if not chunk.choices or not chunk.choices[0].delta: - continue - - delta = chunk.choices[0].delta - - # Handle text - if delta.content: - if not state.text_content_index_and_output: - # Initialize a content tracker for streaming text - state.text_content_index_and_output = ( - 0 if not state.refusal_content_index_and_output else 1, - ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - ) - # Start a new assistant message stream - assistant_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="in_progress", - ) - # Notify consumers of the start of a new output message + first content part - yield ResponseOutputItemAddedEvent( - item=assistant_item, - output_index=0, - type="response.output_item.added", - ) - yield ResponseContentPartAddedEvent( - content_index=state.text_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - type="response.content_part.added", - ) - # Emit the delta for this segment of content - yield ResponseTextDeltaEvent( - content_index=state.text_content_index_and_output[0], - delta=delta.content, - item_id=FAKE_RESPONSES_ID, - output_index=0, - type="response.output_text.delta", - ) - # Accumulate the text into the response part - state.text_content_index_and_output[1].text += delta.content - - # Handle refusals (model declines to answer) - if delta.refusal: - if not state.refusal_content_index_and_output: - # Initialize a content tracker for streaming refusal text - state.refusal_content_index_and_output = ( - 0 if not state.text_content_index_and_output else 1, - ResponseOutputRefusal(refusal="", type="refusal"), - ) - # Start a new assistant message if one doesn't exist yet (in-progress) - assistant_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="in_progress", - ) - # Notify downstream that assistant message + first content part are starting - yield ResponseOutputItemAddedEvent( - item=assistant_item, - output_index=0, - type="response.output_item.added", - ) - yield ResponseContentPartAddedEvent( - content_index=state.refusal_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - type="response.content_part.added", - ) - # Emit the delta for this segment of refusal - yield ResponseRefusalDeltaEvent( - content_index=state.refusal_content_index_and_output[0], - delta=delta.refusal, - item_id=FAKE_RESPONSES_ID, - output_index=0, - type="response.refusal.delta", - ) - # Accumulate the refusal string in the output part - state.refusal_content_index_and_output[1].refusal += delta.refusal - - # Handle tool calls - # Because we don't know the name of the function until the end of the stream, we'll - # save everything and yield events at the end - if delta.tool_calls: - for tc_delta in delta.tool_calls: - if tc_delta.index not in state.function_calls: - state.function_calls[tc_delta.index] = ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - arguments="", - name="", - type="function_call", - call_id="", - ) - tc_function = tc_delta.function - - state.function_calls[tc_delta.index].arguments += ( - tc_function.arguments if tc_function else "" - ) or "" - state.function_calls[tc_delta.index].name += ( - tc_function.name if tc_function else "" - ) or "" - state.function_calls[tc_delta.index].call_id += tc_delta.id or "" - - function_call_starting_index = 0 - if state.text_content_index_and_output: - function_call_starting_index += 1 - # Send end event for this content part - yield ResponseContentPartDoneEvent( - content_index=state.text_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=state.text_content_index_and_output[1], - type="response.content_part.done", - ) - - if state.refusal_content_index_and_output: - function_call_starting_index += 1 - # Send end event for this content part - yield ResponseContentPartDoneEvent( - content_index=state.refusal_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=state.refusal_content_index_and_output[1], - type="response.content_part.done", - ) - - # Actually send events for the function calls - for function_call in state.function_calls.values(): - # First, a ResponseOutputItemAdded for the function call - yield ResponseOutputItemAddedEvent( - item=ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=function_call.call_id, - arguments=function_call.arguments, - name=function_call.name, - type="function_call", - ), - output_index=function_call_starting_index, - type="response.output_item.added", - ) - # Then, yield the args - yield ResponseFunctionCallArgumentsDeltaEvent( - delta=function_call.arguments, - item_id=FAKE_RESPONSES_ID, - output_index=function_call_starting_index, - type="response.function_call_arguments.delta", - ) - # Finally, the ResponseOutputItemDone - yield ResponseOutputItemDoneEvent( - item=ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=function_call.call_id, - arguments=function_call.arguments, - name=function_call.name, - type="function_call", - ), - output_index=function_call_starting_index, - type="response.output_item.done", - ) - - # Finally, send the Response completed event - outputs: list[ResponseOutputItem] = [] - if state.text_content_index_and_output or state.refusal_content_index_and_output: - assistant_msg = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="completed", - ) - if state.text_content_index_and_output: - assistant_msg.content.append(state.text_content_index_and_output[1]) - if state.refusal_content_index_and_output: - assistant_msg.content.append(state.refusal_content_index_and_output[1]) - outputs.append(assistant_msg) - - # send a ResponseOutputItemDone for the assistant message - yield ResponseOutputItemDoneEvent( - item=assistant_msg, - output_index=0, - type="response.output_item.done", - ) - - for function_call in state.function_calls.values(): - outputs.append(function_call) - - final_response = response.model_copy(update={"output": outputs, "usage": usage}) - - yield ResponseCompletedEvent( - response=final_response, - type="response.completed", - ) - if tracing.include_data(): - span_generation.span_data.output = [final_response.model_dump()] - - if usage: - span_generation.span_data.usage = { - "input_tokens": usage.prompt_tokens, - "output_tokens": usage.completion_tokens, - } - - @overload - async def _fetch_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - span: Span[GenerationSpanData], - tracing: ModelTracing, - stream: Literal[True], - ) -> tuple[Response, AsyncStream[ChatCompletionChunk]]: ... - - @overload - async def _fetch_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - span: Span[GenerationSpanData], - tracing: ModelTracing, - stream: Literal[False], - ) -> ChatCompletion: ... - - async def _fetch_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - span: Span[GenerationSpanData], - tracing: ModelTracing, - stream: bool = False, - ) -> ChatCompletion | tuple[Response, AsyncStream[ChatCompletionChunk]]: - converted_messages = _Converter.items_to_messages(input) - - if system_instructions: - converted_messages.insert( - 0, - { - "content": system_instructions, - "role": "system", - }, - ) - if tracing.include_data(): - span.span_data.input = converted_messages - - parallel_tool_calls = ( - True if model_settings.parallel_tool_calls and tools and len(tools) > 0 else NOT_GIVEN - ) - tool_choice = _Converter.convert_tool_choice(model_settings.tool_choice) - response_format = _Converter.convert_response_format(output_schema) - - converted_tools = [ToolConverter.to_openai(tool) for tool in tools] if tools else [] - - for handoff in handoffs: - converted_tools.append(ToolConverter.convert_handoff_tool(handoff)) - - if _debug.DONT_LOG_MODEL_DATA: - logger.debug("Calling LLM") - else: - logger.debug( - f"{json.dumps(converted_messages, indent=2)}\n" - f"Tools:\n{json.dumps(converted_tools, indent=2)}\n" - f"Stream: {stream}\n" - f"Tool choice: {tool_choice}\n" - f"Response format: {response_format}\n" - ) - - ret = await self._get_client().chat.completions.create( - model=self.model, - messages=converted_messages, - tools=converted_tools or NOT_GIVEN, - temperature=self._non_null_or_not_given(model_settings.temperature), - top_p=self._non_null_or_not_given(model_settings.top_p), - frequency_penalty=self._non_null_or_not_given(model_settings.frequency_penalty), - presence_penalty=self._non_null_or_not_given(model_settings.presence_penalty), - tool_choice=tool_choice, - response_format=response_format, - parallel_tool_calls=parallel_tool_calls, - stream=stream, - stream_options={"include_usage": True} if stream else NOT_GIVEN, - extra_headers=_HEADERS, - ) - - if isinstance(ret, ChatCompletion): - return ret - - response = Response( - id=FAKE_RESPONSES_ID, - created_at=time.time(), - model=self.model, - object="response", - output=[], - tool_choice=cast(Literal["auto", "required", "none"], tool_choice) - if tool_choice != NOT_GIVEN - else "auto", - top_p=model_settings.top_p, - temperature=model_settings.temperature, - tools=[], - parallel_tool_calls=parallel_tool_calls or False, - ) - return response, ret - - def _get_client(self) -> AsyncOpenAI: - if self._client is None: - self._client = AsyncOpenAI() - return self._client - - -class _Converter: - @classmethod - def convert_tool_choice( - cls, tool_choice: Literal["auto", "required", "none"] | str | None - ) -> ChatCompletionToolChoiceOptionParam | NotGiven: - if tool_choice is None: - return NOT_GIVEN - elif tool_choice == "auto": - return "auto" - elif tool_choice == "required": - return "required" - elif tool_choice == "none": - return "none" - else: - return { - "type": "function", - "function": { - "name": tool_choice, - }, - } - - @classmethod - def convert_response_format( - cls, final_output_schema: AgentOutputSchema | None - ) -> ResponseFormat | NotGiven: - if not final_output_schema or final_output_schema.is_plain_text(): - return NOT_GIVEN - - return { - "type": "json_schema", - "json_schema": { - "name": "final_output", - "strict": final_output_schema.strict_json_schema, - "schema": final_output_schema.json_schema(), - }, - } - - @classmethod - def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]: - items: list[TResponseOutputItem] = [] - - message_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="completed", - ) - if message.content: - message_item.content.append( - ResponseOutputText(text=message.content, type="output_text", annotations=[]) - ) - if message.refusal: - message_item.content.append( - ResponseOutputRefusal(refusal=message.refusal, type="refusal") - ) - if message.audio: - raise AgentsException("Audio is not currently supported") - - if message_item.content: - items.append(message_item) - - if message.tool_calls: - for tool_call in message.tool_calls: - items.append( - ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=tool_call.id, - arguments=tool_call.function.arguments, - name=tool_call.function.name, - type="function_call", - ) - ) - - return items - - @classmethod - def maybe_easy_input_message(cls, item: Any) -> EasyInputMessageParam | None: - if not isinstance(item, dict): - return None - - keys = item.keys() - # EasyInputMessageParam only has these two keys - if keys != {"content", "role"}: - return None - - role = item.get("role", None) - if role not in ("user", "assistant", "system", "developer"): - return None - - if "content" not in item: - return None - - return cast(EasyInputMessageParam, item) - - @classmethod - def maybe_input_message(cls, item: Any) -> Message | None: - if ( - isinstance(item, dict) - and item.get("type") == "message" - and item.get("role") - in ( - "user", - "system", - "developer", - ) - ): - return cast(Message, item) - - return None - - @classmethod - def maybe_file_search_call(cls, item: Any) -> ResponseFileSearchToolCallParam | None: - if isinstance(item, dict) and item.get("type") == "file_search_call": - return cast(ResponseFileSearchToolCallParam, item) - return None - - @classmethod - def maybe_function_tool_call(cls, item: Any) -> ResponseFunctionToolCallParam | None: - if isinstance(item, dict) and item.get("type") == "function_call": - return cast(ResponseFunctionToolCallParam, item) - return None - - @classmethod - def maybe_function_tool_call_output( - cls, - item: Any, - ) -> FunctionCallOutput | None: - if isinstance(item, dict) and item.get("type") == "function_call_output": - return cast(FunctionCallOutput, item) - return None - - @classmethod - def maybe_item_reference(cls, item: Any) -> ItemReference | None: - if isinstance(item, dict) and item.get("type") == "item_reference": - return cast(ItemReference, item) - return None - - @classmethod - def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam | None: - # ResponseOutputMessage is only used for messages with role assistant - if ( - isinstance(item, dict) - and item.get("type") == "message" - and item.get("role") == "assistant" - ): - return cast(ResponseOutputMessageParam, item) - return None - - @classmethod - def extract_text_content( - cls, content: str | Iterable[ResponseInputContentParam] - ) -> str | list[ChatCompletionContentPartTextParam]: - all_content = cls.extract_all_content(content) - if isinstance(all_content, str): - return all_content - out: list[ChatCompletionContentPartTextParam] = [] - for c in all_content: - if c.get("type") == "text": - out.append(cast(ChatCompletionContentPartTextParam, c)) - return out - - @classmethod - def extract_all_content( - cls, content: str | Iterable[ResponseInputContentParam] - ) -> str | list[ChatCompletionContentPartParam]: - if isinstance(content, str): - return content - out: list[ChatCompletionContentPartParam] = [] - - for c in content: - if isinstance(c, dict) and c.get("type") == "input_text": - casted_text_param = cast(ResponseInputTextParam, c) - out.append( - ChatCompletionContentPartTextParam( - type="text", - text=casted_text_param["text"], - ) - ) - elif isinstance(c, dict) and c.get("type") == "input_image": - casted_image_param = cast(ResponseInputImageParam, c) - if "image_url" not in casted_image_param or not casted_image_param["image_url"]: - raise UserError( - f"Only image URLs are supported for input_image {casted_image_param}" - ) - out.append( - ChatCompletionContentPartImageParam( - type="image_url", - image_url={ - "url": casted_image_param["image_url"], - "detail": casted_image_param["detail"], - }, - ) - ) - elif isinstance(c, dict) and c.get("type") == "input_file": - raise UserError(f"File uploads are not supported for chat completions {c}") - else: - raise UserError(f"Unknonw content: {c}") - return out - - @classmethod - def items_to_messages( - cls, - items: str | Iterable[TResponseInputItem], - ) -> list[ChatCompletionMessageParam]: - """ - Convert a sequence of 'Item' objects into a list of ChatCompletionMessageParam. - - Rules: - - EasyInputMessage or InputMessage (role=user) => ChatCompletionUserMessageParam - - EasyInputMessage or InputMessage (role=system) => ChatCompletionSystemMessageParam - - EasyInputMessage or InputMessage (role=developer) => ChatCompletionDeveloperMessageParam - - InputMessage (role=assistant) => Start or flush a ChatCompletionAssistantMessageParam - - response_output_message => Also produces/flushes a ChatCompletionAssistantMessageParam - - tool calls get attached to the *current* assistant message, or create one if none. - - tool outputs => ChatCompletionToolMessageParam - """ - - if isinstance(items, str): - return [ - ChatCompletionUserMessageParam( - role="user", - content=items, - ) - ] - - result: list[ChatCompletionMessageParam] = [] - current_assistant_msg: ChatCompletionAssistantMessageParam | None = None - - def flush_assistant_message() -> None: - nonlocal current_assistant_msg - if current_assistant_msg is not None: - # The API doesn't support empty arrays for tool_calls - if not current_assistant_msg.get("tool_calls"): - del current_assistant_msg["tool_calls"] - result.append(current_assistant_msg) - current_assistant_msg = None - - def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: - nonlocal current_assistant_msg - if current_assistant_msg is None: - current_assistant_msg = ChatCompletionAssistantMessageParam(role="assistant") - current_assistant_msg["tool_calls"] = [] - return current_assistant_msg - - for item in items: - # 1) Check easy input message - if easy_msg := cls.maybe_easy_input_message(item): - role = easy_msg["role"] - content = easy_msg["content"] - - if role == "user": - flush_assistant_message() - msg_user: ChatCompletionUserMessageParam = { - "role": "user", - "content": cls.extract_all_content(content), - } - result.append(msg_user) - elif role == "system": - flush_assistant_message() - msg_system: ChatCompletionSystemMessageParam = { - "role": "system", - "content": cls.extract_text_content(content), - } - result.append(msg_system) - elif role == "developer": - flush_assistant_message() - msg_developer: ChatCompletionDeveloperMessageParam = { - "role": "developer", - "content": cls.extract_text_content(content), - } - result.append(msg_developer) - else: - raise UserError(f"Unexpected role in easy_input_message: {role}") - - # 2) Check input message - elif in_msg := cls.maybe_input_message(item): - role = in_msg["role"] - content = in_msg["content"] - flush_assistant_message() - - if role == "user": - msg_user = { - "role": "user", - "content": cls.extract_all_content(content), - } - result.append(msg_user) - elif role == "system": - msg_system = { - "role": "system", - "content": cls.extract_text_content(content), - } - result.append(msg_system) - elif role == "developer": - msg_developer = { - "role": "developer", - "content": cls.extract_text_content(content), - } - result.append(msg_developer) - else: - raise UserError(f"Unexpected role in input_message: {role}") - - # 3) response output message => assistant - elif resp_msg := cls.maybe_response_output_message(item): - flush_assistant_message() - new_asst = ChatCompletionAssistantMessageParam(role="assistant") - contents = resp_msg["content"] - - text_segments = [] - for c in contents: - if c["type"] == "output_text": - text_segments.append(c["text"]) - elif c["type"] == "refusal": - new_asst["refusal"] = c["refusal"] - elif c["type"] == "output_audio": - # Can't handle this, b/c chat completions expects an ID which we dont have - raise UserError( - f"Only audio IDs are supported for chat completions, but got: {c}" - ) - else: - raise UserError(f"Unknown content type in ResponseOutputMessage: {c}") - - if text_segments: - combined = "\n".join(text_segments) - new_asst["content"] = combined - - new_asst["tool_calls"] = [] - current_assistant_msg = new_asst - - # 4) function/file-search calls => attach to assistant - elif file_search := cls.maybe_file_search_call(item): - asst = ensure_assistant_message() - tool_calls = list(asst.get("tool_calls", [])) - new_tool_call = ChatCompletionMessageToolCallParam( - id=file_search["id"], - type="function", - function={ - "name": "file_search_call", - "arguments": json.dumps( - { - "queries": file_search.get("queries", []), - "status": file_search.get("status"), - } - ), - }, - ) - tool_calls.append(new_tool_call) - asst["tool_calls"] = tool_calls - - elif func_call := cls.maybe_function_tool_call(item): - asst = ensure_assistant_message() - tool_calls = list(asst.get("tool_calls", [])) - new_tool_call = ChatCompletionMessageToolCallParam( - id=func_call["call_id"], - type="function", - function={ - "name": func_call["name"], - "arguments": func_call["arguments"], - }, - ) - tool_calls.append(new_tool_call) - asst["tool_calls"] = tool_calls - # 5) function call output => tool message - elif func_output := cls.maybe_function_tool_call_output(item): - flush_assistant_message() - msg: ChatCompletionToolMessageParam = { - "role": "tool", - "tool_call_id": func_output["call_id"], - "content": func_output["output"], - } - result.append(msg) - - # 6) item reference => handle or raise - elif item_ref := cls.maybe_item_reference(item): - raise UserError( - f"Encountered an item_reference, which is not supported: {item_ref}" - ) - - # 7) If we haven't recognized it => fail or ignore - else: - raise UserError(f"Unhandled item type or structure: {item}") - - flush_assistant_message() - return result - - -class ToolConverter: - @classmethod - def to_openai(cls, tool: Tool) -> ChatCompletionToolParam: - if isinstance(tool, FunctionTool): - return { - "type": "function", - "function": { - "name": tool.name, - "description": tool.description or "", - "parameters": tool.params_json_schema, - }, - } - - raise UserError( - f"Hosted tools are not supported with the ChatCompletions API. FGot tool type: " - f"{type(tool)}, tool: {tool}" - ) - - @classmethod - def convert_handoff_tool(cls, handoff: Handoff[Any]) -> ChatCompletionToolParam: - return { - "type": "function", - "function": { - "name": handoff.tool_name, - "description": handoff.tool_description, - "parameters": handoff.input_json_schema, - }, - } diff --git a/tests/src/agents/models/openai_provider.py b/tests/src/agents/models/openai_provider.py deleted file mode 100644 index 51946638..00000000 --- a/tests/src/agents/models/openai_provider.py +++ /dev/null @@ -1,65 +0,0 @@ -from __future__ import annotations - -import httpx -from openai import AsyncOpenAI, DefaultAsyncHttpxClient - -from . import _openai_shared -from .interface import Model, ModelProvider -from .openai_chatcompletions import OpenAIChatCompletionsModel -from .openai_responses import OpenAIResponsesModel - -DEFAULT_MODEL: str = "gpt-4o" - - -_http_client: httpx.AsyncClient | None = None - - -# If we create a new httpx client for each request, that would mean no sharing of connection pools, -# which would mean worse latency and resource usage. So, we share the client across requests. -def shared_http_client() -> httpx.AsyncClient: - global _http_client - if _http_client is None: - _http_client = DefaultAsyncHttpxClient() - return _http_client - - -class OpenAIProvider(ModelProvider): - def __init__( - self, - *, - api_key: str | None = None, - base_url: str | None = None, - openai_client: AsyncOpenAI | None = None, - organization: str | None = None, - project: str | None = None, - use_responses: bool | None = None, - ) -> None: - if openai_client is not None: - assert api_key is None and base_url is None, ( - "Don't provide api_key or base_url if you provide openai_client" - ) - self._client = openai_client - else: - self._client = _openai_shared.get_default_openai_client() or AsyncOpenAI( - api_key=api_key or _openai_shared.get_default_openai_key(), - base_url=base_url, - organization=organization, - project=project, - http_client=shared_http_client(), - ) - - self._is_openai_model = self._client.base_url.host.startswith("api.openai.com") - if use_responses is not None: - self._use_responses = use_responses - else: - self._use_responses = _openai_shared.get_use_responses_by_default() - - def get_model(self, model_name: str | None) -> Model: - if model_name is None: - model_name = DEFAULT_MODEL - - return ( - OpenAIResponsesModel(model=model_name, openai_client=self._client) - if self._use_responses - else OpenAIChatCompletionsModel(model=model_name, openai_client=self._client) - ) diff --git a/tests/src/agents/models/openai_responses.py b/tests/src/agents/models/openai_responses.py deleted file mode 100644 index a10d7b98..00000000 --- a/tests/src/agents/models/openai_responses.py +++ /dev/null @@ -1,384 +0,0 @@ -from __future__ import annotations - -import json -from collections.abc import AsyncIterator -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Literal, overload - -from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream, NotGiven -from openai.types import ChatModel -from openai.types.responses import ( - Response, - ResponseCompletedEvent, - ResponseStreamEvent, - ResponseTextConfigParam, - ToolParam, - WebSearchToolParam, - response_create_params, -) - -from .. import _debug -from ..agent_output import AgentOutputSchema -from ..exceptions import UserError -from ..handoffs import Handoff -from ..items import ItemHelpers, ModelResponse, TResponseInputItem -from ..logger import logger -from ..tool import ComputerTool, FileSearchTool, FunctionTool, Tool, WebSearchTool -from ..tracing import SpanError, response_span -from ..usage import Usage -from ..version import __version__ -from .interface import Model, ModelTracing - -if TYPE_CHECKING: - from ..model_settings import ModelSettings - - -_USER_AGENT = f"Agents/Python {__version__}" -_HEADERS = {"User-Agent": _USER_AGENT} - -# From the Responses API -IncludeLiteral = Literal[ - "file_search_call.results", - "message.input_image.image_url", - "computer_call_output.output.image_url", -] - - -class OpenAIResponsesModel(Model): - """ - Implementation of `Model` that uses the OpenAI Responses API. - """ - - def __init__( - self, - model: str | ChatModel, - openai_client: AsyncOpenAI, - ) -> None: - self.model = model - self._client = openai_client - - def _non_null_or_not_given(self, value: Any) -> Any: - return value if value is not None else NOT_GIVEN - - async def get_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - tracing: ModelTracing, - ) -> ModelResponse: - with response_span(disabled=tracing.is_disabled()) as span_response: - try: - response = await self._fetch_response( - system_instructions, - input, - model_settings, - tools, - output_schema, - handoffs, - stream=False, - ) - - if _debug.DONT_LOG_MODEL_DATA: - logger.debug("LLM responsed") - else: - logger.debug( - "LLM resp:\n" - f"{json.dumps([x.model_dump() for x in response.output], indent=2)}\n" - ) - - usage = ( - Usage( - requests=1, - input_tokens=response.usage.input_tokens, - output_tokens=response.usage.output_tokens, - total_tokens=response.usage.total_tokens, - ) - if response.usage - else Usage() - ) - - if tracing.include_data(): - span_response.span_data.response = response - span_response.span_data.input = input - except Exception as e: - span_response.set_error( - SpanError( - message="Error getting response", - data={ - "error": str(e) if tracing.include_data() else e.__class__.__name__, - }, - ) - ) - logger.error(f"Error getting response: {e}") - raise - - return ModelResponse( - output=response.output, - usage=usage, - referenceable_id=response.id, - ) - - async def stream_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - tracing: ModelTracing, - ) -> AsyncIterator[ResponseStreamEvent]: - """ - Yields a partial message as it is generated, as well as the usage information. - """ - with response_span(disabled=tracing.is_disabled()) as span_response: - try: - stream = await self._fetch_response( - system_instructions, - input, - model_settings, - tools, - output_schema, - handoffs, - stream=True, - ) - - final_response: Response | None = None - - async for chunk in stream: - if isinstance(chunk, ResponseCompletedEvent): - final_response = chunk.response - yield chunk - - if final_response and tracing.include_data(): - span_response.span_data.response = final_response - span_response.span_data.input = input - - except Exception as e: - span_response.set_error( - SpanError( - message="Error streaming response", - data={ - "error": str(e) if tracing.include_data() else e.__class__.__name__, - }, - ) - ) - logger.error(f"Error streaming response: {e}") - raise - - @overload - async def _fetch_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - stream: Literal[True], - ) -> AsyncStream[ResponseStreamEvent]: ... - - @overload - async def _fetch_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - stream: Literal[False], - ) -> Response: ... - - async def _fetch_response( - self, - system_instructions: str | None, - input: str | list[TResponseInputItem], - model_settings: ModelSettings, - tools: list[Tool], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - stream: Literal[True] | Literal[False] = False, - ) -> Response | AsyncStream[ResponseStreamEvent]: - list_input = ItemHelpers.input_to_new_input_list(input) - - parallel_tool_calls = ( - True if model_settings.parallel_tool_calls and tools and len(tools) > 0 else NOT_GIVEN - ) - - tool_choice = Converter.convert_tool_choice(model_settings.tool_choice) - converted_tools = Converter.convert_tools(tools, handoffs) - response_format = Converter.get_response_format(output_schema) - - if _debug.DONT_LOG_MODEL_DATA: - logger.debug("Calling LLM") - else: - logger.debug( - f"Calling LLM {self.model} with input:\n" - f"{json.dumps(list_input, indent=2)}\n" - f"Tools:\n{json.dumps(converted_tools.tools, indent=2)}\n" - f"Stream: {stream}\n" - f"Tool choice: {tool_choice}\n" - f"Response format: {response_format}\n" - ) - - return await self._client.responses.create( - instructions=self._non_null_or_not_given(system_instructions), - model=self.model, - input=list_input, - include=converted_tools.includes, - tools=converted_tools.tools, - temperature=self._non_null_or_not_given(model_settings.temperature), - top_p=self._non_null_or_not_given(model_settings.top_p), - truncation=self._non_null_or_not_given(model_settings.truncation), - tool_choice=tool_choice, - parallel_tool_calls=parallel_tool_calls, - stream=stream, - extra_headers=_HEADERS, - text=response_format, - ) - - def _get_client(self) -> AsyncOpenAI: - if self._client is None: - self._client = AsyncOpenAI() - return self._client - - -@dataclass -class ConvertedTools: - tools: list[ToolParam] - includes: list[IncludeLiteral] - - -class Converter: - @classmethod - def convert_tool_choice( - cls, tool_choice: Literal["auto", "required", "none"] | str | None - ) -> response_create_params.ToolChoice | NotGiven: - if tool_choice is None: - return NOT_GIVEN - elif tool_choice == "required": - return "required" - elif tool_choice == "auto": - return "auto" - elif tool_choice == "none": - return "none" - elif tool_choice == "file_search": - return { - "type": "file_search", - } - elif tool_choice == "web_search_preview": - return { - "type": "web_search_preview", - } - elif tool_choice == "computer_use_preview": - return { - "type": "computer_use_preview", - } - else: - return { - "type": "function", - "name": tool_choice, - } - - @classmethod - def get_response_format( - cls, output_schema: AgentOutputSchema | None - ) -> ResponseTextConfigParam | NotGiven: - if output_schema is None or output_schema.is_plain_text(): - return NOT_GIVEN - else: - return { - "format": { - "type": "json_schema", - "name": "final_output", - "schema": output_schema.json_schema(), - "strict": output_schema.strict_json_schema, - } - } - - @classmethod - def convert_tools( - cls, - tools: list[Tool], - handoffs: list[Handoff[Any]], - ) -> ConvertedTools: - converted_tools: list[ToolParam] = [] - includes: list[IncludeLiteral] = [] - - computer_tools = [tool for tool in tools if isinstance(tool, ComputerTool)] - if len(computer_tools) > 1: - raise UserError(f"You can only provide one computer tool. Got {len(computer_tools)}") - - for tool in tools: - converted_tool, include = cls._convert_tool(tool) - converted_tools.append(converted_tool) - if include: - includes.append(include) - - for handoff in handoffs: - converted_tools.append(cls._convert_handoff_tool(handoff)) - - return ConvertedTools(tools=converted_tools, includes=includes) - - @classmethod - def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, IncludeLiteral | None]: - """Returns converted tool and includes""" - - if isinstance(tool, FunctionTool): - converted_tool: ToolParam = { - "name": tool.name, - "parameters": tool.params_json_schema, - "strict": tool.strict_json_schema, - "type": "function", - "description": tool.description, - } - includes: IncludeLiteral | None = None - elif isinstance(tool, WebSearchTool): - ws: WebSearchToolParam = { - "type": "web_search_preview", - "user_location": tool.user_location, - "search_context_size": tool.search_context_size, - } - converted_tool = ws - includes = None - elif isinstance(tool, FileSearchTool): - converted_tool = { - "type": "file_search", - "vector_store_ids": tool.vector_store_ids, - } - if tool.max_num_results: - converted_tool["max_num_results"] = tool.max_num_results - if tool.ranking_options: - converted_tool["ranking_options"] = tool.ranking_options - if tool.filters: - converted_tool["filters"] = tool.filters - - includes = "file_search_call.results" if tool.include_search_results else None - elif isinstance(tool, ComputerTool): - converted_tool = { - "type": "computer-preview", - "environment": tool.computer.environment, - "display_width": tool.computer.dimensions[0], - "display_height": tool.computer.dimensions[1], - } - includes = None - - else: - raise UserError(f"Unknown tool type: {type(tool)}, tool") - - return converted_tool, includes - - @classmethod - def _convert_handoff_tool(cls, handoff: Handoff) -> ToolParam: - return { - "name": handoff.tool_name, - "parameters": handoff.input_json_schema, - "strict": handoff.strict_json_schema, - "type": "function", - "description": handoff.tool_description, - } diff --git a/tests/src/agents/result.py b/tests/src/agents/result.py deleted file mode 100644 index 56838273..00000000 --- a/tests/src/agents/result.py +++ /dev/null @@ -1,220 +0,0 @@ -from __future__ import annotations - -import abc -import asyncio -from collections.abc import AsyncIterator -from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any, cast - -from typing_extensions import TypeVar - -from ._run_impl import QueueCompleteSentinel -from .agent import Agent -from .agent_output import AgentOutputSchema -from .exceptions import InputGuardrailTripwireTriggered, MaxTurnsExceeded -from .guardrail import InputGuardrailResult, OutputGuardrailResult -from .items import ItemHelpers, ModelResponse, RunItem, TResponseInputItem -from .logger import logger -from .stream_events import StreamEvent -from .tracing import Trace - -if TYPE_CHECKING: - from ._run_impl import QueueCompleteSentinel - from .agent import Agent - -T = TypeVar("T") - - -@dataclass -class RunResultBase(abc.ABC): - input: str | list[TResponseInputItem] - """The original input items i.e. the items before run() was called. This may be a mutated - version of the input, if there are handoff input filters that mutate the input. - """ - - new_items: list[RunItem] - """The new items generated during the agent run. These include things like new messages, tool - calls and their outputs, etc. - """ - - raw_responses: list[ModelResponse] - """The raw LLM responses generated by the model during the agent run.""" - - final_output: Any - """The output of the last agent.""" - - input_guardrail_results: list[InputGuardrailResult] - """Guardrail results for the input messages.""" - - output_guardrail_results: list[OutputGuardrailResult] - """Guardrail results for the final output of the agent.""" - - @property - @abc.abstractmethod - def last_agent(self) -> Agent[Any]: - """The last agent that was run.""" - - def final_output_as(self, cls: type[T], raise_if_incorrect_type: bool = False) -> T: - """A convenience method to cast the final output to a specific type. By default, the cast - is only for the typechecker. If you set `raise_if_incorrect_type` to True, we'll raise a - TypeError if the final output is not of the given type. - - Args: - cls: The type to cast the final output to. - raise_if_incorrect_type: If True, we'll raise a TypeError if the final output is not of - the given type. - - Returns: - The final output casted to the given type. - """ - if raise_if_incorrect_type and not isinstance(self.final_output, cls): - raise TypeError(f"Final output is not of type {cls.__name__}") - - return cast(T, self.final_output) - - def to_input_list(self) -> list[TResponseInputItem]: - """Creates a new input list, merging the original input with all the new items generated.""" - original_items: list[TResponseInputItem] = ItemHelpers.input_to_new_input_list(self.input) - new_items = [item.to_input_item() for item in self.new_items] - - return original_items + new_items - - -@dataclass -class RunResult(RunResultBase): - _last_agent: Agent[Any] - - @property - def last_agent(self) -> Agent[Any]: - """The last agent that was run.""" - return self._last_agent - - -@dataclass -class RunResultStreaming(RunResultBase): - """The result of an agent run in streaming mode. You can use the `stream_events` method to - receive semantic events as they are generated. - - The streaming method will raise: - - A MaxTurnsExceeded exception if the agent exceeds the max_turns limit. - - A GuardrailTripwireTriggered exception if a guardrail is tripped. - """ - - current_agent: Agent[Any] - """The current agent that is running.""" - - current_turn: int - """The current turn number.""" - - max_turns: int - """The maximum number of turns the agent can run for.""" - - final_output: Any - """The final output of the agent. This is None until the agent has finished running.""" - - _current_agent_output_schema: AgentOutputSchema | None = field(repr=False) - - _trace: Trace | None = field(repr=False) - - is_complete: bool = False - """Whether the agent has finished running.""" - - # Queues that the background run_loop writes to - _event_queue: asyncio.Queue[StreamEvent | QueueCompleteSentinel] = field( - default_factory=asyncio.Queue, repr=False - ) - _input_guardrail_queue: asyncio.Queue[InputGuardrailResult] = field( - default_factory=asyncio.Queue, repr=False - ) - - # Store the asyncio tasks that we're waiting on - _run_impl_task: asyncio.Task[Any] | None = field(default=None, repr=False) - _input_guardrails_task: asyncio.Task[Any] | None = field(default=None, repr=False) - _output_guardrails_task: asyncio.Task[Any] | None = field(default=None, repr=False) - _stored_exception: Exception | None = field(default=None, repr=False) - - @property - def last_agent(self) -> Agent[Any]: - """The last agent that was run. Updates as the agent run progresses, so the true last agent - is only available after the agent run is complete. - """ - return self.current_agent - - async def stream_events(self) -> AsyncIterator[StreamEvent]: - """Stream deltas for new items as they are generated. We're using the types from the - OpenAI Responses API, so these are semantic events: each event has a `type` field that - describes the type of the event, along with the data for that event. - - This will raise: - - A MaxTurnsExceeded exception if the agent exceeds the max_turns limit. - - A GuardrailTripwireTriggered exception if a guardrail is tripped. - """ - while True: - self._check_errors() - if self._stored_exception: - logger.debug("Breaking due to stored exception") - self.is_complete = True - break - - if self.is_complete and self._event_queue.empty(): - break - - try: - item = await self._event_queue.get() - except asyncio.CancelledError: - break - - if isinstance(item, QueueCompleteSentinel): - self._event_queue.task_done() - # Check for errors, in case the queue was completed due to an exception - self._check_errors() - break - - yield item - self._event_queue.task_done() - - if self._trace: - self._trace.finish(reset_current=True) - - self._cleanup_tasks() - - if self._stored_exception: - raise self._stored_exception - - def _check_errors(self): - if self.current_turn > self.max_turns: - self._stored_exception = MaxTurnsExceeded(f"Max turns ({self.max_turns}) exceeded") - - # Fetch all the completed guardrail results from the queue and raise if needed - while not self._input_guardrail_queue.empty(): - guardrail_result = self._input_guardrail_queue.get_nowait() - if guardrail_result.output.tripwire_triggered: - self._stored_exception = InputGuardrailTripwireTriggered(guardrail_result) - - # Check the tasks for any exceptions - if self._run_impl_task and self._run_impl_task.done(): - exc = self._run_impl_task.exception() - if exc and isinstance(exc, Exception): - self._stored_exception = exc - - if self._input_guardrails_task and self._input_guardrails_task.done(): - exc = self._input_guardrails_task.exception() - if exc and isinstance(exc, Exception): - self._stored_exception = exc - - if self._output_guardrails_task and self._output_guardrails_task.done(): - exc = self._output_guardrails_task.exception() - if exc and isinstance(exc, Exception): - self._stored_exception = exc - - def _cleanup_tasks(self): - if self._run_impl_task and not self._run_impl_task.done(): - self._run_impl_task.cancel() - - if self._input_guardrails_task and not self._input_guardrails_task.done(): - self._input_guardrails_task.cancel() - - if self._output_guardrails_task and not self._output_guardrails_task.done(): - self._output_guardrails_task.cancel() - self._output_guardrails_task.cancel() - self._output_guardrails_task.cancel() diff --git a/tests/src/agents/run.py b/tests/src/agents/run.py deleted file mode 100644 index dfff7e38..00000000 --- a/tests/src/agents/run.py +++ /dev/null @@ -1,904 +0,0 @@ -from __future__ import annotations - -import asyncio -import copy -from dataclasses import dataclass, field -from typing import Any, cast - -from openai.types.responses import ResponseCompletedEvent - -from . import Model, _utils -from ._run_impl import ( - NextStepFinalOutput, - NextStepHandoff, - NextStepRunAgain, - QueueCompleteSentinel, - RunImpl, - SingleStepResult, - TraceCtxManager, - get_model_tracing_impl, -) -from .agent import Agent -from .agent_output import AgentOutputSchema -from .exceptions import ( - AgentsException, - InputGuardrailTripwireTriggered, - MaxTurnsExceeded, - ModelBehaviorError, - OutputGuardrailTripwireTriggered, -) -from .guardrail import InputGuardrail, InputGuardrailResult, OutputGuardrail, OutputGuardrailResult -from .handoffs import Handoff, HandoffInputFilter, handoff -from .items import ItemHelpers, ModelResponse, RunItem, TResponseInputItem -from .lifecycle import RunHooks -from .logger import logger -from .model_settings import ModelSettings -from .models.interface import ModelProvider -from .models.openai_provider import OpenAIProvider -from .result import RunResult, RunResultStreaming -from .run_context import RunContextWrapper, TContext -from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent -from .tracing import Span, SpanError, agent_span, get_current_trace, trace -from .tracing.span_data import AgentSpanData -from .usage import Usage - -DEFAULT_MAX_TURNS = 10 - - -@dataclass -class RunConfig: - """Configures settings for the entire agent run.""" - - model: str | Model | None = None - """The model to use for the entire agent run. If set, will override the model set on every - agent. The model_provider passed in below must be able to resolve this model name. - """ - - model_provider: ModelProvider = field(default_factory=OpenAIProvider) - """The model provider to use when looking up string model names. Defaults to OpenAI.""" - - model_settings: ModelSettings | None = None - """Configure global model settings. Any non-null values will override the agent-specific model - settings. - """ - - handoff_input_filter: HandoffInputFilter | None = None - """A global input filter to apply to all handoffs. If `Handoff.input_filter` is set, then that - will take precedence. The input filter allows you to edit the inputs that are sent to the new - agent. See the documentation in `Handoff.input_filter` for more details. - """ - - input_guardrails: list[InputGuardrail[Any]] | None = None - """A list of input guardrails to run on the initial run input.""" - - output_guardrails: list[OutputGuardrail[Any]] | None = None - """A list of output guardrails to run on the final output of the run.""" - - tracing_disabled: bool = False - """Whether tracing is disabled for the agent run. If disabled, we will not trace the agent run. - """ - - trace_include_sensitive_data: bool = True - """Whether we include potentially sensitive data (for example: inputs/outputs of tool calls or - LLM generations) in traces. If False, we'll still create spans for these events, but the - sensitive data will not be included. - """ - - workflow_name: str = "Agent workflow" - """The name of the run, used for tracing. Should be a logical name for the run, like - "Code generation workflow" or "Customer support agent". - """ - - trace_id: str | None = None - """A custom trace ID to use for tracing. If not provided, we will generate a new trace ID.""" - - group_id: str | None = None - """ - A grouping identifier to use for tracing, to link multiple traces from the same conversation - or process. For example, you might use a chat thread ID. - """ - - trace_metadata: dict[str, Any] | None = None - """ - An optional dictionary of additional metadata to include with the trace. - """ - - -class Runner: - @classmethod - async def run( - cls, - starting_agent: Agent[TContext], - input: str | list[TResponseInputItem], - *, - context: TContext | None = None, - max_turns: int = DEFAULT_MAX_TURNS, - hooks: RunHooks[TContext] | None = None, - run_config: RunConfig | None = None, - ) -> RunResult: - """Run a workflow starting at the given agent. The agent will run in a loop until a final - output is generated. The loop runs like so: - 1. The agent is invoked with the given input. - 2. If there is a final output (i.e. the agent produces something of type - `agent.output_type`, the loop terminates. - 3. If there's a handoff, we run the loop again, with the new agent. - 4. Else, we run tool calls (if any), and re-run the loop. - - In two cases, the agent may raise an exception: - 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. - 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised. - - Note that only the first agent's input guardrails are run. - - Args: - starting_agent: The starting agent to run. - input: The initial input to the agent. You can pass a single string for a user message, - or a list of input items. - context: The context to run the agent with. - max_turns: The maximum number of turns to run the agent for. A turn is defined as one - AI invocation (including any tool calls that might occur). - hooks: An object that receives callbacks on various lifecycle events. - run_config: Global settings for the entire agent run. - - Returns: - A run result containing all the inputs, guardrail results and the output of the last - agent. Agents may perform handoffs, so we don't know the specific type of the output. - """ - if hooks is None: - hooks = RunHooks[Any]() - if run_config is None: - run_config = RunConfig() - - with TraceCtxManager( - workflow_name=run_config.workflow_name, - trace_id=run_config.trace_id, - group_id=run_config.group_id, - metadata=run_config.trace_metadata, - disabled=run_config.tracing_disabled, - ): - current_turn = 0 - original_input: str | list[TResponseInputItem] = copy.deepcopy(input) - generated_items: list[RunItem] = [] - model_responses: list[ModelResponse] = [] - - context_wrapper: RunContextWrapper[TContext] = RunContextWrapper( - context=context, # type: ignore - ) - - input_guardrail_results: list[InputGuardrailResult] = [] - - current_span: Span[AgentSpanData] | None = None - current_agent = starting_agent - should_run_agent_start_hooks = True - - try: - while True: - # Start an agent span if we don't have one. This span is ended if the current - # agent changes, or if the agent loop ends. - if current_span is None: - handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)] - tool_names = [t.name for t in current_agent.tools] - if output_schema := cls._get_output_schema(current_agent): - output_type_name = output_schema.output_type_name() - else: - output_type_name = "str" - - current_span = agent_span( - name=current_agent.name, - handoffs=handoff_names, - tools=tool_names, - output_type=output_type_name, - ) - current_span.start(mark_as_current=True) - - current_turn += 1 - if current_turn > max_turns: - _utils.attach_error_to_span( - current_span, - SpanError( - message="Max turns exceeded", - data={"max_turns": max_turns}, - ), - ) - raise MaxTurnsExceeded(f"Max turns ({max_turns}) exceeded") - - logger.debug( - f"Running agent {current_agent.name} (turn {current_turn})", - ) - - if current_turn == 1: - input_guardrail_results, turn_result = await asyncio.gather( - cls._run_input_guardrails( - starting_agent, - starting_agent.input_guardrails - + (run_config.input_guardrails or []), - copy.deepcopy(input), - context_wrapper, - ), - cls._run_single_turn( - agent=current_agent, - original_input=original_input, - generated_items=generated_items, - hooks=hooks, - context_wrapper=context_wrapper, - run_config=run_config, - should_run_agent_start_hooks=should_run_agent_start_hooks, - ), - ) - else: - turn_result = await cls._run_single_turn( - agent=current_agent, - original_input=original_input, - generated_items=generated_items, - hooks=hooks, - context_wrapper=context_wrapper, - run_config=run_config, - should_run_agent_start_hooks=should_run_agent_start_hooks, - ) - should_run_agent_start_hooks = False - - model_responses.append(turn_result.model_response) - original_input = turn_result.original_input - generated_items = turn_result.generated_items - - if isinstance(turn_result.next_step, NextStepFinalOutput): - output_guardrail_results = await cls._run_output_guardrails( - current_agent.output_guardrails + (run_config.output_guardrails or []), - current_agent, - turn_result.next_step.output, - context_wrapper, - ) - return RunResult( - input=original_input, - new_items=generated_items, - raw_responses=model_responses, - final_output=turn_result.next_step.output, - _last_agent=current_agent, - input_guardrail_results=input_guardrail_results, - output_guardrail_results=output_guardrail_results, - ) - elif isinstance(turn_result.next_step, NextStepHandoff): - current_agent = cast(Agent[TContext], turn_result.next_step.new_agent) - current_span.finish(reset_current=True) - current_span = None - should_run_agent_start_hooks = True - elif isinstance(turn_result.next_step, NextStepRunAgain): - pass - else: - raise AgentsException( - f"Unknown next step type: {type(turn_result.next_step)}" - ) - finally: - if current_span: - current_span.finish(reset_current=True) - - @classmethod - def run_sync( - cls, - starting_agent: Agent[TContext], - input: str | list[TResponseInputItem], - *, - context: TContext | None = None, - max_turns: int = DEFAULT_MAX_TURNS, - hooks: RunHooks[TContext] | None = None, - run_config: RunConfig | None = None, - ) -> RunResult: - """Run a workflow synchronously, starting at the given agent. Note that this just wraps the - `run` method, so it will not work if there's already an event loop (e.g. inside an async - function, or in a Jupyter notebook or async context like FastAPI). For those cases, use - the `run` method instead. - - The agent will run in a loop until a final output is generated. The loop runs like so: - 1. The agent is invoked with the given input. - 2. If there is a final output (i.e. the agent produces something of type - `agent.output_type`, the loop terminates. - 3. If there's a handoff, we run the loop again, with the new agent. - 4. Else, we run tool calls (if any), and re-run the loop. - - In two cases, the agent may raise an exception: - 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. - 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised. - - Note that only the first agent's input guardrails are run. - - Args: - starting_agent: The starting agent to run. - input: The initial input to the agent. You can pass a single string for a user message, - or a list of input items. - context: The context to run the agent with. - max_turns: The maximum number of turns to run the agent for. A turn is defined as one - AI invocation (including any tool calls that might occur). - hooks: An object that receives callbacks on various lifecycle events. - run_config: Global settings for the entire agent run. - - Returns: - A run result containing all the inputs, guardrail results and the output of the last - agent. Agents may perform handoffs, so we don't know the specific type of the output. - """ - return asyncio.get_event_loop().run_until_complete( - cls.run( - starting_agent, - input, - context=context, - max_turns=max_turns, - hooks=hooks, - run_config=run_config, - ) - ) - - @classmethod - def run_streamed( - cls, - starting_agent: Agent[TContext], - input: str | list[TResponseInputItem], - context: TContext | None = None, - max_turns: int = DEFAULT_MAX_TURNS, - hooks: RunHooks[TContext] | None = None, - run_config: RunConfig | None = None, - ) -> RunResultStreaming: - """Run a workflow starting at the given agent in streaming mode. The returned result object - contains a method you can use to stream semantic events as they are generated. - - The agent will run in a loop until a final output is generated. The loop runs like so: - 1. The agent is invoked with the given input. - 2. If there is a final output (i.e. the agent produces something of type - `agent.output_type`, the loop terminates. - 3. If there's a handoff, we run the loop again, with the new agent. - 4. Else, we run tool calls (if any), and re-run the loop. - - In two cases, the agent may raise an exception: - 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. - 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised. - - Note that only the first agent's input guardrails are run. - - Args: - starting_agent: The starting agent to run. - input: The initial input to the agent. You can pass a single string for a user message, - or a list of input items. - context: The context to run the agent with. - max_turns: The maximum number of turns to run the agent for. A turn is defined as one - AI invocation (including any tool calls that might occur). - hooks: An object that receives callbacks on various lifecycle events. - run_config: Global settings for the entire agent run. - - Returns: - A result object that contains data about the run, as well as a method to stream events. - """ - if hooks is None: - hooks = RunHooks[Any]() - if run_config is None: - run_config = RunConfig() - - # If there's already a trace, we don't create a new one. In addition, we can't end the - # trace here, because the actual work is done in `stream_events` and this method ends - # before that. - new_trace = ( - None - if get_current_trace() - else trace( - workflow_name=run_config.workflow_name, - trace_id=run_config.trace_id, - group_id=run_config.group_id, - metadata=run_config.trace_metadata, - disabled=run_config.tracing_disabled, - ) - ) - # Need to start the trace here, because the current trace contextvar is captured at - # asyncio.create_task time - if new_trace: - new_trace.start(mark_as_current=True) - - output_schema = cls._get_output_schema(starting_agent) - context_wrapper: RunContextWrapper[TContext] = RunContextWrapper( - context=context # type: ignore - ) - - streamed_result = RunResultStreaming( - input=copy.deepcopy(input), - new_items=[], - current_agent=starting_agent, - raw_responses=[], - final_output=None, - is_complete=False, - current_turn=0, - max_turns=max_turns, - input_guardrail_results=[], - output_guardrail_results=[], - _current_agent_output_schema=output_schema, - _trace=new_trace, - ) - - # Kick off the actual agent loop in the background and return the streamed result object. - streamed_result._run_impl_task = asyncio.create_task( - cls._run_streamed_impl( - starting_input=input, - streamed_result=streamed_result, - starting_agent=starting_agent, - max_turns=max_turns, - hooks=hooks, - context_wrapper=context_wrapper, - run_config=run_config, - ) - ) - return streamed_result - - @classmethod - async def _run_input_guardrails_with_queue( - cls, - agent: Agent[Any], - guardrails: list[InputGuardrail[TContext]], - input: str | list[TResponseInputItem], - context: RunContextWrapper[TContext], - streamed_result: RunResultStreaming, - parent_span: Span[Any], - ): - queue = streamed_result._input_guardrail_queue - - # We'll run the guardrails and push them onto the queue as they complete - guardrail_tasks = [ - asyncio.create_task( - RunImpl.run_single_input_guardrail(agent, guardrail, input, context) - ) - for guardrail in guardrails - ] - guardrail_results = [] - try: - for done in asyncio.as_completed(guardrail_tasks): - result = await done - if result.output.tripwire_triggered: - _utils.attach_error_to_span( - parent_span, - SpanError( - message="Guardrail tripwire triggered", - data={ - "guardrail": result.guardrail.get_name(), - "type": "input_guardrail", - }, - ), - ) - queue.put_nowait(result) - guardrail_results.append(result) - except Exception: - for t in guardrail_tasks: - t.cancel() - raise - - streamed_result.input_guardrail_results = guardrail_results - - @classmethod - async def _run_streamed_impl( - cls, - starting_input: str | list[TResponseInputItem], - streamed_result: RunResultStreaming, - starting_agent: Agent[TContext], - max_turns: int, - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - run_config: RunConfig, - ): - current_span: Span[AgentSpanData] | None = None - current_agent = starting_agent - current_turn = 0 - should_run_agent_start_hooks = True - - streamed_result._event_queue.put_nowait(AgentUpdatedStreamEvent(new_agent=current_agent)) - - try: - while True: - if streamed_result.is_complete: - break - - # Start an agent span if we don't have one. This span is ended if the current - # agent changes, or if the agent loop ends. - if current_span is None: - handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)] - tool_names = [t.name for t in current_agent.tools] - if output_schema := cls._get_output_schema(current_agent): - output_type_name = output_schema.output_type_name() - else: - output_type_name = "str" - - current_span = agent_span( - name=current_agent.name, - handoffs=handoff_names, - tools=tool_names, - output_type=output_type_name, - ) - current_span.start(mark_as_current=True) - - current_turn += 1 - streamed_result.current_turn = current_turn - - if current_turn > max_turns: - _utils.attach_error_to_span( - current_span, - SpanError( - message="Max turns exceeded", - data={"max_turns": max_turns}, - ), - ) - streamed_result._event_queue.put_nowait(QueueCompleteSentinel()) - break - - if current_turn == 1: - # Run the input guardrails in the background and put the results on the queue - streamed_result._input_guardrails_task = asyncio.create_task( - cls._run_input_guardrails_with_queue( - starting_agent, - starting_agent.input_guardrails + (run_config.input_guardrails or []), - copy.deepcopy(ItemHelpers.input_to_new_input_list(starting_input)), - context_wrapper, - streamed_result, - current_span, - ) - ) - try: - turn_result = await cls._run_single_turn_streamed( - streamed_result, - current_agent, - hooks, - context_wrapper, - run_config, - should_run_agent_start_hooks, - ) - should_run_agent_start_hooks = False - - streamed_result.raw_responses = streamed_result.raw_responses + [ - turn_result.model_response - ] - streamed_result.input = turn_result.original_input - streamed_result.new_items = turn_result.generated_items - - if isinstance(turn_result.next_step, NextStepHandoff): - current_agent = turn_result.next_step.new_agent - current_span.finish(reset_current=True) - current_span = None - should_run_agent_start_hooks = True - streamed_result._event_queue.put_nowait( - AgentUpdatedStreamEvent(new_agent=current_agent) - ) - elif isinstance(turn_result.next_step, NextStepFinalOutput): - streamed_result._output_guardrails_task = asyncio.create_task( - cls._run_output_guardrails( - current_agent.output_guardrails - + (run_config.output_guardrails or []), - current_agent, - turn_result.next_step.output, - context_wrapper, - ) - ) - - try: - output_guardrail_results = await streamed_result._output_guardrails_task - except Exception: - # Exceptions will be checked in the stream_events loop - output_guardrail_results = [] - - streamed_result.output_guardrail_results = output_guardrail_results - streamed_result.final_output = turn_result.next_step.output - streamed_result.is_complete = True - streamed_result._event_queue.put_nowait(QueueCompleteSentinel()) - elif isinstance(turn_result.next_step, NextStepRunAgain): - pass - except Exception as e: - if current_span: - _utils.attach_error_to_span( - current_span, - SpanError( - message="Error in agent run", - data={"error": str(e)}, - ), - ) - streamed_result.is_complete = True - streamed_result._event_queue.put_nowait(QueueCompleteSentinel()) - raise - - streamed_result.is_complete = True - finally: - if current_span: - current_span.finish(reset_current=True) - - @classmethod - async def _run_single_turn_streamed( - cls, - streamed_result: RunResultStreaming, - agent: Agent[TContext], - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - run_config: RunConfig, - should_run_agent_start_hooks: bool, - ) -> SingleStepResult: - if should_run_agent_start_hooks: - await asyncio.gather( - hooks.on_agent_start(context_wrapper, agent), - ( - agent.hooks.on_start(context_wrapper, agent) - if agent.hooks - else _utils.noop_coroutine() - ), - ) - - output_schema = cls._get_output_schema(agent) - - streamed_result.current_agent = agent - streamed_result._current_agent_output_schema = output_schema - - system_prompt = await agent.get_system_prompt(context_wrapper) - - handoffs = cls._get_handoffs(agent) - - model = cls._get_model(agent, run_config) - model_settings = agent.model_settings.resolve(run_config.model_settings) - final_response: ModelResponse | None = None - - input = ItemHelpers.input_to_new_input_list(streamed_result.input) - input.extend([item.to_input_item() for item in streamed_result.new_items]) - - # 1. Stream the output events - async for event in model.stream_response( - system_prompt, - input, - model_settings, - agent.tools, - output_schema, - handoffs, - get_model_tracing_impl( - run_config.tracing_disabled, run_config.trace_include_sensitive_data - ), - ): - if isinstance(event, ResponseCompletedEvent): - usage = ( - Usage( - requests=1, - input_tokens=event.response.usage.input_tokens, - output_tokens=event.response.usage.output_tokens, - total_tokens=event.response.usage.total_tokens, - ) - if event.response.usage - else Usage() - ) - final_response = ModelResponse( - output=event.response.output, - usage=usage, - referenceable_id=event.response.id, - ) - - streamed_result._event_queue.put_nowait(RawResponsesStreamEvent(data=event)) - - # 2. At this point, the streaming is complete for this turn of the agent loop. - if not final_response: - raise ModelBehaviorError("Model did not produce a final response!") - - # 3. Now, we can process the turn as we do in the non-streaming case - single_step_result = await cls._get_single_step_result_from_response( - agent=agent, - original_input=streamed_result.input, - pre_step_items=streamed_result.new_items, - new_response=final_response, - output_schema=output_schema, - handoffs=handoffs, - hooks=hooks, - context_wrapper=context_wrapper, - run_config=run_config, - ) - - RunImpl.stream_step_result_to_queue(single_step_result, streamed_result._event_queue) - return single_step_result - - @classmethod - async def _run_single_turn( - cls, - *, - agent: Agent[TContext], - original_input: str | list[TResponseInputItem], - generated_items: list[RunItem], - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - run_config: RunConfig, - should_run_agent_start_hooks: bool, - ) -> SingleStepResult: - # Ensure we run the hooks before anything else - if should_run_agent_start_hooks: - await asyncio.gather( - hooks.on_agent_start(context_wrapper, agent), - ( - agent.hooks.on_start(context_wrapper, agent) - if agent.hooks - else _utils.noop_coroutine() - ), - ) - - system_prompt = await agent.get_system_prompt(context_wrapper) - - output_schema = cls._get_output_schema(agent) - handoffs = cls._get_handoffs(agent) - input = ItemHelpers.input_to_new_input_list(original_input) - input.extend([generated_item.to_input_item() for generated_item in generated_items]) - - new_response = await cls._get_new_response( - agent, - system_prompt, - input, - output_schema, - handoffs, - context_wrapper, - run_config, - ) - - return await cls._get_single_step_result_from_response( - agent=agent, - original_input=original_input, - pre_step_items=generated_items, - new_response=new_response, - output_schema=output_schema, - handoffs=handoffs, - hooks=hooks, - context_wrapper=context_wrapper, - run_config=run_config, - ) - - @classmethod - async def _get_single_step_result_from_response( - cls, - *, - agent: Agent[TContext], - original_input: str | list[TResponseInputItem], - pre_step_items: list[RunItem], - new_response: ModelResponse, - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - hooks: RunHooks[TContext], - context_wrapper: RunContextWrapper[TContext], - run_config: RunConfig, - ) -> SingleStepResult: - processed_response = RunImpl.process_model_response( - agent=agent, - response=new_response, - output_schema=output_schema, - handoffs=handoffs, - ) - return await RunImpl.execute_tools_and_side_effects( - agent=agent, - original_input=original_input, - pre_step_items=pre_step_items, - new_response=new_response, - processed_response=processed_response, - output_schema=output_schema, - hooks=hooks, - context_wrapper=context_wrapper, - run_config=run_config, - ) - - @classmethod - async def _run_input_guardrails( - cls, - agent: Agent[Any], - guardrails: list[InputGuardrail[TContext]], - input: str | list[TResponseInputItem], - context: RunContextWrapper[TContext], - ) -> list[InputGuardrailResult]: - if not guardrails: - return [] - - guardrail_tasks = [ - asyncio.create_task( - RunImpl.run_single_input_guardrail(agent, guardrail, input, context) - ) - for guardrail in guardrails - ] - - guardrail_results = [] - - for done in asyncio.as_completed(guardrail_tasks): - result = await done - if result.output.tripwire_triggered: - # Cancel all guardrail tasks if a tripwire is triggered. - for t in guardrail_tasks: - t.cancel() - _utils.attach_error_to_current_span( - SpanError( - message="Guardrail tripwire triggered", - data={"guardrail": result.guardrail.get_name()}, - ) - ) - raise InputGuardrailTripwireTriggered(result) - else: - guardrail_results.append(result) - - return guardrail_results - - @classmethod - async def _run_output_guardrails( - cls, - guardrails: list[OutputGuardrail[TContext]], - agent: Agent[TContext], - agent_output: Any, - context: RunContextWrapper[TContext], - ) -> list[OutputGuardrailResult]: - if not guardrails: - return [] - - guardrail_tasks = [ - asyncio.create_task( - RunImpl.run_single_output_guardrail(guardrail, agent, agent_output, context) - ) - for guardrail in guardrails - ] - - guardrail_results = [] - - for done in asyncio.as_completed(guardrail_tasks): - result = await done - if result.output.tripwire_triggered: - # Cancel all guardrail tasks if a tripwire is triggered. - for t in guardrail_tasks: - t.cancel() - _utils.attach_error_to_current_span( - SpanError( - message="Guardrail tripwire triggered", - data={"guardrail": result.guardrail.get_name()}, - ) - ) - raise OutputGuardrailTripwireTriggered(result) - else: - guardrail_results.append(result) - - return guardrail_results - - @classmethod - async def _get_new_response( - cls, - agent: Agent[TContext], - system_prompt: str | None, - input: list[TResponseInputItem], - output_schema: AgentOutputSchema | None, - handoffs: list[Handoff], - context_wrapper: RunContextWrapper[TContext], - run_config: RunConfig, - ) -> ModelResponse: - model = cls._get_model(agent, run_config) - model_settings = agent.model_settings.resolve(run_config.model_settings) - new_response = await model.get_response( - system_instructions=system_prompt, - input=input, - model_settings=model_settings, - tools=agent.tools, - output_schema=output_schema, - handoffs=handoffs, - tracing=get_model_tracing_impl( - run_config.tracing_disabled, run_config.trace_include_sensitive_data - ), - ) - - context_wrapper.usage.add(new_response.usage) - - return new_response - - @classmethod - def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchema | None: - if agent.output_type is None or agent.output_type is str: - return None - - return AgentOutputSchema(agent.output_type) - - @classmethod - def _get_handoffs(cls, agent: Agent[Any]) -> list[Handoff]: - handoffs = [] - for handoff_item in agent.handoffs: - if isinstance(handoff_item, Handoff): - handoffs.append(handoff_item) - elif isinstance(handoff_item, Agent): - handoffs.append(handoff(handoff_item)) - return handoffs - - @classmethod - def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model: - if isinstance(run_config.model, Model): - return run_config.model - elif isinstance(run_config.model, str): - return run_config.model_provider.get_model(run_config.model) - elif isinstance(agent.model, Model): - return agent.model - - return run_config.model_provider.get_model(agent.model) diff --git a/tests/src/agents/run_context.py b/tests/src/agents/run_context.py deleted file mode 100644 index 579a215f..00000000 --- a/tests/src/agents/run_context.py +++ /dev/null @@ -1,26 +0,0 @@ -from dataclasses import dataclass, field -from typing import Any, Generic - -from typing_extensions import TypeVar - -from .usage import Usage - -TContext = TypeVar("TContext", default=Any) - - -@dataclass -class RunContextWrapper(Generic[TContext]): - """This wraps the context object that you passed to `Runner.run()`. It also contains - information about the usage of the agent run so far. - - NOTE: Contexts are not passed to the LLM. They're a way to pass dependencies and data to code - you implement, like tool functions, callbacks, hooks, etc. - """ - - context: TContext - """The context object (or None), passed by you to `Runner.run()`""" - - usage: Usage = field(default_factory=Usage) - """The usage of the agent run so far. For streamed responses, the usage will be stale until the - last chunk of the stream is processed. - """ diff --git a/tests/src/agents/stream_events.py b/tests/src/agents/stream_events.py deleted file mode 100644 index bd37d11f..00000000 --- a/tests/src/agents/stream_events.py +++ /dev/null @@ -1,58 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass -from typing import Any, Literal, Union - -from typing_extensions import TypeAlias - -from .agent import Agent -from .items import RunItem, TResponseStreamEvent - - -@dataclass -class RawResponsesStreamEvent: - """Streaming event from the LLM. These are 'raw' events, i.e. they are directly passed through - from the LLM. - """ - - data: TResponseStreamEvent - """The raw responses streaming event from the LLM.""" - - type: Literal["raw_response_event"] = "raw_response_event" - """The type of the event.""" - - -@dataclass -class RunItemStreamEvent: - """Streaming events that wrap a `RunItem`. As the agent processes the LLM response, it will - generate these events for new messages, tool calls, tool outputs, handoffs, etc. - """ - - name: Literal[ - "message_output_created", - "handoff_requested", - "handoff_occured", - "tool_called", - "tool_output", - "reasoning_item_created", - ] - """The name of the event.""" - - item: RunItem - """The item that was created.""" - - type: Literal["run_item_stream_event"] = "run_item_stream_event" - - -@dataclass -class AgentUpdatedStreamEvent: - """Event that notifies that there is a new agent running.""" - - new_agent: Agent[Any] - """The new agent.""" - - type: Literal["agent_updated_stream_event"] = "agent_updated_stream_event" - - -StreamEvent: TypeAlias = Union[RawResponsesStreamEvent, RunItemStreamEvent, AgentUpdatedStreamEvent] -"""A streaming event from an agent.""" diff --git a/tests/src/agents/strict_schema.py b/tests/src/agents/strict_schema.py deleted file mode 100644 index 910ad85f..00000000 --- a/tests/src/agents/strict_schema.py +++ /dev/null @@ -1,167 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from openai import NOT_GIVEN -from typing_extensions import TypeGuard - -from .exceptions import UserError - -_EMPTY_SCHEMA = { - "additionalProperties": False, - "type": "object", - "properties": {}, - "required": [], -} - - -def ensure_strict_json_schema( - schema: dict[str, Any], -) -> dict[str, Any]: - """Mutates the given JSON schema to ensure it conforms to the `strict` standard - that the OpenAI API expects. - """ - if schema == {}: - return _EMPTY_SCHEMA - return _ensure_strict_json_schema(schema, path=(), root=schema) - - -# Adapted from https://github.com/openai/openai-python/blob/main/src/openai/lib/_pydantic.py -def _ensure_strict_json_schema( - json_schema: object, - *, - path: tuple[str, ...], - root: dict[str, object], -) -> dict[str, Any]: - if not is_dict(json_schema): - raise TypeError(f"Expected {json_schema} to be a dictionary; path={path}") - - defs = json_schema.get("$defs") - if is_dict(defs): - for def_name, def_schema in defs.items(): - _ensure_strict_json_schema(def_schema, path=(*path, "$defs", def_name), root=root) - - definitions = json_schema.get("definitions") - if is_dict(definitions): - for definition_name, definition_schema in definitions.items(): - _ensure_strict_json_schema( - definition_schema, path=(*path, "definitions", definition_name), root=root - ) - - typ = json_schema.get("type") - if typ == "object" and "additionalProperties" not in json_schema: - json_schema["additionalProperties"] = False - elif ( - typ == "object" - and "additionalProperties" in json_schema - and json_schema["additionalProperties"] is True - ): - raise UserError( - "additionalProperties should not be set for object types. This could be because " - "you're using an older version of Pydantic, or because you configured additional " - "properties to be allowed. If you really need this, update the function or output tool " - "to not use a strict schema." - ) - - # object types - # { 'type': 'object', 'properties': { 'a': {...} } } - properties = json_schema.get("properties") - if is_dict(properties): - json_schema["required"] = list(properties.keys()) - json_schema["properties"] = { - key: _ensure_strict_json_schema(prop_schema, path=(*path, "properties", key), root=root) - for key, prop_schema in properties.items() - } - - # arrays - # { 'type': 'array', 'items': {...} } - items = json_schema.get("items") - if is_dict(items): - json_schema["items"] = _ensure_strict_json_schema(items, path=(*path, "items"), root=root) - - # unions - any_of = json_schema.get("anyOf") - if is_list(any_of): - json_schema["anyOf"] = [ - _ensure_strict_json_schema(variant, path=(*path, "anyOf", str(i)), root=root) - for i, variant in enumerate(any_of) - ] - - # intersections - all_of = json_schema.get("allOf") - if is_list(all_of): - if len(all_of) == 1: - json_schema.update( - _ensure_strict_json_schema(all_of[0], path=(*path, "allOf", "0"), root=root) - ) - json_schema.pop("allOf") - else: - json_schema["allOf"] = [ - _ensure_strict_json_schema(entry, path=(*path, "allOf", str(i)), root=root) - for i, entry in enumerate(all_of) - ] - - # strip `None` defaults as there's no meaningful distinction here - # the schema will still be `nullable` and the model will default - # to using `None` anyway - if json_schema.get("default", NOT_GIVEN) is None: - json_schema.pop("default") - - # we can't use `$ref`s if there are also other properties defined, e.g. - # `{"$ref": "...", "description": "my description"}` - # - # so we unravel the ref - # `{"type": "string", "description": "my description"}` - ref = json_schema.get("$ref") - if ref and has_more_than_n_keys(json_schema, 1): - assert isinstance(ref, str), f"Received non-string $ref - {ref}" - - resolved = resolve_ref(root=root, ref=ref) - if not is_dict(resolved): - raise ValueError( - f"Expected `$ref: {ref}` to resolved to a dictionary but got {resolved}" - ) - - # properties from the json schema take priority over the ones on the `$ref` - json_schema.update({**resolved, **json_schema}) - json_schema.pop("$ref") - # Since the schema expanded from `$ref` might not have `additionalProperties: false` applied - # we call `_ensure_strict_json_schema` again to fix the inlined schema and ensure it's valid - return _ensure_strict_json_schema(json_schema, path=path, root=root) - - return json_schema - - -def resolve_ref(*, root: dict[str, object], ref: str) -> object: - if not ref.startswith("#/"): - raise ValueError(f"Unexpected $ref format {ref!r}; Does not start with #/") - - path = ref[2:].split("/") - resolved = root - for key in path: - value = resolved[key] - assert is_dict(value), ( - f"encountered non-dictionary entry while resolving {ref} - {resolved}" - ) - resolved = value - - return resolved - - -def is_dict(obj: object) -> TypeGuard[dict[str, object]]: - # just pretend that we know there are only `str` keys - # as that check is not worth the performance cost - return isinstance(obj, dict) - - -def is_list(obj: object) -> TypeGuard[list[object]]: - return isinstance(obj, list) - - -def has_more_than_n_keys(obj: dict[str, object], n: int) -> bool: - i = 0 - for _ in obj.keys(): - i += 1 - if i > n: - return True - return False diff --git a/tests/src/agents/tool.py b/tests/src/agents/tool.py deleted file mode 100644 index 75872680..00000000 --- a/tests/src/agents/tool.py +++ /dev/null @@ -1,286 +0,0 @@ -from __future__ import annotations - -import inspect -import json -from collections.abc import Awaitable -from dataclasses import dataclass -from typing import Any, Callable, Literal, Union, overload - -from openai.types.responses.file_search_tool_param import Filters, RankingOptions -from openai.types.responses.web_search_tool_param import UserLocation -from pydantic import ValidationError -from typing_extensions import Concatenate, ParamSpec - -from . import _debug, _utils -from ._utils import MaybeAwaitable -from .computer import AsyncComputer, Computer -from .exceptions import ModelBehaviorError -from .function_schema import DocstringStyle, function_schema -from .logger import logger -from .run_context import RunContextWrapper -from .tracing import SpanError - -ToolParams = ParamSpec("ToolParams") - -ToolFunctionWithoutContext = Callable[ToolParams, Any] -ToolFunctionWithContext = Callable[Concatenate[RunContextWrapper[Any], ToolParams], Any] - -ToolFunction = Union[ToolFunctionWithoutContext[ToolParams], ToolFunctionWithContext[ToolParams]] - - -@dataclass -class FunctionTool: - """A tool that wraps a function. In most cases, you should use the `function_tool` helpers to - create a FunctionTool, as they let you easily wrap a Python function. - """ - - name: str - """The name of the tool, as shown to the LLM. Generally the name of the function.""" - - description: str - """A description of the tool, as shown to the LLM.""" - - params_json_schema: dict[str, Any] - """The JSON schema for the tool's parameters.""" - - on_invoke_tool: Callable[[RunContextWrapper[Any], str], Awaitable[str]] - """A function that invokes the tool with the given context and parameters. The params passed - are: - 1. The tool run context. - 2. The arguments from the LLM, as a JSON string. - - You must return a string representation of the tool output. In case of errors, you can either - raise an Exception (which will cause the run to fail) or return a string error message (which - will be sent back to the LLM). - """ - - strict_json_schema: bool = True - """Whether the JSON schema is in strict mode. We **strongly** recommend setting this to True, - as it increases the likelihood of correct JSON input.""" - - -@dataclass -class FileSearchTool: - """A hosted tool that lets the LLM search through a vector store. Currently only supported with - OpenAI models, using the Responses API. - """ - - vector_store_ids: list[str] - """The IDs of the vector stores to search.""" - - max_num_results: int | None = None - """The maximum number of results to return.""" - - include_search_results: bool = False - """Whether to include the search results in the output produced by the LLM.""" - - ranking_options: RankingOptions | None = None - """Ranking options for search.""" - - filters: Filters | None = None - """A filter to apply based on file attributes.""" - - @property - def name(self): - return "file_search" - - -@dataclass -class WebSearchTool: - """A hosted tool that lets the LLM search the web. Currently only supported with OpenAI models, - using the Responses API. - """ - - user_location: UserLocation | None = None - """Optional location for the search. Lets you customize results to be relevant to a location.""" - - search_context_size: Literal["low", "medium", "high"] = "medium" - """The amount of context to use for the search.""" - - @property - def name(self): - return "web_search_preview" - - -@dataclass -class ComputerTool: - """A hosted tool that lets the LLM control a computer.""" - - computer: Computer | AsyncComputer - """The computer implementation, which describes the environment and dimensions of the computer, - as well as implements the computer actions like click, screenshot, etc. - """ - - @property - def name(self): - return "computer_use_preview" - - -Tool = Union[FunctionTool, FileSearchTool, WebSearchTool, ComputerTool] -"""A tool that can be used in an agent.""" - - -def default_tool_error_function(ctx: RunContextWrapper[Any], error: Exception) -> str: - """The default tool error function, which just returns a generic error message.""" - return f"An error occurred while running the tool. Please try again. Error: {str(error)}" - - -ToolErrorFunction = Callable[[RunContextWrapper[Any], Exception], MaybeAwaitable[str]] - - -@overload -def function_tool( - func: ToolFunction[...], - *, - name_override: str | None = None, - description_override: str | None = None, - docstring_style: DocstringStyle | None = None, - use_docstring_info: bool = True, - failure_error_function: ToolErrorFunction | None = None, -) -> FunctionTool: - """Overload for usage as @function_tool (no parentheses).""" - ... - - -@overload -def function_tool( - *, - name_override: str | None = None, - description_override: str | None = None, - docstring_style: DocstringStyle | None = None, - use_docstring_info: bool = True, - failure_error_function: ToolErrorFunction | None = None, -) -> Callable[[ToolFunction[...]], FunctionTool]: - """Overload for usage as @function_tool(...).""" - ... - - -def function_tool( - func: ToolFunction[...] | None = None, - *, - name_override: str | None = None, - description_override: str | None = None, - docstring_style: DocstringStyle | None = None, - use_docstring_info: bool = True, - failure_error_function: ToolErrorFunction | None = default_tool_error_function, -) -> FunctionTool | Callable[[ToolFunction[...]], FunctionTool]: - """ - Decorator to create a FunctionTool from a function. By default, we will: - 1. Parse the function signature to create a JSON schema for the tool's parameters. - 2. Use the function's docstring to populate the tool's description. - 3. Use the function's docstring to populate argument descriptions. - The docstring style is detected automatically, but you can override it. - - If the function takes a `RunContextWrapper` as the first argument, it *must* match the - context type of the agent that uses the tool. - - Args: - func: The function to wrap. - name_override: If provided, use this name for the tool instead of the function's name. - description_override: If provided, use this description for the tool instead of the - function's docstring. - docstring_style: If provided, use this style for the tool's docstring. If not provided, - we will attempt to auto-detect the style. - use_docstring_info: If True, use the function's docstring to populate the tool's - description and argument descriptions. - failure_error_function: If provided, use this function to generate an error message when - the tool call fails. The error message is sent to the LLM. If you pass None, then no - error message will be sent and instead an Exception will be raised. - """ - - def _create_function_tool(the_func: ToolFunction[...]) -> FunctionTool: - schema = function_schema( - func=the_func, - name_override=name_override, - description_override=description_override, - docstring_style=docstring_style, - use_docstring_info=use_docstring_info, - ) - - async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> str: - try: - json_data: dict[str, Any] = json.loads(input) if input else {} - except Exception as e: - if _debug.DONT_LOG_TOOL_DATA: - logger.debug(f"Invalid JSON input for tool {schema.name}") - else: - logger.debug(f"Invalid JSON input for tool {schema.name}: {input}") - raise ModelBehaviorError( - f"Invalid JSON input for tool {schema.name}: {input}" - ) from e - - if _debug.DONT_LOG_TOOL_DATA: - logger.debug(f"Invoking tool {schema.name}") - else: - logger.debug(f"Invoking tool {schema.name} with input {input}") - - try: - parsed = ( - schema.params_pydantic_model(**json_data) - if json_data - else schema.params_pydantic_model() - ) - except ValidationError as e: - raise ModelBehaviorError(f"Invalid JSON input for tool {schema.name}: {e}") from e - - args, kwargs_dict = schema.to_call_args(parsed) - - if not _debug.DONT_LOG_TOOL_DATA: - logger.debug(f"Tool call args: {args}, kwargs: {kwargs_dict}") - - if inspect.iscoroutinefunction(the_func): - if schema.takes_context: - result = await the_func(ctx, *args, **kwargs_dict) - else: - result = await the_func(*args, **kwargs_dict) - else: - if schema.takes_context: - result = the_func(ctx, *args, **kwargs_dict) - else: - result = the_func(*args, **kwargs_dict) - - if _debug.DONT_LOG_TOOL_DATA: - logger.debug(f"Tool {schema.name} completed.") - else: - logger.debug(f"Tool {schema.name} returned {result}") - - return str(result) - - async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> str: - try: - return await _on_invoke_tool_impl(ctx, input) - except Exception as e: - if failure_error_function is None: - raise - - result = failure_error_function(ctx, e) - if inspect.isawaitable(result): - return await result - - _utils.attach_error_to_current_span( - SpanError( - message="Error running tool (non-fatal)", - data={ - "tool_name": schema.name, - "error": str(e), - }, - ) - ) - return result - - return FunctionTool( - name=schema.name, - description=schema.description or "", - params_json_schema=schema.params_json_schema, - on_invoke_tool=_on_invoke_tool, - ) - - # If func is actually a callable, we were used as @function_tool with no parentheses - if callable(func): - return _create_function_tool(func) - - # Otherwise, we were used as @function_tool(...), so return a decorator - def decorator(real_func: ToolFunction[...]) -> FunctionTool: - return _create_function_tool(real_func) - - return decorator diff --git a/tests/src/agents/tracing/__init__.py b/tests/src/agents/tracing/__init__.py deleted file mode 100644 index 8e802018..00000000 --- a/tests/src/agents/tracing/__init__.py +++ /dev/null @@ -1,97 +0,0 @@ -import atexit - -from .create import ( - agent_span, - custom_span, - function_span, - generation_span, - get_current_span, - get_current_trace, - guardrail_span, - handoff_span, - response_span, - trace, -) -from .processor_interface import TracingProcessor -from .processors import default_exporter, default_processor -from .setup import GLOBAL_TRACE_PROVIDER -from .span_data import ( - AgentSpanData, - CustomSpanData, - FunctionSpanData, - GenerationSpanData, - GuardrailSpanData, - HandoffSpanData, - ResponseSpanData, - SpanData, -) -from .spans import Span, SpanError -from .traces import Trace -from .util import gen_span_id, gen_trace_id - -__all__ = [ - "add_trace_processor", - "agent_span", - "custom_span", - "function_span", - "generation_span", - "get_current_span", - "get_current_trace", - "guardrail_span", - "handoff_span", - "response_span", - "set_trace_processors", - "set_tracing_disabled", - "trace", - "Trace", - "SpanError", - "Span", - "SpanData", - "AgentSpanData", - "CustomSpanData", - "FunctionSpanData", - "GenerationSpanData", - "GuardrailSpanData", - "HandoffSpanData", - "ResponseSpanData", - "TracingProcessor", - "gen_trace_id", - "gen_span_id", -] - - -def add_trace_processor(span_processor: TracingProcessor) -> None: - """ - Adds a new trace processor. This processor will receive all traces/spans. - """ - GLOBAL_TRACE_PROVIDER.register_processor(span_processor) - - -def set_trace_processors(processors: list[TracingProcessor]) -> None: - """ - Set the list of trace processors. This will replace the current list of processors. - """ - GLOBAL_TRACE_PROVIDER.set_processors(processors) - - -def set_tracing_disabled(disabled: bool) -> None: - """ - Set whether tracing is globally disabled. - """ - GLOBAL_TRACE_PROVIDER.set_disabled(disabled) - - -def set_tracing_export_api_key(api_key: str) -> None: - """ - Set the OpenAI API key for the backend exporter. - """ - default_exporter().set_api_key(api_key) - - -# Add the default processor, which exports traces and spans to the backend in batches. You can -# change the default behavior by either: -# 1. calling add_trace_processor(), which adds additional processors, or -# 2. calling set_trace_processors(), which replaces the default processor. -add_trace_processor(default_processor()) - -atexit.register(GLOBAL_TRACE_PROVIDER.shutdown) diff --git a/tests/src/agents/tracing/create.py b/tests/src/agents/tracing/create.py deleted file mode 100644 index 8d7fc493..00000000 --- a/tests/src/agents/tracing/create.py +++ /dev/null @@ -1,306 +0,0 @@ -from __future__ import annotations - -from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any - -from .logger import logger -from .setup import GLOBAL_TRACE_PROVIDER -from .span_data import ( - AgentSpanData, - CustomSpanData, - FunctionSpanData, - GenerationSpanData, - GuardrailSpanData, - HandoffSpanData, - ResponseSpanData, -) -from .spans import Span -from .traces import Trace - -if TYPE_CHECKING: - from openai.types.responses import Response - - -def trace( - workflow_name: str, - trace_id: str | None = None, - group_id: str | None = None, - metadata: dict[str, Any] | None = None, - disabled: bool = False, -) -> Trace: - """ - Create a new trace. The trace will not be started automatically; you should either use - it as a context manager (`with trace(...):`) or call `trace.start()` + `trace.finish()` - manually. - - In addition to the workflow name and optional grouping identifier, you can provide - an arbitrary metadata dictionary to attach additional user-defined information to - the trace. - - Args: - workflow_name: The name of the logical app or workflow. For example, you might provide - "code_bot" for a coding agent, or "customer_support_agent" for a customer support agent. - trace_id: The ID of the trace. Optional. If not provided, we will generate an ID. We - recommend using `util.gen_trace_id()` to generate a trace ID, to guarantee that IDs are - correctly formatted. - group_id: Optional grouping identifier to link multiple traces from the same conversation - or process. For instance, you might use a chat thread ID. - metadata: Optional dictionary of additional metadata to attach to the trace. - disabled: If True, we will return a Trace but the Trace will not be recorded. This will - not be checked if there's an existing trace and `even_if_trace_running` is True. - - Returns: - The newly created trace object. - """ - current_trace = GLOBAL_TRACE_PROVIDER.get_current_trace() - if current_trace: - logger.warning( - "Trace already exists. Creating a new trace, but this is probably a mistake." - ) - - return GLOBAL_TRACE_PROVIDER.create_trace( - name=workflow_name, - trace_id=trace_id, - group_id=group_id, - metadata=metadata, - disabled=disabled, - ) - - -def get_current_trace() -> Trace | None: - """Returns the currently active trace, if present.""" - return GLOBAL_TRACE_PROVIDER.get_current_trace() - - -def get_current_span() -> Span[Any] | None: - """Returns the currently active span, if present.""" - return GLOBAL_TRACE_PROVIDER.get_current_span() - - -def agent_span( - name: str, - handoffs: list[str] | None = None, - tools: list[str] | None = None, - output_type: str | None = None, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, -) -> Span[AgentSpanData]: - """Create a new agent span. The span will not be started automatically, you should either do - `with agent_span() ...` or call `span.start()` + `span.finish()` manually. - - Args: - name: The name of the agent. - handoffs: Optional list of agent names to which this agent could hand off control. - tools: Optional list of tool names available to this agent. - output_type: Optional name of the output type produced by the agent. - span_id: The ID of the span. Optional. If not provided, we will generate an ID. We - recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are - correctly formatted. - parent: The parent span or trace. If not provided, we will automatically use the current - trace/span as the parent. - disabled: If True, we will return a Span but the Span will not be recorded. - - Returns: - The newly created agent span. - """ - return GLOBAL_TRACE_PROVIDER.create_span( - span_data=AgentSpanData(name=name, handoffs=handoffs, tools=tools, output_type=output_type), - span_id=span_id, - parent=parent, - disabled=disabled, - ) - - -def function_span( - name: str, - input: str | None = None, - output: str | None = None, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, -) -> Span[FunctionSpanData]: - """Create a new function span. The span will not be started automatically, you should either do - `with function_span() ...` or call `span.start()` + `span.finish()` manually. - - Args: - name: The name of the function. - input: The input to the function. - output: The output of the function. - span_id: The ID of the span. Optional. If not provided, we will generate an ID. We - recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are - correctly formatted. - parent: The parent span or trace. If not provided, we will automatically use the current - trace/span as the parent. - disabled: If True, we will return a Span but the Span will not be recorded. - - Returns: - The newly created function span. - """ - return GLOBAL_TRACE_PROVIDER.create_span( - span_data=FunctionSpanData(name=name, input=input, output=output), - span_id=span_id, - parent=parent, - disabled=disabled, - ) - - -def generation_span( - input: Sequence[Mapping[str, Any]] | None = None, - output: Sequence[Mapping[str, Any]] | None = None, - model: str | None = None, - model_config: Mapping[str, Any] | None = None, - usage: dict[str, Any] | None = None, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, -) -> Span[GenerationSpanData]: - """Create a new generation span. The span will not be started automatically, you should either - do `with generation_span() ...` or call `span.start()` + `span.finish()` manually. - - This span captures the details of a model generation, including the - input message sequence, any generated outputs, the model name and - configuration, and usage data. If you only need to capture a model - response identifier, use `response_span()` instead. - - Args: - input: The sequence of input messages sent to the model. - output: The sequence of output messages received from the model. - model: The model identifier used for the generation. - model_config: The model configuration (hyperparameters) used. - usage: A dictionary of usage information (input tokens, output tokens, etc.). - span_id: The ID of the span. Optional. If not provided, we will generate an ID. We - recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are - correctly formatted. - parent: The parent span or trace. If not provided, we will automatically use the current - trace/span as the parent. - disabled: If True, we will return a Span but the Span will not be recorded. - - Returns: - The newly created generation span. - """ - return GLOBAL_TRACE_PROVIDER.create_span( - span_data=GenerationSpanData( - input=input, output=output, model=model, model_config=model_config, usage=usage - ), - span_id=span_id, - parent=parent, - disabled=disabled, - ) - - -def response_span( - response: Response | None = None, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, -) -> Span[ResponseSpanData]: - """Create a new response span. The span will not be started automatically, you should either do - `with response_span() ...` or call `span.start()` + `span.finish()` manually. - - Args: - response: The OpenAI Response object. - span_id: The ID of the span. Optional. If not provided, we will generate an ID. We - recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are - correctly formatted. - parent: The parent span or trace. If not provided, we will automatically use the current - trace/span as the parent. - disabled: If True, we will return a Span but the Span will not be recorded. - """ - return GLOBAL_TRACE_PROVIDER.create_span( - span_data=ResponseSpanData(response=response), - span_id=span_id, - parent=parent, - disabled=disabled, - ) - - -def handoff_span( - from_agent: str | None = None, - to_agent: str | None = None, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, -) -> Span[HandoffSpanData]: - """Create a new handoff span. The span will not be started automatically, you should either do - `with handoff_span() ...` or call `span.start()` + `span.finish()` manually. - - Args: - from_agent: The name of the agent that is handing off. - to_agent: The name of the agent that is receiving the handoff. - span_id: The ID of the span. Optional. If not provided, we will generate an ID. We - recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are - correctly formatted. - parent: The parent span or trace. If not provided, we will automatically use the current - trace/span as the parent. - disabled: If True, we will return a Span but the Span will not be recorded. - - Returns: - The newly created handoff span. - """ - return GLOBAL_TRACE_PROVIDER.create_span( - span_data=HandoffSpanData(from_agent=from_agent, to_agent=to_agent), - span_id=span_id, - parent=parent, - disabled=disabled, - ) - - -def custom_span( - name: str, - data: dict[str, Any] | None = None, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, -) -> Span[CustomSpanData]: - """Create a new custom span, to which you can add your own metadata. The span will not be - started automatically, you should either do `with custom_span() ...` or call - `span.start()` + `span.finish()` manually. - - Args: - name: The name of the custom span. - data: Arbitrary structured data to associate with the span. - span_id: The ID of the span. Optional. If not provided, we will generate an ID. We - recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are - correctly formatted. - parent: The parent span or trace. If not provided, we will automatically use the current - trace/span as the parent. - disabled: If True, we will return a Span but the Span will not be recorded. - - Returns: - The newly created custom span. - """ - return GLOBAL_TRACE_PROVIDER.create_span( - span_data=CustomSpanData(name=name, data=data or {}), - span_id=span_id, - parent=parent, - disabled=disabled, - ) - - -def guardrail_span( - name: str, - triggered: bool = False, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, -) -> Span[GuardrailSpanData]: - """Create a new guardrail span. The span will not be started automatically, you should either - do `with guardrail_span() ...` or call `span.start()` + `span.finish()` manually. - - Args: - name: The name of the guardrail. - triggered: Whether the guardrail was triggered. - span_id: The ID of the span. Optional. If not provided, we will generate an ID. We - recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are - correctly formatted. - parent: The parent span or trace. If not provided, we will automatically use the current - trace/span as the parent. - disabled: If True, we will return a Span but the Span will not be recorded. - """ - return GLOBAL_TRACE_PROVIDER.create_span( - span_data=GuardrailSpanData(name=name, triggered=triggered), - span_id=span_id, - parent=parent, - disabled=disabled, - ) diff --git a/tests/src/agents/tracing/logger.py b/tests/src/agents/tracing/logger.py deleted file mode 100644 index 661d09b5..00000000 --- a/tests/src/agents/tracing/logger.py +++ /dev/null @@ -1,3 +0,0 @@ -import logging - -logger = logging.getLogger("openai.agents.tracing") diff --git a/tests/src/agents/tracing/processor_interface.py b/tests/src/agents/tracing/processor_interface.py deleted file mode 100644 index 4dcd897c..00000000 --- a/tests/src/agents/tracing/processor_interface.py +++ /dev/null @@ -1,69 +0,0 @@ -import abc -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from .spans import Span - from .traces import Trace - - -class TracingProcessor(abc.ABC): - """Interface for processing spans.""" - - @abc.abstractmethod - def on_trace_start(self, trace: "Trace") -> None: - """Called when a trace is started. - - Args: - trace: The trace that started. - """ - pass - - @abc.abstractmethod - def on_trace_end(self, trace: "Trace") -> None: - """Called when a trace is finished. - - Args: - trace: The trace that started. - """ - pass - - @abc.abstractmethod - def on_span_start(self, span: "Span[Any]") -> None: - """Called when a span is started. - - Args: - span: The span that started. - """ - pass - - @abc.abstractmethod - def on_span_end(self, span: "Span[Any]") -> None: - """Called when a span is finished. Should not block or raise exceptions. - - Args: - span: The span that finished. - """ - pass - - @abc.abstractmethod - def shutdown(self) -> None: - """Called when the application stops.""" - pass - - @abc.abstractmethod - def force_flush(self) -> None: - """Forces an immediate flush of all queued spans/traces.""" - pass - - -class TracingExporter(abc.ABC): - """Exports traces and spans. For example, could log them or send them to a backend.""" - - @abc.abstractmethod - def export(self, items: list["Trace | Span[Any]"]) -> None: - """Exports a list of traces and spans. - - Args: - items: The items to export. - """ - pass diff --git a/tests/src/agents/tracing/processors.py b/tests/src/agents/tracing/processors.py deleted file mode 100644 index 282bc23c..00000000 --- a/tests/src/agents/tracing/processors.py +++ /dev/null @@ -1,261 +0,0 @@ -from __future__ import annotations - -import os -import queue -import random -import threading -import time -from typing import Any - -import httpx - -from .logger import logger -from .processor_interface import TracingExporter, TracingProcessor -from .spans import Span -from .traces import Trace - - -class ConsoleSpanExporter(TracingExporter): - """Prints the traces and spans to the console.""" - - def export(self, items: list[Trace | Span[Any]]) -> None: - for item in items: - if isinstance(item, Trace): - print(f"[Exporter] Export trace_id={item.trace_id}, name={item.name}, ") - else: - print(f"[Exporter] Export span: {item.export()}") - - -class BackendSpanExporter(TracingExporter): - def __init__( - self, - api_key: str | None = None, - organization: str | None = None, - project: str | None = None, - endpoint: str = "https://api.openai.com/v1/traces/ingest", - max_retries: int = 3, - base_delay: float = 1.0, - max_delay: float = 30.0, - ): - """ - Args: - api_key: The API key for the "Authorization" header. Defaults to - `os.environ["OPENAI_TRACE_API_KEY"]` if not provided. - organization: The OpenAI organization to use. Defaults to - `os.environ["OPENAI_ORG_ID"]` if not provided. - project: The OpenAI project to use. Defaults to - `os.environ["OPENAI_PROJECT_ID"]` if not provided. - endpoint: The HTTP endpoint to which traces/spans are posted. - max_retries: Maximum number of retries upon failures. - base_delay: Base delay (in seconds) for the first backoff. - max_delay: Maximum delay (in seconds) for backoff growth. - """ - self.api_key = api_key or os.environ.get("OPENAI_API_KEY") - self.organization = organization or os.environ.get("OPENAI_ORG_ID") - self.project = project or os.environ.get("OPENAI_PROJECT_ID") - self.endpoint = endpoint - self.max_retries = max_retries - self.base_delay = base_delay - self.max_delay = max_delay - - # Keep a client open for connection pooling across multiple export calls - self._client = httpx.Client(timeout=httpx.Timeout(timeout=60, connect=5.0)) - - def set_api_key(self, api_key: str): - """Set the OpenAI API key for the exporter. - - Args: - api_key: The OpenAI API key to use. This is the same key used by the OpenAI Python - client. - """ - self.api_key = api_key - - def export(self, items: list[Trace | Span[Any]]) -> None: - if not items: - return - - if not self.api_key: - logger.warning("OPENAI_API_KEY is not set, skipping trace export") - return - - traces: list[dict[str, Any]] = [] - spans: list[dict[str, Any]] = [] - - data = [item.export() for item in items if item.export()] - payload = {"data": data} - - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json", - "OpenAI-Beta": "traces=v1", - } - - # Exponential backoff loop - attempt = 0 - delay = self.base_delay - while True: - attempt += 1 - try: - response = self._client.post(url=self.endpoint, headers=headers, json=payload) - - # If the response is successful, break out of the loop - if response.status_code < 300: - logger.debug(f"Exported {len(traces)} traces, {len(spans)} spans") - return - - # If the response is a client error (4xx), we wont retry - if 400 <= response.status_code < 500: - logger.error(f"Tracing client error {response.status_code}: {response.text}") - return - - # For 5xx or other unexpected codes, treat it as transient and retry - logger.warning(f"Server error {response.status_code}, retrying.") - except httpx.RequestError as exc: - # Network or other I/O error, we'll retry - logger.warning(f"Request failed: {exc}") - - # If we reach here, we need to retry or give up - if attempt >= self.max_retries: - logger.error("Max retries reached, giving up on this batch.") - return - - # Exponential backoff + jitter - sleep_time = delay + random.uniform(0, 0.1 * delay) # 10% jitter - time.sleep(sleep_time) - delay = min(delay * 2, self.max_delay) - - def close(self): - """Close the underlying HTTP client.""" - self._client.close() - - -class BatchTraceProcessor(TracingProcessor): - """Some implementation notes: - 1. Using Queue, which is thread-safe. - 2. Using a background thread to export spans, to minimize any performance issues. - 3. Spans are stored in memory until they are exported. - """ - - def __init__( - self, - exporter: TracingExporter, - max_queue_size: int = 8192, - max_batch_size: int = 128, - schedule_delay: float = 5.0, - export_trigger_ratio: float = 0.7, - ): - """ - Args: - exporter: The exporter to use. - max_queue_size: The maximum number of spans to store in the queue. After this, we will - start dropping spans. - max_batch_size: The maximum number of spans to export in a single batch. - schedule_delay: The delay between checks for new spans to export. - export_trigger_ratio: The ratio of the queue size at which we will trigger an export. - """ - self._exporter = exporter - self._queue: queue.Queue[Trace | Span[Any]] = queue.Queue(maxsize=max_queue_size) - self._max_queue_size = max_queue_size - self._max_batch_size = max_batch_size - self._schedule_delay = schedule_delay - self._shutdown_event = threading.Event() - - # The queue size threshold at which we export immediately. - self._export_trigger_size = int(max_queue_size * export_trigger_ratio) - - # Track when we next *must* perform a scheduled export - self._next_export_time = time.time() + self._schedule_delay - - self._shutdown_event = threading.Event() - self._worker_thread = threading.Thread(target=self._run, daemon=True) - self._worker_thread.start() - - def on_trace_start(self, trace: Trace) -> None: - try: - self._queue.put_nowait(trace) - except queue.Full: - logger.warning("Queue is full, dropping trace.") - - def on_trace_end(self, trace: Trace) -> None: - # We send traces via on_trace_start, so we don't need to do anything here. - pass - - def on_span_start(self, span: Span[Any]) -> None: - # We send spans via on_span_end, so we don't need to do anything here. - pass - - def on_span_end(self, span: Span[Any]) -> None: - try: - self._queue.put_nowait(span) - except queue.Full: - logger.warning("Queue is full, dropping span.") - - def shutdown(self, timeout: float | None = None): - """ - Called when the application stops. We signal our thread to stop, then join it. - """ - self._shutdown_event.set() - self._worker_thread.join(timeout=timeout) - - def force_flush(self): - """ - Forces an immediate flush of all queued spans. - """ - self._export_batches(force=True) - - def _run(self): - while not self._shutdown_event.is_set(): - current_time = time.time() - queue_size = self._queue.qsize() - - # If it's time for a scheduled flush or queue is above the trigger threshold - if current_time >= self._next_export_time or queue_size >= self._export_trigger_size: - self._export_batches(force=False) - # Reset the next scheduled flush time - self._next_export_time = time.time() + self._schedule_delay - else: - # Sleep a short interval so we don't busy-wait. - time.sleep(0.2) - - # Final drain after shutdown - self._export_batches(force=True) - - def _export_batches(self, force: bool = False): - """Drains the queue and exports in batches. If force=True, export everything. - Otherwise, export up to `max_batch_size` repeatedly until the queue is empty or below a - certain threshold. - """ - while True: - items_to_export: list[Span[Any] | Trace] = [] - - # Gather a batch of spans up to max_batch_size - while not self._queue.empty() and ( - force or len(items_to_export) < self._max_batch_size - ): - try: - items_to_export.append(self._queue.get_nowait()) - except queue.Empty: - # Another thread might have emptied the queue between checks - break - - # If we collected nothing, we're done - if not items_to_export: - break - - # Export the batch - self._exporter.export(items_to_export) - - -# Create a shared global instance: -_global_exporter = BackendSpanExporter() -_global_processor = BatchTraceProcessor(_global_exporter) - - -def default_exporter() -> BackendSpanExporter: - """The default exporter, which exports traces and spans to the backend in batches.""" - return _global_exporter - - -def default_processor() -> BatchTraceProcessor: - """The default processor, which exports traces and spans to the backend in batches.""" - return _global_processor diff --git a/tests/src/agents/tracing/scope.py b/tests/src/agents/tracing/scope.py deleted file mode 100644 index 9ccd9f87..00000000 --- a/tests/src/agents/tracing/scope.py +++ /dev/null @@ -1,45 +0,0 @@ -# Holds the current active span -import contextvars -from typing import TYPE_CHECKING, Any - -from .logger import logger - -if TYPE_CHECKING: - from .spans import Span - from .traces import Trace - -_current_span: contextvars.ContextVar["Span[Any] | None"] = contextvars.ContextVar( - "current_span", default=None -) - -_current_trace: contextvars.ContextVar["Trace | None"] = contextvars.ContextVar( - "current_trace", default=None -) - - -class Scope: - @classmethod - def get_current_span(cls) -> "Span[Any] | None": - return _current_span.get() - - @classmethod - def set_current_span(cls, span: "Span[Any] | None") -> "contextvars.Token[Span[Any] | None]": - return _current_span.set(span) - - @classmethod - def reset_current_span(cls, token: "contextvars.Token[Span[Any] | None]") -> None: - _current_span.reset(token) - - @classmethod - def get_current_trace(cls) -> "Trace | None": - return _current_trace.get() - - @classmethod - def set_current_trace(cls, trace: "Trace | None") -> "contextvars.Token[Trace | None]": - logger.debug(f"Setting current trace: {trace.trace_id if trace else None}") - return _current_trace.set(trace) - - @classmethod - def reset_current_trace(cls, token: "contextvars.Token[Trace | None]") -> None: - logger.debug("Resetting current trace") - _current_trace.reset(token) diff --git a/tests/src/agents/tracing/setup.py b/tests/src/agents/tracing/setup.py deleted file mode 100644 index bc340c9f..00000000 --- a/tests/src/agents/tracing/setup.py +++ /dev/null @@ -1,211 +0,0 @@ -from __future__ import annotations - -import os -import threading -from typing import Any - -from . import util -from .logger import logger -from .processor_interface import TracingProcessor -from .scope import Scope -from .spans import NoOpSpan, Span, SpanImpl, TSpanData -from .traces import NoOpTrace, Trace, TraceImpl - - -class SynchronousMultiTracingProcessor(TracingProcessor): - """ - Forwards all calls to a list of TracingProcessors, in order of registration. - """ - - def __init__(self): - # Using a tuple to avoid race conditions when iterating over processors - self._processors: tuple[TracingProcessor, ...] = () - self._lock = threading.Lock() - - def add_tracing_processor(self, tracing_processor: TracingProcessor): - """ - Add a processor to the list of processors. Each processor will receive all traces/spans. - """ - with self._lock: - self._processors += (tracing_processor,) - - def set_processors(self, processors: list[TracingProcessor]): - """ - Set the list of processors. This will replace the current list of processors. - """ - with self._lock: - self._processors = tuple(processors) - - def on_trace_start(self, trace: Trace) -> None: - """ - Called when a trace is started. - """ - for processor in self._processors: - processor.on_trace_start(trace) - - def on_trace_end(self, trace: Trace) -> None: - """ - Called when a trace is finished. - """ - for processor in self._processors: - processor.on_trace_end(trace) - - def on_span_start(self, span: Span[Any]) -> None: - """ - Called when a span is started. - """ - for processor in self._processors: - processor.on_span_start(span) - - def on_span_end(self, span: Span[Any]) -> None: - """ - Called when a span is finished. - """ - for processor in self._processors: - processor.on_span_end(span) - - def shutdown(self) -> None: - """ - Called when the application stops. - """ - for processor in self._processors: - logger.debug(f"Shutting down trace processor {processor}") - processor.shutdown() - - def force_flush(self): - """ - Force the processors to flush their buffers. - """ - for processor in self._processors: - processor.force_flush() - - -class TraceProvider: - def __init__(self): - self._multi_processor = SynchronousMultiTracingProcessor() - self._disabled = os.environ.get("OPENAI_AGENTS_DISABLE_TRACING", "false").lower() in ( - "true", - "1", - ) - - def register_processor(self, processor: TracingProcessor): - """ - Add a processor to the list of processors. Each processor will receive all traces/spans. - """ - self._multi_processor.add_tracing_processor(processor) - - def set_processors(self, processors: list[TracingProcessor]): - """ - Set the list of processors. This will replace the current list of processors. - """ - self._multi_processor.set_processors(processors) - - def get_current_trace(self) -> Trace | None: - """ - Returns the currently active trace, if any. - """ - return Scope.get_current_trace() - - def get_current_span(self) -> Span[Any] | None: - """ - Returns the currently active span, if any. - """ - return Scope.get_current_span() - - def set_disabled(self, disabled: bool) -> None: - """ - Set whether tracing is disabled. - """ - self._disabled = disabled - - def create_trace( - self, - name: str, - trace_id: str | None = None, - group_id: str | None = None, - metadata: dict[str, Any] | None = None, - disabled: bool = False, - ) -> Trace: - """ - Create a new trace. - """ - if self._disabled or disabled: - logger.debug(f"Tracing is disabled. Not creating trace {name}") - return NoOpTrace() - - trace_id = trace_id or util.gen_trace_id() - - logger.debug(f"Creating trace {name} with id {trace_id}") - - return TraceImpl( - name=name, - trace_id=trace_id, - group_id=group_id, - metadata=metadata, - processor=self._multi_processor, - ) - - def create_span( - self, - span_data: TSpanData, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, - ) -> Span[TSpanData]: - """ - Create a new span. - """ - if self._disabled or disabled: - logger.debug(f"Tracing is disabled. Not creating span {span_data}") - return NoOpSpan(span_data) - - if not parent: - current_span = Scope.get_current_span() - current_trace = Scope.get_current_trace() - if current_trace is None: - logger.error( - "No active trace. Make sure to start a trace with `trace()` first" - "Returning NoOpSpan." - ) - return NoOpSpan(span_data) - elif isinstance(current_trace, NoOpTrace) or isinstance(current_span, NoOpSpan): - logger.debug( - f"Parent {current_span} or {current_trace} is no-op, returning NoOpSpan" - ) - return NoOpSpan(span_data) - - parent_id = current_span.span_id if current_span else None - trace_id = current_trace.trace_id - - elif isinstance(parent, Trace): - if isinstance(parent, NoOpTrace): - logger.debug(f"Parent {parent} is no-op, returning NoOpSpan") - return NoOpSpan(span_data) - trace_id = parent.trace_id - parent_id = None - elif isinstance(parent, Span): - if isinstance(parent, NoOpSpan): - logger.debug(f"Parent {parent} is no-op, returning NoOpSpan") - return NoOpSpan(span_data) - parent_id = parent.span_id - trace_id = parent.trace_id - - logger.debug(f"Creating span {span_data} with id {span_id}") - - return SpanImpl( - trace_id=trace_id, - span_id=span_id, - parent_id=parent_id, - processor=self._multi_processor, - span_data=span_data, - ) - - def shutdown(self) -> None: - try: - logger.debug("Shutting down trace provider") - self._multi_processor.shutdown() - except Exception as e: - logger.error(f"Error shutting down trace provider: {e}") - - -GLOBAL_TRACE_PROVIDER = TraceProvider() diff --git a/tests/src/agents/tracing/span_data.py b/tests/src/agents/tracing/span_data.py deleted file mode 100644 index 5e5d38cb..00000000 --- a/tests/src/agents/tracing/span_data.py +++ /dev/null @@ -1,188 +0,0 @@ -from __future__ import annotations - -import abc -from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from openai.types.responses import Response, ResponseInputItemParam - - -class SpanData(abc.ABC): - @abc.abstractmethod - def export(self) -> dict[str, Any]: - pass - - @property - @abc.abstractmethod - def type(self) -> str: - pass - - -class AgentSpanData(SpanData): - __slots__ = ("name", "handoffs", "tools", "output_type") - - def __init__( - self, - name: str, - handoffs: list[str] | None = None, - tools: list[str] | None = None, - output_type: str | None = None, - ): - self.name = name - self.handoffs: list[str] | None = handoffs - self.tools: list[str] | None = tools - self.output_type: str | None = output_type - - @property - def type(self) -> str: - return "agent" - - def export(self) -> dict[str, Any]: - return { - "type": self.type, - "name": self.name, - "handoffs": self.handoffs, - "tools": self.tools, - "output_type": self.output_type, - } - - -class FunctionSpanData(SpanData): - __slots__ = ("name", "input", "output") - - def __init__(self, name: str, input: str | None, output: str | None): - self.name = name - self.input = input - self.output = output - - @property - def type(self) -> str: - return "function" - - def export(self) -> dict[str, Any]: - return { - "type": self.type, - "name": self.name, - "input": self.input, - "output": self.output, - } - - -class GenerationSpanData(SpanData): - __slots__ = ( - "input", - "output", - "model", - "model_config", - "usage", - ) - - def __init__( - self, - input: Sequence[Mapping[str, Any]] | None = None, - output: Sequence[Mapping[str, Any]] | None = None, - model: str | None = None, - model_config: Mapping[str, Any] | None = None, - usage: dict[str, Any] | None = None, - ): - self.input = input - self.output = output - self.model = model - self.model_config = model_config - self.usage = usage - - @property - def type(self) -> str: - return "generation" - - def export(self) -> dict[str, Any]: - return { - "type": self.type, - "input": self.input, - "output": self.output, - "model": self.model, - "model_config": self.model_config, - "usage": self.usage, - } - - -class ResponseSpanData(SpanData): - __slots__ = ("response", "input") - - def __init__( - self, - response: Response | None = None, - input: str | list[ResponseInputItemParam] | None = None, - ) -> None: - self.response = response - # This is not used by the OpenAI trace processors, but is useful for other tracing - # processor implementations - self.input = input - - @property - def type(self) -> str: - return "response" - - def export(self) -> dict[str, Any]: - return { - "type": self.type, - "response_id": self.response.id if self.response else None, - } - - -class HandoffSpanData(SpanData): - __slots__ = ("from_agent", "to_agent") - - def __init__(self, from_agent: str | None, to_agent: str | None): - self.from_agent = from_agent - self.to_agent = to_agent - - @property - def type(self) -> str: - return "handoff" - - def export(self) -> dict[str, Any]: - return { - "type": self.type, - "from_agent": self.from_agent, - "to_agent": self.to_agent, - } - - -class CustomSpanData(SpanData): - __slots__ = ("name", "data") - - def __init__(self, name: str, data: dict[str, Any]): - self.name = name - self.data = data - - @property - def type(self) -> str: - return "custom" - - def export(self) -> dict[str, Any]: - return { - "type": self.type, - "name": self.name, - "data": self.data, - } - - -class GuardrailSpanData(SpanData): - __slots__ = ("name", "triggered") - - def __init__(self, name: str, triggered: bool = False): - self.name = name - self.triggered = triggered - - @property - def type(self) -> str: - return "guardrail" - - def export(self) -> dict[str, Any]: - return { - "type": self.type, - "name": self.name, - "triggered": self.triggered, - } diff --git a/tests/src/agents/tracing/spans.py b/tests/src/agents/tracing/spans.py deleted file mode 100644 index d682a9a0..00000000 --- a/tests/src/agents/tracing/spans.py +++ /dev/null @@ -1,264 +0,0 @@ -from __future__ import annotations - -import abc -import contextvars -from typing import Any, Generic, TypeVar - -from typing_extensions import TypedDict - -from . import util -from .logger import logger -from .processor_interface import TracingProcessor -from .scope import Scope -from .span_data import SpanData - -TSpanData = TypeVar("TSpanData", bound=SpanData) - - -class SpanError(TypedDict): - message: str - data: dict[str, Any] | None - - -class Span(abc.ABC, Generic[TSpanData]): - @property - @abc.abstractmethod - def trace_id(self) -> str: - pass - - @property - @abc.abstractmethod - def span_id(self) -> str: - pass - - @property - @abc.abstractmethod - def span_data(self) -> TSpanData: - pass - - @abc.abstractmethod - def start(self, mark_as_current: bool = False): - """ - Start the span. - - Args: - mark_as_current: If true, the span will be marked as the current span. - """ - pass - - @abc.abstractmethod - def finish(self, reset_current: bool = False) -> None: - """ - Finish the span. - - Args: - reset_current: If true, the span will be reset as the current span. - """ - pass - - @abc.abstractmethod - def __enter__(self) -> Span[TSpanData]: - pass - - @abc.abstractmethod - def __exit__(self, exc_type, exc_val, exc_tb): - pass - - @property - @abc.abstractmethod - def parent_id(self) -> str | None: - pass - - @abc.abstractmethod - def set_error(self, error: SpanError) -> None: - pass - - @property - @abc.abstractmethod - def error(self) -> SpanError | None: - pass - - @abc.abstractmethod - def export(self) -> dict[str, Any] | None: - pass - - @property - @abc.abstractmethod - def started_at(self) -> str | None: - pass - - @property - @abc.abstractmethod - def ended_at(self) -> str | None: - pass - - -class NoOpSpan(Span[TSpanData]): - __slots__ = ("_span_data", "_prev_span_token") - - def __init__(self, span_data: TSpanData): - self._span_data = span_data - self._prev_span_token: contextvars.Token[Span[TSpanData] | None] | None = None - - @property - def trace_id(self) -> str: - return "no-op" - - @property - def span_id(self) -> str: - return "no-op" - - @property - def span_data(self) -> TSpanData: - return self._span_data - - @property - def parent_id(self) -> str | None: - return None - - def start(self, mark_as_current: bool = False): - if mark_as_current: - self._prev_span_token = Scope.set_current_span(self) - - def finish(self, reset_current: bool = False) -> None: - if reset_current and self._prev_span_token is not None: - Scope.reset_current_span(self._prev_span_token) - self._prev_span_token = None - - def __enter__(self) -> Span[TSpanData]: - self.start(mark_as_current=True) - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - reset_current = True - if exc_type is GeneratorExit: - logger.debug("GeneratorExit, skipping span reset") - reset_current = False - - self.finish(reset_current=reset_current) - - def set_error(self, error: SpanError) -> None: - pass - - @property - def error(self) -> SpanError | None: - return None - - def export(self) -> dict[str, Any] | None: - return None - - @property - def started_at(self) -> str | None: - return None - - @property - def ended_at(self) -> str | None: - return None - - -class SpanImpl(Span[TSpanData]): - __slots__ = ( - "_trace_id", - "_span_id", - "_parent_id", - "_started_at", - "_ended_at", - "_error", - "_prev_span_token", - "_processor", - "_span_data", - ) - - def __init__( - self, - trace_id: str, - span_id: str | None, - parent_id: str | None, - processor: TracingProcessor, - span_data: TSpanData, - ): - self._trace_id = trace_id - self._span_id = span_id or util.gen_span_id() - self._parent_id = parent_id - self._started_at: str | None = None - self._ended_at: str | None = None - self._processor = processor - self._error: SpanError | None = None - self._prev_span_token: contextvars.Token[Span[TSpanData] | None] | None = None - self._span_data = span_data - - @property - def trace_id(self) -> str: - return self._trace_id - - @property - def span_id(self) -> str: - return self._span_id - - @property - def span_data(self) -> TSpanData: - return self._span_data - - @property - def parent_id(self) -> str | None: - return self._parent_id - - def start(self, mark_as_current: bool = False): - if self.started_at is not None: - logger.warning("Span already started") - return - - self._started_at = util.time_iso() - self._processor.on_span_start(self) - if mark_as_current: - self._prev_span_token = Scope.set_current_span(self) - - def finish(self, reset_current: bool = False) -> None: - if self.ended_at is not None: - logger.warning("Span already finished") - return - - self._ended_at = util.time_iso() - self._processor.on_span_end(self) - if reset_current and self._prev_span_token is not None: - Scope.reset_current_span(self._prev_span_token) - self._prev_span_token = None - - def __enter__(self) -> Span[TSpanData]: - self.start(mark_as_current=True) - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - reset_current = True - if exc_type is GeneratorExit: - logger.debug("GeneratorExit, skipping span reset") - reset_current = False - - self.finish(reset_current=reset_current) - - def set_error(self, error: SpanError) -> None: - self._error = error - - @property - def error(self) -> SpanError | None: - return self._error - - @property - def started_at(self) -> str | None: - return self._started_at - - @property - def ended_at(self) -> str | None: - return self._ended_at - - def export(self) -> dict[str, Any] | None: - return { - "object": "trace.span", - "id": self.span_id, - "trace_id": self.trace_id, - "parent_id": self._parent_id, - "started_at": self._started_at, - "ended_at": self._ended_at, - "span_data": self.span_data.export(), - "error": self._error, - } diff --git a/tests/src/agents/tracing/traces.py b/tests/src/agents/tracing/traces.py deleted file mode 100644 index bf3b43df..00000000 --- a/tests/src/agents/tracing/traces.py +++ /dev/null @@ -1,195 +0,0 @@ -from __future__ import annotations - -import abc -import contextvars -from typing import Any - -from . import util -from .logger import logger -from .processor_interface import TracingProcessor -from .scope import Scope - - -class Trace: - """ - A trace is the root level object that tracing creates. It represents a logical "workflow". - """ - - @abc.abstractmethod - def __enter__(self) -> Trace: - pass - - @abc.abstractmethod - def __exit__(self, exc_type, exc_val, exc_tb): - pass - - @abc.abstractmethod - def start(self, mark_as_current: bool = False): - """ - Start the trace. - - Args: - mark_as_current: If true, the trace will be marked as the current trace. - """ - pass - - @abc.abstractmethod - def finish(self, reset_current: bool = False): - """ - Finish the trace. - - Args: - reset_current: If true, the trace will be reset as the current trace. - """ - pass - - @property - @abc.abstractmethod - def trace_id(self) -> str: - """ - The trace ID. - """ - pass - - @property - @abc.abstractmethod - def name(self) -> str: - """ - The name of the workflow being traced. - """ - pass - - @abc.abstractmethod - def export(self) -> dict[str, Any] | None: - """ - Export the trace as a dictionary. - """ - pass - - -class NoOpTrace(Trace): - """ - A no-op trace that will not be recorded. - """ - - def __init__(self): - self._started = False - self._prev_context_token: contextvars.Token[Trace | None] | None = None - - def __enter__(self) -> Trace: - if self._started: - if not self._prev_context_token: - logger.error("Trace already started but no context token set") - return self - - self._started = True - self.start(mark_as_current=True) - - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - self.finish(reset_current=True) - - def start(self, mark_as_current: bool = False): - if mark_as_current: - self._prev_context_token = Scope.set_current_trace(self) - - def finish(self, reset_current: bool = False): - if reset_current and self._prev_context_token is not None: - Scope.reset_current_trace(self._prev_context_token) - self._prev_context_token = None - - @property - def trace_id(self) -> str: - return "no-op" - - @property - def name(self) -> str: - return "no-op" - - def export(self) -> dict[str, Any] | None: - return None - - -NO_OP_TRACE = NoOpTrace() - - -class TraceImpl(Trace): - """ - A trace that will be recorded by the tracing library. - """ - - __slots__ = ( - "_name", - "_trace_id", - "group_id", - "metadata", - "_prev_context_token", - "_processor", - "_started", - ) - - def __init__( - self, - name: str, - trace_id: str | None, - group_id: str | None, - metadata: dict[str, Any] | None, - processor: TracingProcessor, - ): - self._name = name - self._trace_id = trace_id or util.gen_trace_id() - self.group_id = group_id - self.metadata = metadata - self._prev_context_token: contextvars.Token[Trace | None] | None = None - self._processor = processor - self._started = False - - @property - def trace_id(self) -> str: - return self._trace_id - - @property - def name(self) -> str: - return self._name - - def start(self, mark_as_current: bool = False): - if self._started: - return - - self._started = True - self._processor.on_trace_start(self) - - if mark_as_current: - self._prev_context_token = Scope.set_current_trace(self) - - def finish(self, reset_current: bool = False): - if not self._started: - return - - self._processor.on_trace_end(self) - - if reset_current and self._prev_context_token is not None: - Scope.reset_current_trace(self._prev_context_token) - self._prev_context_token = None - - def __enter__(self) -> Trace: - if self._started: - if not self._prev_context_token: - logger.error("Trace already started but no context token set") - return self - - self.start(mark_as_current=True) - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - self.finish(reset_current=exc_type is not GeneratorExit) - - def export(self) -> dict[str, Any] | None: - return { - "object": "trace", - "id": self.trace_id, - "workflow_name": self.name, - "group_id": self.group_id, - "metadata": self.metadata, - } diff --git a/tests/src/agents/tracing/util.py b/tests/src/agents/tracing/util.py deleted file mode 100644 index 3e5cad90..00000000 --- a/tests/src/agents/tracing/util.py +++ /dev/null @@ -1,17 +0,0 @@ -import uuid -from datetime import datetime, timezone - - -def time_iso() -> str: - """Returns the current time in ISO 8601 format.""" - return datetime.now(timezone.utc).isoformat() - - -def gen_trace_id() -> str: - """Generates a new trace ID.""" - return f"trace_{uuid.uuid4().hex}" - - -def gen_span_id() -> str: - """Generates a new span ID.""" - return f"span_{uuid.uuid4().hex[:24]}" diff --git a/tests/src/agents/usage.py b/tests/src/agents/usage.py deleted file mode 100644 index 23d989b4..00000000 --- a/tests/src/agents/usage.py +++ /dev/null @@ -1,22 +0,0 @@ -from dataclasses import dataclass - - -@dataclass -class Usage: - requests: int = 0 - """Total requests made to the LLM API.""" - - input_tokens: int = 0 - """Total input tokens sent, across all requests.""" - - output_tokens: int = 0 - """Total output tokens received, across all requests.""" - - total_tokens: int = 0 - """Total tokens sent and received, across all requests.""" - - def add(self, other: "Usage") -> None: - self.requests += other.requests if other.requests else 0 - self.input_tokens += other.input_tokens if other.input_tokens else 0 - self.output_tokens += other.output_tokens if other.output_tokens else 0 - self.total_tokens += other.total_tokens if other.total_tokens else 0 diff --git a/tests/src/agents/version.py b/tests/src/agents/version.py deleted file mode 100644 index a0b7e9be..00000000 --- a/tests/src/agents/version.py +++ /dev/null @@ -1,7 +0,0 @@ -import importlib.metadata - -try: - __version__ = importlib.metadata.version("agents") -except importlib.metadata.PackageNotFoundError: - # Fallback if running from source without being installed - __version__ = "0.0.0" diff --git a/tests/src/openai_agents.egg-info/PKG-INFO b/tests/src/openai_agents.egg-info/PKG-INFO deleted file mode 100644 index ebf2d7c2..00000000 --- a/tests/src/openai_agents.egg-info/PKG-INFO +++ /dev/null @@ -1,217 +0,0 @@ -Metadata-Version: 2.2 -Name: openai-agents -Version: 0.0.1 -Summary: OpenAI Agents SDK -Author-email: OpenAI -Project-URL: Homepage, https://github.com/openai/openai-agents-python -Project-URL: Repository, https://github.com/openai/openai-agents-python -Classifier: Typing :: Typed -Classifier: Intended Audience :: Developers -Classifier: Programming Language :: Python :: 3 -Classifier: Programming Language :: Python :: 3.9 -Classifier: Programming Language :: Python :: 3.10 -Classifier: Programming Language :: Python :: 3.11 -Classifier: Programming Language :: Python :: 3.12 -Classifier: Intended Audience :: Developers -Classifier: Intended Audience :: Information Technology -Classifier: Operating System :: OS Independent -Classifier: Operating System :: POSIX -Classifier: Operating System :: MacOS -Classifier: Operating System :: POSIX :: Linux -Classifier: Operating System :: Microsoft :: Windows -Classifier: Topic :: Software Development :: Libraries :: Python Modules -Requires-Python: >=3.9 -Description-Content-Type: text/markdown -Requires-Dist: openai@ {root:parent:uri}/openai-1.30.1-py3-none-any.whl -Requires-Dist: pydantic<3,>=2.10 -Requires-Dist: griffe<2,>=1.5.6 -Requires-Dist: typing-extensions<5,>=4.12.2 -Requires-Dist: requests<3,>=2.0 -Requires-Dist: types-requests<3,>=2.0 - -# OpenAI Agents SDK - -The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. - -### Core concepts: -1. [**Agents,**](docs/agents.md) which are LLMs configured with instructions, tools, guardrails, and handoffs -2. [**Handoffs,**](docs/handoffs.md) which allow agents to transfer control to other agents for specific tasks -3. [**Guardrails,**](docs/guardrails.md) which makes it easy to watch an agent execution and validate inputs/outputs -4. [**Tracing,**](docs/tracing.md) which automatically captures the entire agentic run, allowing you to view, debug and optimize your workflows - -Explore examples of the SDK in action in the [examples](examples) directory. - -## Using the SDK - -1. Set up python env - -``` -python -m venv env -source env/bin/activate -``` - -2. Install Agents SDK - -``` -pip install git+ssh://git@github.com/openai/agentsdk_prototype.git#subdirectory=agents -``` - -## Development (only needed if you need to edit the SDK/examples) - -0. Ensure you have [`uv`](https://docs.astral.sh/uv/) installed. - -```bash -uv --version -``` - -1. Install dependencies/setup virtual environment - -```bash -uv sync -``` - -2. Install the dependencies - -```bash -uv sync --all-extras --all-packages -``` - -3. Activate the virtual environment - -```bash -source .venv/bin/activate -``` - -## Tests - -Make sure the virtual environment is activated first. - -```bash -pytest -``` - -## Hello world example - -```py -from agents.agent import Agent -from agents.run import Runner -import asyncio - -agent = Agent( - name="Hello world", - instructions="You are a helpful agent." -) - -async def main(): - out = await Runner.run(agent, input="Hola, ¿cómo estás?") - print(out) - - -if __name__ == "__main__": - asyncio.run(main()) - -# The capital of the United States is Washington, D.C. -``` - -## Handoffs example - -```py -from agents.agent import Agent -from agents.run import Runner -import asyncio - -spanish_agent = Agent( - name="spanish_agent", - instructions="You only speak Spanish.", -) - -english_agent = Agent( - name="english_agent", - instructions="You only speak English", -) - -triage_agent = Agent( - name="triage_agent", - instructions="Handoff to the appropriate agent based on the language of the request.", - handoffs=[spanish_agent, english_agent], -) - - -async def main(): - out = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") - print(out) - - -if __name__ == "__main__": - asyncio.run(main()) - -# ¡Hola! Estoy bien, gracias por preguntar. ¿Y tú, cómo estás? -``` - -## Functions example - -```python -from agents.agent import Agent -from agents.run import Runner -import asyncio -from agents.tool import function_tool - - -@function_tool -def get_weather(city: str) -> str: - print(f"Getting weather for {city}") - return f"The weather in {city} is sunny." - - -agent = Agent( - name="Hello world", - instructions="You are a helpful agent.", - tools=[get_weather], -) - - -async def main(): - out = await Runner.run(agent, input="What's the weather in Tokyo?") - print(out.final_output) - - -if __name__ == "__main__": - asyncio.run(main()) -``` - -For more complex systems, we recommend including detailed instructions about handoffs. We have a recommendation in `handoff.RECOMMENDED_PROMPT_PREFIX` that can be used to add these instructions to an agent. - -```py -agent = Agent( - ..., - instructions=f"{handoff.RECOMMENDED_PROMPT_PREFIX}\n\n{instructions}" -) -``` - -## The agent loop - -When you call `Runner.run()`, we run a loop until we get a final output. - -1. We call the LLM, using the model and settings on the agent, and the message history. -2. The LLM returns a response, which may include tool calls. -3. If the response has a final output (see below for the more on this), we return it and end the loop. -4. If the response has a handoff, we set the agent to the new agent and go back to step 1. -5. We process the tool calls (if any) and append the tool responses messsages. Then we go to step 1. - -There is a `max_turns` parameter that you can use to limit the number of times the loop executes. - -### Final output - -There are two ways to get a **final output**: - -1. If you set an `output_type` on the agent, the LLM is given a special tool called `final_output`. If it uses this tool, the output of the tool is the final output. -2. If there's no `output_type`, then we assume the final output is a string. As soon as the LLM produces a message without any tool calls, that is considered the final output. - -As a result, the mental model for the agent loop is: - -1. If the current agent has an `output_type`, the loop runs until the agent uses that tool to return the final output. -2. If the current agent does not have an `output_type`, the loop runs until the current agent produces a message without any tool calls. - -## Common agent patterns - -There are a number of useful patterns in agentic apps. There are a number of examples in [`examples/agent_patterns`](examples/agent_patterns), and we recommend reading them. diff --git a/tests/src/openai_agents.egg-info/SOURCES.txt b/tests/src/openai_agents.egg-info/SOURCES.txt deleted file mode 100644 index 695ad1fc..00000000 --- a/tests/src/openai_agents.egg-info/SOURCES.txt +++ /dev/null @@ -1,81 +0,0 @@ -README.md -pyproject.toml -src/agents/__init__.py -src/agents/_config.py -src/agents/_debug.py -src/agents/_run_impl.py -src/agents/_utils.py -src/agents/agent.py -src/agents/agent_output.py -src/agents/call_agent_tool.py -src/agents/computer.py -src/agents/exceptions.py -src/agents/function_schema.py -src/agents/guardrail.py -src/agents/handoffs.py -src/agents/items.py -src/agents/lifecycle.py -src/agents/logger.py -src/agents/model_settings.py -src/agents/result.py -src/agents/run.py -src/agents/run_context.py -src/agents/strict_schema.py -src/agents/tool.py -src/agents/usage.py -src/agents/version.py -src/agents/extensions/__init__.py -src/agents/extensions/handoff_filters.py -src/agents/extensions/handoff_prompt.py -src/agents/models/__init__.py -src/agents/models/_openai_shared.py -src/agents/models/fake_id.py -src/agents/models/interface.py -src/agents/models/map.py -src/agents/models/openai_chatcompletions.py -src/agents/models/openai_responses.py -src/agents/tracing/__init__.py -src/agents/tracing/create.py -src/agents/tracing/logger.py -src/agents/tracing/processor_interface.py -src/agents/tracing/processors.py -src/agents/tracing/scope.py -src/agents/tracing/setup.py -src/agents/tracing/span_data.py -src/agents/tracing/spans.py -src/agents/tracing/traces.py -src/agents/tracing/util.py -src/openai_agents.egg-info/PKG-INFO -src/openai_agents.egg-info/SOURCES.txt -src/openai_agents.egg-info/dependency_links.txt -src/openai_agents.egg-info/requires.txt -src/openai_agents.egg-info/top_level.txt -tests/test_agent_config.py -tests/test_agent_hooks.py -tests/test_agent_runner.py -tests/test_agent_runner_streamed.py -tests/test_agent_tracing.py -tests/test_config.py -tests/test_doc_parsing.py -tests/test_function_schema.py -tests/test_function_tool.py -tests/test_function_tool_decorator.py -tests/test_global_hooks.py -tests/test_guardrails.py -tests/test_handoff_tool.py -tests/test_items_helpers.py -tests/test_max_turns.py -tests/test_model_mapper.py -tests/test_openai_chatcompletions_converter.py -tests/test_openai_responses_converter.py -tests/test_output_tool.py -tests/test_responses.py -tests/test_run_config.py -tests/test_run_step_execution.py -tests/test_run_step_processing.py -tests/test_tool_converter.py -tests/test_trace_processor.py -tests/test_tracing.py -tests/test_tracing_errors.py -tests/test_tracing_errors_streamed.py -tests/testing_processor.py \ No newline at end of file diff --git a/tests/src/openai_agents.egg-info/dependency_links.txt b/tests/src/openai_agents.egg-info/dependency_links.txt deleted file mode 100644 index 8b137891..00000000 --- a/tests/src/openai_agents.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/tests/src/openai_agents.egg-info/requires.txt b/tests/src/openai_agents.egg-info/requires.txt deleted file mode 100644 index 3dbad2b8..00000000 --- a/tests/src/openai_agents.egg-info/requires.txt +++ /dev/null @@ -1,6 +0,0 @@ -openai@ {root:parent:uri}/openai-1.30.1-py3-none-any.whl -pydantic<3,>=2.10 -griffe<2,>=1.5.6 -typing-extensions<5,>=4.12.2 -requests<3,>=2.0 -types-requests<3,>=2.0 diff --git a/tests/src/openai_agents.egg-info/top_level.txt b/tests/src/openai_agents.egg-info/top_level.txt deleted file mode 100644 index 4a33ff62..00000000 --- a/tests/src/openai_agents.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -agents diff --git a/tests/test_agent_config.py b/tests/test_agent_config.py index 44339dad..f79c0cf8 100644 --- a/tests/test_agent_config.py +++ b/tests/test_agent_config.py @@ -1,7 +1,7 @@ import pytest from pydantic import BaseModel -from agents import Agent, Handoff, RunContextWrapper, Runner, handoff +from agents import Agent, AgentOutputSchema, Handoff, RunContextWrapper, Runner, handoff @pytest.mark.asyncio @@ -160,8 +160,9 @@ async def test_agent_final_output(): ) schema = Runner._get_output_schema(agent) + assert isinstance(schema, AgentOutputSchema) assert schema is not None assert schema.output_type == Foo - assert schema.strict_json_schema is True + assert schema.is_strict_json_schema() is True assert schema.json_schema() is not None assert not schema.is_plain_text() diff --git a/tests/test_agent_hooks.py b/tests/test_agent_hooks.py index 33107cba..a6c302dc 100644 --- a/tests/test_agent_hooks.py +++ b/tests/test_agent_hooks.py @@ -224,7 +224,7 @@ class Foo(TypedDict): @pytest.mark.asyncio -async def test_structed_output_non_streamed_agent_hooks(): +async def test_structured_output_non_streamed_agent_hooks(): hooks = AgentHooksForTests() model = FakeModel() agent_1 = Agent(name="test_1", model=model) @@ -295,7 +295,7 @@ async def test_structed_output_non_streamed_agent_hooks(): @pytest.mark.asyncio -async def test_structed_output_streamed_agent_hooks(): +async def test_structured_output_streamed_agent_hooks(): hooks = AgentHooksForTests() model = FakeModel() agent_1 = Agent(name="test_1", model=model) diff --git a/tests/test_agent_runner.py b/tests/test_agent_runner.py index c124915a..14a278a9 100644 --- a/tests/test_agent_runner.py +++ b/tests/test_agent_runner.py @@ -14,13 +14,17 @@ InputGuardrail, InputGuardrailTripwireTriggered, ModelBehaviorError, + ModelSettings, OutputGuardrail, OutputGuardrailTripwireTriggered, + RunConfig, RunContextWrapper, Runner, UserError, handoff, ) +from agents.agent import ToolsToFinalOutputResult +from agents.tool import FunctionToolResult, function_tool from .fake_model import FakeModel from .test_responses import ( @@ -552,3 +556,192 @@ def guardrail_function( with pytest.raises(OutputGuardrailTripwireTriggered): await Runner.run(agent, input="user_message") + + +@function_tool +def test_tool_one(): + return Foo(bar="tool_one_result") + + +@function_tool +def test_tool_two(): + return "tool_two_result" + + +@pytest.mark.asyncio +async def test_tool_use_behavior_first_output(): + model = FakeModel() + agent = Agent( + name="test", + model=model, + tools=[get_function_tool("foo", "tool_result"), test_tool_one, test_tool_two], + tool_use_behavior="stop_on_first_tool", + output_type=Foo, + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [ + get_text_message("a_message"), + get_function_tool_call("test_tool_one", None), + get_function_tool_call("test_tool_two", None), + ], + ] + ) + + result = await Runner.run(agent, input="user_message") + + assert result.final_output == Foo(bar="tool_one_result"), ( + "should have used the first tool result" + ) + + +def custom_tool_use_behavior( + context: RunContextWrapper[Any], results: list[FunctionToolResult] +) -> ToolsToFinalOutputResult: + if "test_tool_one" in [result.tool.name for result in results]: + return ToolsToFinalOutputResult(is_final_output=True, final_output="the_final_output") + else: + return ToolsToFinalOutputResult(is_final_output=False, final_output=None) + + +@pytest.mark.asyncio +async def test_tool_use_behavior_custom_function(): + model = FakeModel() + agent = Agent( + name="test", + model=model, + tools=[get_function_tool("foo", "tool_result"), test_tool_one, test_tool_two], + tool_use_behavior=custom_tool_use_behavior, + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [ + get_text_message("a_message"), + get_function_tool_call("test_tool_two", None), + ], + # Second turn: a message and tool call + [ + get_text_message("a_message"), + get_function_tool_call("test_tool_one", None), + get_function_tool_call("test_tool_two", None), + ], + ] + ) + + result = await Runner.run(agent, input="user_message") + + assert len(result.raw_responses) == 2, "should have two model responses" + assert result.final_output == "the_final_output", "should have used the custom function" + + +@pytest.mark.asyncio +async def test_model_settings_override(): + model = FakeModel() + agent = Agent( + name="test", model=model, model_settings=ModelSettings(temperature=1.0, max_tokens=1000) + ) + + model.add_multiple_turn_outputs( + [ + [ + get_text_message("a_message"), + ], + ] + ) + + await Runner.run( + agent, + input="user_message", + run_config=RunConfig(model_settings=ModelSettings(0.5)), + ) + + # temperature is overridden by Runner.run, but max_tokens is not + assert model.last_turn_args["model_settings"].temperature == 0.5 + assert model.last_turn_args["model_settings"].max_tokens == 1000 + + +@pytest.mark.asyncio +async def test_previous_response_id_passed_between_runs(): + """Test that previous_response_id is passed to the model on subsequent runs.""" + model = FakeModel() + model.set_next_output([get_text_message("done")]) + agent = Agent(name="test", model=model) + + assert model.last_turn_args.get("previous_response_id") is None + await Runner.run(agent, input="test", previous_response_id="resp-non-streamed-test") + assert model.last_turn_args.get("previous_response_id") == "resp-non-streamed-test" + + +@pytest.mark.asyncio +async def test_multi_turn_previous_response_id_passed_between_runs(): + """Test that previous_response_id is passed to the model on subsequent runs.""" + + model = FakeModel() + agent = Agent( + name="test", + model=model, + tools=[get_function_tool("foo", "tool_result")], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("foo", json.dumps({"a": "b"}))], + # Second turn: text message + [get_text_message("done")], + ] + ) + + assert model.last_turn_args.get("previous_response_id") is None + await Runner.run(agent, input="test", previous_response_id="resp-test-123") + assert model.last_turn_args.get("previous_response_id") == "resp-test-123" + + +@pytest.mark.asyncio +async def test_previous_response_id_passed_between_runs_streamed(): + """Test that previous_response_id is passed to the model on subsequent streamed runs.""" + model = FakeModel() + model.set_next_output([get_text_message("done")]) + agent = Agent( + name="test", + model=model, + ) + + assert model.last_turn_args.get("previous_response_id") is None + result = Runner.run_streamed(agent, input="test", previous_response_id="resp-stream-test") + async for _ in result.stream_events(): + pass + + assert model.last_turn_args.get("previous_response_id") == "resp-stream-test" + + +@pytest.mark.asyncio +async def test_previous_response_id_passed_between_runs_streamed_multi_turn(): + """Test that previous_response_id is passed to the model on subsequent streamed runs.""" + + model = FakeModel() + agent = Agent( + name="test", + model=model, + tools=[get_function_tool("foo", "tool_result")], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [get_text_message("a_message"), get_function_tool_call("foo", json.dumps({"a": "b"}))], + # Second turn: text message + [get_text_message("done")], + ] + ) + + assert model.last_turn_args.get("previous_response_id") is None + result = Runner.run_streamed(agent, input="test", previous_response_id="resp-stream-test") + async for _ in result.stream_events(): + pass + + assert model.last_turn_args.get("previous_response_id") == "resp-stream-test" diff --git a/tests/test_agent_runner_streamed.py b/tests/test_agent_runner_streamed.py index 4c7c7efd..87a76a70 100644 --- a/tests/test_agent_runner_streamed.py +++ b/tests/test_agent_runner_streamed.py @@ -674,7 +674,7 @@ async def test_streaming_events(): total_expected_item_count = sum(expected_item_type_map.values()) assert event_counts["run_item_stream_event"] == total_expected_item_count, ( - f"Expectd {total_expected_item_count} events, got {event_counts['run_item_stream_event']}" + f"Expected {total_expected_item_count} events, got {event_counts['run_item_stream_event']}" f"Expected events were: {expected_item_type_map}, got {event_counts}" ) diff --git a/tests/test_agent_tracing.py b/tests/test_agent_tracing.py index 24bd72f1..bb16cab2 100644 --- a/tests/test_agent_tracing.py +++ b/tests/test_agent_tracing.py @@ -3,12 +3,13 @@ import asyncio import pytest +from inline_snapshot import snapshot from agents import Agent, RunConfig, Runner, trace from .fake_model import FakeModel from .test_responses import get_text_message -from .testing_processor import fetch_ordered_spans, fetch_traces +from .testing_processor import assert_no_traces, fetch_normalized_spans @pytest.mark.asyncio @@ -22,13 +23,23 @@ async def test_single_run_is_single_trace(): await Runner.run(agent, input="first_test") - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 1, ( - f"Got {len(spans)}, but expected 1: the agent span. data:" - f"{[span.span_data for span in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + } + ], + } + ] ) @@ -49,11 +60,38 @@ async def test_multiple_runs_are_multiple_traces(): await Runner.run(agent, input="first_test") await Runner.run(agent, input="second_test") - traces = fetch_traces() - assert len(traces) == 2, f"Expected 2 traces, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 2, f"Got {len(spans)}, but expected 2: agent span per run" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + } + ], + }, + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + } + ], + }, + ] + ) @pytest.mark.asyncio @@ -76,11 +114,42 @@ async def test_wrapped_trace_is_single_trace(): await Runner.run(agent, input="second_test") await Runner.run(agent, input="third_test") - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 3, f"Got {len(spans)}, but expected 3: the agent span per run" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "test_workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + ], + } + ] + ) @pytest.mark.asyncio @@ -95,12 +164,7 @@ async def test_parent_disabled_trace_disabled_agent_trace(): await Runner.run(agent, input="first_test") - traces = fetch_traces() - assert len(traces) == 0, f"Expected 0 traces, got {len(traces)}" - spans = fetch_ordered_spans() - assert len(spans) == 0, ( - f"Expected no spans, got {len(spans)}, with {[x.span_data for x in spans]}" - ) + assert_no_traces() @pytest.mark.asyncio @@ -114,10 +178,7 @@ async def test_manual_disabling_works(): await Runner.run(agent, input="first_test", run_config=RunConfig(tracing_disabled=True)) - traces = fetch_traces() - assert len(traces) == 0, f"Expected 0 traces, got {len(traces)}" - spans = fetch_ordered_spans() - assert len(spans) == 0, f"Got {len(spans)}, but expected no spans" + assert_no_traces() @pytest.mark.asyncio @@ -132,16 +193,29 @@ async def test_trace_config_works(): await Runner.run( agent, input="first_test", - run_config=RunConfig(workflow_name="Foo bar", group_id="123", trace_id="456"), + run_config=RunConfig(workflow_name="Foo bar", group_id="123", trace_id="trace_456"), ) - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - export = traces[0].export() - assert export is not None, "Trace export should not be None" - assert export["workflow_name"] == "Foo bar" - assert export["group_id"] == "123" - assert export["id"] == "456" + assert fetch_normalized_spans(keep_trace_id=True) == snapshot( + [ + { + "id": "trace_456", + "workflow_name": "Foo bar", + "group_id": "123", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + } + ], + } + ] + ) @pytest.mark.asyncio @@ -161,11 +235,24 @@ async def test_not_starting_streaming_creates_trace(): break await asyncio.sleep(0.1) - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 1, f"Got {len(spans)}, but expected 1: the agent span" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + } + ], + } + ] + ) # Await the stream to avoid warnings about it not being awaited async for _ in result.stream_events(): @@ -185,8 +272,24 @@ async def test_streaming_single_run_is_single_trace(): async for _ in x.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + } + ], + } + ] + ) @pytest.mark.asyncio @@ -211,8 +314,38 @@ async def test_multiple_streamed_runs_are_multiple_traces(): async for _ in x.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 2, f"Expected 2 traces, got {len(traces)}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + } + ], + }, + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + } + ], + }, + ] + ) @pytest.mark.asyncio @@ -243,8 +376,42 @@ async def test_wrapped_streaming_trace_is_single_trace(): async for _ in x.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "test_workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + ], + } + ] + ) @pytest.mark.asyncio @@ -273,8 +440,42 @@ async def test_wrapped_mixed_trace_is_single_trace(): async for _ in x.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "test_workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + }, + ], + } + ] + ) @pytest.mark.asyncio @@ -296,8 +497,7 @@ async def test_parent_disabled_trace_disables_streaming_agent_trace(): async for _ in x.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 0, f"Expected 0 traces, got {len(traces)}" + assert_no_traces() @pytest.mark.asyncio @@ -318,5 +518,4 @@ async def test_manual_streaming_disabling_works(): async for _ in x.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 0, f"Expected 0 traces, got {len(traces)}" + assert_no_traces() diff --git a/tests/test_cancel_streaming.py b/tests/test_cancel_streaming.py new file mode 100644 index 00000000..3417a3c5 --- /dev/null +++ b/tests/test_cancel_streaming.py @@ -0,0 +1,116 @@ +import json + +import pytest + +from agents import Agent, Runner + +from .fake_model import FakeModel +from .test_responses import get_function_tool, get_function_tool_call, get_text_message + + +@pytest.mark.asyncio +async def test_simple_streaming_with_cancel(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + num_events = 0 + stop_after = 1 # There are two that the model gives back. + + async for _event in result.stream_events(): + num_events += 1 + if num_events == stop_after: + result.cancel() + + assert num_events == 1, f"Expected {stop_after} visible events, but got {num_events}" + + +@pytest.mark.asyncio +async def test_multiple_events_streaming_with_cancel(): + model = FakeModel() + agent = Agent( + name="Joker", + model=model, + tools=[get_function_tool("foo", "tool_result")], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [ + get_text_message("a_message"), + get_function_tool_call("foo", json.dumps({"a": "b"})), + ], + # Second turn: text message + [get_text_message("done")], + ] + ) + + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + num_events = 0 + stop_after = 2 + + async for _ in result.stream_events(): + num_events += 1 + if num_events == stop_after: + result.cancel() + + assert num_events == stop_after, f"Expected {stop_after} visible events, but got {num_events}" + + +@pytest.mark.asyncio +async def test_cancel_prevents_further_events(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + events = [] + async for event in result.stream_events(): + events.append(event) + result.cancel() + break # Cancel after first event + # Try to get more events after cancel + more_events = [e async for e in result.stream_events()] + assert len(events) == 1 + assert more_events == [], "No events should be yielded after cancel()" + + +@pytest.mark.asyncio +async def test_cancel_is_idempotent(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + events = [] + async for event in result.stream_events(): + events.append(event) + result.cancel() + result.cancel() # Call cancel again + break + # Should not raise or misbehave + assert len(events) == 1 + + +@pytest.mark.asyncio +async def test_cancel_before_streaming(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + result.cancel() # Cancel before streaming + events = [e async for e in result.stream_events()] + assert events == [], "No events should be yielded if cancel() is called before streaming." + + +@pytest.mark.asyncio +async def test_cancel_cleans_up_resources(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + # Start streaming, then cancel + async for _ in result.stream_events(): + result.cancel() + break + # After cancel, queues should be empty and is_complete True + assert result.is_complete, "Result should be marked complete after cancel." + assert result._event_queue.empty(), "Event queue should be empty after cancel." + assert result._input_guardrail_queue.empty(), ( + "Input guardrail queue should be empty after cancel." + ) diff --git a/tests/test_config.py b/tests/test_config.py index 8f37200a..dba854db 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -49,13 +49,16 @@ def test_resp_set_default_openai_client(): def test_set_default_openai_api(): - assert isinstance(OpenAIProvider().get_model("gpt-4"), OpenAIResponsesModel), \ + assert isinstance(OpenAIProvider().get_model("gpt-4"), OpenAIResponsesModel), ( "Default should be responses" + ) set_default_openai_api("chat_completions") - assert isinstance(OpenAIProvider().get_model("gpt-4"), OpenAIChatCompletionsModel), \ + assert isinstance(OpenAIProvider().get_model("gpt-4"), OpenAIChatCompletionsModel), ( "Should be chat completions model" + ) set_default_openai_api("responses") - assert isinstance(OpenAIProvider().get_model("gpt-4"), OpenAIResponsesModel), \ + assert isinstance(OpenAIProvider().get_model("gpt-4"), OpenAIResponsesModel), ( "Should be responses model" + ) diff --git a/tests/test_extra_headers.py b/tests/test_extra_headers.py new file mode 100644 index 00000000..a6af3007 --- /dev/null +++ b/tests/test_extra_headers.py @@ -0,0 +1,100 @@ +import pytest +from openai.types.chat.chat_completion import ChatCompletion, Choice +from openai.types.chat.chat_completion_message import ChatCompletionMessage +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails + +from agents import ModelSettings, ModelTracing, OpenAIChatCompletionsModel, OpenAIResponsesModel + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_extra_headers_passed_to_openai_responses_model(): + """ + Ensure extra_headers in ModelSettings is passed to the OpenAIResponsesModel client. + """ + called_kwargs = {} + + class DummyResponses: + async def create(self, **kwargs): + nonlocal called_kwargs + called_kwargs = kwargs + + class DummyResponse: + id = "dummy" + output = [] + usage = type( + "Usage", + (), + { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "input_tokens_details": InputTokensDetails(cached_tokens=0), + "output_tokens_details": OutputTokensDetails(reasoning_tokens=0), + }, + )() + + return DummyResponse() + + class DummyClient: + def __init__(self): + self.responses = DummyResponses() + + model = OpenAIResponsesModel(model="gpt-4", openai_client=DummyClient()) # type: ignore + extra_headers = {"X-Test-Header": "test-value"} + await model.get_response( + system_instructions=None, + input="hi", + model_settings=ModelSettings(extra_headers=extra_headers), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + assert "extra_headers" in called_kwargs + assert called_kwargs["extra_headers"]["X-Test-Header"] == "test-value" + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_extra_headers_passed_to_openai_client(): + """ + Ensure extra_headers in ModelSettings is passed to the OpenAI client. + """ + called_kwargs = {} + + class DummyCompletions: + async def create(self, **kwargs): + nonlocal called_kwargs + called_kwargs = kwargs + msg = ChatCompletionMessage(role="assistant", content="Hello") + choice = Choice(index=0, finish_reason="stop", message=msg) + return ChatCompletion( + id="resp-id", + created=0, + model="fake", + object="chat.completion", + choices=[choice], + usage=None, + ) + + class DummyClient: + def __init__(self): + self.chat = type("_Chat", (), {"completions": DummyCompletions()})() + self.base_url = "https://api.openai.com" + + model = OpenAIChatCompletionsModel(model="gpt-4", openai_client=DummyClient()) # type: ignore + extra_headers = {"X-Test-Header": "test-value"} + await model.get_response( + system_instructions=None, + input="hi", + model_settings=ModelSettings(extra_headers=extra_headers), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + assert "extra_headers" in called_kwargs + assert called_kwargs["extra_headers"]["X-Test-Header"] == "test-value" diff --git a/tests/test_function_schema.py b/tests/test_function_schema.py index 2407ab03..5618d8ae 100644 --- a/tests/test_function_schema.py +++ b/tests/test_function_schema.py @@ -1,3 +1,4 @@ +from collections.abc import Mapping from enum import Enum from typing import Any, Literal @@ -98,7 +99,7 @@ def varargs_function(x: int, *numbers: float, flag: bool = False, **kwargs: Any) def test_varargs_function(): """Test a function that uses *args and **kwargs.""" - func_schema = function_schema(varargs_function) + func_schema = function_schema(varargs_function, strict_json_schema=False) # Check JSON schema structure assert isinstance(func_schema.params_json_schema, dict) assert func_schema.params_json_schema.get("title") == "varargs_function_args" @@ -421,10 +422,20 @@ def test_var_keyword_dict_annotation(): def func(**kwargs: dict[str, int]): return kwargs - fs = function_schema(func, use_docstring_info=False) + fs = function_schema(func, use_docstring_info=False, strict_json_schema=False) properties = fs.params_json_schema.get("properties", {}) # The name of the field is "kwargs", and it's a JSON object i.e. a dict. assert properties.get("kwargs").get("type") == "object" # The values in the dict are integers. assert properties.get("kwargs").get("additionalProperties").get("type") == "integer" + + +def test_schema_with_mapping_raises_strict_mode_error(): + """A mapping type is not allowed in strict mode. Same for dicts. Ensure we raise a UserError.""" + + def func_with_mapping(test_one: Mapping[str, int]) -> str: + return "foo" + + with pytest.raises(UserError): + function_schema(func_with_mapping) diff --git a/tests/test_function_tool.py b/tests/test_function_tool.py index 6a78309b..0a57aea8 100644 --- a/tests/test_function_tool.py +++ b/tests/test_function_tool.py @@ -49,10 +49,10 @@ async def test_simple_function(): assert tool.name == "simple_function" result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1}') - assert result == "6" + assert result == 6 result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1, "b": 2}') - assert result == "3" + assert result == 3 # Missing required argument should raise an error with pytest.raises(ModelBehaviorError): diff --git a/tests/test_function_tool_decorator.py b/tests/test_function_tool_decorator.py index 3a47deb4..3b52788f 100644 --- a/tests/test_function_tool_decorator.py +++ b/tests/test_function_tool_decorator.py @@ -1,8 +1,9 @@ import asyncio import json -from typing import Any +from typing import Any, Optional import pytest +from inline_snapshot import snapshot from agents import function_tool from agents.run_context import RunContextWrapper @@ -142,3 +143,93 @@ async def test_no_error_on_invalid_json_async(): tool = will_not_fail_on_bad_json_async result = await tool.on_invoke_tool(ctx_wrapper(), "{not valid json}") assert result == "error_ModelBehaviorError" + + +@function_tool(strict_mode=False) +def optional_param_function(a: int, b: Optional[int] = None) -> str: + if b is None: + return f"{a}_no_b" + return f"{a}_{b}" + + +@pytest.mark.asyncio +async def test_non_strict_mode_function(): + tool = optional_param_function + + assert tool.strict_json_schema is False, "strict_json_schema should be False" + + assert tool.params_json_schema.get("required") == ["a"], "required should only be a" + + input_data = {"a": 5} + output = await tool.on_invoke_tool(ctx_wrapper(), json.dumps(input_data)) + assert output == "5_no_b" + + input_data = {"a": 5, "b": 10} + output = await tool.on_invoke_tool(ctx_wrapper(), json.dumps(input_data)) + assert output == "5_10" + + +@function_tool(strict_mode=False) +def all_optional_params_function( + x: int = 42, + y: str = "hello", + z: Optional[int] = None, +) -> str: + if z is None: + return f"{x}_{y}_no_z" + return f"{x}_{y}_{z}" + + +@pytest.mark.asyncio +async def test_all_optional_params_function(): + tool = all_optional_params_function + + assert tool.strict_json_schema is False, "strict_json_schema should be False" + + assert tool.params_json_schema.get("required") is None, "required should be empty" + + input_data: dict[str, Any] = {} + output = await tool.on_invoke_tool(ctx_wrapper(), json.dumps(input_data)) + assert output == "42_hello_no_z" + + input_data = {"x": 10, "y": "world"} + output = await tool.on_invoke_tool(ctx_wrapper(), json.dumps(input_data)) + assert output == "10_world_no_z" + + input_data = {"x": 10, "y": "world", "z": 99} + output = await tool.on_invoke_tool(ctx_wrapper(), json.dumps(input_data)) + assert output == "10_world_99" + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city. + + Args: + city: The city to get the weather for. + """ + return f"The weather in {city} is sunny." + + +@pytest.mark.asyncio +async def test_extract_descriptions_from_docstring(): + """Ensure that we extract function and param descriptions from docstrings.""" + + tool = get_weather + assert tool.description == "Get the weather for a given city." + params_json_schema = tool.params_json_schema + assert params_json_schema == snapshot( + { + "type": "object", + "properties": { + "city": { + "description": "The city to get the weather for.", + "title": "City", + "type": "string", + } + }, + "title": "get_weather_args", + "required": ["city"], + "additionalProperties": False, + } + ) diff --git a/tests/test_global_hooks.py b/tests/test_global_hooks.py index 6ac35b90..45854410 100644 --- a/tests/test_global_hooks.py +++ b/tests/test_global_hooks.py @@ -223,7 +223,7 @@ class Foo(TypedDict): @pytest.mark.asyncio -async def test_structed_output_non_streamed_agent_hooks(): +async def test_structured_output_non_streamed_agent_hooks(): hooks = RunHooksForTests() model = FakeModel() agent_1 = Agent(name="test_1", model=model) @@ -296,7 +296,7 @@ async def test_structed_output_non_streamed_agent_hooks(): @pytest.mark.asyncio -async def test_structed_output_streamed_agent_hooks(): +async def test_structured_output_streamed_agent_hooks(): hooks = RunHooksForTests() model = FakeModel() agent_1 = Agent(name="test_1", model=model) diff --git a/tests/test_items_helpers.py b/tests/test_items_helpers.py index 64e2dcda..5dba21d8 100644 --- a/tests/test_items_helpers.py +++ b/tests/test_items_helpers.py @@ -13,12 +13,12 @@ from openai.types.responses.response_function_tool_call_param import ResponseFunctionToolCallParam from openai.types.responses.response_function_web_search import ResponseFunctionWebSearch from openai.types.responses.response_function_web_search_param import ResponseFunctionWebSearchParam -from openai.types.responses.response_input_item_param import Reasoning as ReasoningInputParam -from openai.types.responses.response_output_item import Reasoning, ReasoningContent from openai.types.responses.response_output_message import ResponseOutputMessage from openai.types.responses.response_output_message_param import ResponseOutputMessageParam from openai.types.responses.response_output_refusal import ResponseOutputRefusal from openai.types.responses.response_output_text import ResponseOutputText +from openai.types.responses.response_reasoning_item import ResponseReasoningItem, Summary +from openai.types.responses.response_reasoning_item_param import ResponseReasoningItemParam from agents import ( Agent, @@ -129,7 +129,7 @@ def test_text_message_outputs_across_list_of_runitems() -> None: item1: RunItem = MessageOutputItem(agent=Agent(name="test"), raw_item=message1) item2: RunItem = MessageOutputItem(agent=Agent(name="test"), raw_item=message2) # Create a non-message run item of a different type, e.g., a reasoning trace. - reasoning = Reasoning(id="rid", content=[], type="reasoning") + reasoning = ResponseReasoningItem(id="rid", summary=[], type="reasoning") non_message_item: RunItem = ReasoningItem(agent=Agent(name="test"), raw_item=reasoning) # Confirm only the message outputs are concatenated. assert ItemHelpers.text_message_outputs([item1, non_message_item, item2]) == "foobar" @@ -168,7 +168,7 @@ def test_to_input_items_for_message() -> None: message = ResponseOutputMessage( id="m1", content=[content], role="assistant", status="completed", type="message" ) - resp = ModelResponse(output=[message], usage=Usage(), referenceable_id=None) + resp = ModelResponse(output=[message], usage=Usage(), response_id=None) input_items = resp.to_input_items() assert isinstance(input_items, list) and len(input_items) == 1 # The dict should contain exactly the primitive values of the message @@ -193,7 +193,7 @@ def test_to_input_items_for_function_call() -> None: tool_call = ResponseFunctionToolCall( id="f1", arguments="{}", call_id="c1", name="func", type="function_call" ) - resp = ModelResponse(output=[tool_call], usage=Usage(), referenceable_id=None) + resp = ModelResponse(output=[tool_call], usage=Usage(), response_id=None) input_items = resp.to_input_items() assert isinstance(input_items, list) and len(input_items) == 1 expected: ResponseFunctionToolCallParam = { @@ -211,7 +211,7 @@ def test_to_input_items_for_file_search_call() -> None: fs_call = ResponseFileSearchToolCall( id="fs1", queries=["query"], status="completed", type="file_search_call" ) - resp = ModelResponse(output=[fs_call], usage=Usage(), referenceable_id=None) + resp = ModelResponse(output=[fs_call], usage=Usage(), response_id=None) input_items = resp.to_input_items() assert isinstance(input_items, list) and len(input_items) == 1 expected: ResponseFileSearchToolCallParam = { @@ -226,7 +226,7 @@ def test_to_input_items_for_file_search_call() -> None: def test_to_input_items_for_web_search_call() -> None: """A web search tool call output should produce the same dict as a web search input.""" ws_call = ResponseFunctionWebSearch(id="w1", status="completed", type="web_search_call") - resp = ModelResponse(output=[ws_call], usage=Usage(), referenceable_id=None) + resp = ModelResponse(output=[ws_call], usage=Usage(), response_id=None) input_items = resp.to_input_items() assert isinstance(input_items, list) and len(input_items) == 1 expected: ResponseFunctionWebSearchParam = { @@ -248,7 +248,7 @@ def test_to_input_items_for_computer_call_click() -> None: pending_safety_checks=[], status="completed", ) - resp = ModelResponse(output=[comp_call], usage=Usage(), referenceable_id=None) + resp = ModelResponse(output=[comp_call], usage=Usage(), response_id=None) input_items = resp.to_input_items() assert isinstance(input_items, list) and len(input_items) == 1 converted_dict = input_items[0] @@ -266,16 +266,18 @@ def test_to_input_items_for_computer_call_click() -> None: def test_to_input_items_for_reasoning() -> None: """A reasoning output should produce the same dict as a reasoning input item.""" - rc = ReasoningContent(text="why", type="reasoning_summary") - reasoning = Reasoning(id="rid1", content=[rc], type="reasoning") - resp = ModelResponse(output=[reasoning], usage=Usage(), referenceable_id=None) + rc = Summary(text="why", type="summary_text") + reasoning = ResponseReasoningItem(id="rid1", summary=[rc], type="reasoning") + resp = ModelResponse(output=[reasoning], usage=Usage(), response_id=None) input_items = resp.to_input_items() assert isinstance(input_items, list) and len(input_items) == 1 converted_dict = input_items[0] - expected: ReasoningInputParam = { + expected: ResponseReasoningItemParam = { "id": "rid1", - "content": [{"text": "why", "type": "reasoning_summary"}], + "summary": [{"text": "why", "type": "summary_text"}], "type": "reasoning", } + print(converted_dict) + print(expected) assert converted_dict == expected diff --git a/tests/test_openai_chatcompletions.py b/tests/test_openai_chatcompletions.py index 95216476..ba4605d0 100644 --- a/tests/test_openai_chatcompletions.py +++ b/tests/test_openai_chatcompletions.py @@ -5,7 +5,7 @@ import httpx import pytest -from openai import NOT_GIVEN +from openai import NOT_GIVEN, AsyncOpenAI from openai.types.chat.chat_completion import ChatCompletion, Choice from openai.types.chat.chat_completion_chunk import ChatCompletionChunk from openai.types.chat.chat_completion_message import ChatCompletionMessage @@ -13,7 +13,10 @@ ChatCompletionMessageToolCall, Function, ) -from openai.types.completion_usage import CompletionUsage +from openai.types.completion_usage import ( + CompletionUsage, + PromptTokensDetails, +) from openai.types.responses import ( Response, ResponseFunctionToolCall, @@ -30,6 +33,7 @@ OpenAIProvider, generation_span, ) +from agents.models.chatcmpl_helpers import ChatCmplHelpers from agents.models.fake_id import FAKE_RESPONSES_ID @@ -50,7 +54,13 @@ async def test_get_response_with_text_message(monkeypatch) -> None: model="fake", object="chat.completion", choices=[choice], - usage=CompletionUsage(completion_tokens=5, prompt_tokens=7, total_tokens=12), + usage=CompletionUsage( + completion_tokens=5, + prompt_tokens=7, + total_tokens=12, + # completion_tokens_details left blank to test default + prompt_tokens_details=PromptTokensDetails(cached_tokens=3), + ), ) async def patched_fetch_response(self, *args, **kwargs): @@ -66,6 +76,7 @@ async def patched_fetch_response(self, *args, **kwargs): output_schema=None, handoffs=[], tracing=ModelTracing.DISABLED, + previous_response_id=None, ) # Should have produced exactly one output message with one text part assert isinstance(resp, ModelResponse) @@ -79,7 +90,9 @@ async def patched_fetch_response(self, *args, **kwargs): assert resp.usage.input_tokens == 7 assert resp.usage.output_tokens == 5 assert resp.usage.total_tokens == 12 - assert resp.referenceable_id is None + assert resp.usage.input_tokens_details.cached_tokens == 3 + assert resp.usage.output_tokens_details.reasoning_tokens == 0 + assert resp.response_id is None @pytest.mark.allow_call_model_methods @@ -114,6 +127,7 @@ async def patched_fetch_response(self, *args, **kwargs): output_schema=None, handoffs=[], tracing=ModelTracing.DISABLED, + previous_response_id=None, ) assert len(resp.output) == 1 assert isinstance(resp.output[0], ResponseOutputMessage) @@ -124,6 +138,8 @@ async def patched_fetch_response(self, *args, **kwargs): assert resp.usage.requests == 0 assert resp.usage.input_tokens == 0 assert resp.usage.output_tokens == 0 + assert resp.usage.input_tokens_details.cached_tokens == 0 + assert resp.usage.output_tokens_details.reasoning_tokens == 0 @pytest.mark.allow_call_model_methods @@ -163,6 +179,7 @@ async def patched_fetch_response(self, *args, **kwargs): output_schema=None, handoffs=[], tracing=ModelTracing.DISABLED, + previous_response_id=None, ) # Expect a message item followed by a function tool call item. assert len(resp.output) == 2 @@ -226,6 +243,7 @@ def __init__(self, completions: DummyCompletions) -> None: # Ensure expected args were passed through to OpenAI client. kwargs = completions.kwargs assert kwargs["stream"] is False + assert kwargs["store"] is NOT_GIVEN assert kwargs["model"] == "gpt-4" assert kwargs["messages"][0]["role"] == "system" assert kwargs["messages"][0]["content"] == "sys" @@ -279,7 +297,8 @@ def __init__(self, completions: DummyCompletions) -> None: ) # Check OpenAI client was called for streaming assert completions.kwargs["stream"] is True - assert completions.kwargs["stream_options"] == {"include_usage": True} + assert completions.kwargs["store"] is NOT_GIVEN + assert completions.kwargs["stream_options"] is NOT_GIVEN # Response is a proper openai Response assert isinstance(response, Response) assert response.id == FAKE_RESPONSES_ID @@ -288,3 +307,39 @@ def __init__(self, completions: DummyCompletions) -> None: assert response.output == [] # We returned the async iterator produced by our dummy. assert hasattr(stream, "__aiter__") + + +def test_store_param(): + """Should default to True for OpenAI API calls, and False otherwise.""" + + model_settings = ModelSettings() + client = AsyncOpenAI() + assert ChatCmplHelpers.get_store_param(client, model_settings) is True, ( + "Should default to True for OpenAI API calls" + ) + + model_settings = ModelSettings(store=False) + assert ChatCmplHelpers.get_store_param(client, model_settings) is False, ( + "Should respect explicitly set store=False" + ) + + model_settings = ModelSettings(store=True) + assert ChatCmplHelpers.get_store_param(client, model_settings) is True, ( + "Should respect explicitly set store=True" + ) + + client = AsyncOpenAI(base_url="http://www.notopenai.com") + model_settings = ModelSettings() + assert ChatCmplHelpers.get_store_param(client, model_settings) is None, ( + "Should default to None for non-OpenAI API calls" + ) + + model_settings = ModelSettings(store=False) + assert ChatCmplHelpers.get_store_param(client, model_settings) is False, ( + "Should respect explicitly set store=False" + ) + + model_settings = ModelSettings(store=True) + assert ChatCmplHelpers.get_store_param(client, model_settings) is True, ( + "Should respect explicitly set store=True" + ) diff --git a/tests/test_openai_chatcompletions_converter.py b/tests/test_openai_chatcompletions_converter.py index 8cf07d7c..bcfca549 100644 --- a/tests/test_openai_chatcompletions_converter.py +++ b/tests/test_openai_chatcompletions_converter.py @@ -4,7 +4,7 @@ # See LICENSE file in the project root for full license information. """ -Unit tests for the internal `_Converter` class defined in +Unit tests for the internal `Converter` class defined in `agents.models.openai_chatcompletions`. The converter is responsible for translating between internal "item" structures (e.g., `ResponseOutputMessage` and related types from `openai.types.responses`) and the ChatCompletion message @@ -12,10 +12,10 @@ These tests exercise both conversion directions: -- `_Converter.message_to_output_items` turns a `ChatCompletionMessage` (as +- `Converter.message_to_output_items` turns a `ChatCompletionMessage` (as returned by the OpenAI API) into a list of `ResponseOutputItem` instances. -- `_Converter.items_to_messages` takes in either a simple string prompt, or a +- `Converter.items_to_messages` takes in either a simple string prompt, or a list of input/output items such as `ResponseOutputMessage` and `ResponseFunctionToolCallParam` dicts, and constructs a list of `ChatCompletionMessageParam` dicts suitable for sending back to the API. @@ -41,8 +41,8 @@ from agents.agent_output import AgentOutputSchema from agents.exceptions import UserError from agents.items import TResponseInputItem +from agents.models.chatcmpl_converter import Converter from agents.models.fake_id import FAKE_RESPONSES_ID -from agents.models.openai_chatcompletions import _Converter def test_message_to_output_items_with_text_only(): @@ -51,7 +51,7 @@ def test_message_to_output_items_with_text_only(): into a single ResponseOutputMessage containing one ResponseOutputText. """ msg = ChatCompletionMessage(role="assistant", content="Hello") - items = _Converter.message_to_output_items(msg) + items = Converter.message_to_output_items(msg) # Expect exactly one output item (the message) assert len(items) == 1 message_item = cast(ResponseOutputMessage, items[0]) @@ -72,7 +72,7 @@ def test_message_to_output_items_with_refusal(): with a ResponseOutputRefusal content part. """ msg = ChatCompletionMessage(role="assistant", refusal="I'm sorry") - items = _Converter.message_to_output_items(msg) + items = Converter.message_to_output_items(msg) assert len(items) == 1 message_item = cast(ResponseOutputMessage, items[0]) assert len(message_item.content) == 1 @@ -93,7 +93,7 @@ def test_message_to_output_items_with_tool_call(): function=Function(name="myfn", arguments='{"x":1}'), ) msg = ChatCompletionMessage(role="assistant", content="Hi", tool_calls=[tool_call]) - items = _Converter.message_to_output_items(msg) + items = Converter.message_to_output_items(msg) # Should produce a message item followed by one function tool call item assert len(items) == 2 message_item = cast(ResponseOutputMessage, items[0]) @@ -111,7 +111,7 @@ def test_items_to_messages_with_string_user_content(): A simple string as the items argument should be converted into a user message param dict with the same content. """ - result = _Converter.items_to_messages("Ask me anything") + result = Converter.items_to_messages("Ask me anything") assert isinstance(result, list) assert len(result) == 1 msg = result[0] @@ -130,7 +130,7 @@ def test_items_to_messages_with_easy_input_message(): "content": "How are you?", } ] - messages = _Converter.items_to_messages(items) + messages = Converter.items_to_messages(items) assert len(messages) == 1 out = messages[0] assert out["role"] == "user" @@ -174,7 +174,7 @@ def test_items_to_messages_with_output_message_and_function_call(): resp_msg.model_dump(), # type:ignore func_item, ] - messages = _Converter.items_to_messages(items) + messages = Converter.items_to_messages(items) # Should return a single assistant message assert len(messages) == 1 assistant = messages[0] @@ -197,16 +197,16 @@ def test_items_to_messages_with_output_message_and_function_call(): def test_convert_tool_choice_handles_standard_and_named_options() -> None: """ - The `_Converter.convert_tool_choice` method should return NOT_GIVEN + The `Converter.convert_tool_choice` method should return NOT_GIVEN if no choice is provided, pass through values like "auto", "required", or "none" unchanged, and translate any other string into a function selection dict. """ - assert _Converter.convert_tool_choice(None).__class__.__name__ == "NotGiven" - assert _Converter.convert_tool_choice("auto") == "auto" - assert _Converter.convert_tool_choice("required") == "required" - assert _Converter.convert_tool_choice("none") == "none" - tool_choice_dict = _Converter.convert_tool_choice("mytool") + assert Converter.convert_tool_choice(None).__class__.__name__ == "NotGiven" + assert Converter.convert_tool_choice("auto") == "auto" + assert Converter.convert_tool_choice("required") == "required" + assert Converter.convert_tool_choice("none") == "none" + tool_choice_dict = Converter.convert_tool_choice("mytool") assert isinstance(tool_choice_dict, dict) assert tool_choice_dict["type"] == "function" assert tool_choice_dict["function"]["name"] == "mytool" @@ -214,25 +214,25 @@ def test_convert_tool_choice_handles_standard_and_named_options() -> None: def test_convert_response_format_returns_not_given_for_plain_text_and_dict_for_schemas() -> None: """ - The `_Converter.convert_response_format` method should return NOT_GIVEN + The `Converter.convert_response_format` method should return NOT_GIVEN when no output schema is provided or if the output schema indicates plain text. For structured output schemas, it should return a dict with type `json_schema` and include the generated JSON schema and strict flag from the provided `AgentOutputSchema`. """ # when output is plain text (schema None or output_type str), do not include response_format - assert _Converter.convert_response_format(None).__class__.__name__ == "NotGiven" + assert Converter.convert_response_format(None).__class__.__name__ == "NotGiven" assert ( - _Converter.convert_response_format(AgentOutputSchema(str)).__class__.__name__ == "NotGiven" + Converter.convert_response_format(AgentOutputSchema(str)).__class__.__name__ == "NotGiven" ) # For e.g. integer output, we expect a response_format dict schema = AgentOutputSchema(int) - resp_format = _Converter.convert_response_format(schema) + resp_format = Converter.convert_response_format(schema) assert isinstance(resp_format, dict) assert resp_format["type"] == "json_schema" assert resp_format["json_schema"]["name"] == "final_output" assert "strict" in resp_format["json_schema"] - assert resp_format["json_schema"]["strict"] == schema.strict_json_schema + assert resp_format["json_schema"]["strict"] == schema.is_strict_json_schema() assert "schema" in resp_format["json_schema"] assert resp_format["json_schema"]["schema"] == schema.json_schema() @@ -247,7 +247,7 @@ def test_items_to_messages_with_function_output_item(): "call_id": "somecall", "output": '{"foo": "bar"}', } - messages = _Converter.items_to_messages([func_output_item]) + messages = Converter.items_to_messages([func_output_item]) assert len(messages) == 1 tool_msg = messages[0] assert tool_msg["role"] == "tool" @@ -266,16 +266,16 @@ def test_extract_all_and_text_content_for_strings_and_lists(): should filter to only the textual parts. """ prompt = "just text" - assert _Converter.extract_all_content(prompt) == prompt - assert _Converter.extract_text_content(prompt) == prompt + assert Converter.extract_all_content(prompt) == prompt + assert Converter.extract_text_content(prompt) == prompt text1: ResponseInputTextParam = {"type": "input_text", "text": "one"} text2: ResponseInputTextParam = {"type": "input_text", "text": "two"} - all_parts = _Converter.extract_all_content([text1, text2]) + all_parts = Converter.extract_all_content([text1, text2]) assert isinstance(all_parts, list) assert len(all_parts) == 2 assert all_parts[0]["type"] == "text" and all_parts[0]["text"] == "one" assert all_parts[1]["type"] == "text" and all_parts[1]["text"] == "two" - text_parts = _Converter.extract_text_content([text1, text2]) + text_parts = Converter.extract_text_content([text1, text2]) assert isinstance(text_parts, list) assert all(p["type"] == "text" for p in text_parts) assert [p["text"] for p in text_parts] == ["one", "two"] @@ -288,12 +288,12 @@ def test_items_to_messages_handles_system_and_developer_roles(): `message` typed dicts. """ sys_items: list[TResponseInputItem] = [{"role": "system", "content": "setup"}] - sys_msgs = _Converter.items_to_messages(sys_items) + sys_msgs = Converter.items_to_messages(sys_items) assert len(sys_msgs) == 1 assert sys_msgs[0]["role"] == "system" assert sys_msgs[0]["content"] == "setup" dev_items: list[TResponseInputItem] = [{"role": "developer", "content": "debug"}] - dev_msgs = _Converter.items_to_messages(dev_items) + dev_msgs = Converter.items_to_messages(dev_items) assert len(dev_msgs) == 1 assert dev_msgs[0]["role"] == "developer" assert dev_msgs[0]["content"] == "debug" @@ -301,7 +301,7 @@ def test_items_to_messages_handles_system_and_developer_roles(): def test_maybe_input_message_allows_message_typed_dict(): """ - The `_Converter.maybe_input_message` should recognize a dict with + The `Converter.maybe_input_message` should recognize a dict with "type": "message" and a supported role as an input message. Ensure that such dicts are passed through by `items_to_messages`. """ @@ -311,9 +311,9 @@ def test_maybe_input_message_allows_message_typed_dict(): "role": "user", "content": "hi", } - assert _Converter.maybe_input_message(message_dict) is not None + assert Converter.maybe_input_message(message_dict) is not None # items_to_messages should process this correctly - msgs = _Converter.items_to_messages([message_dict]) + msgs = Converter.items_to_messages([message_dict]) assert len(msgs) == 1 assert msgs[0]["role"] == "user" assert msgs[0]["content"] == "hi" @@ -331,7 +331,7 @@ def test_tool_call_conversion(): type="function_call", ) - messages = _Converter.items_to_messages([function_call]) + messages = Converter.items_to_messages([function_call]) assert len(messages) == 1 tool_msg = messages[0] assert tool_msg["role"] == "assistant" @@ -348,7 +348,7 @@ def test_tool_call_conversion(): @pytest.mark.parametrize("role", ["user", "system", "developer"]) def test_input_message_with_all_roles(role: str): """ - The `_Converter.maybe_input_message` should recognize a dict with + The `Converter.maybe_input_message` should recognize a dict with "type": "message" and a supported role as an input message. Ensure that such dicts are passed through by `items_to_messages`. """ @@ -359,9 +359,9 @@ def test_input_message_with_all_roles(role: str): "role": casted_role, "content": "hi", } - assert _Converter.maybe_input_message(message_dict) is not None + assert Converter.maybe_input_message(message_dict) is not None # items_to_messages should process this correctly - msgs = _Converter.items_to_messages([message_dict]) + msgs = Converter.items_to_messages([message_dict]) assert len(msgs) == 1 assert msgs[0]["role"] == casted_role assert msgs[0]["content"] == "hi" @@ -372,7 +372,7 @@ def test_item_reference_errors(): Test that item references are converted correctly. """ with pytest.raises(UserError): - _Converter.items_to_messages( + Converter.items_to_messages( [ { "type": "item_reference", @@ -392,4 +392,39 @@ def test_unknown_object_errors(): """ with pytest.raises(UserError, match="Unhandled item type or structure"): # Purposely ignore the type error - _Converter.items_to_messages([TestObject()]) # type: ignore + Converter.items_to_messages([TestObject()]) # type: ignore + + +def test_assistant_messages_in_history(): + """ + Test that assistant messages are added to the history. + """ + messages = Converter.items_to_messages( + [ + { + "role": "user", + "content": "Hello", + }, + { + "role": "assistant", + "content": "Hello?", + }, + { + "role": "user", + "content": "What was my Name?", + }, + ] + ) + + assert messages == [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hello?"}, + {"role": "user", "content": "What was my Name?"}, + ] + assert len(messages) == 3 + assert messages[0]["role"] == "user" + assert messages[0]["content"] == "Hello" + assert messages[1]["role"] == "assistant" + assert messages[1]["content"] == "Hello?" + assert messages[2]["role"] == "user" + assert messages[2]["content"] == "What was my Name?" diff --git a/tests/test_openai_chatcompletions_stream.py b/tests/test_openai_chatcompletions_stream.py index 2a15f7f0..5c8bb9e3 100644 --- a/tests/test_openai_chatcompletions_stream.py +++ b/tests/test_openai_chatcompletions_stream.py @@ -8,7 +8,11 @@ ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, ) -from openai.types.completion_usage import CompletionUsage +from openai.types.completion_usage import ( + CompletionTokensDetails, + CompletionUsage, + PromptTokensDetails, +) from openai.types.responses import ( Response, ResponseFunctionToolCall, @@ -46,7 +50,13 @@ async def test_stream_response_yields_events_for_text_content(monkeypatch) -> No model="fake", object="chat.completion.chunk", choices=[Choice(index=0, delta=ChoiceDelta(content="llo"))], - usage=CompletionUsage(completion_tokens=5, prompt_tokens=7, total_tokens=12), + usage=CompletionUsage( + completion_tokens=5, + prompt_tokens=7, + total_tokens=12, + prompt_tokens_details=PromptTokensDetails(cached_tokens=2), + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=3), + ), ) async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: @@ -79,6 +89,7 @@ async def patched_fetch_response(self, *args, **kwargs): output_schema=None, handoffs=[], tracing=ModelTracing.DISABLED, + previous_response_id=None, ): output_events.append(event) # We expect a response.created, then a response.output_item.added, content part added, @@ -107,6 +118,13 @@ async def patched_fetch_response(self, *args, **kwargs): assert isinstance(completed_resp.output[0].content[0], ResponseOutputText) assert completed_resp.output[0].content[0].text == "Hello" + assert completed_resp.usage, "usage should not be None" + assert completed_resp.usage.input_tokens == 7 + assert completed_resp.usage.output_tokens == 5 + assert completed_resp.usage.total_tokens == 12 + assert completed_resp.usage.input_tokens_details.cached_tokens == 2 + assert completed_resp.usage.output_tokens_details.reasoning_tokens == 3 + @pytest.mark.allow_call_model_methods @pytest.mark.asyncio @@ -163,6 +181,7 @@ async def patched_fetch_response(self, *args, **kwargs): output_schema=None, handoffs=[], tracing=ModelTracing.DISABLED, + previous_response_id=None, ): output_events.append(event) # Expect sequence similar to text: created, output_item.added, content part added, @@ -250,6 +269,7 @@ async def patched_fetch_response(self, *args, **kwargs): output_schema=None, handoffs=[], tracing=ModelTracing.DISABLED, + previous_response_id=None, ): output_events.append(event) # Sequence should be: response.created, then after loop we expect function call-related events: diff --git a/tests/test_openai_responses_converter.py b/tests/test_openai_responses_converter.py index 58204265..8e486665 100644 --- a/tests/test_openai_responses_converter.py +++ b/tests/test_openai_responses_converter.py @@ -92,7 +92,7 @@ class OutModel(BaseModel): assert inner.get("name") == "final_output" assert isinstance(inner.get("schema"), dict) # Should include a strict flag matching the schema's strictness setting. - assert inner.get("strict") == out_schema.strict_json_schema + assert inner.get("strict") == out_schema.is_strict_json_schema() def test_convert_tools_basic_types_and_includes(): @@ -163,7 +163,7 @@ def drag(self, path: list[tuple[int, int]]) -> None: assert "function" in types assert "file_search" in types assert "web_search_preview" in types - assert "computer-preview" in types + assert "computer_use_preview" in types # Verify file search tool contains max_num_results and vector_store_ids file_params = next(ct for ct in converted.tools if ct["type"] == "file_search") assert file_params.get("max_num_results") == file_tool.max_num_results @@ -173,7 +173,7 @@ def drag(self, path: list[tuple[int, int]]) -> None: assert web_params.get("user_location") == web_tool.user_location assert web_params.get("search_context_size") == web_tool.search_context_size # Verify computer tool contains environment and computed dimensions - comp_params = next(ct for ct in converted.tools if ct["type"] == "computer-preview") + comp_params = next(ct for ct in converted.tools if ct["type"] == "computer_use_preview") assert comp_params.get("environment") == "mac" assert comp_params.get("display_width") == 800 assert comp_params.get("display_height") == 600 diff --git a/tests/test_output_tool.py b/tests/test_output_tool.py index 31ac984d..37c1b1b6 100644 --- a/tests/test_output_tool.py +++ b/tests/test_output_tool.py @@ -1,11 +1,20 @@ import json +from typing import Any import pytest from pydantic import BaseModel from typing_extensions import TypedDict -from agents import Agent, AgentOutputSchema, ModelBehaviorError, Runner, UserError, _utils +from agents import ( + Agent, + AgentOutputSchema, + AgentOutputSchemaBase, + ModelBehaviorError, + Runner, + UserError, +) from agents.agent_output import _WRAPPER_DICT_KEY +from agents.util import _json def test_plain_text_output(): @@ -26,6 +35,7 @@ def test_structured_output_pydantic(): output_schema = Runner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" + assert isinstance(output_schema, AgentOutputSchema) assert output_schema.output_type == Foo, "Should have the correct output type" assert not output_schema._is_wrapped, "Pydantic objects should not be wrapped" for key, value in Foo.model_json_schema().items(): @@ -44,6 +54,7 @@ def test_structured_output_typed_dict(): agent = Agent(name="test", output_type=Bar) output_schema = Runner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" + assert isinstance(output_schema, AgentOutputSchema) assert output_schema.output_type == Bar, "Should have the correct output type" assert not output_schema._is_wrapped, "TypedDicts should not be wrapped" @@ -56,6 +67,7 @@ def test_structured_output_list(): agent = Agent(name="test", output_type=list[str]) output_schema = Runner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" + assert isinstance(output_schema, AgentOutputSchema) assert output_schema.output_type == list[str], "Should have the correct output type" assert output_schema._is_wrapped, "Lists should be wrapped" @@ -77,7 +89,7 @@ def test_bad_json_raises_error(mocker): output_schema = Runner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" - mock_validate_json = mocker.patch.object(_utils, "validate_json") + mock_validate_json = mocker.patch.object(_json, "validate_json") mock_validate_json.return_value = ["foo"] with pytest.raises(ModelBehaviorError): @@ -97,7 +109,7 @@ def test_plain_text_obj_doesnt_produce_schema(): def test_structured_output_is_strict(): output_wrapper = AgentOutputSchema(output_type=Foo) - assert output_wrapper.strict_json_schema + assert output_wrapper.is_strict_json_schema() for key, value in Foo.model_json_schema().items(): assert output_wrapper.json_schema()[key] == value @@ -109,5 +121,48 @@ def test_structured_output_is_strict(): def test_setting_strict_false_works(): output_wrapper = AgentOutputSchema(output_type=Foo, strict_json_schema=False) - assert not output_wrapper.strict_json_schema + assert not output_wrapper.is_strict_json_schema() assert output_wrapper.json_schema() == Foo.model_json_schema() + assert output_wrapper.json_schema() == Foo.model_json_schema() + + +_CUSTOM_OUTPUT_SCHEMA_JSON_SCHEMA = { + "type": "object", + "properties": { + "foo": {"type": "string"}, + }, + "required": ["foo"], +} + + +class CustomOutputSchema(AgentOutputSchemaBase): + def is_plain_text(self) -> bool: + return False + + def name(self) -> str: + return "FooBarBaz" + + def json_schema(self) -> dict[str, Any]: + return _CUSTOM_OUTPUT_SCHEMA_JSON_SCHEMA + + def is_strict_json_schema(self) -> bool: + return False + + def validate_json(self, json_str: str) -> Any: + return ["some", "output"] + + +def test_custom_output_schema(): + custom_output_schema = CustomOutputSchema() + agent = Agent(name="test", output_type=custom_output_schema) + output_schema = Runner._get_output_schema(agent) + + assert output_schema, "Should have an output tool config with a structured output type" + assert isinstance(output_schema, CustomOutputSchema) + assert output_schema.json_schema() == _CUSTOM_OUTPUT_SCHEMA_JSON_SCHEMA + assert not output_schema.is_strict_json_schema() + assert not output_schema.is_plain_text() + + json_str = json.dumps({"foo": "bar"}) + validated = output_schema.validate_json(json_str) + assert validated == ["some", "output"] diff --git a/tests/test_pretty_print.py b/tests/test_pretty_print.py new file mode 100644 index 00000000..b2218a27 --- /dev/null +++ b/tests/test_pretty_print.py @@ -0,0 +1,201 @@ +import json + +import pytest +from inline_snapshot import snapshot +from pydantic import BaseModel + +from agents import Agent, Runner +from agents.agent_output import _WRAPPER_DICT_KEY +from agents.util._pretty_print import pretty_print_result, pretty_print_run_result_streaming +from tests.fake_model import FakeModel + +from .test_responses import get_final_output_message, get_text_message + + +@pytest.mark.asyncio +async def test_pretty_result(): + model = FakeModel() + model.set_next_output([get_text_message("Hi there")]) + + agent = Agent(name="test_agent", model=model) + result = await Runner.run(agent, input="Hello") + + assert pretty_print_result(result) == snapshot("""\ +RunResult: +- Last agent: Agent(name="test_agent", ...) +- Final output (str): + Hi there +- 1 new item(s) +- 1 raw response(s) +- 0 input guardrail result(s) +- 0 output guardrail result(s) +(See `RunResult` for more details)\ +""") + + +@pytest.mark.asyncio +async def test_pretty_run_result_streaming(): + model = FakeModel() + model.set_next_output([get_text_message("Hi there")]) + + agent = Agent(name="test_agent", model=model) + result = Runner.run_streamed(agent, input="Hello") + async for _ in result.stream_events(): + pass + + assert pretty_print_run_result_streaming(result) == snapshot("""\ +RunResultStreaming: +- Current agent: Agent(name="test_agent", ...) +- Current turn: 1 +- Max turns: 10 +- Is complete: True +- Final output (str): + Hi there +- 1 new item(s) +- 1 raw response(s) +- 0 input guardrail result(s) +- 0 output guardrail result(s) +(See `RunResultStreaming` for more details)\ +""") + + +class Foo(BaseModel): + bar: str + + +@pytest.mark.asyncio +async def test_pretty_run_result_structured_output(): + model = FakeModel() + model.set_next_output( + [ + get_text_message("Test"), + get_final_output_message(Foo(bar="Hi there").model_dump_json()), + ] + ) + + agent = Agent(name="test_agent", model=model, output_type=Foo) + result = await Runner.run(agent, input="Hello") + + assert pretty_print_result(result) == snapshot("""\ +RunResult: +- Last agent: Agent(name="test_agent", ...) +- Final output (Foo): + { + "bar": "Hi there" + } +- 2 new item(s) +- 1 raw response(s) +- 0 input guardrail result(s) +- 0 output guardrail result(s) +(See `RunResult` for more details)\ +""") + + +@pytest.mark.asyncio +async def test_pretty_run_result_streaming_structured_output(): + model = FakeModel() + model.set_next_output( + [ + get_text_message("Test"), + get_final_output_message(Foo(bar="Hi there").model_dump_json()), + ] + ) + + agent = Agent(name="test_agent", model=model, output_type=Foo) + result = Runner.run_streamed(agent, input="Hello") + + async for _ in result.stream_events(): + pass + + assert pretty_print_run_result_streaming(result) == snapshot("""\ +RunResultStreaming: +- Current agent: Agent(name="test_agent", ...) +- Current turn: 1 +- Max turns: 10 +- Is complete: True +- Final output (Foo): + { + "bar": "Hi there" + } +- 2 new item(s) +- 1 raw response(s) +- 0 input guardrail result(s) +- 0 output guardrail result(s) +(See `RunResultStreaming` for more details)\ +""") + + +@pytest.mark.asyncio +async def test_pretty_run_result_list_structured_output(): + model = FakeModel() + model.set_next_output( + [ + get_text_message("Test"), + get_final_output_message( + json.dumps( + { + _WRAPPER_DICT_KEY: [ + Foo(bar="Hi there").model_dump(), + Foo(bar="Hi there 2").model_dump(), + ] + } + ) + ), + ] + ) + + agent = Agent(name="test_agent", model=model, output_type=list[Foo]) + result = await Runner.run(agent, input="Hello") + + assert pretty_print_result(result) == snapshot("""\ +RunResult: +- Last agent: Agent(name="test_agent", ...) +- Final output (list): + [Foo(bar='Hi there'), Foo(bar='Hi there 2')] +- 2 new item(s) +- 1 raw response(s) +- 0 input guardrail result(s) +- 0 output guardrail result(s) +(See `RunResult` for more details)\ +""") + + +@pytest.mark.asyncio +async def test_pretty_run_result_streaming_list_structured_output(): + model = FakeModel() + model.set_next_output( + [ + get_text_message("Test"), + get_final_output_message( + json.dumps( + { + _WRAPPER_DICT_KEY: [ + Foo(bar="Test").model_dump(), + Foo(bar="Test 2").model_dump(), + ] + } + ) + ), + ] + ) + + agent = Agent(name="test_agent", model=model, output_type=list[Foo]) + result = Runner.run_streamed(agent, input="Hello") + + async for _ in result.stream_events(): + pass + + assert pretty_print_run_result_streaming(result) == snapshot("""\ +RunResultStreaming: +- Current agent: Agent(name="test_agent", ...) +- Current turn: 1 +- Max turns: 10 +- Is complete: True +- Final output (list): + [Foo(bar='Test'), Foo(bar='Test 2')] +- 2 new item(s) +- 1 raw response(s) +- 0 input guardrail result(s) +- 0 output guardrail result(s) +(See `RunResultStreaming` for more details)\ +""") diff --git a/tests/test_responses_tracing.py b/tests/test_responses_tracing.py index 82b8e75b..db24fe49 100644 --- a/tests/test_responses_tracing.py +++ b/tests/test_responses_tracing.py @@ -1,12 +1,16 @@ +from typing import Optional + import pytest +from inline_snapshot import snapshot from openai import AsyncOpenAI from openai.types.responses import ResponseCompletedEvent +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails from agents import ModelSettings, ModelTracing, OpenAIResponsesModel, trace from agents.tracing.span_data import ResponseSpanData from tests import fake_model -from .testing_processor import fetch_ordered_spans +from .testing_processor import assert_no_spans, fetch_normalized_spans, fetch_ordered_spans class DummyTracing: @@ -15,10 +19,25 @@ def is_disabled(self): class DummyUsage: - def __init__(self, input_tokens=1, output_tokens=1, total_tokens=2): + def __init__( + self, + input_tokens: int = 1, + input_tokens_details: Optional[InputTokensDetails] = None, + output_tokens: int = 1, + output_tokens_details: Optional[OutputTokensDetails] = None, + total_tokens: int = 2, + ): self.input_tokens = input_tokens self.output_tokens = output_tokens self.total_tokens = total_tokens + self.input_tokens_details = ( + input_tokens_details if input_tokens_details else InputTokensDetails(cached_tokens=0) + ) + self.output_tokens_details = ( + output_tokens_details + if output_tokens_details + else OutputTokensDetails(reasoning_tokens=0) + ) class DummyResponse: @@ -31,6 +50,7 @@ def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj(self.output), + sequence_number=0, ) @@ -43,7 +63,14 @@ async def test_get_response_creates_trace(monkeypatch): # Mock _fetch_response to return a dummy response with a known id async def dummy_fetch_response( - system_instructions, input, model_settings, tools, output_schema, handoffs, stream + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + prev_response_id, + stream, ): return DummyResponse() @@ -51,15 +78,24 @@ async def dummy_fetch_response( # Call get_response await model.get_response( - "instr", "input", ModelSettings(), [], None, [], ModelTracing.ENABLED + "instr", + "input", + ModelSettings(), + [], + None, + [], + ModelTracing.ENABLED, + previous_response_id=None, ) - spans = fetch_ordered_spans() - assert len(spans) == 1 - - assert isinstance(spans[0].span_data, ResponseSpanData) - assert spans[0].span_data.response is not None - assert spans[0].span_data.response.id == "dummy-id" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "test", + "children": [{"type": "response", "data": {"response_id": "dummy-id"}}], + } + ] + ) @pytest.mark.allow_call_model_methods @@ -71,7 +107,14 @@ async def test_non_data_tracing_doesnt_set_response_id(monkeypatch): # Mock _fetch_response to return a dummy response with a known id async def dummy_fetch_response( - system_instructions, input, model_settings, tools, output_schema, handoffs, stream + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + prev_response_id, + stream, ): return DummyResponse() @@ -79,12 +122,22 @@ async def dummy_fetch_response( # Call get_response await model.get_response( - "instr", "input", ModelSettings(), [], None, [], ModelTracing.ENABLED_WITHOUT_DATA + "instr", + "input", + ModelSettings(), + [], + None, + [], + ModelTracing.ENABLED_WITHOUT_DATA, + previous_response_id=None, ) - spans = fetch_ordered_spans() - assert len(spans) == 1 - assert spans[0].span_data.response is None + assert fetch_normalized_spans() == snapshot( + [{"workflow_name": "test", "children": [{"type": "response"}]}] + ) + + [span] = fetch_ordered_spans() + assert span.span_data.response is None @pytest.mark.allow_call_model_methods @@ -96,7 +149,14 @@ async def test_disable_tracing_does_not_create_span(monkeypatch): # Mock _fetch_response to return a dummy response with a known id async def dummy_fetch_response( - system_instructions, input, model_settings, tools, output_schema, handoffs, stream + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + prev_response_id, + stream, ): return DummyResponse() @@ -104,11 +164,19 @@ async def dummy_fetch_response( # Call get_response await model.get_response( - "instr", "input", ModelSettings(), [], None, [], ModelTracing.DISABLED + "instr", + "input", + ModelSettings(), + [], + None, + [], + ModelTracing.DISABLED, + previous_response_id=None, ) - spans = fetch_ordered_spans() - assert len(spans) == 0 + assert fetch_normalized_spans() == snapshot([{"workflow_name": "test"}]) + + assert_no_spans() @pytest.mark.allow_call_model_methods @@ -120,13 +188,21 @@ async def test_stream_response_creates_trace(monkeypatch): # Define a dummy fetch function that returns an async stream with a dummy response async def dummy_fetch_response( - system_instructions, input, model_settings, tools, output_schema, handoffs, stream + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + prev_response_id, + stream, ): class DummyStream: async def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj([], "dummy-id-123"), + sequence_number=0, ) return DummyStream() @@ -135,15 +211,25 @@ async def __aiter__(self): # Consume the stream to trigger processing of the final response async for _ in model.stream_response( - "instr", "input", ModelSettings(), [], None, [], ModelTracing.ENABLED + "instr", + "input", + ModelSettings(), + [], + None, + [], + ModelTracing.ENABLED, + previous_response_id=None, ): pass - spans = fetch_ordered_spans() - assert len(spans) == 1 - assert isinstance(spans[0].span_data, ResponseSpanData) - assert spans[0].span_data.response is not None - assert spans[0].span_data.response.id == "dummy-id-123" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "test", + "children": [{"type": "response", "data": {"response_id": "dummy-id-123"}}], + } + ] + ) @pytest.mark.allow_call_model_methods @@ -155,13 +241,21 @@ async def test_stream_non_data_tracing_doesnt_set_response_id(monkeypatch): # Define a dummy fetch function that returns an async stream with a dummy response async def dummy_fetch_response( - system_instructions, input, model_settings, tools, output_schema, handoffs, stream + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + prev_response_id, + stream, ): class DummyStream: async def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj([], "dummy-id-123"), + sequence_number=0, ) return DummyStream() @@ -170,14 +264,24 @@ async def __aiter__(self): # Consume the stream to trigger processing of the final response async for _ in model.stream_response( - "instr", "input", ModelSettings(), [], None, [], ModelTracing.ENABLED_WITHOUT_DATA + "instr", + "input", + ModelSettings(), + [], + None, + [], + ModelTracing.ENABLED_WITHOUT_DATA, + previous_response_id=None, ): pass - spans = fetch_ordered_spans() - assert len(spans) == 1 - assert isinstance(spans[0].span_data, ResponseSpanData) - assert spans[0].span_data.response is None + assert fetch_normalized_spans() == snapshot( + [{"workflow_name": "test", "children": [{"type": "response"}]}] + ) + + [span] = fetch_ordered_spans() + assert isinstance(span.span_data, ResponseSpanData) + assert span.span_data.response is None @pytest.mark.allow_call_model_methods @@ -189,13 +293,21 @@ async def test_stream_disabled_tracing_doesnt_create_span(monkeypatch): # Define a dummy fetch function that returns an async stream with a dummy response async def dummy_fetch_response( - system_instructions, input, model_settings, tools, output_schema, handoffs, stream + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + prev_response_id, + stream, ): class DummyStream: async def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj([], "dummy-id-123"), + sequence_number=0, ) return DummyStream() @@ -204,9 +316,17 @@ async def __aiter__(self): # Consume the stream to trigger processing of the final response async for _ in model.stream_response( - "instr", "input", ModelSettings(), [], None, [], ModelTracing.DISABLED + "instr", + "input", + ModelSettings(), + [], + None, + [], + ModelTracing.DISABLED, + previous_response_id=None, ): pass - spans = fetch_ordered_spans() - assert len(spans) == 0 + assert fetch_normalized_spans() == snapshot([{"workflow_name": "test"}]) + + assert_no_spans() diff --git a/tests/test_result_cast.py b/tests/test_result_cast.py index ec17e327..c621e735 100644 --- a/tests/test_result_cast.py +++ b/tests/test_result_cast.py @@ -3,7 +3,7 @@ import pytest from pydantic import BaseModel -from agents import Agent, RunResult +from agents import Agent, RunContextWrapper, RunResult def create_run_result(final_output: Any) -> RunResult: @@ -15,6 +15,7 @@ def create_run_result(final_output: Any) -> RunResult: input_guardrail_results=[], output_guardrail_results=[], _last_agent=Agent(name="test"), + context_wrapper=RunContextWrapper(context=None), ) diff --git a/tests/test_run_step_execution.py b/tests/test_run_step_execution.py index 2d581bf6..6ae25fbd 100644 --- a/tests/test_run_step_execution.py +++ b/tests/test_run_step_execution.py @@ -43,7 +43,7 @@ async def test_empty_response_is_final_output(): response = ModelResponse( output=[], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent, response) @@ -59,7 +59,7 @@ async def test_plaintext_agent_no_tool_calls_is_final_output(): response = ModelResponse( output=[get_text_message("hello_world")], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent, response) @@ -79,7 +79,7 @@ async def test_plaintext_agent_no_tool_calls_multiple_messages_is_final_output() get_text_message("bye"), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result( agent, @@ -105,7 +105,7 @@ async def test_plaintext_agent_with_tool_call_is_run_again(): response = ModelResponse( output=[get_text_message("hello_world"), get_function_tool_call("test", "")], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent, response) @@ -140,7 +140,7 @@ async def test_multiple_tool_calls(): get_function_tool_call("test_2"), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent, response) @@ -166,7 +166,7 @@ async def test_handoff_output_leads_to_handoff_next_step(): response = ModelResponse( output=[get_text_message("Hello, world!"), get_handoff_tool_call(agent_1)], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent_3, response) @@ -186,7 +186,7 @@ async def test_final_output_without_tool_runs_again(): response = ModelResponse( output=[get_function_tool_call("tool_1")], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent, response) @@ -203,7 +203,7 @@ async def test_final_output_leads_to_final_output_next_step(): get_final_output_message(Foo(bar="123").model_dump_json()), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent, response) @@ -222,7 +222,7 @@ async def test_handoff_and_final_output_leads_to_handoff_next_step(): get_handoff_tool_call(agent_1), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent_3, response) @@ -241,7 +241,7 @@ async def test_multiple_final_output_leads_to_final_output_next_step(): get_final_output_message(Foo(bar="456").model_dump_json()), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = await get_execute_result(agent_3, response) @@ -290,6 +290,7 @@ async def get_execute_result( processed_response = RunImpl.process_model_response( agent=agent, + all_tools=await agent.get_all_tools(), response=response, output_schema=output_schema, handoffs=handoffs, diff --git a/tests/test_run_step_processing.py b/tests/test_run_step_processing.py index 41f65c4c..2ea98f06 100644 --- a/tests/test_run_step_processing.py +++ b/tests/test_run_step_processing.py @@ -7,7 +7,7 @@ ResponseFunctionWebSearch, ) from openai.types.responses.response_computer_tool_call import ActionClick -from openai.types.responses.response_output_item import Reasoning, ReasoningContent +from openai.types.responses.response_reasoning_item import ResponseReasoningItem, Summary from pydantic import BaseModel from agents import ( @@ -39,11 +39,15 @@ def test_empty_response(): response = ModelResponse( output=[], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=agent, response=response, output_schema=None, handoffs=[] + agent=agent, + response=response, + output_schema=None, + handoffs=[], + all_tools=[], ) assert not result.handoffs assert not result.functions @@ -54,16 +58,17 @@ def test_no_tool_calls(): response = ModelResponse( output=[get_text_message("Hello, world!")], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=agent, response=response, output_schema=None, handoffs=[] + agent=agent, response=response, output_schema=None, handoffs=[], all_tools=[] ) assert not result.handoffs assert not result.functions -def test_single_tool_call(): +@pytest.mark.asyncio +async def test_single_tool_call(): agent = Agent(name="test", tools=[get_function_tool(name="test")]) response = ModelResponse( output=[ @@ -71,10 +76,14 @@ def test_single_tool_call(): get_function_tool_call("test", ""), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=agent, response=response, output_schema=None, handoffs=[] + agent=agent, + response=response, + output_schema=None, + handoffs=[], + all_tools=await agent.get_all_tools(), ) assert not result.handoffs assert result.functions and len(result.functions) == 1 @@ -84,7 +93,8 @@ def test_single_tool_call(): assert func.tool_call.arguments == "" -def test_missing_tool_call_raises_error(): +@pytest.mark.asyncio +async def test_missing_tool_call_raises_error(): agent = Agent(name="test", tools=[get_function_tool(name="test")]) response = ModelResponse( output=[ @@ -92,16 +102,21 @@ def test_missing_tool_call_raises_error(): get_function_tool_call("missing", ""), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) with pytest.raises(ModelBehaviorError): RunImpl.process_model_response( - agent=agent, response=response, output_schema=None, handoffs=[] + agent=agent, + response=response, + output_schema=None, + handoffs=[], + all_tools=await agent.get_all_tools(), ) -def test_multiple_tool_calls(): +@pytest.mark.asyncio +async def test_multiple_tool_calls(): agent = Agent( name="test", tools=[ @@ -117,11 +132,15 @@ def test_multiple_tool_calls(): get_function_tool_call("test_2", "xyz"), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=agent, response=response, output_schema=None, handoffs=[] + agent=agent, + response=response, + output_schema=None, + handoffs=[], + all_tools=await agent.get_all_tools(), ) assert not result.handoffs assert result.functions and len(result.functions) == 2 @@ -143,23 +162,28 @@ async def test_handoffs_parsed_correctly(): response = ModelResponse( output=[get_text_message("Hello, world!")], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=agent_3, response=response, output_schema=None, handoffs=[] + agent=agent_3, + response=response, + output_schema=None, + handoffs=[], + all_tools=await agent_3.get_all_tools(), ) assert not result.handoffs, "Shouldn't have a handoff here" response = ModelResponse( output=[get_text_message("Hello, world!"), get_handoff_tool_call(agent_1)], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( agent=agent_3, response=response, output_schema=None, handoffs=Runner._get_handoffs(agent_3), + all_tools=await agent_3.get_all_tools(), ) assert len(result.handoffs) == 1, "Should have a handoff here" handoff = result.handoffs[0] @@ -181,7 +205,7 @@ async def test_missing_handoff_fails(): response = ModelResponse( output=[get_text_message("Hello, world!"), get_handoff_tool_call(agent_2)], usage=Usage(), - referenceable_id=None, + response_id=None, ) with pytest.raises(ModelBehaviorError): RunImpl.process_model_response( @@ -189,10 +213,12 @@ async def test_missing_handoff_fails(): response=response, output_schema=None, handoffs=Runner._get_handoffs(agent_3), + all_tools=await agent_3.get_all_tools(), ) -def test_multiple_handoffs_doesnt_error(): +@pytest.mark.asyncio +async def test_multiple_handoffs_doesnt_error(): agent_1 = Agent(name="test_1") agent_2 = Agent(name="test_2") agent_3 = Agent(name="test_3", handoffs=[agent_1, agent_2]) @@ -203,13 +229,14 @@ def test_multiple_handoffs_doesnt_error(): get_handoff_tool_call(agent_2), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( agent=agent_3, response=response, output_schema=None, handoffs=Runner._get_handoffs(agent_3), + all_tools=await agent_3.get_all_tools(), ) assert len(result.handoffs) == 2, "Should have multiple handoffs here" @@ -218,7 +245,8 @@ class Foo(BaseModel): bar: str -def test_final_output_parsed_correctly(): +@pytest.mark.asyncio +async def test_final_output_parsed_correctly(): agent = Agent(name="test", output_type=Foo) response = ModelResponse( output=[ @@ -226,7 +254,7 @@ def test_final_output_parsed_correctly(): get_final_output_message(Foo(bar="123").model_dump_json()), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) RunImpl.process_model_response( @@ -234,10 +262,12 @@ def test_final_output_parsed_correctly(): response=response, output_schema=Runner._get_output_schema(agent), handoffs=[], + all_tools=await agent.get_all_tools(), ) -def test_file_search_tool_call_parsed_correctly(): +@pytest.mark.asyncio +async def test_file_search_tool_call_parsed_correctly(): # Ensure that a ResponseFileSearchToolCall output is parsed into a ToolCallItem and that no tool # runs are scheduled. @@ -251,10 +281,14 @@ def test_file_search_tool_call_parsed_correctly(): response = ModelResponse( output=[get_text_message("hello"), file_search_call], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=agent, response=response, output_schema=None, handoffs=[] + agent=agent, + response=response, + output_schema=None, + handoffs=[], + all_tools=await agent.get_all_tools(), ) # The final item should be a ToolCallItem for the file search call assert any( @@ -265,16 +299,21 @@ def test_file_search_tool_call_parsed_correctly(): assert not result.handoffs -def test_function_web_search_tool_call_parsed_correctly(): +@pytest.mark.asyncio +async def test_function_web_search_tool_call_parsed_correctly(): agent = Agent(name="test") web_search_call = ResponseFunctionWebSearch(id="w1", status="completed", type="web_search_call") response = ModelResponse( output=[get_text_message("hello"), web_search_call], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=agent, response=response, output_schema=None, handoffs=[] + agent=agent, + response=response, + output_schema=None, + handoffs=[], + all_tools=await agent.get_all_tools(), ) assert any( isinstance(item, ToolCallItem) and item.raw_item is web_search_call @@ -284,19 +323,24 @@ def test_function_web_search_tool_call_parsed_correctly(): assert not result.handoffs -def test_reasoning_item_parsed_correctly(): +@pytest.mark.asyncio +async def test_reasoning_item_parsed_correctly(): # Verify that a Reasoning output item is converted into a ReasoningItem. - reasoning = Reasoning( - id="r1", type="reasoning", content=[ReasoningContent(text="why", type="reasoning_summary")] + reasoning = ResponseReasoningItem( + id="r1", type="reasoning", summary=[Summary(text="why", type="summary_text")] ) response = ModelResponse( output=[reasoning], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=Agent(name="test"), response=response, output_schema=None, handoffs=[] + agent=Agent(name="test"), + response=response, + output_schema=None, + handoffs=[], + all_tools=await Agent(name="test").get_all_tools(), ) assert any( isinstance(item, ReasoningItem) and item.raw_item is reasoning for item in result.new_items @@ -342,7 +386,8 @@ def drag(self, path: list[tuple[int, int]]) -> None: return None # pragma: no cover -def test_computer_tool_call_without_computer_tool_raises_error(): +@pytest.mark.asyncio +async def test_computer_tool_call_without_computer_tool_raises_error(): # If the agent has no ComputerTool in its tools, process_model_response should raise a # ModelBehaviorError when encountering a ResponseComputerToolCall. computer_call = ResponseComputerToolCall( @@ -356,15 +401,20 @@ def test_computer_tool_call_without_computer_tool_raises_error(): response = ModelResponse( output=[computer_call], usage=Usage(), - referenceable_id=None, + response_id=None, ) with pytest.raises(ModelBehaviorError): RunImpl.process_model_response( - agent=Agent(name="test"), response=response, output_schema=None, handoffs=[] + agent=Agent(name="test"), + response=response, + output_schema=None, + handoffs=[], + all_tools=await Agent(name="test").get_all_tools(), ) -def test_computer_tool_call_with_computer_tool_parsed_correctly(): +@pytest.mark.asyncio +async def test_computer_tool_call_with_computer_tool_parsed_correctly(): # If the agent contains a ComputerTool, ensure that a ResponseComputerToolCall is parsed into a # ToolCallItem and scheduled to run in computer_actions. dummy_computer = DummyComputer() @@ -380,10 +430,14 @@ def test_computer_tool_call_with_computer_tool_parsed_correctly(): response = ModelResponse( output=[computer_call], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( - agent=agent, response=response, output_schema=None, handoffs=[] + agent=agent, + response=response, + output_schema=None, + handoffs=[], + all_tools=await agent.get_all_tools(), ) assert any( isinstance(item, ToolCallItem) and item.raw_item is computer_call @@ -392,7 +446,8 @@ def test_computer_tool_call_with_computer_tool_parsed_correctly(): assert result.computer_actions and result.computer_actions[0].tool_call == computer_call -def test_tool_and_handoff_parsed_correctly(): +@pytest.mark.asyncio +async def test_tool_and_handoff_parsed_correctly(): agent_1 = Agent(name="test_1") agent_2 = Agent(name="test_2") agent_3 = Agent( @@ -405,7 +460,7 @@ def test_tool_and_handoff_parsed_correctly(): get_handoff_tool_call(agent_1), ], usage=Usage(), - referenceable_id=None, + response_id=None, ) result = RunImpl.process_model_response( @@ -413,6 +468,7 @@ def test_tool_and_handoff_parsed_correctly(): response=response, output_schema=None, handoffs=Runner._get_handoffs(agent_3), + all_tools=await agent_3.get_all_tools(), ) assert result.functions and len(result.functions) == 1 assert len(result.handoffs) == 1, "Should have a handoff here" diff --git a/tests/test_tool_choice_reset.py b/tests/test_tool_choice_reset.py new file mode 100644 index 00000000..f95117fd --- /dev/null +++ b/tests/test_tool_choice_reset.py @@ -0,0 +1,210 @@ +import pytest + +from agents import Agent, ModelSettings, Runner +from agents._run_impl import AgentToolUseTracker, RunImpl + +from .fake_model import FakeModel +from .test_responses import get_function_tool, get_function_tool_call, get_text_message + + +class TestToolChoiceReset: + def test_should_reset_tool_choice_direct(self): + """ + Test the _should_reset_tool_choice method directly with various inputs + to ensure it correctly identifies cases where reset is needed. + """ + agent = Agent(name="test_agent") + + # Case 1: Empty tool use tracker should not change the "None" tool choice + model_settings = ModelSettings(tool_choice=None) + tracker = AgentToolUseTracker() + new_settings = RunImpl.maybe_reset_tool_choice(agent, tracker, model_settings) + assert new_settings.tool_choice == model_settings.tool_choice + + # Case 2: Empty tool use tracker should not change the "auto" tool choice + model_settings = ModelSettings(tool_choice="auto") + tracker = AgentToolUseTracker() + new_settings = RunImpl.maybe_reset_tool_choice(agent, tracker, model_settings) + assert model_settings.tool_choice == new_settings.tool_choice + + # Case 3: Empty tool use tracker should not change the "required" tool choice + model_settings = ModelSettings(tool_choice="required") + tracker = AgentToolUseTracker() + new_settings = RunImpl.maybe_reset_tool_choice(agent, tracker, model_settings) + assert model_settings.tool_choice == new_settings.tool_choice + + # Case 4: tool_choice = "required" with one tool should reset + model_settings = ModelSettings(tool_choice="required") + tracker = AgentToolUseTracker() + tracker.add_tool_use(agent, ["tool1"]) + new_settings = RunImpl.maybe_reset_tool_choice(agent, tracker, model_settings) + assert new_settings.tool_choice is None + + # Case 5: tool_choice = "required" with multiple tools should reset + model_settings = ModelSettings(tool_choice="required") + tracker = AgentToolUseTracker() + tracker.add_tool_use(agent, ["tool1", "tool2"]) + new_settings = RunImpl.maybe_reset_tool_choice(agent, tracker, model_settings) + assert new_settings.tool_choice is None + + # Case 6: Tool usage on a different agent should not affect the tool choice + model_settings = ModelSettings(tool_choice="foo_bar") + tracker = AgentToolUseTracker() + tracker.add_tool_use(Agent(name="other_agent"), ["foo_bar", "baz"]) + new_settings = RunImpl.maybe_reset_tool_choice(agent, tracker, model_settings) + assert new_settings.tool_choice == model_settings.tool_choice + + # Case 7: tool_choice = "foo_bar" with multiple tools should reset + model_settings = ModelSettings(tool_choice="foo_bar") + tracker = AgentToolUseTracker() + tracker.add_tool_use(agent, ["foo_bar", "baz"]) + new_settings = RunImpl.maybe_reset_tool_choice(agent, tracker, model_settings) + assert new_settings.tool_choice is None + + @pytest.mark.asyncio + async def test_required_tool_choice_with_multiple_runs(self): + """ + Test scenario 1: When multiple runs are executed with tool_choice="required", ensure each + run works correctly and doesn't get stuck in an infinite loop. Also verify that tool_choice + remains "required" between runs. + """ + # Set up our fake model with responses for two runs + fake_model = FakeModel() + fake_model.add_multiple_turn_outputs( + [[get_text_message("First run response")], [get_text_message("Second run response")]] + ) + + # Create agent with a custom tool and tool_choice="required" + custom_tool = get_function_tool("custom_tool") + agent = Agent( + name="test_agent", + model=fake_model, + tools=[custom_tool], + model_settings=ModelSettings(tool_choice="required"), + ) + + # First run should work correctly and preserve tool_choice + result1 = await Runner.run(agent, "first run") + assert result1.final_output == "First run response" + assert fake_model.last_turn_args["model_settings"].tool_choice == "required", ( + "tool_choice should stay required" + ) + + # Second run should also work correctly with tool_choice still required + result2 = await Runner.run(agent, "second run") + assert result2.final_output == "Second run response" + assert fake_model.last_turn_args["model_settings"].tool_choice == "required", ( + "tool_choice should stay required" + ) + + @pytest.mark.asyncio + async def test_required_with_stop_at_tool_name(self): + """ + Test scenario 2: When using required tool_choice with stop_at_tool_names behavior, ensure + it correctly stops at the specified tool + """ + # Set up fake model to return a tool call for second_tool + fake_model = FakeModel() + fake_model.set_next_output([get_function_tool_call("second_tool", "{}")]) + + # Create agent with two tools and tool_choice="required" and stop_at_tool behavior + first_tool = get_function_tool("first_tool", return_value="first tool result") + second_tool = get_function_tool("second_tool", return_value="second tool result") + + agent = Agent( + name="test_agent", + model=fake_model, + tools=[first_tool, second_tool], + model_settings=ModelSettings(tool_choice="required"), + tool_use_behavior={"stop_at_tool_names": ["second_tool"]}, + ) + + # Run should stop after using second_tool + result = await Runner.run(agent, "run test") + assert result.final_output == "second tool result" + + @pytest.mark.asyncio + async def test_specific_tool_choice(self): + """ + Test scenario 3: When using a specific tool choice name, ensure it doesn't cause infinite + loops. + """ + # Set up fake model to return a text message + fake_model = FakeModel() + fake_model.set_next_output([get_text_message("Test message")]) + + # Create agent with specific tool_choice + tool1 = get_function_tool("tool1") + tool2 = get_function_tool("tool2") + tool3 = get_function_tool("tool3") + + agent = Agent( + name="test_agent", + model=fake_model, + tools=[tool1, tool2, tool3], + model_settings=ModelSettings(tool_choice="tool1"), # Specific tool + ) + + # Run should complete without infinite loops + result = await Runner.run(agent, "first run") + assert result.final_output == "Test message" + + @pytest.mark.asyncio + async def test_required_with_single_tool(self): + """ + Test scenario 4: When using required tool_choice with only one tool, ensure it doesn't cause + infinite loops. + """ + # Set up fake model to return a tool call followed by a text message + fake_model = FakeModel() + fake_model.add_multiple_turn_outputs( + [ + # First call returns a tool call + [get_function_tool_call("custom_tool", "{}")], + # Second call returns a text message + [get_text_message("Final response")], + ] + ) + + # Create agent with a single tool and tool_choice="required" + custom_tool = get_function_tool("custom_tool", return_value="tool result") + agent = Agent( + name="test_agent", + model=fake_model, + tools=[custom_tool], + model_settings=ModelSettings(tool_choice="required"), + ) + + # Run should complete without infinite loops + result = await Runner.run(agent, "first run") + assert result.final_output == "Final response" + + @pytest.mark.asyncio + async def test_dont_reset_tool_choice_if_not_required(self): + """ + Test scenario 5: When agent.reset_tool_choice is False, ensure tool_choice is not reset. + """ + # Set up fake model to return a tool call followed by a text message + fake_model = FakeModel() + fake_model.add_multiple_turn_outputs( + [ + # First call returns a tool call + [get_function_tool_call("custom_tool", "{}")], + # Second call returns a text message + [get_text_message("Final response")], + ] + ) + + # Create agent with a single tool and tool_choice="required" and reset_tool_choice=False + custom_tool = get_function_tool("custom_tool", return_value="tool result") + agent = Agent( + name="test_agent", + model=fake_model, + tools=[custom_tool], + model_settings=ModelSettings(tool_choice="required"), + reset_tool_choice=False, + ) + + await Runner.run(agent, "test") + + assert fake_model.last_turn_args["model_settings"].tool_choice == "required" diff --git a/tests/test_tool_converter.py b/tests/test_tool_converter.py index 1b6ebcf9..918de015 100644 --- a/tests/test_tool_converter.py +++ b/tests/test_tool_converter.py @@ -3,7 +3,7 @@ from agents import Agent, Handoff, function_tool, handoff from agents.exceptions import UserError -from agents.models.openai_chatcompletions import ToolConverter +from agents.models.chatcmpl_converter import Converter from agents.tool import FileSearchTool, WebSearchTool @@ -15,7 +15,7 @@ def test_to_openai_with_function_tool(): some_function(a="foo", b=[1, 2, 3]) tool = function_tool(some_function) - result = ToolConverter.to_openai(tool) + result = Converter.tool_to_openai(tool) assert result["type"] == "function" assert result["function"]["name"] == "some_function" @@ -34,7 +34,7 @@ class Foo(BaseModel): def test_convert_handoff_tool(): agent = Agent(name="test_1", handoff_description="test_2") handoff_obj = handoff(agent=agent) - result = ToolConverter.convert_handoff_tool(handoff_obj) + result = Converter.convert_handoff_tool(handoff_obj) assert result["type"] == "function" assert result["function"]["name"] == Handoff.default_tool_name(agent) @@ -48,7 +48,7 @@ def test_convert_handoff_tool(): def test_tool_converter_hosted_tools_errors(): with pytest.raises(UserError): - ToolConverter.to_openai(WebSearchTool()) + Converter.tool_to_openai(WebSearchTool()) with pytest.raises(UserError): - ToolConverter.to_openai(FileSearchTool(vector_store_ids=["abc"], max_num_results=1)) + Converter.tool_to_openai(FileSearchTool(vector_store_ids=["abc"], max_num_results=1)) diff --git a/tests/test_tool_use_behavior.py b/tests/test_tool_use_behavior.py new file mode 100644 index 00000000..6a673b7a --- /dev/null +++ b/tests/test_tool_use_behavior.py @@ -0,0 +1,194 @@ +# Copyright + +from __future__ import annotations + +from typing import cast + +import pytest +from openai.types.responses.response_input_item_param import FunctionCallOutput + +from agents import ( + Agent, + FunctionToolResult, + RunConfig, + RunContextWrapper, + ToolCallOutputItem, + ToolsToFinalOutputResult, + UserError, +) +from agents._run_impl import RunImpl + +from .test_responses import get_function_tool + + +def _make_function_tool_result( + agent: Agent, output: str, tool_name: str | None = None +) -> FunctionToolResult: + # Construct a FunctionToolResult with the given output using a simple function tool. + tool = get_function_tool(tool_name or "dummy", return_value=output) + raw_item: FunctionCallOutput = cast( + FunctionCallOutput, + { + "call_id": "1", + "output": output, + "type": "function_call_output", + }, + ) + # For this test we don't care about the specific RunItem subclass, only the output field + run_item = ToolCallOutputItem(agent=agent, raw_item=raw_item, output=output) + return FunctionToolResult(tool=tool, output=output, run_item=run_item) + + +@pytest.mark.asyncio +async def test_no_tool_results_returns_not_final_output() -> None: + # If there are no tool results at all, tool_use_behavior should not produce a final output. + agent = Agent(name="test") + result = await RunImpl._check_for_final_output_from_tools( + agent=agent, + tool_results=[], + context_wrapper=RunContextWrapper(context=None), + config=RunConfig(), + ) + assert result.is_final_output is False + assert result.final_output is None + + +@pytest.mark.asyncio +async def test_run_llm_again_behavior() -> None: + # With the default run_llm_again behavior, even with tools we still expect to keep running. + agent = Agent(name="test", tool_use_behavior="run_llm_again") + tool_results = [_make_function_tool_result(agent, "ignored")] + result = await RunImpl._check_for_final_output_from_tools( + agent=agent, + tool_results=tool_results, + context_wrapper=RunContextWrapper(context=None), + config=RunConfig(), + ) + assert result.is_final_output is False + assert result.final_output is None + + +@pytest.mark.asyncio +async def test_stop_on_first_tool_behavior() -> None: + # When tool_use_behavior is stop_on_first_tool, we should surface first tool output as final. + agent = Agent(name="test", tool_use_behavior="stop_on_first_tool") + tool_results = [ + _make_function_tool_result(agent, "first_tool_output"), + _make_function_tool_result(agent, "ignored"), + ] + result = await RunImpl._check_for_final_output_from_tools( + agent=agent, + tool_results=tool_results, + context_wrapper=RunContextWrapper(context=None), + config=RunConfig(), + ) + assert result.is_final_output is True + assert result.final_output == "first_tool_output" + + +@pytest.mark.asyncio +async def test_custom_tool_use_behavior_sync() -> None: + """If tool_use_behavior is a sync function, we should call it and propagate its return.""" + + def behavior( + context: RunContextWrapper, results: list[FunctionToolResult] + ) -> ToolsToFinalOutputResult: + assert len(results) == 3 + return ToolsToFinalOutputResult(is_final_output=True, final_output="custom") + + agent = Agent(name="test", tool_use_behavior=behavior) + tool_results = [ + _make_function_tool_result(agent, "ignored1"), + _make_function_tool_result(agent, "ignored2"), + _make_function_tool_result(agent, "ignored3"), + ] + result = await RunImpl._check_for_final_output_from_tools( + agent=agent, + tool_results=tool_results, + context_wrapper=RunContextWrapper(context=None), + config=RunConfig(), + ) + assert result.is_final_output is True + assert result.final_output == "custom" + + +@pytest.mark.asyncio +async def test_custom_tool_use_behavior_async() -> None: + """If tool_use_behavior is an async function, we should await it and propagate its return.""" + + async def behavior( + context: RunContextWrapper, results: list[FunctionToolResult] + ) -> ToolsToFinalOutputResult: + assert len(results) == 3 + return ToolsToFinalOutputResult(is_final_output=True, final_output="async_custom") + + agent = Agent(name="test", tool_use_behavior=behavior) + tool_results = [ + _make_function_tool_result(agent, "ignored1"), + _make_function_tool_result(agent, "ignored2"), + _make_function_tool_result(agent, "ignored3"), + ] + result = await RunImpl._check_for_final_output_from_tools( + agent=agent, + tool_results=tool_results, + context_wrapper=RunContextWrapper(context=None), + config=RunConfig(), + ) + assert result.is_final_output is True + assert result.final_output == "async_custom" + + +@pytest.mark.asyncio +async def test_invalid_tool_use_behavior_raises() -> None: + """If tool_use_behavior is invalid, we should raise a UserError.""" + agent = Agent(name="test") + # Force an invalid value; mypy will complain, so ignore the type here. + agent.tool_use_behavior = "bad_value" # type: ignore[assignment] + tool_results = [_make_function_tool_result(agent, "ignored")] + with pytest.raises(UserError): + await RunImpl._check_for_final_output_from_tools( + agent=agent, + tool_results=tool_results, + context_wrapper=RunContextWrapper(context=None), + config=RunConfig(), + ) + + +@pytest.mark.asyncio +async def test_tool_names_to_stop_at_behavior() -> None: + agent = Agent( + name="test", + tools=[ + get_function_tool("tool1", return_value="tool1_output"), + get_function_tool("tool2", return_value="tool2_output"), + get_function_tool("tool3", return_value="tool3_output"), + ], + tool_use_behavior={"stop_at_tool_names": ["tool1"]}, + ) + + tool_results = [ + _make_function_tool_result(agent, "ignored1", "tool2"), + _make_function_tool_result(agent, "ignored3", "tool3"), + ] + result = await RunImpl._check_for_final_output_from_tools( + agent=agent, + tool_results=tool_results, + context_wrapper=RunContextWrapper(context=None), + config=RunConfig(), + ) + assert result.is_final_output is False, "We should not have stopped at tool1" + + # Now test with a tool that matches the list + tool_results = [ + _make_function_tool_result(agent, "output1", "tool1"), + _make_function_tool_result(agent, "ignored2", "tool2"), + _make_function_tool_result(agent, "ignored3", "tool3"), + ] + result = await RunImpl._check_for_final_output_from_tools( + agent=agent, + tool_results=tool_results, + context_wrapper=RunContextWrapper(context=None), + config=RunConfig(), + ) + assert result.is_final_output is True, "We should have stopped at tool1" + assert result.final_output == "output1" diff --git a/tests/test_tracing.py b/tests/test_tracing.py index c54c3d86..8f763509 100644 --- a/tests/test_tracing.py +++ b/tests/test_tracing.py @@ -4,6 +4,7 @@ from typing import Any import pytest +from inline_snapshot import snapshot from agents.tracing import ( Span, @@ -17,7 +18,12 @@ ) from agents.tracing.spans import SpanError -from .testing_processor import fetch_events, fetch_ordered_spans, fetch_traces +from .testing_processor import ( + SPAN_PROCESSOR_TESTING, + assert_no_traces, + fetch_events, + fetch_normalized_spans, +) ### HELPERS @@ -47,7 +53,7 @@ def simple_tracing(): x = trace("test") x.start() - span_1 = agent_span(name="agent_1", parent=x) + span_1 = agent_span(name="agent_1", span_id="span_1", parent=x) span_1.start() span_1.finish() @@ -66,33 +72,36 @@ def simple_tracing(): def test_simple_tracing() -> None: simple_tracing() - spans, traces = fetch_ordered_spans(), fetch_traces() - assert len(spans) == 3 - assert len(traces) == 1 - - trace = traces[0] - standard_trace_checks(trace, name_check="test") - trace_id = trace.trace_id - - first_span = spans[0] - standard_span_checks(first_span, trace_id=trace_id, parent_id=None, span_type="agent") - assert first_span.span_data.name == "agent_1" - - second_span = spans[1] - standard_span_checks(second_span, trace_id=trace_id, parent_id=None, span_type="custom") - assert second_span.span_id == "span_2" - assert second_span.span_data.name == "custom_1" - - third_span = spans[2] - standard_span_checks( - third_span, trace_id=trace_id, parent_id=second_span.span_id, span_type="custom" + assert fetch_normalized_spans(keep_span_id=True) == snapshot( + [ + { + "workflow_name": "test", + "children": [ + { + "type": "agent", + "id": "span_1", + "data": {"name": "agent_1"}, + }, + { + "type": "custom", + "id": "span_2", + "data": {"name": "custom_1", "data": {}}, + "children": [ + { + "type": "custom", + "id": "span_3", + "data": {"name": "custom_2", "data": {}}, + } + ], + }, + ], + } + ] ) - assert third_span.span_id == "span_3" - assert third_span.span_data.name == "custom_2" def ctxmanager_spans(): - with trace(workflow_name="test", trace_id="123", group_id="456"): + with trace(workflow_name="test", trace_id="trace_123", group_id="456"): with custom_span(name="custom_1", span_id="span_1"): with custom_span(name="custom_2", span_id="span_1_inner"): pass @@ -104,36 +113,38 @@ def ctxmanager_spans(): def test_ctxmanager_spans() -> None: ctxmanager_spans() - spans, traces = fetch_ordered_spans(), fetch_traces() - assert len(spans) == 3 - assert len(traces) == 1 - - trace = traces[0] - standard_trace_checks(trace, name_check="test") - trace_id = trace.trace_id - - first_span = spans[0] - standard_span_checks(first_span, trace_id=trace_id, parent_id=None, span_type="custom") - assert first_span.span_id == "span_1" - - first_inner_span = spans[1] - standard_span_checks( - first_inner_span, trace_id=trace_id, parent_id=first_span.span_id, span_type="custom" + assert fetch_normalized_spans(keep_span_id=True) == snapshot( + [ + { + "workflow_name": "test", + "group_id": "456", + "children": [ + { + "type": "custom", + "id": "span_1", + "data": {"name": "custom_1", "data": {}}, + "children": [ + { + "type": "custom", + "id": "span_1_inner", + "data": {"name": "custom_2", "data": {}}, + } + ], + }, + {"type": "custom", "id": "span_2", "data": {"name": "custom_2", "data": {}}}, + ], + } + ] ) - assert first_inner_span.span_id == "span_1_inner" - - second_span = spans[2] - standard_span_checks(second_span, trace_id=trace_id, parent_id=None, span_type="custom") - assert second_span.span_id == "span_2" async def run_subtask(span_id: str | None = None) -> None: with generation_span(span_id=span_id): - await asyncio.sleep(0.01) + await asyncio.sleep(0.0001) async def simple_async_tracing(): - with trace(workflow_name="test", trace_id="123", group_id="456"): + with trace(workflow_name="test", trace_id="trace_123", group_id="group_456"): await run_subtask(span_id="span_1") await run_subtask(span_id="span_2") @@ -142,21 +153,18 @@ async def simple_async_tracing(): async def test_async_tracing() -> None: await simple_async_tracing() - spans, traces = fetch_ordered_spans(), fetch_traces() - assert len(spans) == 2 - assert len(traces) == 1 - - trace = traces[0] - standard_trace_checks(trace, name_check="test") - trace_id = trace.trace_id - - # We don't care about ordering here, just that they're there - for s in spans: - standard_span_checks(s, trace_id=trace_id, parent_id=None, span_type="generation") - - ids = [span.span_id for span in spans] - assert "span_1" in ids - assert "span_2" in ids + assert fetch_normalized_spans(keep_span_id=True) == snapshot( + [ + { + "workflow_name": "test", + "group_id": "group_456", + "children": [ + {"type": "generation", "id": "span_1"}, + {"type": "generation", "id": "span_2"}, + ], + } + ] + ) async def run_tasks_parallel(span_ids: list[str]) -> None: @@ -171,13 +179,11 @@ async def run_tasks_as_children(first_span_id: str, second_span_id: str) -> None async def complex_async_tracing(): - with trace(workflow_name="test", trace_id="123", group_id="456"): - await asyncio.sleep(0.01) + with trace(workflow_name="test", trace_id="trace_123", group_id="456"): await asyncio.gather( run_tasks_parallel(["span_1", "span_2"]), run_tasks_parallel(["span_3", "span_4"]), ) - await asyncio.sleep(0.01) await asyncio.gather( run_tasks_as_children("span_5", "span_6"), run_tasks_as_children("span_7", "span_8"), @@ -186,39 +192,38 @@ async def complex_async_tracing(): @pytest.mark.asyncio async def test_complex_async_tracing() -> None: - await complex_async_tracing() - - spans, traces = fetch_ordered_spans(), fetch_traces() - assert len(spans) == 8 - assert len(traces) == 1 - - trace = traces[0] - standard_trace_checks(trace, name_check="test") - trace_id = trace.trace_id - - # First ensure 1,2,3,4 exist and are in parallel with the trace as parent - for span_id in ["span_1", "span_2", "span_3", "span_4"]: - span = next((s for s in spans if s.span_id == span_id), None) - assert span is not None - standard_span_checks(span, trace_id=trace_id, parent_id=None, span_type="generation") - - # Ensure 5 and 7 exist and have the trace as parent - for span_id in ["span_5", "span_7"]: - span = next((s for s in spans if s.span_id == span_id), None) - assert span is not None - standard_span_checks(span, trace_id=trace_id, parent_id=None, span_type="generation") - - # Ensure 6 and 8 exist and have 5 and 7 as parents - six = next((s for s in spans if s.span_id == "span_6"), None) - assert six is not None - standard_span_checks(six, trace_id=trace_id, parent_id="span_5", span_type="generation") - eight = next((s for s in spans if s.span_id == "span_8"), None) - assert eight is not None - standard_span_checks(eight, trace_id=trace_id, parent_id="span_7", span_type="generation") + for _ in range(300): + SPAN_PROCESSOR_TESTING.clear() + await complex_async_tracing() + + assert fetch_normalized_spans(keep_span_id=True) == ( + [ + { + "workflow_name": "test", + "group_id": "456", + "children": [ + {"type": "generation", "id": "span_1"}, + {"type": "generation", "id": "span_2"}, + {"type": "generation", "id": "span_3"}, + {"type": "generation", "id": "span_4"}, + { + "type": "generation", + "id": "span_5", + "children": [{"type": "generation", "id": "span_6"}], + }, + { + "type": "generation", + "id": "span_7", + "children": [{"type": "generation", "id": "span_8"}], + }, + ], + } + ] + ) def spans_with_setters(): - with trace(workflow_name="test", trace_id="123", group_id="456"): + with trace(workflow_name="test", trace_id="trace_123", group_id="456"): with agent_span(name="agent_1") as span_a: span_a.span_data.name = "agent_2" @@ -236,34 +241,33 @@ def spans_with_setters(): def test_spans_with_setters() -> None: spans_with_setters() - spans, traces = fetch_ordered_spans(), fetch_traces() - assert len(spans) == 4 - assert len(traces) == 1 - - trace = traces[0] - standard_trace_checks(trace, name_check="test") - trace_id = trace.trace_id - - # Check the spans - first_span = spans[0] - standard_span_checks(first_span, trace_id=trace_id, parent_id=None, span_type="agent") - assert first_span.span_data.name == "agent_2" - - second_span = spans[1] - standard_span_checks( - second_span, trace_id=trace_id, parent_id=first_span.span_id, span_type="function" - ) - assert second_span.span_data.input == "i" - assert second_span.span_data.output == "o" - - third_span = spans[2] - standard_span_checks( - third_span, trace_id=trace_id, parent_id=first_span.span_id, span_type="generation" - ) - - fourth_span = spans[3] - standard_span_checks( - fourth_span, trace_id=trace_id, parent_id=first_span.span_id, span_type="handoff" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "test", + "group_id": "456", + "children": [ + { + "type": "agent", + "data": {"name": "agent_2"}, + "children": [ + { + "type": "function", + "data": {"name": "function_1", "input": "i", "output": "o"}, + }, + { + "type": "generation", + "data": {"input": [{"foo": "bar"}]}, + }, + { + "type": "handoff", + "data": {"from_agent": "agent_1", "to_agent": "agent_2"}, + }, + ], + } + ], + } + ] ) @@ -276,14 +280,11 @@ def disabled_tracing(): def test_disabled_tracing(): disabled_tracing() - - spans, traces = fetch_ordered_spans(), fetch_traces() - assert len(spans) == 0 - assert len(traces) == 0 + assert_no_traces() def enabled_trace_disabled_span(): - with trace(workflow_name="test", trace_id="123"): + with trace(workflow_name="test", trace_id="trace_123"): with agent_span(name="agent_1"): with function_span(name="function_1", disabled=True): with generation_span(): @@ -293,17 +294,19 @@ def enabled_trace_disabled_span(): def test_enabled_trace_disabled_span(): enabled_trace_disabled_span() - spans, traces = fetch_ordered_spans(), fetch_traces() - assert len(spans) == 1 # Only the agent span is recorded - assert len(traces) == 1 # The trace is recorded - - trace = traces[0] - standard_trace_checks(trace, name_check="test") - trace_id = trace.trace_id - - first_span = spans[0] - standard_span_checks(first_span, trace_id=trace_id, parent_id=None, span_type="agent") - assert first_span.span_data.name == "agent_1" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "test", + "children": [ + { + "type": "agent", + "data": {"name": "agent_1"}, + } + ], + } + ] + ) def test_start_and_end_called_manual(): @@ -367,9 +370,7 @@ async def test_noop_span_doesnt_record(): with custom_span(name="span_1") as span: span.set_error(SpanError(message="test", data={})) - spans, traces = fetch_ordered_spans(), fetch_traces() - assert len(spans) == 0 - assert len(traces) == 0 + assert_no_traces() assert t.export() is None assert span.export() is None diff --git a/tests/test_tracing_errors.py b/tests/test_tracing_errors.py index d57e1a84..72bd39ed 100644 --- a/tests/test_tracing_errors.py +++ b/tests/test_tracing_errors.py @@ -4,6 +4,7 @@ from typing import Any import pytest +from inline_snapshot import snapshot from typing_extensions import TypedDict from agents import ( @@ -17,7 +18,6 @@ Runner, TResponseInputItem, ) -from agents.tracing import AgentSpanData, FunctionSpanData, GenerationSpanData from .fake_model import FakeModel from .test_responses import ( @@ -27,7 +27,7 @@ get_handoff_tool_call, get_text_message, ) -from .testing_processor import fetch_ordered_spans, fetch_traces +from .testing_processor import fetch_normalized_spans @pytest.mark.asyncio @@ -42,15 +42,33 @@ async def test_single_turn_model_error(): with pytest.raises(ValueError): await Runner.run(agent, input="first_test") - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 2, f"should have agent and generation spans, got {len(spans)}" - - generation_span = spans[1] - assert isinstance(generation_span.span_data, GenerationSpanData) - assert generation_span.error, "should have error" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + "children": [ + { + "type": "generation", + "error": { + "message": "Error", + "data": {"name": "ValueError", "message": "test error"}, + }, + } + ], + } + ], + } + ] + ) @pytest.mark.asyncio @@ -77,18 +95,43 @@ async def test_multi_turn_no_handoffs(): with pytest.raises(ValueError): await Runner.run(agent, input="first_test") - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 4, ( - f"should have agent, generation, tool, generation, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent", + "handoffs": [], + "tools": ["foo"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": { + "name": "foo", + "input": '{"a": "b"}', + "output": "tool_result", + }, + }, + { + "type": "generation", + "error": { + "message": "Error", + "data": {"name": "ValueError", "message": "test error"}, + }, + }, + ], + } + ], + } + ] ) - last_generation_span = [x for x in spans if isinstance(x.span_data, GenerationSpanData)][-1] - assert last_generation_span.error, "should have error" - @pytest.mark.asyncio async def test_tool_call_error(): @@ -107,18 +150,39 @@ async def test_tool_call_error(): with pytest.raises(ModelBehaviorError): await Runner.run(agent, input="first_test") - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 3, ( - f"should have agent, generation, tool spans, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent", + "handoffs": [], + "tools": ["foo"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "error": { + "message": "Error running tool", + "data": { + "tool_name": "foo", + "error": "Invalid JSON input for tool foo: bad_json", + }, + }, + "data": {"name": "foo", "input": "bad_json"}, + }, + ], + } + ], + } + ] ) - function_span = [x for x in spans if isinstance(x.span_data, FunctionSpanData)][0] - assert function_span.error, "should have error" - @pytest.mark.asyncio async def test_multiple_handoff_doesnt_error(): @@ -156,13 +220,53 @@ async def test_multiple_handoff_doesnt_error(): result = await Runner.run(agent_3, input="user_message") assert result.last_agent == agent_1, "should have picked first handoff" - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 7, ( - f"should have 2 agent, 1 function, 3 generation, 1 handoff, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test", + "handoffs": ["test", "test"], + "tools": ["some_function"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": { + "name": "some_function", + "input": '{"a": "b"}', + "output": "result", + }, + }, + {"type": "generation"}, + { + "type": "handoff", + "data": {"from_agent": "test", "to_agent": "test"}, + "error": { + "data": { + "requested_agents": [ + "test", + "test", + ], + }, + "message": "Multiple handoffs requested", + }, + }, + ], + }, + { + "type": "agent", + "data": {"name": "test", "handoffs": [], "tools": [], "output_type": "str"}, + "children": [{"type": "generation"}], + }, + ], + } + ] ) @@ -190,13 +294,19 @@ async def test_multiple_final_output_doesnt_error(): result = await Runner.run(agent_1, input="user_message") assert result.final_output == Foo(bar="abc") - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 2, ( - f"should have 1 agent, 1 generation, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": {"name": "test", "handoffs": [], "tools": [], "output_type": "Foo"}, + "children": [{"type": "generation"}], + } + ], + } + ] ) @@ -248,13 +358,83 @@ async def test_handoffs_lead_to_correct_agent_spans(): f"should have ended on the third agent, got {result.last_agent.name}" ) - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 12, ( - f"should have 3 agents, 2 function, 5 generation, 2 handoff, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_3", + "handoffs": ["test_agent_1", "test_agent_2"], + "tools": ["some_function"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": { + "name": "some_function", + "input": '{"a": "b"}', + "output": "result", + }, + }, + {"type": "generation"}, + { + "type": "handoff", + "data": {"from_agent": "test_agent_3", "to_agent": "test_agent_1"}, + "error": { + "data": { + "requested_agents": [ + "test_agent_1", + "test_agent_2", + ], + }, + "message": "Multiple handoffs requested", + }, + }, + ], + }, + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": ["test_agent_3"], + "tools": ["some_function"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": { + "name": "some_function", + "input": '{"a": "b"}', + "output": "result", + }, + }, + {"type": "generation"}, + { + "type": "handoff", + "data": {"from_agent": "test_agent_1", "to_agent": "test_agent_3"}, + }, + ], + }, + { + "type": "agent", + "data": { + "name": "test_agent_3", + "handoffs": ["test_agent_1", "test_agent_2"], + "tools": ["some_function"], + "output_type": "str", + }, + "children": [{"type": "generation"}], + }, + ], + } + ] ) @@ -282,18 +462,38 @@ async def test_max_turns_exceeded(): with pytest.raises(MaxTurnsExceeded): await Runner.run(agent, input="user_message", max_turns=2) - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 5, ( - f"should have 1 agent span, 2 generations, 2 function calls, got " - f"{len(spans)} with data: {[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "error": {"message": "Max turns exceeded", "data": {"max_turns": 2}}, + "data": { + "name": "test", + "handoffs": [], + "tools": ["foo"], + "output_type": "Foo", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": {"name": "foo", "input": "", "output": "result"}, + }, + {"type": "generation"}, + { + "type": "function", + "data": {"name": "foo", "input": "", "output": "result"}, + }, + ], + } + ], + } + ] ) - agent_span = [x for x in spans if isinstance(x.span_data, AgentSpanData)][-1] - assert agent_span.error, "last agent should have error" - def guardrail_function( context: RunContextWrapper[Any], agent: Agent[Any], input: str | list[TResponseInputItem] @@ -315,14 +515,26 @@ async def test_guardrail_error(): with pytest.raises(InputGuardrailTripwireTriggered): await Runner.run(agent, input="user_message") - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 2, ( - f"should have 1 agent, 1 guardrail, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "error": { + "message": "Guardrail tripwire triggered", + "data": {"guardrail": "guardrail_function"}, + }, + "data": {"name": "test", "handoffs": [], "tools": [], "output_type": "str"}, + "children": [ + { + "type": "guardrail", + "data": {"name": "guardrail_function", "triggered": True}, + } + ], + } + ], + } + ] ) - - agent_span = [x for x in spans if isinstance(x.span_data, AgentSpanData)][-1] - assert agent_span.error, "last agent should have error" diff --git a/tests/test_tracing_errors_streamed.py b/tests/test_tracing_errors_streamed.py index 00f440ee..416793e7 100644 --- a/tests/test_tracing_errors_streamed.py +++ b/tests/test_tracing_errors_streamed.py @@ -5,13 +5,11 @@ from typing import Any import pytest +from inline_snapshot import snapshot from typing_extensions import TypedDict from agents import ( Agent, - AgentSpanData, - FunctionSpanData, - GenerationSpanData, GuardrailFunctionOutput, InputGuardrail, InputGuardrailTripwireTriggered, @@ -32,7 +30,7 @@ get_handoff_tool_call, get_text_message, ) -from .testing_processor import fetch_ordered_spans, fetch_traces +from .testing_processor import fetch_normalized_spans @pytest.mark.asyncio @@ -49,15 +47,34 @@ async def test_single_turn_model_error(): async for _ in result.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 2, f"should have agent and generation spans, got {len(spans)}" - - generation_span = spans[1] - assert isinstance(generation_span.span_data, GenerationSpanData) - assert generation_span.error, "should have error" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "error": {"message": "Error in agent run", "data": {"error": "test error"}}, + "data": { + "name": "test_agent", + "handoffs": [], + "tools": [], + "output_type": "str", + }, + "children": [ + { + "type": "generation", + "error": { + "message": "Error", + "data": {"name": "ValueError", "message": "test error"}, + }, + } + ], + } + ], + } + ] + ) @pytest.mark.asyncio @@ -86,18 +103,44 @@ async def test_multi_turn_no_handoffs(): async for _ in result.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 4, ( - f"should have agent, generation, tool, generation, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "error": {"message": "Error in agent run", "data": {"error": "test error"}}, + "data": { + "name": "test_agent", + "handoffs": [], + "tools": ["foo"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": { + "name": "foo", + "input": '{"a": "b"}', + "output": "tool_result", + }, + }, + { + "type": "generation", + "error": { + "message": "Error", + "data": {"name": "ValueError", "message": "test error"}, + }, + }, + ], + } + ], + } + ] ) - last_generation_span = [x for x in spans if isinstance(x.span_data, GenerationSpanData)][-1] - assert last_generation_span.error, "should have error" - @pytest.mark.asyncio async def test_tool_call_error(): @@ -118,18 +161,43 @@ async def test_tool_call_error(): async for _ in result.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 3, ( - f"should have agent, generation, tool spans, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "error": { + "message": "Error in agent run", + "data": {"error": "Invalid JSON input for tool foo: bad_json"}, + }, + "data": { + "name": "test_agent", + "handoffs": [], + "tools": ["foo"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "error": { + "message": "Error running tool", + "data": { + "tool_name": "foo", + "error": "Invalid JSON input for tool foo: bad_json", + }, + }, + "data": {"name": "foo", "input": "bad_json"}, + }, + ], + } + ], + } + ] ) - function_span = [x for x in spans if isinstance(x.span_data, FunctionSpanData)][0] - assert function_span.error, "should have error" - @pytest.mark.asyncio async def test_multiple_handoff_doesnt_error(): @@ -170,13 +238,48 @@ async def test_multiple_handoff_doesnt_error(): assert result.last_agent == agent_1, "should have picked first handoff" - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 7, ( - f"should have 2 agent, 1 function, 3 generation, 1 handoff, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test", + "handoffs": ["test", "test"], + "tools": ["some_function"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": { + "name": "some_function", + "input": '{"a": "b"}', + "output": "result", + }, + }, + {"type": "generation"}, + { + "type": "handoff", + "data": {"from_agent": "test", "to_agent": "test"}, + "error": { + "data": {"requested_agents": ["test", "test"]}, + "message": "Multiple handoffs requested", + }, + }, + ], + }, + { + "type": "agent", + "data": {"name": "test", "handoffs": [], "tools": [], "output_type": "str"}, + "children": [{"type": "generation"}], + }, + ], + } + ] ) @@ -208,13 +311,19 @@ async def test_multiple_final_output_no_error(): assert isinstance(result.final_output, dict) assert result.final_output["bar"] == "abc" - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 2, ( - f"should have 1 agent, 1 generation, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": {"name": "test", "handoffs": [], "tools": [], "output_type": "Foo"}, + "children": [{"type": "generation"}], + } + ], + } + ] ) @@ -268,13 +377,78 @@ async def test_handoffs_lead_to_correct_agent_spans(): f"should have ended on the third agent, got {result.last_agent.name}" ) - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 12, ( - f"should have 3 agents, 2 function, 5 generation, 2 handoff, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "data": { + "name": "test_agent_3", + "handoffs": ["test_agent_1", "test_agent_2"], + "tools": ["some_function"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": { + "name": "some_function", + "input": '{"a": "b"}', + "output": "result", + }, + }, + {"type": "generation"}, + { + "type": "handoff", + "error": { + "message": "Multiple handoffs requested", + "data": {"requested_agents": ["test_agent_1", "test_agent_2"]}, + }, + "data": {"from_agent": "test_agent_3", "to_agent": "test_agent_1"}, + }, + ], + }, + { + "type": "agent", + "data": { + "name": "test_agent_1", + "handoffs": ["test_agent_3"], + "tools": ["some_function"], + "output_type": "str", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": { + "name": "some_function", + "input": '{"a": "b"}', + "output": "result", + }, + }, + {"type": "generation"}, + { + "type": "handoff", + "data": {"from_agent": "test_agent_1", "to_agent": "test_agent_3"}, + }, + ], + }, + { + "type": "agent", + "data": { + "name": "test_agent_3", + "handoffs": ["test_agent_1", "test_agent_2"], + "tools": ["some_function"], + "output_type": "str", + }, + "children": [{"type": "generation"}], + }, + ], + } + ] ) @@ -304,18 +478,38 @@ async def test_max_turns_exceeded(): async for _ in result.stream_events(): pass - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 5, ( - f"should have 1 agent, 2 generations, 2 function calls, got " - f"{len(spans)} with data: {[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "error": {"message": "Max turns exceeded", "data": {"max_turns": 2}}, + "data": { + "name": "test", + "handoffs": [], + "tools": ["foo"], + "output_type": "Foo", + }, + "children": [ + {"type": "generation"}, + { + "type": "function", + "data": {"name": "foo", "input": "", "output": "result"}, + }, + {"type": "generation"}, + { + "type": "function", + "data": {"name": "foo", "input": "", "output": "result"}, + }, + ], + } + ], + } + ] ) - agent_span = [x for x in spans if isinstance(x.span_data, AgentSpanData)][-1] - assert agent_span.error, "last agent should have error" - def input_guardrail_function( context: RunContextWrapper[Any], agent: Agent[Any], input: str | list[TResponseInputItem] @@ -344,18 +538,33 @@ async def test_input_guardrail_error(): await asyncio.sleep(1) - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 2, ( - f"should have 1 agent, 1 guardrail, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "error": { + "message": "Guardrail tripwire triggered", + "data": { + "guardrail": "input_guardrail_function", + "type": "input_guardrail", + }, + }, + "data": {"name": "test", "handoffs": [], "tools": [], "output_type": "str"}, + "children": [ + { + "type": "guardrail", + "data": {"name": "input_guardrail_function", "triggered": True}, + } + ], + } + ], + } + ] ) - agent_span = [x for x in spans if isinstance(x.span_data, AgentSpanData)][-1] - assert agent_span.error, "last agent should have error" - def output_guardrail_function( context: RunContextWrapper[Any], agent: Agent[Any], agent_output: Any @@ -384,14 +593,26 @@ async def test_output_guardrail_error(): await asyncio.sleep(1) - traces = fetch_traces() - assert len(traces) == 1, f"Expected 1 trace, got {len(traces)}" - - spans = fetch_ordered_spans() - assert len(spans) == 2, ( - f"should have 1 agent, 1 guardrail, got {len(spans)} with data: " - f"{[x.span_data for x in spans]}" + assert fetch_normalized_spans() == snapshot( + [ + { + "workflow_name": "Agent workflow", + "children": [ + { + "type": "agent", + "error": { + "message": "Guardrail tripwire triggered", + "data": {"guardrail": "output_guardrail_function"}, + }, + "data": {"name": "test", "handoffs": [], "tools": [], "output_type": "str"}, + "children": [ + { + "type": "guardrail", + "data": {"name": "output_guardrail_function", "triggered": True}, + } + ], + } + ], + } + ] ) - - agent_span = [x for x in spans if isinstance(x.span_data, AgentSpanData)][-1] - assert agent_span.error, "last agent should have error" diff --git a/tests/test_usage.py b/tests/test_usage.py new file mode 100644 index 00000000..405f99dd --- /dev/null +++ b/tests/test_usage.py @@ -0,0 +1,52 @@ +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails + +from agents.usage import Usage + + +def test_usage_add_aggregates_all_fields(): + u1 = Usage( + requests=1, + input_tokens=10, + input_tokens_details=InputTokensDetails(cached_tokens=3), + output_tokens=20, + output_tokens_details=OutputTokensDetails(reasoning_tokens=5), + total_tokens=30, + ) + u2 = Usage( + requests=2, + input_tokens=7, + input_tokens_details=InputTokensDetails(cached_tokens=4), + output_tokens=8, + output_tokens_details=OutputTokensDetails(reasoning_tokens=6), + total_tokens=15, + ) + + u1.add(u2) + + assert u1.requests == 3 + assert u1.input_tokens == 17 + assert u1.output_tokens == 28 + assert u1.total_tokens == 45 + assert u1.input_tokens_details.cached_tokens == 7 + assert u1.output_tokens_details.reasoning_tokens == 11 + + +def test_usage_add_aggregates_with_none_values(): + u1 = Usage() + u2 = Usage( + requests=2, + input_tokens=7, + input_tokens_details=InputTokensDetails(cached_tokens=4), + output_tokens=8, + output_tokens_details=OutputTokensDetails(reasoning_tokens=6), + total_tokens=15, + ) + + u1.add(u2) + + assert u1.requests == 2 + assert u1.input_tokens == 7 + assert u1.output_tokens == 8 + assert u1.total_tokens == 15 + assert u1.input_tokens_details.cached_tokens == 4 + assert u1.output_tokens_details.reasoning_tokens == 6 diff --git a/tests/test_visualization.py b/tests/test_visualization.py new file mode 100644 index 00000000..6aa86774 --- /dev/null +++ b/tests/test_visualization.py @@ -0,0 +1,136 @@ +from unittest.mock import Mock + +import graphviz # type: ignore +import pytest + +from agents import Agent +from agents.extensions.visualization import ( + draw_graph, + get_all_edges, + get_all_nodes, + get_main_graph, +) +from agents.handoffs import Handoff + + +@pytest.fixture +def mock_agent(): + tool1 = Mock() + tool1.name = "Tool1" + tool2 = Mock() + tool2.name = "Tool2" + + handoff1 = Mock(spec=Handoff) + handoff1.agent_name = "Handoff1" + + agent = Mock(spec=Agent) + agent.name = "Agent1" + agent.tools = [tool1, tool2] + agent.handoffs = [handoff1] + + return agent + + +def test_get_main_graph(mock_agent): + result = get_main_graph(mock_agent) + print(result) + assert "digraph G" in result + assert "graph [splines=true];" in result + assert 'node [fontname="Arial"];' in result + assert "edge [penwidth=1.5];" in result + assert ( + '"__start__" [label="__start__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" in result + ) + assert ( + '"__end__" [label="__end__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" in result + ) + assert ( + '"Agent1" [label="Agent1", shape=box, style=filled, ' + "fillcolor=lightyellow, width=1.5, height=0.8];" in result + ) + assert ( + '"Tool1" [label="Tool1", shape=ellipse, style=filled, ' + "fillcolor=lightgreen, width=0.5, height=0.3];" in result + ) + assert ( + '"Tool2" [label="Tool2", shape=ellipse, style=filled, ' + "fillcolor=lightgreen, width=0.5, height=0.3];" in result + ) + assert ( + '"Handoff1" [label="Handoff1", shape=box, style=filled, style=rounded, ' + "fillcolor=lightyellow, width=1.5, height=0.8];" in result + ) + + +def test_get_all_nodes(mock_agent): + result = get_all_nodes(mock_agent) + assert ( + '"__start__" [label="__start__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" in result + ) + assert ( + '"__end__" [label="__end__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" in result + ) + assert ( + '"Agent1" [label="Agent1", shape=box, style=filled, ' + "fillcolor=lightyellow, width=1.5, height=0.8];" in result + ) + assert ( + '"Tool1" [label="Tool1", shape=ellipse, style=filled, ' + "fillcolor=lightgreen, width=0.5, height=0.3];" in result + ) + assert ( + '"Tool2" [label="Tool2", shape=ellipse, style=filled, ' + "fillcolor=lightgreen, width=0.5, height=0.3];" in result + ) + assert ( + '"Handoff1" [label="Handoff1", shape=box, style=filled, style=rounded, ' + "fillcolor=lightyellow, width=1.5, height=0.8];" in result + ) + + +def test_get_all_edges(mock_agent): + result = get_all_edges(mock_agent) + assert '"__start__" -> "Agent1";' in result + assert '"Agent1" -> "__end__";' + assert '"Agent1" -> "Tool1" [style=dotted, penwidth=1.5];' in result + assert '"Tool1" -> "Agent1" [style=dotted, penwidth=1.5];' in result + assert '"Agent1" -> "Tool2" [style=dotted, penwidth=1.5];' in result + assert '"Tool2" -> "Agent1" [style=dotted, penwidth=1.5];' in result + assert '"Agent1" -> "Handoff1";' in result + + +def test_draw_graph(mock_agent): + graph = draw_graph(mock_agent) + assert isinstance(graph, graphviz.Source) + assert "digraph G" in graph.source + assert "graph [splines=true];" in graph.source + assert 'node [fontname="Arial"];' in graph.source + assert "edge [penwidth=1.5];" in graph.source + assert ( + '"__start__" [label="__start__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" in graph.source + ) + assert ( + '"__end__" [label="__end__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" in graph.source + ) + assert ( + '"Agent1" [label="Agent1", shape=box, style=filled, ' + "fillcolor=lightyellow, width=1.5, height=0.8];" in graph.source + ) + assert ( + '"Tool1" [label="Tool1", shape=ellipse, style=filled, ' + "fillcolor=lightgreen, width=0.5, height=0.3];" in graph.source + ) + assert ( + '"Tool2" [label="Tool2", shape=ellipse, style=filled, ' + "fillcolor=lightgreen, width=0.5, height=0.3];" in graph.source + ) + assert ( + '"Handoff1" [label="Handoff1", shape=box, style=filled, style=rounded, ' + "fillcolor=lightyellow, width=1.5, height=0.8];" in graph.source + ) diff --git a/tests/testing_processor.py b/tests/testing_processor.py index 258a08dc..a38c3956 100644 --- a/tests/testing_processor.py +++ b/tests/testing_processor.py @@ -1,6 +1,7 @@ from __future__ import annotations import threading +from datetime import datetime from typing import Any, Literal from agents.tracing import Span, Trace, TracingProcessor @@ -77,3 +78,55 @@ def fetch_traces() -> list[Trace]: def fetch_events() -> list[TestSpanProcessorEvent]: return SPAN_PROCESSOR_TESTING._events + + +def assert_no_spans(): + spans = fetch_ordered_spans() + if spans: + raise AssertionError(f"Expected 0 spans, got {len(spans)}") + + +def assert_no_traces(): + traces = fetch_traces() + if traces: + raise AssertionError(f"Expected 0 traces, got {len(traces)}") + assert_no_spans() + + +def fetch_normalized_spans( + keep_span_id: bool = False, keep_trace_id: bool = False +) -> list[dict[str, Any]]: + nodes: dict[tuple[str, str | None], dict[str, Any]] = {} + traces = [] + for trace_obj in fetch_traces(): + trace = trace_obj.export() + assert trace + assert trace.pop("object") == "trace" + assert trace["id"].startswith("trace_") + if not keep_trace_id: + del trace["id"] + trace = {k: v for k, v in trace.items() if v is not None} + nodes[(trace_obj.trace_id, None)] = trace + traces.append(trace) + + assert traces, "Use assert_no_traces() to check for empty traces" + + for span_obj in fetch_ordered_spans(): + span = span_obj.export() + assert span + assert span.pop("object") == "trace.span" + assert span["id"].startswith("span_") + if not keep_span_id: + del span["id"] + assert datetime.fromisoformat(span.pop("started_at")) + assert datetime.fromisoformat(span.pop("ended_at")) + parent_id = span.pop("parent_id") + assert "type" not in span + span_data = span.pop("span_data") + span = {"type": span_data.pop("type")} | {k: v for k, v in span.items() if v is not None} + span_data = {k: v for k, v in span_data.items() if v is not None} + if span_data: + span["data"] = span_data + nodes[(span_obj.trace_id, span_obj.span_id)] = span + nodes[(span.pop("trace_id"), parent_id)].setdefault("children", []).append(span) + return traces diff --git a/tests/tracing/test_processor_api_key.py b/tests/tracing/test_processor_api_key.py new file mode 100644 index 00000000..b0a0218a --- /dev/null +++ b/tests/tracing/test_processor_api_key.py @@ -0,0 +1,27 @@ +import pytest + +from agents.tracing.processors import BackendSpanExporter + + +@pytest.mark.asyncio +async def test_processor_api_key(monkeypatch): + # If the API key is not set, it should be None + monkeypatch.delenv("OPENAI_API_KEY", None) + processor = BackendSpanExporter() + assert processor.api_key is None + + # If we set it afterwards, it should be the new value + processor.set_api_key("test_api_key") + assert processor.api_key == "test_api_key" + + +@pytest.mark.asyncio +async def test_processor_api_key_from_env(monkeypatch): + # If the API key is not set at creation time but set before access time, it should be the new + # value + monkeypatch.delenv("OPENAI_API_KEY", None) + processor = BackendSpanExporter() + + # If we set it afterwards, it should be the new value + monkeypatch.setenv("OPENAI_API_KEY", "foo_bar_123") + assert processor.api_key == "foo_bar_123" diff --git a/tests/voice/__init__.py b/tests/voice/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/voice/conftest.py b/tests/voice/conftest.py new file mode 100644 index 00000000..79d85d8b --- /dev/null +++ b/tests/voice/conftest.py @@ -0,0 +1,11 @@ +import os +import sys + + +# Skip voice tests on Python 3.9 +def pytest_ignore_collect(collection_path, config): + if sys.version_info[:2] == (3, 9): + this_dir = os.path.dirname(__file__) + + if str(collection_path).startswith(this_dir): + return True diff --git a/tests/voice/fake_models.py b/tests/voice/fake_models.py new file mode 100644 index 00000000..109ee4cb --- /dev/null +++ b/tests/voice/fake_models.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator +from typing import Literal + +import numpy as np +import numpy.typing as npt + +try: + from agents.voice import ( + AudioInput, + StreamedAudioInput, + StreamedTranscriptionSession, + STTModel, + STTModelSettings, + TTSModel, + TTSModelSettings, + VoiceWorkflowBase, + ) +except ImportError: + pass + + +class FakeTTS(TTSModel): + """Fakes TTS by just returning string bytes.""" + + def __init__(self, strategy: Literal["default", "split_words"] = "default"): + self.strategy = strategy + + @property + def model_name(self) -> str: + return "fake_tts" + + async def run(self, text: str, settings: TTSModelSettings) -> AsyncIterator[bytes]: + if self.strategy == "default": + yield np.zeros(2, dtype=np.int16).tobytes() + elif self.strategy == "split_words": + for _ in text.split(): + yield np.zeros(2, dtype=np.int16).tobytes() + + async def verify_audio(self, text: str, audio: bytes, dtype: npt.DTypeLike = np.int16) -> None: + assert audio == np.zeros(2, dtype=dtype).tobytes() + + async def verify_audio_chunks( + self, text: str, audio_chunks: list[bytes], dtype: npt.DTypeLike = np.int16 + ) -> None: + assert audio_chunks == [np.zeros(2, dtype=dtype).tobytes() for _word in text.split()] + + +class FakeSession(StreamedTranscriptionSession): + """A fake streamed transcription session that yields preconfigured transcripts.""" + + def __init__(self): + self.outputs: list[str] = [] + + async def transcribe_turns(self) -> AsyncIterator[str]: + for t in self.outputs: + yield t + + async def close(self) -> None: + return None + + +class FakeSTT(STTModel): + """A fake STT model that either returns a single transcript or yields multiple.""" + + def __init__(self, outputs: list[str] | None = None): + self.outputs = outputs or [] + + @property + def model_name(self) -> str: + return "fake_stt" + + async def transcribe(self, _: AudioInput, __: STTModelSettings, ___: bool, ____: bool) -> str: + return self.outputs.pop(0) + + async def create_session( + self, + _: StreamedAudioInput, + __: STTModelSettings, + ___: bool, + ____: bool, + ) -> StreamedTranscriptionSession: + session = FakeSession() + session.outputs = self.outputs + return session + + +class FakeWorkflow(VoiceWorkflowBase): + """A fake workflow that yields preconfigured outputs.""" + + def __init__(self, outputs: list[list[str]] | None = None): + self.outputs = outputs or [] + + def add_output(self, output: list[str]) -> None: + self.outputs.append(output) + + def add_multiple_outputs(self, outputs: list[list[str]]) -> None: + self.outputs.extend(outputs) + + async def run(self, _: str) -> AsyncIterator[str]: + if not self.outputs: + raise ValueError("No output configured") + output = self.outputs.pop(0) + for t in output: + yield t + + +class FakeStreamedAudioInput: + @classmethod + async def get(cls, count: int) -> StreamedAudioInput: + input = StreamedAudioInput() + for _ in range(count): + await input.add_audio(np.zeros(2, dtype=np.int16)) + return input diff --git a/tests/voice/helpers.py b/tests/voice/helpers.py new file mode 100644 index 00000000..ae902dc1 --- /dev/null +++ b/tests/voice/helpers.py @@ -0,0 +1,21 @@ +try: + from agents.voice import StreamedAudioResult +except ImportError: + pass + + +async def extract_events(result: StreamedAudioResult) -> tuple[list[str], list[bytes]]: + """Collapse pipeline stream events to simple labels for ordering assertions.""" + flattened: list[str] = [] + audio_chunks: list[bytes] = [] + + async for ev in result.stream(): + if ev.type == "voice_stream_event_audio": + if ev.data is not None: + audio_chunks.append(ev.data.tobytes()) + flattened.append("audio") + elif ev.type == "voice_stream_event_lifecycle": + flattened.append(ev.event) + elif ev.type == "voice_stream_event_error": + flattened.append("error") + return flattened, audio_chunks diff --git a/tests/voice/test_input.py b/tests/voice/test_input.py new file mode 100644 index 00000000..d41d870d --- /dev/null +++ b/tests/voice/test_input.py @@ -0,0 +1,127 @@ +import io +import wave + +import numpy as np +import pytest + +try: + from agents import UserError + from agents.voice import AudioInput, StreamedAudioInput + from agents.voice.input import DEFAULT_SAMPLE_RATE, _buffer_to_audio_file +except ImportError: + pass + + +def test_buffer_to_audio_file_int16(): + # Create a simple sine wave in int16 format + t = np.linspace(0, 1, DEFAULT_SAMPLE_RATE) + buffer = (np.sin(2 * np.pi * 440 * t) * 32767).astype(np.int16) + + filename, audio_file, content_type = _buffer_to_audio_file(buffer) + + assert filename == "audio.wav" + assert content_type == "audio/wav" + assert isinstance(audio_file, io.BytesIO) + + # Verify the WAV file contents + with wave.open(audio_file, "rb") as wav_file: + assert wav_file.getnchannels() == 1 + assert wav_file.getsampwidth() == 2 + assert wav_file.getframerate() == DEFAULT_SAMPLE_RATE + assert wav_file.getnframes() == len(buffer) + + +def test_buffer_to_audio_file_float32(): + # Create a simple sine wave in float32 format + t = np.linspace(0, 1, DEFAULT_SAMPLE_RATE) + buffer = np.sin(2 * np.pi * 440 * t).astype(np.float32) + + filename, audio_file, content_type = _buffer_to_audio_file(buffer) + + assert filename == "audio.wav" + assert content_type == "audio/wav" + assert isinstance(audio_file, io.BytesIO) + + # Verify the WAV file contents + with wave.open(audio_file, "rb") as wav_file: + assert wav_file.getnchannels() == 1 + assert wav_file.getsampwidth() == 2 + assert wav_file.getframerate() == DEFAULT_SAMPLE_RATE + assert wav_file.getnframes() == len(buffer) + + +def test_buffer_to_audio_file_invalid_dtype(): + # Create a buffer with invalid dtype (float64) + buffer = np.array([1.0, 2.0, 3.0], dtype=np.float64) + + with pytest.raises(UserError, match="Buffer must be a numpy array of int16 or float32"): + # Purposely ignore the type error + _buffer_to_audio_file(buffer) # type: ignore + + +class TestAudioInput: + def test_audio_input_default_params(self): + # Create a simple sine wave + t = np.linspace(0, 1, DEFAULT_SAMPLE_RATE) + buffer = np.sin(2 * np.pi * 440 * t).astype(np.float32) + + audio_input = AudioInput(buffer=buffer) + + assert audio_input.frame_rate == DEFAULT_SAMPLE_RATE + assert audio_input.sample_width == 2 + assert audio_input.channels == 1 + assert np.array_equal(audio_input.buffer, buffer) + + def test_audio_input_custom_params(self): + # Create a simple sine wave + t = np.linspace(0, 1, 48000) + buffer = np.sin(2 * np.pi * 440 * t).astype(np.float32) + + audio_input = AudioInput(buffer=buffer, frame_rate=48000, sample_width=4, channels=2) + + assert audio_input.frame_rate == 48000 + assert audio_input.sample_width == 4 + assert audio_input.channels == 2 + assert np.array_equal(audio_input.buffer, buffer) + + def test_audio_input_to_audio_file(self): + # Create a simple sine wave + t = np.linspace(0, 1, DEFAULT_SAMPLE_RATE) + buffer = np.sin(2 * np.pi * 440 * t).astype(np.float32) + + audio_input = AudioInput(buffer=buffer) + filename, audio_file, content_type = audio_input.to_audio_file() + + assert filename == "audio.wav" + assert content_type == "audio/wav" + assert isinstance(audio_file, io.BytesIO) + + # Verify the WAV file contents + with wave.open(audio_file, "rb") as wav_file: + assert wav_file.getnchannels() == 1 + assert wav_file.getsampwidth() == 2 + assert wav_file.getframerate() == DEFAULT_SAMPLE_RATE + assert wav_file.getnframes() == len(buffer) + + +class TestStreamedAudioInput: + @pytest.mark.asyncio + async def test_streamed_audio_input(self): + streamed_input = StreamedAudioInput() + + # Create some test audio data + t = np.linspace(0, 1, DEFAULT_SAMPLE_RATE) + audio1 = np.sin(2 * np.pi * 440 * t).astype(np.float32) + audio2 = np.sin(2 * np.pi * 880 * t).astype(np.float32) + + # Add audio to the queue + await streamed_input.add_audio(audio1) + await streamed_input.add_audio(audio2) + + # Verify the queue contents + assert streamed_input.queue.qsize() == 2 + # Test non-blocking get + assert np.array_equal(streamed_input.queue.get_nowait(), audio1) + # Test blocking get + assert np.array_equal(await streamed_input.queue.get(), audio2) + assert streamed_input.queue.empty() diff --git a/tests/voice/test_openai_stt.py b/tests/voice/test_openai_stt.py new file mode 100644 index 00000000..89b5cca7 --- /dev/null +++ b/tests/voice/test_openai_stt.py @@ -0,0 +1,367 @@ +# test_openai_stt_transcription_session.py + +import asyncio +import json +import time +from unittest.mock import AsyncMock, patch + +import numpy as np +import pytest + +try: + from agents.voice import OpenAISTTTranscriptionSession, StreamedAudioInput, STTModelSettings + from agents.voice.exceptions import STTWebsocketConnectionError + from agents.voice.models.openai_stt import EVENT_INACTIVITY_TIMEOUT + + from .fake_models import FakeStreamedAudioInput +except ImportError: + pass + + +# ===== Helpers ===== + + +def create_mock_websocket(messages: list[str]) -> AsyncMock: + """ + Creates a mock websocket (AsyncMock) that will return the provided incoming_messages + from __aiter__() as if they came from the server. + """ + + mock_ws = AsyncMock() + mock_ws.__aenter__.return_value = mock_ws + # The incoming_messages are strings that we pretend come from the server + mock_ws.__aiter__.return_value = iter(messages) + return mock_ws + + +def fake_time(increment: int): + current = 1000 + while True: + yield current + current += increment + + +# ===== Tests ===== +@pytest.mark.asyncio +async def test_non_json_messages_should_crash(): + """This tests that non-JSON messages will raise an exception""" + # Setup: mock websockets.connect + mock_ws = create_mock_websocket(["not a json message"]) + with patch("websockets.connect", return_value=mock_ws): + # Instantiate the session + input_audio = await FakeStreamedAudioInput.get(count=2) + stt_settings = STTModelSettings() + + session = OpenAISTTTranscriptionSession( + input=input_audio, + client=AsyncMock(api_key="FAKE_KEY"), + model="whisper-1", + settings=stt_settings, + trace_include_sensitive_data=False, + trace_include_sensitive_audio_data=False, + ) + + with pytest.raises(STTWebsocketConnectionError): + # Start reading from transcribe_turns, which triggers _process_websocket_connection + turns = session.transcribe_turns() + + async for _ in turns: + pass + + await session.close() + + +@pytest.mark.asyncio +async def test_session_connects_and_configures_successfully(): + """ + Test that the session: + 1) Connects to the correct URL with correct headers. + 2) Receives a 'session.created' event. + 3) Sends an update message for session config. + 4) Receives a 'session.updated' event. + """ + # Setup: mock websockets.connect + mock_ws = create_mock_websocket( + [ + json.dumps({"type": "transcription_session.created"}), + json.dumps({"type": "transcription_session.updated"}), + ] + ) + with patch("websockets.connect", return_value=mock_ws) as mock_connect: + # Instantiate the session + input_audio = await FakeStreamedAudioInput.get(count=2) + stt_settings = STTModelSettings() + + session = OpenAISTTTranscriptionSession( + input=input_audio, + client=AsyncMock(api_key="FAKE_KEY"), + model="whisper-1", + settings=stt_settings, + trace_include_sensitive_data=False, + trace_include_sensitive_audio_data=False, + ) + + # Start reading from transcribe_turns, which triggers _process_websocket_connection + turns = session.transcribe_turns() + + async for _ in turns: + pass + + # Check connect call + args, kwargs = mock_connect.call_args + assert "wss://api.openai.com/v1/realtime?intent=transcription" in args[0] + headers = kwargs.get("additional_headers", {}) + assert headers.get("Authorization") == "Bearer FAKE_KEY" + assert headers.get("OpenAI-Beta") == "realtime=v1" + assert headers.get("OpenAI-Log-Session") == "1" + + # Check that we sent a 'transcription_session.update' message + sent_messages = [call.args[0] for call in mock_ws.send.call_args_list] + assert any('"type": "transcription_session.update"' in msg for msg in sent_messages), ( + f"Expected 'transcription_session.update' in {sent_messages}" + ) + + await session.close() + + +@pytest.mark.asyncio +async def test_stream_audio_sends_correct_json(): + """ + Test that when audio is placed on the input queue, the session: + 1) Base64-encodes the data. + 2) Sends the correct JSON message over the websocket. + """ + # Simulate a single "transcription_session.created" and "transcription_session.updated" event, + # before we test streaming. + mock_ws = create_mock_websocket( + [ + json.dumps({"type": "transcription_session.created"}), + json.dumps({"type": "transcription_session.updated"}), + ] + ) + + with patch("websockets.connect", return_value=mock_ws): + # Prepare + audio_input = StreamedAudioInput() + stt_settings = STTModelSettings() + + session = OpenAISTTTranscriptionSession( + input=audio_input, + client=AsyncMock(api_key="FAKE_KEY"), + model="whisper-1", + settings=stt_settings, + trace_include_sensitive_data=False, + trace_include_sensitive_audio_data=False, + ) + + # Kick off the transcribe_turns generator + turn_iter = session.transcribe_turns() + async for _ in turn_iter: + pass + + # Now push some audio data + + buffer1 = np.array([1, 2, 3, 4], dtype=np.int16) + await audio_input.add_audio(buffer1) + await asyncio.sleep(0.1) # give time for _stream_audio to consume + await asyncio.sleep(4) + + # Check that the websocket sent an "input_audio_buffer.append" message + found_audio_append = False + for call_arg in mock_ws.send.call_args_list: + print("call_arg", call_arg) + print("test", session._turn_audio_buffer) + sent_str = call_arg.args[0] + print("sent_str", sent_str) + if '"type": "input_audio_buffer.append"' in sent_str: + msg_dict = json.loads(sent_str) + assert msg_dict["type"] == "input_audio_buffer.append" + assert "audio" in msg_dict + found_audio_append = True + assert found_audio_append, "No 'input_audio_buffer.append' message was sent." + + await session.close() + + +@pytest.mark.asyncio +async def test_transcription_event_puts_output_in_queue(): + """ + Test that a 'conversation.item.input_audio_transcription.completed' event + yields a transcript from transcribe_turns(). + """ + mock_ws = create_mock_websocket( + [ + json.dumps({"type": "transcription_session.created"}), + json.dumps({"type": "transcription_session.updated"}), + # Once configured, we mock a completed transcription event: + json.dumps( + { + "type": "conversation.item.input_audio_transcription.completed", + "transcript": "Hello world!", + } + ), + ] + ) + + with patch("websockets.connect", return_value=mock_ws): + # Prepare + audio_input = await FakeStreamedAudioInput.get(count=2) + stt_settings = STTModelSettings() + + session = OpenAISTTTranscriptionSession( + input=audio_input, + client=AsyncMock(api_key="FAKE_KEY"), + model="whisper-1", + settings=stt_settings, + trace_include_sensitive_data=False, + trace_include_sensitive_audio_data=False, + ) + turns = session.transcribe_turns() + + # We'll collect transcribed turns in a list + collected_turns = [] + async for turn in turns: + collected_turns.append(turn) + await session.close() + + # Check we got "Hello world!" + assert "Hello world!" in collected_turns + # Cleanup + + +@pytest.mark.asyncio +async def test_timeout_waiting_for_created_event(monkeypatch): + """ + If the 'session.created' event does not arrive before SESSION_CREATION_TIMEOUT, + the session should raise a TimeoutError. + """ + time_gen = fake_time(increment=30) # increment by 30 seconds each time + + # Define a replacement function that returns the next time + def fake_time_func(): + return next(time_gen) + + # Monkey-patch time.time with our fake_time_func + monkeypatch.setattr(time, "time", fake_time_func) + + mock_ws = create_mock_websocket( + [ + json.dumps({"type": "unknown"}), + ] + ) # add a fake event to the mock websocket to make sure it doesn't raise a different exception + + with patch("websockets.connect", return_value=mock_ws): + audio_input = await FakeStreamedAudioInput.get(count=2) + stt_settings = STTModelSettings() + + session = OpenAISTTTranscriptionSession( + input=audio_input, + client=AsyncMock(api_key="FAKE_KEY"), + model="whisper-1", + settings=stt_settings, + trace_include_sensitive_data=False, + trace_include_sensitive_audio_data=False, + ) + turns = session.transcribe_turns() + + # We expect an exception once the generator tries to connect + wait for event + with pytest.raises(STTWebsocketConnectionError) as exc_info: + async for _ in turns: + pass + + assert "Timeout waiting for transcription_session.created event" in str(exc_info.value) + + await session.close() + + +@pytest.mark.asyncio +async def test_session_error_event(): + """ + If the session receives an event with "type": "error", it should propagate an exception + and put an ErrorSentinel in the output queue. + """ + mock_ws = create_mock_websocket( + [ + json.dumps({"type": "transcription_session.created"}), + json.dumps({"type": "transcription_session.updated"}), + # Then an error from the server + json.dumps({"type": "error", "error": "Simulated server error!"}), + ] + ) + + with patch("websockets.connect", return_value=mock_ws): + audio_input = await FakeStreamedAudioInput.get(count=2) + stt_settings = STTModelSettings() + + session = OpenAISTTTranscriptionSession( + input=audio_input, + client=AsyncMock(api_key="FAKE_KEY"), + model="whisper-1", + settings=stt_settings, + trace_include_sensitive_data=False, + trace_include_sensitive_audio_data=False, + ) + + with pytest.raises(STTWebsocketConnectionError): + turns = session.transcribe_turns() + async for _ in turns: + pass + + await session.close() + + +@pytest.mark.asyncio +async def test_inactivity_timeout(): + """ + Test that if no events arrive in EVENT_INACTIVITY_TIMEOUT ms, + _handle_events breaks out and a SessionCompleteSentinel is placed in the output queue. + """ + # We'll feed only the creation + updated events. Then do nothing. + # The handle_events loop should eventually time out. + mock_ws = create_mock_websocket( + [ + json.dumps({"type": "unknown"}), + json.dumps({"type": "unknown"}), + json.dumps({"type": "transcription_session.created"}), + json.dumps({"type": "transcription_session.updated"}), + ] + ) + + # We'll artificially manipulate the "time" to simulate inactivity quickly. + # The code checks time.time() for inactivity over EVENT_INACTIVITY_TIMEOUT. + # We'll increment the return_value manually. + with ( + patch("websockets.connect", return_value=mock_ws), + patch( + "time.time", + side_effect=[ + 1000.0, + 1000.0 + EVENT_INACTIVITY_TIMEOUT + 1, + 2000.0 + EVENT_INACTIVITY_TIMEOUT + 1, + 3000.0 + EVENT_INACTIVITY_TIMEOUT + 1, + 9999, + ], + ), + ): + audio_input = await FakeStreamedAudioInput.get(count=2) + stt_settings = STTModelSettings() + + session = OpenAISTTTranscriptionSession( + input=audio_input, + client=AsyncMock(api_key="FAKE_KEY"), + model="whisper-1", + settings=stt_settings, + trace_include_sensitive_data=False, + trace_include_sensitive_audio_data=False, + ) + + collected_turns: list[str] = [] + with pytest.raises(STTWebsocketConnectionError) as exc_info: + async for turn in session.transcribe_turns(): + collected_turns.append(turn) + + assert "Timeout waiting for transcription_session" in str(exc_info.value) + + assert len(collected_turns) == 0, "No transcripts expected, but we got something?" + + await session.close() diff --git a/tests/voice/test_openai_tts.py b/tests/voice/test_openai_tts.py new file mode 100644 index 00000000..b18f9e8c --- /dev/null +++ b/tests/voice/test_openai_tts.py @@ -0,0 +1,94 @@ +# Tests for the OpenAI text-to-speech model (OpenAITTSModel). + +from types import SimpleNamespace +from typing import Any + +import pytest + +try: + from agents.voice import OpenAITTSModel, TTSModelSettings +except ImportError: + pass + + +class _FakeStreamResponse: + """A minimal async context manager to simulate streaming audio bytes.""" + + def __init__(self, chunks: list[bytes]): + self._chunks = chunks + + async def __aenter__(self) -> "_FakeStreamResponse": + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb) -> None: + return None + + async def iter_bytes(self, chunk_size: int = 1024): + for chunk in self._chunks: + yield chunk + + +def _make_fake_openai_client(fake_create) -> SimpleNamespace: + """Construct an object with nested audio.speech.with_streaming_response.create.""" + return SimpleNamespace( + audio=SimpleNamespace( + speech=SimpleNamespace(with_streaming_response=SimpleNamespace(create=fake_create)) + ) + ) + + +@pytest.mark.asyncio +async def test_openai_tts_default_voice_and_instructions() -> None: + """If no voice is specified, OpenAITTSModel uses its default voice and passes instructions.""" + chunks = [b"abc", b"def"] + captured: dict[str, object] = {} + + def fake_create( + *, model: str, voice: str, input: str, response_format: str, extra_body: dict[str, Any] + ) -> _FakeStreamResponse: + captured["model"] = model + captured["voice"] = voice + captured["input"] = input + captured["response_format"] = response_format + captured["extra_body"] = extra_body + return _FakeStreamResponse(chunks) + + client = _make_fake_openai_client(fake_create) + tts_model = OpenAITTSModel(model="test-model", openai_client=client) # type: ignore[arg-type] + settings = TTSModelSettings() + out: list[bytes] = [] + async for b in tts_model.run("hello world", settings): + out.append(b) + assert out == chunks + assert captured["model"] == "test-model" + assert captured["voice"] == "ash" + assert captured["input"] == "hello world" + assert captured["response_format"] == "pcm" + assert captured["extra_body"] == {"instructions": settings.instructions} + + +@pytest.mark.asyncio +async def test_openai_tts_custom_voice_and_instructions() -> None: + """Specifying voice and instructions are forwarded to the API.""" + chunks = [b"x"] + captured: dict[str, object] = {} + + def fake_create( + *, model: str, voice: str, input: str, response_format: str, extra_body: dict[str, Any] + ) -> _FakeStreamResponse: + captured["model"] = model + captured["voice"] = voice + captured["input"] = input + captured["response_format"] = response_format + captured["extra_body"] = extra_body + return _FakeStreamResponse(chunks) + + client = _make_fake_openai_client(fake_create) + tts_model = OpenAITTSModel(model="my-model", openai_client=client) # type: ignore[arg-type] + settings = TTSModelSettings(voice="fable", instructions="Custom instructions") + out: list[bytes] = [] + async for b in tts_model.run("hi", settings): + out.append(b) + assert out == chunks + assert captured["voice"] == "fable" + assert captured["extra_body"] == {"instructions": "Custom instructions"} diff --git a/tests/voice/test_pipeline.py b/tests/voice/test_pipeline.py new file mode 100644 index 00000000..51904468 --- /dev/null +++ b/tests/voice/test_pipeline.py @@ -0,0 +1,179 @@ +from __future__ import annotations + +import numpy as np +import numpy.typing as npt +import pytest + +try: + from agents.voice import AudioInput, TTSModelSettings, VoicePipeline, VoicePipelineConfig + + from .fake_models import FakeStreamedAudioInput, FakeSTT, FakeTTS, FakeWorkflow + from .helpers import extract_events +except ImportError: + pass + + +@pytest.mark.asyncio +async def test_voicepipeline_run_single_turn() -> None: + # Single turn. Should produce a single audio output, which is the TTS output for "out_1". + + fake_stt = FakeSTT(["first"]) + workflow = FakeWorkflow([["out_1"]]) + fake_tts = FakeTTS() + config = VoicePipelineConfig(tts_settings=TTSModelSettings(buffer_size=1)) + pipeline = VoicePipeline( + workflow=workflow, stt_model=fake_stt, tts_model=fake_tts, config=config + ) + audio_input = AudioInput(buffer=np.zeros(2, dtype=np.int16)) + result = await pipeline.run(audio_input) + events, audio_chunks = await extract_events(result) + assert events == [ + "turn_started", + "audio", + "turn_ended", + "session_ended", + ] + await fake_tts.verify_audio("out_1", audio_chunks[0]) + + +@pytest.mark.asyncio +async def test_voicepipeline_streamed_audio_input() -> None: + # Multi turn. Should produce 2 audio outputs, which are the TTS outputs of "out_1" and "out_2" + + fake_stt = FakeSTT(["first", "second"]) + workflow = FakeWorkflow([["out_1"], ["out_2"]]) + fake_tts = FakeTTS() + pipeline = VoicePipeline(workflow=workflow, stt_model=fake_stt, tts_model=fake_tts) + + streamed_audio_input = await FakeStreamedAudioInput.get(count=2) + + result = await pipeline.run(streamed_audio_input) + events, audio_chunks = await extract_events(result) + assert events == [ + "turn_started", + "audio", # out_1 + "turn_ended", + "turn_started", + "audio", # out_2 + "turn_ended", + "session_ended", + ] + assert len(audio_chunks) == 2 + await fake_tts.verify_audio("out_1", audio_chunks[0]) + await fake_tts.verify_audio("out_2", audio_chunks[1]) + + +@pytest.mark.asyncio +async def test_voicepipeline_run_single_turn_split_words() -> None: + # Single turn. Should produce multiple audio outputs, which are the TTS outputs of "foo bar baz" + # split into words and then "foo2 bar2 baz2" split into words. + + fake_stt = FakeSTT(["first"]) + workflow = FakeWorkflow([["foo bar baz"]]) + fake_tts = FakeTTS(strategy="split_words") + config = VoicePipelineConfig(tts_settings=TTSModelSettings(buffer_size=1)) + pipeline = VoicePipeline( + workflow=workflow, stt_model=fake_stt, tts_model=fake_tts, config=config + ) + audio_input = AudioInput(buffer=np.zeros(2, dtype=np.int16)) + result = await pipeline.run(audio_input) + events, audio_chunks = await extract_events(result) + assert events == [ + "turn_started", + "audio", # foo + "audio", # bar + "audio", # baz + "turn_ended", + "session_ended", + ] + await fake_tts.verify_audio_chunks("foo bar baz", audio_chunks) + + +@pytest.mark.asyncio +async def test_voicepipeline_run_multi_turn_split_words() -> None: + # Multi turn. Should produce multiple audio outputs, which are the TTS outputs of "foo bar baz" + # split into words. + + fake_stt = FakeSTT(["first", "second"]) + workflow = FakeWorkflow([["foo bar baz"], ["foo2 bar2 baz2"]]) + fake_tts = FakeTTS(strategy="split_words") + config = VoicePipelineConfig(tts_settings=TTSModelSettings(buffer_size=1)) + pipeline = VoicePipeline( + workflow=workflow, stt_model=fake_stt, tts_model=fake_tts, config=config + ) + streamed_audio_input = await FakeStreamedAudioInput.get(count=6) + result = await pipeline.run(streamed_audio_input) + events, audio_chunks = await extract_events(result) + assert events == [ + "turn_started", + "audio", # foo + "audio", # bar + "audio", # baz + "turn_ended", + "turn_started", + "audio", # foo2 + "audio", # bar2 + "audio", # baz2 + "turn_ended", + "session_ended", + ] + assert len(audio_chunks) == 6 + await fake_tts.verify_audio_chunks("foo bar baz", audio_chunks[:3]) + await fake_tts.verify_audio_chunks("foo2 bar2 baz2", audio_chunks[3:]) + + +@pytest.mark.asyncio +async def test_voicepipeline_float32() -> None: + # Single turn. Should produce a single audio output, which is the TTS output for "out_1". + + fake_stt = FakeSTT(["first"]) + workflow = FakeWorkflow([["out_1"]]) + fake_tts = FakeTTS() + config = VoicePipelineConfig(tts_settings=TTSModelSettings(buffer_size=1, dtype=np.float32)) + pipeline = VoicePipeline( + workflow=workflow, stt_model=fake_stt, tts_model=fake_tts, config=config + ) + audio_input = AudioInput(buffer=np.zeros(2, dtype=np.int16)) + result = await pipeline.run(audio_input) + events, audio_chunks = await extract_events(result) + assert events == [ + "turn_started", + "audio", + "turn_ended", + "session_ended", + ] + await fake_tts.verify_audio("out_1", audio_chunks[0], dtype=np.float32) + + +@pytest.mark.asyncio +async def test_voicepipeline_transform_data() -> None: + # Single turn. Should produce a single audio output, which is the TTS output for "out_1". + + def _transform_data( + data_chunk: npt.NDArray[np.int16 | np.float32], + ) -> npt.NDArray[np.int16]: + return data_chunk.astype(np.int16) + + fake_stt = FakeSTT(["first"]) + workflow = FakeWorkflow([["out_1"]]) + fake_tts = FakeTTS() + config = VoicePipelineConfig( + tts_settings=TTSModelSettings( + buffer_size=1, + dtype=np.float32, + transform_data=_transform_data, + ) + ) + pipeline = VoicePipeline( + workflow=workflow, stt_model=fake_stt, tts_model=fake_tts, config=config + ) + audio_input = AudioInput(buffer=np.zeros(2, dtype=np.int16)) + result = await pipeline.run(audio_input) + events, audio_chunks = await extract_events(result) + assert events == [ + "turn_started", + "audio", + "turn_ended", + "session_ended", + ] + await fake_tts.verify_audio("out_1", audio_chunks[0], dtype=np.int16) diff --git a/tests/voice/test_workflow.py b/tests/voice/test_workflow.py new file mode 100644 index 00000000..035a05d5 --- /dev/null +++ b/tests/voice/test_workflow.py @@ -0,0 +1,194 @@ +from __future__ import annotations + +import json +from collections.abc import AsyncIterator + +import pytest +from inline_snapshot import snapshot +from openai.types.responses import ResponseCompletedEvent +from openai.types.responses.response_text_delta_event import ResponseTextDeltaEvent + +from agents import Agent, Model, ModelSettings, ModelTracing, Tool +from agents.agent_output import AgentOutputSchemaBase +from agents.handoffs import Handoff +from agents.items import ( + ModelResponse, + TResponseInputItem, + TResponseOutputItem, + TResponseStreamEvent, +) + +try: + from agents.voice import SingleAgentVoiceWorkflow + + from ..fake_model import get_response_obj + from ..test_responses import get_function_tool, get_function_tool_call, get_text_message +except ImportError: + pass + + +class FakeStreamingModel(Model): + def __init__(self): + self.turn_outputs: list[list[TResponseOutputItem]] = [] + + def set_next_output(self, output: list[TResponseOutputItem]): + self.turn_outputs.append(output) + + def add_multiple_turn_outputs(self, outputs: list[list[TResponseOutputItem]]): + self.turn_outputs.extend(outputs) + + def get_next_output(self) -> list[TResponseOutputItem]: + if not self.turn_outputs: + return [] + return self.turn_outputs.pop(0) + + async def get_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + *, + previous_response_id: str | None, + ) -> ModelResponse: + raise NotImplementedError("Not implemented") + + async def stream_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + *, + previous_response_id: str | None, + ) -> AsyncIterator[TResponseStreamEvent]: + output = self.get_next_output() + for item in output: + if ( + item.type == "message" + and len(item.content) == 1 + and item.content[0].type == "output_text" + ): + yield ResponseTextDeltaEvent( + content_index=0, + delta=item.content[0].text, + type="response.output_text.delta", + output_index=0, + item_id=item.id, + sequence_number=0, + ) + + yield ResponseCompletedEvent( + type="response.completed", + response=get_response_obj(output), + sequence_number=1, + ) + + +@pytest.mark.asyncio +async def test_single_agent_workflow(monkeypatch) -> None: + model = FakeStreamingModel() + model.add_multiple_turn_outputs( + [ + # First turn: a message and a tool call + [ + get_function_tool_call("some_function", json.dumps({"a": "b"})), + get_text_message("a_message"), + ], + # Second turn: text message + [get_text_message("done")], + ] + ) + + agent = Agent( + "initial_agent", + model=model, + tools=[get_function_tool("some_function", "tool_result")], + ) + + workflow = SingleAgentVoiceWorkflow(agent) + output = [] + async for chunk in workflow.run("transcription_1"): + output.append(chunk) + + # Validate that the text yielded matches our fake events + assert output == ["a_message", "done"] + # Validate that internal state was updated + assert workflow._input_history == snapshot( + [ + {"content": "transcription_1", "role": "user"}, + { + "arguments": '{"a": "b"}', + "call_id": "2", + "name": "some_function", + "type": "function_call", + "id": "1", + }, + { + "id": "1", + "content": [{"annotations": [], "text": "a_message", "type": "output_text"}], + "role": "assistant", + "status": "completed", + "type": "message", + }, + {"call_id": "2", "output": "tool_result", "type": "function_call_output"}, + { + "id": "1", + "content": [{"annotations": [], "text": "done", "type": "output_text"}], + "role": "assistant", + "status": "completed", + "type": "message", + }, + ] + ) + assert workflow._current_agent == agent + + model.set_next_output([get_text_message("done_2")]) + + # Run it again with a new transcription to make sure the input history is updated + output = [] + async for chunk in workflow.run("transcription_2"): + output.append(chunk) + + assert workflow._input_history == snapshot( + [ + {"role": "user", "content": "transcription_1"}, + { + "arguments": '{"a": "b"}', + "call_id": "2", + "name": "some_function", + "type": "function_call", + "id": "1", + }, + { + "id": "1", + "content": [{"annotations": [], "text": "a_message", "type": "output_text"}], + "role": "assistant", + "status": "completed", + "type": "message", + }, + {"call_id": "2", "output": "tool_result", "type": "function_call_output"}, + { + "id": "1", + "content": [{"annotations": [], "text": "done", "type": "output_text"}], + "role": "assistant", + "status": "completed", + "type": "message", + }, + {"role": "user", "content": "transcription_2"}, + { + "id": "1", + "content": [{"annotations": [], "text": "done_2", "type": "output_text"}], + "role": "assistant", + "status": "completed", + "type": "message", + }, + ] + ) + assert workflow._current_agent == agent diff --git a/uv.lock b/uv.lock index 2bceea75..6f2f3f84 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,129 @@ version = 1 revision = 1 requires-python = ">=3.9" +resolution-markers = [ + "python_full_version >= '3.10'", + "python_full_version < '3.10'", +] + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = 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