diff --git a/.github/workflows/issues.yml b/.github/workflows/issues.yml index 6447f83ef..dca717752 100644 --- a/.github/workflows/issues.yml +++ b/.github/workflows/issues.yml @@ -21,6 +21,7 @@ jobs: days-before-pr-stale: 10 days-before-pr-close: 7 stale-pr-label: "stale" + exempt-issue-labels: "skip-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: ${{ secrets.GITHUB_TOKEN }} diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 000000000..a75c1414f --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,14 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python Debugger: Python File", + "type": "debugpy", + "request": "launch", + "program": "${file}" + } + ] +} \ No newline at end of file diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 000000000..291c31837 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,71 @@ +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`. + +All python commands should be run via `uv run python ...` + +## 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 `uv run ruff format`, imports sorted, and type hints passing `uv run mypy .`. +- Clear documentation for any public API changes. +- Clean history and a helpful PR description. diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 000000000..5e01a1c3d --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1 @@ +Read the AGENTS.md file for instructions. \ No newline at end of file diff --git a/Makefile b/Makefile index 5c6aba425..9a88f93a1 100644 --- a/Makefile +++ b/Makefile @@ -7,6 +7,10 @@ format: uv run ruff format uv run ruff check --fix +.PHONY: format-check +format-check: + uv run ruff format --check + .PHONY: lint lint: uv run ruff check @@ -55,5 +59,5 @@ serve-docs: deploy-docs: uv run mkdocs gh-deploy --force --verbose - - +.PHONY: check +check: format-check lint mypy tests diff --git a/README.md b/README.md index 7dcd97b33..638f94b21 100644 --- a/README.md +++ b/README.md @@ -4,27 +4,146 @@ The OpenAI Agents SDK is a lightweight yet powerful framework for building multi Image of the Agents Tracing UI +> [!NOTE] +> Looking for the JavaScript/TypeScript version? Check out [Agents SDK JS/TS](https://github.com/openai/openai-agents-js). + ### Core concepts: 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 +4. [**Sessions**](#sessions): Automatic conversation history management across agent runs +5. [**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. +## Sessions + +The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the need to manually handle `.to_input_list()` between turns. + +### Quick start + +```python +from agents import Agent, Runner, SQLiteSession + +# Create agent +agent = Agent( + name="Assistant", + instructions="Reply very concisely.", +) + +# Create a session instance +session = SQLiteSession("conversation_123") + +# First turn +result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session +) +print(result.final_output) # "San Francisco" + +# Second turn - agent automatically remembers previous context +result = await Runner.run( + agent, + "What state is it in?", + session=session +) +print(result.final_output) # "California" + +# Also works with synchronous runner +result = Runner.run_sync( + agent, + "What's the population?", + session=session +) +print(result.final_output) # "Approximately 39 million" +``` + +### Session options + +- **No memory** (default): No session memory when session parameter is omitted +- **`session: Session = DatabaseSession(...)`**: Use a Session instance to manage conversation history + +```python +from agents import Agent, Runner, SQLiteSession + +# Custom SQLite database file +session = SQLiteSession("user_123", "conversations.db") +agent = Agent(name="Assistant") + +# Different session IDs maintain separate conversation histories +result1 = await Runner.run( + agent, + "Hello", + session=session +) +result2 = await Runner.run( + agent, + "Hello", + session=SQLiteSession("user_456", "conversations.db") +) +``` + +### Custom session implementations + +You can implement your own session memory by creating a class that follows the `Session` protocol: + +```python +from agents.memory import Session +from typing import List + +class MyCustomSession: + """Custom session implementation following the Session protocol.""" + + def __init__(self, session_id: str): + self.session_id = session_id + # Your initialization here + + async def get_items(self, limit: int | None = None) -> List[dict]: + # Retrieve conversation history for the session + pass + + async def add_items(self, items: List[dict]) -> None: + # Store new items for the session + pass + + async def pop_item(self) -> dict | None: + # Remove and return the most recent item from the session + pass + + async def clear_session(self) -> None: + # Clear all items for the session + pass + +# Use your custom session +agent = Agent(name="Assistant") +result = await Runner.run( + agent, + "Hello", + session=MyCustomSession("my_session") +) +``` + ## Get started 1. Set up your Python environment -``` +- Option A: Using venv (traditional method) +```bash python -m venv env -source env/bin/activate +source env/bin/activate # On Windows: env\Scripts\activate +``` + +- Option B: Using uv (recommended) +```bash +uv venv +source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 2. Install Agents SDK -``` +```bash pip install openai-agents ``` @@ -47,7 +166,7 @@ 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)_) +(_For Jupyter notebook users, see [hello_world_jupyter.ipynb](examples/basic/hello_world_jupyter.ipynb)_) ## Handoffs example @@ -160,10 +279,16 @@ make sync 2. (After making changes) lint/test +``` +make check # run tests linter and typechecker +``` + +Or to run them individually: ``` make tests # run tests make mypy # run typechecker make lint # run linter +make format-check # run style checker ``` ## Acknowledgements diff --git a/docs/agents.md b/docs/agents.md index 39d4afd57..b11a4dd68 100644 --- a/docs/agents.md +++ b/docs/agents.md @@ -6,6 +6,7 @@ Agents are the core building block in your apps. An agent is a large language mo The most common properties of an agent you'll configure are: +- `name`: A required string that identifies your agent. - `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. diff --git a/docs/context.md b/docs/context.md index 4176ec51f..6e54565e0 100644 --- a/docs/context.md +++ b/docs/context.md @@ -38,7 +38,8 @@ class UserInfo: # (1)! @function_tool async def fetch_user_age(wrapper: RunContextWrapper[UserInfo]) -> str: # (2)! - return f"User {wrapper.context.name} is 47 years old" + """Fetch the age of the user. Call this function to get user's age information.""" + return f"The user {wrapper.context.name} is 47 years old" async def main(): user_info = UserInfo(name="John", uid=123) diff --git a/docs/guardrails.md b/docs/guardrails.md index 2f0be0f2a..8df904a4c 100644 --- a/docs/guardrails.md +++ b/docs/guardrails.md @@ -23,7 +23,7 @@ Input guardrails run in 3 steps: Output guardrails run in 3 steps: -1. First, the guardrail receives the same input passed to the agent. +1. First, the guardrail receives the output produced by 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. diff --git a/docs/index.md b/docs/index.md index 8aef6574e..935c4be5b 100644 --- a/docs/index.md +++ b/docs/index.md @@ -5,6 +5,7 @@ The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) enables - **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 +- **Sessions**, which automatically maintains conversation history across agent runs 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. @@ -21,6 +22,7 @@ Here are the main features of the SDK: - 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. +- Sessions: Automatic conversation history management across agent runs, eliminating manual state handling. - 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. diff --git a/docs/ja/guardrails.md b/docs/ja/guardrails.md index e7b02a6ed..b67bb8bad 100644 --- a/docs/ja/guardrails.md +++ b/docs/ja/guardrails.md @@ -4,44 +4,44 @@ search: --- # 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。 +ガードレールは エージェント と _並行して_ 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えます。例えば、とても賢い（つまり遅く/高価な）モデルを使用してカスタマーリクエストを処理する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しょう。そこで、速く/安価なモデルで動く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すると、直ちにエラーを送出して高価なモデルの実行を停止し、時間とコストを節約できます。 -ガードレールには 2 種類あります。 +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 +1. 入力ガードレール は初期 ユーザー 入力に対して実行されます +2. 出力ガードレール 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 +## 入力ガードレール -Input ガードレールは 3 つのステップで実行されます。 +入力ガードレールは 3 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。 +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] にラップされます。 +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、適切に ユーザー に応答したり例外を処理できます。 !!! 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。 + 入力ガードレールは ユーザー 入力に対して実行されることを意図しているため、ガードレールは *最初* のエージェントでのみ実行されます。「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」と疑問に思うかもしれません。これは、ガードレールが実際の エージェント と密接に関連していることが多いからです。異なるエージェントには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します。 -## Output ガードレール +## 出力ガードレール -Output ガードレールは 3 つのステップで実行されます。 +出力ガードレールは 3 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。 +1. まず、ガードレールはエージェントが生成した出力を受け取ります。 +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] にラップされます。 +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、適切に ユーザー に応答したり例外を処理できます。 !!! 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。 + 出力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意図しているため、ガードレールは *最後* のエージェントでのみ実行されます。入力ガードレールの場合と同様、ガードレールが実際の エージェント と密接に関連していることが多いため、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 +## トリップワイヤー -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。 +入力または出力がガードレールを通過できなかった場合、ガードレールはトリップワイヤーでそれを示すことができます。トリップワイヤーがトリガーされたガードレールを検知した時点で、直ちに `{Input,Output}GuardrailTripwireTriggered` 例外を送出し、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。 +入力を受け取り、[`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を返す関数を提供する必要があります。この例では、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 @@ -94,12 +94,12 @@ async def main(): 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。 +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。 +出力ガードレールも同様です。 ```python from pydantic import BaseModel @@ -155,4 +155,4 @@ async def main(): 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 +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/mcp.md b/docs/ja/mcp.md index 7cdaa57ee..1e394a5e6 100644 --- a/docs/ja/mcp.md +++ b/docs/ja/mcp.md @@ -12,23 +12,31 @@ Agents SDK は MCP をサポートしており、これにより幅広い MCP ## 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。 +現在、MCP 仕様では使用するトランスポート方式に基づき 3 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。 +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。 +3. **Streamable HTTP** サーバー: MCP 仕様に定義された Streamable HTTP トランスポートを使用してリモートで動作します。 -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。 +これらのサーバーへは [`MCPServerStdio`][agents.mcp.server.MCPServerStdio]、[`MCPServerSse`][agents.mcp.server.MCPServerSse]、[`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp] 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 +from agents.run_context import RunContextWrapper + async with MCPServerStdio( params={ "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], } ) as server: - tools = await server.list_tools() + # 注意：実際には通常は MCP サーバーをエージェントに追加し、 + # フレームワークがツール一覧の取得を自動的に処理するようにします。 + # list_tools() への直接呼び出しには run_context と agent パラメータが必要です。 + run_context = RunContextWrapper(context=None) + agent = Agent(name="test", instructions="test") + tools = await server.list_tools(run_context, agent) ``` ## MCP サーバーの利用 @@ -46,7 +54,7 @@ agent=Agent( ## 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。 +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]、[`MCPServerSse`][agents.mcp.server.MCPServerSse]、[`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp] の各クラスに `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。 diff --git a/docs/ja/models.md b/docs/ja/models.md deleted file mode 100644 index 5a76d60ec..000000000 --- a/docs/ja/models.md +++ /dev/null @@ -1,106 +0,0 @@ -# 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 index a40ae38f6..410c01676 100644 --- a/docs/ja/models/index.md +++ b/docs/ja/models/index.md @@ -4,21 +4,52 @@ search: --- # 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。 +Agents SDK は OpenAI モデルを 2 つの形態で即利用できます。 -- **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。 +- **推奨**: [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] は、新しい [Responses API](https://platform.openai.com/docs/api-reference/responses) を使用して 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) を使用して OpenAI API を呼び出します。 + +## 非 OpenAI モデル + +ほとんどの非 OpenAI モデルは [LiteLLM インテグレーション](./litellm.md) 経由で利用できます。まず、litellm 依存グループをインストールします: + +```bash +pip install "openai-agents[litellm]" +``` + +次に、`litellm/` 接頭辞を付けて任意の [サポート対象モデル](https://docs.litellm.ai/docs/providers) を使用します: + +```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", ...) +``` + +### 非 OpenAI モデルを利用するその他の方法 + +他の LLM プロバイダーを統合する方法は、あと 3 つあります（[こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/) に例があります）。 + +1. [`set_default_openai_client`][agents.set_default_openai_client] + `AsyncOpenAI` インスタンスを LLM クライアントとしてグローバルに使用したい場合に便利です。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用します。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) にあります。 +2. [`ModelProvider`][agents.models.interface.ModelProvider] + `Runner.run` レベルでカスタムモデルプロバイダーを指定できます。これにより「この run のすべてのエージェントでカスタム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) にあります。 +3. [`Agent.model`][agents.agent.Agent.model] + 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。エージェントごとに異なるプロバイダーを組み合わせることが可能です。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) にあります。ほとんどのモデルを簡単に利用する方法として [LiteLLM インテグレーション](./litellm.md) を利用できます。 + +`platform.openai.com` の API キーを持っていない場合は、`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) を設定することをお勧めします。 + +!!! note + これらの例では、Responses API をまだサポートしていない LLM プロバイダーが多いため、Chat Completions API/モデルを使用しています。LLM プロバイダーが Responses API をサポートしている場合は、Responses を使用することを推奨します。 ## 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 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用するといったケースです。[`Agent`][agents.Agent] を設定する際、次のいずれかの方法で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 +1. モデル名を直接指定する +2. 任意のモデル名と、その名前を Model インスタンスへマッピングできる [`ModelProvider`][agents.models.interface.ModelProvider] を指定する 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。 + SDK は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] と [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] の両形態をサポートしていますが、各ワークフローで 1 つのモデル形態に統一することを推奨します。2 つの形態はサポートする機能とツールが異なるためです。混在させる場合は、使用する機能が双方で利用可能かを必ず確認してください。 ```python from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel @@ -51,10 +82,10 @@ async def main(): print(result.final_output) ``` -1. OpenAI モデル名を直接指定 +1. OpenAI のモデル名を直接設定 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。 +エージェントで使用するモデルをさらに構成したい場合は、`temperature` などのオプションパラメーターを指定できる [`ModelSettings`][agents.models.interface.ModelSettings] を渡せます。 ```python from agents import Agent, ModelSettings @@ -67,50 +98,58 @@ english_agent = Agent( ) ``` -## 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)。 +OpenAI の Responses API を使用する場合、`user` や `service_tier` など[その他のオプションパラメーター](https://platform.openai.com/docs/api-reference/responses/create) があります。トップレベルで指定できない場合は、`extra_args` で渡してください。 -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。 +```python +from agents import Agent, ModelSettings -!!! 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。 +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4o", + model_settings=ModelSettings( + temperature=0.1, + extra_args={"service_tier": "flex", "user": "user_12345"}, + ), +) +``` -## 他の LLM プロバイダーでよくある問題 +## 他の LLM プロバイダー使用時の一般的な問題 ### 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。 +Tracing 関連のエラーが発生する場合、トレースは OpenAI サーバーへアップロードされるため、OpenAI API キーが必要です。対応方法は次の 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。 +2. トレース用に 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用され、[platform.openai.com](https://platform.openai.com/) で取得したものが必要です。 +3. OpenAI 以外のトレースプロセッサーを使用する。詳細は [tracing のドキュメント](../tracing.md#custom-tracing-processors) を参照してください。 -### Responses API サポート +### Responses API のサポート -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。 +SDK はデフォルトで Responses API を使用しますが、ほとんどの LLM プロバイダーはまだ非対応です。その結果、404 などのエラーが発生することがあります。対処方法は次の 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。 + `OPENAI_API_KEY` と `OPENAI_BASE_URL` を環境変数で設定している場合に有効です。 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。 + 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 +これは一部プロバイダーの制限で、JSON 出力自体はサポートしていても `json_schema` を指定できないことが原因です。修正に向けて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使用することをお勧めします。そうでないと、不正な JSON が返されてアプリが頻繁に壊れる可能性があります。 + +## プロバイダーを跨いだモデルの組み合わせ + +モデルプロバイダーごとの機能差に注意しないと、エラーが発生します。たとえば OpenAI は structured outputs、マルチモーダル入力、ホスト型の file search や web search をサポートしていますが、多くの他プロバイダーは非対応です。以下の制限に留意してください。 + +- 対応していないプロバイダーには未サポートの `tools` を送らない +- テキストのみのモデルを呼び出す前にマルチモーダル入力を除外する +- structured JSON 出力をサポートしていないプロバイダーでは、不正な JSON が返ることがある点に注意する \ No newline at end of file diff --git a/docs/ja/repl.md b/docs/ja/repl.md new file mode 100644 index 000000000..108c12b90 --- /dev/null +++ b/docs/ja/repl.md @@ -0,0 +1,22 @@ +--- +search: + exclude: true +--- +# REPL ユーティリティ + +`run_demo_loop` を使うと、ターミナルから手軽にエージェントを試せます。 + +```python +import asyncio +from agents import Agent, run_demo_loop + +async def main() -> None: + agent = Agent(name="Assistant", instructions="あなたは親切なアシスタントです") + await run_demo_loop(agent) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +`run_demo_loop` は入力を繰り返し受け取り、会話履歴を保持したままエージェントを実行します。既定ではストリーミング出力を表示します。 +`quit` または `exit` と入力するか `Ctrl-D` を押すと終了します。 diff --git a/docs/ja/tools.md b/docs/ja/tools.md index 7ab15e472..cd80092d5 100644 --- a/docs/ja/tools.md +++ b/docs/ja/tools.md @@ -17,6 +17,10 @@ OpenAI は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIRespons - [`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。 +- [`CodeInterpreterTool`][agents.tool.CodeInterpreterTool] はサンドボックス環境でコードを実行します。 +- [`HostedMCPTool`][agents.tool.HostedMCPTool] はリモート MCP サーバーのツールをモデルから直接利用できるようにします。 +- [`ImageGenerationTool`][agents.tool.ImageGenerationTool] はプロンプトから画像を生成します。 +- [`LocalShellTool`][agents.tool.LocalShellTool] はローカルマシンでシェルコマンドを実行します。 ```python from agents import Agent, FileSearchTool, Runner, WebSearchTool diff --git a/docs/ja/tracing.md b/docs/ja/tracing.md index 0e0d0e77d..d67200ce4 100644 --- a/docs/ja/tracing.md +++ b/docs/ja/tracing.md @@ -119,4 +119,5 @@ async def main(): - [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 +- [Okahu‑Monocle](https://github.com/monocle2ai/monocle) +- [Portkey AI](https://portkey.ai/docs/integrations/agents/openai-agents) diff --git a/docs/mcp.md b/docs/mcp.md index e279a25e0..eef61a047 100644 --- a/docs/mcp.md +++ b/docs/mcp.md @@ -4,27 +4,35 @@ The [Model context protocol](https://modelcontextprotocol.io/introduction) (aka > 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. +The Agents SDK has support for MCP. This enables you to use a wide range of MCP servers to provide tools and prompts to your Agents. ## MCP servers -Currently, the MCP spec defines two kinds of servers, based on the transport mechanism they use: +Currently, the MCP spec defines three 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. +3. **Streamable HTTP** servers run remotely using the Streamable HTTP transport defined in the MCP spec. -You can use the [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse] classes to connect to these servers. +You can use the [`MCPServerStdio`][agents.mcp.server.MCPServerStdio], [`MCPServerSse`][agents.mcp.server.MCPServerSse], and [`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp] 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 +from agents.run_context import RunContextWrapper + async with MCPServerStdio( params={ "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], } ) as server: - tools = await server.list_tools() + # Note: In practice, you typically add the server to an Agent + # and let the framework handle tool listing automatically. + # Direct calls to list_tools() require run_context and agent parameters. + run_context = RunContextWrapper(context=None) + agent = Agent(name="test", instructions="test") + tools = await server.list_tools(run_context, agent) ``` ## Using MCP servers @@ -40,9 +48,128 @@ agent=Agent( ) ``` +## Tool filtering + +You can filter which tools are available to your Agent by configuring tool filters on MCP servers. The SDK supports both static and dynamic tool filtering. + +### Static tool filtering + +For simple allow/block lists, you can use static filtering: + +```python +from agents.mcp import create_static_tool_filter + +# Only expose specific tools from this server +server = MCPServerStdio( + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + }, + tool_filter=create_static_tool_filter( + allowed_tool_names=["read_file", "write_file"] + ) +) + +# Exclude specific tools from this server +server = MCPServerStdio( + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + }, + tool_filter=create_static_tool_filter( + blocked_tool_names=["delete_file"] + ) +) + +``` + +**When both `allowed_tool_names` and `blocked_tool_names` are configured, the processing order is:** +1. First apply `allowed_tool_names` (allowlist) - only keep the specified tools +2. Then apply `blocked_tool_names` (blocklist) - exclude specified tools from the remaining tools + +For example, if you configure `allowed_tool_names=["read_file", "write_file", "delete_file"]` and `blocked_tool_names=["delete_file"]`, only `read_file` and `write_file` tools will be available. + +### Dynamic tool filtering + +For more complex filtering logic, you can use dynamic filters with functions: + +```python +from agents.mcp import ToolFilterContext + +# Simple synchronous filter +def custom_filter(context: ToolFilterContext, tool) -> bool: + """Example of a custom tool filter.""" + # Filter logic based on tool name patterns + return tool.name.startswith("allowed_prefix") + +# Context-aware filter +def context_aware_filter(context: ToolFilterContext, tool) -> bool: + """Filter tools based on context information.""" + # Access agent information + agent_name = context.agent.name + + # Access server information + server_name = context.server_name + + # Implement your custom filtering logic here + return some_filtering_logic(agent_name, server_name, tool) + +# Asynchronous filter +async def async_filter(context: ToolFilterContext, tool) -> bool: + """Example of an asynchronous filter.""" + # Perform async operations if needed + result = await some_async_check(context, tool) + return result + +server = MCPServerStdio( + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + }, + tool_filter=custom_filter # or context_aware_filter or async_filter +) +``` + +The `ToolFilterContext` provides access to: +- `run_context`: The current run context +- `agent`: The agent requesting the tools +- `server_name`: The name of the MCP server + +## Prompts + +MCP servers can also provide prompts that can be used to dynamically generate agent instructions. This allows you to create reusable instruction templates that can be customized with parameters. + +### Using prompts + +MCP servers that support prompts provide two key methods: + +- `list_prompts()`: Lists all available prompts on the server +- `get_prompt(name, arguments)`: Gets a specific prompt with optional parameters + +```python +# List available prompts +prompts_result = await server.list_prompts() +for prompt in prompts_result.prompts: + print(f"Prompt: {prompt.name} - {prompt.description}") + +# Get a specific prompt with parameters +prompt_result = await server.get_prompt( + "generate_code_review_instructions", + {"focus": "security vulnerabilities", "language": "python"} +) +instructions = prompt_result.messages[0].content.text + +# Use the prompt-generated instructions with an Agent +agent = Agent( + name="Code Reviewer", + instructions=instructions, # Instructions from MCP prompt + mcp_servers=[server] +) +``` + ## 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. +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 [`MCPServerStdio`][agents.mcp.server.MCPServerStdio], [`MCPServerSse`][agents.mcp.server.MCPServerSse], and [`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp]. 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. diff --git a/docs/models/index.md b/docs/models/index.md index 1c89d778a..b3b2b7f0b 100644 --- a/docs/models/index.md +++ b/docs/models/index.md @@ -93,6 +93,22 @@ english_agent = Agent( ) ``` +Also, when you use OpenAI's Responses API, [there are a few other optional parameters](https://platform.openai.com/docs/api-reference/responses/create) (e.g., `user`, `service_tier`, and so on). If they are not available at the top level, you can use `extra_args` to pass them as well. + +```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, + extra_args={"service_tier": "flex", "user": "user_12345"}, + ), +) +``` + ## Common issues with using other LLM providers ### Tracing client error 401 diff --git a/docs/ref/memory.md b/docs/ref/memory.md new file mode 100644 index 000000000..04a2258bf --- /dev/null +++ b/docs/ref/memory.md @@ -0,0 +1,8 @@ +# Memory + +::: agents.memory + + options: + members: + - Session + - SQLiteSession diff --git a/docs/ref/repl.md b/docs/ref/repl.md new file mode 100644 index 000000000..a064a9bff --- /dev/null +++ b/docs/ref/repl.md @@ -0,0 +1,6 @@ +# `repl` + +::: agents.repl + options: + members: + - run_demo_loop diff --git a/docs/release.md b/docs/release.md new file mode 100644 index 000000000..a86103f96 --- /dev/null +++ b/docs/release.md @@ -0,0 +1,24 @@ +# Release process/changelog + +The project follows a slightly modified version of semantic versioning using the form `0.Y.Z`. The leading `0` indicates the SDK is still evolving rapidly. Increment the components as follows: + +## Minor (`Y`) versions + +We will increase minor versions `Y` for **breaking changes** to any public interfaces that are not marked as beta. For example, going from `0.0.x` to `0.1.x` might include breaking changes. + +If you don't want breaking changes, we recommend pinning to `0.0.x` versions in your project. + +## Patch (`Z`) versions + +We will increment `Z` for non-breaking changes: + +- Bug fixes +- New features +- Changes to private interfaces +- Updates to beta features + +## Breaking change changelog + +### 0.1.0 + +In this version, [`MCPServer.list_tools()`][agents.mcp.server.MCPServer] has two new params: `run_context` and `agent`. You'll need to add these params to any classes that subclass `MCPServer`. diff --git a/docs/repl.md b/docs/repl.md new file mode 100644 index 000000000..073b87f51 --- /dev/null +++ b/docs/repl.md @@ -0,0 +1,19 @@ +# REPL utility + +The SDK provides `run_demo_loop` for quick interactive testing. + +```python +import asyncio +from agents import Agent, run_demo_loop + +async def main() -> None: + agent = Agent(name="Assistant", instructions="You are a helpful assistant.") + await run_demo_loop(agent) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +`run_demo_loop` prompts for user input in a loop, keeping the conversation +history between turns. By default it streams model output as it is produced. +Type `quit` or `exit` (or press `Ctrl-D`) to leave the loop. diff --git a/docs/running_agents.md b/docs/running_agents.md index f631cf46f..6898f5101 100644 --- a/docs/running_agents.md +++ b/docs/running_agents.md @@ -65,7 +65,9 @@ Calling any of the run methods can result in one or more agents running (and hen 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. +### Manual conversation management + +You can manually manage conversation history using the [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] method to get the inputs for the next turn: ```python async def main(): @@ -84,6 +86,39 @@ async def main(): # California ``` +### Automatic conversation management with Sessions + +For a simpler approach, you can use [Sessions](sessions.md) to automatically handle conversation history without manually calling `.to_input_list()`: + +```python +from agents import Agent, Runner, SQLiteSession + +async def main(): + agent = Agent(name="Assistant", instructions="Reply very concisely.") + + # Create session instance + session = SQLiteSession("conversation_123") + + with trace(workflow_name="Conversation", group_id=thread_id): + # First turn + result = await Runner.run(agent, "What city is the Golden Gate Bridge in?", session=session) + print(result.final_output) + # San Francisco + + # Second turn - agent automatically remembers previous context + result = await Runner.run(agent, "What state is it in?", session=session) + print(result.final_output) + # California +``` + +Sessions automatically: + +- Retrieves conversation history before each run +- Stores new messages after each run +- Maintains separate conversations for different session IDs + +See the [Sessions documentation](sessions.md) for more details. + ## Exceptions The SDK raises exceptions in certain cases. The full list is in [`agents.exceptions`][]. As an overview: diff --git a/docs/scripts/translate_docs.py b/docs/scripts/translate_docs.py index b2e8b44fc..ac40b6fa8 100644 --- a/docs/scripts/translate_docs.py +++ b/docs/scripts/translate_docs.py @@ -1,5 +1,7 @@ # ruff: noqa import os +import sys +import argparse from openai import OpenAI from concurrent.futures import ThreadPoolExecutor @@ -263,24 +265,47 @@ def translate_single_source_file(file_path: str) -> None: 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.") + parser = argparse.ArgumentParser(description="Translate documentation files") + parser.add_argument( + "--file", type=str, help="Specific file to translate (relative to docs directory)" + ) + args = parser.parse_args() + + if args.file: + # Translate a single file + # Handle both "foo.md" and "docs/foo.md" formats + if args.file.startswith("docs/"): + # Remove "docs/" prefix if present + relative_file = args.file[5:] + else: + relative_file = args.file + + file_path = os.path.join(source_dir, relative_file) + if os.path.exists(file_path): + translate_single_source_file(file_path) + print(f"Translation completed for {relative_file}") + else: + print(f"Error: File {file_path} does not exist") + sys.exit(1) + else: + # Traverse the source directory (original behavior) + 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__": diff --git a/docs/sessions.md b/docs/sessions.md new file mode 100644 index 000000000..8906f0e6d --- /dev/null +++ b/docs/sessions.md @@ -0,0 +1,319 @@ +# Sessions + +The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the need to manually handle `.to_input_list()` between turns. + +Sessions stores conversation history for a specific session, allowing agents to maintain context without requiring explicit manual memory management. This is particularly useful for building chat applications or multi-turn conversations where you want the agent to remember previous interactions. + +## Quick start + +```python +from agents import Agent, Runner, SQLiteSession + +# Create agent +agent = Agent( + name="Assistant", + instructions="Reply very concisely.", +) + +# Create a session instance with a session ID +session = SQLiteSession("conversation_123") + +# First turn +result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session +) +print(result.final_output) # "San Francisco" + +# Second turn - agent automatically remembers previous context +result = await Runner.run( + agent, + "What state is it in?", + session=session +) +print(result.final_output) # "California" + +# Also works with synchronous runner +result = Runner.run_sync( + agent, + "What's the population?", + session=session +) +print(result.final_output) # "Approximately 39 million" +``` + +## How it works + +When session memory is enabled: + +1. **Before each run**: The runner automatically retrieves the conversation history for the session and prepends it to the input items. +2. **After each run**: All new items generated during the run (user input, assistant responses, tool calls, etc.) are automatically stored in the session. +3. **Context preservation**: Each subsequent run with the same session includes the full conversation history, allowing the agent to maintain context. + +This eliminates the need to manually call `.to_input_list()` and manage conversation state between runs. + +## Memory operations + +### Basic operations + +Sessions supports several operations for managing conversation history: + +```python +from agents import SQLiteSession + +session = SQLiteSession("user_123", "conversations.db") + +# Get all items in a session +items = await session.get_items() + +# Add new items to a session +new_items = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"} +] +await session.add_items(new_items) + +# Remove and return the most recent item +last_item = await session.pop_item() +print(last_item) # {"role": "assistant", "content": "Hi there!"} + +# Clear all items from a session +await session.clear_session() +``` + +### Using pop_item for corrections + +The `pop_item` method is particularly useful when you want to undo or modify the last item in a conversation: + +```python +from agents import Agent, Runner, SQLiteSession + +agent = Agent(name="Assistant") +session = SQLiteSession("correction_example") + +# Initial conversation +result = await Runner.run( + agent, + "What's 2 + 2?", + session=session +) +print(f"Agent: {result.final_output}") + +# User wants to correct their question +user_item = await session.pop_item() # Remove user's question +assistant_item = await session.pop_item() # Remove agent's response + +# Ask a corrected question +result = await Runner.run( + agent, + "What's 2 + 3?", + session=session +) +print(f"Agent: {result.final_output}") +``` + +## Memory options + +### No memory (default) + +```python +# Default behavior - no session memory +result = await Runner.run(agent, "Hello") +``` + +### SQLite memory + +```python +from agents import SQLiteSession + +# In-memory database (lost when process ends) +session = SQLiteSession("user_123") + +# Persistent file-based database +session = SQLiteSession("user_123", "conversations.db") + +# Use the session +result = await Runner.run( + agent, + "Hello", + session=session +) +``` + +### Multiple sessions + +```python +from agents import Agent, Runner, SQLiteSession + +agent = Agent(name="Assistant") + +# Different sessions maintain separate conversation histories +session_1 = SQLiteSession("user_123", "conversations.db") +session_2 = SQLiteSession("user_456", "conversations.db") + +result1 = await Runner.run( + agent, + "Hello", + session=session_1 +) +result2 = await Runner.run( + agent, + "Hello", + session=session_2 +) +``` + +## Custom memory implementations + +You can implement your own session memory by creating a class that follows the [`Session`][agents.memory.session.Session] protocol: + +````python +from agents.memory import Session +from typing import List + +class MyCustomSession: + """Custom session implementation following the Session protocol.""" + + def __init__(self, session_id: str): + self.session_id = session_id + # Your initialization here + + async def get_items(self, limit: int | None = None) -> List[dict]: + """Retrieve conversation history for this session.""" + # Your implementation here + pass + + async def add_items(self, items: List[dict]) -> None: + """Store new items for this session.""" + # Your implementation here + pass + + async def pop_item(self) -> dict | None: + """Remove and return the most recent item from this session.""" + # Your implementation here + pass + + async def clear_session(self) -> None: + """Clear all items for this session.""" + # Your implementation here + pass + +# Use your custom session +agent = Agent(name="Assistant") +result = await Runner.run( + agent, + "Hello", + session=MyCustomSession("my_session") +) + +## Session management + +### Session ID naming + +Use meaningful session IDs that help you organize conversations: + +- User-based: `"user_12345"` +- Thread-based: `"thread_abc123"` +- Context-based: `"support_ticket_456"` + +### Memory persistence + +- Use in-memory SQLite (`SQLiteSession("session_id")`) for temporary conversations +- Use file-based SQLite (`SQLiteSession("session_id", "path/to/db.sqlite")`) for persistent conversations +- Consider implementing custom session backends for production systems (Redis, PostgreSQL, etc.) + +### Session management + +```python +# Clear a session when conversation should start fresh +await session.clear_session() + +# Different agents can share the same session +support_agent = Agent(name="Support") +billing_agent = Agent(name="Billing") +session = SQLiteSession("user_123") + +# Both agents will see the same conversation history +result1 = await Runner.run( + support_agent, + "Help me with my account", + session=session +) +result2 = await Runner.run( + billing_agent, + "What are my charges?", + session=session +) +```` + +## Complete example + +Here's a complete example showing session memory in action: + +```python +import asyncio +from agents import Agent, Runner, SQLiteSession + + +async def main(): + # Create an agent + agent = Agent( + name="Assistant", + instructions="Reply very concisely.", + ) + + # Create a session instance that will persist across runs + session = SQLiteSession("conversation_123", "conversation_history.db") + + print("=== Sessions Example ===") + print("The agent will remember previous messages automatically.\n") + + # First turn + print("First turn:") + print("User: What city is the Golden Gate Bridge in?") + result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + # Second turn - the agent will remember the previous conversation + print("Second turn:") + print("User: What state is it in?") + result = await Runner.run( + agent, + "What state is it in?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + # Third turn - continuing the conversation + print("Third turn:") + print("User: What's the population of that state?") + result = await Runner.run( + agent, + "What's the population of that state?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + print("=== Conversation Complete ===") + print("Notice how the agent remembered the context from previous turns!") + print("Sessions automatically handles conversation history.") + + +if __name__ == "__main__": + asyncio.run(main()) +``` + +## API Reference + +For detailed API documentation, see: + +- [`Session`][agents.memory.Session] - Protocol interface +- [`SQLiteSession`][agents.memory.SQLiteSession] - SQLite implementation diff --git a/docs/tools.md b/docs/tools.md index 5fe2ecedb..6dba1a853 100644 --- a/docs/tools.md +++ b/docs/tools.md @@ -13,6 +13,10 @@ OpenAI offers a few built-in tools when using the [`OpenAIResponsesModel`][agent - 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. +- The [`CodeInterpreterTool`][agents.tool.CodeInterpreterTool] lets the LLM execute code in a sandboxed environment. +- The [`HostedMCPTool`][agents.tool.HostedMCPTool] exposes a remote MCP server's tools to the model. +- The [`ImageGenerationTool`][agents.tool.ImageGenerationTool] generates images from a prompt. +- The [`LocalShellTool`][agents.tool.LocalShellTool] runs shell commands on your machine. ```python from agents import Agent, FileSearchTool, Runner, WebSearchTool @@ -266,7 +270,7 @@ The `agent.as_tool` function is a convenience method to make it easy to turn an ```python @function_tool async def run_my_agent() -> str: - """A tool that runs the agent with custom configs". + """A tool that runs the agent with custom configs""" agent = Agent(name="My agent", instructions="...") @@ -280,6 +284,33 @@ async def run_my_agent() -> str: return str(result.final_output) ``` +### Custom output extraction + +In certain cases, you might want to modify the output of the tool-agents before returning it to the central agent. This may be useful if you want to: + +- Extract a specific piece of information (e.g., a JSON payload) from the sub-agent's chat history. +- Convert or reformat the agent’s final answer (e.g., transform Markdown into plain text or CSV). +- Validate the output or provide a fallback value when the agent’s response is missing or malformed. + +You can do this by supplying the `custom_output_extractor` argument to the `as_tool` method: + +```python +async def extract_json_payload(run_result: RunResult) -> str: + # Scan the agent’s outputs in reverse order until we find a JSON-like message from a tool call. + for item in reversed(run_result.new_items): + if isinstance(item, ToolCallOutputItem) and item.output.strip().startswith("{"): + return item.output.strip() + # Fallback to an empty JSON object if nothing was found + return "{}" + + +json_tool = data_agent.as_tool( + tool_name="get_data_json", + tool_description="Run the data agent and return only its JSON payload", + custom_output_extractor=extract_json_payload, +) +``` + ## 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 dd883c5aa..c7776ad7b 100644 --- a/docs/tracing.md +++ b/docs/tracing.md @@ -115,3 +115,5 @@ To customize this default setup, to send traces to alternative or additional bac - [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) +- [Portkey AI](https://portkey.ai/docs/integrations/agents/openai-agents) diff --git a/examples/agent_patterns/input_guardrails.py b/examples/agent_patterns/input_guardrails.py index 154535511..18ab9d2a7 100644 --- a/examples/agent_patterns/input_guardrails.py +++ b/examples/agent_patterns/input_guardrails.py @@ -20,7 +20,7 @@ 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 +- Check that input 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. diff --git a/examples/agent_patterns/llm_as_a_judge.py b/examples/agent_patterns/llm_as_a_judge.py index 5a46cc3eb..81dec7501 100644 --- a/examples/agent_patterns/llm_as_a_judge.py +++ b/examples/agent_patterns/llm_as_a_judge.py @@ -32,7 +32,7 @@ class EvaluationFeedback: 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." + "Never give it a pass on the first try. After 5 attempts, you can give it a pass if story outline is good enough - do not go for perfection" ), output_type=EvaluationFeedback, ) diff --git a/examples/basic/agent_lifecycle_example.py b/examples/basic/agent_lifecycle_example.py index 29bb18c96..b4334a83b 100644 --- a/examples/basic/agent_lifecycle_example.py +++ b/examples/basic/agent_lifecycle_example.py @@ -101,12 +101,10 @@ async def main() -> None: ### (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 +### (Start Agent) 4: 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 +### (Multiply Agent) 4: Agent Multiply Agent ended with output number=74 Done! """ diff --git a/examples/basic/hello_world_jupyter.ipynb b/examples/basic/hello_world_jupyter.ipynb new file mode 100644 index 000000000..8dd3bb379 --- /dev/null +++ b/examples/basic/hello_world_jupyter.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8a77ee2e-22f2-409c-837d-b994978b0aa2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A function calls self, \n", + "Unraveling layers deep, \n", + "Base case ends the quest. \n", + "\n", + "Infinite loops lurk, \n", + "Mind the base condition well, \n", + "Or it will not work. \n", + "\n", + "Trees and lists unfold, \n", + "Elegant solutions bloom, \n", + "Recursion's art told.\n" + ] + } + ], + "source": [ + "from agents import Agent, Runner\n", + "\n", + "agent = Agent(name=\"Assistant\", instructions=\"You are a helpful assistant\")\n", + "\n", + "# Intended for Jupyter notebooks where there's an existing event loop\n", + "result = await Runner.run(agent, \"Write a haiku about recursion in programming.\") # type: ignore[top-level-await] # noqa: F704\n", + "print(result.final_output)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/basic/hello_world_jupyter.py b/examples/basic/hello_world_jupyter.py deleted file mode 100644 index c929a7c68..000000000 --- a/examples/basic/hello_world_jupyter.py +++ /dev/null @@ -1,11 +0,0 @@ -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 285bfecd6..02ce449f4 100644 --- a/examples/basic/lifecycle_example.py +++ b/examples/basic/lifecycle_example.py @@ -105,14 +105,12 @@ async def main() -> None: 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 +### 3: Tool random_number ended with result 101. Usage: 1 requests, 148 input tokens, 15 output tokens, 163 total token +### 4: Handoff from Start Agent to Multiply Agent. Usage: 2 requests, 323 input tokens, 30 output tokens, 353 total tokens +### 5: Agent Multiply Agent started. Usage: 2 requests, 323 input tokens, 30 output tokens, 353 total tokens +### 6: Tool multiply_by_two started. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens +### 7: Tool multiply_by_two ended with result 202. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens +### 8: Agent Multiply Agent ended with output number=202. Usage: 4 requests, 714 input tokens, 63 output tokens, 777 total tokens Done! """ diff --git a/examples/basic/prompt_template.py b/examples/basic/prompt_template.py new file mode 100644 index 000000000..59251935e --- /dev/null +++ b/examples/basic/prompt_template.py @@ -0,0 +1,79 @@ +import argparse +import asyncio +import random + +from agents import Agent, GenerateDynamicPromptData, Runner + +""" +NOTE: This example will not work out of the box, because the default prompt ID will not be available +in your project. + +To use it, please: +1. Go to https://platform.openai.com/playground/prompts +2. Create a new prompt variable, `poem_style`. +3. Create a system prompt with the content: +``` +Write a poem in {{poem_style}} +``` +4. Run the example with the `--prompt-id` flag. +""" + +DEFAULT_PROMPT_ID = "pmpt_6850729e8ba481939fd439e058c69ee004afaa19c520b78b" + + +class DynamicContext: + def __init__(self, prompt_id: str): + self.prompt_id = prompt_id + self.poem_style = random.choice(["limerick", "haiku", "ballad"]) + print(f"[debug] DynamicContext initialized with poem_style: {self.poem_style}") + + +async def _get_dynamic_prompt(data: GenerateDynamicPromptData): + ctx: DynamicContext = data.context.context + return { + "id": ctx.prompt_id, + "version": "1", + "variables": { + "poem_style": ctx.poem_style, + }, + } + + +async def dynamic_prompt(prompt_id: str): + context = DynamicContext(prompt_id) + + agent = Agent( + name="Assistant", + prompt=_get_dynamic_prompt, + ) + + result = await Runner.run(agent, "Tell me about recursion in programming.", context=context) + print(result.final_output) + + +async def static_prompt(prompt_id: str): + agent = Agent( + name="Assistant", + prompt={ + "id": prompt_id, + "version": "1", + "variables": { + "poem_style": "limerick", + }, + }, + ) + + result = await Runner.run(agent, "Tell me about recursion in programming.") + print(result.final_output) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--dynamic", action="store_true") + parser.add_argument("--prompt-id", type=str, default=DEFAULT_PROMPT_ID) + args = parser.parse_args() + + if args.dynamic: + asyncio.run(dynamic_prompt(args.prompt_id)) + else: + asyncio.run(static_prompt(args.prompt_id)) diff --git a/examples/basic/session_example.py b/examples/basic/session_example.py new file mode 100644 index 000000000..63d1d1b7c --- /dev/null +++ b/examples/basic/session_example.py @@ -0,0 +1,77 @@ +""" +Example demonstrating session memory functionality. + +This example shows how to use session memory to maintain conversation history +across multiple agent runs without manually handling .to_input_list(). +""" + +import asyncio + +from agents import Agent, Runner, SQLiteSession + + +async def main(): + # Create an agent + agent = Agent( + name="Assistant", + instructions="Reply very concisely.", + ) + + # Create a session instance that will persist across runs + session_id = "conversation_123" + session = SQLiteSession(session_id) + + print("=== Session Example ===") + print("The agent will remember previous messages automatically.\n") + + # First turn + print("First turn:") + print("User: What city is the Golden Gate Bridge in?") + result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session, + ) + print(f"Assistant: {result.final_output}") + print() + + # Second turn - the agent will remember the previous conversation + print("Second turn:") + print("User: What state is it in?") + result = await Runner.run(agent, "What state is it in?", session=session) + print(f"Assistant: {result.final_output}") + print() + + # Third turn - continuing the conversation + print("Third turn:") + print("User: What's the population of that state?") + result = await Runner.run( + agent, + "What's the population of that state?", + session=session, + ) + print(f"Assistant: {result.final_output}") + print() + + print("=== Conversation Complete ===") + print("Notice how the agent remembered the context from previous turns!") + print("Sessions automatically handles conversation history.") + + # Demonstrate the limit parameter - get only the latest 2 items + print("\n=== Latest Items Demo ===") + latest_items = await session.get_items(limit=2) + print("Latest 2 items:") + for i, msg in enumerate(latest_items, 1): + role = msg.get("role", "unknown") + content = msg.get("content", "") + print(f" {i}. {role}: {content}") + + print(f"\nFetched {len(latest_items)} out of total conversation history.") + + # Get all items to show the difference + all_items = await session.get_items() + print(f"Total items in session: {len(all_items)}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/hosted_mcp/__init__.py b/examples/hosted_mcp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/hosted_mcp/approvals.py b/examples/hosted_mcp/approvals.py new file mode 100644 index 000000000..3080a1d63 --- /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 res.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 000000000..895fdfbe0 --- /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 res.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/prompt_server/README.md b/examples/mcp/prompt_server/README.md new file mode 100644 index 000000000..c1b1c3b37 --- /dev/null +++ b/examples/mcp/prompt_server/README.md @@ -0,0 +1,29 @@ +# MCP Prompt Server Example + +This example uses a local MCP prompt server in [server.py](server.py). + +Run the example via: + +``` +uv run python examples/mcp/prompt_server/main.py +``` + +## Details + +The example uses the `MCPServerStreamableHttp` class from `agents.mcp`. The server runs in a sub-process at `http://localhost:8000/mcp` and provides user-controlled prompts that generate agent instructions. + +The server exposes prompts like `generate_code_review_instructions` that take parameters such as focus area and programming language. The agent calls these prompts to dynamically generate its system instructions based on user-provided parameters. + +## Workflow + +The example demonstrates two key functions: + +1. **`show_available_prompts`** - Lists all available prompts on the MCP server, showing users what prompts they can select from. This demonstrates the discovery aspect of MCP prompts. + +2. **`demo_code_review`** - Shows the complete user-controlled prompt workflow: + - Calls `generate_code_review_instructions` with specific parameters (focus: "security vulnerabilities", language: "python") + - Uses the generated instructions to create an Agent with specialized code review capabilities + - Runs the agent against vulnerable sample code (command injection via `os.system`) + - The agent analyzes the code and provides security-focused feedback using available tools + +This pattern allows users to dynamically configure agent behavior through MCP prompts rather than hardcoded instructions. \ No newline at end of file diff --git a/examples/mcp/prompt_server/main.py b/examples/mcp/prompt_server/main.py new file mode 100644 index 000000000..8f2991fc0 --- /dev/null +++ b/examples/mcp/prompt_server/main.py @@ -0,0 +1,110 @@ +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 get_instructions_from_prompt(mcp_server: MCPServer, prompt_name: str, **kwargs) -> str: + """Get agent instructions by calling MCP prompt endpoint (user-controlled)""" + print(f"Getting instructions from prompt: {prompt_name}") + + try: + prompt_result = await mcp_server.get_prompt(prompt_name, kwargs) + content = prompt_result.messages[0].content + if hasattr(content, 'text'): + instructions = content.text + else: + instructions = str(content) + print("Generated instructions") + return instructions + except Exception as e: + print(f"Failed to get instructions: {e}") + return f"You are a helpful assistant. Error: {e}" + + +async def demo_code_review(mcp_server: MCPServer): + """Demo: Code review with user-selected prompt""" + print("=== CODE REVIEW DEMO ===") + + # User explicitly selects prompt and parameters + instructions = await get_instructions_from_prompt( + mcp_server, + "generate_code_review_instructions", + focus="security vulnerabilities", + language="python", + ) + + agent = Agent( + name="Code Reviewer Agent", + instructions=instructions, # Instructions from MCP prompt + model_settings=ModelSettings(tool_choice="auto"), + ) + + message = """Please review this code: + +def process_user_input(user_input): + command = f"echo {user_input}" + os.system(command) + return "Command executed" + +""" + + print(f"Running: {message[:60]}...") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + print("\n" + "=" * 50 + "\n") + + +async def show_available_prompts(mcp_server: MCPServer): + """Show available prompts for user selection""" + print("=== AVAILABLE PROMPTS ===") + + prompts_result = await mcp_server.list_prompts() + print("User can select from these prompts:") + for i, prompt in enumerate(prompts_result.prompts, 1): + print(f" {i}. {prompt.name} - {prompt.description}") + print() + + +async def main(): + async with MCPServerStreamableHttp( + name="Simple Prompt Server", + params={"url": "http://localhost:8000/mcp"}, + ) as server: + trace_id = gen_trace_id() + with trace(workflow_name="Simple Prompt Demo", trace_id=trace_id): + print(f"Trace: https://platform.openai.com/traces/trace?trace_id={trace_id}\n") + + await show_available_prompts(server) + await demo_code_review(server) + + +if __name__ == "__main__": + if not shutil.which("uv"): + raise RuntimeError("uv is not installed") + + 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 Simple Prompt Server...") + process = subprocess.Popen(["uv", "run", server_file]) + time.sleep(3) + print("Server started\n") + except Exception as e: + print(f"Error starting server: {e}") + exit(1) + + try: + asyncio.run(main()) + finally: + if process: + process.terminate() + print("Server terminated.") diff --git a/examples/mcp/prompt_server/server.py b/examples/mcp/prompt_server/server.py new file mode 100644 index 000000000..01dcbac34 --- /dev/null +++ b/examples/mcp/prompt_server/server.py @@ -0,0 +1,37 @@ +from mcp.server.fastmcp import FastMCP + +# Create server +mcp = FastMCP("Prompt Server") + + +# Instruction-generating prompts (user-controlled) +@mcp.prompt() +def generate_code_review_instructions( + focus: str = "general code quality", language: str = "python" +) -> str: + """Generate agent instructions for code review tasks""" + print(f"[debug-server] generate_code_review_instructions({focus}, {language})") + + return f"""You are a senior {language} code review specialist. Your role is to provide comprehensive code analysis with focus on {focus}. + +INSTRUCTIONS: +- Analyze code for quality, security, performance, and best practices +- Provide specific, actionable feedback with examples +- Identify potential bugs, vulnerabilities, and optimization opportunities +- Suggest improvements with code examples when applicable +- Be constructive and educational in your feedback +- Focus particularly on {focus} aspects + +RESPONSE FORMAT: +1. Overall Assessment +2. Specific Issues Found +3. Security Considerations +4. Performance Notes +5. Recommended Improvements +6. Best Practices Suggestions + +Use the available tools to check current time if you need timestamps for your analysis.""" + + +if __name__ == "__main__": + mcp.run(transport="streamable-http") diff --git a/examples/mcp/streamablehttp_example/README.md b/examples/mcp/streamablehttp_example/README.md new file mode 100644 index 000000000..a07fe19be --- /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 000000000..cc95e798b --- /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 000000000..d8f839652 --- /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/realtime/demo.py b/examples/realtime/demo.py new file mode 100644 index 000000000..61834a4e5 --- /dev/null +++ b/examples/realtime/demo.py @@ -0,0 +1,114 @@ +import asyncio +import os +import sys +from typing import TYPE_CHECKING + +import numpy as np + +from agents.realtime import RealtimeSession + +# Add the current directory to path so we can import ui +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from agents import function_tool +from agents.realtime import RealtimeAgent, RealtimeRunner, RealtimeSessionEvent + +if TYPE_CHECKING: + from .ui import AppUI +else: + # Try both import styles + try: + # Try relative import first (when used as a package) + from .ui import AppUI + except ImportError: + # Fall back to direct import (when run as a script) + from ui import AppUI + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather in a city.""" + return f"The weather in {city} is sunny." + + +agent = RealtimeAgent( + name="Assistant", + instructions="You always greet the user with 'Top of the morning to you'.", + tools=[get_weather], +) + + +class Example: + def __init__(self) -> None: + self.ui = AppUI() + self.ui.connected = asyncio.Event() + self.ui.last_audio_item_id = None + # Set the audio callback + self.ui.set_audio_callback(self.on_audio_recorded) + + self.session: RealtimeSession | None = None + + async def run(self) -> None: + # Start UI in a separate task instead of waiting for it to complete + ui_task = asyncio.create_task(self.ui.run_async()) + + # Set up session immediately without waiting for UI to finish + runner = RealtimeRunner(agent) + async with await runner.run() as session: + self.session = session + self.ui.set_is_connected(True) + async for event in session: + await self.on_event(event) + + # Wait for UI task to complete when session ends + await ui_task + + async def on_audio_recorded(self, audio_bytes: bytes) -> None: + """Called when audio is recorded by the UI.""" + try: + # Send the audio to the session + assert self.session is not None + await self.session.send_audio(audio_bytes) + except Exception as e: + self.ui.log_message(f"Error sending audio: {e}") + + async def on_event(self, event: RealtimeSessionEvent) -> None: + # Display event in the UI + try: + if event.type == "agent_start": + self.ui.add_transcript(f"Agent started: {event.agent.name}") + elif event.type == "agent_end": + self.ui.add_transcript(f"Agent ended: {event.agent.name}") + elif event.type == "handoff": + self.ui.add_transcript( + f"Handoff from {event.from_agent.name} to {event.to_agent.name}" + ) + elif event.type == "tool_start": + self.ui.add_transcript(f"Tool started: {event.tool.name}") + elif event.type == "tool_end": + self.ui.add_transcript(f"Tool ended: {event.tool.name}; output: {event.output}") + elif event.type == "audio_end": + self.ui.add_transcript("Audio ended") + elif event.type == "audio": + np_audio = np.frombuffer(event.audio.data, dtype=np.int16) + self.ui.play_audio(np_audio) + elif event.type == "audio_interrupted": + self.ui.add_transcript("Audio interrupted") + elif event.type == "error": + self.ui.add_transcript(f"Error: {event.error}") + elif event.type == "history_updated": + pass + elif event.type == "history_added": + pass + elif event.type == "raw_model_event": + self.ui.log_message(f"Raw model event: {event.data}") + else: + self.ui.log_message(f"Unknown event type: {event.type}") + except Exception as e: + # This can happen if the UI has already exited + self.ui.log_message(f"Event handling error: {str(e)}") + + +if __name__ == "__main__": + example = Example() + asyncio.run(example.run()) diff --git a/examples/realtime/ui.py b/examples/realtime/ui.py new file mode 100644 index 000000000..1ba055835 --- /dev/null +++ b/examples/realtime/ui.py @@ -0,0 +1,271 @@ +from __future__ import annotations + +import asyncio +from collections.abc import Coroutine +from typing import Any, Callable + +import numpy as np +import numpy.typing as npt +import sounddevice as sd +from textual import events +from textual.app import App, ComposeResult +from textual.containers import Container, Horizontal +from textual.reactive import reactive +from textual.widgets import RichLog, Static +from typing_extensions import override + +CHUNK_LENGTH_S = 0.05 # 50ms +SAMPLE_RATE = 24000 +FORMAT = np.int16 +CHANNELS = 1 + + +class Header(Static): + """A header widget.""" + + @override + def render(self) -> str: + return "Realtime Demo" + + +class AudioStatusIndicator(Static): + """A widget that shows the current audio recording status.""" + + is_recording = reactive(False) + + @override + def render(self) -> str: + status = ( + "🔴 Conversation started." + if self.is_recording + else "⚪ Press SPACE to start the conversation (q to quit)" + ) + return status + + +class AppUI(App[None]): + CSS = """ + Screen { + background: #1a1b26; /* Dark blue-grey background */ + } + + Container { + border: double rgb(91, 164, 91); + } + + #input-container { + height: 5; /* Explicit height for input container */ + margin: 1 1; + padding: 1 2; + } + + #bottom-pane { + width: 100%; + height: 82%; /* Reduced to make room for session display */ + border: round rgb(205, 133, 63); + } + + #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; + } + + #transcripts { + width: 50%; + height: 100%; + border-right: solid rgb(91, 164, 91); + } + + #transcripts-header { + height: 2; + background: #2a2b36; + content-align: center middle; + border-bottom: solid rgb(91, 164, 91); + } + + #transcripts-content { + height: 100%; + } + + #event-log { + width: 50%; + height: 100%; + } + + #event-log-header { + height: 2; + background: #2a2b36; + content-align: center middle; + border-bottom: solid rgb(91, 164, 91); + } + + #event-log-content { + height: 100%; + } + + Static { + color: white; + } + """ + + should_send_audio: asyncio.Event + connected: asyncio.Event + last_audio_item_id: str | None + audio_callback: Callable[[bytes], Coroutine[Any, Any, None]] | None + + def __init__(self) -> None: + super().__init__() + self.audio_player = sd.OutputStream( + samplerate=SAMPLE_RATE, + channels=CHANNELS, + dtype=FORMAT, + ) + self.should_send_audio = asyncio.Event() + self.connected = asyncio.Event() + self.audio_callback = None + + @override + def compose(self) -> ComposeResult: + """Create child widgets for the app.""" + with Container(): + yield Header(id="session-display") + yield AudioStatusIndicator(id="status-indicator") + with Container(id="bottom-pane"): + with Horizontal(): + with Container(id="transcripts"): + yield Static("Conversation transcript", id="transcripts-header") + yield RichLog( + id="transcripts-content", wrap=True, highlight=True, markup=True + ) + with Container(id="event-log"): + yield Static("Raw event log", id="event-log-header") + yield RichLog( + id="event-log-content", wrap=True, highlight=True, markup=True + ) + + def set_is_connected(self, is_connected: bool) -> None: + self.connected.set() if is_connected else self.connected.clear() + + def set_audio_callback(self, callback: Callable[[bytes], Coroutine[Any, Any, None]]) -> None: + """Set a callback function to be called when audio is recorded.""" + self.audio_callback = callback + + # High-level methods for UI operations + def set_header_text(self, text: str) -> None: + """Update the header text.""" + header = self.query_one("#session-display", Header) + header.update(text) + + def set_recording_status(self, is_recording: bool) -> None: + """Set the recording status indicator.""" + status_indicator = self.query_one(AudioStatusIndicator) + status_indicator.is_recording = is_recording + + def log_message(self, message: str) -> None: + """Add a message to the event log.""" + try: + log_pane = self.query_one("#event-log-content", RichLog) + log_pane.write(message) + except Exception: + # Handle the case where the widget might not be available + pass + + def add_transcript(self, message: str) -> None: + """Add a transcript message to the transcripts panel.""" + try: + transcript_pane = self.query_one("#transcripts-content", RichLog) + transcript_pane.write(message) + except Exception: + # Handle the case where the widget might not be available + pass + + def play_audio(self, audio_data: npt.NDArray[np.int16]) -> None: + """Play audio data through the audio player.""" + try: + self.audio_player.write(audio_data) + except Exception as e: + self.log_message(f"Audio play error: {e}") + + async def on_mount(self) -> None: + """Set up audio player and start the audio capture worker.""" + self.audio_player.start() + self.run_worker(self.capture_audio()) + + async def capture_audio(self) -> None: + """Capture audio from the microphone and send to the session.""" + # Wait for connection to be established + await self.connected.wait() + + # Set up audio input stream + stream = sd.InputStream( + channels=CHANNELS, + samplerate=SAMPLE_RATE, + dtype=FORMAT, + ) + + try: + # Wait for user to press spacebar to start + await self.should_send_audio.wait() + + stream.start() + self.set_recording_status(True) + self.log_message("Recording started - speak to the agent") + + # Buffer size in samples + read_size = int(SAMPLE_RATE * CHUNK_LENGTH_S) + + while True: + # Check if there's enough data to read + if stream.read_available < read_size: + await asyncio.sleep(0.01) # Small sleep to avoid CPU hogging + continue + + # Read audio data + data, _ = stream.read(read_size) + + # Convert numpy array to bytes + audio_bytes = data.tobytes() + + # Call audio callback if set + if self.audio_callback: + try: + await self.audio_callback(audio_bytes) + except Exception as e: + self.log_message(f"Audio callback error: {e}") + + # Yield control back to event loop + await asyncio.sleep(0) + + except Exception as e: + self.log_message(f"Audio capture error: {e}") + finally: + if stream.active: + stream.stop() + stream.close() + + async def on_key(self, event: events.Key) -> None: + """Handle key press events.""" + # add the keypress to the log + self.log_message(f"Key pressed: {event.key}") + + if event.key == "q": + self.audio_player.stop() + self.audio_player.close() + self.exit() + return + + if event.key == "space": # Spacebar + if not self.should_send_audio.is_set(): + self.should_send_audio.set() + self.set_recording_status(True) diff --git a/examples/reasoning_content/__init__.py b/examples/reasoning_content/__init__.py new file mode 100644 index 000000000..f24b2606d --- /dev/null +++ b/examples/reasoning_content/__init__.py @@ -0,0 +1,3 @@ +""" +Examples demonstrating how to use models that provide reasoning content. +""" diff --git a/examples/reasoning_content/main.py b/examples/reasoning_content/main.py new file mode 100644 index 000000000..c23b04254 --- /dev/null +++ b/examples/reasoning_content/main.py @@ -0,0 +1,124 @@ +""" +Example demonstrating how to use the reasoning content feature with models that support it. + +Some models, like deepseek-reasoner, provide a reasoning_content field in addition to the regular content. +This example shows how to access and use this reasoning content from both streaming and non-streaming responses. + +To run this example, you need to: +1. Set your OPENAI_API_KEY environment variable +2. Use a model that supports reasoning content (e.g., deepseek-reasoner) +""" + +import asyncio +import os +from typing import Any, cast + +from agents import ModelSettings +from agents.models.interface import ModelTracing +from agents.models.openai_provider import OpenAIProvider +from agents.types import ResponseOutputRefusal, ResponseOutputText # type: ignore + +MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "deepseek-reasoner" + + +async def stream_with_reasoning_content(): + """ + Example of streaming a response from a model that provides reasoning content. + The reasoning content will be emitted as separate events. + """ + provider = OpenAIProvider() + model = provider.get_model(MODEL_NAME) + + print("\n=== Streaming Example ===") + print("Prompt: Write a haiku about recursion in programming") + + reasoning_content = "" + regular_content = "" + + async for event in model.stream_response( + system_instructions="You are a helpful assistant that writes creative content.", + input="Write a haiku about recursion in programming", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ): + if event.type == "response.reasoning_summary_text.delta": + print( + f"\033[33m{event.delta}\033[0m", end="", flush=True + ) # Yellow for reasoning content + reasoning_content += event.delta + elif event.type == "response.output_text.delta": + print(f"\033[32m{event.delta}\033[0m", end="", flush=True) # Green for regular content + regular_content += event.delta + + print("\n\nReasoning Content:") + print(reasoning_content) + print("\nRegular Content:") + print(regular_content) + print("\n") + + +async def get_response_with_reasoning_content(): + """ + Example of getting a complete response from a model that provides reasoning content. + The reasoning content will be available as a separate item in the response. + """ + provider = OpenAIProvider() + model = provider.get_model(MODEL_NAME) + + print("\n=== Non-streaming Example ===") + print("Prompt: Explain the concept of recursion in programming") + + response = await model.get_response( + system_instructions="You are a helpful assistant that explains technical concepts clearly.", + input="Explain the concept of recursion in programming", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ) + + # Extract reasoning content and regular content from the response + reasoning_content = None + regular_content = None + + for item in response.output: + if hasattr(item, "type") and item.type == "reasoning": + reasoning_content = item.summary[0].text + elif hasattr(item, "type") and item.type == "message": + if item.content and len(item.content) > 0: + content_item = item.content[0] + if isinstance(content_item, ResponseOutputText): + regular_content = content_item.text + elif isinstance(content_item, ResponseOutputRefusal): + refusal_item = cast(Any, content_item) + regular_content = refusal_item.refusal + + print("\nReasoning Content:") + print(reasoning_content or "No reasoning content provided") + + print("\nRegular Content:") + print(regular_content or "No regular content provided") + + print("\n") + + +async def main(): + try: + await stream_with_reasoning_content() + await get_response_with_reasoning_content() + except Exception as e: + print(f"Error: {e}") + print("\nNote: This example requires a model that supports reasoning content.") + print("You may need to use a specific model like deepseek-reasoner or similar.") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/reasoning_content/runner_example.py b/examples/reasoning_content/runner_example.py new file mode 100644 index 000000000..e51f85799 --- /dev/null +++ b/examples/reasoning_content/runner_example.py @@ -0,0 +1,88 @@ +""" +Example demonstrating how to use the reasoning content feature with the Runner API. + +This example shows how to extract and use reasoning content from responses when using +the Runner API, which is the most common way users interact with the Agents library. + +To run this example, you need to: +1. Set your OPENAI_API_KEY environment variable +2. Use a model that supports reasoning content (e.g., deepseek-reasoner) +""" + +import asyncio +import os +from typing import Any + +from agents import Agent, Runner, trace +from agents.items import ReasoningItem + +MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "deepseek-reasoner" + + +async def main(): + print(f"Using model: {MODEL_NAME}") + + # Create an agent with a model that supports reasoning content + agent = Agent( + name="Reasoning Agent", + instructions="You are a helpful assistant that explains your reasoning step by step.", + model=MODEL_NAME, + ) + + # Example 1: Non-streaming response + with trace("Reasoning Content - Non-streaming"): + print("\n=== Example 1: Non-streaming response ===") + result = await Runner.run( + agent, "What is the square root of 841? Please explain your reasoning." + ) + + # Extract reasoning content from the result items + reasoning_content = None + # RunResult has 'response' attribute which has 'output' attribute + for item in result.response.output: # type: ignore + if isinstance(item, ReasoningItem): + reasoning_content = item.summary[0].text # type: ignore + break + + print("\nReasoning Content:") + print(reasoning_content or "No reasoning content provided") + + print("\nFinal Output:") + print(result.final_output) + + # Example 2: Streaming response + with trace("Reasoning Content - Streaming"): + print("\n=== Example 2: Streaming response ===") + print("\nStreaming response:") + + # Buffers to collect reasoning and regular content + reasoning_buffer = "" + content_buffer = "" + + # RunResultStreaming is async iterable + stream = Runner.run_streamed(agent, "What is 15 x 27? Please explain your reasoning.") + + async for event in stream: # type: ignore + if isinstance(event, ReasoningItem): + # This is reasoning content + reasoning_item: Any = event + reasoning_buffer += reasoning_item.summary[0].text + print( + f"\033[33m{reasoning_item.summary[0].text}\033[0m", end="", flush=True + ) # Yellow for reasoning + elif hasattr(event, "text"): + # This is regular content + content_buffer += event.text + print( + f"\033[32m{event.text}\033[0m", end="", flush=True + ) # Green for regular content + + print("\n\nCollected Reasoning Content:") + print(reasoning_buffer) + + print("\nCollected Final Answer:") + print(content_buffer) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/research_bot/agents/search_agent.py b/examples/research_bot/agents/search_agent.py index 0212ce5b5..61f91701f 100644 --- a/examples/research_bot/agents/search_agent.py +++ b/examples/research_bot/agents/search_agent.py @@ -3,7 +3,7 @@ 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 " + "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 " diff --git a/examples/tools/code_interpreter.py b/examples/tools/code_interpreter.py new file mode 100644 index 000000000..a5843ce3f --- /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/image_generator.py b/examples/tools/image_generator.py new file mode 100644 index 000000000..fd6fcc6ba --- /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/mkdocs.yml b/mkdocs.yml index ad719670c..a465bfefa 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -57,8 +57,10 @@ plugins: - Documentation: - agents.md - running_agents.md + - sessions.md - results.md - streaming.md + - repl.md - tools.md - mcp.md - handoffs.md @@ -71,6 +73,7 @@ plugins: - models/litellm.md - config.md - visualization.md + - release.md - Voice agents: - voice/quickstart.md - voice/pipeline.md @@ -80,6 +83,8 @@ plugins: - ref/index.md - ref/agent.md - ref/run.md + - ref/memory.md + - ref/repl.md - ref/tool.md - ref/result.md - ref/stream_events.md @@ -137,8 +142,10 @@ plugins: - ドキュメント: - agents.md - running_agents.md + - sessions.md - results.md - streaming.md + - repl.md - tools.md - mcp.md - handoffs.md diff --git a/pyproject.toml b/pyproject.toml index c1ae467a2..dfceb88f9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,19 +1,19 @@ [project] name = "openai-agents" -version = "0.0.13" +version = "0.1.0" description = "OpenAI Agents SDK" readme = "README.md" requires-python = ">=3.9" license = "MIT" authors = [{ name = "OpenAI", email = "support@openai.com" }] dependencies = [ - "openai>=1.76.0", + "openai>=1.93.1, <2", "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>=1.6.0, <2; python_version >= '3.10'", + "mcp>=1.9.4, <2; python_version >= '3.10'", ] classifiers = [ "Typing :: Typed", @@ -23,20 +23,21 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", - "Intended Audience :: Developers", + "Programming Language :: Python :: 3.13", "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" +Homepage = "https://openai.github.io/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>=1.65.0, <2"] +litellm = ["litellm>=1.67.4.post1, <2"] +realtime = ["websockets>=15.0, <16"] [dependency-groups] dev = [ diff --git a/src/agents/__init__.py b/src/agents/__init__.py index 6d7c90b4f..7de17efdb 100644 --- a/src/agents/__init__.py +++ b/src/agents/__init__.py @@ -5,7 +5,7 @@ from openai import AsyncOpenAI from . import _config -from .agent import Agent, ToolsToFinalOutputFunction, ToolsToFinalOutputResult +from .agent import Agent, AgentBase, ToolsToFinalOutputFunction, ToolsToFinalOutputResult from .agent_output import AgentOutputSchema, AgentOutputSchemaBase from .computer import AsyncComputer, Button, Computer, Environment from .exceptions import ( @@ -14,6 +14,7 @@ MaxTurnsExceeded, ModelBehaviorError, OutputGuardrailTripwireTriggered, + RunErrorDetails, UserError, ) from .guardrail import ( @@ -39,11 +40,14 @@ TResponseInputItem, ) from .lifecycle import AgentHooks, RunHooks +from .memory import Session, SQLiteSession 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 .prompts import DynamicPromptFunction, GenerateDynamicPromptData, Prompt +from .repl import run_demo_loop from .result import RunResult, RunResultStreaming from .run import RunConfig, Runner from .run_context import RunContextWrapper, TContext @@ -54,10 +58,19 @@ StreamEvent, ) from .tool import ( + CodeInterpreterTool, ComputerTool, FileSearchTool, FunctionTool, FunctionToolResult, + HostedMCPTool, + ImageGenerationTool, + LocalShellCommandRequest, + LocalShellExecutor, + LocalShellTool, + MCPToolApprovalFunction, + MCPToolApprovalFunctionResult, + MCPToolApprovalRequest, Tool, WebSearchTool, default_tool_error_function, @@ -92,6 +105,7 @@ handoff_span, mcp_tools_span, set_trace_processors, + set_trace_provider, set_tracing_disabled, set_tracing_export_api_key, speech_group_span, @@ -147,9 +161,11 @@ def enable_verbose_stdout_logging(): __all__ = [ "Agent", + "AgentBase", "ToolsToFinalOutputFunction", "ToolsToFinalOutputResult", "Runner", + "run_demo_loop", "Model", "ModelProvider", "ModelTracing", @@ -166,6 +182,9 @@ def enable_verbose_stdout_logging(): "AgentsException", "InputGuardrailTripwireTriggered", "OutputGuardrailTripwireTriggered", + "DynamicPromptFunction", + "GenerateDynamicPromptData", + "Prompt", "MaxTurnsExceeded", "ModelBehaviorError", "UserError", @@ -189,12 +208,14 @@ def enable_verbose_stdout_logging(): "ToolCallItem", "ToolCallOutputItem", "ReasoningItem", - "ModelResponse", "ItemHelpers", "RunHooks", "AgentHooks", + "Session", + "SQLiteSession", "RunContextWrapper", "TContext", + "RunErrorDetails", "RunResult", "RunResultStreaming", "RunConfig", @@ -206,8 +227,17 @@ def enable_verbose_stdout_logging(): "FunctionToolResult", "ComputerTool", "FileSearchTool", + "CodeInterpreterTool", + "ImageGenerationTool", + "LocalShellCommandRequest", + "LocalShellExecutor", + "LocalShellTool", "Tool", "WebSearchTool", + "HostedMCPTool", + "MCPToolApprovalFunction", + "MCPToolApprovalRequest", + "MCPToolApprovalFunctionResult", "function_tool", "Usage", "add_trace_processor", @@ -220,6 +250,7 @@ def enable_verbose_stdout_logging(): "guardrail_span", "handoff_span", "set_trace_processors", + "set_trace_provider", "set_tracing_disabled", "speech_group_span", "transcription_span", diff --git a/src/agents/_run_impl.py b/src/agents/_run_impl.py index b5a83685c..4ac8b316b 100644 --- a/src/agents/_run_impl.py +++ b/src/agents/_run_impl.py @@ -14,6 +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, @@ -25,7 +28,17 @@ ActionType, ActionWait, ) -from openai.types.responses.response_input_param import ComputerCallOutput +from openai.types.responses.response_input_item_param import ( + ComputerCallOutputAcknowledgedSafetyCheck, +) +from openai.types.responses.response_input_param import ComputerCallOutput, McpApprovalResponse +from openai.types.responses.response_output_item import ( + ImageGenerationCall, + LocalShellCall, + McpApprovalRequest, + McpCall, + McpListTools, +) from openai.types.responses.response_reasoning_item import ResponseReasoningItem from .agent import Agent, ToolsToFinalOutputResult @@ -38,6 +51,9 @@ HandoffCallItem, HandoffOutputItem, ItemHelpers, + MCPApprovalRequestItem, + MCPApprovalResponseItem, + MCPListToolsItem, MessageOutputItem, ModelResponse, ReasoningItem, @@ -52,7 +68,18 @@ from .models.interface import ModelTracing from .run_context import RunContextWrapper, TContext from .stream_events import RunItemStreamEvent, StreamEvent -from .tool import ComputerTool, FunctionTool, FunctionToolResult, Tool +from .tool import ( + ComputerTool, + ComputerToolSafetyCheckData, + FunctionTool, + FunctionToolResult, + HostedMCPTool, + LocalShellCommandRequest, + LocalShellTool, + MCPToolApprovalRequest, + Tool, +) +from .tool_context import ToolContext from .tracing import ( SpanError, Trace, @@ -112,15 +139,29 @@ 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( @@ -128,6 +169,8 @@ def has_tools_to_run(self) -> bool: self.handoffs, self.functions, self.computer_actions, + self.local_shell_calls, + self.mcp_approval_requests, ] ) @@ -226,7 +269,16 @@ async def execute_tools_and_side_effects( 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, @@ -240,7 +292,7 @@ async def execute_tools_and_side_effects( run_config=run_config, ) - # Third, we'll check if the tool use should result in a final output + # 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, @@ -295,7 +347,7 @@ 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, @@ -343,10 +395,20 @@ 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 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): @@ -375,6 +437,57 @@ 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, McpCall): + items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("mcp") + 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 @@ -416,7 +529,9 @@ 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 @@ -433,23 +548,24 @@ async def run_single_tool( func_tool: FunctionTool, tool_call: ResponseFunctionToolCall ) -> Any: with function_span(func_tool.name) as span_fn: + tool_context = ToolContext.from_agent_context(context_wrapper, tool_call.call_id) 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), + hooks.on_tool_start(tool_context, agent, func_tool), ( - agent.hooks.on_tool_start(context_wrapper, agent, func_tool) + agent.hooks.on_tool_start(tool_context, agent, func_tool) if agent.hooks else _coro.noop_coroutine() ), - func_tool.on_invoke_tool(context_wrapper, tool_call.arguments), + func_tool.on_invoke_tool(tool_context, tool_call.arguments), ) await asyncio.gather( - hooks.on_tool_end(context_wrapper, agent, func_tool, result), + hooks.on_tool_end(tool_context, agent, func_tool, result), ( - agent.hooks.on_tool_end(context_wrapper, agent, func_tool, result) + agent.hooks.on_tool_end(tool_context, agent, func_tool, result) if agent.hooks else _coro.noop_coroutine() ), @@ -489,6 +605,30 @@ async def run_single_tool( 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, @@ -502,6 +642,29 @@ async def execute_computer_actions( results: list[RunItem] = [] # Need to run these serially, because each action can affect the computer state for action in actions: + acknowledged: list[ComputerCallOutputAcknowledgedSafetyCheck] | None = None + if action.tool_call.pending_safety_checks and action.computer_tool.on_safety_check: + acknowledged = [] + for check in action.tool_call.pending_safety_checks: + data = ComputerToolSafetyCheckData( + ctx_wrapper=context_wrapper, + agent=agent, + tool_call=action.tool_call, + safety_check=check, + ) + maybe = action.computer_tool.on_safety_check(data) + ack = await maybe if inspect.isawaitable(maybe) else maybe + if ack: + acknowledged.append( + ComputerCallOutputAcknowledgedSafetyCheck( + id=check.id, + code=check.code, + message=check.message, + ) + ) + else: + raise UserError("Computer tool safety check was not acknowledged") + results.append( await ComputerAction.execute( agent=agent, @@ -509,6 +672,7 @@ async def execute_computer_actions( hooks=hooks, context_wrapper=context_wrapper, config=config, + acknowledged_safety_checks=acknowledged, ) ) @@ -643,6 +807,40 @@ 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, @@ -727,6 +925,11 @@ 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 @@ -823,6 +1026,7 @@ async def execute( hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], config: RunConfig, + acknowledged_safety_checks: list[ComputerCallOutputAcknowledgedSafetyCheck] | None = None, ) -> RunItem: output_func = ( cls._get_screenshot_async(action.computer_tool.computer, action.tool_call) @@ -861,6 +1065,7 @@ async def execute( "image_url": image_url, }, type="computer_call_output", + acknowledged_safety_checks=acknowledged_safety_checks, ), ) @@ -919,3 +1124,54 @@ 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/agent.py b/src/agents/agent.py index e22f579fa..e418b67da 100644 --- a/src/agents/agent.py +++ b/src/agents/agent.py @@ -1,11 +1,13 @@ from __future__ import annotations +import asyncio import dataclasses import inspect from collections.abc import Awaitable from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, cast +from openai.types.responses.response_prompt_param import ResponsePromptParam from typing_extensions import NotRequired, TypeAlias, TypedDict from .agent_output import AgentOutputSchemaBase @@ -16,8 +18,9 @@ from .mcp import MCPUtil from .model_settings import ModelSettings from .models.interface import Model +from .prompts import DynamicPromptFunction, Prompt, PromptUtil from .run_context import RunContextWrapper, TContext -from .tool import FunctionToolResult, Tool, function_tool +from .tool import FunctionTool, FunctionToolResult, Tool, function_tool from .util import _transforms from .util._types import MaybeAwaitable @@ -64,7 +67,63 @@ class MCPConfig(TypedDict): @dataclass -class Agent(Generic[TContext]): +class AgentBase(Generic[TContext]): + """Base class for `Agent` and `RealtimeAgent`.""" + + name: str + """The name of the agent.""" + + 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. + """ + + 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.""" + + async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> 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, run_context, self + ) + + async def get_all_tools(self, run_context: RunContextWrapper[Any]) -> list[Tool]: + """All agent tools, including MCP tools and function tools.""" + mcp_tools = await self.get_mcp_tools(run_context) + + async def _check_tool_enabled(tool: Tool) -> bool: + if not isinstance(tool, FunctionTool): + return True + + attr = tool.is_enabled + if isinstance(attr, bool): + return attr + res = attr(run_context, self) + if inspect.isawaitable(res): + return bool(await res) + return bool(res) + + results = await asyncio.gather(*(_check_tool_enabled(t) for t in self.tools)) + enabled: list[Tool] = [t for t, ok in zip(self.tools, results) if ok] + return [*mcp_tools, *enabled] + + +@dataclass +class Agent(AgentBase, 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 @@ -73,10 +132,9 @@ class Agent(Generic[TContext]): 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.""" + See `AgentBase` for base parameters that are shared with `RealtimeAgent`s. + """ instructions: ( str @@ -94,9 +152,10 @@ class Agent(Generic[TContext]): 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. + prompt: Prompt | DynamicPromptFunction | None = None + """A prompt object (or a function that returns a Prompt). Prompts allow you to dynamically + configure the instructions, tools and other config for an agent outside of your code. Only + usable with OpenAI models, using the Responses API. """ handoffs: list[Agent[Any] | Handoff[TContext]] = field(default_factory=list) @@ -116,22 +175,6 @@ class Agent(Generic[TContext]): """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.""" - - 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. @@ -241,12 +284,35 @@ async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> s return None - async def get_mcp_tools(self) -> list[Tool]: + async def get_prompt( + self, run_context: RunContextWrapper[TContext] + ) -> ResponsePromptParam | None: + """Get the prompt for the agent.""" + return await PromptUtil.to_model_input(self.prompt, run_context, self) + + async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> 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) + return await MCPUtil.get_all_function_tools( + self.mcp_servers, convert_schemas_to_strict, run_context, self + ) - async def get_all_tools(self) -> list[Tool]: + async def get_all_tools(self, run_context: RunContextWrapper[Any]) -> list[Tool]: """All agent tools, including MCP tools and function tools.""" - mcp_tools = await self.get_mcp_tools() - return mcp_tools + self.tools + mcp_tools = await self.get_mcp_tools(run_context) + + async def _check_tool_enabled(tool: Tool) -> bool: + if not isinstance(tool, FunctionTool): + return True + + attr = tool.is_enabled + if isinstance(attr, bool): + return attr + res = attr(run_context, self) + if inspect.isawaitable(res): + return bool(await res) + return bool(res) + + results = await asyncio.gather(*(_check_tool_enabled(t) for t in self.tools)) + enabled: list[Tool] = [t for t, ok in zip(self.tools, results) if ok] + return [*mcp_tools, *enabled] diff --git a/src/agents/agent_output.py b/src/agents/agent_output.py index 066bbd835..ee14956e3 100644 --- a/src/agents/agent_output.py +++ b/src/agents/agent_output.py @@ -38,7 +38,7 @@ def json_schema(self) -> dict[str, Any]: @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: + features, but guarantees valid JSON. See here for details: https://platform.openai.com/docs/guides/structured-outputs#supported-schemas """ pass diff --git a/src/agents/exceptions.py b/src/agents/exceptions.py index 78898f017..c00024c2e 100644 --- a/src/agents/exceptions.py +++ b/src/agents/exceptions.py @@ -1,12 +1,42 @@ -from typing import TYPE_CHECKING +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any if TYPE_CHECKING: + from .agent import Agent from .guardrail import InputGuardrailResult, OutputGuardrailResult + from .items import ModelResponse, RunItem, TResponseInputItem + from .run_context import RunContextWrapper + +from .util._pretty_print import pretty_print_run_error_details + + +@dataclass +class RunErrorDetails: + """Data collected from an agent run when an exception occurs.""" + + input: str | list[TResponseInputItem] + new_items: list[RunItem] + raw_responses: list[ModelResponse] + last_agent: Agent[Any] + context_wrapper: RunContextWrapper[Any] + input_guardrail_results: list[InputGuardrailResult] + output_guardrail_results: list[OutputGuardrailResult] + + def __str__(self) -> str: + return pretty_print_run_error_details(self) class AgentsException(Exception): """Base class for all exceptions in the Agents SDK.""" + run_data: RunErrorDetails | None + + def __init__(self, *args: object) -> None: + super().__init__(*args) + self.run_data = None + class MaxTurnsExceeded(AgentsException): """Exception raised when the maximum number of turns is exceeded.""" @@ -15,6 +45,7 @@ class MaxTurnsExceeded(AgentsException): def __init__(self, message: str): self.message = message + super().__init__(message) class ModelBehaviorError(AgentsException): @@ -26,6 +57,7 @@ class ModelBehaviorError(AgentsException): def __init__(self, message: str): self.message = message + super().__init__(message) class UserError(AgentsException): @@ -35,15 +67,16 @@ class UserError(AgentsException): def __init__(self, message: str): self.message = message + super().__init__(message) class InputGuardrailTripwireTriggered(AgentsException): """Exception raised when a guardrail tripwire is triggered.""" - guardrail_result: "InputGuardrailResult" + guardrail_result: InputGuardrailResult """The result data of the guardrail that was triggered.""" - def __init__(self, guardrail_result: "InputGuardrailResult"): + def __init__(self, guardrail_result: InputGuardrailResult): self.guardrail_result = guardrail_result super().__init__( f"Guardrail {guardrail_result.guardrail.__class__.__name__} triggered tripwire" @@ -53,10 +86,10 @@ def __init__(self, guardrail_result: "InputGuardrailResult"): class OutputGuardrailTripwireTriggered(AgentsException): """Exception raised when a guardrail tripwire is triggered.""" - guardrail_result: "OutputGuardrailResult" + guardrail_result: OutputGuardrailResult """The result data of the guardrail that was triggered.""" - def __init__(self, guardrail_result: "OutputGuardrailResult"): + 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/src/agents/extensions/models/litellm_model.py b/src/agents/extensions/models/litellm_model.py index dc672acd4..a06c61dc3 100644 --- a/src/agents/extensions/models/litellm_model.py +++ b/src/agents/extensions/models/litellm_model.py @@ -5,7 +5,7 @@ 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 @@ -71,6 +71,7 @@ async def get_response( handoffs: list[Handoff], tracing: ModelTracing, previous_response_id: str | None, + prompt: Any | None = None, ) -> ModelResponse: with generation_span( model=str(self.model), @@ -88,6 +89,7 @@ async def get_response( span_generation, tracing, stream=False, + prompt=prompt, ) assert isinstance(response.choices[0], litellm.types.utils.Choices) @@ -96,7 +98,11 @@ async def get_response( logger.debug("Received model response") else: logger.debug( - f"LLM resp:\n{json.dumps(response.choices[0].message.model_dump(), indent=2)}\n" + f"""LLM resp:\n{ + json.dumps( + response.choices[0].message.model_dump(), indent=2, ensure_ascii=False + ) + }\n""" ) if hasattr(response, "usage"): @@ -107,6 +113,18 @@ 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() @@ -141,8 +159,8 @@ async def stream_response( output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, - *, previous_response_id: str | None, + prompt: Any | None = None, ) -> AsyncIterator[TResponseStreamEvent]: with generation_span( model=str(self.model), @@ -160,6 +178,7 @@ async def stream_response( span_generation, tracing, stream=True, + prompt=prompt, ) final_response: Response | None = None @@ -190,6 +209,7 @@ async def _fetch_response( span: Span[GenerationSpanData], tracing: ModelTracing, stream: Literal[True], + prompt: Any | None = None, ) -> tuple[Response, AsyncStream[ChatCompletionChunk]]: ... @overload @@ -204,6 +224,7 @@ async def _fetch_response( span: Span[GenerationSpanData], tracing: ModelTracing, stream: Literal[False], + prompt: Any | None = None, ) -> litellm.types.utils.ModelResponse: ... async def _fetch_response( @@ -217,6 +238,7 @@ async def _fetch_response( span: Span[GenerationSpanData], tracing: ModelTracing, stream: bool = False, + prompt: Any | None = None, ) -> litellm.types.utils.ModelResponse | tuple[Response, AsyncStream[ChatCompletionChunk]]: converted_messages = Converter.items_to_messages(input) @@ -251,8 +273,8 @@ async def _fetch_response( 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"{json.dumps(converted_messages, indent=2, ensure_ascii=False)}\n" + f"Tools:\n{json.dumps(converted_tools, indent=2, ensure_ascii=False)}\n" f"Stream: {stream}\n" f"Tool choice: {tool_choice}\n" f"Response format: {response_format}\n" @@ -269,6 +291,12 @@ async def _fetch_response( 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) + + # Add kwargs from model_settings.extra_args, filtering out None values + if model_settings.extra_args: + extra_kwargs.update(model_settings.extra_args) ret = await litellm.acompletion( model=self.model, diff --git a/src/agents/extensions/visualization.py b/src/agents/extensions/visualization.py index 888e262c3..be762a330 100644 --- a/src/agents/extensions/visualization.py +++ b/src/agents/extensions/visualization.py @@ -1,4 +1,4 @@ -from typing import Optional +from __future__ import annotations import graphviz # type: ignore @@ -31,7 +31,9 @@ def get_main_graph(agent: Agent) -> str: return "".join(parts) -def get_all_nodes(agent: Agent, parent: Optional[Agent] = None) -> str: +def get_all_nodes( + agent: Agent, parent: Agent | None = None, visited: set[str] | None = None +) -> str: """ Recursively generates the nodes for the given agent and its handoffs in DOT format. @@ -41,17 +43,23 @@ def get_all_nodes(agent: Agent, parent: Optional[Agent] = None) -> str: Returns: str: The DOT format string representing the nodes. """ + if visited is None: + visited = set() + if agent.name in visited: + return "" + visited.add(agent.name) + 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( + '"__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 parts.append( f'"{agent.name}" [label="{agent.name}", shape=box, style=filled, ' "fillcolor=lightyellow, width=1.5, height=0.8];" @@ -71,17 +79,20 @@ def get_all_nodes(agent: Agent, parent: Optional[Agent] = None) -> str: 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)) + if handoff.name not in visited: + 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, agent, visited)) return "".join(parts) -def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: +def get_all_edges( + agent: Agent, parent: Agent | None = None, visited: set[str] | None = None +) -> str: """ Recursively generates the edges for the given agent and its handoffs in DOT format. @@ -92,6 +103,12 @@ def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: Returns: str: The DOT format string representing the edges. """ + if visited is None: + visited = set() + if agent.name in visited: + return "" + visited.add(agent.name) + parts = [] if not parent: @@ -109,7 +126,7 @@ def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: if isinstance(handoff, Agent): parts.append(f""" "{agent.name}" -> "{handoff.name}";""") - parts.append(get_all_edges(handoff, agent)) + parts.append(get_all_edges(handoff, agent, visited)) if not agent.handoffs and not isinstance(agent, Tool): # type: ignore parts.append(f'"{agent.name}" -> "__end__";') @@ -117,7 +134,7 @@ def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: return "".join(parts) -def draw_graph(agent: Agent, filename: Optional[str] = None) -> graphviz.Source: +def draw_graph(agent: Agent, filename: str | None = None) -> graphviz.Source: """ Draws the graph for the given agent and optionally saves it as a PNG file. diff --git a/src/agents/function_schema.py b/src/agents/function_schema.py index 0e5868965..e1a91e189 100644 --- a/src/agents/function_schema.py +++ b/src/agents/function_schema.py @@ -13,6 +13,7 @@ from .exceptions import UserError from .run_context import RunContextWrapper from .strict_schema import ensure_strict_json_schema +from .tool_context import ToolContext @dataclass @@ -222,7 +223,8 @@ def function_schema( doc_info = None param_descs = {} - func_name = name_override or doc_info.name if doc_info else func.__name__ + # Ensure name_override takes precedence even if docstring info is disabled. + 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) @@ -237,21 +239,21 @@ def function_schema( ann = type_hints.get(first_name, first_param.annotation) if ann != inspect._empty: origin = get_origin(ann) or ann - if origin is RunContextWrapper: + if origin is RunContextWrapper or origin is ToolContext: 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 parameters other than the first, raise error if any use RunContextWrapper or ToolContext. 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: + if origin is RunContextWrapper or origin is ToolContext: raise UserError( - f"RunContextWrapper param found at non-first position in function" + f"RunContextWrapper/ToolContext param found at non-first position in function" f" {func.__name__}" ) filtered_params.append((name, param)) @@ -335,7 +337,8 @@ def function_schema( # 5. Return as a FuncSchema dataclass return FuncSchema( name=func_name, - description=description_override or doc_info.description if doc_info else None, + # Ensure description_override takes precedence even if docstring info is disabled. + description=description_override or (doc_info.description if doc_info else None), params_pydantic_model=dynamic_model, params_json_schema=json_schema, signature=sig, diff --git a/src/agents/handoffs.py b/src/agents/handoffs.py index 686191f3d..cb2752e4f 100644 --- a/src/agents/handoffs.py +++ b/src/agents/handoffs.py @@ -1,6 +1,7 @@ from __future__ import annotations import inspect +import json from collections.abc import Awaitable from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Callable, Generic, cast, overload @@ -14,6 +15,7 @@ from .strict_schema import ensure_strict_json_schema from .tracing.spans import SpanError from .util import _error_tracing, _json, _transforms +from .util._types import MaybeAwaitable if TYPE_CHECKING: from .agent import Agent @@ -98,9 +100,13 @@ class Handoff(Generic[TContext]): True, as it increases the likelihood of correct JSON input. """ + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True + """Whether the handoff is enabled. Either a bool or a Callable that takes the run context and + agent and returns whether the handoff is enabled. You can use this to dynamically enable/disable + a handoff based on your context/state.""" + def get_transfer_message(self, agent: Agent[Any]) -> str: - base = f"{{'assistant': '{agent.name}'}}" - return base + return json.dumps({"assistant": agent.name}) @classmethod def default_tool_name(cls, agent: Agent[Any]) -> str: @@ -121,6 +127,7 @@ def handoff( tool_name_override: str | None = None, tool_description_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True, ) -> Handoff[TContext]: ... @@ -133,6 +140,7 @@ def handoff( tool_description_override: str | None = None, tool_name_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True, ) -> Handoff[TContext]: ... @@ -144,6 +152,7 @@ def handoff( tool_description_override: str | None = None, tool_name_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True, ) -> Handoff[TContext]: ... @@ -154,6 +163,7 @@ def handoff( on_handoff: OnHandoffWithInput[THandoffInput] | OnHandoffWithoutInput | None = None, input_type: type[THandoffInput] | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True, ) -> Handoff[TContext]: """Create a handoff from an agent. @@ -166,9 +176,12 @@ def handoff( 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. + is_enabled: Whether the handoff is enabled. Can be a bool or a callable that takes the run + context and agent and returns whether the handoff is enabled. Disabled handoffs are + hidden from the LLM at runtime. """ 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" + "You must provide either both on_handoff and input_type, or neither" ) type_adapter: TypeAdapter[Any] | None if input_type is not None: @@ -233,4 +246,5 @@ async def _invoke_handoff( on_invoke_handoff=_invoke_handoff, input_filter=input_filter, agent_name=agent.name, + is_enabled=is_enabled, ) diff --git a/src/agents/items.py b/src/agents/items.py index 8fb2b52a3..64797ad22 100644 --- a/src/agents/items.py +++ b/src/agents/items.py @@ -18,7 +18,22 @@ ResponseOutputText, ResponseStreamEvent, ) -from openai.types.responses.response_input_item_param import ComputerCallOutput, FunctionCallOutput +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.""" @@ -123,10 +142,12 @@ 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: Any @@ -147,6 +168,36 @@ class ReasoningItem(RunItemBase[ResponseReasoningItem]): 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, @@ -154,6 +205,9 @@ class ReasoningItem(RunItemBase[ResponseReasoningItem]): ToolCallItem, ToolCallOutputItem, ReasoningItem, + MCPListToolsItem, + MCPApprovalRequestItem, + MCPApprovalResponseItem, ] """An item generated by an agent.""" diff --git a/src/agents/lifecycle.py b/src/agents/lifecycle.py index 8643248b1..2cce496c8 100644 --- a/src/agents/lifecycle.py +++ b/src/agents/lifecycle.py @@ -1,25 +1,27 @@ from typing import Any, Generic -from .agent import Agent +from typing_extensions import TypeVar + +from .agent import Agent, AgentBase from .run_context import RunContextWrapper, TContext from .tool import Tool +TAgent = TypeVar("TAgent", bound=AgentBase, default=AgentBase) + -class RunHooks(Generic[TContext]): +class RunHooksBase(Generic[TContext, TAgent]): """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: + async def on_agent_start(self, context: RunContextWrapper[TContext], agent: TAgent) -> 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], + agent: TAgent, output: Any, ) -> None: """Called when the agent produces a final output.""" @@ -28,8 +30,8 @@ async def on_agent_end( async def on_handoff( self, context: RunContextWrapper[TContext], - from_agent: Agent[TContext], - to_agent: Agent[TContext], + from_agent: TAgent, + to_agent: TAgent, ) -> None: """Called when a handoff occurs.""" pass @@ -37,7 +39,7 @@ async def on_handoff( async def on_tool_start( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, tool: Tool, ) -> None: """Called before a tool is invoked.""" @@ -46,7 +48,7 @@ async def on_tool_start( async def on_tool_end( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, tool: Tool, result: str, ) -> None: @@ -54,14 +56,14 @@ async def on_tool_end( pass -class AgentHooks(Generic[TContext]): +class AgentHooksBase(Generic[TContext, TAgent]): """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: + async def on_start(self, context: RunContextWrapper[TContext], agent: TAgent) -> None: """Called before the agent is invoked. Called each time the running agent is changed to this agent.""" pass @@ -69,7 +71,7 @@ async def on_start(self, context: RunContextWrapper[TContext], agent: Agent[TCon async def on_end( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, output: Any, ) -> None: """Called when the agent produces a final output.""" @@ -78,8 +80,8 @@ async def on_end( async def on_handoff( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], - source: Agent[TContext], + agent: TAgent, + source: TAgent, ) -> None: """Called when the agent is being handed off to. The `source` is the agent that is handing off to this agent.""" @@ -88,7 +90,7 @@ async def on_handoff( async def on_tool_start( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, tool: Tool, ) -> None: """Called before a tool is invoked.""" @@ -97,9 +99,16 @@ async def on_tool_start( async def on_tool_end( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, tool: Tool, result: str, ) -> None: """Called after a tool is invoked.""" pass + + +RunHooks = RunHooksBase[TContext, Agent] +"""Run hooks when using `Agent`.""" + +AgentHooks = AgentHooksBase[TContext, Agent] +"""Agent hooks for `Agent`s.""" diff --git a/src/agents/mcp/__init__.py b/src/agents/mcp/__init__.py index 1a72a89f0..da5a68b16 100644 --- a/src/agents/mcp/__init__.py +++ b/src/agents/mcp/__init__.py @@ -5,11 +5,20 @@ MCPServerSseParams, MCPServerStdio, MCPServerStdioParams, + MCPServerStreamableHttp, + MCPServerStreamableHttpParams, ) except ImportError: pass -from .util import MCPUtil +from .util import ( + MCPUtil, + ToolFilter, + ToolFilterCallable, + ToolFilterContext, + ToolFilterStatic, + create_static_tool_filter, +) __all__ = [ "MCPServer", @@ -17,5 +26,12 @@ "MCPServerSseParams", "MCPServerStdio", "MCPServerStdioParams", + "MCPServerStreamableHttp", + "MCPServerStreamableHttpParams", "MCPUtil", + "ToolFilter", + "ToolFilterCallable", + "ToolFilterContext", + "ToolFilterStatic", + "create_static_tool_filter", ] diff --git a/src/agents/mcp/server.py b/src/agents/mcp/server.py index 9916c92b0..91a9274fc 100644 --- a/src/agents/mcp/server.py +++ b/src/agents/mcp/server.py @@ -2,19 +2,27 @@ import abc import asyncio +import inspect from contextlib import AbstractAsyncContextManager, AsyncExitStack from datetime import timedelta from pathlib import Path -from typing import Any, Literal +from typing import TYPE_CHECKING, Any, Literal, cast 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.types import CallToolResult, JSONRPCMessage +from mcp.client.streamable_http import GetSessionIdCallback, streamablehttp_client +from mcp.shared.message import SessionMessage +from mcp.types import CallToolResult, GetPromptResult, InitializeResult, ListPromptsResult from typing_extensions import NotRequired, TypedDict from ..exceptions import UserError from ..logger import logger +from ..run_context import RunContextWrapper +from .util import ToolFilter, ToolFilterCallable, ToolFilterContext, ToolFilterStatic + +if TYPE_CHECKING: + from ..agent import AgentBase class MCPServer(abc.ABC): @@ -42,7 +50,11 @@ async def cleanup(self): pass @abc.abstractmethod - async def list_tools(self) -> list[MCPTool]: + async def list_tools( + self, + run_context: RunContextWrapper[Any] | None = None, + agent: AgentBase | None = None, + ) -> list[MCPTool]: """List the tools available on the server.""" pass @@ -51,11 +63,30 @@ async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> C """Invoke a tool on the server.""" pass + @abc.abstractmethod + async def list_prompts( + self, + ) -> ListPromptsResult: + """List the prompts available on the server.""" + pass + + @abc.abstractmethod + async def get_prompt( + self, name: str, arguments: dict[str, Any] | None = None + ) -> GetPromptResult: + """Get a specific prompt from 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): + def __init__( + self, + cache_tools_list: bool, + client_session_timeout_seconds: float | None, + tool_filter: ToolFilter = None, + ): """ Args: cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be @@ -66,11 +97,13 @@ def __init__(self, cache_tools_list: bool, client_session_timeout_seconds: float (by avoiding a round-trip to the server every time). client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + tool_filter: The tool filter to use for filtering tools. """ 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 @@ -78,13 +111,94 @@ def __init__(self, cache_tools_list: bool, client_session_timeout_seconds: float self._cache_dirty = True self._tools_list: list[MCPTool] | None = None + self.tool_filter = tool_filter + + async def _apply_tool_filter( + self, + tools: list[MCPTool], + run_context: RunContextWrapper[Any], + agent: AgentBase, + ) -> list[MCPTool]: + """Apply the tool filter to the list of tools.""" + if self.tool_filter is None: + return tools + + # Handle static tool filter + if isinstance(self.tool_filter, dict): + return self._apply_static_tool_filter(tools, self.tool_filter) + + # Handle callable tool filter (dynamic filter) + else: + return await self._apply_dynamic_tool_filter(tools, run_context, agent) + + def _apply_static_tool_filter( + self, tools: list[MCPTool], static_filter: ToolFilterStatic + ) -> list[MCPTool]: + """Apply static tool filtering based on allowlist and blocklist.""" + filtered_tools = tools + + # Apply allowed_tool_names filter (whitelist) + if "allowed_tool_names" in static_filter: + allowed_names = static_filter["allowed_tool_names"] + filtered_tools = [t for t in filtered_tools if t.name in allowed_names] + + # Apply blocked_tool_names filter (blacklist) + if "blocked_tool_names" in static_filter: + blocked_names = static_filter["blocked_tool_names"] + filtered_tools = [t for t in filtered_tools if t.name not in blocked_names] + + return filtered_tools + + async def _apply_dynamic_tool_filter( + self, + tools: list[MCPTool], + run_context: RunContextWrapper[Any], + agent: AgentBase, + ) -> list[MCPTool]: + """Apply dynamic tool filtering using a callable filter function.""" + + # Ensure we have a callable filter and cast to help mypy + if not callable(self.tool_filter): + raise ValueError("Tool filter must be callable for dynamic filtering") + tool_filter_func = cast(ToolFilterCallable, self.tool_filter) + + # Create filter context + filter_context = ToolFilterContext( + run_context=run_context, + agent=agent, + server_name=self.name, + ) + + filtered_tools = [] + for tool in tools: + try: + # Call the filter function with context + result = tool_filter_func(filter_context, tool) + + if inspect.isawaitable(result): + should_include = await result + else: + should_include = result + + if should_include: + filtered_tools.append(tool) + except Exception as e: + logger.error( + f"Error applying tool filter to tool '{tool.name}' on server '{self.name}': {e}" + ) + # On error, exclude the tool for safety + continue + + return filtered_tools + @abc.abstractmethod def create_streams( self, ) -> AbstractAsyncContextManager[ tuple[ - MemoryObjectReceiveStream[JSONRPCMessage | Exception], - MemoryObjectSendStream[JSONRPCMessage], + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -105,7 +219,11 @@ async def connect(self): """Connect to the server.""" try: transport = await self.exit_stack.enter_async_context(self.create_streams()) - read, write = transport + # 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, @@ -115,28 +233,40 @@ async def connect(self): else None, ) ) - await session.initialize() + 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]: + async def list_tools( + self, + run_context: RunContextWrapper[Any] | None = None, + agent: AgentBase | None = None, + ) -> 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 + tools = self._tools_list + else: + # 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 + tools = self._tools_list + + # Filter tools based on tool_filter + filtered_tools = tools + if self.tool_filter is not None: + if run_context is None or agent is None: + raise UserError("run_context and agent are required for dynamic tool filtering") + filtered_tools = await self._apply_tool_filter(filtered_tools, run_context, agent) + return filtered_tools async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> CallToolResult: """Invoke a tool on the server.""" @@ -145,6 +275,24 @@ async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> C return await self.session.call_tool(tool_name, arguments) + async def list_prompts( + self, + ) -> ListPromptsResult: + """List the prompts available on the server.""" + if not self.session: + raise UserError("Server not initialized. Make sure you call `connect()` first.") + + return await self.session.list_prompts() + + async def get_prompt( + self, name: str, arguments: dict[str, Any] | None = None + ) -> GetPromptResult: + """Get a specific prompt from the server.""" + if not self.session: + raise UserError("Server not initialized. Make sure you call `connect()` first.") + + return await self.session.get_prompt(name, arguments) + async def cleanup(self): """Cleanup the server.""" async with self._cleanup_lock: @@ -197,6 +345,7 @@ def __init__( cache_tools_list: bool = False, name: str | None = None, client_session_timeout_seconds: float | None = 5, + tool_filter: ToolFilter = None, ): """Create a new MCP server based on the stdio transport. @@ -214,8 +363,13 @@ def __init__( 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. + tool_filter: The tool filter to use for filtering tools. """ - super().__init__(cache_tools_list, client_session_timeout_seconds) + super().__init__( + cache_tools_list, + client_session_timeout_seconds, + tool_filter, + ) self.params = StdioServerParameters( command=params["command"], @@ -232,8 +386,9 @@ def create_streams( self, ) -> AbstractAsyncContextManager[ tuple[ - MemoryObjectReceiveStream[JSONRPCMessage | Exception], - MemoryObjectSendStream[JSONRPCMessage], + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -273,6 +428,7 @@ def __init__( cache_tools_list: bool = False, name: str | None = None, client_session_timeout_seconds: float | None = 5, + tool_filter: ToolFilter = None, ): """Create a new MCP server based on the HTTP with SSE transport. @@ -292,8 +448,13 @@ def __init__( URL. client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + tool_filter: The tool filter to use for filtering tools. """ - super().__init__(cache_tools_list, client_session_timeout_seconds) + super().__init__( + cache_tools_list, + client_session_timeout_seconds, + tool_filter, + ) self.params = params self._name = name or f"sse: {self.params['url']}" @@ -302,8 +463,9 @@ def create_streams( self, ) -> AbstractAsyncContextManager[ tuple[ - MemoryObjectReceiveStream[JSONRPCMessage | Exception], - MemoryObjectSendStream[JSONRPCMessage], + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -318,3 +480,90 @@ def create_streams( 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 | float] + """The timeout for the HTTP request. Defaults to 5 seconds.""" + + sse_read_timeout: NotRequired[timedelta | float] + """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, + tool_filter: ToolFilter = None, + ): + """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. + tool_filter: The tool filter to use for filtering tools. + """ + super().__init__( + cache_tools_list, + client_session_timeout_seconds, + tool_filter, + ) + + 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", 5), + sse_read_timeout=self.params.get("sse_read_timeout", 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 index bbfe1885c..18cf4440a 100644 --- a/src/agents/mcp/util.py +++ b/src/agents/mcp/util.py @@ -1,34 +1,113 @@ import functools import json -from typing import TYPE_CHECKING, Any +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Callable, Optional, Union -from agents.strict_schema import ensure_strict_json_schema +from typing_extensions import NotRequired, TypedDict from .. import _debug from ..exceptions import AgentsException, ModelBehaviorError, UserError from ..logger import logger from ..run_context import RunContextWrapper +from ..strict_schema import ensure_strict_json_schema from ..tool import FunctionTool, Tool from ..tracing import FunctionSpanData, get_current_span, mcp_tools_span +from ..util._types import MaybeAwaitable if TYPE_CHECKING: from mcp.types import Tool as MCPTool + from ..agent import AgentBase from .server import MCPServer +@dataclass +class ToolFilterContext: + """Context information available to tool filter functions.""" + + run_context: RunContextWrapper[Any] + """The current run context.""" + + agent: "AgentBase" + """The agent that is requesting the tool list.""" + + server_name: str + """The name of the MCP server.""" + + +ToolFilterCallable = Callable[["ToolFilterContext", "MCPTool"], MaybeAwaitable[bool]] +"""A function that determines whether a tool should be available. + +Args: + context: The context information including run context, agent, and server name. + tool: The MCP tool to filter. + +Returns: + Whether the tool should be available (True) or filtered out (False). +""" + + +class ToolFilterStatic(TypedDict): + """Static tool filter configuration using allowlists and blocklists.""" + + allowed_tool_names: NotRequired[list[str]] + """Optional list of tool names to allow (whitelist). + If set, only these tools will be available.""" + + blocked_tool_names: NotRequired[list[str]] + """Optional list of tool names to exclude (blacklist). + If set, these tools will be filtered out.""" + + +ToolFilter = Union[ToolFilterCallable, ToolFilterStatic, None] +"""A tool filter that can be either a function, static configuration, or None (no filtering).""" + + +def create_static_tool_filter( + allowed_tool_names: Optional[list[str]] = None, + blocked_tool_names: Optional[list[str]] = None, +) -> Optional[ToolFilterStatic]: + """Create a static tool filter from allowlist and blocklist parameters. + + This is a convenience function for creating a ToolFilterStatic. + + Args: + allowed_tool_names: Optional list of tool names to allow (whitelist). + blocked_tool_names: Optional list of tool names to exclude (blacklist). + + Returns: + A ToolFilterStatic if any filtering is specified, None otherwise. + """ + if allowed_tool_names is None and blocked_tool_names is None: + return None + + filter_dict: ToolFilterStatic = {} + if allowed_tool_names is not None: + filter_dict["allowed_tool_names"] = allowed_tool_names + if blocked_tool_names is not None: + filter_dict["blocked_tool_names"] = blocked_tool_names + + return filter_dict + + 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 + cls, + servers: list["MCPServer"], + convert_schemas_to_strict: bool, + run_context: RunContextWrapper[Any], + agent: "AgentBase", ) -> 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_tools = await cls.get_function_tools( + server, convert_schemas_to_strict, run_context, agent + ) server_tool_names = {tool.name for tool in server_tools} if len(server_tool_names & tool_names) > 0: raise UserError( @@ -42,12 +121,16 @@ async def get_all_function_tools( @classmethod async def get_function_tools( - cls, server: "MCPServer", convert_schemas_to_strict: bool + cls, + server: "MCPServer", + convert_schemas_to_strict: bool, + run_context: RunContextWrapper[Any], + agent: "AgentBase", ) -> 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() + tools = await server.list_tools(run_context, agent) 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] @@ -116,7 +199,7 @@ async def invoke_mcp_tool( 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]) + tool_output = json.dumps([item.model_dump(mode="json") for item in result.content]) else: logger.error(f"Errored MCP tool result: {result}") tool_output = "Error running tool." diff --git a/src/agents/memory/__init__.py b/src/agents/memory/__init__.py new file mode 100644 index 000000000..059ca57ab --- /dev/null +++ b/src/agents/memory/__init__.py @@ -0,0 +1,3 @@ +from .session import Session, SQLiteSession + +__all__ = ["Session", "SQLiteSession"] diff --git a/src/agents/memory/session.py b/src/agents/memory/session.py new file mode 100644 index 000000000..8db0971eb --- /dev/null +++ b/src/agents/memory/session.py @@ -0,0 +1,369 @@ +from __future__ import annotations + +import asyncio +import json +import sqlite3 +import threading +from abc import ABC, abstractmethod +from pathlib import Path +from typing import TYPE_CHECKING, Protocol, runtime_checkable + +if TYPE_CHECKING: + from ..items import TResponseInputItem + + +@runtime_checkable +class Session(Protocol): + """Protocol for session implementations. + + Session stores conversation history for a specific session, allowing + agents to maintain context without requiring explicit manual memory management. + """ + + session_id: str + + async def get_items(self, limit: int | None = None) -> list[TResponseInputItem]: + """Retrieve the conversation history for this session. + + Args: + limit: Maximum number of items to retrieve. If None, retrieves all items. + When specified, returns the latest N items in chronological order. + + Returns: + List of input items representing the conversation history + """ + ... + + async def add_items(self, items: list[TResponseInputItem]) -> None: + """Add new items to the conversation history. + + Args: + items: List of input items to add to the history + """ + ... + + async def pop_item(self) -> TResponseInputItem | None: + """Remove and return the most recent item from the session. + + Returns: + The most recent item if it exists, None if the session is empty + """ + ... + + async def clear_session(self) -> None: + """Clear all items for this session.""" + ... + + +class SessionABC(ABC): + """Abstract base class for session implementations. + + Session stores conversation history for a specific session, allowing + agents to maintain context without requiring explicit manual memory management. + + This ABC is intended for internal use and as a base class for concrete implementations. + Third-party libraries should implement the Session protocol instead. + """ + + session_id: str + + @abstractmethod + async def get_items(self, limit: int | None = None) -> list[TResponseInputItem]: + """Retrieve the conversation history for this session. + + Args: + limit: Maximum number of items to retrieve. If None, retrieves all items. + When specified, returns the latest N items in chronological order. + + Returns: + List of input items representing the conversation history + """ + ... + + @abstractmethod + async def add_items(self, items: list[TResponseInputItem]) -> None: + """Add new items to the conversation history. + + Args: + items: List of input items to add to the history + """ + ... + + @abstractmethod + async def pop_item(self) -> TResponseInputItem | None: + """Remove and return the most recent item from the session. + + Returns: + The most recent item if it exists, None if the session is empty + """ + ... + + @abstractmethod + async def clear_session(self) -> None: + """Clear all items for this session.""" + ... + + +class SQLiteSession(SessionABC): + """SQLite-based implementation of session storage. + + This implementation stores conversation history in a SQLite database. + By default, uses an in-memory database that is lost when the process ends. + For persistent storage, provide a file path. + """ + + def __init__( + self, + session_id: str, + db_path: str | Path = ":memory:", + sessions_table: str = "agent_sessions", + messages_table: str = "agent_messages", + ): + """Initialize the SQLite session. + + Args: + session_id: Unique identifier for the conversation session + db_path: Path to the SQLite database file. Defaults to ':memory:' (in-memory database) + sessions_table: Name of the table to store session metadata. Defaults to + 'agent_sessions' + messages_table: Name of the table to store message data. Defaults to 'agent_messages' + """ + self.session_id = session_id + self.db_path = db_path + self.sessions_table = sessions_table + self.messages_table = messages_table + self._local = threading.local() + self._lock = threading.Lock() + + # For in-memory databases, we need a shared connection to avoid thread isolation + # For file databases, we use thread-local connections for better concurrency + self._is_memory_db = str(db_path) == ":memory:" + if self._is_memory_db: + self._shared_connection = sqlite3.connect(":memory:", check_same_thread=False) + self._shared_connection.execute("PRAGMA journal_mode=WAL") + self._init_db_for_connection(self._shared_connection) + else: + # For file databases, initialize the schema once since it persists + init_conn = sqlite3.connect(str(self.db_path), check_same_thread=False) + init_conn.execute("PRAGMA journal_mode=WAL") + self._init_db_for_connection(init_conn) + init_conn.close() + + def _get_connection(self) -> sqlite3.Connection: + """Get a database connection.""" + if self._is_memory_db: + # Use shared connection for in-memory database to avoid thread isolation + return self._shared_connection + else: + # Use thread-local connections for file databases + if not hasattr(self._local, "connection"): + self._local.connection = sqlite3.connect( + str(self.db_path), + check_same_thread=False, + ) + self._local.connection.execute("PRAGMA journal_mode=WAL") + assert isinstance(self._local.connection, sqlite3.Connection), ( + f"Expected sqlite3.Connection, got {type(self._local.connection)}" + ) + return self._local.connection + + def _init_db_for_connection(self, conn: sqlite3.Connection) -> None: + """Initialize the database schema for a specific connection.""" + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.sessions_table} ( + session_id TEXT PRIMARY KEY, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """ + ) + + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.messages_table} ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + session_id TEXT NOT NULL, + message_data TEXT NOT NULL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (session_id) REFERENCES {self.sessions_table} (session_id) + ON DELETE CASCADE + ) + """ + ) + + conn.execute( + f""" + CREATE INDEX IF NOT EXISTS idx_{self.messages_table}_session_id + ON {self.messages_table} (session_id, created_at) + """ + ) + + conn.commit() + + async def get_items(self, limit: int | None = None) -> list[TResponseInputItem]: + """Retrieve the conversation history for this session. + + Args: + limit: Maximum number of items to retrieve. If None, retrieves all items. + When specified, returns the latest N items in chronological order. + + Returns: + List of input items representing the conversation history + """ + + def _get_items_sync(): + conn = self._get_connection() + with self._lock if self._is_memory_db else threading.Lock(): + if limit is None: + # Fetch all items in chronological order + cursor = conn.execute( + f""" + SELECT message_data FROM {self.messages_table} + WHERE session_id = ? + ORDER BY created_at ASC + """, + (self.session_id,), + ) + else: + # Fetch the latest N items in chronological order + cursor = conn.execute( + f""" + SELECT message_data FROM {self.messages_table} + WHERE session_id = ? + ORDER BY created_at DESC + LIMIT ? + """, + (self.session_id, limit), + ) + + rows = cursor.fetchall() + + # Reverse to get chronological order when using DESC + if limit is not None: + rows = list(reversed(rows)) + + items = [] + for (message_data,) in rows: + try: + item = json.loads(message_data) + items.append(item) + except json.JSONDecodeError: + # Skip invalid JSON entries + continue + + return items + + return await asyncio.to_thread(_get_items_sync) + + async def add_items(self, items: list[TResponseInputItem]) -> None: + """Add new items to the conversation history. + + Args: + items: List of input items to add to the history + """ + if not items: + return + + def _add_items_sync(): + conn = self._get_connection() + + with self._lock if self._is_memory_db else threading.Lock(): + # Ensure session exists + conn.execute( + f""" + INSERT OR IGNORE INTO {self.sessions_table} (session_id) VALUES (?) + """, + (self.session_id,), + ) + + # Add items + message_data = [(self.session_id, json.dumps(item)) for item in items] + conn.executemany( + f""" + INSERT INTO {self.messages_table} (session_id, message_data) VALUES (?, ?) + """, + message_data, + ) + + # Update session timestamp + conn.execute( + f""" + UPDATE {self.sessions_table} + SET updated_at = CURRENT_TIMESTAMP + WHERE session_id = ? + """, + (self.session_id,), + ) + + conn.commit() + + await asyncio.to_thread(_add_items_sync) + + async def pop_item(self) -> TResponseInputItem | None: + """Remove and return the most recent item from the session. + + Returns: + The most recent item if it exists, None if the session is empty + """ + + def _pop_item_sync(): + conn = self._get_connection() + with self._lock if self._is_memory_db else threading.Lock(): + # Use DELETE with RETURNING to atomically delete and return the most recent item + cursor = conn.execute( + f""" + DELETE FROM {self.messages_table} + WHERE id = ( + SELECT id FROM {self.messages_table} + WHERE session_id = ? + ORDER BY created_at DESC + LIMIT 1 + ) + RETURNING message_data + """, + (self.session_id,), + ) + + result = cursor.fetchone() + conn.commit() + + if result: + message_data = result[0] + try: + item = json.loads(message_data) + return item + except json.JSONDecodeError: + # Return None for corrupted JSON entries (already deleted) + return None + + return None + + return await asyncio.to_thread(_pop_item_sync) + + async def clear_session(self) -> None: + """Clear all items for this session.""" + + def _clear_session_sync(): + conn = self._get_connection() + with self._lock if self._is_memory_db else threading.Lock(): + conn.execute( + f"DELETE FROM {self.messages_table} WHERE session_id = ?", + (self.session_id,), + ) + conn.execute( + f"DELETE FROM {self.sessions_table} WHERE session_id = ?", + (self.session_id,), + ) + conn.commit() + + await asyncio.to_thread(_clear_session_sync) + + def close(self) -> None: + """Close the database connection.""" + if self._is_memory_db: + if hasattr(self, "_shared_connection"): + self._shared_connection.close() + else: + if hasattr(self._local, "connection"): + self._local.connection.close() diff --git a/src/agents/model_settings.py b/src/agents/model_settings.py index 7b016c98f..1e9edcbc6 100644 --- a/src/agents/model_settings.py +++ b/src/agents/model_settings.py @@ -1,13 +1,55 @@ from __future__ import annotations import dataclasses +from collections.abc import Mapping from dataclasses import dataclass, fields, replace -from typing import Any, Literal +from typing import Annotated, Any, Literal, Union -from openai._types import Body, Headers, Query +from openai import Omit as _Omit +from openai._types import Body, Query +from openai.types.responses import ResponseIncludable from openai.types.shared import Reasoning -from pydantic import BaseModel +from pydantic import BaseModel, GetCoreSchemaHandler +from pydantic_core import core_schema +from typing_extensions import TypeAlias + + +class _OmitTypeAnnotation: + @classmethod + def __get_pydantic_core_schema__( + cls, + _source_type: Any, + _handler: GetCoreSchemaHandler, + ) -> core_schema.CoreSchema: + def validate_from_none(value: None) -> _Omit: + return _Omit() + + from_none_schema = core_schema.chain_schema( + [ + core_schema.none_schema(), + core_schema.no_info_plain_validator_function(validate_from_none), + ] + ) + return core_schema.json_or_python_schema( + json_schema=from_none_schema, + python_schema=core_schema.union_schema( + [ + # check if it's an instance first before doing any further work + core_schema.is_instance_schema(_Omit), + from_none_schema, + ] + ), + serialization=core_schema.plain_serializer_function_ser_schema(lambda instance: None), + ) +@dataclass +class MCPToolChoice: + server_label: str + name: str + +Omit = Annotated[_Omit, _OmitTypeAnnotation] +Headers: TypeAlias = Mapping[str, Union[str, Omit]] +ToolChoice: TypeAlias = Union[Literal["auto", "required", "none"], str, MCPToolChoice, None] @dataclass class ModelSettings: @@ -32,12 +74,17 @@ class ModelSettings: presence_penalty: float | None = None """The presence penalty to use when calling the model.""" - tool_choice: Literal["auto", "required", "none"] | str | None = None + tool_choice: ToolChoice | None = None """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.""" + """Controls whether the model can make multiple parallel tool calls in a single turn. + If not provided (i.e., set to None), this behavior defers to the underlying + model provider's default. For most current providers (e.g., OpenAI), this typically + means parallel tool calls are enabled (True). + Set to True to explicitly enable parallel tool calls, or False to restrict the + model to at most one tool call per turn. + """ truncation: Literal["auto", "disabled"] | None = None """The truncation strategy to use when calling the model.""" @@ -61,6 +108,10 @@ class ModelSettings: """Whether to include usage chunk. Defaults to True if not provided.""" + response_include: list[ResponseIncludable] | None = None + """Additional output data to include in the model response. + [include parameter](https://platform.openai.com/docs/api-reference/responses/create#responses-create-include)""" + extra_query: Query | None = None """Additional query fields to provide with the request. Defaults to None if not provided.""" @@ -73,6 +124,11 @@ class ModelSettings: """Additional headers to provide with the request. Defaults to None if not provided.""" + extra_args: dict[str, Any] | None = None + """Arbitrary keyword arguments to pass to the model API call. + These will be passed directly to the underlying model provider's API. + Use with caution as not all models support all parameters.""" + 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.""" @@ -84,6 +140,16 @@ def resolve(self, override: ModelSettings | None) -> ModelSettings: for field in fields(self) if getattr(override, field.name) is not None } + + # Handle extra_args merging specially - merge dictionaries instead of replacing + if self.extra_args is not None or override.extra_args is not None: + merged_args = {} + if self.extra_args: + merged_args.update(self.extra_args) + if override.extra_args: + merged_args.update(override.extra_args) + changes["extra_args"] = merged_args if merged_args else None + return replace(self, **changes) def to_json_dict(self) -> dict[str, Any]: diff --git a/src/agents/models/chatcmpl_converter.py b/src/agents/models/chatcmpl_converter.py index 613a37453..d3c71c24e 100644 --- a/src/agents/models/chatcmpl_converter.py +++ b/src/agents/models/chatcmpl_converter.py @@ -19,6 +19,7 @@ ChatCompletionToolMessageParam, ChatCompletionUserMessageParam, ) +from openai.types.chat.chat_completion_content_part_param import File, FileFile from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam from openai.types.chat.completion_create_params import ResponseFormat from openai.types.responses import ( @@ -27,19 +28,23 @@ ResponseFunctionToolCall, ResponseFunctionToolCallParam, ResponseInputContentParam, + ResponseInputFileParam, ResponseInputImageParam, ResponseInputTextParam, ResponseOutputMessage, ResponseOutputMessageParam, ResponseOutputRefusal, ResponseOutputText, + ResponseReasoningItem, ) from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message +from openai.types.responses.response_reasoning_item import Summary from ..agent_output import AgentOutputSchemaBase from ..exceptions import AgentsException, UserError from ..handoffs import Handoff from ..items import TResponseInputItem, TResponseOutputItem +from ..model_settings import MCPToolChoice from ..tool import FunctionTool, Tool from .fake_id import FAKE_RESPONSES_ID @@ -47,10 +52,12 @@ class Converter: @classmethod def convert_tool_choice( - cls, tool_choice: Literal["auto", "required", "none"] | str | None + cls, tool_choice: Literal["auto", "required", "none"] | str | MCPToolChoice | None ) -> ChatCompletionToolChoiceOptionParam | NotGiven: if tool_choice is None: return NOT_GIVEN + elif isinstance(tool_choice, MCPToolChoice): + raise UserError("MCPToolChoice is not supported for Chat Completions models") elif tool_choice == "auto": return "auto" elif tool_choice == "required": @@ -85,6 +92,16 @@ def convert_response_format( def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]: items: list[TResponseOutputItem] = [] + # Handle reasoning content if available + if hasattr(message, "reasoning_content") and message.reasoning_content: + items.append( + ResponseReasoningItem( + id=FAKE_RESPONSES_ID, + summary=[Summary(text=message.reasoning_content, type="summary_text")], + type="reasoning", + ) + ) + message_item = ResponseOutputMessage( id=FAKE_RESPONSES_ID, content=[], @@ -234,12 +251,24 @@ def extract_all_content( type="image_url", image_url={ "url": casted_image_param["image_url"], - "detail": casted_image_param["detail"], + "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}") + casted_file_param = cast(ResponseInputFileParam, c) + if "file_data" not in casted_file_param or not casted_file_param["file_data"]: + raise UserError( + f"Only file_data is supported for input_file {casted_file_param}" + ) + out.append( + File( + type="file", + file=FileFile( + file_data=casted_file_param["file_data"], + ), + ) + ) else: raise UserError(f"Unknown content: {c}") return out diff --git a/src/agents/models/chatcmpl_stream_handler.py b/src/agents/models/chatcmpl_stream_handler.py index c71adeb55..83fa32abc 100644 --- a/src/agents/models/chatcmpl_stream_handler.py +++ b/src/agents/models/chatcmpl_stream_handler.py @@ -20,24 +20,51 @@ ResponseOutputMessage, ResponseOutputRefusal, ResponseOutputText, + ResponseReasoningItem, + ResponseReasoningSummaryPartAddedEvent, + ResponseReasoningSummaryPartDoneEvent, + ResponseReasoningSummaryTextDeltaEvent, ResponseRefusalDeltaEvent, ResponseTextDeltaEvent, ResponseUsage, ) +from openai.types.responses.response_reasoning_item import Summary +from openai.types.responses.response_reasoning_summary_part_added_event import ( + Part as AddedEventPart, +) +from openai.types.responses.response_reasoning_summary_part_done_event import Part as DoneEventPart from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails from ..items import TResponseStreamEvent from .fake_id import FAKE_RESPONSES_ID +# Define a Part class for internal use +class Part: + def __init__(self, text: str, type: str): + self.text = text + self.type = type + + @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 + reasoning_content_index_and_output: tuple[int, ResponseReasoningItem] | 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( @@ -47,13 +74,14 @@ async def handle_stream( ) -> 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 @@ -64,12 +92,65 @@ async def handle_stream( delta = chunk.choices[0].delta - # Handle text - if delta.content: + # Handle reasoning content + if hasattr(delta, "reasoning_content"): + reasoning_content = delta.reasoning_content + if reasoning_content and not state.reasoning_content_index_and_output: + state.reasoning_content_index_and_output = ( + 0, + ResponseReasoningItem( + id=FAKE_RESPONSES_ID, + summary=[Summary(text="", type="summary_text")], + type="reasoning", + ), + ) + yield ResponseOutputItemAddedEvent( + item=ResponseReasoningItem( + id=FAKE_RESPONSES_ID, + summary=[Summary(text="", type="summary_text")], + type="reasoning", + ), + output_index=0, + type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), + ) + + yield ResponseReasoningSummaryPartAddedEvent( + item_id=FAKE_RESPONSES_ID, + output_index=0, + summary_index=0, + part=AddedEventPart(text="", type="summary_text"), + type="response.reasoning_summary_part.added", + sequence_number=sequence_number.get_and_increment(), + ) + + if reasoning_content and state.reasoning_content_index_and_output: + yield ResponseReasoningSummaryTextDeltaEvent( + delta=reasoning_content, + item_id=FAKE_RESPONSES_ID, + output_index=0, + summary_index=0, + type="response.reasoning_summary_text.delta", + sequence_number=sequence_number.get_and_increment(), + ) + + # Create a new summary with updated text + current_summary = state.reasoning_content_index_and_output[1].summary[0] + updated_text = current_summary.text + reasoning_content + new_summary = Summary(text=updated_text, type="summary_text") + state.reasoning_content_index_and_output[1].summary[0] = new_summary + + # Handle regular content + if delta.content is not None: if not state.text_content_index_and_output: - # Initialize a content tracker for streaming text + content_index = 0 + if state.reasoning_content_index_and_output: + content_index += 1 + if state.refusal_content_index_and_output: + content_index += 1 + state.text_content_index_and_output = ( - 0 if not state.refusal_content_index_and_output else 1, + content_index, ResponseOutputText( text="", type="output_text", @@ -87,27 +168,33 @@ async def handle_stream( # Notify consumers of the start of a new output message + first content part yield ResponseOutputItemAddedEvent( item=assistant_item, - output_index=0, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 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, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 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, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 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 @@ -116,9 +203,14 @@ async def handle_stream( # 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 + refusal_index = 0 + if state.reasoning_content_index_and_output: + refusal_index += 1 + if state.text_content_index_and_output: + refusal_index += 1 + state.refusal_content_index_and_output = ( - 0 if not state.text_content_index_and_output else 1, + refusal_index, ResponseOutputRefusal(refusal="", type="refusal"), ) # Start a new assistant message if one doesn't exist yet (in-progress) @@ -132,27 +224,33 @@ async def handle_stream( # Notify downstream that assistant message + first content part are starting yield ResponseOutputItemAddedEvent( item=assistant_item, - output_index=0, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 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, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 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, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 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 @@ -178,18 +276,42 @@ async def handle_stream( 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 "" + state.function_calls[tc_delta.index].call_id = tc_delta.id or "" + + if state.reasoning_content_index_and_output: + yield ResponseReasoningSummaryPartDoneEvent( + item_id=FAKE_RESPONSES_ID, + output_index=0, + summary_index=0, + part=DoneEventPart( + text=state.reasoning_content_index_and_output[1].summary[0].text, + type="summary_text", + ), + type="response.reasoning_summary_part.done", + sequence_number=sequence_number.get_and_increment(), + ) + yield ResponseOutputItemDoneEvent( + item=state.reasoning_content_index_and_output[1], + output_index=0, + type="response.output_item.done", + sequence_number=sequence_number.get_and_increment(), + ) function_call_starting_index = 0 + if state.reasoning_content_index_and_output: + function_call_starting_index += 1 + 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, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 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: @@ -198,9 +320,11 @@ async def handle_stream( yield ResponseContentPartDoneEvent( content_index=state.refusal_content_index_and_output[0], item_id=FAKE_RESPONSES_ID, - output_index=0, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 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 @@ -216,6 +340,7 @@ async def handle_stream( ), output_index=function_call_starting_index, type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), ) # Then, yield the args yield ResponseFunctionCallArgumentsDeltaEvent( @@ -223,6 +348,7 @@ async def handle_stream( 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( @@ -235,10 +361,17 @@ async def handle_stream( ), 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] = [] + + # include Reasoning item if it exists + if state.reasoning_content_index_and_output: + outputs.append(state.reasoning_content_index_and_output[1]) + + # include text or refusal content if they exist if state.text_content_index_and_output or state.refusal_content_index_and_output: assistant_msg = ResponseOutputMessage( id=FAKE_RESPONSES_ID, @@ -256,8 +389,10 @@ async def handle_stream( # send a ResponseOutputItemDone for the assistant message yield ResponseOutputItemDoneEvent( item=assistant_msg, - output_index=0, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 type="response.output_item.done", + sequence_number=sequence_number.get_and_increment(), ) for function_call in state.function_calls.values(): @@ -289,4 +424,5 @@ async def handle_stream( 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 3a79e5640..5a185806c 100644 --- a/src/agents/models/interface.py +++ b/src/agents/models/interface.py @@ -5,6 +5,8 @@ from collections.abc import AsyncIterator from typing import TYPE_CHECKING +from openai.types.responses.response_prompt_param import ResponsePromptParam + from ..agent_output import AgentOutputSchemaBase from ..handoffs import Handoff from ..items import ModelResponse, TResponseInputItem, TResponseStreamEvent @@ -46,6 +48,7 @@ async def get_response( tracing: ModelTracing, *, previous_response_id: str | None, + prompt: ResponsePromptParam | None, ) -> ModelResponse: """Get a response from the model. @@ -59,6 +62,7 @@ async def get_response( tracing: Tracing configuration. previous_response_id: the ID of the previous response. Generally not used by the model, except for the OpenAI Responses API. + prompt: The prompt config to use for the model. Returns: The full model response. @@ -77,6 +81,7 @@ def stream_response( tracing: ModelTracing, *, previous_response_id: str | None, + prompt: ResponsePromptParam | None, ) -> AsyncIterator[TResponseStreamEvent]: """Stream a response from the model. @@ -90,6 +95,7 @@ def stream_response( tracing: Tracing configuration. previous_response_id: the ID of the previous response. Generally not used by the model, except for the OpenAI Responses API. + prompt: The prompt config to use for the model. Returns: An iterator of response stream events, in OpenAI Responses format. diff --git a/src/agents/models/openai_chatcompletions.py b/src/agents/models/openai_chatcompletions.py index 89619f838..6de431b4d 100644 --- a/src/agents/models/openai_chatcompletions.py +++ b/src/agents/models/openai_chatcompletions.py @@ -7,8 +7,11 @@ from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream from openai.types import ChatModel -from openai.types.chat import ChatCompletion, ChatCompletionChunk +from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage +from openai.types.chat.chat_completion import Choice from openai.types.responses import Response +from openai.types.responses.response_prompt_param import ResponsePromptParam +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails from .. import _debug from ..agent_output import AgentOutputSchemaBase @@ -52,6 +55,7 @@ async def get_response( handoffs: list[Handoff], tracing: ModelTracing, previous_response_id: str | None, + prompt: ResponsePromptParam | None = None, ) -> ModelResponse: with generation_span( model=str(self.model), @@ -68,14 +72,26 @@ async def get_response( span_generation, tracing, stream=False, + prompt=prompt, ) + message: ChatCompletionMessage | None = None + first_choice: Choice | None = None + if response.choices and len(response.choices) > 0: + first_choice = response.choices[0] + message = first_choice.message + 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 message is not None: + logger.debug( + "LLM resp:\n%s\n", + json.dumps(message.model_dump(), indent=2, ensure_ascii=False), + ) + else: + finish_reason = first_choice.finish_reason if first_choice else "-" + logger.debug(f"LLM resp had no message. finish_reason: {finish_reason}") usage = ( Usage( @@ -83,18 +99,32 @@ 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() ) if tracing.include_data(): - span_generation.span_data.output = [response.choices[0].message.model_dump()] + span_generation.span_data.output = ( + [message.model_dump()] if message is not None else [] + ) 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) + items = Converter.message_to_output_items(message) if message is not None else [] return ModelResponse( output=items, @@ -111,8 +141,8 @@ async def stream_response( output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, - *, previous_response_id: str | None, + prompt: ResponsePromptParam | None = None, ) -> AsyncIterator[TResponseStreamEvent]: """ Yields a partial message as it is generated, as well as the usage information. @@ -132,6 +162,7 @@ async def stream_response( span_generation, tracing, stream=True, + prompt=prompt, ) final_response: Response | None = None @@ -162,6 +193,7 @@ async def _fetch_response( span: Span[GenerationSpanData], tracing: ModelTracing, stream: Literal[True], + prompt: ResponsePromptParam | None = None, ) -> tuple[Response, AsyncStream[ChatCompletionChunk]]: ... @overload @@ -176,6 +208,7 @@ async def _fetch_response( span: Span[GenerationSpanData], tracing: ModelTracing, stream: Literal[False], + prompt: ResponsePromptParam | None = None, ) -> ChatCompletion: ... async def _fetch_response( @@ -189,6 +222,7 @@ async def _fetch_response( span: Span[GenerationSpanData], tracing: ModelTracing, stream: bool = False, + prompt: ResponsePromptParam | None = None, ) -> ChatCompletion | tuple[Response, AsyncStream[ChatCompletionChunk]]: converted_messages = Converter.items_to_messages(input) @@ -222,8 +256,8 @@ async def _fetch_response( 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"{json.dumps(converted_messages, indent=2, ensure_ascii=False)}\n" + f"Tools:\n{json.dumps(converted_tools, indent=2, ensure_ascii=False)}\n" f"Stream: {stream}\n" f"Tool choice: {tool_choice}\n" f"Response format: {response_format}\n" @@ -252,10 +286,11 @@ async def _fetch_response( 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_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), + **(model_settings.extra_args or {}), ) if isinstance(ret, ChatCompletion): diff --git a/src/agents/models/openai_responses.py b/src/agents/models/openai_responses.py index c1ff85b98..d25613aee 100644 --- a/src/agents/models/openai_responses.py +++ b/src/agents/models/openai_responses.py @@ -10,12 +10,14 @@ from openai.types.responses import ( Response, ResponseCompletedEvent, + ResponseIncludable, ResponseStreamEvent, ResponseTextConfigParam, ToolParam, WebSearchToolParam, response_create_params, ) +from openai.types.responses.response_prompt_param import ResponsePromptParam from .. import _debug from ..agent_output import AgentOutputSchemaBase @@ -23,7 +25,18 @@ from ..handoffs import Handoff from ..items import ItemHelpers, ModelResponse, TResponseInputItem from ..logger import logger -from ..tool import ComputerTool, FileSearchTool, FunctionTool, Tool, WebSearchTool +from ..model_settings import MCPToolChoice +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 +49,6 @@ _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): """ @@ -70,6 +76,7 @@ async def get_response( handoffs: list[Handoff], tracing: ModelTracing, previous_response_id: str | None, + prompt: ResponsePromptParam | None = None, ) -> ModelResponse: with response_span(disabled=tracing.is_disabled()) as span_response: try: @@ -82,6 +89,7 @@ async def get_response( handoffs, previous_response_id, stream=False, + prompt=prompt, ) if _debug.DONT_LOG_MODEL_DATA: @@ -89,7 +97,13 @@ async def get_response( else: logger.debug( "LLM resp:\n" - f"{json.dumps([x.model_dump() for x in response.output], indent=2)}\n" + f"""{ + json.dumps( + [x.model_dump() for x in response.output], + indent=2, + ensure_ascii=False, + ) + }\n""" ) usage = ( @@ -98,6 +112,8 @@ 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() @@ -135,6 +151,7 @@ async def stream_response( handoffs: list[Handoff], tracing: ModelTracing, previous_response_id: str | None, + prompt: ResponsePromptParam | None = None, ) -> AsyncIterator[ResponseStreamEvent]: """ Yields a partial message as it is generated, as well as the usage information. @@ -150,6 +167,7 @@ async def stream_response( handoffs, previous_response_id, stream=True, + prompt=prompt, ) final_response: Response | None = None @@ -186,6 +204,7 @@ async def _fetch_response( handoffs: list[Handoff], previous_response_id: str | None, stream: Literal[True], + prompt: ResponsePromptParam | None = None, ) -> AsyncStream[ResponseStreamEvent]: ... @overload @@ -199,6 +218,7 @@ async def _fetch_response( handoffs: list[Handoff], previous_response_id: str | None, stream: Literal[False], + prompt: ResponsePromptParam | None = None, ) -> Response: ... async def _fetch_response( @@ -211,6 +231,7 @@ async def _fetch_response( handoffs: list[Handoff], previous_response_id: str | None, stream: Literal[True] | Literal[False] = False, + prompt: ResponsePromptParam | None = None, ) -> Response | AsyncStream[ResponseStreamEvent]: list_input = ItemHelpers.input_to_new_input_list(input) @@ -226,13 +247,17 @@ async def _fetch_response( converted_tools = Converter.convert_tools(tools, handoffs) response_format = Converter.get_response_format(output_schema) + include: list[ResponseIncludable] = converted_tools.includes + if model_settings.response_include is not None: + include = list({*include, *model_settings.response_include}) + 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"{json.dumps(list_input, indent=2, ensure_ascii=False)}\n" + f"Tools:\n{json.dumps(converted_tools.tools, indent=2, ensure_ascii=False)}\n" f"Stream: {stream}\n" f"Tool choice: {tool_choice}\n" f"Response format: {response_format}\n" @@ -244,8 +269,9 @@ async def _fetch_response( instructions=self._non_null_or_not_given(system_instructions), model=self.model, input=list_input, - include=converted_tools.includes, + include=include, tools=converted_tools.tools, + prompt=self._non_null_or_not_given(prompt), 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), @@ -260,6 +286,7 @@ async def _fetch_response( 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), + **(model_settings.extra_args or {}), ) def _get_client(self) -> AsyncOpenAI: @@ -271,16 +298,22 @@ def _get_client(self) -> AsyncOpenAI: @dataclass class ConvertedTools: tools: list[ToolParam] - includes: list[IncludeLiteral] + includes: list[ResponseIncludable] class Converter: @classmethod def convert_tool_choice( - cls, tool_choice: Literal["auto", "required", "none"] | str | None + cls, tool_choice: Literal["auto", "required", "none"] | str | MCPToolChoice | None ) -> response_create_params.ToolChoice | NotGiven: if tool_choice is None: return NOT_GIVEN + elif isinstance(tool_choice, MCPToolChoice): + return { + "server_label": tool_choice.server_label, + "type": "mcp", + "name": tool_choice.name, + } elif tool_choice == "required": return "required" elif tool_choice == "auto": @@ -299,6 +332,18 @@ 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": + # Note that this is still here for backwards compatibility, + # but migrating to MCPToolChoice is recommended. + return { "type": "mcp" } # type: ignore [typeddict-item] else: return { "type": "function", @@ -328,7 +373,7 @@ 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: @@ -346,7 +391,7 @@ 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): @@ -357,7 +402,7 @@ 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", @@ -387,7 +432,20 @@ def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, IncludeLiteral | None]: "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/src/agents/prompts.py b/src/agents/prompts.py new file mode 100644 index 000000000..aa627d033 --- /dev/null +++ b/src/agents/prompts.py @@ -0,0 +1,76 @@ +from __future__ import annotations + +import inspect +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Callable + +from openai.types.responses.response_prompt_param import ( + ResponsePromptParam, + Variables as ResponsesPromptVariables, +) +from typing_extensions import NotRequired, TypedDict + +from agents.util._types import MaybeAwaitable + +from .exceptions import UserError +from .run_context import RunContextWrapper + +if TYPE_CHECKING: + from .agent import Agent + + +class Prompt(TypedDict): + """Prompt configuration to use for interacting with an OpenAI model.""" + + id: str + """The unique ID of the prompt.""" + + version: NotRequired[str] + """Optional version of the prompt.""" + + variables: NotRequired[dict[str, ResponsesPromptVariables]] + """Optional variables to substitute into the prompt.""" + + +@dataclass +class GenerateDynamicPromptData: + """Inputs to a function that allows you to dynamically generate a prompt.""" + + context: RunContextWrapper[Any] + """The run context.""" + + agent: Agent[Any] + """The agent for which the prompt is being generated.""" + + +DynamicPromptFunction = Callable[[GenerateDynamicPromptData], MaybeAwaitable[Prompt]] +"""A function that dynamically generates a prompt.""" + + +class PromptUtil: + @staticmethod + async def to_model_input( + prompt: Prompt | DynamicPromptFunction | None, + context: RunContextWrapper[Any], + agent: Agent[Any], + ) -> ResponsePromptParam | None: + if prompt is None: + return None + + resolved_prompt: Prompt + if isinstance(prompt, dict): + resolved_prompt = prompt + else: + func_result = prompt(GenerateDynamicPromptData(context=context, agent=agent)) + if inspect.isawaitable(func_result): + resolved_prompt = await func_result + else: + resolved_prompt = func_result + if not isinstance(resolved_prompt, dict): + raise UserError("Dynamic prompt function must return a Prompt") + + return { + "id": resolved_prompt["id"], + "version": resolved_prompt.get("version"), + "variables": resolved_prompt.get("variables"), + } diff --git a/src/agents/realtime/README.md b/src/agents/realtime/README.md new file mode 100644 index 000000000..9acc23160 --- /dev/null +++ b/src/agents/realtime/README.md @@ -0,0 +1,3 @@ +# Realtime + +Realtime agents are in beta: expect some breaking changes over the next few weeks as we find issues and fix them. diff --git a/src/agents/realtime/__init__.py b/src/agents/realtime/__init__.py new file mode 100644 index 000000000..52b59edc3 --- /dev/null +++ b/src/agents/realtime/__init__.py @@ -0,0 +1,69 @@ +from .agent import RealtimeAgent, RealtimeAgentHooks, RealtimeRunHooks +from .config import ( + RealtimeAudioFormat, + RealtimeClientMessage, + RealtimeInputAudioTranscriptionConfig, + RealtimeModelName, + RealtimeRunConfig, + RealtimeSessionModelSettings, + RealtimeTurnDetectionConfig, + RealtimeUserInput, + RealtimeUserInputMessage, + RealtimeUserInputText, +) +from .events import ( + RealtimeAgentEndEvent, + RealtimeAgentStartEvent, + RealtimeAudio, + RealtimeAudioEnd, + RealtimeAudioInterrupted, + RealtimeError, + RealtimeHandoffEvent, + RealtimeHistoryAdded, + RealtimeHistoryUpdated, + RealtimeRawModelEvent, + RealtimeSessionEvent, + RealtimeToolEnd, + RealtimeToolStart, +) +from .model import ( + RealtimeModel, + RealtimeModelConfig, + RealtimeModelListener, +) +from .runner import RealtimeRunner +from .session import RealtimeSession + +__all__ = [ + "RealtimeAgent", + "RealtimeAgentHooks", + "RealtimeRunHooks", + "RealtimeRunner", + "RealtimeRunConfig", + "RealtimeSessionModelSettings", + "RealtimeInputAudioTranscriptionConfig", + "RealtimeTurnDetectionConfig", + "RealtimeAudioFormat", + "RealtimeClientMessage", + "RealtimeUserInput", + "RealtimeUserInputMessage", + "RealtimeUserInputText", + "RealtimeModelName", + "RealtimeModel", + "RealtimeModelListener", + "RealtimeModelConfig", + "RealtimeSessionEvent", + "RealtimeAgentStartEvent", + "RealtimeAgentEndEvent", + "RealtimeHandoffEvent", + "RealtimeToolStart", + "RealtimeToolEnd", + "RealtimeRawModelEvent", + "RealtimeAudioEnd", + "RealtimeAudio", + "RealtimeAudioInterrupted", + "RealtimeError", + "RealtimeHistoryUpdated", + "RealtimeHistoryAdded", + "RealtimeSession", +] diff --git a/src/agents/realtime/agent.py b/src/agents/realtime/agent.py new file mode 100644 index 000000000..9bbed8cb4 --- /dev/null +++ b/src/agents/realtime/agent.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +import dataclasses +import inspect +from collections.abc import Awaitable +from dataclasses import dataclass +from typing import Any, Callable, Generic, cast + +from ..agent import AgentBase +from ..lifecycle import AgentHooksBase, RunHooksBase +from ..logger import logger +from ..run_context import RunContextWrapper, TContext +from ..util._types import MaybeAwaitable + +RealtimeAgentHooks = AgentHooksBase[TContext, "RealtimeAgent[TContext]"] +"""Agent hooks for `RealtimeAgent`s.""" + +RealtimeRunHooks = RunHooksBase[TContext, "RealtimeAgent[TContext]"] +"""Run hooks for `RealtimeAgent`s.""" + + +@dataclass +class RealtimeAgent(AgentBase, Generic[TContext]): + """A specialized agent instance that is meant to be used within a `RealtimeSession` to build + voice agents. Due to the nature of this agent, some configuration options are not supported + that are supported by regular `Agent` instances. For example: + - `model` choice is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `modelSettings` is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `outputType` is not supported, as RealtimeAgents do not support structured outputs. + - `toolUseBehavior` is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `voice` can be configured on an `Agent` level; however, it cannot be changed after the first + agent within a `RealtimeSession` has spoken. + + See `AgentBase` for base parameters that are shared with `Agent`s. + """ + + instructions: ( + str + | Callable[ + [RunContextWrapper[TContext], RealtimeAgent[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. + """ + + hooks: RealtimeAgentHooks | None = None + """A class that receives callbacks on various lifecycle events for this agent. + """ + + def clone(self, **kwargs: Any) -> RealtimeAgent[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) + + 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/src/agents/realtime/config.py b/src/agents/realtime/config.py new file mode 100644 index 000000000..ece72e755 --- /dev/null +++ b/src/agents/realtime/config.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +from typing import ( + Any, + Literal, + Union, +) + +from typing_extensions import NotRequired, TypeAlias, TypedDict + +from ..guardrail import OutputGuardrail +from ..model_settings import ToolChoice +from ..tool import Tool + +RealtimeModelName: TypeAlias = Union[ + Literal[ + "gpt-4o-realtime-preview", + "gpt-4o-mini-realtime-preview", + "gpt-4o-realtime-preview-2025-06-03", + "gpt-4o-realtime-preview-2024-12-17", + "gpt-4o-realtime-preview-2024-10-01", + "gpt-4o-mini-realtime-preview-2024-12-17", + ], + str, +] +"""The name of a realtime model.""" + + +class RealtimeClientMessage(TypedDict): + type: str # explicitly required + other_data: NotRequired[dict[str, Any]] + + +class RealtimeUserInputText(TypedDict): + type: Literal["input_text"] + text: str + + +class RealtimeUserInputMessage(TypedDict): + type: Literal["message"] + role: Literal["user"] + content: list[RealtimeUserInputText] + + +RealtimeUserInput: TypeAlias = Union[str, RealtimeUserInputMessage] + + +RealtimeAudioFormat: TypeAlias = Union[Literal["pcm16", "g711_ulaw", "g711_alaw"], str] + + +class RealtimeInputAudioTranscriptionConfig(TypedDict): + language: NotRequired[str] + model: NotRequired[Literal["gpt-4o-transcribe", "gpt-4o-mini-transcribe", "whisper-1"] | str] + prompt: NotRequired[str] + + +class RealtimeTurnDetectionConfig(TypedDict): + """Turn detection config. Allows extra vendor keys if needed.""" + + type: NotRequired[Literal["semantic_vad", "server_vad"]] + create_response: NotRequired[bool] + eagerness: NotRequired[Literal["auto", "low", "medium", "high"]] + interrupt_response: NotRequired[bool] + prefix_padding_ms: NotRequired[int] + silence_duration_ms: NotRequired[int] + threshold: NotRequired[float] + + +class RealtimeSessionModelSettings(TypedDict): + """Model settings for a realtime model session.""" + + model_name: NotRequired[RealtimeModelName] + + instructions: NotRequired[str] + modalities: NotRequired[list[Literal["text", "audio"]]] + voice: NotRequired[str] + + input_audio_format: NotRequired[RealtimeAudioFormat] + output_audio_format: NotRequired[RealtimeAudioFormat] + input_audio_transcription: NotRequired[RealtimeInputAudioTranscriptionConfig] + turn_detection: NotRequired[RealtimeTurnDetectionConfig] + + tool_choice: NotRequired[ToolChoice] + tools: NotRequired[list[Tool]] + + tracing: NotRequired[RealtimeModelTracingConfig | None] + + +class RealtimeGuardrailsSettings(TypedDict): + """Settings for output guardrails in realtime sessions.""" + + debounce_text_length: NotRequired[int] + """ + The minimum number of characters to accumulate before running guardrails on transcript + deltas. Defaults to 100. Guardrails run every time the accumulated text reaches + 1x, 2x, 3x, etc. times this threshold. + """ + + +class RealtimeModelTracingConfig(TypedDict): + """Configuration for tracing in realtime model sessions.""" + + workflow_name: NotRequired[str] + """The workflow name to use for tracing.""" + + group_id: NotRequired[str] + """A group identifier to use for tracing, to link multiple traces together.""" + + metadata: NotRequired[dict[str, Any]] + """Additional metadata to include with the trace.""" + + +class RealtimeRunConfig(TypedDict): + model_settings: NotRequired[RealtimeSessionModelSettings] + + output_guardrails: NotRequired[list[OutputGuardrail[Any]]] + """List of output guardrails to run on the agent's responses.""" + + guardrails_settings: NotRequired[RealtimeGuardrailsSettings] + """Settings for guardrail execution.""" + + tracing_disabled: NotRequired[bool] + """Whether tracing is disabled for this run.""" + + # TODO (rm) Add history audio storage config diff --git a/src/agents/realtime/events.py b/src/agents/realtime/events.py new file mode 100644 index 000000000..e1b3cfea3 --- /dev/null +++ b/src/agents/realtime/events.py @@ -0,0 +1,215 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Literal, Union + +from typing_extensions import TypeAlias + +from ..guardrail import OutputGuardrailResult +from ..run_context import RunContextWrapper +from ..tool import Tool +from .agent import RealtimeAgent +from .items import RealtimeItem +from .model_events import RealtimeModelAudioEvent, RealtimeModelEvent + + +@dataclass +class RealtimeEventInfo: + context: RunContextWrapper + """The context for the event.""" + + +@dataclass +class RealtimeAgentStartEvent: + """A new agent has started.""" + + agent: RealtimeAgent + """The new agent.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["agent_start"] = "agent_start" + + +@dataclass +class RealtimeAgentEndEvent: + """An agent has ended.""" + + agent: RealtimeAgent + """The agent that ended.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["agent_end"] = "agent_end" + + +@dataclass +class RealtimeHandoffEvent: + """An agent has handed off to another agent.""" + + from_agent: RealtimeAgent + """The agent that handed off.""" + + to_agent: RealtimeAgent + """The agent that was handed off to.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["handoff"] = "handoff" + + +@dataclass +class RealtimeToolStart: + """An agent is starting a tool call.""" + + agent: RealtimeAgent + """The agent that updated.""" + + tool: Tool + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["tool_start"] = "tool_start" + + +@dataclass +class RealtimeToolEnd: + """An agent has ended a tool call.""" + + agent: RealtimeAgent + """The agent that ended the tool call.""" + + tool: Tool + """The tool that was called.""" + + output: Any + """The output of the tool call.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["tool_end"] = "tool_end" + + +@dataclass +class RealtimeRawModelEvent: + """Forwards raw events from the model layer.""" + + data: RealtimeModelEvent + """The raw data from the model layer.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["raw_model_event"] = "raw_model_event" + + +@dataclass +class RealtimeAudioEnd: + """Triggered when the agent stops generating audio.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["audio_end"] = "audio_end" + + +@dataclass +class RealtimeAudio: + """Triggered when the agent generates new audio to be played.""" + + audio: RealtimeModelAudioEvent + """The audio event from the model layer.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["audio"] = "audio" + + +@dataclass +class RealtimeAudioInterrupted: + """Triggered when the agent is interrupted. Can be listened to by the user to stop audio + playback or give visual indicators to the user. + """ + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["audio_interrupted"] = "audio_interrupted" + + +@dataclass +class RealtimeError: + """An error has occurred.""" + + error: Any + """The error that occurred.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["error"] = "error" + + +@dataclass +class RealtimeHistoryUpdated: + """The history has been updated. Contains the full history of the session.""" + + history: list[RealtimeItem] + """The full history of the session.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["history_updated"] = "history_updated" + + +@dataclass +class RealtimeHistoryAdded: + """A new item has been added to the history.""" + + item: RealtimeItem + """The new item that was added to the history.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["history_added"] = "history_added" + + +@dataclass +class RealtimeGuardrailTripped: + """A guardrail has been tripped and the agent has been interrupted.""" + + guardrail_results: list[OutputGuardrailResult] + """The results from all triggered guardrails.""" + + message: str + """The message that was being generated when the guardrail was triggered.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["guardrail_tripped"] = "guardrail_tripped" + +RealtimeSessionEvent: TypeAlias = Union[ + RealtimeAgentStartEvent, + RealtimeAgentEndEvent, + RealtimeHandoffEvent, + RealtimeToolStart, + RealtimeToolEnd, + RealtimeRawModelEvent, + RealtimeAudioEnd, + RealtimeAudio, + RealtimeAudioInterrupted, + RealtimeError, + RealtimeHistoryUpdated, + RealtimeHistoryAdded, + RealtimeGuardrailTripped, +] +"""An event emitted by the realtime session.""" diff --git a/src/agents/realtime/items.py b/src/agents/realtime/items.py new file mode 100644 index 000000000..a027b2a39 --- /dev/null +++ b/src/agents/realtime/items.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +from typing import Annotated, Literal, Union + +from pydantic import BaseModel, ConfigDict, Field + + +class InputText(BaseModel): + type: Literal["input_text"] = "input_text" + text: str + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class InputAudio(BaseModel): + type: Literal["input_audio"] = "input_audio" + audio: str | None = None + transcript: str | None = None + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class AssistantText(BaseModel): + type: Literal["text"] = "text" + text: str + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class AssistantAudio(BaseModel): + type: Literal["audio"] = "audio" + audio: str | None = None + transcript: str | None = None + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class SystemMessageItem(BaseModel): + item_id: str + previous_item_id: str | None = None + type: Literal["message"] = "message" + role: Literal["system"] = "system" + content: list[InputText] + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class UserMessageItem(BaseModel): + item_id: str + previous_item_id: str | None = None + type: Literal["message"] = "message" + role: Literal["user"] = "user" + content: list[InputText | InputAudio] + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class AssistantMessageItem(BaseModel): + item_id: str + previous_item_id: str | None = None + type: Literal["message"] = "message" + role: Literal["assistant"] = "assistant" + status: Literal["in_progress", "completed", "incomplete"] | None = None + content: list[AssistantText | AssistantAudio] + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +RealtimeMessageItem = Annotated[ + Union[SystemMessageItem, UserMessageItem, AssistantMessageItem], + Field(discriminator="role"), +] + + +class RealtimeToolCallItem(BaseModel): + item_id: str + previous_item_id: str | None = None + type: Literal["function_call"] = "function_call" + status: Literal["in_progress", "completed"] + arguments: str + name: str + output: str | None = None + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +RealtimeItem = Union[RealtimeMessageItem, RealtimeToolCallItem] + + +class RealtimeResponse(BaseModel): + id: str + output: list[RealtimeMessageItem] diff --git a/src/agents/realtime/model.py b/src/agents/realtime/model.py new file mode 100644 index 000000000..abb3a1eac --- /dev/null +++ b/src/agents/realtime/model.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import abc +from typing import Any, Callable + +from typing_extensions import NotRequired, TypedDict + +from ..util._types import MaybeAwaitable +from .config import ( + RealtimeClientMessage, + RealtimeSessionModelSettings, + RealtimeUserInput, +) +from .model_events import RealtimeModelEvent, RealtimeModelToolCallEvent + + +class RealtimeModelListener(abc.ABC): + """A listener for realtime transport events.""" + + @abc.abstractmethod + async def on_event(self, event: RealtimeModelEvent) -> None: + """Called when an event is emitted by the realtime transport.""" + pass + + +class RealtimeModelConfig(TypedDict): + """Options for connecting to a realtime model.""" + + api_key: NotRequired[str | Callable[[], MaybeAwaitable[str]]] + """The API key (or function that returns a key) to use when connecting. If unset, the model will + try to use a sane default. For example, the OpenAI Realtime model will try to use the + `OPENAI_API_KEY` environment variable. + """ + + url: NotRequired[str] + """The URL to use when connecting. If unset, the model will use a sane default. For example, + the OpenAI Realtime model will use the default OpenAI WebSocket URL. + """ + + initial_model_settings: NotRequired[RealtimeSessionModelSettings] + """The initial model settings to use when connecting.""" + + +class RealtimeModel(abc.ABC): + """Interface for connecting to a realtime model and sending/receiving events.""" + + @abc.abstractmethod + async def connect(self, options: RealtimeModelConfig) -> None: + """Establish a connection to the model and keep it alive.""" + pass + + @abc.abstractmethod + def add_listener(self, listener: RealtimeModelListener) -> None: + """Add a listener to the model.""" + pass + + @abc.abstractmethod + def remove_listener(self, listener: RealtimeModelListener) -> None: + """Remove a listener from the model.""" + pass + + @abc.abstractmethod + async def send_event(self, event: RealtimeClientMessage) -> None: + """Send an event to the model.""" + pass + + @abc.abstractmethod + async def send_message( + self, message: RealtimeUserInput, other_event_data: dict[str, Any] | None = None + ) -> None: + """Send a message to the model.""" + pass + + @abc.abstractmethod + async def send_audio(self, audio: bytes, *, commit: bool = False) -> None: + """Send a raw audio chunk to the model. + + Args: + audio: The audio data to send. + commit: Whether to commit the audio buffer to the model. If the model does not do turn + detection, this can be used to indicate the turn is completed. + """ + pass + + @abc.abstractmethod + async def send_tool_output( + self, tool_call: RealtimeModelToolCallEvent, output: str, start_response: bool + ) -> None: + """Send tool output to the model.""" + pass + + @abc.abstractmethod + async def interrupt(self) -> None: + """Interrupt the model. For example, could be triggered by a guardrail.""" + pass + + @abc.abstractmethod + async def close(self) -> None: + """Close the session.""" + pass diff --git a/src/agents/realtime/model_events.py b/src/agents/realtime/model_events.py new file mode 100644 index 000000000..a9769cebd --- /dev/null +++ b/src/agents/realtime/model_events.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Literal, Union + +from typing_extensions import TypeAlias + +from .items import RealtimeItem + +RealtimeConnectionStatus: TypeAlias = Literal["connecting", "connected", "disconnected"] + + +@dataclass +class RealtimeModelErrorEvent: + """Represents a transport‑layer error.""" + + error: Any + + type: Literal["error"] = "error" + + +@dataclass +class RealtimeModelToolCallEvent: + """Model attempted a tool/function call.""" + + name: str + call_id: str + arguments: str + + id: str | None = None + previous_item_id: str | None = None + + type: Literal["function_call"] = "function_call" + + +@dataclass +class RealtimeModelAudioEvent: + """Raw audio bytes emitted by the model.""" + + data: bytes + response_id: str + + type: Literal["audio"] = "audio" + + +@dataclass +class RealtimeModelAudioInterruptedEvent: + """Audio interrupted.""" + + type: Literal["audio_interrupted"] = "audio_interrupted" + + +@dataclass +class RealtimeModelAudioDoneEvent: + """Audio done.""" + + type: Literal["audio_done"] = "audio_done" + + +@dataclass +class RealtimeModelInputAudioTranscriptionCompletedEvent: + """Input audio transcription completed.""" + + item_id: str + transcript: str + + type: Literal["conversation.item.input_audio_transcription.completed"] = ( + "conversation.item.input_audio_transcription.completed" + ) + + +@dataclass +class RealtimeModelTranscriptDeltaEvent: + """Partial transcript update.""" + + item_id: str + delta: str + response_id: str + + type: Literal["transcript_delta"] = "transcript_delta" + + +@dataclass +class RealtimeModelItemUpdatedEvent: + """Item added to the history or updated.""" + + item: RealtimeItem + + type: Literal["item_updated"] = "item_updated" + + +@dataclass +class RealtimeModelItemDeletedEvent: + """Item deleted from the history.""" + + item_id: str + + type: Literal["item_deleted"] = "item_deleted" + + +@dataclass +class RealtimeModelConnectionStatusEvent: + """Connection status changed.""" + + status: RealtimeConnectionStatus + + type: Literal["connection_status"] = "connection_status" + + +@dataclass +class RealtimeModelTurnStartedEvent: + """Triggered when the model starts generating a response for a turn.""" + + type: Literal["turn_started"] = "turn_started" + + +@dataclass +class RealtimeModelTurnEndedEvent: + """Triggered when the model finishes generating a response for a turn.""" + + type: Literal["turn_ended"] = "turn_ended" + + +@dataclass +class RealtimeModelOtherEvent: + """Used as a catchall for vendor-specific events.""" + + data: Any + + type: Literal["other"] = "other" + + +# TODO (rm) Add usage events + + +RealtimeModelEvent: TypeAlias = Union[ + RealtimeModelErrorEvent, + RealtimeModelToolCallEvent, + RealtimeModelAudioEvent, + RealtimeModelAudioInterruptedEvent, + RealtimeModelAudioDoneEvent, + RealtimeModelInputAudioTranscriptionCompletedEvent, + RealtimeModelTranscriptDeltaEvent, + RealtimeModelItemUpdatedEvent, + RealtimeModelItemDeletedEvent, + RealtimeModelConnectionStatusEvent, + RealtimeModelTurnStartedEvent, + RealtimeModelTurnEndedEvent, + RealtimeModelOtherEvent, +] diff --git a/src/agents/realtime/openai_realtime.py b/src/agents/realtime/openai_realtime.py new file mode 100644 index 000000000..797753242 --- /dev/null +++ b/src/agents/realtime/openai_realtime.py @@ -0,0 +1,410 @@ +from __future__ import annotations + +import asyncio +import base64 +import inspect +import json +import os +from datetime import datetime +from typing import Any, Callable, Literal + +import websockets +from openai.types.beta.realtime.conversation_item import ConversationItem +from openai.types.beta.realtime.realtime_server_event import ( + RealtimeServerEvent as OpenAIRealtimeServerEvent, +) +from openai.types.beta.realtime.response_audio_delta_event import ResponseAudioDeltaEvent +from pydantic import TypeAdapter +from websockets.asyncio.client import ClientConnection + +from agents.util._types import MaybeAwaitable + +from ..exceptions import UserError +from ..logger import logger +from .config import ( + RealtimeClientMessage, + RealtimeModelTracingConfig, + RealtimeSessionModelSettings, + RealtimeUserInput, +) +from .items import RealtimeMessageItem, RealtimeToolCallItem +from .model import ( + RealtimeModel, + RealtimeModelConfig, + RealtimeModelListener, +) +from .model_events import ( + RealtimeModelAudioDoneEvent, + RealtimeModelAudioEvent, + RealtimeModelAudioInterruptedEvent, + RealtimeModelErrorEvent, + RealtimeModelEvent, + RealtimeModelInputAudioTranscriptionCompletedEvent, + RealtimeModelItemDeletedEvent, + RealtimeModelItemUpdatedEvent, + RealtimeModelToolCallEvent, + RealtimeModelTranscriptDeltaEvent, + RealtimeModelTurnEndedEvent, + RealtimeModelTurnStartedEvent, +) + + +async def get_api_key(key: str | Callable[[], MaybeAwaitable[str]] | None) -> str | None: + if isinstance(key, str): + return key + elif callable(key): + result = key() + if inspect.isawaitable(result): + return await result + return result + + return os.getenv("OPENAI_API_KEY") + + +class OpenAIRealtimeWebSocketModel(RealtimeModel): + """A model that uses OpenAI's WebSocket API.""" + + def __init__(self) -> None: + self.model = "gpt-4o-realtime-preview" # Default model + self._websocket: ClientConnection | None = None + self._websocket_task: asyncio.Task[None] | None = None + self._listeners: list[RealtimeModelListener] = [] + self._current_item_id: str | None = None + self._audio_start_time: datetime | None = None + self._audio_length_ms: float = 0.0 + self._ongoing_response: bool = False + self._current_audio_content_index: int | None = None + self._tracing_config: RealtimeModelTracingConfig | Literal["auto"] | None = None + + async def connect(self, options: RealtimeModelConfig) -> None: + """Establish a connection to the model and keep it alive.""" + assert self._websocket is None, "Already connected" + assert self._websocket_task is None, "Already connected" + + model_settings: RealtimeSessionModelSettings = options.get("initial_model_settings", {}) + + self.model = model_settings.get("model_name", self.model) + api_key = await get_api_key(options.get("api_key")) + + if "tracing" in model_settings: + self._tracing_config = model_settings["tracing"] + else: + self._tracing_config = "auto" + + if not api_key: + raise UserError("API key is required but was not provided.") + + url = options.get("url", f"wss://api.openai.com/v1/realtime?model={self.model}") + + headers = { + "Authorization": f"Bearer {api_key}", + "OpenAI-Beta": "realtime=v1", + } + self._websocket = await websockets.connect(url, additional_headers=headers) + self._websocket_task = asyncio.create_task(self._listen_for_messages()) + + async def _send_tracing_config( + self, tracing_config: RealtimeModelTracingConfig | Literal["auto"] | None + ) -> None: + """Update tracing configuration via session.update event.""" + if tracing_config is not None: + await self.send_event( + {"type": "session.update", "other_data": {"session": {"tracing": tracing_config}}} + ) + + def add_listener(self, listener: RealtimeModelListener) -> None: + """Add a listener to the model.""" + self._listeners.append(listener) + + def remove_listener(self, listener: RealtimeModelListener) -> None: + """Remove a listener from the model.""" + self._listeners.remove(listener) + + async def _emit_event(self, event: RealtimeModelEvent) -> None: + """Emit an event to the listeners.""" + for listener in self._listeners: + await listener.on_event(event) + + async def _listen_for_messages(self): + assert self._websocket is not None, "Not connected" + + try: + async for message in self._websocket: + parsed = json.loads(message) + await self._handle_ws_event(parsed) + + except websockets.exceptions.ConnectionClosed: + # TODO connection closed handling (event, cleanup) + logger.warning("WebSocket connection closed") + except Exception as e: + logger.error(f"WebSocket error: {e}") + + async def send_event(self, event: RealtimeClientMessage) -> None: + """Send an event to the model.""" + assert self._websocket is not None, "Not connected" + converted_event = { + "type": event["type"], + } + + converted_event.update(event.get("other_data", {})) + + await self._websocket.send(json.dumps(converted_event)) + + async def send_message( + self, message: RealtimeUserInput, other_event_data: dict[str, Any] | None = None + ) -> None: + """Send a message to the model.""" + message = ( + message + if isinstance(message, dict) + else { + "type": "message", + "role": "user", + "content": [{"type": "input_text", "text": message}], + } + ) + other_data = { + "item": message, + } + if other_event_data: + other_data.update(other_event_data) + + await self.send_event({"type": "conversation.item.create", "other_data": other_data}) + + await self.send_event({"type": "response.create"}) + + async def send_audio(self, audio: bytes, *, commit: bool = False) -> None: + """Send a raw audio chunk to the model. + + Args: + audio: The audio data to send. + commit: Whether to commit the audio buffer to the model. If the model does not do turn + detection, this can be used to indicate the turn is completed. + """ + assert self._websocket is not None, "Not connected" + base64_audio = base64.b64encode(audio).decode("utf-8") + await self.send_event( + { + "type": "input_audio_buffer.append", + "other_data": { + "audio": base64_audio, + }, + } + ) + if commit: + await self.send_event({"type": "input_audio_buffer.commit"}) + + async def send_tool_output( + self, tool_call: RealtimeModelToolCallEvent, output: str, start_response: bool + ) -> None: + """Send tool output to the model.""" + await self.send_event( + { + "type": "conversation.item.create", + "other_data": { + "item": { + "type": "function_call_output", + "output": output, + "call_id": tool_call.id, + }, + }, + } + ) + + tool_item = RealtimeToolCallItem( + item_id=tool_call.id or "", + previous_item_id=tool_call.previous_item_id, + type="function_call", + status="completed", + arguments=tool_call.arguments, + name=tool_call.name, + output=output, + ) + await self._emit_event(RealtimeModelItemUpdatedEvent(item=tool_item)) + + if start_response: + await self.send_event({"type": "response.create"}) + + async def interrupt(self) -> None: + """Interrupt the model.""" + if not self._current_item_id or not self._audio_start_time: + return + + await self._cancel_response() + + elapsed_time_ms = (datetime.now() - self._audio_start_time).total_seconds() * 1000 + if elapsed_time_ms > 0 and elapsed_time_ms < self._audio_length_ms: + await self._emit_event(RealtimeModelAudioInterruptedEvent()) + await self.send_event( + { + "type": "conversation.item.truncate", + "other_data": { + "item_id": self._current_item_id, + "content_index": self._current_audio_content_index, + "audio_end_ms": elapsed_time_ms, + }, + } + ) + + self._current_item_id = None + self._audio_start_time = None + self._audio_length_ms = 0.0 + self._current_audio_content_index = None + + async def _handle_audio_delta(self, parsed: ResponseAudioDeltaEvent) -> None: + """Handle audio delta events and update audio tracking state.""" + self._current_audio_content_index = parsed.content_index + self._current_item_id = parsed.item_id + if self._audio_start_time is None: + self._audio_start_time = datetime.now() + self._audio_length_ms = 0.0 + + audio_bytes = base64.b64decode(parsed.delta) + # Calculate audio length in ms using 24KHz pcm16le + self._audio_length_ms += self._calculate_audio_length_ms(audio_bytes) + await self._emit_event( + RealtimeModelAudioEvent(data=audio_bytes, response_id=parsed.response_id) + ) + + def _calculate_audio_length_ms(self, audio_bytes: bytes) -> float: + """Calculate audio length in milliseconds for 24KHz PCM16LE format.""" + return len(audio_bytes) / 24 / 2 + + async def _handle_output_item(self, item: ConversationItem) -> None: + """Handle response output item events (function calls and messages).""" + if item.type == "function_call" and item.status == "completed": + tool_call = RealtimeToolCallItem( + item_id=item.id or "", + previous_item_id=None, + type="function_call", + # We use the same item for tool call and output, so it will be completed by the + # output being added + status="in_progress", + arguments=item.arguments or "", + name=item.name or "", + output=None, + ) + await self._emit_event(RealtimeModelItemUpdatedEvent(item=tool_call)) + await self._emit_event( + RealtimeModelToolCallEvent( + call_id=item.id or "", + name=item.name or "", + arguments=item.arguments or "", + id=item.id or "", + ) + ) + elif item.type == "message": + # Handle message items from output_item events (no previous_item_id) + message_item: RealtimeMessageItem = TypeAdapter(RealtimeMessageItem).validate_python( + { + "item_id": item.id or "", + "type": item.type, + "role": item.role, + "content": item.content, + "status": "in_progress", + } + ) + await self._emit_event(RealtimeModelItemUpdatedEvent(item=message_item)) + + async def _handle_conversation_item( + self, item: ConversationItem, previous_item_id: str | None + ) -> None: + """Handle conversation item creation/retrieval events.""" + message_item: RealtimeMessageItem = TypeAdapter(RealtimeMessageItem).validate_python( + { + "item_id": item.id or "", + "previous_item_id": previous_item_id, + "type": item.type, + "role": item.role, + "content": item.content, + "status": "in_progress", + } + ) + await self._emit_event(RealtimeModelItemUpdatedEvent(item=message_item)) + + async def close(self) -> None: + """Close the session.""" + if self._websocket: + await self._websocket.close() + self._websocket = None + if self._websocket_task: + self._websocket_task.cancel() + self._websocket_task = None + + async def _cancel_response(self) -> None: + if self._ongoing_response: + await self.send_event({"type": "response.cancel"}) + self._ongoing_response = False + + async def _handle_ws_event(self, event: dict[str, Any]): + try: + parsed: OpenAIRealtimeServerEvent = TypeAdapter( + OpenAIRealtimeServerEvent + ).validate_python(event) + except Exception as e: + logger.error(f"Invalid event: {event} - {e}") + # await self._emit_event(RealtimeModelErrorEvent(error=f"Invalid event: {event} - {e}")) + return + + if parsed.type == "response.audio.delta": + await self._handle_audio_delta(parsed) + elif parsed.type == "response.audio.done": + await self._emit_event(RealtimeModelAudioDoneEvent()) + elif parsed.type == "input_audio_buffer.speech_started": + await self.interrupt() + elif parsed.type == "response.created": + self._ongoing_response = True + await self._emit_event(RealtimeModelTurnStartedEvent()) + elif parsed.type == "response.done": + self._ongoing_response = False + await self._emit_event(RealtimeModelTurnEndedEvent()) + elif parsed.type == "session.created": + await self._send_tracing_config(self._tracing_config) + elif parsed.type == "error": + await self._emit_event(RealtimeModelErrorEvent(error=parsed.error)) + elif parsed.type == "conversation.item.deleted": + await self._emit_event(RealtimeModelItemDeletedEvent(item_id=parsed.item_id)) + elif ( + parsed.type == "conversation.item.created" + or parsed.type == "conversation.item.retrieved" + ): + previous_item_id = ( + parsed.previous_item_id if parsed.type == "conversation.item.created" else None + ) + await self._handle_conversation_item(parsed.item, previous_item_id) + elif ( + parsed.type == "conversation.item.input_audio_transcription.completed" + or parsed.type == "conversation.item.truncated" + ): + await self.send_event( + { + "type": "conversation.item.retrieve", + "other_data": { + "item_id": self._current_item_id, + }, + } + ) + if parsed.type == "conversation.item.input_audio_transcription.completed": + await self._emit_event( + RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id=parsed.item_id, transcript=parsed.transcript + ) + ) + elif parsed.type == "response.audio_transcript.delta": + await self._emit_event( + RealtimeModelTranscriptDeltaEvent( + item_id=parsed.item_id, delta=parsed.delta, response_id=parsed.response_id + ) + ) + elif ( + parsed.type == "conversation.item.input_audio_transcription.delta" + or parsed.type == "response.text.delta" + or parsed.type == "response.function_call_arguments.delta" + ): + # No support for partials yet + pass + elif ( + parsed.type == "response.output_item.added" + or parsed.type == "response.output_item.done" + ): + await self._handle_output_item(parsed.item) diff --git a/src/agents/realtime/runner.py b/src/agents/realtime/runner.py new file mode 100644 index 000000000..a7047a6f5 --- /dev/null +++ b/src/agents/realtime/runner.py @@ -0,0 +1,118 @@ +"""Minimal realtime session implementation for voice agents.""" + +from __future__ import annotations + +import asyncio + +from ..run_context import RunContextWrapper, TContext +from .agent import RealtimeAgent +from .config import ( + RealtimeRunConfig, + RealtimeSessionModelSettings, +) +from .model import ( + RealtimeModel, + RealtimeModelConfig, +) +from .openai_realtime import OpenAIRealtimeWebSocketModel +from .session import RealtimeSession + + +class RealtimeRunner: + """A `RealtimeRunner` is the equivalent of `Runner` for realtime agents. It automatically + handles multiple turns by maintaining a persistent connection with the underlying model + layer. + + The session manages the local history copy, executes tools, runs guardrails and facilitates + handoffs between agents. + + Since this code runs on your server, it uses WebSockets by default. You can optionally create + your own custom model layer by implementing the `RealtimeModel` interface. + """ + + def __init__( + self, + starting_agent: RealtimeAgent, + *, + model: RealtimeModel | None = None, + config: RealtimeRunConfig | None = None, + ) -> None: + """Initialize the realtime runner. + + Args: + starting_agent: The agent to start the session with. + context: The context to use for the session. + model: The model to use. If not provided, will use a default OpenAI realtime model. + config: Override parameters to use for the entire run. + """ + self._starting_agent = starting_agent + self._config = config + self._model = model or OpenAIRealtimeWebSocketModel() + + async def run( + self, *, context: TContext | None = None, model_config: RealtimeModelConfig | None = None + ) -> RealtimeSession: + """Start and returns a realtime session. + + Returns: + RealtimeSession: A session object that allows bidirectional communication with the + realtime model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + await session.send_message("Hello") + async for event in session: + print(event) + ``` + """ + model_settings = await self._get_model_settings( + agent=self._starting_agent, + disable_tracing=self._config.get("tracing_disabled", False) if self._config else False, + initial_settings=model_config.get("initial_model_settings") if model_config else None, + overrides=self._config.get("model_settings") if self._config else None, + ) + + model_config = model_config.copy() if model_config else {} + model_config["initial_model_settings"] = model_settings + + # Create and return the connection + session = RealtimeSession( + model=self._model, + agent=self._starting_agent, + context=context, + model_config=model_config, + run_config=self._config, + ) + + return session + + async def _get_model_settings( + self, + agent: RealtimeAgent, + disable_tracing: bool, + context: TContext | None = None, + initial_settings: RealtimeSessionModelSettings | None = None, + overrides: RealtimeSessionModelSettings | None = None, + ) -> RealtimeSessionModelSettings: + context_wrapper = RunContextWrapper(context) + model_settings = initial_settings.copy() if initial_settings else {} + + instructions, tools = await asyncio.gather( + agent.get_system_prompt(context_wrapper), + agent.get_all_tools(context_wrapper), + ) + + if instructions is not None: + model_settings["instructions"] = instructions + if tools is not None: + model_settings["tools"] = tools + + if overrides: + model_settings.update(overrides) + + if disable_tracing: + model_settings["tracing"] = None + + return model_settings diff --git a/src/agents/realtime/session.py b/src/agents/realtime/session.py new file mode 100644 index 000000000..0ca3bf7af --- /dev/null +++ b/src/agents/realtime/session.py @@ -0,0 +1,368 @@ +from __future__ import annotations + +import asyncio +from collections.abc import AsyncIterator +from typing import Any, cast + +from typing_extensions import assert_never + +from ..agent import Agent +from ..handoffs import Handoff +from ..run_context import RunContextWrapper, TContext +from ..tool import FunctionTool +from ..tool_context import ToolContext +from .agent import RealtimeAgent +from .config import RealtimeRunConfig, RealtimeUserInput +from .events import ( + RealtimeAgentEndEvent, + RealtimeAgentStartEvent, + RealtimeAudio, + RealtimeAudioEnd, + RealtimeAudioInterrupted, + RealtimeError, + RealtimeEventInfo, + RealtimeGuardrailTripped, + RealtimeHistoryAdded, + RealtimeHistoryUpdated, + RealtimeRawModelEvent, + RealtimeSessionEvent, + RealtimeToolEnd, + RealtimeToolStart, +) +from .items import InputAudio, InputText, RealtimeItem +from .model import RealtimeModel, RealtimeModelConfig, RealtimeModelListener +from .model_events import ( + RealtimeModelEvent, + RealtimeModelInputAudioTranscriptionCompletedEvent, + RealtimeModelToolCallEvent, +) + + +class RealtimeSession(RealtimeModelListener): + """A connection to a realtime model. It streams events from the model to you, and allows you to + send messages and audio to the model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + # Send messages + await session.send_message("Hello") + await session.send_audio(audio_bytes) + + # Stream events + async for event in session: + if event.type == "audio": + # Handle audio event + pass + ``` + """ + + def __init__( + self, + model: RealtimeModel, + agent: RealtimeAgent, + context: TContext | None, + model_config: RealtimeModelConfig | None = None, + run_config: RealtimeRunConfig | None = None, + ) -> None: + """Initialize the session. + + Args: + model: The model to use. + agent: The current agent. + context: The context object. + model_config: Model configuration. + run_config: Runtime configuration including guardrails. + """ + self._model = model + self._current_agent = agent + self._context_wrapper = RunContextWrapper(context) + self._event_info = RealtimeEventInfo(context=self._context_wrapper) + self._history: list[RealtimeItem] = [] + self._model_config = model_config or {} + self._run_config = run_config or {} + self._event_queue: asyncio.Queue[RealtimeSessionEvent] = asyncio.Queue() + self._closed = False + + # Guardrails state tracking + self._interrupted_by_guardrail = False + self._item_transcripts: dict[str, str] = {} # item_id -> accumulated transcript + self._item_guardrail_run_counts: dict[str, int] = {} # item_id -> run count + self._debounce_text_length = self._run_config.get("guardrails_settings", {}).get( + "debounce_text_length", 100 + ) + + async def __aenter__(self) -> RealtimeSession: + """Start the session by connecting to the model. After this, you will be able to stream + events from the model and send messages and audio to the model. + """ + # Add ourselves as a listener + self._model.add_listener(self) + + # Connect to the model + await self._model.connect(self._model_config) + + # Emit initial history update + await self._put_event( + RealtimeHistoryUpdated( + history=self._history, + info=self._event_info, + ) + ) + + return self + + async def enter(self) -> RealtimeSession: + """Enter the async context manager. We strongly recommend using the async context manager + pattern instead of this method. If you use this, you need to manually call `close()` when + you are done. + """ + return await self.__aenter__() + + async def __aexit__(self, _exc_type: Any, _exc_val: Any, _exc_tb: Any) -> None: + """End the session.""" + await self.close() + + async def __aiter__(self) -> AsyncIterator[RealtimeSessionEvent]: + """Iterate over events from the session.""" + while not self._closed: + try: + event = await self._event_queue.get() + yield event + except asyncio.CancelledError: + break + + async def close(self) -> None: + """Close the session.""" + self._closed = True + self._model.remove_listener(self) + await self._model.close() + + async def send_message(self, message: RealtimeUserInput) -> None: + """Send a message to the model.""" + await self._model.send_message(message) + + async def send_audio(self, audio: bytes, *, commit: bool = False) -> None: + """Send a raw audio chunk to the model.""" + await self._model.send_audio(audio, commit=commit) + + async def interrupt(self) -> None: + """Interrupt the model.""" + await self._model.interrupt() + + async def on_event(self, event: RealtimeModelEvent) -> None: + await self._put_event(RealtimeRawModelEvent(data=event, info=self._event_info)) + + if event.type == "error": + await self._put_event(RealtimeError(info=self._event_info, error=event.error)) + elif event.type == "function_call": + await self._handle_tool_call(event) + elif event.type == "audio": + await self._put_event(RealtimeAudio(info=self._event_info, audio=event)) + elif event.type == "audio_interrupted": + await self._put_event(RealtimeAudioInterrupted(info=self._event_info)) + elif event.type == "audio_done": + await self._put_event(RealtimeAudioEnd(info=self._event_info)) + elif event.type == "conversation.item.input_audio_transcription.completed": + self._history = RealtimeSession._get_new_history(self._history, event) + await self._put_event( + RealtimeHistoryUpdated(info=self._event_info, history=self._history) + ) + elif event.type == "transcript_delta": + # Accumulate transcript text for guardrail debouncing per item_id + item_id = event.item_id + if item_id not in self._item_transcripts: + self._item_transcripts[item_id] = "" + self._item_guardrail_run_counts[item_id] = 0 + + self._item_transcripts[item_id] += event.delta + + # Check if we should run guardrails based on debounce threshold + current_length = len(self._item_transcripts[item_id]) + threshold = self._debounce_text_length + next_run_threshold = (self._item_guardrail_run_counts[item_id] + 1) * threshold + + if current_length >= next_run_threshold: + self._item_guardrail_run_counts[item_id] += 1 + await self._run_output_guardrails(self._item_transcripts[item_id]) + elif event.type == "item_updated": + is_new = not any(item.item_id == event.item.item_id for item in self._history) + self._history = self._get_new_history(self._history, event.item) + if is_new: + new_item = next( + item for item in self._history if item.item_id == event.item.item_id + ) + await self._put_event(RealtimeHistoryAdded(info=self._event_info, item=new_item)) + else: + await self._put_event( + RealtimeHistoryUpdated(info=self._event_info, history=self._history) + ) + elif event.type == "item_deleted": + deleted_id = event.item_id + self._history = [item for item in self._history if item.item_id != deleted_id] + await self._put_event( + RealtimeHistoryUpdated(info=self._event_info, history=self._history) + ) + elif event.type == "connection_status": + pass + elif event.type == "turn_started": + await self._put_event( + RealtimeAgentStartEvent( + agent=self._current_agent, + info=self._event_info, + ) + ) + elif event.type == "turn_ended": + # Clear guardrail state for next turn + self._item_transcripts.clear() + self._item_guardrail_run_counts.clear() + self._interrupted_by_guardrail = False + + await self._put_event( + RealtimeAgentEndEvent( + agent=self._current_agent, + info=self._event_info, + ) + ) + elif event.type == "other": + pass + else: + assert_never(event) + + async def _put_event(self, event: RealtimeSessionEvent) -> None: + """Put an event into the queue.""" + await self._event_queue.put(event) + + async def _handle_tool_call(self, event: RealtimeModelToolCallEvent) -> None: + """Handle a tool call event.""" + all_tools = await self._current_agent.get_all_tools(self._context_wrapper) + function_map = {tool.name: tool for tool in all_tools if isinstance(tool, FunctionTool)} + handoff_map = {tool.name: tool for tool in all_tools if isinstance(tool, Handoff)} + + if event.name in function_map: + await self._put_event( + RealtimeToolStart( + info=self._event_info, + tool=function_map[event.name], + agent=self._current_agent, + ) + ) + + func_tool = function_map[event.name] + tool_context = ToolContext.from_agent_context(self._context_wrapper, event.call_id) + result = await func_tool.on_invoke_tool(tool_context, event.arguments) + + await self._model.send_tool_output(event, str(result), True) + + await self._put_event( + RealtimeToolEnd( + info=self._event_info, + tool=func_tool, + output=result, + agent=self._current_agent, + ) + ) + elif event.name in handoff_map: + # TODO (rm) Add support for handoffs + pass + else: + # TODO (rm) Add error handling + pass + + @classmethod + def _get_new_history( + cls, + old_history: list[RealtimeItem], + event: RealtimeModelInputAudioTranscriptionCompletedEvent | RealtimeItem, + ) -> list[RealtimeItem]: + # Merge transcript into placeholder input_audio message. + if isinstance(event, RealtimeModelInputAudioTranscriptionCompletedEvent): + new_history: list[RealtimeItem] = [] + for item in old_history: + if item.item_id == event.item_id and item.type == "message" and item.role == "user": + content: list[InputText | InputAudio] = [] + for entry in item.content: + if entry.type == "input_audio": + copied_entry = entry.model_copy(update={"transcript": event.transcript}) + content.append(copied_entry) + else: + content.append(entry) # type: ignore + new_history.append( + item.model_copy(update={"content": content, "status": "completed"}) + ) + else: + new_history.append(item) + return new_history + + # Otherwise it's just a new item + # TODO (rm) Add support for audio storage config + + # If the item already exists, update it + existing_index = next( + (i for i, item in enumerate(old_history) if item.item_id == event.item_id), None + ) + if existing_index is not None: + new_history = old_history.copy() + new_history[existing_index] = event + return new_history + # Otherwise, insert it after the previous_item_id if that is set + elif event.previous_item_id: + # Insert the new item after the previous item + previous_index = next( + (i for i, item in enumerate(old_history) if item.item_id == event.previous_item_id), + None, + ) + if previous_index is not None: + new_history = old_history.copy() + new_history.insert(previous_index + 1, event) + return new_history + + # Otherwise, add it to the end + return old_history + [event] + + async def _run_output_guardrails(self, text: str) -> bool: + """Run output guardrails on the given text. Returns True if any guardrail was triggered.""" + output_guardrails = self._run_config.get("output_guardrails", []) + if not output_guardrails or self._interrupted_by_guardrail: + return False + + triggered_results = [] + + for guardrail in output_guardrails: + try: + result = await guardrail.run( + # TODO (rm) Remove this cast, it's wrong + self._context_wrapper, + cast(Agent[Any], self._current_agent), + text, + ) + if result.output.tripwire_triggered: + triggered_results.append(result) + except Exception: + # Continue with other guardrails if one fails + continue + + if triggered_results: + # Mark as interrupted to prevent multiple interrupts + self._interrupted_by_guardrail = True + + # Emit guardrail tripped event + await self._put_event( + RealtimeGuardrailTripped( + guardrail_results=triggered_results, + message=text, + info=self._event_info, + ) + ) + + # Interrupt the model + await self._model.interrupt() + + # Send guardrail triggered message + guardrail_names = [result.guardrail.get_name() for result in triggered_results] + await self._model.send_message(f"guardrail triggered: {', '.join(guardrail_names)}") + + return True + + return False diff --git a/src/agents/repl.py b/src/agents/repl.py new file mode 100644 index 000000000..f7142555f --- /dev/null +++ b/src/agents/repl.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +from typing import Any + +from openai.types.responses.response_text_delta_event import ResponseTextDeltaEvent + +from .agent import Agent +from .items import TResponseInputItem +from .result import RunResultBase +from .run import Runner +from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent, RunItemStreamEvent + + +async def run_demo_loop(agent: Agent[Any], *, stream: bool = True) -> None: + """Run a simple REPL loop with the given agent. + + This utility allows quick manual testing and debugging of an agent from the + command line. Conversation state is preserved across turns. Enter ``exit`` + or ``quit`` to stop the loop. + + Args: + agent: The starting agent to run. + stream: Whether to stream the agent output. + """ + + current_agent = agent + input_items: list[TResponseInputItem] = [] + while True: + try: + user_input = input(" > ") + except (EOFError, KeyboardInterrupt): + print() + break + if user_input.strip().lower() in {"exit", "quit"}: + break + if not user_input: + continue + + input_items.append({"role": "user", "content": user_input}) + + result: RunResultBase + if stream: + result = Runner.run_streamed(current_agent, input=input_items) + async for event in result.stream_events(): + if isinstance(event, RawResponsesStreamEvent): + if isinstance(event.data, ResponseTextDeltaEvent): + print(event.data.delta, end="", flush=True) + elif isinstance(event, RunItemStreamEvent): + if event.item.type == "tool_call_item": + print("\n[tool called]", flush=True) + elif event.item.type == "tool_call_output_item": + print(f"\n[tool output: {event.item.output}]", flush=True) + elif isinstance(event, AgentUpdatedStreamEvent): + print(f"\n[Agent updated: {event.new_agent.name}]", flush=True) + print() + else: + result = await Runner.run(current_agent, input_items) + if result.final_output is not None: + print(result.final_output) + + current_agent = result.last_agent + input_items = result.to_input_list() diff --git a/src/agents/result.py b/src/agents/result.py index 243db155c..5cf0e74c8 100644 --- a/src/agents/result.py +++ b/src/agents/result.py @@ -11,14 +11,22 @@ from ._run_impl import QueueCompleteSentinel from .agent import Agent from .agent_output import AgentOutputSchemaBase -from .exceptions import InputGuardrailTripwireTriggered, MaxTurnsExceeded +from .exceptions import ( + AgentsException, + InputGuardrailTripwireTriggered, + MaxTurnsExceeded, + RunErrorDetails, +) 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 +from .util._pretty_print import ( + pretty_print_result, + pretty_print_run_result_streaming, +) if TYPE_CHECKING: from ._run_impl import QueueCompleteSentinel @@ -206,31 +214,53 @@ async def stream_events(self) -> AsyncIterator[StreamEvent]: if self._stored_exception: raise self._stored_exception + def _create_error_details(self) -> RunErrorDetails: + """Return a `RunErrorDetails` object considering the current attributes of the class.""" + return RunErrorDetails( + input=self.input, + new_items=self.new_items, + raw_responses=self.raw_responses, + last_agent=self.current_agent, + context_wrapper=self.context_wrapper, + input_guardrail_results=self.input_guardrail_results, + output_guardrail_results=self.output_guardrail_results, + ) + def _check_errors(self): if self.current_turn > self.max_turns: - self._stored_exception = MaxTurnsExceeded(f"Max turns ({self.max_turns}) exceeded") + max_turns_exc = MaxTurnsExceeded(f"Max turns ({self.max_turns}) exceeded") + max_turns_exc.run_data = self._create_error_details() + self._stored_exception = max_turns_exc # 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) + tripwire_exc = InputGuardrailTripwireTriggered(guardrail_result) + tripwire_exc.run_data = self._create_error_details() + self._stored_exception = tripwire_exc # 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 + run_impl_exc = self._run_impl_task.exception() + if run_impl_exc and isinstance(run_impl_exc, Exception): + if isinstance(run_impl_exc, AgentsException) and run_impl_exc.run_data is None: + run_impl_exc.run_data = self._create_error_details() + self._stored_exception = run_impl_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 + in_guard_exc = self._input_guardrails_task.exception() + if in_guard_exc and isinstance(in_guard_exc, Exception): + if isinstance(in_guard_exc, AgentsException) and in_guard_exc.run_data is None: + in_guard_exc.run_data = self._create_error_details() + self._stored_exception = in_guard_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 + out_guard_exc = self._output_guardrails_task.exception() + if out_guard_exc and isinstance(out_guard_exc, Exception): + if isinstance(out_guard_exc, AgentsException) and out_guard_exc.run_data is None: + out_guard_exc.run_data = self._create_error_details() + self._stored_exception = out_guard_exc def _cleanup_tasks(self): if self._run_impl_task and not self._run_impl_task.done(): diff --git a/src/agents/run.py b/src/agents/run.py index 849da7bfc..2dd9524bb 100644 --- a/src/agents/run.py +++ b/src/agents/run.py @@ -2,10 +2,15 @@ import asyncio import copy +import inspect from dataclasses import dataclass, field -from typing import Any, cast +from typing import Any, Generic, cast from openai.types.responses import ResponseCompletedEvent +from openai.types.responses.response_prompt_param import ( + ResponsePromptParam, +) +from typing_extensions import NotRequired, TypedDict, Unpack from ._run_impl import ( AgentToolUseTracker, @@ -26,12 +31,20 @@ MaxTurnsExceeded, ModelBehaviorError, OutputGuardrailTripwireTriggered, + RunErrorDetails, + UserError, +) +from .guardrail import ( + InputGuardrail, + InputGuardrailResult, + OutputGuardrail, + OutputGuardrailResult, ) -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 .memory import Session from .model_settings import ModelSettings from .models.interface import Model, ModelProvider from .models.multi_provider import MultiProvider @@ -46,6 +59,27 @@ DEFAULT_MAX_TURNS = 10 +DEFAULT_AGENT_RUNNER: AgentRunner = None # type: ignore +# the value is set at the end of the module + + +def set_default_agent_runner(runner: AgentRunner | None) -> None: + """ + WARNING: this class is experimental and not part of the public API + It should not be used directly. + """ + global DEFAULT_AGENT_RUNNER + DEFAULT_AGENT_RUNNER = runner or AgentRunner() + + +def get_default_agent_runner() -> AgentRunner: + """ + WARNING: this class is experimental and not part of the public API + It should not be used directly. + """ + global DEFAULT_AGENT_RUNNER + return DEFAULT_AGENT_RUNNER + @dataclass class RunConfig: @@ -106,6 +140,28 @@ class RunConfig: """ +class RunOptions(TypedDict, Generic[TContext]): + """Arguments for ``AgentRunner`` methods.""" + + context: NotRequired[TContext | None] + """The context for the run.""" + + max_turns: NotRequired[int] + """The maximum number of turns to run for.""" + + hooks: NotRequired[RunHooks[TContext] | None] + """Lifecycle hooks for the run.""" + + run_config: NotRequired[RunConfig | None] + """Run configuration.""" + + previous_response_id: NotRequired[str | None] + """The ID of the previous response, if any.""" + + session: NotRequired[Session | None] + """The session for the run.""" + + class Runner: @classmethod async def run( @@ -118,6 +174,7 @@ async def run( hooks: RunHooks[TContext] | None = None, run_config: RunConfig | None = None, previous_response_id: str | None = None, + session: Session | 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: @@ -126,13 +183,10 @@ async def run( `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, @@ -144,16 +198,153 @@ async def run( 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 + agent. Agents may perform handoffs, so we don't know the specific type of the output. + """ + runner = DEFAULT_AGENT_RUNNER + return await runner.run( + starting_agent, + input, + context=context, + max_turns=max_turns, + hooks=hooks, + run_config=run_config, + previous_response_id=previous_response_id, + session=session, + ) + @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, + previous_response_id: str | None = None, + session: Session | 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. + 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 agent. Agents may perform handoffs, so we don't know the specific type of the output. """ + runner = DEFAULT_AGENT_RUNNER + return runner.run_sync( + starting_agent, + input, + context=context, + max_turns=max_turns, + hooks=hooks, + run_config=run_config, + previous_response_id=previous_response_id, + session=session, + ) + + @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, + previous_response_id: str | None = None, + session: Session | 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. + 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. + """ + runner = DEFAULT_AGENT_RUNNER + return runner.run_streamed( + starting_agent, + input, + context=context, + max_turns=max_turns, + hooks=hooks, + run_config=run_config, + previous_response_id=previous_response_id, + session=session, + ) + + +class AgentRunner: + """ + WARNING: this class is experimental and not part of the public API + It should not be used directly or subclassed. + """ + + async def run( + self, + starting_agent: Agent[TContext], + input: str | list[TResponseInputItem], + **kwargs: Unpack[RunOptions[TContext]], + ) -> RunResult: + context = kwargs.get("context") + max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS) + hooks = kwargs.get("hooks") + run_config = kwargs.get("run_config") + previous_response_id = kwargs.get("previous_response_id") + session = kwargs.get("session") if hooks is None: hooks = RunHooks[Any]() if run_config is None: run_config = RunConfig() + # Prepare input with session if enabled + prepared_input = await self._prepare_input_with_session(input, session) + tool_use_tracker = AgentToolUseTracker() with TraceCtxManager( @@ -164,7 +355,7 @@ async def run( disabled=run_config.tracing_disabled, ): current_turn = 0 - original_input: str | list[TResponseInputItem] = copy.deepcopy(input) + original_input: str | list[TResponseInputItem] = copy.deepcopy(prepared_input) generated_items: list[RunItem] = [] model_responses: list[ModelResponse] = [] @@ -180,11 +371,16 @@ async def run( try: while True: + all_tools = await AgentRunner._get_all_tools(current_agent, context_wrapper) + # 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)] - if output_schema := cls._get_output_schema(current_agent): + handoff_names = [ + h.agent_name + for h in await AgentRunner._get_handoffs(current_agent, context_wrapper) + ] + if output_schema := AgentRunner._get_output_schema(current_agent): output_type_name = output_schema.name() else: output_type_name = "str" @@ -195,8 +391,6 @@ async def run( 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 @@ -216,14 +410,14 @@ async def run( if current_turn == 1: input_guardrail_results, turn_result = await asyncio.gather( - cls._run_input_guardrails( + self._run_input_guardrails( starting_agent, starting_agent.input_guardrails + (run_config.input_guardrails or []), - copy.deepcopy(input), + copy.deepcopy(prepared_input), context_wrapper, ), - cls._run_single_turn( + self._run_single_turn( agent=current_agent, all_tools=all_tools, original_input=original_input, @@ -237,7 +431,7 @@ async def run( ), ) else: - turn_result = await cls._run_single_turn( + turn_result = await self._run_single_turn( agent=current_agent, all_tools=all_tools, original_input=original_input, @@ -256,13 +450,13 @@ async def run( generated_items = turn_result.generated_items if isinstance(turn_result.next_step, NextStepFinalOutput): - output_guardrail_results = await cls._run_output_guardrails( + output_guardrail_results = await self._run_output_guardrails( current_agent.output_guardrails + (run_config.output_guardrails or []), current_agent, turn_result.next_step.output, context_wrapper, ) - return RunResult( + result = RunResult( input=original_input, new_items=generated_items, raw_responses=model_responses, @@ -272,6 +466,11 @@ async def run( output_guardrail_results=output_guardrail_results, context_wrapper=context_wrapper, ) + + # Save the conversation to session if enabled + await self._save_result_to_session(session, input, result) + + return result elif isinstance(turn_result.next_step, NextStepHandoff): current_agent = cast(Agent[TContext], turn_result.next_step.new_agent) current_span.finish(reset_current=True) @@ -283,60 +482,39 @@ async def run( raise AgentsException( f"Unknown next step type: {type(turn_result.next_step)}" ) + except AgentsException as exc: + exc.run_data = RunErrorDetails( + input=original_input, + new_items=generated_items, + raw_responses=model_responses, + last_agent=current_agent, + context_wrapper=context_wrapper, + input_guardrail_results=input_guardrail_results, + output_guardrail_results=[], + ) + raise finally: if current_span: current_span.finish(reset_current=True) - @classmethod def run_sync( - cls, + self, 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, - previous_response_id: str | None = None, + **kwargs: Unpack[RunOptions[TContext]], ) -> 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. - 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. + context = kwargs.get("context") + max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS) + hooks = kwargs.get("hooks") + run_config = kwargs.get("run_config") + previous_response_id = kwargs.get("previous_response_id") + session = kwargs.get("session") - 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( + self.run( starting_agent, input, + session=session, context=context, max_turns=max_turns, hooks=hooks, @@ -345,47 +523,19 @@ def run_sync( ) ) - @classmethod def run_streamed( - cls, + self, 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, - previous_response_id: str | None = None, + **kwargs: Unpack[RunOptions[TContext]], ) -> 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. + context = kwargs.get("context") + max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS) + hooks = kwargs.get("hooks") + run_config = kwargs.get("run_config") + previous_response_id = kwargs.get("previous_response_id") + session = kwargs.get("session") - 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. - 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. - """ if hooks is None: hooks = RunHooks[Any]() if run_config is None: @@ -406,7 +556,7 @@ def run_streamed( ) ) - output_schema = cls._get_output_schema(starting_agent) + output_schema = AgentRunner._get_output_schema(starting_agent) context_wrapper: RunContextWrapper[TContext] = RunContextWrapper( context=context # type: ignore ) @@ -429,7 +579,7 @@ def run_streamed( # 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( + self._start_streaming( starting_input=input, streamed_result=streamed_result, starting_agent=starting_agent, @@ -438,6 +588,7 @@ def run_streamed( context_wrapper=context_wrapper, run_config=run_config, previous_response_id=previous_response_id, + session=session, ) ) return streamed_result @@ -486,7 +637,7 @@ async def _run_input_guardrails_with_queue( streamed_result.input_guardrail_results = guardrail_results @classmethod - async def _run_streamed_impl( + async def _start_streaming( cls, starting_input: str | list[TResponseInputItem], streamed_result: RunResultStreaming, @@ -496,6 +647,7 @@ async def _run_streamed_impl( context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, previous_response_id: str | None, + session: Session | None, ): if streamed_result.trace: streamed_result.trace.start(mark_as_current=True) @@ -509,14 +661,25 @@ async def _run_streamed_impl( streamed_result._event_queue.put_nowait(AgentUpdatedStreamEvent(new_agent=current_agent)) try: + # Prepare input with session if enabled + prepared_input = await AgentRunner._prepare_input_with_session(starting_input, session) + + # Update the streamed result with the prepared input + streamed_result.input = prepared_input + while True: if streamed_result.is_complete: break + all_tools = await cls._get_all_tools(current_agent, context_wrapper) + # 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)] + handoff_names = [ + h.agent_name + for h in await cls._get_handoffs(current_agent, context_wrapper) + ] if output_schema := cls._get_output_schema(current_agent): output_type_name = output_schema.name() else: @@ -528,8 +691,6 @@ async def _run_streamed_impl( 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 @@ -552,7 +713,7 @@ async def _run_streamed_impl( 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)), + copy.deepcopy(ItemHelpers.input_to_new_input_list(prepared_input)), context_wrapper, streamed_result, current_span, @@ -606,9 +767,39 @@ async def _run_streamed_impl( streamed_result.output_guardrail_results = output_guardrail_results streamed_result.final_output = turn_result.next_step.output streamed_result.is_complete = True + + # Save the conversation to session if enabled + # Create a temporary RunResult for session saving + temp_result = RunResult( + input=streamed_result.input, + new_items=streamed_result.new_items, + raw_responses=streamed_result.raw_responses, + final_output=streamed_result.final_output, + _last_agent=current_agent, + input_guardrail_results=streamed_result.input_guardrail_results, + output_guardrail_results=streamed_result.output_guardrail_results, + context_wrapper=context_wrapper, + ) + await AgentRunner._save_result_to_session( + session, starting_input, temp_result + ) + streamed_result._event_queue.put_nowait(QueueCompleteSentinel()) elif isinstance(turn_result.next_step, NextStepRunAgain): pass + except AgentsException as exc: + streamed_result.is_complete = True + streamed_result._event_queue.put_nowait(QueueCompleteSentinel()) + exc.run_data = RunErrorDetails( + input=streamed_result.input, + new_items=streamed_result.new_items, + raw_responses=streamed_result.raw_responses, + last_agent=current_agent, + context_wrapper=context_wrapper, + input_guardrail_results=streamed_result.input_guardrail_results, + output_guardrail_results=streamed_result.output_guardrail_results, + ) + raise except Exception as e: if current_span: _error_tracing.attach_error_to_span( @@ -657,9 +848,12 @@ async def _run_single_turn_streamed( streamed_result.current_agent = agent streamed_result._current_agent_output_schema = output_schema - system_prompt = await agent.get_system_prompt(context_wrapper) + system_prompt, prompt_config = await asyncio.gather( + agent.get_system_prompt(context_wrapper), + agent.get_prompt(context_wrapper), + ) - handoffs = cls._get_handoffs(agent) + handoffs = await cls._get_handoffs(agent, context_wrapper) 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) @@ -681,6 +875,7 @@ async def _run_single_turn_streamed( run_config.tracing_disabled, run_config.trace_include_sensitive_data ), previous_response_id=previous_response_id, + prompt=prompt_config, ): if isinstance(event, ResponseCompletedEvent): usage = ( @@ -689,6 +884,8 @@ 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() @@ -750,10 +947,13 @@ async def _run_single_turn( ), ) - system_prompt = await agent.get_system_prompt(context_wrapper) + system_prompt, prompt_config = await asyncio.gather( + agent.get_system_prompt(context_wrapper), + agent.get_prompt(context_wrapper), + ) output_schema = cls._get_output_schema(agent) - handoffs = cls._get_handoffs(agent) + handoffs = await cls._get_handoffs(agent, context_wrapper) input = ItemHelpers.input_to_new_input_list(original_input) input.extend([generated_item.to_input_item() for generated_item in generated_items]) @@ -768,6 +968,7 @@ async def _run_single_turn( run_config, tool_use_tracker, previous_response_id, + prompt_config, ) return await cls._get_single_step_result_from_response( @@ -911,6 +1112,7 @@ async def _get_new_response( run_config: RunConfig, tool_use_tracker: AgentToolUseTracker, previous_response_id: str | None, + prompt_config: ResponsePromptParam | None, ) -> ModelResponse: model = cls._get_model(agent, run_config) model_settings = agent.model_settings.resolve(run_config.model_settings) @@ -927,6 +1129,7 @@ async def _get_new_response( run_config.tracing_disabled, run_config.trace_include_sensitive_data ), previous_response_id=previous_response_id, + prompt=prompt_config, ) context_wrapper.usage.add(new_response.usage) @@ -943,18 +1146,34 @@ def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchemaBase | None: return AgentOutputSchema(agent.output_type) @classmethod - def _get_handoffs(cls, agent: Agent[Any]) -> list[Handoff]: + async def _get_handoffs( + cls, agent: Agent[Any], context_wrapper: RunContextWrapper[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 + + async def _check_handoff_enabled(handoff_obj: Handoff) -> bool: + attr = handoff_obj.is_enabled + if isinstance(attr, bool): + return attr + res = attr(context_wrapper, agent) + if inspect.isawaitable(res): + return bool(await res) + return bool(res) + + results = await asyncio.gather(*(_check_handoff_enabled(h) for h in handoffs)) + enabled: list[Handoff] = [h for h, ok in zip(handoffs, results) if ok] + return enabled @classmethod - async def _get_all_tools(cls, agent: Agent[Any]) -> list[Tool]: - return await agent.get_all_tools() + async def _get_all_tools( + cls, agent: Agent[Any], context_wrapper: RunContextWrapper[Any] + ) -> list[Tool]: + return await agent.get_all_tools(context_wrapper) @classmethod def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model: @@ -966,3 +1185,58 @@ def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model: return agent.model return run_config.model_provider.get_model(agent.model) + + @classmethod + async def _prepare_input_with_session( + cls, + input: str | list[TResponseInputItem], + session: Session | None, + ) -> str | list[TResponseInputItem]: + """Prepare input by combining it with session history if enabled.""" + if session is None: + return input + + # Validate that we don't have both a session and a list input, as this creates + # ambiguity about whether the list should append to or replace existing session history + if isinstance(input, list): + raise UserError( + "Cannot provide both a session and a list of input items. " + "When using session memory, provide only a string input to append to the " + "conversation, or use session=None and provide a list to manually manage " + "conversation history." + ) + + # Get previous conversation history + history = await session.get_items() + + # Convert input to list format + new_input_list = ItemHelpers.input_to_new_input_list(input) + + # Combine history with new input + combined_input = history + new_input_list + + return combined_input + + @classmethod + async def _save_result_to_session( + cls, + session: Session | None, + original_input: str | list[TResponseInputItem], + result: RunResult, + ) -> None: + """Save the conversation turn to session.""" + if session is None: + return + + # Convert original input to list format if needed + input_list = ItemHelpers.input_to_new_input_list(original_input) + + # Convert new items to input format + new_items_as_input = [item.to_input_item() for item in result.new_items] + + # Save all items from this turn + items_to_save = input_list + new_items_as_input + await session.add_items(items_to_save) + + +DEFAULT_AGENT_RUNNER = AgentRunner() diff --git a/src/agents/stream_events.py b/src/agents/stream_events.py index bd37d11f3..a271e8acd 100644 --- a/src/agents/stream_events.py +++ b/src/agents/stream_events.py @@ -31,10 +31,13 @@ class RunItemStreamEvent: name: Literal[ "message_output_created", "handoff_requested", + # This is misspelled, but we can't change it because that would be a breaking change "handoff_occured", "tool_called", "tool_output", "reasoning_item_created", + "mcp_approval_requested", + "mcp_list_tools", ] """The name of the event.""" diff --git a/src/agents/tool.py b/src/agents/tool.py index c1c162423..0faf09846 100644 --- a/src/agents/tool.py +++ b/src/agents/tool.py @@ -4,12 +4,18 @@ import json from collections.abc import Awaitable from dataclasses import dataclass -from typing import Any, Callable, Literal, Union, overload +from typing import TYPE_CHECKING, Any, Callable, Literal, Union, overload from openai.types.responses.file_search_tool_param import Filters, RankingOptions +from openai.types.responses.response_computer_tool_call import ( + PendingSafetyCheck, + ResponseComputerToolCall, +) +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 from .computer import AsyncComputer, Computer @@ -18,16 +24,25 @@ from .items import RunItem from .logger import logger from .run_context import RunContextWrapper +from .tool_context import ToolContext from .tracing import SpanError from .util import _error_tracing from .util._types import MaybeAwaitable +if TYPE_CHECKING: + from .agent import Agent, AgentBase + ToolParams = ParamSpec("ToolParams") ToolFunctionWithoutContext = Callable[ToolParams, Any] ToolFunctionWithContext = Callable[Concatenate[RunContextWrapper[Any], ToolParams], Any] +ToolFunctionWithToolContext = Callable[Concatenate[ToolContext, ToolParams], Any] -ToolFunction = Union[ToolFunctionWithoutContext[ToolParams], ToolFunctionWithContext[ToolParams]] +ToolFunction = Union[ + ToolFunctionWithoutContext[ToolParams], + ToolFunctionWithContext[ToolParams], + ToolFunctionWithToolContext[ToolParams], +] @dataclass @@ -57,7 +72,7 @@ class FunctionTool: params_json_schema: dict[str, Any] """The JSON schema for the tool's parameters.""" - on_invoke_tool: Callable[[RunContextWrapper[Any], str], Awaitable[Any]] + on_invoke_tool: Callable[[ToolContext[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. @@ -72,6 +87,11 @@ class FunctionTool: """Whether the JSON schema is in strict mode. We **strongly** recommend setting this to True, as it increases the likelihood of correct JSON input.""" + is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase], MaybeAwaitable[bool]] = True + """Whether the tool is enabled. Either a bool or a Callable that takes the run context and agent + and returns whether the tool is enabled. You can use this to dynamically enable/disable a tool + based on your context/state.""" + @dataclass class FileSearchTool: @@ -125,12 +145,140 @@ class ComputerTool: as well as implements the computer actions like click, screenshot, etc. """ + on_safety_check: Callable[[ComputerToolSafetyCheckData], MaybeAwaitable[bool]] | None = None + """Optional callback to acknowledge computer tool safety checks.""" + @property def name(self): return "computer_use_preview" -Tool = Union[FunctionTool, FileSearchTool, WebSearchTool, ComputerTool] +@dataclass +class ComputerToolSafetyCheckData: + """Information about a computer tool safety check.""" + + ctx_wrapper: RunContextWrapper[Any] + """The run context.""" + + agent: Agent[Any] + """The agent performing the computer action.""" + + tool_call: ResponseComputerToolCall + """The computer tool call.""" + + safety_check: PendingSafetyCheck + """The pending safety check to acknowledge.""" + + +@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.""" @@ -152,6 +300,7 @@ def function_tool( use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = None, strict_mode: bool = True, + is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase], MaybeAwaitable[bool]] = True, ) -> FunctionTool: """Overload for usage as @function_tool (no parentheses).""" ... @@ -166,6 +315,7 @@ def function_tool( use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = None, strict_mode: bool = True, + is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase], MaybeAwaitable[bool]] = True, ) -> Callable[[ToolFunction[...]], FunctionTool]: """Overload for usage as @function_tool(...).""" ... @@ -180,6 +330,7 @@ def function_tool( use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = default_tool_error_function, strict_mode: bool = True, + is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase], MaybeAwaitable[bool]] = True, ) -> FunctionTool | Callable[[ToolFunction[...]], FunctionTool]: """ Decorator to create a FunctionTool from a function. By default, we will: @@ -208,6 +359,9 @@ def function_tool( 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 + is_enabled: Whether the tool is enabled. Can be a bool or a callable that takes the run + context and agent and returns whether the tool is enabled. Disabled tools are hidden + from the LLM at runtime. """ def _create_function_tool(the_func: ToolFunction[...]) -> FunctionTool: @@ -220,7 +374,7 @@ def _create_function_tool(the_func: ToolFunction[...]) -> FunctionTool: strict_json_schema=strict_mode, ) - async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> Any: + async def _on_invoke_tool_impl(ctx: ToolContext[Any], input: str) -> Any: try: json_data: dict[str, Any] = json.loads(input) if input else {} except Exception as e: @@ -269,7 +423,7 @@ async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> Any: return result - async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> Any: + async def _on_invoke_tool(ctx: ToolContext[Any], input: str) -> Any: try: return await _on_invoke_tool_impl(ctx, input) except Exception as e: @@ -297,6 +451,7 @@ async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> Any: params_json_schema=schema.params_json_schema, on_invoke_tool=_on_invoke_tool, strict_json_schema=strict_mode, + is_enabled=is_enabled, ) # If func is actually a callable, we were used as @function_tool with no parentheses diff --git a/src/agents/tool_context.py b/src/agents/tool_context.py new file mode 100644 index 000000000..c4329b8af --- /dev/null +++ b/src/agents/tool_context.py @@ -0,0 +1,29 @@ +from dataclasses import dataclass, field, fields +from typing import Any + +from .run_context import RunContextWrapper, TContext + + +def _assert_must_pass_tool_call_id() -> str: + raise ValueError("tool_call_id must be passed to ToolContext") + + +@dataclass +class ToolContext(RunContextWrapper[TContext]): + """The context of a tool call.""" + + tool_call_id: str = field(default_factory=_assert_must_pass_tool_call_id) + """The ID of the tool call.""" + + @classmethod + def from_agent_context( + cls, context: RunContextWrapper[TContext], tool_call_id: str + ) -> "ToolContext": + """ + Create a ToolContext from a RunContextWrapper. + """ + # Grab the names of the RunContextWrapper's init=True fields + base_values: dict[str, Any] = { + f.name: getattr(context, f.name) for f in fields(RunContextWrapper) if f.init + } + return cls(tool_call_id=tool_call_id, **base_values) diff --git a/src/agents/tracing/__init__.py b/src/agents/tracing/__init__.py index 9df944263..b45c06d75 100644 --- a/src/agents/tracing/__init__.py +++ b/src/agents/tracing/__init__.py @@ -18,7 +18,8 @@ ) from .processor_interface import TracingProcessor from .processors import default_exporter, default_processor -from .setup import GLOBAL_TRACE_PROVIDER +from .provider import DefaultTraceProvider, TraceProvider +from .setup import get_trace_provider, set_trace_provider from .span_data import ( AgentSpanData, CustomSpanData, @@ -45,10 +46,12 @@ "generation_span", "get_current_span", "get_current_trace", + "get_trace_provider", "guardrail_span", "handoff_span", "response_span", "set_trace_processors", + "set_trace_provider", "set_tracing_disabled", "trace", "Trace", @@ -67,6 +70,7 @@ "SpeechSpanData", "TranscriptionSpanData", "TracingProcessor", + "TraceProvider", "gen_trace_id", "gen_span_id", "speech_group_span", @@ -80,21 +84,21 @@ 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) + get_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) + get_trace_provider().set_processors(processors) def set_tracing_disabled(disabled: bool) -> None: """ Set whether tracing is globally disabled. """ - GLOBAL_TRACE_PROVIDER.set_disabled(disabled) + get_trace_provider().set_disabled(disabled) def set_tracing_export_api_key(api_key: str) -> None: @@ -104,10 +108,11 @@ def set_tracing_export_api_key(api_key: str) -> None: default_exporter().set_api_key(api_key) +set_trace_provider(DefaultTraceProvider()) # 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) +atexit.register(get_trace_provider().shutdown) diff --git a/src/agents/tracing/create.py b/src/agents/tracing/create.py index b6fe46104..ac451abf5 100644 --- a/src/agents/tracing/create.py +++ b/src/agents/tracing/create.py @@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, Any from ..logger import logger -from .setup import GLOBAL_TRACE_PROVIDER +from .setup import get_trace_provider from .span_data import ( AgentSpanData, CustomSpanData, @@ -56,13 +56,13 @@ def trace( Returns: The newly created trace object. """ - current_trace = GLOBAL_TRACE_PROVIDER.get_current_trace() + current_trace = get_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( + return get_trace_provider().create_trace( name=workflow_name, trace_id=trace_id, group_id=group_id, @@ -73,12 +73,12 @@ def trace( def get_current_trace() -> Trace | None: """Returns the currently active trace, if present.""" - return GLOBAL_TRACE_PROVIDER.get_current_trace() + return get_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() + return get_trace_provider().get_current_span() def agent_span( @@ -108,7 +108,7 @@ def agent_span( Returns: The newly created agent span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=AgentSpanData(name=name, handoffs=handoffs, tools=tools, output_type=output_type), span_id=span_id, parent=parent, @@ -141,7 +141,7 @@ def function_span( Returns: The newly created function span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=FunctionSpanData(name=name, input=input, output=output), span_id=span_id, parent=parent, @@ -183,7 +183,7 @@ def generation_span( Returns: The newly created generation span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=GenerationSpanData( input=input, output=output, @@ -215,7 +215,7 @@ def response_span( 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( + return get_trace_provider().create_span( span_data=ResponseSpanData(response=response), span_id=span_id, parent=parent, @@ -246,7 +246,7 @@ def handoff_span( Returns: The newly created handoff span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=HandoffSpanData(from_agent=from_agent, to_agent=to_agent), span_id=span_id, parent=parent, @@ -278,7 +278,7 @@ def custom_span( Returns: The newly created custom span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=CustomSpanData(name=name, data=data or {}), span_id=span_id, parent=parent, @@ -306,7 +306,7 @@ def guardrail_span( 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( + return get_trace_provider().create_span( span_data=GuardrailSpanData(name=name, triggered=triggered), span_id=span_id, parent=parent, @@ -344,7 +344,7 @@ def transcription_span( Returns: The newly created speech-to-text span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=TranscriptionSpanData( input=input, input_format=input_format, @@ -386,7 +386,7 @@ def speech_span( 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( + return get_trace_provider().create_span( span_data=SpeechSpanData( model=model, input=input, @@ -419,7 +419,7 @@ def speech_group_span( 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( + return get_trace_provider().create_span( span_data=SpeechGroupSpanData(input=input), span_id=span_id, parent=parent, @@ -447,7 +447,7 @@ def mcp_tools_span( 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( + return get_trace_provider().create_span( span_data=MCPListToolsSpanData(server=server, result=result), span_id=span_id, parent=parent, diff --git a/src/agents/tracing/processor_interface.py b/src/agents/tracing/processor_interface.py index 4dcd897c7..0a05bcae2 100644 --- a/src/agents/tracing/processor_interface.py +++ b/src/agents/tracing/processor_interface.py @@ -23,7 +23,7 @@ def on_trace_end(self, trace: "Trace") -> None: """Called when a trace is finished. Args: - trace: The trace that started. + trace: The trace that finished. """ pass diff --git a/src/agents/tracing/processors.py b/src/agents/tracing/processors.py index 2913b11a4..73f733125 100644 --- a/src/agents/tracing/processors.py +++ b/src/agents/tracing/processors.py @@ -188,10 +188,27 @@ def __init__( # Track when we next *must* perform a scheduled export self._next_export_time = time.time() + self._schedule_delay - self._worker_thread = threading.Thread(target=self._run, daemon=True) - self._worker_thread.start() + # We lazily start the background worker thread the first time a span/trace is queued. + self._worker_thread: threading.Thread | None = None + self._thread_start_lock = threading.Lock() + + def _ensure_thread_started(self) -> None: + # Fast path without holding the lock + if self._worker_thread and self._worker_thread.is_alive(): + return + + # Double-checked locking to avoid starting multiple threads + with self._thread_start_lock: + if self._worker_thread and self._worker_thread.is_alive(): + return + + self._worker_thread = threading.Thread(target=self._run, daemon=True) + self._worker_thread.start() def on_trace_start(self, trace: Trace) -> None: + # Ensure the background worker is running before we enqueue anything. + self._ensure_thread_started() + try: self._queue.put_nowait(trace) except queue.Full: @@ -206,6 +223,9 @@ def on_span_start(self, span: Span[Any]) -> None: pass def on_span_end(self, span: Span[Any]) -> None: + # Ensure the background worker is running before we enqueue anything. + self._ensure_thread_started() + try: self._queue.put_nowait(span) except queue.Full: @@ -216,7 +236,13 @@ 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) + + # Only join if we ever started the background thread; otherwise flush synchronously. + if self._worker_thread and self._worker_thread.is_alive(): + self._worker_thread.join(timeout=timeout) + else: + # No background thread: process any remaining items synchronously. + self._export_batches(force=True) def force_flush(self): """ diff --git a/src/agents/tracing/provider.py b/src/agents/tracing/provider.py new file mode 100644 index 000000000..38d3b7ab3 --- /dev/null +++ b/src/agents/tracing/provider.py @@ -0,0 +1,294 @@ +from __future__ import annotations + +import os +import threading +import uuid +from abc import ABC, abstractmethod +from datetime import datetime, timezone +from typing import Any + +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(ABC): + """Interface for creating traces and spans.""" + + @abstractmethod + def register_processor(self, processor: TracingProcessor) -> None: + """Add a processor that will receive all traces and spans.""" + + @abstractmethod + def set_processors(self, processors: list[TracingProcessor]) -> None: + """Replace the list of processors with ``processors``.""" + + @abstractmethod + def get_current_trace(self) -> Trace | None: + """Return the currently active trace, if any.""" + + @abstractmethod + def get_current_span(self) -> Span[Any] | None: + """Return the currently active span, if any.""" + + @abstractmethod + def set_disabled(self, disabled: bool) -> None: + """Enable or disable tracing globally.""" + + @abstractmethod + def time_iso(self) -> str: + """Return the current time in ISO 8601 format.""" + + @abstractmethod + def gen_trace_id(self) -> str: + """Generate a new trace identifier.""" + + @abstractmethod + def gen_span_id(self) -> str: + """Generate a new span identifier.""" + + @abstractmethod + def gen_group_id(self) -> str: + """Generate a new group identifier.""" + + @abstractmethod + 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.""" + + @abstractmethod + 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.""" + + @abstractmethod + def shutdown(self) -> None: + """Clean up any resources used by the provider.""" + + +class DefaultTraceProvider(TraceProvider): + def __init__(self) -> None: + 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 time_iso(self) -> str: + """Return the current time in ISO 8601 format.""" + return datetime.now(timezone.utc).isoformat() + + def gen_trace_id(self) -> str: + """Generate a new trace ID.""" + return f"trace_{uuid.uuid4().hex}" + + def gen_span_id(self) -> str: + """Generate a new span ID.""" + return f"span_{uuid.uuid4().hex[:24]}" + + def gen_group_id(self) -> str: + """Generate a new group ID.""" + return f"group_{uuid.uuid4().hex[:24]}" + + 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 self.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 or self.gen_span_id(), + parent_id=parent_id, + processor=self._multi_processor, + span_data=span_data, + ) + + def shutdown(self) -> None: + if self._disabled: + return + + try: + logger.debug("Shutting down trace provider") + self._multi_processor.shutdown() + except Exception as e: + logger.error(f"Error shutting down trace provider: {e}") diff --git a/src/agents/tracing/setup.py b/src/agents/tracing/setup.py index 9e27d210b..3a56b728f 100644 --- a/src/agents/tracing/setup.py +++ b/src/agents/tracing/setup.py @@ -1,214 +1,21 @@ from __future__ import annotations -import os -import threading -from typing import Any +from typing import TYPE_CHECKING -from ..logger import logger -from . import util -from .processor_interface import TracingProcessor -from .scope import Scope -from .spans import NoOpSpan, Span, SpanImpl, TSpanData -from .traces import NoOpTrace, Trace, TraceImpl +if TYPE_CHECKING: + from .provider import TraceProvider +GLOBAL_TRACE_PROVIDER: TraceProvider | None = None -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 set_trace_provider(provider: TraceProvider) -> None: + """Set the global trace provider used by tracing utilities.""" + global GLOBAL_TRACE_PROVIDER + GLOBAL_TRACE_PROVIDER = provider - 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: - if self._disabled: - return - - 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() +def get_trace_provider() -> TraceProvider: + """Get the global trace provider used by tracing utilities.""" + if GLOBAL_TRACE_PROVIDER is None: + raise RuntimeError("Trace provider not set") + return GLOBAL_TRACE_PROVIDER diff --git a/src/agents/tracing/util.py b/src/agents/tracing/util.py index f546b4e50..7f436d019 100644 --- a/src/agents/tracing/util.py +++ b/src/agents/tracing/util.py @@ -1,22 +1,21 @@ -import uuid -from datetime import datetime, timezone +from .setup import get_trace_provider def time_iso() -> str: - """Returns the current time in ISO 8601 format.""" - return datetime.now(timezone.utc).isoformat() + """Return the current time in ISO 8601 format.""" + return get_trace_provider().time_iso() def gen_trace_id() -> str: - """Generates a new trace ID.""" - return f"trace_{uuid.uuid4().hex}" + """Generate a new trace ID.""" + return get_trace_provider().gen_trace_id() def gen_span_id() -> str: - """Generates a new span ID.""" - return f"span_{uuid.uuid4().hex[:24]}" + """Generate a new span ID.""" + return get_trace_provider().gen_span_id() def gen_group_id() -> str: - """Generates a new group ID.""" - return f"group_{uuid.uuid4().hex[:24]}" + """Generate a new group ID.""" + return get_trace_provider().gen_group_id() diff --git a/src/agents/usage.py b/src/agents/usage.py index 23d989b4b..843f62937 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/_pretty_print.py b/src/agents/util/_pretty_print.py index afd3e2b1b..29df3562e 100644 --- a/src/agents/util/_pretty_print.py +++ b/src/agents/util/_pretty_print.py @@ -3,6 +3,7 @@ from pydantic import BaseModel if TYPE_CHECKING: + from ..exceptions import RunErrorDetails from ..result import RunResult, RunResultBase, RunResultStreaming @@ -38,6 +39,17 @@ def pretty_print_result(result: "RunResult") -> str: return output +def pretty_print_run_error_details(result: "RunErrorDetails") -> str: + output = "RunErrorDetails:" + output += f'\n- Last agent: Agent(name="{result.last_agent.name}", ...)' + 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 += "\n(See `RunErrorDetails` 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}", ...)' diff --git a/src/agents/voice/model.py b/src/agents/voice/model.py index c36a4de76..b048a452d 100644 --- a/src/agents/voice/model.py +++ b/src/agents/voice/model.py @@ -17,9 +17,11 @@ 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 diff --git a/src/agents/voice/pipeline.py b/src/agents/voice/pipeline.py index d1dac57cf..5addd995f 100644 --- a/src/agents/voice/pipeline.py +++ b/src/agents/voice/pipeline.py @@ -125,6 +125,12 @@ async def _run_multi_turn(self, audio_input: StreamedAudioInput) -> StreamedAudi self._get_tts_model(), self.config.tts_settings, self.config ) + try: + async for intro_text in self.workflow.on_start(): + await output._add_text(intro_text) + except Exception as e: + logger.warning(f"on_start() failed: {e}") + transcription_session = await self._get_stt_model().create_session( audio_input, self.config.stt_settings, diff --git a/src/agents/voice/workflow.py b/src/agents/voice/workflow.py index c706ec413..538676ad1 100644 --- a/src/agents/voice/workflow.py +++ b/src/agents/voice/workflow.py @@ -32,6 +32,14 @@ def run(self, transcription: str) -> AsyncIterator[str]: """ pass + async def on_start(self) -> AsyncIterator[str]: + """ + Optional method that runs before any user input is received. Can be used + to deliver a greeting or instruction via TTS. Defaults to doing nothing. + """ + return + yield + class VoiceWorkflowHelper: @classmethod diff --git a/tests/conftest.py b/tests/conftest.py index ba0d88221..b73d734d1 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,8 +5,9 @@ from agents.models import _openai_shared from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel from agents.models.openai_responses import OpenAIResponsesModel +from agents.run import set_default_agent_runner from agents.tracing import set_trace_processors -from agents.tracing.setup import GLOBAL_TRACE_PROVIDER +from agents.tracing.setup import get_trace_provider from .testing_processor import SPAN_PROCESSOR_TESTING @@ -33,11 +34,16 @@ def clear_openai_settings(): _openai_shared._use_responses_by_default = True +@pytest.fixture(autouse=True) +def clear_default_runner(): + set_default_agent_runner(None) + + # This fixture will run after all tests end @pytest.fixture(autouse=True, scope="session") def shutdown_trace_provider(): yield - GLOBAL_TRACE_PROVIDER.shutdown() + get_trace_provider().shutdown() @pytest.fixture(autouse=True) diff --git a/tests/fake_model.py b/tests/fake_model.py index 32f919ef1..6c1377e6d 100644 --- a/tests/fake_model.py +++ b/tests/fake_model.py @@ -61,6 +61,7 @@ async def get_response( tracing: ModelTracing, *, previous_response_id: str | None, + prompt: Any | None, ) -> ModelResponse: self.last_turn_args = { "system_instructions": system_instructions, @@ -103,6 +104,7 @@ async def stream_response( tracing: ModelTracing, *, previous_response_id: str | None, + prompt: Any | None, ) -> AsyncIterator[TResponseStreamEvent]: self.last_turn_args = { "system_instructions": system_instructions, @@ -129,6 +131,7 @@ async def stream_response( yield ResponseCompletedEvent( type="response.completed", response=get_response_obj(output, usage=self.hardcoded_usage), + sequence_number=0, ) diff --git a/tests/mcp/helpers.py b/tests/mcp/helpers.py index 8ff153c18..31d43c228 100644 --- a/tests/mcp/helpers.py +++ b/tests/mcp/helpers.py @@ -1,11 +1,14 @@ +import asyncio import json import shutil from typing import Any from mcp import Tool as MCPTool -from mcp.types import CallToolResult, TextContent +from mcp.types import CallToolResult, GetPromptResult, ListPromptsResult, PromptMessage, TextContent from agents.mcp import MCPServer +from agents.mcp.server import _MCPServerWithClientSession +from agents.mcp.util import ToolFilter tee = shutil.which("tee") or "" assert tee, "tee not found" @@ -28,11 +31,41 @@ async def __aexit__(self, exc_type, exc_val, exc_tb): pass +class _TestFilterServer(_MCPServerWithClientSession): + """Minimal implementation of _MCPServerWithClientSession for testing tool filtering""" + + def __init__(self, tool_filter: ToolFilter, server_name: str): + # Initialize parent class properly to avoid type errors + super().__init__( + cache_tools_list=False, + client_session_timeout_seconds=None, + tool_filter=tool_filter, + ) + self._server_name: str = server_name + # Override some attributes for test isolation + self.session = None + self._cleanup_lock = asyncio.Lock() + + def create_streams(self): + raise NotImplementedError("Not needed for filtering tests") + + @property + def name(self) -> str: + return self._server_name + + class FakeMCPServer(MCPServer): - def __init__(self, tools: list[MCPTool] | None = None): + def __init__( + self, + tools: list[MCPTool] | None = None, + tool_filter: ToolFilter = None, + server_name: str = "fake_mcp_server", + ): self.tools: list[MCPTool] = tools or [] self.tool_calls: list[str] = [] self.tool_results: list[str] = [] + self.tool_filter = tool_filter + self._server_name = server_name def add_tool(self, name: str, input_schema: dict[str, Any]): self.tools.append(MCPTool(name=name, inputSchema=input_schema)) @@ -43,8 +76,16 @@ async def connect(self): async def cleanup(self): pass - async def list_tools(self): - return self.tools + async def list_tools(self, run_context=None, agent=None): + tools = self.tools + + # Apply tool filtering using the REAL implementation + if self.tool_filter is not None: + # Use the real _MCPServerWithClientSession filtering logic + filter_server = _TestFilterServer(self.tool_filter, self.name) + tools = await filter_server._apply_tool_filter(tools, run_context, agent) + + return tools async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> CallToolResult: self.tool_calls.append(tool_name) @@ -53,6 +94,18 @@ async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> C content=[TextContent(text=self.tool_results[-1], type="text")], ) + async def list_prompts(self, run_context=None, agent=None) -> ListPromptsResult: + """Return empty list of prompts for fake server""" + return ListPromptsResult(prompts=[]) + + async def get_prompt( + self, name: str, arguments: dict[str, Any] | None = None + ) -> GetPromptResult: + """Return a simple prompt result for fake server""" + content = f"Fake prompt content for {name}" + message = PromptMessage(role="user", content=TextContent(type="text", text=content)) + return GetPromptResult(description=f"Fake prompt: {name}", messages=[message]) + @property def name(self) -> str: - return "fake_mcp_server" + return self._server_name diff --git a/tests/mcp/test_caching.py b/tests/mcp/test_caching.py index cac409e6e..f31cdf951 100644 --- a/tests/mcp/test_caching.py +++ b/tests/mcp/test_caching.py @@ -3,7 +3,9 @@ import pytest from mcp.types import ListToolsResult, Tool as MCPTool +from agents import Agent from agents.mcp import MCPServerStdio +from agents.run_context import RunContextWrapper from .helpers import DummyStreamsContextManager, tee @@ -33,25 +35,29 @@ async def test_server_caching_works( mock_list_tools.return_value = ListToolsResult(tools=tools) async with server: + # Create test context and agent + run_context = RunContextWrapper(context=None) + agent = Agent(name="test_agent", instructions="Test agent") + # Call list_tools() multiple times - tools = await server.list_tools() - assert tools == tools + result_tools = await server.list_tools(run_context, agent) + assert result_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 + result_tools = await server.list_tools(run_context, agent) + assert result_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 + result_tools = await server.list_tools(run_context, agent) + assert result_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 + result_tools = await server.list_tools(run_context, agent) + assert result_tools == tools diff --git a/tests/mcp/test_mcp_tracing.py b/tests/mcp/test_mcp_tracing.py index b71954b5b..54575dcb5 100644 --- a/tests/mcp/test_mcp_tracing.py +++ b/tests/mcp/test_mcp_tracing.py @@ -44,6 +44,10 @@ async def test_mcp_tracing(): { "workflow_name": "Agent workflow", "children": [ + { + "type": "mcp_tools", + "data": {"server": "fake_mcp_server", "result": ["test_tool_1"]}, + }, { "type": "agent", "data": { @@ -53,10 +57,6 @@ async def test_mcp_tracing(): "output_type": "str", }, "children": [ - { - "type": "mcp_tools", - "data": {"server": "fake_mcp_server", "result": ["test_tool_1"]}, - }, { "type": "function", "data": { @@ -66,8 +66,12 @@ async def test_mcp_tracing(): "mcp_data": {"server": "fake_mcp_server"}, }, }, + { + "type": "mcp_tools", + "data": {"server": "fake_mcp_server", "result": ["test_tool_1"]}, + }, ], - } + }, ], } ] @@ -100,6 +104,13 @@ async def test_mcp_tracing(): { "workflow_name": "Agent workflow", "children": [ + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2"], + }, + }, { "type": "agent", "data": { @@ -109,13 +120,6 @@ async def test_mcp_tracing(): "output_type": "str", }, "children": [ - { - "type": "mcp_tools", - "data": { - "server": "fake_mcp_server", - "result": ["test_tool_1", "test_tool_2"], - }, - }, { "type": "function", "data": { @@ -133,8 +137,15 @@ async def test_mcp_tracing(): "mcp_data": {"server": "fake_mcp_server"}, }, }, + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2"], + }, + }, ], - } + }, ], } ] @@ -165,6 +176,13 @@ async def test_mcp_tracing(): { "workflow_name": "Agent workflow", "children": [ + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2", "test_tool_3"], + }, + }, { "type": "agent", "data": { @@ -174,13 +192,6 @@ async def test_mcp_tracing(): "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": { @@ -190,8 +201,15 @@ async def test_mcp_tracing(): "mcp_data": {"server": "fake_mcp_server"}, }, }, + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2", "test_tool_3"], + }, + }, ], - } + }, ], } ] diff --git a/tests/mcp/test_mcp_util.py b/tests/mcp/test_mcp_util.py index 74356a16d..3230e63dd 100644 --- a/tests/mcp/test_mcp_util.py +++ b/tests/mcp/test_mcp_util.py @@ -57,7 +57,10 @@ async def test_get_all_function_tools(): 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) + run_context = RunContextWrapper(context=None) + agent = Agent(name="test_agent", instructions="Test agent") + + tools = await MCPUtil.get_all_function_tools(servers, False, run_context, agent) assert len(tools) == 5 assert all(tool.name in names for tool in tools) @@ -70,7 +73,7 @@ async def test_get_all_function_tools(): 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) + tools = await MCPUtil.get_all_function_tools(servers, True, run_context, agent) assert len(tools) == 5 assert all(tool.name in names for tool in tools) @@ -144,7 +147,8 @@ async def test_agent_convert_schemas_true(): agent = Agent( name="test_agent", mcp_servers=[server], mcp_config={"convert_schemas_to_strict": True} ) - tools = await agent.get_mcp_tools() + run_context = RunContextWrapper(context=None) + tools = await agent.get_mcp_tools(run_context) foo_tool = next(tool for tool in tools if tool.name == "foo") assert isinstance(foo_tool, FunctionTool) @@ -208,7 +212,8 @@ async def test_agent_convert_schemas_false(): agent = Agent( name="test_agent", mcp_servers=[server], mcp_config={"convert_schemas_to_strict": False} ) - tools = await agent.get_mcp_tools() + run_context = RunContextWrapper(context=None) + tools = await agent.get_mcp_tools(run_context) foo_tool = next(tool for tool in tools if tool.name == "foo") assert isinstance(foo_tool, FunctionTool) @@ -245,7 +250,8 @@ async def test_agent_convert_schemas_unset(): 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() + run_context = RunContextWrapper(context=None) + tools = await agent.get_mcp_tools(run_context) foo_tool = next(tool for tool in tools if tool.name == "foo") assert isinstance(foo_tool, FunctionTool) @@ -279,7 +285,9 @@ async def test_util_adds_properties(): server = FakeMCPServer() server.add_tool("test_tool", schema) - tools = await MCPUtil.get_all_function_tools([server], convert_schemas_to_strict=False) + run_context = RunContextWrapper(context=None) + agent = Agent(name="test_agent", instructions="Test agent") + tools = await MCPUtil.get_all_function_tools([server], False, run_context, agent) tool = next(tool for tool in tools if tool.name == "test_tool") assert isinstance(tool, FunctionTool) diff --git a/tests/mcp/test_prompt_server.py b/tests/mcp/test_prompt_server.py new file mode 100644 index 000000000..15afe28e4 --- /dev/null +++ b/tests/mcp/test_prompt_server.py @@ -0,0 +1,301 @@ +from typing import Any + +import pytest + +from agents import Agent, Runner +from agents.mcp import MCPServer + +from ..fake_model import FakeModel +from ..test_responses import get_text_message + + +class FakeMCPPromptServer(MCPServer): + """Fake MCP server for testing prompt functionality""" + + def __init__(self, server_name: str = "fake_prompt_server"): + self.prompts: list[Any] = [] + self.prompt_results: dict[str, str] = {} + self._server_name = server_name + + def add_prompt(self, name: str, description: str, arguments: dict[str, Any] | None = None): + """Add a prompt to the fake server""" + from mcp.types import Prompt + + prompt = Prompt(name=name, description=description, arguments=[]) + self.prompts.append(prompt) + + def set_prompt_result(self, name: str, result: str): + """Set the result that should be returned for a prompt""" + self.prompt_results[name] = result + + async def connect(self): + pass + + async def cleanup(self): + pass + + async def list_prompts(self, run_context=None, agent=None): + """List available prompts""" + from mcp.types import ListPromptsResult + + return ListPromptsResult(prompts=self.prompts) + + async def get_prompt(self, name: str, arguments: dict[str, Any] | None = None): + """Get a prompt with arguments""" + from mcp.types import GetPromptResult, PromptMessage, TextContent + + if name not in self.prompt_results: + raise ValueError(f"Prompt '{name}' not found") + + content = self.prompt_results[name] + + # If it's a format string, try to format it with arguments + if arguments and "{" in content: + try: + content = content.format(**arguments) + except KeyError: + pass # Use original content if formatting fails + + message = PromptMessage(role="user", content=TextContent(type="text", text=content)) + + return GetPromptResult(description=f"Generated prompt for {name}", messages=[message]) + + async def list_tools(self, run_context=None, agent=None): + return [] + + async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None = None): + raise NotImplementedError("This fake server doesn't support tools") + + @property + def name(self) -> str: + return self._server_name + + +@pytest.mark.asyncio +async def test_list_prompts(): + """Test listing available prompts""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + + result = await server.list_prompts() + + assert len(result.prompts) == 1 + assert result.prompts[0].name == "generate_code_review_instructions" + assert "code review" in result.prompts[0].description + + +@pytest.mark.asyncio +async def test_get_prompt_without_arguments(): + """Test getting a prompt without arguments""" + server = FakeMCPPromptServer() + server.add_prompt("simple_prompt", "A simple prompt") + server.set_prompt_result("simple_prompt", "You are a helpful assistant.") + + result = await server.get_prompt("simple_prompt") + + assert len(result.messages) == 1 + assert result.messages[0].content.text == "You are a helpful assistant." + + +@pytest.mark.asyncio +async def test_get_prompt_with_arguments(): + """Test getting a prompt with arguments""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + server.set_prompt_result( + "generate_code_review_instructions", + "You are a senior {language} code review specialist. Focus on {focus}.", + ) + + result = await server.get_prompt( + "generate_code_review_instructions", + {"focus": "security vulnerabilities", "language": "python"}, + ) + + assert len(result.messages) == 1 + expected_text = ( + "You are a senior python code review specialist. Focus on security vulnerabilities." + ) + assert result.messages[0].content.text == expected_text + + +@pytest.mark.asyncio +async def test_get_prompt_not_found(): + """Test getting a prompt that doesn't exist""" + server = FakeMCPPromptServer() + + with pytest.raises(ValueError, match="Prompt 'nonexistent' not found"): + await server.get_prompt("nonexistent") + + +@pytest.mark.asyncio +async def test_agent_with_prompt_instructions(): + """Test using prompt-generated instructions with an agent""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + server.set_prompt_result( + "generate_code_review_instructions", + "You are a code reviewer. Analyze the provided code for security issues.", + ) + + # Get instructions from prompt + prompt_result = await server.get_prompt("generate_code_review_instructions") + instructions = prompt_result.messages[0].content.text + + # Create agent with prompt-generated instructions + model = FakeModel() + agent = Agent(name="prompt_agent", instructions=instructions, model=model, mcp_servers=[server]) + + # Mock model response + model.add_multiple_turn_outputs( + [[get_text_message("Code analysis complete. Found security vulnerability.")]] + ) + + # Run the agent + result = await Runner.run(agent, input="Review this code: def unsafe_exec(cmd): os.system(cmd)") + + assert "Code analysis complete" in result.final_output + assert ( + agent.instructions + == "You are a code reviewer. Analyze the provided code for security issues." + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("streaming", [False, True]) +async def test_agent_with_prompt_instructions_streaming(streaming: bool): + """Test using prompt-generated instructions with streaming and non-streaming""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + server.set_prompt_result( + "generate_code_review_instructions", + "You are a {language} code reviewer focusing on {focus}.", + ) + + # Get instructions from prompt with arguments + prompt_result = await server.get_prompt( + "generate_code_review_instructions", {"language": "Python", "focus": "security"} + ) + instructions = prompt_result.messages[0].content.text + + # Create agent + model = FakeModel() + agent = Agent( + name="streaming_prompt_agent", instructions=instructions, model=model, mcp_servers=[server] + ) + + model.add_multiple_turn_outputs([[get_text_message("Security analysis complete.")]]) + + if streaming: + streaming_result = Runner.run_streamed(agent, input="Review code") + async for _ in streaming_result.stream_events(): + pass + final_result = streaming_result.final_output + else: + result = await Runner.run(agent, input="Review code") + final_result = result.final_output + + assert "Security analysis complete" in final_result + assert agent.instructions == "You are a Python code reviewer focusing on security." + + +@pytest.mark.asyncio +async def test_multiple_prompts(): + """Test server with multiple prompts""" + server = FakeMCPPromptServer() + + # Add multiple prompts + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + server.add_prompt( + "generate_testing_instructions", "Generate agent instructions for testing tasks" + ) + + server.set_prompt_result("generate_code_review_instructions", "You are a code reviewer.") + server.set_prompt_result("generate_testing_instructions", "You are a test engineer.") + + # Test listing prompts + prompts_result = await server.list_prompts() + assert len(prompts_result.prompts) == 2 + + prompt_names = [p.name for p in prompts_result.prompts] + assert "generate_code_review_instructions" in prompt_names + assert "generate_testing_instructions" in prompt_names + + # Test getting each prompt + review_result = await server.get_prompt("generate_code_review_instructions") + assert review_result.messages[0].content.text == "You are a code reviewer." + + testing_result = await server.get_prompt("generate_testing_instructions") + assert testing_result.messages[0].content.text == "You are a test engineer." + + +@pytest.mark.asyncio +async def test_prompt_with_complex_arguments(): + """Test prompt with complex argument formatting""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_detailed_instructions", "Generate detailed instructions with multiple parameters" + ) + server.set_prompt_result( + "generate_detailed_instructions", + "You are a {role} specialist. Your focus is on {focus}. " + + "You work with {language} code. Your experience level is {level}.", + ) + + arguments = { + "role": "security", + "focus": "vulnerability detection", + "language": "Python", + "level": "senior", + } + + result = await server.get_prompt("generate_detailed_instructions", arguments) + + expected = ( + "You are a security specialist. Your focus is on vulnerability detection. " + "You work with Python code. Your experience level is senior." + ) + assert result.messages[0].content.text == expected + + +@pytest.mark.asyncio +async def test_prompt_with_missing_arguments(): + """Test prompt with missing arguments in format string""" + server = FakeMCPPromptServer() + server.add_prompt("incomplete_prompt", "Prompt with missing arguments") + server.set_prompt_result("incomplete_prompt", "You are a {role} working on {task}.") + + # Only provide one of the required arguments + result = await server.get_prompt("incomplete_prompt", {"role": "developer"}) + + # Should return the original string since formatting fails + assert result.messages[0].content.text == "You are a {role} working on {task}." + + +@pytest.mark.asyncio +async def test_prompt_server_cleanup(): + """Test that prompt server cleanup works correctly""" + server = FakeMCPPromptServer() + server.add_prompt("test_prompt", "Test prompt") + server.set_prompt_result("test_prompt", "Test result") + + # Test that server works before cleanup + result = await server.get_prompt("test_prompt") + assert result.messages[0].content.text == "Test result" + + # Cleanup should not raise any errors + await server.cleanup() + + # Server should still work after cleanup (in this fake implementation) + result = await server.get_prompt("test_prompt") + assert result.messages[0].content.text == "Test result" diff --git a/tests/mcp/test_server_errors.py b/tests/mcp/test_server_errors.py index fbd8db17d..9e0455115 100644 --- a/tests/mcp/test_server_errors.py +++ b/tests/mcp/test_server_errors.py @@ -1,7 +1,9 @@ import pytest +from agents import Agent from agents.exceptions import UserError from agents.mcp.server import _MCPServerWithClientSession +from agents.run_context import RunContextWrapper class CrashingClientSessionServer(_MCPServerWithClientSession): @@ -35,8 +37,11 @@ async def test_server_errors_cause_error_and_cleanup_called(): async def test_not_calling_connect_causes_error(): server = CrashingClientSessionServer() + run_context = RunContextWrapper(context=None) + agent = Agent(name="test_agent", instructions="Test agent") + with pytest.raises(UserError): - await server.list_tools() + await server.list_tools(run_context, agent) with pytest.raises(UserError): await server.call_tool("foo", {}) diff --git a/tests/mcp/test_tool_filtering.py b/tests/mcp/test_tool_filtering.py new file mode 100644 index 000000000..0127df806 --- /dev/null +++ b/tests/mcp/test_tool_filtering.py @@ -0,0 +1,246 @@ +""" +Tool filtering tests use FakeMCPServer instead of real MCPServer implementations to avoid +external dependencies (processes, network connections) and ensure fast, reliable unit tests. +FakeMCPServer delegates filtering logic to the real _MCPServerWithClientSession implementation. +""" + +import asyncio + +import pytest +from mcp import Tool as MCPTool + +from agents import Agent +from agents.mcp import ToolFilterContext, create_static_tool_filter +from agents.run_context import RunContextWrapper + +from .helpers import FakeMCPServer + + +def create_test_agent(name: str = "test_agent") -> Agent: + """Create a test agent for filtering tests.""" + return Agent(name=name, instructions="Test agent") + + +def create_test_context() -> RunContextWrapper: + """Create a test run context for filtering tests.""" + return RunContextWrapper(context=None) + + +# === Static Tool Filtering Tests === + + +@pytest.mark.asyncio +async def test_static_tool_filtering(): + """Test all static tool filtering scenarios: allowed, blocked, both, none, etc.""" + server = FakeMCPServer(server_name="test_server") + server.add_tool("tool1", {}) + server.add_tool("tool2", {}) + server.add_tool("tool3", {}) + server.add_tool("tool4", {}) + + # Create test context and agent for all calls + run_context = create_test_context() + agent = create_test_agent() + + # Test allowed_tool_names only + server.tool_filter = {"allowed_tool_names": ["tool1", "tool2"]} + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"tool1", "tool2"} + + # Test blocked_tool_names only + server.tool_filter = {"blocked_tool_names": ["tool3", "tool4"]} + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"tool1", "tool2"} + + # Test both filters together (allowed first, then blocked) + server.tool_filter = { + "allowed_tool_names": ["tool1", "tool2", "tool3"], + "blocked_tool_names": ["tool3"], + } + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"tool1", "tool2"} + + # Test no filter + server.tool_filter = None + tools = await server.list_tools(run_context, agent) + assert len(tools) == 4 + + # Test helper function + server.tool_filter = create_static_tool_filter( + allowed_tool_names=["tool1", "tool2"], blocked_tool_names=["tool2"] + ) + tools = await server.list_tools(run_context, agent) + assert len(tools) == 1 + assert tools[0].name == "tool1" + + +# === Dynamic Tool Filtering Core Tests === + + +@pytest.mark.asyncio +async def test_dynamic_filter_sync_and_async(): + """Test both synchronous and asynchronous dynamic filters""" + server = FakeMCPServer(server_name="test_server") + server.add_tool("allowed_tool", {}) + server.add_tool("blocked_tool", {}) + server.add_tool("restricted_tool", {}) + + # Create test context and agent + run_context = create_test_context() + agent = create_test_agent() + + # Test sync filter + def sync_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + return tool.name.startswith("allowed") + + server.tool_filter = sync_filter + tools = await server.list_tools(run_context, agent) + assert len(tools) == 1 + assert tools[0].name == "allowed_tool" + + # Test async filter + async def async_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + await asyncio.sleep(0.001) # Simulate async operation + return "restricted" not in tool.name + + server.tool_filter = async_filter + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"allowed_tool", "blocked_tool"} + + +@pytest.mark.asyncio +async def test_dynamic_filter_context_handling(): + """Test dynamic filters with context access""" + server = FakeMCPServer(server_name="test_server") + server.add_tool("admin_tool", {}) + server.add_tool("user_tool", {}) + server.add_tool("guest_tool", {}) + + # Test context-independent filter + def context_independent_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + return not tool.name.startswith("admin") + + server.tool_filter = context_independent_filter + run_context = create_test_context() + agent = create_test_agent() + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"user_tool", "guest_tool"} + + # Test context-dependent filter (needs context) + def context_dependent_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + assert context is not None + assert context.run_context is not None + assert context.agent is not None + assert context.server_name == "test_server" + + # Only admin tools for agents with "admin" in name + if "admin" in context.agent.name.lower(): + return True + else: + return not tool.name.startswith("admin") + + server.tool_filter = context_dependent_filter + + # Should work with context + run_context = RunContextWrapper(context=None) + regular_agent = create_test_agent("regular_user") + tools = await server.list_tools(run_context, regular_agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"user_tool", "guest_tool"} + + admin_agent = create_test_agent("admin_user") + tools = await server.list_tools(run_context, admin_agent) + assert len(tools) == 3 + + +@pytest.mark.asyncio +async def test_dynamic_filter_error_handling(): + """Test error handling in dynamic filters""" + server = FakeMCPServer(server_name="test_server") + server.add_tool("good_tool", {}) + server.add_tool("error_tool", {}) + server.add_tool("another_good_tool", {}) + + def error_prone_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + if tool.name == "error_tool": + raise ValueError("Simulated filter error") + return True + + server.tool_filter = error_prone_filter + + # Test with server call + run_context = create_test_context() + agent = create_test_agent() + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"good_tool", "another_good_tool"} + + +# === Integration Tests === + + +@pytest.mark.asyncio +async def test_agent_dynamic_filtering_integration(): + """Test dynamic filtering integration with Agent methods""" + server = FakeMCPServer() + server.add_tool("file_read", {"type": "object", "properties": {"path": {"type": "string"}}}) + server.add_tool( + "file_write", + { + "type": "object", + "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, + }, + ) + server.add_tool( + "database_query", {"type": "object", "properties": {"query": {"type": "string"}}} + ) + server.add_tool( + "network_request", {"type": "object", "properties": {"url": {"type": "string"}}} + ) + + # Role-based filter for comprehensive testing + async def role_based_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + # Simulate async permission check + await asyncio.sleep(0.001) + + agent_name = context.agent.name.lower() + if "admin" in agent_name: + return True + elif "readonly" in agent_name: + return "read" in tool.name or "query" in tool.name + else: + return tool.name.startswith("file_") + + server.tool_filter = role_based_filter + + # Test admin agent + admin_agent = Agent(name="admin_user", instructions="Admin", mcp_servers=[server]) + run_context = RunContextWrapper(context=None) + admin_tools = await admin_agent.get_mcp_tools(run_context) + assert len(admin_tools) == 4 + + # Test readonly agent + readonly_agent = Agent(name="readonly_viewer", instructions="Read-only", mcp_servers=[server]) + readonly_tools = await readonly_agent.get_mcp_tools(run_context) + assert len(readonly_tools) == 2 + assert {t.name for t in readonly_tools} == {"file_read", "database_query"} + + # Test regular agent + regular_agent = Agent(name="regular_user", instructions="Regular", mcp_servers=[server]) + regular_tools = await regular_agent.get_mcp_tools(run_context) + assert len(regular_tools) == 2 + assert {t.name for t in regular_tools} == {"file_read", "file_write"} + + # Test get_all_tools method + all_tools = await regular_agent.get_all_tools(run_context) + mcp_tool_names = { + t.name + for t in all_tools + if t.name in {"file_read", "file_write", "database_query", "network_request"} + } + assert mcp_tool_names == {"file_read", "file_write"} diff --git a/tests/model_settings/test_serialization.py b/tests/model_settings/test_serialization.py index d76a58d17..6e8c65180 100644 --- a/tests/model_settings/test_serialization.py +++ b/tests/model_settings/test_serialization.py @@ -2,8 +2,10 @@ from dataclasses import fields from openai.types.shared import Reasoning +from pydantic import TypeAdapter +from pydantic_core import to_json -from agents.model_settings import ModelSettings +from agents.model_settings import MCPToolChoice, ModelSettings def verify_serialization(model_settings: ModelSettings) -> None: @@ -27,6 +29,17 @@ def test_basic_serialization() -> None: verify_serialization(model_settings) +def test_mcp_tool_choice_serialization() -> None: + """Tests whether ModelSettings with MCPToolChoice can be serialized to a JSON string.""" + # First, lets create a ModelSettings instance + model_settings = ModelSettings( + temperature=0.5, + tool_choice=MCPToolChoice(server_label="mcp", name="mcp_tool"), + ) + # 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.""" @@ -44,9 +57,11 @@ def test_all_fields_serialization() -> None: metadata={"foo": "bar"}, store=False, include_usage=False, + response_include=["reasoning.encrypted_content"], extra_query={"foo": "bar"}, extra_body={"foo": "bar"}, extra_headers={"foo": "bar"}, + extra_args={"custom_param": "value", "another_param": 42}, ) # Verify that every single field is set to a non-None value @@ -57,3 +72,105 @@ def test_all_fields_serialization() -> None: # Now, lets serialize the ModelSettings instance to a JSON string verify_serialization(model_settings) + + +def test_extra_args_serialization() -> None: + """Test that extra_args are properly serialized.""" + model_settings = ModelSettings( + temperature=0.5, + extra_args={"custom_param": "value", "another_param": 42, "nested": {"key": "value"}}, + ) + + json_dict = model_settings.to_json_dict() + assert json_dict["extra_args"] == { + "custom_param": "value", + "another_param": 42, + "nested": {"key": "value"}, + } + + # Verify serialization works + verify_serialization(model_settings) + + +def test_extra_args_resolve() -> None: + """Test that extra_args are properly merged in the resolve method.""" + base_settings = ModelSettings( + temperature=0.5, extra_args={"param1": "base_value", "param2": "base_only"} + ) + + override_settings = ModelSettings( + top_p=0.9, extra_args={"param1": "override_value", "param3": "override_only"} + ) + + resolved = base_settings.resolve(override_settings) + + # Check that regular fields are properly resolved + assert resolved.temperature == 0.5 # from base + assert resolved.top_p == 0.9 # from override + + # Check that extra_args are properly merged + expected_extra_args = { + "param1": "override_value", # override wins + "param2": "base_only", # from base + "param3": "override_only", # from override + } + assert resolved.extra_args == expected_extra_args + + +def test_extra_args_resolve_with_none() -> None: + """Test that resolve works properly when one side has None extra_args.""" + # Base with extra_args, override with None + base_settings = ModelSettings(extra_args={"param1": "value1"}) + override_settings = ModelSettings(temperature=0.8) + + resolved = base_settings.resolve(override_settings) + assert resolved.extra_args == {"param1": "value1"} + assert resolved.temperature == 0.8 + + # Base with None, override with extra_args + base_settings = ModelSettings(temperature=0.5) + override_settings = ModelSettings(extra_args={"param2": "value2"}) + + resolved = base_settings.resolve(override_settings) + assert resolved.extra_args == {"param2": "value2"} + assert resolved.temperature == 0.5 + + +def test_extra_args_resolve_both_none() -> None: + """Test that resolve works when both sides have None extra_args.""" + base_settings = ModelSettings(temperature=0.5) + override_settings = ModelSettings(top_p=0.9) + + resolved = base_settings.resolve(override_settings) + assert resolved.extra_args is None + assert resolved.temperature == 0.5 + assert resolved.top_p == 0.9 + + +def test_pydantic_serialization() -> None: + """Tests whether ModelSettings can be serialized with Pydantic.""" + + # 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"}, + extra_args={"custom_param": "value", "another_param": 42}, + ) + + json = to_json(model_settings) + deserialized = TypeAdapter(ModelSettings).validate_json(json) + + assert model_settings == deserialized diff --git a/tests/models/test_kwargs_functionality.py b/tests/models/test_kwargs_functionality.py new file mode 100644 index 000000000..210610a02 --- /dev/null +++ b/tests/models/test_kwargs_functionality.py @@ -0,0 +1,177 @@ +import litellm +import pytest +from litellm.types.utils import Choices, Message, ModelResponse, Usage +from openai.types.chat.chat_completion import ChatCompletion, Choice +from openai.types.chat.chat_completion_message import ChatCompletionMessage +from openai.types.completion_usage import CompletionUsage + +from agents.extensions.models.litellm_model import LitellmModel +from agents.model_settings import ModelSettings +from agents.models.interface import ModelTracing +from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_litellm_kwargs_forwarded(monkeypatch): + """ + Test that kwargs from ModelSettings are forwarded to litellm.acompletion. + """ + captured: dict[str, object] = {} + + async def fake_acompletion(model, messages=None, **kwargs): + captured.update(kwargs) + msg = Message(role="assistant", content="test response") + choice = Choices(index=0, message=msg) + return ModelResponse(choices=[choice], usage=Usage(0, 0, 0)) + + monkeypatch.setattr(litellm, "acompletion", fake_acompletion) + + settings = ModelSettings( + temperature=0.5, + extra_args={ + "custom_param": "custom_value", + "seed": 42, + "stop": ["END"], + "logit_bias": {123: -100}, + }, + ) + model = LitellmModel(model="test-model") + + await model.get_response( + system_instructions=None, + input="test input", + model_settings=settings, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + # Verify that all kwargs were passed through + assert captured["custom_param"] == "custom_value" + assert captured["seed"] == 42 + assert captured["stop"] == ["END"] + assert captured["logit_bias"] == {123: -100} + + # Verify regular parameters are still passed + assert captured["temperature"] == 0.5 + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_openai_chatcompletions_kwargs_forwarded(monkeypatch): + """ + Test that kwargs from ModelSettings are forwarded to OpenAI chat completions API. + """ + captured: dict[str, object] = {} + + class MockChatCompletions: + async def create(self, **kwargs): + captured.update(kwargs) + msg = ChatCompletionMessage(role="assistant", content="test response") + choice = Choice(index=0, message=msg, finish_reason="stop") + return ChatCompletion( + id="test-id", + created=0, + model="gpt-4", + object="chat.completion", + choices=[choice], + usage=CompletionUsage(completion_tokens=5, prompt_tokens=10, total_tokens=15), + ) + + class MockChat: + def __init__(self): + self.completions = MockChatCompletions() + + class MockClient: + def __init__(self): + self.chat = MockChat() + self.base_url = "https://api.openai.com/v1" + + settings = ModelSettings( + temperature=0.7, + extra_args={ + "seed": 123, + "logit_bias": {456: 10}, + "stop": ["STOP", "END"], + "user": "test-user", + }, + ) + + mock_client = MockClient() + model = OpenAIChatCompletionsModel(model="gpt-4", openai_client=mock_client) # type: ignore + + await model.get_response( + system_instructions="Test system", + input="test input", + model_settings=settings, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + # Verify that all kwargs were passed through + assert captured["seed"] == 123 + assert captured["logit_bias"] == {456: 10} + assert captured["stop"] == ["STOP", "END"] + assert captured["user"] == "test-user" + + # Verify regular parameters are still passed + assert captured["temperature"] == 0.7 + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_empty_kwargs_handling(monkeypatch): + """ + Test that empty or None kwargs are handled gracefully. + """ + captured: dict[str, object] = {} + + async def fake_acompletion(model, messages=None, **kwargs): + captured.update(kwargs) + msg = Message(role="assistant", content="test response") + choice = Choices(index=0, message=msg) + return ModelResponse(choices=[choice], usage=Usage(0, 0, 0)) + + monkeypatch.setattr(litellm, "acompletion", fake_acompletion) + + # Test with None kwargs + settings_none = ModelSettings(temperature=0.5, extra_args=None) + model = LitellmModel(model="test-model") + + await model.get_response( + system_instructions=None, + input="test input", + model_settings=settings_none, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + # Should work without error and include regular parameters + assert captured["temperature"] == 0.5 + + # Test with empty dict + captured.clear() + settings_empty = ModelSettings(temperature=0.3, extra_args={}) + + await model.get_response( + system_instructions=None, + input="test input", + model_settings=settings_empty, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + # Should work without error and include regular parameters + assert captured["temperature"] == 0.3 diff --git a/tests/models/test_litellm_chatcompletions_stream.py b/tests/models/test_litellm_chatcompletions_stream.py index 80bd8ea22..cd342e444 100644 --- a/tests/models/test_litellm_chatcompletions_stream.py +++ b/tests/models/test_litellm_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, + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=2), + prompt_tokens_details=PromptTokensDetails(cached_tokens=6), + ), ) async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: @@ -80,6 +90,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # We expect a response.created, then a response.output_item.added, content part added, @@ -112,6 +123,8 @@ async def patched_fetch_response(self, *args, **kwargs): 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 @@ -170,6 +183,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # Expect sequence similar to text: created, output_item.added, content part added, @@ -258,6 +272,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # Sequence should be: response.created, then after loop we expect function call-related events: diff --git a/tests/models/test_litellm_extra_body.py b/tests/models/test_litellm_extra_body.py new file mode 100644 index 000000000..3c83b0607 --- /dev/null +++ b/tests/models/test_litellm_extra_body.py @@ -0,0 +1,44 @@ +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 index 6b65fc094..b1a129667 100644 --- a/tests/models/test_map.py +++ b/tests/models/test_map.py @@ -1,20 +1,21 @@ -from agents import Agent, OpenAIResponsesModel, RunConfig, Runner +from agents import Agent, OpenAIResponsesModel, RunConfig from agents.extensions.models.litellm_model import LitellmModel +from agents.run import AgentRunner def test_no_prefix_is_openai(): agent = Agent(model="gpt-4o", instructions="", name="test") - model = Runner._get_model(agent, RunConfig()) + model = AgentRunner._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()) + model = AgentRunner._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()) + model = AgentRunner._get_model(agent, RunConfig()) assert isinstance(model, LitellmModel) diff --git a/tests/realtime/__init__.py b/tests/realtime/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/realtime/test_agent.py b/tests/realtime/test_agent.py new file mode 100644 index 000000000..aae8bc47c --- /dev/null +++ b/tests/realtime/test_agent.py @@ -0,0 +1,25 @@ +import pytest + +from agents import RunContextWrapper +from agents.realtime.agent import RealtimeAgent + + +def test_can_initialize_realtime_agent(): + agent = RealtimeAgent(name="test", instructions="Hello") + assert agent.name == "test" + assert agent.instructions == "Hello" + + +@pytest.mark.asyncio +async def test_dynamic_instructions(): + agent = RealtimeAgent(name="test") + assert agent.instructions is None + + def _instructions(ctx, agt) -> str: + assert ctx.context is None + assert agt == agent + return "Dynamic" + + agent = RealtimeAgent(name="test", instructions=_instructions) + instructions = await agent.get_system_prompt(RunContextWrapper(context=None)) + assert instructions == "Dynamic" diff --git a/tests/realtime/test_model_events.py b/tests/realtime/test_model_events.py new file mode 100644 index 000000000..b8696cc29 --- /dev/null +++ b/tests/realtime/test_model_events.py @@ -0,0 +1,12 @@ +from typing import get_args + +from agents.realtime.model_events import RealtimeModelEvent + + +def test_all_events_have_type() -> None: + """Test that all events have a type.""" + events = get_args(RealtimeModelEvent) + assert len(events) > 0 + for event in events: + assert event.type is not None + assert isinstance(event.type, str) diff --git a/tests/realtime/test_openai_realtime.py b/tests/realtime/test_openai_realtime.py new file mode 100644 index 000000000..9fe2f9acb --- /dev/null +++ b/tests/realtime/test_openai_realtime.py @@ -0,0 +1,385 @@ +from datetime import datetime +from typing import Any +from unittest.mock import AsyncMock, Mock, patch + +import pytest +import websockets + +from agents.exceptions import UserError +from agents.realtime.model_events import ( + RealtimeModelAudioEvent, + RealtimeModelErrorEvent, + RealtimeModelToolCallEvent, +) +from agents.realtime.openai_realtime import OpenAIRealtimeWebSocketModel + + +class TestOpenAIRealtimeWebSocketModel: + """Test suite for OpenAIRealtimeWebSocketModel connection and event handling.""" + + @pytest.fixture + def model(self): + """Create a fresh model instance for each test.""" + return OpenAIRealtimeWebSocketModel() + + @pytest.fixture + def mock_websocket(self): + """Create a mock websocket connection.""" + mock_ws = AsyncMock() + mock_ws.send = AsyncMock() + mock_ws.close = AsyncMock() + return mock_ws + + +class TestConnectionLifecycle(TestOpenAIRealtimeWebSocketModel): + """Test connection establishment, configuration, and error handling.""" + + @pytest.mark.asyncio + async def test_connect_missing_api_key_raises_error(self, model): + """Test that missing API key raises UserError.""" + config: dict[str, Any] = {"initial_model_settings": {}} + + with patch.dict("os.environ", {}, clear=True): + with pytest.raises(UserError, match="API key is required"): + await model.connect(config) + + @pytest.mark.asyncio + async def test_connect_with_string_api_key(self, model, mock_websocket): + """Test successful connection with string API key.""" + config = { + "api_key": "test-api-key-123", + "initial_model_settings": {"model_name": "gpt-4o-realtime-preview"}, + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket) as mock_connect: + with patch("asyncio.create_task") as mock_create_task: + # Mock create_task to return a mock task and properly handle the coroutine + mock_task = AsyncMock() + + def mock_create_task_func(coro): + # Properly close the coroutine to avoid RuntimeWarning + coro.close() + return mock_task + + mock_create_task.side_effect = mock_create_task_func + + await model.connect(config) + + # Verify WebSocket connection called with correct parameters + mock_connect.assert_called_once() + call_args = mock_connect.call_args + assert ( + call_args[0][0] + == "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview" + ) + assert ( + call_args[1]["additional_headers"]["Authorization"] == "Bearer test-api-key-123" + ) + assert call_args[1]["additional_headers"]["OpenAI-Beta"] == "realtime=v1" + + # Verify task was created for message listening + mock_create_task.assert_called_once() + + # Verify internal state + assert model._websocket == mock_websocket + assert model._websocket_task is not None + assert model.model == "gpt-4o-realtime-preview" + + @pytest.mark.asyncio + async def test_connect_with_callable_api_key(self, model, mock_websocket): + """Test connection with callable API key provider.""" + + def get_api_key(): + return "callable-api-key" + + config = {"api_key": get_api_key} + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + # Mock create_task to return a mock task and properly handle the coroutine + mock_task = AsyncMock() + + def mock_create_task_func(coro): + # Properly close the coroutine to avoid RuntimeWarning + coro.close() + return mock_task + + mock_create_task.side_effect = mock_create_task_func + + await model.connect(config) + # Should succeed with callable API key + assert model._websocket == mock_websocket + + @pytest.mark.asyncio + async def test_connect_with_async_callable_api_key(self, model, mock_websocket): + """Test connection with async callable API key provider.""" + + async def get_api_key(): + return "async-api-key" + + config = {"api_key": get_api_key} + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + # Mock create_task to return a mock task and properly handle the coroutine + mock_task = AsyncMock() + + def mock_create_task_func(coro): + # Properly close the coroutine to avoid RuntimeWarning + coro.close() + return mock_task + + mock_create_task.side_effect = mock_create_task_func + + await model.connect(config) + assert model._websocket == mock_websocket + + @pytest.mark.asyncio + async def test_connect_websocket_failure_propagates(self, model): + """Test that WebSocket connection failures are properly propagated.""" + config = {"api_key": "test-key"} + + with patch( + "websockets.connect", side_effect=websockets.exceptions.ConnectionClosed(None, None) + ): + with pytest.raises(websockets.exceptions.ConnectionClosed): + await model.connect(config) + + # Verify internal state remains clean after failure + assert model._websocket is None + assert model._websocket_task is None + + @pytest.mark.asyncio + async def test_connect_already_connected_assertion(self, model, mock_websocket): + """Test that connecting when already connected raises assertion error.""" + model._websocket = mock_websocket # Simulate already connected + + config = {"api_key": "test-key"} + + with pytest.raises(AssertionError, match="Already connected"): + await model.connect(config) + + +class TestEventHandlingRobustness(TestOpenAIRealtimeWebSocketModel): + """Test event parsing, validation, and error handling robustness.""" + + @pytest.mark.asyncio + async def test_handle_malformed_json_logs_error_continues(self, model): + """Test that malformed JSON is logged as error but doesn't crash.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Malformed JSON should not crash the handler + with patch("agents.realtime.openai_realtime.logger") as mock_logger: + await model._handle_ws_event("invalid json {") + + # Should log error but not crash + mock_logger.error.assert_called_once() + assert "Invalid event" in mock_logger.error.call_args[0][0] + + # Should not emit any events to listeners + mock_listener.on_event.assert_not_called() + + @pytest.mark.asyncio + async def test_handle_invalid_event_schema_logs_error(self, model): + """Test that events with invalid schema are logged but don't crash.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + invalid_event = {"type": "response.audio.delta"} # Missing required fields + + with patch("agents.realtime.openai_realtime.logger") as mock_logger: + await model._handle_ws_event(invalid_event) + + # Should log validation error + mock_logger.error.assert_called_once() + error_msg = mock_logger.error.call_args[0][0] + assert "Invalid event" in error_msg + + # Should not emit events to listeners + mock_listener.on_event.assert_not_called() + + @pytest.mark.asyncio + async def test_handle_unknown_event_type_ignored(self, model): + """Test that unknown event types are ignored gracefully.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Create a well-formed but unknown event type + unknown_event = {"type": "unknown.event.type", "data": "some data"} + + # Should not raise error or log anything for unknown types + with patch("agents.realtime.openai_realtime.logger"): + await model._handle_ws_event(unknown_event) + + # Should not log errors for unknown events (they're just ignored) + # This will depend on the TypeAdapter validation behavior + # If it fails validation, it should log; if it passes but type is + # unknown, it should be ignored + pass + + @pytest.mark.asyncio + async def test_handle_audio_delta_event_success(self, model): + """Test successful handling of audio delta events.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Valid audio delta event (minimal required fields for OpenAI spec) + audio_event = { + "type": "response.audio.delta", + "event_id": "event_123", + "response_id": "resp_123", + "item_id": "item_456", + "output_index": 0, + "content_index": 0, + "delta": "dGVzdCBhdWRpbw==", # base64 encoded "test audio" + } + + with patch("agents.realtime.openai_realtime.datetime") as mock_datetime: + mock_now = datetime(2024, 1, 1, 12, 0, 0) + mock_datetime.now.return_value = mock_now + + await model._handle_ws_event(audio_event) + + # Should emit audio event to listeners + mock_listener.on_event.assert_called_once() + emitted_event = mock_listener.on_event.call_args[0][0] + assert isinstance(emitted_event, RealtimeModelAudioEvent) + assert emitted_event.response_id == "resp_123" + assert emitted_event.data == b"test audio" # decoded from base64 + + # Should update internal audio tracking state + assert model._current_item_id == "item_456" + assert model._current_audio_content_index == 0 + assert model._audio_start_time == mock_now + + @pytest.mark.asyncio + async def test_handle_error_event_success(self, model): + """Test successful handling of error events.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + error_event = { + "type": "error", + "event_id": "event_456", + "error": { + "type": "invalid_request_error", + "code": "invalid_api_key", + "message": "Invalid API key provided", + }, + } + + await model._handle_ws_event(error_event) + + # Should emit error event to listeners + mock_listener.on_event.assert_called_once() + emitted_event = mock_listener.on_event.call_args[0][0] + assert isinstance(emitted_event, RealtimeModelErrorEvent) + + @pytest.mark.asyncio + async def test_handle_tool_call_event_success(self, model): + """Test successful handling of function call events.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Test response.output_item.done with function_call + tool_call_event = { + "type": "response.output_item.done", + "event_id": "event_789", + "response_id": "resp_789", + "output_index": 0, + "item": { + "id": "call_123", + "type": "function_call", + "status": "completed", + "name": "get_weather", + "arguments": '{"location": "San Francisco"}', + }, + } + + await model._handle_ws_event(tool_call_event) + + # Should emit both item updated and tool call events + assert mock_listener.on_event.call_count == 2 + + # First should be item updated, second should be tool call + calls = mock_listener.on_event.call_args_list + tool_call_emitted = calls[1][0][0] + assert isinstance(tool_call_emitted, RealtimeModelToolCallEvent) + assert tool_call_emitted.name == "get_weather" + assert tool_call_emitted.arguments == '{"location": "San Francisco"}' + assert tool_call_emitted.call_id == "call_123" + + @pytest.mark.asyncio + async def test_audio_timing_calculation_accuracy(self, model): + """Test that audio timing calculations are accurate for interruption handling.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Send multiple audio deltas to test cumulative timing + audio_deltas = [ + { + "type": "response.audio.delta", + "event_id": "event_1", + "response_id": "resp_1", + "item_id": "item_1", + "output_index": 0, + "content_index": 0, + "delta": "dGVzdA==", # 4 bytes -> "test" + }, + { + "type": "response.audio.delta", + "event_id": "event_2", + "response_id": "resp_1", + "item_id": "item_1", + "output_index": 0, + "content_index": 0, + "delta": "bW9yZQ==", # 4 bytes -> "more" + }, + ] + + for event in audio_deltas: + await model._handle_ws_event(event) + + # Should accumulate audio length: 8 bytes / 24 / 2 = ~0.167ms per byte + # Total: 8 bytes / 24 / 2 = 0.167ms + expected_length = 8 / 24 / 2 + assert abs(model._audio_length_ms - expected_length) < 0.001 + + def test_calculate_audio_length_ms_pure_function(self, model): + """Test the pure audio length calculation function.""" + # Test various audio buffer sizes + assert model._calculate_audio_length_ms(b"test") == 4 / 24 / 2 # 4 bytes + assert model._calculate_audio_length_ms(b"") == 0 # empty + assert model._calculate_audio_length_ms(b"a" * 48) == 1.0 # exactly 1ms worth + + @pytest.mark.asyncio + async def test_handle_audio_delta_state_management(self, model): + """Test that _handle_audio_delta properly manages internal state.""" + # Create mock parsed event + mock_parsed = Mock() + mock_parsed.content_index = 5 + mock_parsed.item_id = "test_item" + mock_parsed.delta = "dGVzdA==" # "test" in base64 + mock_parsed.response_id = "resp_123" + + with patch("agents.realtime.openai_realtime.datetime") as mock_datetime: + mock_now = datetime(2024, 1, 1, 12, 0, 0) + mock_datetime.now.return_value = mock_now + + await model._handle_audio_delta(mock_parsed) + + # Check state was updated correctly + assert model._current_audio_content_index == 5 + assert model._current_item_id == "test_item" + assert model._audio_start_time == mock_now + assert model._audio_length_ms == 4 / 24 / 2 # 4 bytes diff --git a/tests/realtime/test_runner.py b/tests/realtime/test_runner.py new file mode 100644 index 000000000..aabdff140 --- /dev/null +++ b/tests/realtime/test_runner.py @@ -0,0 +1,224 @@ +from unittest.mock import AsyncMock, Mock, patch + +import pytest +from inline_snapshot import snapshot + +from agents.realtime.agent import RealtimeAgent +from agents.realtime.config import RealtimeRunConfig, RealtimeSessionModelSettings +from agents.realtime.model import RealtimeModel, RealtimeModelConfig +from agents.realtime.runner import RealtimeRunner +from agents.realtime.session import RealtimeSession + + +class MockRealtimeModel(RealtimeModel): + async def connect(self, options=None): + pass + + def add_listener(self, listener): + pass + + def remove_listener(self, listener): + pass + + async def send_event(self, event): + pass + + async def send_message(self, message, other_event_data=None): + pass + + async def send_audio(self, audio, commit=False): + pass + + async def send_tool_output(self, tool_call, output, start_response=True): + pass + + async def interrupt(self): + pass + + async def close(self): + pass + + +@pytest.fixture +def mock_agent(): + agent = Mock(spec=RealtimeAgent) + agent.get_system_prompt = AsyncMock(return_value="Test instructions") + agent.get_all_tools = AsyncMock(return_value=[{"type": "function", "name": "test_tool"}]) + return agent + + +@pytest.fixture +def mock_model(): + return MockRealtimeModel() + + +@pytest.mark.asyncio +async def test_run_creates_session_with_no_settings(mock_agent, mock_model): + """Test that run() creates a session correctly if no settings are provided""" + runner = RealtimeRunner(mock_agent, model=mock_model) + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + session = await runner.run() + + # Verify session was created with correct parameters + mock_session_class.assert_called_once() + call_args = mock_session_class.call_args + + assert call_args[1]["model"] == mock_model + assert call_args[1]["agent"] == mock_agent + assert call_args[1]["context"] is None + + # Verify model_config contains expected settings from agent + model_config = call_args[1]["model_config"] + assert model_config == snapshot( + { + "initial_model_settings": { + "instructions": "Test instructions", + "tools": [{"type": "function", "name": "test_tool"}], + } + } + ) + + assert session == mock_session + + +@pytest.mark.asyncio +async def test_run_creates_session_with_settings_only_in_init(mock_agent, mock_model): + """Test that it creates a session with the right settings if they are provided only in init""" + config = RealtimeRunConfig( + model_settings=RealtimeSessionModelSettings(model_name="gpt-4o-realtime", voice="nova") + ) + runner = RealtimeRunner(mock_agent, model=mock_model, config=config) + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + _ = await runner.run() + + # Verify session was created with config overrides + call_args = mock_session_class.call_args + model_config = call_args[1]["model_config"] + + # Should have agent settings plus config overrides + assert model_config == snapshot( + { + "initial_model_settings": { + "instructions": "Test instructions", + "tools": [{"type": "function", "name": "test_tool"}], + "model_name": "gpt-4o-realtime", + "voice": "nova", + } + } + ) + + +@pytest.mark.asyncio +async def test_run_creates_session_with_settings_in_both_init_and_run_overrides( + mock_agent, mock_model +): + """Test settings in both init and run() - init should override run()""" + init_config = RealtimeRunConfig( + model_settings=RealtimeSessionModelSettings(model_name="gpt-4o-realtime", voice="nova") + ) + runner = RealtimeRunner(mock_agent, model=mock_model, config=init_config) + + run_model_config: RealtimeModelConfig = { + "initial_model_settings": RealtimeSessionModelSettings( + voice="alloy", input_audio_format="pcm16" + ) + } + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + _ = await runner.run(model_config=run_model_config) + + # Verify run() settings override init settings + call_args = mock_session_class.call_args + model_config = call_args[1]["model_config"] + + # Should have agent settings, then init config, then run config overrides + assert model_config == snapshot( + { + "initial_model_settings": { + "voice": "nova", + "input_audio_format": "pcm16", + "instructions": "Test instructions", + "tools": [{"type": "function", "name": "test_tool"}], + "model_name": "gpt-4o-realtime", + } + } + ) + + +@pytest.mark.asyncio +async def test_run_creates_session_with_settings_only_in_run(mock_agent, mock_model): + """Test settings provided only in run()""" + runner = RealtimeRunner(mock_agent, model=mock_model) + + run_model_config: RealtimeModelConfig = { + "initial_model_settings": RealtimeSessionModelSettings( + model_name="gpt-4o-realtime-preview", voice="shimmer", modalities=["text", "audio"] + ) + } + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + _ = await runner.run(model_config=run_model_config) + + # Verify run() settings are applied + call_args = mock_session_class.call_args + model_config = call_args[1]["model_config"] + + # Should have agent settings plus run() settings + assert model_config == snapshot( + { + "initial_model_settings": { + "model_name": "gpt-4o-realtime-preview", + "voice": "shimmer", + "modalities": ["text", "audio"], + "instructions": "Test instructions", + "tools": [{"type": "function", "name": "test_tool"}], + } + } + ) + + +@pytest.mark.asyncio +async def test_run_with_context_parameter(mock_agent, mock_model): + """Test that context parameter is passed through to session""" + runner = RealtimeRunner(mock_agent, model=mock_model) + test_context = {"user_id": "test123"} + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + await runner.run(context=test_context) + + call_args = mock_session_class.call_args + assert call_args[1]["context"] == test_context + + +@pytest.mark.asyncio +async def test_get_model_settings_with_none_values(mock_model): + """Test _get_model_settings handles None values from agent properly""" + agent = Mock(spec=RealtimeAgent) + agent.get_system_prompt = AsyncMock(return_value=None) + agent.get_all_tools = AsyncMock(return_value=None) + + runner = RealtimeRunner(agent, model=mock_model) + + with patch("agents.realtime.runner.RealtimeSession"): + await runner.run() + + # Should not crash and agent methods should be called + agent.get_system_prompt.assert_called_once() + agent.get_all_tools.assert_called_once() diff --git a/tests/realtime/test_session.py b/tests/realtime/test_session.py new file mode 100644 index 000000000..6003f9443 --- /dev/null +++ b/tests/realtime/test_session.py @@ -0,0 +1,1159 @@ +from typing import cast +from unittest.mock import AsyncMock, Mock + +import pytest + +from agents.guardrail import GuardrailFunctionOutput, OutputGuardrail +from agents.handoffs import Handoff +from agents.realtime.agent import RealtimeAgent +from agents.realtime.config import RealtimeRunConfig +from agents.realtime.events import ( + RealtimeAgentEndEvent, + RealtimeAgentStartEvent, + RealtimeAudio, + RealtimeAudioEnd, + RealtimeAudioInterrupted, + RealtimeError, + RealtimeGuardrailTripped, + RealtimeHistoryAdded, + RealtimeHistoryUpdated, + RealtimeRawModelEvent, + RealtimeToolEnd, + RealtimeToolStart, +) +from agents.realtime.items import ( + AssistantMessageItem, + AssistantText, + InputAudio, + InputText, + RealtimeItem, + UserMessageItem, +) +from agents.realtime.model import RealtimeModel +from agents.realtime.model_events import ( + RealtimeModelAudioDoneEvent, + RealtimeModelAudioEvent, + RealtimeModelAudioInterruptedEvent, + RealtimeModelConnectionStatusEvent, + RealtimeModelErrorEvent, + RealtimeModelInputAudioTranscriptionCompletedEvent, + RealtimeModelItemDeletedEvent, + RealtimeModelItemUpdatedEvent, + RealtimeModelOtherEvent, + RealtimeModelToolCallEvent, + RealtimeModelTranscriptDeltaEvent, + RealtimeModelTurnEndedEvent, + RealtimeModelTurnStartedEvent, +) +from agents.realtime.session import RealtimeSession +from agents.tool import FunctionTool +from agents.tool_context import ToolContext + + +class MockRealtimeModel(RealtimeModel): + def __init__(self): + super().__init__() + self.listeners = [] + self.connect_called = False + self.close_called = False + self.sent_messages = [] + self.sent_audio = [] + self.sent_tool_outputs = [] + self.interrupts_called = 0 + + async def connect(self, options=None): + self.connect_called = True + + def add_listener(self, listener): + self.listeners.append(listener) + + def remove_listener(self, listener): + if listener in self.listeners: + self.listeners.remove(listener) + + async def send_event(self, event): + pass + + async def send_message(self, message, other_event_data=None): + self.sent_messages.append(message) + + async def send_audio(self, audio, commit=False): + self.sent_audio.append((audio, commit)) + + async def send_tool_output(self, tool_call, output, start_response=True): + self.sent_tool_outputs.append((tool_call, output, start_response)) + + async def interrupt(self): + self.interrupts_called += 1 + + async def close(self): + self.close_called = True + + +@pytest.fixture +def mock_agent(): + agent = Mock(spec=RealtimeAgent) + agent.get_all_tools = AsyncMock(return_value=[]) + return agent + + +@pytest.fixture +def mock_model(): + return MockRealtimeModel() + + +@pytest.fixture +def mock_function_tool(): + tool = Mock(spec=FunctionTool) + tool.name = "test_function" + tool.on_invoke_tool = AsyncMock(return_value="function_result") + return tool + + +@pytest.fixture +def mock_handoff(): + handoff = Mock(spec=Handoff) + handoff.name = "test_handoff" + return handoff + + +class TestEventHandling: + """Test suite for event handling and transformation in RealtimeSession.on_event""" + + @pytest.mark.asyncio + async def test_error_event_transformation(self, mock_model, mock_agent): + """Test that error events are properly transformed and queued""" + session = RealtimeSession(mock_model, mock_agent, None) + + error_event = RealtimeModelErrorEvent(error="Test error") + + await session.on_event(error_event) + + # Check that events were queued + assert session._event_queue.qsize() == 2 + + # First event should be raw model event + raw_event = await session._event_queue.get() + assert isinstance(raw_event, RealtimeRawModelEvent) + assert raw_event.data == error_event + + # Second event should be transformed error event + error_session_event = await session._event_queue.get() + assert isinstance(error_session_event, RealtimeError) + assert error_session_event.error == "Test error" + + @pytest.mark.asyncio + async def test_audio_events_transformation(self, mock_model, mock_agent): + """Test that audio-related events are properly transformed""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Test audio event + audio_event = RealtimeModelAudioEvent(data=b"audio_data", response_id="resp_1") + await session.on_event(audio_event) + + # Test audio interrupted event + interrupted_event = RealtimeModelAudioInterruptedEvent() + await session.on_event(interrupted_event) + + # Test audio done event + done_event = RealtimeModelAudioDoneEvent() + await session.on_event(done_event) + + # Should have 6 events total (2 per event: raw + transformed) + assert session._event_queue.qsize() == 6 + + # Check audio event transformation + await session._event_queue.get() # raw event + audio_session_event = await session._event_queue.get() + assert isinstance(audio_session_event, RealtimeAudio) + assert audio_session_event.audio == audio_event + + # Check audio interrupted transformation + await session._event_queue.get() # raw event + interrupted_session_event = await session._event_queue.get() + assert isinstance(interrupted_session_event, RealtimeAudioInterrupted) + + # Check audio done transformation + await session._event_queue.get() # raw event + done_session_event = await session._event_queue.get() + assert isinstance(done_session_event, RealtimeAudioEnd) + + @pytest.mark.asyncio + async def test_turn_events_transformation(self, mock_model, mock_agent): + """Test that turn start/end events are properly transformed""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Test turn started event + turn_started = RealtimeModelTurnStartedEvent() + await session.on_event(turn_started) + + # Test turn ended event + turn_ended = RealtimeModelTurnEndedEvent() + await session.on_event(turn_ended) + + # Should have 4 events total (2 per event: raw + transformed) + assert session._event_queue.qsize() == 4 + + # Check turn started transformation + await session._event_queue.get() # raw event + start_session_event = await session._event_queue.get() + assert isinstance(start_session_event, RealtimeAgentStartEvent) + assert start_session_event.agent == mock_agent + + # Check turn ended transformation + await session._event_queue.get() # raw event + end_session_event = await session._event_queue.get() + assert isinstance(end_session_event, RealtimeAgentEndEvent) + assert end_session_event.agent == mock_agent + + @pytest.mark.asyncio + async def test_transcription_completed_event_updates_history(self, mock_model, mock_agent): + """Test that transcription completed events update history and emit events""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Set up initial history with an audio message + initial_item = UserMessageItem( + item_id="item_1", role="user", content=[InputAudio(transcript=None)] + ) + session._history = [initial_item] + + # Create transcription completed event + transcription_event = RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id="item_1", transcript="Hello world" + ) + + await session.on_event(transcription_event) + + # Check that history was updated + assert len(session._history) == 1 + updated_item = session._history[0] + assert updated_item.content[0].transcript == "Hello world" # type: ignore + assert updated_item.status == "completed" # type: ignore + + # Should have 2 events: raw + history updated + assert session._event_queue.qsize() == 2 + + await session._event_queue.get() # raw event + history_event = await session._event_queue.get() + assert isinstance(history_event, RealtimeHistoryUpdated) + assert len(history_event.history) == 1 + + @pytest.mark.asyncio + async def test_item_updated_event_adds_new_item(self, mock_model, mock_agent): + """Test that item_updated events add new items to history""" + session = RealtimeSession(mock_model, mock_agent, None) + + new_item = AssistantMessageItem( + item_id="new_item", role="assistant", content=[AssistantText(text="Hello")] + ) + + item_updated_event = RealtimeModelItemUpdatedEvent(item=new_item) + + await session.on_event(item_updated_event) + + # Check that item was added to history + assert len(session._history) == 1 + assert session._history[0] == new_item + + # Should have 2 events: raw + history added + assert session._event_queue.qsize() == 2 + + await session._event_queue.get() # raw event + history_event = await session._event_queue.get() + assert isinstance(history_event, RealtimeHistoryAdded) + assert history_event.item == new_item + + @pytest.mark.asyncio + async def test_item_updated_event_updates_existing_item(self, mock_model, mock_agent): + """Test that item_updated events update existing items in history""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Set up initial history + initial_item = AssistantMessageItem( + item_id="existing_item", role="assistant", content=[AssistantText(text="Initial")] + ) + session._history = [initial_item] + + # Create updated version + updated_item = AssistantMessageItem( + item_id="existing_item", role="assistant", content=[AssistantText(text="Updated")] + ) + + item_updated_event = RealtimeModelItemUpdatedEvent(item=updated_item) + + await session.on_event(item_updated_event) + + # Check that item was updated + assert len(session._history) == 1 + updated_item = cast(AssistantMessageItem, session._history[0]) + assert cast(AssistantText, updated_item.content[0]).text == "Updated" + + # Should have 2 events: raw + history updated (not added) + assert session._event_queue.qsize() == 2 + + await session._event_queue.get() # raw event + history_event = await session._event_queue.get() + assert isinstance(history_event, RealtimeHistoryUpdated) + + @pytest.mark.asyncio + async def test_item_deleted_event_removes_item(self, mock_model, mock_agent): + """Test that item_deleted events remove items from history""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Set up initial history with multiple items + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + item2 = AssistantMessageItem( + item_id="item_2", role="assistant", content=[AssistantText(text="Second")] + ) + session._history = [item1, item2] + + # Delete first item + delete_event = RealtimeModelItemDeletedEvent(item_id="item_1") + + await session.on_event(delete_event) + + # Check that item was removed + assert len(session._history) == 1 + assert session._history[0].item_id == "item_2" + + # Should have 2 events: raw + history updated + assert session._event_queue.qsize() == 2 + + await session._event_queue.get() # raw event + history_event = await session._event_queue.get() + assert isinstance(history_event, RealtimeHistoryUpdated) + assert len(history_event.history) == 1 + + @pytest.mark.asyncio + async def test_ignored_events_only_generate_raw_events(self, mock_model, mock_agent): + """Test that ignored events (transcript_delta, connection_status, other) only generate raw + events""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Test transcript delta (should be ignored per TODO comment) + transcript_event = RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="hello", response_id="resp_1" + ) + await session.on_event(transcript_event) + + # Test connection status (should be ignored) + connection_event = RealtimeModelConnectionStatusEvent(status="connected") + await session.on_event(connection_event) + + # Test other event (should be ignored) + other_event = RealtimeModelOtherEvent(data={"custom": "data"}) + await session.on_event(other_event) + + # Should only have 3 raw events (no transformed events) + assert session._event_queue.qsize() == 3 + + for _ in range(3): + event = await session._event_queue.get() + assert isinstance(event, RealtimeRawModelEvent) + + @pytest.mark.asyncio + async def test_function_call_event_triggers_tool_handling(self, mock_model, mock_agent): + """Test that function_call events trigger tool call handling""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Create function call event + function_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_123", arguments='{"param": "value"}' + ) + + # We'll test the detailed tool handling in a separate test class + # Here we just verify that it gets to the handler + with pytest.MonkeyPatch().context() as m: + handle_tool_call_mock = AsyncMock() + m.setattr(session, "_handle_tool_call", handle_tool_call_mock) + + await session.on_event(function_call_event) + + # Should have called the tool handler + handle_tool_call_mock.assert_called_once_with(function_call_event) + + # Should still have raw event + assert session._event_queue.qsize() == 1 + raw_event = await session._event_queue.get() + assert isinstance(raw_event, RealtimeRawModelEvent) + assert raw_event.data == function_call_event + + +class TestHistoryManagement: + """Test suite for history management and audio transcription in + RealtimeSession._get_new_history""" + + def test_merge_transcript_into_existing_audio_message(self): + """Test merging audio transcript into existing placeholder input_audio message""" + # Create initial history with audio message without transcript + initial_item = UserMessageItem( + item_id="item_1", + role="user", + content=[ + InputText(text="Before audio"), + InputAudio(transcript=None, audio="audio_data"), + InputText(text="After audio"), + ], + ) + old_history = [initial_item] + + # Create transcription completed event + transcription_event = RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id="item_1", transcript="Hello world" + ) + + # Apply the history update + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), transcription_event + ) + + # Verify the transcript was merged + assert len(new_history) == 1 + updated_item = cast(UserMessageItem, new_history[0]) + assert updated_item.item_id == "item_1" + assert hasattr(updated_item, "status") and updated_item.status == "completed" + assert len(updated_item.content) == 3 + + # Check that audio content got transcript but other content unchanged + assert cast(InputText, updated_item.content[0]).text == "Before audio" + assert cast(InputAudio, updated_item.content[1]).transcript == "Hello world" + # Should preserve audio data + assert cast(InputAudio, updated_item.content[1]).audio == "audio_data" + assert cast(InputText, updated_item.content[2]).text == "After audio" + + def test_merge_transcript_preserves_other_items(self): + """Test that merging transcript preserves other items in history""" + # Create history with multiple items + item1 = UserMessageItem( + item_id="item_1", role="user", content=[InputText(text="First message")] + ) + item2 = UserMessageItem( + item_id="item_2", role="user", content=[InputAudio(transcript=None)] + ) + item3 = AssistantMessageItem( + item_id="item_3", role="assistant", content=[AssistantText(text="Third message")] + ) + old_history = [item1, item2, item3] + + # Create transcription event for item_2 + transcription_event = RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id="item_2", transcript="Transcribed audio" + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), transcription_event + ) + + # Should have same number of items + assert len(new_history) == 3 + + # First and third items should be unchanged + assert new_history[0] == item1 + assert new_history[2] == item3 + + # Second item should have transcript + updated_item2 = cast(UserMessageItem, new_history[1]) + assert updated_item2.item_id == "item_2" + assert cast(InputAudio, updated_item2.content[0]).transcript == "Transcribed audio" + assert hasattr(updated_item2, "status") and updated_item2.status == "completed" + + def test_merge_transcript_only_affects_matching_audio_content(self): + """Test that transcript merge only affects audio content, not text content""" + # Create item with mixed content including multiple audio items + item = UserMessageItem( + item_id="item_1", + role="user", + content=[ + InputText(text="Text content"), + InputAudio(transcript=None, audio="audio1"), + InputAudio(transcript="existing", audio="audio2"), + InputText(text="More text"), + ], + ) + old_history = [item] + + transcription_event = RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id="item_1", transcript="New transcript" + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), transcription_event + ) + + updated_item = cast(UserMessageItem, new_history[0]) + + # Text content should be unchanged + assert cast(InputText, updated_item.content[0]).text == "Text content" + assert cast(InputText, updated_item.content[3]).text == "More text" + + # All audio content should have the new transcript (current implementation overwrites all) + assert cast(InputAudio, updated_item.content[1]).transcript == "New transcript" + assert ( + cast(InputAudio, updated_item.content[2]).transcript == "New transcript" + ) # Implementation overwrites existing + + def test_update_existing_item_by_id(self): + """Test updating an existing item by item_id""" + # Create initial history + original_item = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="Original")] + ) + old_history = [original_item] + + # Create updated version of same item + updated_item = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="Updated")] + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), updated_item + ) + + # Should have same number of items + assert len(new_history) == 1 + + # Item should be updated + result_item = cast(AssistantMessageItem, new_history[0]) + assert result_item.item_id == "item_1" + assert cast(AssistantText, result_item.content[0]).text == "Updated" + + def test_update_existing_item_preserves_order(self): + """Test that updating existing item preserves its position in history""" + # Create history with multiple items + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + item2 = AssistantMessageItem( + item_id="item_2", role="assistant", content=[AssistantText(text="Second")] + ) + item3 = AssistantMessageItem( + item_id="item_3", role="assistant", content=[AssistantText(text="Third")] + ) + old_history = [item1, item2, item3] + + # Update middle item + updated_item2 = AssistantMessageItem( + item_id="item_2", role="assistant", content=[AssistantText(text="Updated Second")] + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), updated_item2 + ) + + # Should have same number of items in same order + assert len(new_history) == 3 + assert new_history[0].item_id == "item_1" + assert new_history[1].item_id == "item_2" + assert new_history[2].item_id == "item_3" + + # Middle item should be updated + updated_result = cast(AssistantMessageItem, new_history[1]) + assert cast(AssistantText, updated_result.content[0]).text == "Updated Second" + + # Other items should be unchanged + item1_result = cast(AssistantMessageItem, new_history[0]) + item3_result = cast(AssistantMessageItem, new_history[2]) + assert cast(AssistantText, item1_result.content[0]).text == "First" + assert cast(AssistantText, item3_result.content[0]).text == "Third" + + def test_insert_new_item_after_previous_item(self): + """Test inserting new item after specified previous_item_id""" + # Create initial history + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + item3 = AssistantMessageItem( + item_id="item_3", role="assistant", content=[AssistantText(text="Third")] + ) + old_history = [item1, item3] + + # Create new item to insert between them + new_item = AssistantMessageItem( + item_id="item_2", + previous_item_id="item_1", + role="assistant", + content=[AssistantText(text="Second")], + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), new_item + ) + + # Should have one more item + assert len(new_history) == 3 + + # Items should be in correct order + assert new_history[0].item_id == "item_1" + assert new_history[1].item_id == "item_2" + assert new_history[2].item_id == "item_3" + + # Content should be correct + item2_result = cast(AssistantMessageItem, new_history[1]) + assert cast(AssistantText, item2_result.content[0]).text == "Second" + + def test_insert_new_item_after_nonexistent_previous_item(self): + """Test that item with nonexistent previous_item_id gets added to end""" + # Create initial history + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + old_history = [item1] + + # Create new item with nonexistent previous_item_id + new_item = AssistantMessageItem( + item_id="item_2", + previous_item_id="nonexistent", + role="assistant", + content=[AssistantText(text="Second")], + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), new_item + ) + + # Should add to end when previous_item_id not found + assert len(new_history) == 2 + assert new_history[0].item_id == "item_1" + assert new_history[1].item_id == "item_2" + + def test_add_new_item_to_end_when_no_previous_item_id(self): + """Test adding new item to end when no previous_item_id is specified""" + # Create initial history + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + old_history = [item1] + + # Create new item without previous_item_id + new_item = AssistantMessageItem( + item_id="item_2", role="assistant", content=[AssistantText(text="Second")] + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), new_item + ) + + # Should add to end + assert len(new_history) == 2 + assert new_history[0].item_id == "item_1" + assert new_history[1].item_id == "item_2" + + def test_add_first_item_to_empty_history(self): + """Test adding first item to empty history""" + old_history: list[RealtimeItem] = [] + + new_item = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + + new_history = RealtimeSession._get_new_history(old_history, new_item) + + assert len(new_history) == 1 + assert new_history[0].item_id == "item_1" + + def test_complex_insertion_scenario(self): + """Test complex scenario with multiple insertions and updates""" + # Start with items A and C + itemA = AssistantMessageItem( + item_id="A", role="assistant", content=[AssistantText(text="A")] + ) + itemC = AssistantMessageItem( + item_id="C", role="assistant", content=[AssistantText(text="C")] + ) + history: list[RealtimeItem] = [itemA, itemC] + + # Insert B after A + itemB = AssistantMessageItem( + item_id="B", previous_item_id="A", role="assistant", content=[AssistantText(text="B")] + ) + history = RealtimeSession._get_new_history(history, itemB) + + # Should be A, B, C + assert len(history) == 3 + assert [item.item_id for item in history] == ["A", "B", "C"] + + # Insert D after B + itemD = AssistantMessageItem( + item_id="D", previous_item_id="B", role="assistant", content=[AssistantText(text="D")] + ) + history = RealtimeSession._get_new_history(history, itemD) + + # Should be A, B, D, C + assert len(history) == 4 + assert [item.item_id for item in history] == ["A", "B", "D", "C"] + + # Update B + updated_itemB = AssistantMessageItem( + item_id="B", role="assistant", content=[AssistantText(text="Updated B")] + ) + history = RealtimeSession._get_new_history(history, updated_itemB) + + # Should still be A, B, D, C but B is updated + assert len(history) == 4 + assert [item.item_id for item in history] == ["A", "B", "D", "C"] + itemB_result = cast(AssistantMessageItem, history[1]) + assert cast(AssistantText, itemB_result.content[0]).text == "Updated B" + + +# Test 3: Tool call execution flow (_handle_tool_call method) +class TestToolCallExecution: + """Test suite for tool call execution flow in RealtimeSession._handle_tool_call""" + + @pytest.mark.asyncio + async def test_function_tool_execution_success( + self, mock_model, mock_agent, mock_function_tool + ): + """Test successful function tool execution""" + # Set up agent to return our mock tool + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + # Create function call event + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_123", arguments='{"param": "value"}' + ) + + await session._handle_tool_call(tool_call_event) + + # Verify the flow + mock_agent.get_all_tools.assert_called_once() + mock_function_tool.on_invoke_tool.assert_called_once() + + # Check the tool context was created correctly + call_args = mock_function_tool.on_invoke_tool.call_args + tool_context = call_args[0][0] + assert isinstance(tool_context, ToolContext) + assert call_args[0][1] == '{"param": "value"}' + + # Verify tool output was sent to model + assert len(mock_model.sent_tool_outputs) == 1 + sent_call, sent_output, start_response = mock_model.sent_tool_outputs[0] + assert sent_call == tool_call_event + assert sent_output == "function_result" + assert start_response is True + + # Verify events were queued + assert session._event_queue.qsize() == 2 + + # Check tool start event + tool_start_event = await session._event_queue.get() + assert isinstance(tool_start_event, RealtimeToolStart) + assert tool_start_event.tool == mock_function_tool + assert tool_start_event.agent == mock_agent + + # Check tool end event + tool_end_event = await session._event_queue.get() + assert isinstance(tool_end_event, RealtimeToolEnd) + assert tool_end_event.tool == mock_function_tool + assert tool_end_event.output == "function_result" + assert tool_end_event.agent == mock_agent + + @pytest.mark.asyncio + async def test_function_tool_with_multiple_tools_available(self, mock_model, mock_agent): + """Test function tool execution when multiple tools are available""" + # Create multiple mock tools + tool1 = Mock(spec=FunctionTool) + tool1.name = "tool_one" + tool1.on_invoke_tool = AsyncMock(return_value="result_one") + + tool2 = Mock(spec=FunctionTool) + tool2.name = "tool_two" + tool2.on_invoke_tool = AsyncMock(return_value="result_two") + + handoff = Mock(spec=Handoff) + handoff.name = "handoff_tool" + + # Set up agent to return all tools + mock_agent.get_all_tools.return_value = [tool1, tool2, handoff] + + session = RealtimeSession(mock_model, mock_agent, None) + + # Call tool_two + tool_call_event = RealtimeModelToolCallEvent( + name="tool_two", call_id="call_456", arguments='{"test": "data"}' + ) + + await session._handle_tool_call(tool_call_event) + + # Only tool2 should have been called + tool1.on_invoke_tool.assert_not_called() + tool2.on_invoke_tool.assert_called_once() + + # Verify correct result was sent + sent_call, sent_output, _ = mock_model.sent_tool_outputs[0] + assert sent_output == "result_two" + + @pytest.mark.asyncio + async def test_handoff_tool_handling_todo(self, mock_model, mock_agent, mock_handoff): + """Test that handoff tools are recognized but not yet implemented""" + # Set up agent to return handoff tool + mock_agent.get_all_tools.return_value = [mock_handoff] + + session = RealtimeSession(mock_model, mock_agent, None) + + tool_call_event = RealtimeModelToolCallEvent( + name="test_handoff", call_id="call_789", arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Should not have sent any tool outputs (handoffs not implemented) + assert len(mock_model.sent_tool_outputs) == 0 + + # Should not have queued any events (handoffs not implemented) + assert session._event_queue.qsize() == 0 + + @pytest.mark.asyncio + async def test_unknown_tool_handling_todo(self, mock_model, mock_agent, mock_function_tool): + """Test that unknown tools are handled gracefully (TODO: add error handling)""" + # Set up agent to return different tool than what's called + mock_function_tool.name = "known_tool" + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + # Call unknown tool + tool_call_event = RealtimeModelToolCallEvent( + name="unknown_tool", call_id="call_unknown", arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Should not have called any tools + mock_function_tool.on_invoke_tool.assert_not_called() + + # Should not have sent any tool outputs + assert len(mock_model.sent_tool_outputs) == 0 + + # Should not have queued any events + assert session._event_queue.qsize() == 0 + + @pytest.mark.asyncio + async def test_function_tool_exception_handling( + self, mock_model, mock_agent, mock_function_tool + ): + """Test that exceptions in function tools are handled (currently they propagate)""" + # Set up tool to raise exception + mock_function_tool.on_invoke_tool.side_effect = ValueError("Tool error") + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_error", arguments="{}" + ) + + # Currently exceptions propagate (no error handling implemented) + with pytest.raises(ValueError, match="Tool error"): + await session._handle_tool_call(tool_call_event) + + # Tool start event should have been queued before the error + assert session._event_queue.qsize() == 1 + tool_start_event = await session._event_queue.get() + assert isinstance(tool_start_event, RealtimeToolStart) + + # But no tool output should have been sent and no end event queued + assert len(mock_model.sent_tool_outputs) == 0 + + @pytest.mark.asyncio + async def test_tool_call_with_complex_arguments( + self, mock_model, mock_agent, mock_function_tool + ): + """Test tool call with complex JSON arguments""" + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + # Complex arguments + complex_args = '{"nested": {"data": [1, 2, 3]}, "bool": true, "null": null}' + + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_complex", arguments=complex_args + ) + + await session._handle_tool_call(tool_call_event) + + # Verify arguments were passed correctly + call_args = mock_function_tool.on_invoke_tool.call_args + assert call_args[0][1] == complex_args + + @pytest.mark.asyncio + async def test_tool_call_with_custom_call_id(self, mock_model, mock_agent, mock_function_tool): + """Test that tool context receives correct call_id""" + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + custom_call_id = "custom_call_id_12345" + + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id=custom_call_id, arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Verify tool context was created with correct call_id + call_args = mock_function_tool.on_invoke_tool.call_args + tool_context = call_args[0][0] + # The call_id is used internally in ToolContext.from_agent_context + # We can't directly access it, but we can verify the context was created + assert isinstance(tool_context, ToolContext) + + @pytest.mark.asyncio + async def test_tool_result_conversion_to_string(self, mock_model, mock_agent): + """Test that tool results are converted to strings for model output""" + # Create tool that returns non-string result + tool = Mock(spec=FunctionTool) + tool.name = "test_function" + tool.on_invoke_tool = AsyncMock(return_value={"result": "data", "count": 42}) + + mock_agent.get_all_tools.return_value = [tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_conversion", arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Verify result was converted to string + sent_call, sent_output, _ = mock_model.sent_tool_outputs[0] + assert isinstance(sent_output, str) + assert sent_output == "{'result': 'data', 'count': 42}" + + @pytest.mark.asyncio + async def test_mixed_tool_types_filtering(self, mock_model, mock_agent): + """Test that function tools and handoffs are properly separated""" + # Create mixed tools + func_tool1 = Mock(spec=FunctionTool) + func_tool1.name = "func1" + func_tool1.on_invoke_tool = AsyncMock(return_value="result1") + + handoff1 = Mock(spec=Handoff) + handoff1.name = "handoff1" + + func_tool2 = Mock(spec=FunctionTool) + func_tool2.name = "func2" + func_tool2.on_invoke_tool = AsyncMock(return_value="result2") + + handoff2 = Mock(spec=Handoff) + handoff2.name = "handoff2" + + # Add some other object that's neither (should be ignored) + other_tool = Mock() + other_tool.name = "other" + + all_tools = [func_tool1, handoff1, func_tool2, handoff2, other_tool] + mock_agent.get_all_tools.return_value = all_tools + + session = RealtimeSession(mock_model, mock_agent, None) + + # Call a function tool + tool_call_event = RealtimeModelToolCallEvent( + name="func2", call_id="call_filtering", arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Only func2 should have been called + func_tool1.on_invoke_tool.assert_not_called() + func_tool2.on_invoke_tool.assert_called_once() + + # Verify result + sent_call, sent_output, _ = mock_model.sent_tool_outputs[0] + assert sent_output == "result2" + + +class TestGuardrailFunctionality: + """Test suite for output guardrail functionality in RealtimeSession""" + + @pytest.fixture + def triggered_guardrail(self): + """Creates a guardrail that always triggers""" + def guardrail_func(context, agent, output): + return GuardrailFunctionOutput( + output_info={"reason": "test trigger"}, + tripwire_triggered=True + ) + return OutputGuardrail(guardrail_function=guardrail_func, name="triggered_guardrail") + + @pytest.fixture + def safe_guardrail(self): + """Creates a guardrail that never triggers""" + def guardrail_func(context, agent, output): + return GuardrailFunctionOutput( + output_info={"reason": "safe content"}, + tripwire_triggered=False + ) + return OutputGuardrail(guardrail_function=guardrail_func, name="safe_guardrail") + + @pytest.mark.asyncio + async def test_transcript_delta_triggers_guardrail_at_threshold( + self, mock_model, mock_agent, triggered_guardrail + ): + """Test that guardrails run when transcript delta reaches debounce threshold""" + run_config: RealtimeRunConfig = { + "output_guardrails": [triggered_guardrail], + "guardrails_settings": {"debounce_text_length": 10} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + # Send transcript delta that exceeds threshold (10 chars) + transcript_event = RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="this is more than ten characters", response_id="resp_1" + ) + + await session.on_event(transcript_event) + + # Should have triggered guardrail and interrupted + assert session._interrupted_by_guardrail is True + assert mock_model.interrupts_called == 1 + assert len(mock_model.sent_messages) == 1 + assert "triggered_guardrail" in mock_model.sent_messages[0] + + # Should have emitted guardrail_tripped event + events = [] + while not session._event_queue.empty(): + events.append(await session._event_queue.get()) + + guardrail_events = [e for e in events if isinstance(e, RealtimeGuardrailTripped)] + assert len(guardrail_events) == 1 + assert guardrail_events[0].message == "this is more than ten characters" + + @pytest.mark.asyncio + async def test_transcript_delta_multiple_thresholds_same_item( + self, mock_model, mock_agent, triggered_guardrail + ): + """Test guardrails run at 1x, 2x, 3x thresholds for same item_id""" + run_config: RealtimeRunConfig = { + "output_guardrails": [triggered_guardrail], + "guardrails_settings": {"debounce_text_length": 5} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + # First delta - reaches 1x threshold (5 chars) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="12345", response_id="resp_1" + )) + + # Second delta - reaches 2x threshold (10 chars total) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="67890", response_id="resp_1" + )) + + # Should only trigger once due to interrupted_by_guardrail flag + assert mock_model.interrupts_called == 1 + assert len(mock_model.sent_messages) == 1 + + @pytest.mark.asyncio + async def test_transcript_delta_different_items_tracked_separately( + self, mock_model, mock_agent, safe_guardrail + ): + """Test that different item_ids are tracked separately for debouncing""" + run_config: RealtimeRunConfig = { + "output_guardrails": [safe_guardrail], + "guardrails_settings": {"debounce_text_length": 10} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + # Add text to item_1 (8 chars - below threshold) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="12345678", response_id="resp_1" + )) + + # Add text to item_2 (8 chars - below threshold) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_2", delta="abcdefgh", response_id="resp_2" + )) + + # Neither should trigger guardrails yet + assert mock_model.interrupts_called == 0 + + # Add more text to item_1 (total 12 chars - above threshold) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="90ab", response_id="resp_1" + )) + + # item_1 should have triggered guardrail run (but not interrupted since safe) + assert session._item_guardrail_run_counts["item_1"] == 1 + assert ( + "item_2" not in session._item_guardrail_run_counts + or session._item_guardrail_run_counts["item_2"] == 0 + ) + + @pytest.mark.asyncio + async def test_turn_ended_clears_guardrail_state( + self, mock_model, mock_agent, triggered_guardrail + ): + """Test that turn_ended event clears guardrail state for next turn""" + run_config: RealtimeRunConfig = { + "output_guardrails": [triggered_guardrail], + "guardrails_settings": {"debounce_text_length": 5} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + # Trigger guardrail + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="trigger", response_id="resp_1" + )) + + assert session._interrupted_by_guardrail is True + assert len(session._item_transcripts) == 1 + + # End turn + await session.on_event(RealtimeModelTurnEndedEvent()) + + # State should be cleared + assert session._interrupted_by_guardrail is False + assert len(session._item_transcripts) == 0 + assert len(session._item_guardrail_run_counts) == 0 + + @pytest.mark.asyncio + async def test_multiple_guardrails_all_triggered( + self, mock_model, mock_agent + ): + """Test that all triggered guardrails are included in the event""" + def create_triggered_guardrail(name): + def guardrail_func(context, agent, output): + return GuardrailFunctionOutput( + output_info={"name": name}, + tripwire_triggered=True + ) + return OutputGuardrail(guardrail_function=guardrail_func, name=name) + + guardrail1 = create_triggered_guardrail("guardrail_1") + guardrail2 = create_triggered_guardrail("guardrail_2") + + run_config: RealtimeRunConfig = { + "output_guardrails": [guardrail1, guardrail2], + "guardrails_settings": {"debounce_text_length": 5} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="trigger", response_id="resp_1" + )) + + # Should have interrupted and sent message with both guardrail names + assert mock_model.interrupts_called == 1 + assert len(mock_model.sent_messages) == 1 + message = mock_model.sent_messages[0] + assert "guardrail_1" in message and "guardrail_2" in message + + # Should have emitted event with both guardrail results + events = [] + while not session._event_queue.empty(): + events.append(await session._event_queue.get()) + + guardrail_events = [e for e in events if isinstance(e, RealtimeGuardrailTripped)] + assert len(guardrail_events) == 1 + assert len(guardrail_events[0].guardrail_results) == 2 diff --git a/tests/realtime/test_tracing.py b/tests/realtime/test_tracing.py new file mode 100644 index 000000000..456ae125f --- /dev/null +++ b/tests/realtime/test_tracing.py @@ -0,0 +1,257 @@ +from unittest.mock import AsyncMock, patch + +import pytest + +from agents.realtime.openai_realtime import OpenAIRealtimeWebSocketModel + + +class TestRealtimeTracingIntegration: + """Test tracing configuration and session.update integration.""" + + @pytest.fixture + def model(self): + """Create a fresh model instance for each test.""" + return OpenAIRealtimeWebSocketModel() + + @pytest.fixture + def mock_websocket(self): + """Create a mock websocket connection.""" + mock_ws = AsyncMock() + mock_ws.send = AsyncMock() + mock_ws.close = AsyncMock() + return mock_ws + + @pytest.mark.asyncio + async def test_tracing_config_storage_and_defaults(self, model, mock_websocket): + """Test that tracing config is stored correctly and defaults to 'auto'.""" + # Test with explicit tracing config + config_with_tracing = { + "api_key": "test-key", + "initial_model_settings": { + "tracing": { + "workflow_name": "test_workflow", + "group_id": "group_123", + "metadata": {"version": "1.0"}, + } + }, + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_task = AsyncMock() + mock_create_task.return_value = mock_task + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model.connect(config_with_tracing) + + # Should store the tracing config + assert model._tracing_config == { + "workflow_name": "test_workflow", + "group_id": "group_123", + "metadata": {"version": "1.0"}, + } + + # Test without tracing config - should default to "auto" + model2 = OpenAIRealtimeWebSocketModel() + config_no_tracing = { + "api_key": "test-key", + "initial_model_settings": {}, + } + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model2.connect(config_no_tracing) # type: ignore[arg-type] + assert model2._tracing_config == "auto" + + @pytest.mark.asyncio + async def test_send_tracing_config_on_session_created(self, model, mock_websocket): + """Test that tracing config is sent when session.created event is received.""" + config = { + "api_key": "test-key", + "initial_model_settings": { + "tracing": {"workflow_name": "test_workflow", "group_id": "group_123"} + }, + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_task = AsyncMock() + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model.connect(config) + + # Simulate session.created event + session_created_event = { + "type": "session.created", + "event_id": "event_123", + "session": {"id": "session_456"}, + } + + with patch.object(model, "send_event") as mock_send_event: + await model._handle_ws_event(session_created_event) + + # Should send session.update with tracing config + mock_send_event.assert_called_once_with( + { + "type": "session.update", + "other_data": { + "session": { + "tracing": { + "workflow_name": "test_workflow", + "group_id": "group_123", + } + } + }, + } + ) + + @pytest.mark.asyncio + async def test_send_tracing_config_auto_mode(self, model, mock_websocket): + """Test that 'auto' tracing config is sent correctly.""" + config = { + "api_key": "test-key", + "initial_model_settings": {}, # No tracing config - defaults to "auto" + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_task = AsyncMock() + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model.connect(config) + + session_created_event = { + "type": "session.created", + "event_id": "event_123", + "session": {"id": "session_456"}, + } + + with patch.object(model, "send_event") as mock_send_event: + await model._handle_ws_event(session_created_event) + + # Should send session.update with "auto" + mock_send_event.assert_called_once_with( + {"type": "session.update", "other_data": {"session": {"tracing": "auto"}}} + ) + + @pytest.mark.asyncio + async def test_tracing_config_none_skips_session_update(self, model, mock_websocket): + """Test that None tracing config skips sending session.update.""" + # Manually set tracing config to None (this would happen if explicitly set) + model._tracing_config = None + + session_created_event = { + "type": "session.created", + "event_id": "event_123", + "session": {"id": "session_456"}, + } + + with patch.object(model, "send_event") as mock_send_event: + await model._handle_ws_event(session_created_event) + + # Should not send any session.update + mock_send_event.assert_not_called() + + @pytest.mark.asyncio + async def test_tracing_config_with_metadata_serialization(self, model, mock_websocket): + """Test that complex metadata in tracing config is handled correctly.""" + complex_metadata = { + "user_id": "user_123", + "session_type": "demo", + "features": ["audio", "tools"], + "config": {"timeout": 30, "retries": 3}, + } + + config = { + "api_key": "test-key", + "initial_model_settings": { + "tracing": {"workflow_name": "complex_workflow", "metadata": complex_metadata} + }, + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_task = AsyncMock() + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model.connect(config) + + session_created_event = { + "type": "session.created", + "event_id": "event_123", + "session": {"id": "session_456"}, + } + + with patch.object(model, "send_event") as mock_send_event: + await model._handle_ws_event(session_created_event) + + # Should send session.update with complete tracing config including metadata + expected_call = { + "type": "session.update", + "other_data": { + "session": { + "tracing": { + "workflow_name": "complex_workflow", + "metadata": complex_metadata, + } + } + }, + } + mock_send_event.assert_called_once_with(expected_call) + + @pytest.mark.asyncio + async def test_tracing_disabled_prevents_tracing(self, mock_websocket): + """Test that tracing_disabled=True prevents tracing configuration.""" + from agents.realtime.agent import RealtimeAgent + from agents.realtime.runner import RealtimeRunner + + # Create a test agent and runner with tracing disabled + agent = RealtimeAgent(name="test_agent", instructions="test") + + runner = RealtimeRunner( + starting_agent=agent, + config={"tracing_disabled": True} + ) + + # Test the _get_model_settings method directly since that's where the logic is + model_settings = await runner._get_model_settings( + agent=agent, + disable_tracing=True, # This should come from config["tracing_disabled"] + initial_settings=None, + overrides=None + ) + + # When tracing is disabled, model settings should have tracing=None + assert model_settings["tracing"] is None + + # Also test that the runner passes disable_tracing=True correctly + with patch.object(runner, '_get_model_settings') as mock_get_settings: + mock_get_settings.return_value = {"tracing": None} + + with patch('agents.realtime.session.RealtimeSession') as mock_session_class: + mock_session = AsyncMock() + mock_session_class.return_value = mock_session + + await runner.run() + + # Verify that _get_model_settings was called with disable_tracing=True + mock_get_settings.assert_called_once_with( + agent=agent, + disable_tracing=True, + initial_settings=None, + overrides=None + ) diff --git a/tests/test_agent_config.py b/tests/test_agent_config.py index f79c0cf8a..a985fd60d 100644 --- a/tests/test_agent_config.py +++ b/tests/test_agent_config.py @@ -1,7 +1,8 @@ import pytest from pydantic import BaseModel -from agents import Agent, AgentOutputSchema, Handoff, RunContextWrapper, Runner, handoff +from agents import Agent, AgentOutputSchema, Handoff, RunContextWrapper, handoff +from agents.run import AgentRunner @pytest.mark.asyncio @@ -42,7 +43,7 @@ async def test_handoff_with_agents(): handoffs=[agent_1, agent_2], ) - handoffs = Runner._get_handoffs(agent_3) + handoffs = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(None)) assert len(handoffs) == 2 assert handoffs[0].agent_name == "agent_1" @@ -77,7 +78,7 @@ async def test_handoff_with_handoff_obj(): ], ) - handoffs = Runner._get_handoffs(agent_3) + handoffs = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(None)) assert len(handoffs) == 2 assert handoffs[0].agent_name == "agent_1" @@ -111,7 +112,7 @@ async def test_handoff_with_handoff_obj_and_agent(): handoffs=[handoff(agent_1), agent_2], ) - handoffs = Runner._get_handoffs(agent_3) + handoffs = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(None)) assert len(handoffs) == 2 assert handoffs[0].agent_name == "agent_1" @@ -159,7 +160,7 @@ async def test_agent_final_output(): output_type=Foo, ) - schema = Runner._get_output_schema(agent) + schema = AgentRunner._get_output_schema(agent) assert isinstance(schema, AgentOutputSchema) assert schema is not None assert schema.output_type == Foo diff --git a/tests/test_agent_prompt.py b/tests/test_agent_prompt.py new file mode 100644 index 000000000..010717d66 --- /dev/null +++ b/tests/test_agent_prompt.py @@ -0,0 +1,97 @@ +import pytest + +from agents import Agent, Prompt, RunContextWrapper, Runner + +from .fake_model import FakeModel +from .test_responses import get_text_message + + +class PromptCaptureFakeModel(FakeModel): + """Subclass of FakeModel that records the prompt passed to the model.""" + + def __init__(self): + super().__init__() + self.last_prompt = None + + async def get_response( + self, + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + tracing, + *, + previous_response_id, + prompt, + ): + # Record the prompt that the agent resolved and passed in. + self.last_prompt = prompt + return await super().get_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + tracing, + previous_response_id=previous_response_id, + prompt=prompt, + ) + + +@pytest.mark.asyncio +async def test_static_prompt_is_resolved_correctly(): + static_prompt: Prompt = { + "id": "my_prompt", + "version": "1", + "variables": {"some_var": "some_value"}, + } + + agent = Agent(name="test", prompt=static_prompt) + context_wrapper = RunContextWrapper(context=None) + + resolved = await agent.get_prompt(context_wrapper) + + assert resolved == { + "id": "my_prompt", + "version": "1", + "variables": {"some_var": "some_value"}, + } + + +@pytest.mark.asyncio +async def test_dynamic_prompt_is_resolved_correctly(): + dynamic_prompt_value: Prompt = {"id": "dyn_prompt", "version": "2"} + + def dynamic_prompt_fn(_data): + return dynamic_prompt_value + + agent = Agent(name="test", prompt=dynamic_prompt_fn) + context_wrapper = RunContextWrapper(context=None) + + resolved = await agent.get_prompt(context_wrapper) + + assert resolved == {"id": "dyn_prompt", "version": "2", "variables": None} + + +@pytest.mark.asyncio +async def test_prompt_is_passed_to_model(): + static_prompt: Prompt = {"id": "model_prompt"} + + model = PromptCaptureFakeModel() + agent = Agent(name="test", model=model, prompt=static_prompt) + + # Ensure the model returns a simple message so the run completes in one turn. + model.set_next_output([get_text_message("done")]) + + await Runner.run(agent, input="hello") + + # The model should have received the prompt resolved by the agent. + expected_prompt = { + "id": "model_prompt", + "version": None, + "variables": None, + } + assert model.last_prompt == expected_prompt diff --git a/tests/test_agent_runner.py b/tests/test_agent_runner.py index 14a278a90..77eb8019e 100644 --- a/tests/test_agent_runner.py +++ b/tests/test_agent_runner.py @@ -745,3 +745,38 @@ async def test_previous_response_id_passed_between_runs_streamed_multi_turn(): pass assert model.last_turn_args.get("previous_response_id") == "resp-stream-test" + + +@pytest.mark.asyncio +async def test_dynamic_tool_addition_run() -> None: + """Test that tools can be added to an agent during a run.""" + model = FakeModel() + + executed: dict[str, bool] = {"called": False} + + agent = Agent(name="test", model=model, tool_use_behavior="run_llm_again") + + @function_tool(name_override="tool2") + def tool2() -> str: + executed["called"] = True + return "result2" + + @function_tool(name_override="add_tool") + async def add_tool() -> str: + agent.tools.append(tool2) + return "added" + + agent.tools.append(add_tool) + + model.add_multiple_turn_outputs( + [ + [get_function_tool_call("add_tool", json.dumps({}))], + [get_function_tool_call("tool2", json.dumps({}))], + [get_text_message("done")], + ] + ) + + result = await Runner.run(agent, input="start") + + assert executed["called"] is True + assert result.final_output == "done" diff --git a/tests/test_agent_runner_streamed.py b/tests/test_agent_runner_streamed.py index 87a76a706..31fe2979b 100644 --- a/tests/test_agent_runner_streamed.py +++ b/tests/test_agent_runner_streamed.py @@ -18,6 +18,7 @@ RunContextWrapper, Runner, UserError, + function_tool, handoff, ) from agents.items import RunItem @@ -684,3 +685,39 @@ async def test_streaming_events(): assert len(agent_data) == 2, "should have 2 agent updated events" assert agent_data[0].new_agent == agent_2, "should have started with agent_2" assert agent_data[1].new_agent == agent_1, "should have handed off to agent_1" + + +@pytest.mark.asyncio +async def test_dynamic_tool_addition_run_streamed() -> None: + model = FakeModel() + + executed: dict[str, bool] = {"called": False} + + agent = Agent(name="test", model=model, tool_use_behavior="run_llm_again") + + @function_tool(name_override="tool2") + def tool2() -> str: + executed["called"] = True + return "result2" + + @function_tool(name_override="add_tool") + async def add_tool() -> str: + agent.tools.append(tool2) + return "added" + + agent.tools.append(add_tool) + + model.add_multiple_turn_outputs( + [ + [get_function_tool_call("add_tool", json.dumps({}))], + [get_function_tool_call("tool2", json.dumps({}))], + [get_text_message("done")], + ] + ) + + result = Runner.run_streamed(agent, input="start") + async for _ in result.stream_events(): + pass + + assert executed["called"] is True + assert result.final_output == "done" diff --git a/tests/test_computer_action.py b/tests/test_computer_action.py index 70dcabd59..a306b1841 100644 --- a/tests/test_computer_action.py +++ b/tests/test_computer_action.py @@ -18,6 +18,7 @@ ActionScroll, ActionType, ActionWait, + PendingSafetyCheck, ResponseComputerToolCall, ) @@ -31,8 +32,9 @@ RunContextWrapper, RunHooks, ) -from agents._run_impl import ComputerAction, ToolRunComputerAction +from agents._run_impl import ComputerAction, RunImpl, ToolRunComputerAction from agents.items import ToolCallOutputItem +from agents.tool import ComputerToolSafetyCheckData class LoggingComputer(Computer): @@ -309,3 +311,44 @@ async def test_execute_invokes_hooks_and_returns_tool_call_output() -> None: assert raw["output"]["type"] == "computer_screenshot" assert "image_url" in raw["output"] assert raw["output"]["image_url"].endswith("xyz") + + +@pytest.mark.asyncio +async def test_pending_safety_check_acknowledged() -> None: + """Safety checks should be acknowledged via the callback.""" + + computer = LoggingComputer(screenshot_return="img") + called: list[ComputerToolSafetyCheckData] = [] + + def on_sc(data: ComputerToolSafetyCheckData) -> bool: + called.append(data) + return True + + tool = ComputerTool(computer=computer, on_safety_check=on_sc) + safety = PendingSafetyCheck(id="sc", code="c", message="m") + tool_call = ResponseComputerToolCall( + id="t1", + type="computer_call", + action=ActionClick(type="click", x=1, y=1, button="left"), + call_id="t1", + pending_safety_checks=[safety], + status="completed", + ) + run_action = ToolRunComputerAction(tool_call=tool_call, computer_tool=tool) + agent = Agent(name="a", tools=[tool]) + ctx = RunContextWrapper(context=None) + + results = await RunImpl.execute_computer_actions( + agent=agent, + actions=[run_action], + hooks=RunHooks[Any](), + context_wrapper=ctx, + config=RunConfig(), + ) + + assert len(results) == 1 + raw = results[0].raw_item + assert isinstance(raw, dict) + assert raw.get("acknowledged_safety_checks") == [{"id": "sc", "code": "c", "message": "m"}] + assert len(called) == 1 + assert called[0].safety_check.id == "sc" diff --git a/tests/test_extra_headers.py b/tests/test_extra_headers.py index f29c25408..a6af30077 100644 --- a/tests/test_extra_headers.py +++ b/tests/test_extra_headers.py @@ -1,6 +1,7 @@ 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 @@ -17,21 +18,29 @@ 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} + "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 + model = OpenAIResponsesModel(model="gpt-4", openai_client=DummyClient()) # type: ignore extra_headers = {"X-Test-Header": "test-value"} await model.get_response( system_instructions=None, @@ -47,7 +56,6 @@ def __init__(self): 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(): @@ -76,7 +84,7 @@ 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 + model = OpenAIChatCompletionsModel(model="gpt-4", openai_client=DummyClient()) # type: ignore extra_headers = {"X-Test-Header": "test-value"} await model.get_response( system_instructions=None, diff --git a/tests/test_function_schema.py b/tests/test_function_schema.py index 5618d8ae9..54e7e2f9e 100644 --- a/tests/test_function_schema.py +++ b/tests/test_function_schema.py @@ -439,3 +439,15 @@ def func_with_mapping(test_one: Mapping[str, int]) -> str: with pytest.raises(UserError): function_schema(func_with_mapping) + + +def test_name_override_without_docstring() -> None: + """name_override should be used even when not parsing docstrings.""" + + def foo(x: int) -> int: + return x + + fs = function_schema(foo, use_docstring_info=False, name_override="custom") + + assert fs.name == "custom" + assert fs.params_json_schema.get("title") == "custom_args" diff --git a/tests/test_function_tool.py b/tests/test_function_tool.py index 0a57aea87..9b02a2c42 100644 --- a/tests/test_function_tool.py +++ b/tests/test_function_tool.py @@ -5,8 +5,16 @@ from pydantic import BaseModel from typing_extensions import TypedDict -from agents import FunctionTool, ModelBehaviorError, RunContextWrapper, function_tool +from agents import ( + Agent, + AgentBase, + FunctionTool, + ModelBehaviorError, + RunContextWrapper, + function_tool, +) from agents.tool import default_tool_error_function +from agents.tool_context import ToolContext def argless_function() -> str: @@ -18,11 +26,11 @@ async def test_argless_function(): tool = function_tool(argless_function) assert tool.name == "argless_function" - result = await tool.on_invoke_tool(RunContextWrapper(None), "") + result = await tool.on_invoke_tool(ToolContext(context=None, tool_call_id="1"), "") assert result == "ok" -def argless_with_context(ctx: RunContextWrapper[str]) -> str: +def argless_with_context(ctx: ToolContext[str]) -> str: return "ok" @@ -31,11 +39,11 @@ async def test_argless_with_context(): tool = function_tool(argless_with_context) assert tool.name == "argless_with_context" - result = await tool.on_invoke_tool(RunContextWrapper(None), "") + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), "") assert result == "ok" # Extra JSON should not raise an error - result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1}') + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"a": 1}') assert result == "ok" @@ -48,15 +56,15 @@ async def test_simple_function(): tool = function_tool(simple_function, failure_error_function=None) assert tool.name == "simple_function" - result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1}') + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"a": 1}') assert result == 6 - result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1, "b": 2}') + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"a": 1, "b": 2}') assert result == 3 # Missing required argument should raise an error with pytest.raises(ModelBehaviorError): - await tool.on_invoke_tool(RunContextWrapper(None), "") + await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), "") class Foo(BaseModel): @@ -84,7 +92,7 @@ async def test_complex_args_function(): "bar": Bar(x="hello", y=10), } ) - result = await tool.on_invoke_tool(RunContextWrapper(None), valid_json) + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), valid_json) assert result == "6 hello10 hello" valid_json = json.dumps( @@ -93,7 +101,7 @@ async def test_complex_args_function(): "bar": Bar(x="hello", y=10), } ) - result = await tool.on_invoke_tool(RunContextWrapper(None), valid_json) + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), valid_json) assert result == "3 hello10 hello" valid_json = json.dumps( @@ -103,12 +111,12 @@ async def test_complex_args_function(): "baz": "world", } ) - result = await tool.on_invoke_tool(RunContextWrapper(None), valid_json) + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), valid_json) assert result == "3 hello10 world" # Missing required argument should raise an error with pytest.raises(ModelBehaviorError): - await tool.on_invoke_tool(RunContextWrapper(None), '{"foo": {"a": 1}}') + await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"foo": {"a": 1}}') def test_function_config_overrides(): @@ -168,7 +176,7 @@ async def run_function(ctx: RunContextWrapper[Any], args: str) -> str: assert tool.params_json_schema[key] == value assert tool.strict_json_schema - result = await tool.on_invoke_tool(RunContextWrapper(None), '{"data": "hello"}') + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"data": "hello"}') assert result == "hello_done" tool_not_strict = FunctionTool( @@ -183,7 +191,7 @@ async def run_function(ctx: RunContextWrapper[Any], args: str) -> str: assert "additionalProperties" not in tool_not_strict.params_json_schema result = await tool_not_strict.on_invoke_tool( - RunContextWrapper(None), '{"data": "hello", "bar": "baz"}' + ToolContext(None, tool_call_id="1"), '{"data": "hello", "bar": "baz"}' ) assert result == "hello_done" @@ -194,7 +202,7 @@ def my_func(a: int, b: int = 5): raise ValueError("test") tool = function_tool(my_func) - ctx = RunContextWrapper(None) + ctx = ToolContext(None, tool_call_id="1") result = await tool.on_invoke_tool(ctx, "") assert "Invalid JSON" in str(result) @@ -218,7 +226,7 @@ def custom_sync_error_function(ctx: RunContextWrapper[Any], error: Exception) -> return f"error_{error.__class__.__name__}" tool = function_tool(my_func, failure_error_function=custom_sync_error_function) - ctx = RunContextWrapper(None) + ctx = ToolContext(None, tool_call_id="1") result = await tool.on_invoke_tool(ctx, "") assert result == "error_ModelBehaviorError" @@ -242,7 +250,7 @@ def custom_sync_error_function(ctx: RunContextWrapper[Any], error: Exception) -> return f"error_{error.__class__.__name__}" tool = function_tool(my_func, failure_error_function=custom_sync_error_function) - ctx = RunContextWrapper(None) + ctx = ToolContext(None, tool_call_id="1") result = await tool.on_invoke_tool(ctx, "") assert result == "error_ModelBehaviorError" @@ -255,3 +263,44 @@ def custom_sync_error_function(ctx: RunContextWrapper[Any], error: Exception) -> result = await tool.on_invoke_tool(ctx, '{"a": 1, "b": 2}') assert result == "error_ValueError" + + +class BoolCtx(BaseModel): + enable_tools: bool + + +@pytest.mark.asyncio +async def test_is_enabled_bool_and_callable(): + @function_tool(is_enabled=False) + def disabled_tool(): + return "nope" + + async def cond_enabled(ctx: RunContextWrapper[BoolCtx], agent: AgentBase) -> bool: + return ctx.context.enable_tools + + @function_tool(is_enabled=cond_enabled) + def another_tool(): + return "hi" + + async def third_tool_on_invoke_tool(ctx: RunContextWrapper[Any], args: str) -> str: + return "third" + + third_tool = FunctionTool( + name="third_tool", + description="third tool", + on_invoke_tool=third_tool_on_invoke_tool, + is_enabled=lambda ctx, agent: ctx.context.enable_tools, + params_json_schema={}, + ) + + agent = Agent(name="t", tools=[disabled_tool, another_tool, third_tool]) + context_1 = RunContextWrapper(BoolCtx(enable_tools=False)) + context_2 = RunContextWrapper(BoolCtx(enable_tools=True)) + + tools_with_ctx = await agent.get_all_tools(context_1) + assert tools_with_ctx == [] + + tools_with_ctx = await agent.get_all_tools(context_2) + assert len(tools_with_ctx) == 2 + assert tools_with_ctx[0].name == "another_tool" + assert tools_with_ctx[1].name == "third_tool" diff --git a/tests/test_function_tool_decorator.py b/tests/test_function_tool_decorator.py index 3b52788fb..d334d8f84 100644 --- a/tests/test_function_tool_decorator.py +++ b/tests/test_function_tool_decorator.py @@ -7,6 +7,7 @@ from agents import function_tool from agents.run_context import RunContextWrapper +from agents.tool_context import ToolContext class DummyContext: @@ -14,8 +15,8 @@ def __init__(self): self.data = "something" -def ctx_wrapper() -> RunContextWrapper[DummyContext]: - return RunContextWrapper(DummyContext()) +def ctx_wrapper() -> ToolContext[DummyContext]: + return ToolContext(context=DummyContext(), tool_call_id="1") @function_tool @@ -44,7 +45,7 @@ async def test_sync_no_context_with_args_invocation(): @function_tool -def sync_with_context(ctx: RunContextWrapper[DummyContext], name: str) -> str: +def sync_with_context(ctx: ToolContext[DummyContext], name: str) -> str: return f"{name}_{ctx.context.data}" @@ -71,7 +72,7 @@ async def test_async_no_context_invocation(): @function_tool -async def async_with_context(ctx: RunContextWrapper[DummyContext], prefix: str, num: int) -> str: +async def async_with_context(ctx: ToolContext[DummyContext], prefix: str, num: int) -> str: await asyncio.sleep(0) return f"{prefix}-{num}-{ctx.context.data}" diff --git a/tests/test_handoff_tool.py b/tests/test_handoff_tool.py index a2a06208f..0f7fc2166 100644 --- a/tests/test_handoff_tool.py +++ b/tests/test_handoff_tool.py @@ -1,3 +1,4 @@ +import json from typing import Any import pytest @@ -11,10 +12,10 @@ MessageOutputItem, ModelBehaviorError, RunContextWrapper, - Runner, UserError, handoff, ) +from agents.run import AgentRunner def message_item(content: str, agent: Agent[Any]) -> MessageOutputItem: @@ -37,16 +38,17 @@ def get_len(data: HandoffInputData) -> int: return input_len + pre_handoff_len + new_items_len -def test_single_handoff_setup(): +@pytest.mark.asyncio +async def test_single_handoff_setup(): agent_1 = Agent(name="test_1") agent_2 = Agent(name="test_2", handoffs=[agent_1]) assert not agent_1.handoffs assert agent_2.handoffs == [agent_1] - assert not Runner._get_handoffs(agent_1) + assert not (await AgentRunner._get_handoffs(agent_1, RunContextWrapper(agent_1))) - handoff_objects = Runner._get_handoffs(agent_2) + handoff_objects = await AgentRunner._get_handoffs(agent_2, RunContextWrapper(agent_2)) assert len(handoff_objects) == 1 obj = handoff_objects[0] assert obj.tool_name == Handoff.default_tool_name(agent_1) @@ -54,7 +56,8 @@ def test_single_handoff_setup(): assert obj.agent_name == agent_1.name -def test_multiple_handoffs_setup(): +@pytest.mark.asyncio +async def test_multiple_handoffs_setup(): agent_1 = Agent(name="test_1") agent_2 = Agent(name="test_2") agent_3 = Agent(name="test_3", handoffs=[agent_1, agent_2]) @@ -63,7 +66,7 @@ def test_multiple_handoffs_setup(): assert not agent_1.handoffs assert not agent_2.handoffs - handoff_objects = Runner._get_handoffs(agent_3) + handoff_objects = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(agent_3)) assert len(handoff_objects) == 2 assert handoff_objects[0].tool_name == Handoff.default_tool_name(agent_1) assert handoff_objects[1].tool_name == Handoff.default_tool_name(agent_2) @@ -75,7 +78,8 @@ def test_multiple_handoffs_setup(): assert handoff_objects[1].agent_name == agent_2.name -def test_custom_handoff_setup(): +@pytest.mark.asyncio +async def test_custom_handoff_setup(): agent_1 = Agent(name="test_1") agent_2 = Agent(name="test_2") agent_3 = Agent( @@ -94,7 +98,7 @@ def test_custom_handoff_setup(): assert not agent_1.handoffs assert not agent_2.handoffs - handoff_objects = Runner._get_handoffs(agent_3) + handoff_objects = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(agent_3)) assert len(handoff_objects) == 2 first_handoff = handoff_objects[0] @@ -276,3 +280,93 @@ def test_handoff_input_schema_is_strict(): "additionalProperties" in obj.input_json_schema and not obj.input_json_schema["additionalProperties"] ), "Input schema should be strict and have additionalProperties=False" + + +def test_get_transfer_message_is_valid_json() -> None: + agent = Agent(name="foo") + obj = handoff(agent) + transfer = obj.get_transfer_message(agent) + assert json.loads(transfer) == {"assistant": agent.name} + + +def test_handoff_is_enabled_bool(): + """Test that handoff respects is_enabled boolean parameter.""" + agent = Agent(name="test") + + # Test enabled handoff (default) + handoff_enabled = handoff(agent) + assert handoff_enabled.is_enabled is True + + # Test explicitly enabled handoff + handoff_explicit_enabled = handoff(agent, is_enabled=True) + assert handoff_explicit_enabled.is_enabled is True + + # Test disabled handoff + handoff_disabled = handoff(agent, is_enabled=False) + assert handoff_disabled.is_enabled is False + + +@pytest.mark.asyncio +async def test_handoff_is_enabled_callable(): + """Test that handoff respects is_enabled callable parameter.""" + agent = Agent(name="test") + + # Test callable that returns True + def always_enabled(ctx: RunContextWrapper[Any], agent: Agent[Any]) -> bool: + return True + + handoff_callable_enabled = handoff(agent, is_enabled=always_enabled) + assert callable(handoff_callable_enabled.is_enabled) + result = handoff_callable_enabled.is_enabled(RunContextWrapper(agent), agent) + assert result is True + + # Test callable that returns False + def always_disabled(ctx: RunContextWrapper[Any], agent: Agent[Any]) -> bool: + return False + + handoff_callable_disabled = handoff(agent, is_enabled=always_disabled) + assert callable(handoff_callable_disabled.is_enabled) + result = handoff_callable_disabled.is_enabled(RunContextWrapper(agent), agent) + assert result is False + + # Test async callable + async def async_enabled(ctx: RunContextWrapper[Any], agent: Agent[Any]) -> bool: + return True + + handoff_async_enabled = handoff(agent, is_enabled=async_enabled) + assert callable(handoff_async_enabled.is_enabled) + result = await handoff_async_enabled.is_enabled(RunContextWrapper(agent), agent) # type: ignore + assert result is True + + +@pytest.mark.asyncio +async def test_handoff_is_enabled_filtering_integration(): + """Integration test that disabled handoffs are filtered out by the runner.""" + + # Set up agents + agent_1 = Agent(name="agent_1") + agent_2 = Agent(name="agent_2") + agent_3 = Agent(name="agent_3") + + # Create main agent with mixed enabled/disabled handoffs + main_agent = Agent( + name="main_agent", + handoffs=[ + handoff(agent_1, is_enabled=True), # enabled + handoff(agent_2, is_enabled=False), # disabled + handoff(agent_3, is_enabled=lambda ctx, agent: True), # enabled callable + ], + ) + + context_wrapper = RunContextWrapper(main_agent) + + # Get filtered handoffs using the runner's method + filtered_handoffs = await AgentRunner._get_handoffs(main_agent, context_wrapper) + + # Should only have 2 handoffs (agent_1 and agent_3), agent_2 should be filtered out + assert len(filtered_handoffs) == 2 + + # Check that the correct agents are present + agent_names = {h.agent_name for h in filtered_handoffs} + assert agent_names == {"agent_1", "agent_3"} + assert "agent_2" not in agent_names diff --git a/tests/test_items_helpers.py b/tests/test_items_helpers.py index 5dba21d88..f711f21e1 100644 --- a/tests/test_items_helpers.py +++ b/tests/test_items_helpers.py @@ -11,7 +11,10 @@ ) from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall 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 import ( + ActionSearch, + ResponseFunctionWebSearch, +) from openai.types.responses.response_function_web_search_param import ResponseFunctionWebSearchParam from openai.types.responses.response_output_message import ResponseOutputMessage from openai.types.responses.response_output_message_param import ResponseOutputMessageParam @@ -225,7 +228,12 @@ 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") + ws_call = ResponseFunctionWebSearch( + id="w1", + action=ActionSearch(type="search", query="query"), + status="completed", + type="web_search_call", + ) 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 @@ -233,6 +241,7 @@ def test_to_input_items_for_web_search_call() -> None: "id": "w1", "status": "completed", "type": "web_search_call", + "action": {"type": "search", "query": "query"}, } assert input_items[0] == expected diff --git a/tests/test_openai_chatcompletions.py b/tests/test_openai_chatcompletions.py index ba3ec68d0..a6909b195 100644 --- a/tests/test_openai_chatcompletions.py +++ b/tests/test_openai_chatcompletions.py @@ -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, @@ -51,7 +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): @@ -68,6 +77,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ) # Should have produced exactly one output message with one text part assert isinstance(resp, ModelResponse) @@ -81,6 +91,8 @@ 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.usage.input_tokens_details.cached_tokens == 3 + assert resp.usage.output_tokens_details.reasoning_tokens == 0 assert resp.response_id is None @@ -117,6 +129,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ) assert len(resp.output) == 1 assert isinstance(resp.output[0], ResponseOutputMessage) @@ -127,6 +140,8 @@ 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 @@ -167,6 +182,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ) # Expect a message item followed by a function tool call item. assert len(resp.output) == 2 @@ -178,6 +194,41 @@ async def patched_fetch_response(self, *args, **kwargs): assert fn_call_item.arguments == "{'x':1}" +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_get_response_with_no_message(monkeypatch) -> None: + """If the model returns no message, get_response should return an empty output.""" + msg = ChatCompletionMessage(role="assistant", content="ignored") + choice = Choice(index=0, finish_reason="content_filter", message=msg) + choice.message = None # type: ignore[assignment] + chat = ChatCompletion( + id="resp-id", + created=0, + model="fake", + object="chat.completion", + choices=[choice], + usage=None, + ) + + async def patched_fetch_response(self, *args, **kwargs): + return chat + + monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response) + model = OpenAIProvider(use_responses=False).get_model("gpt-4") + resp: ModelResponse = await model.get_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ) + assert resp.output == [] + + @pytest.mark.asyncio async def test_fetch_response_non_stream(monkeypatch) -> None: """ diff --git a/tests/test_openai_chatcompletions_stream.py b/tests/test_openai_chatcompletions_stream.py index b82f24303..49e7bc2f4 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]: @@ -80,6 +90,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # We expect a response.created, then a response.output_item.added, content part added, @@ -112,6 +123,8 @@ async def patched_fetch_response(self, *args, **kwargs): 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 @@ -170,6 +183,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # Expect sequence similar to text: created, output_item.added, content part added, @@ -258,6 +272,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # Sequence should be: response.created, then after loop we expect function call-related events: diff --git a/tests/test_output_tool.py b/tests/test_output_tool.py index 37c1b1b67..e98fd3c55 100644 --- a/tests/test_output_tool.py +++ b/tests/test_output_tool.py @@ -10,16 +10,16 @@ AgentOutputSchema, AgentOutputSchemaBase, ModelBehaviorError, - Runner, UserError, ) from agents.agent_output import _WRAPPER_DICT_KEY +from agents.run import AgentRunner from agents.util import _json def test_plain_text_output(): agent = Agent(name="test") - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert not output_schema, "Shouldn't have an output tool config without an output type" agent = Agent(name="test", output_type=str) @@ -32,7 +32,7 @@ class Foo(BaseModel): def test_structured_output_pydantic(): agent = Agent(name="test", output_type=Foo) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" assert isinstance(output_schema, AgentOutputSchema) @@ -52,7 +52,7 @@ class Bar(TypedDict): def test_structured_output_typed_dict(): agent = Agent(name="test", output_type=Bar) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._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" @@ -65,7 +65,7 @@ def test_structured_output_typed_dict(): def test_structured_output_list(): agent = Agent(name="test", output_type=list[str]) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._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" @@ -79,14 +79,14 @@ def test_structured_output_list(): def test_bad_json_raises_error(mocker): agent = Agent(name="test", output_type=Foo) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" with pytest.raises(ModelBehaviorError): output_schema.validate_json("not valid json") agent = Agent(name="test", output_type=list[str]) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" mock_validate_json = mocker.patch.object(_json, "validate_json") @@ -155,7 +155,7 @@ def validate_json(self, json_str: str) -> Any: 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) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" assert isinstance(output_schema, CustomOutputSchema) diff --git a/tests/test_reasoning_content.py b/tests/test_reasoning_content.py new file mode 100644 index 000000000..69e9a7d0c --- /dev/null +++ b/tests/test_reasoning_content.py @@ -0,0 +1,285 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator +from typing import Any, cast + +import pytest +from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage +from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta +from openai.types.completion_usage import ( + CompletionTokensDetails, + CompletionUsage, + PromptTokensDetails, +) +from openai.types.responses import ( + Response, + ResponseOutputMessage, + ResponseOutputText, + ResponseReasoningItem, +) + +from agents.model_settings import ModelSettings +from agents.models.interface import ModelTracing +from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel +from agents.models.openai_provider import OpenAIProvider + + +# Helper functions to create test objects consistently +def create_content_delta(content: str) -> dict[str, Any]: + """Create a delta dictionary with regular content""" + return {"content": content, "role": None, "function_call": None, "tool_calls": None} + + +def create_reasoning_delta(content: str) -> dict[str, Any]: + """Create a delta dictionary with reasoning content. The Only difference is reasoning_content""" + return { + "content": None, + "role": None, + "function_call": None, + "tool_calls": None, + "reasoning_content": content, + } + + +def create_chunk(delta: dict[str, Any], include_usage: bool = False) -> ChatCompletionChunk: + """Create a ChatCompletionChunk with the given delta""" + # Create a ChoiceDelta object from the dictionary + delta_obj = ChoiceDelta( + content=delta.get("content"), + role=delta.get("role"), + function_call=delta.get("function_call"), + tool_calls=delta.get("tool_calls"), + ) + + # Add reasoning_content attribute dynamically if present in the delta + if "reasoning_content" in delta: + # Use direct assignment for the reasoning_content attribute + delta_obj_any = cast(Any, delta_obj) + delta_obj_any.reasoning_content = delta["reasoning_content"] + + # Create the chunk + chunk = ChatCompletionChunk( + id="chunk-id", + created=1, + model="deepseek is usually expected", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=delta_obj)], + ) + + if include_usage: + chunk.usage = CompletionUsage( + completion_tokens=4, + prompt_tokens=2, + total_tokens=6, + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=2), + prompt_tokens_details=PromptTokensDetails(cached_tokens=0), + ) + + return chunk + + +async def create_fake_stream( + chunks: list[ChatCompletionChunk], +) -> AsyncIterator[ChatCompletionChunk]: + for chunk in chunks: + yield chunk + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_yields_events_for_reasoning_content(monkeypatch) -> None: + """ + Validate that when a model streams reasoning content, + `stream_response` emits the appropriate sequence of events including + `response.reasoning_summary_text.delta` events for each chunk of the reasoning content and + constructs a completed response with a `ResponseReasoningItem` part. + """ + # Create test chunks + chunks = [ + # Reasoning content chunks + create_chunk(create_reasoning_delta("Let me think")), + create_chunk(create_reasoning_delta(" about this")), + # Regular content chunks + create_chunk(create_content_delta("The answer")), + create_chunk(create_content_delta(" is 42"), include_usage=True), + ] + + 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, create_fake_stream(chunks) + + monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response) + model = OpenAIProvider(use_responses=False).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, + prompt=None, + ): + output_events.append(event) + + # verify reasoning content events were emitted + reasoning_delta_events = [ + e for e in output_events if e.type == "response.reasoning_summary_text.delta" + ] + assert len(reasoning_delta_events) == 2 + assert reasoning_delta_events[0].delta == "Let me think" + assert reasoning_delta_events[1].delta == " about this" + + # verify regular content events were emitted + content_delta_events = [e for e in output_events if e.type == "response.output_text.delta"] + assert len(content_delta_events) == 2 + assert content_delta_events[0].delta == "The answer" + assert content_delta_events[1].delta == " is 42" + + # verify the final response contains both types of content + response_event = output_events[-1] + assert response_event.type == "response.completed" + assert len(response_event.response.output) == 2 + + # first item should be reasoning + assert isinstance(response_event.response.output[0], ResponseReasoningItem) + assert response_event.response.output[0].summary[0].text == "Let me think about this" + + # second item should be message with text + assert isinstance(response_event.response.output[1], ResponseOutputMessage) + assert isinstance(response_event.response.output[1].content[0], ResponseOutputText) + assert response_event.response.output[1].content[0].text == "The answer is 42" + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_get_response_with_reasoning_content(monkeypatch) -> None: + """ + Test that when a model returns reasoning content in addition to regular content, + `get_response` properly includes both in the response output. + """ + # create a message with reasoning content + msg = ChatCompletionMessage( + role="assistant", + content="The answer is 42", + ) + # Use dynamic attribute for reasoning_content + # We need to cast to Any to avoid mypy errors since reasoning_content is not a defined attribute + msg_with_reasoning = cast(Any, msg) + msg_with_reasoning.reasoning_content = "Let me think about this question carefully" + + # create a choice with the message + mock_choice = { + "index": 0, + "finish_reason": "stop", + "message": msg_with_reasoning, + "delta": None, + } + + chat = ChatCompletion( + id="resp-id", + created=0, + model="deepseek is expected", + object="chat.completion", + choices=[mock_choice], # type: ignore[list-item] + usage=CompletionUsage( + completion_tokens=10, + prompt_tokens=5, + total_tokens=15, + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=6), + prompt_tokens_details=PromptTokensDetails(cached_tokens=0), + ), + ) + + async def patched_fetch_response(self, *args, **kwargs): + return chat + + monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response) + model = OpenAIProvider(use_responses=False).get_model("gpt-4") + resp = await model.get_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ) + + # should have produced a reasoning item and a message with text content + assert len(resp.output) == 2 + + # first output should be the reasoning item + assert isinstance(resp.output[0], ResponseReasoningItem) + assert resp.output[0].summary[0].text == "Let me think about this question carefully" + + # second output should be the message with text content + assert isinstance(resp.output[1], ResponseOutputMessage) + assert isinstance(resp.output[1].content[0], ResponseOutputText) + assert resp.output[1].content[0].text == "The answer is 42" + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_with_empty_reasoning_content(monkeypatch) -> None: + """ + Test that when a model streams empty reasoning content, + the response still processes correctly without errors. + """ + # create test chunks with empty reasoning content + chunks = [ + create_chunk(create_reasoning_delta("")), + create_chunk(create_content_delta("The answer is 42"), include_usage=True), + ] + + 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, create_fake_stream(chunks) + + monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response) + model = OpenAIProvider(use_responses=False).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, + prompt=None, + ): + output_events.append(event) + + # verify the final response contains the content + response_event = output_events[-1] + assert response_event.type == "response.completed" + + # should only have the message, not an empty reasoning item + assert len(response_event.response.output) == 1 + assert isinstance(response_event.response.output[0], ResponseOutputMessage) + assert isinstance(response_event.response.output[0].content[0], ResponseOutputText) + assert response_event.response.output[0].content[0].text == "The answer is 42" diff --git a/tests/test_repl.py b/tests/test_repl.py new file mode 100644 index 000000000..7ba2011be --- /dev/null +++ b/tests/test_repl.py @@ -0,0 +1,28 @@ +import pytest + +from agents import Agent, run_demo_loop + +from .fake_model import FakeModel +from .test_responses import get_text_input_item, get_text_message + + +@pytest.mark.asyncio +async def test_run_demo_loop_conversation(monkeypatch, capsys): + model = FakeModel() + model.add_multiple_turn_outputs([[get_text_message("hello")], [get_text_message("good")]]) + + agent = Agent(name="test", model=model) + + inputs = iter(["Hi", "How are you?", "quit"]) + monkeypatch.setattr("builtins.input", lambda _=" > ": next(inputs)) + + await run_demo_loop(agent, stream=False) + + output = capsys.readouterr().out + assert "hello" in output + assert "good" in output + assert model.last_turn_args["input"] == [ + get_text_input_item("Hi"), + get_text_message("hello").model_dump(exclude_unset=True), + get_text_input_item("How are you?"), + ] diff --git a/tests/test_responses.py b/tests/test_responses.py index 6b91bf8c6..74212f61b 100644 --- a/tests/test_responses.py +++ b/tests/test_responses.py @@ -49,10 +49,12 @@ def _foo() -> str: ) -def get_function_tool_call(name: str, arguments: str | None = None) -> ResponseOutputItem: +def get_function_tool_call( + name: str, arguments: str | None = None, call_id: str | None = None +) -> ResponseOutputItem: return ResponseFunctionToolCall( id="1", - call_id="2", + call_id=call_id or "2", type="function_call", name=name, arguments=arguments or "", diff --git a/tests/test_responses_tracing.py b/tests/test_responses_tracing.py index 0bc97a953..fe63e8ecb 100644 --- a/tests/test_responses_tracing.py +++ b/tests/test_responses_tracing.py @@ -1,7 +1,10 @@ +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 @@ -16,10 +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: @@ -32,6 +50,7 @@ def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj(self.output), + sequence_number=0, ) @@ -52,6 +71,7 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): return DummyResponse() @@ -96,6 +116,7 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): return DummyResponse() @@ -138,6 +159,7 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): return DummyResponse() @@ -177,12 +199,14 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): 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() @@ -229,12 +253,14 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): 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() @@ -280,12 +306,14 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): 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() diff --git a/tests/test_run.py b/tests/test_run.py new file mode 100644 index 000000000..66cfee1f1 --- /dev/null +++ b/tests/test_run.py @@ -0,0 +1,26 @@ +from __future__ import annotations + +from unittest import mock + +import pytest + +from agents import Agent, Runner +from agents.run import AgentRunner, set_default_agent_runner + +from .fake_model import FakeModel + + +@pytest.mark.asyncio +async def test_static_run_methods_call_into_default_runner() -> None: + runner = mock.Mock(spec=AgentRunner) + set_default_agent_runner(runner) + + agent = Agent(name="test", model=FakeModel()) + await Runner.run(agent, input="test") + runner.run.assert_called_once() + + Runner.run_streamed(agent, input="test") + runner.run_streamed.assert_called_once() + + Runner.run_sync(agent, input="test") + runner.run_sync.assert_called_once() diff --git a/tests/test_run_config.py b/tests/test_run_config.py index 51835ab66..e19899006 100644 --- a/tests/test_run_config.py +++ b/tests/test_run_config.py @@ -60,7 +60,7 @@ async def test_run_config_model_name_override_takes_precedence() -> None: async def test_run_config_model_override_object_takes_precedence() -> None: """ When a concrete Model instance is set on the RunConfig, then that instance should be - returned by Runner._get_model regardless of the agent's model. + returned by AgentRunner._get_model regardless of the agent's model. """ fake_model = FakeModel(initial_output=[get_text_message("override-object")]) agent = Agent(name="test", model="agent-model") diff --git a/tests/test_run_error_details.py b/tests/test_run_error_details.py new file mode 100644 index 000000000..104b248fc --- /dev/null +++ b/tests/test_run_error_details.py @@ -0,0 +1,48 @@ +import json + +import pytest + +from agents import Agent, MaxTurnsExceeded, RunErrorDetails, 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_run_error_includes_data(): + model = FakeModel() + agent = Agent(name="test", model=model, tools=[get_function_tool("foo", "res")]) + model.add_multiple_turn_outputs( + [ + [get_text_message("1"), get_function_tool_call("foo", json.dumps({"a": "b"}))], + [get_text_message("done")], + ] + ) + with pytest.raises(MaxTurnsExceeded) as exc: + await Runner.run(agent, input="hello", max_turns=1) + data = exc.value.run_data + assert isinstance(data, RunErrorDetails) + assert data.last_agent == agent + assert len(data.raw_responses) == 1 + assert len(data.new_items) > 0 + + +@pytest.mark.asyncio +async def test_streamed_run_error_includes_data(): + model = FakeModel() + agent = Agent(name="test", model=model, tools=[get_function_tool("foo", "res")]) + model.add_multiple_turn_outputs( + [ + [get_text_message("1"), get_function_tool_call("foo", json.dumps({"a": "b"}))], + [get_text_message("done")], + ] + ) + result = Runner.run_streamed(agent, input="hello", max_turns=1) + with pytest.raises(MaxTurnsExceeded) as exc: + async for _ in result.stream_events(): + pass + data = exc.value.run_data + assert isinstance(data, RunErrorDetails) + assert data.last_agent == agent + assert len(data.raw_responses) == 1 + assert len(data.new_items) > 0 diff --git a/tests/test_run_step_execution.py b/tests/test_run_step_execution.py index 6ae25fbd5..4cf9ae832 100644 --- a/tests/test_run_step_execution.py +++ b/tests/test_run_step_execution.py @@ -1,5 +1,6 @@ from __future__ import annotations +import json from typing import Any import pytest @@ -13,7 +14,6 @@ RunContextWrapper, RunHooks, RunItem, - Runner, ToolCallItem, ToolCallOutputItem, TResponseInputItem, @@ -26,6 +26,9 @@ RunImpl, SingleStepResult, ) +from agents.run import AgentRunner +from agents.tool import function_tool +from agents.tool_context import ToolContext from .test_responses import ( get_final_output_message, @@ -158,6 +161,42 @@ async def test_multiple_tool_calls(): assert isinstance(result.next_step, NextStepRunAgain) +@pytest.mark.asyncio +async def test_multiple_tool_calls_with_tool_context(): + async def _fake_tool(context: ToolContext[str], value: str) -> str: + return f"{value}-{context.tool_call_id}" + + tool = function_tool(_fake_tool, name_override="fake_tool", failure_error_function=None) + + agent = Agent( + name="test", + tools=[tool], + ) + response = ModelResponse( + output=[ + get_function_tool_call("fake_tool", json.dumps({"value": "123"}), call_id="1"), + get_function_tool_call("fake_tool", json.dumps({"value": "456"}), call_id="2"), + ], + usage=Usage(), + response_id=None, + ) + + result = await get_execute_result(agent, response) + assert result.original_input == "hello" + + # 4 items: new message, 2 tool calls, 2 tool call outputs + assert len(result.generated_items) == 4 + assert isinstance(result.next_step, NextStepRunAgain) + + items = result.generated_items + assert_item_is_function_tool_call(items[0], "fake_tool", json.dumps({"value": "123"})) + assert_item_is_function_tool_call(items[1], "fake_tool", json.dumps({"value": "456"})) + assert_item_is_function_tool_call_output(items[2], "123-1") + assert_item_is_function_tool_call_output(items[3], "456-2") + + assert isinstance(result.next_step, NextStepRunAgain) + + @pytest.mark.asyncio async def test_handoff_output_leads_to_handoff_next_step(): agent_1 = Agent(name="test_1") @@ -285,12 +324,12 @@ async def get_execute_result( context_wrapper: RunContextWrapper[Any] | None = None, run_config: RunConfig | None = None, ) -> SingleStepResult: - output_schema = Runner._get_output_schema(agent) - handoffs = Runner._get_handoffs(agent) + output_schema = AgentRunner._get_output_schema(agent) + handoffs = await AgentRunner._get_handoffs(agent, context_wrapper or RunContextWrapper(None)) processed_response = RunImpl.process_model_response( agent=agent, - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(context_wrapper or RunContextWrapper(None)), 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 2ea98f06a..27d36afa8 100644 --- a/tests/test_run_step_processing.py +++ b/tests/test_run_step_processing.py @@ -7,6 +7,7 @@ ResponseFunctionWebSearch, ) from openai.types.responses.response_computer_tool_call import ActionClick +from openai.types.responses.response_function_web_search import ActionSearch from openai.types.responses.response_reasoning_item import ResponseReasoningItem, Summary from pydantic import BaseModel @@ -19,11 +20,11 @@ ModelResponse, ReasoningItem, RunContextWrapper, - Runner, ToolCallItem, Usage, ) from agents._run_impl import RunImpl +from agents.run import AgentRunner from .test_responses import ( get_final_output_message, @@ -34,6 +35,10 @@ ) +def _dummy_ctx() -> RunContextWrapper[None]: + return RunContextWrapper(context=None) + + def test_empty_response(): agent = Agent(name="test") response = ModelResponse( @@ -83,7 +88,7 @@ async def test_single_tool_call(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) assert not result.handoffs assert result.functions and len(result.functions) == 1 @@ -111,7 +116,7 @@ async def test_missing_tool_call_raises_error(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) @@ -140,7 +145,7 @@ async def test_multiple_tool_calls(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) assert not result.handoffs assert result.functions and len(result.functions) == 2 @@ -169,7 +174,7 @@ async def test_handoffs_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await agent_3.get_all_tools(), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) assert not result.handoffs, "Shouldn't have a handoff here" @@ -182,8 +187,8 @@ async def test_handoffs_parsed_correctly(): agent=agent_3, response=response, output_schema=None, - handoffs=Runner._get_handoffs(agent_3), - all_tools=await agent_3.get_all_tools(), + handoffs=await AgentRunner._get_handoffs(agent_3, _dummy_ctx()), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) assert len(result.handoffs) == 1, "Should have a handoff here" handoff = result.handoffs[0] @@ -212,8 +217,8 @@ async def test_missing_handoff_fails(): agent=agent_3, response=response, output_schema=None, - handoffs=Runner._get_handoffs(agent_3), - all_tools=await agent_3.get_all_tools(), + handoffs=await AgentRunner._get_handoffs(agent_3, _dummy_ctx()), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) @@ -235,8 +240,8 @@ async def test_multiple_handoffs_doesnt_error(): agent=agent_3, response=response, output_schema=None, - handoffs=Runner._get_handoffs(agent_3), - all_tools=await agent_3.get_all_tools(), + handoffs=await AgentRunner._get_handoffs(agent_3, _dummy_ctx()), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) assert len(result.handoffs) == 2, "Should have multiple handoffs here" @@ -260,9 +265,9 @@ async def test_final_output_parsed_correctly(): RunImpl.process_model_response( agent=agent, response=response, - output_schema=Runner._get_output_schema(agent), + output_schema=AgentRunner._get_output_schema(agent), handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) @@ -288,7 +293,7 @@ async def test_file_search_tool_call_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) # The final item should be a ToolCallItem for the file search call assert any( @@ -302,7 +307,12 @@ async def test_file_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") + web_search_call = ResponseFunctionWebSearch( + id="w1", + action=ActionSearch(type="search", query="query"), + status="completed", + type="web_search_call", + ) response = ModelResponse( output=[get_text_message("hello"), web_search_call], usage=Usage(), @@ -313,7 +323,7 @@ async def test_function_web_search_tool_call_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) assert any( isinstance(item, ToolCallItem) and item.raw_item is web_search_call @@ -340,7 +350,7 @@ async def test_reasoning_item_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await Agent(name="test").get_all_tools(), + all_tools=await Agent(name="test").get_all_tools(_dummy_ctx()), ) assert any( isinstance(item, ReasoningItem) and item.raw_item is reasoning for item in result.new_items @@ -409,7 +419,7 @@ async def test_computer_tool_call_without_computer_tool_raises_error(): response=response, output_schema=None, handoffs=[], - all_tools=await Agent(name="test").get_all_tools(), + all_tools=await Agent(name="test").get_all_tools(_dummy_ctx()), ) @@ -437,7 +447,7 @@ async def test_computer_tool_call_with_computer_tool_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) assert any( isinstance(item, ToolCallItem) and item.raw_item is computer_call @@ -467,8 +477,8 @@ async def test_tool_and_handoff_parsed_correctly(): agent=agent_3, response=response, output_schema=None, - handoffs=Runner._get_handoffs(agent_3), - all_tools=await agent_3.get_all_tools(), + handoffs=await AgentRunner._get_handoffs(agent_3, _dummy_ctx()), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) assert result.functions and len(result.functions) == 1 assert len(result.handoffs) == 1, "Should have a handoff here" diff --git a/tests/test_session.py b/tests/test_session.py new file mode 100644 index 000000000..032f2bb38 --- /dev/null +++ b/tests/test_session.py @@ -0,0 +1,400 @@ +"""Tests for session memory functionality.""" + +import asyncio +import tempfile +from pathlib import Path + +import pytest + +from agents import Agent, Runner, SQLiteSession, TResponseInputItem +from agents.exceptions import UserError + +from .fake_model import FakeModel +from .test_responses import get_text_message + + +# Helper functions for parametrized testing of different Runner methods +def _run_sync_wrapper(agent, input_data, **kwargs): + """Wrapper for run_sync that properly sets up an event loop.""" + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + return Runner.run_sync(agent, input_data, **kwargs) + finally: + loop.close() + + +async def run_agent_async(runner_method: str, agent, input_data, **kwargs): + """Helper function to run agent with different methods.""" + if runner_method == "run": + return await Runner.run(agent, input_data, **kwargs) + elif runner_method == "run_sync": + # For run_sync, we need to run it in a thread with its own event loop + return await asyncio.to_thread(_run_sync_wrapper, agent, input_data, **kwargs) + elif runner_method == "run_streamed": + result = Runner.run_streamed(agent, input_data, **kwargs) + # For streaming, we first try to get at least one event to trigger any early exceptions + # If there's an exception in setup (like memory validation), it will be raised here + try: + first_event = None + async for event in result.stream_events(): + if first_event is None: + first_event = event + # Continue consuming all events + pass + except Exception: + # If an exception occurs during streaming, we let it propagate up + raise + return result + else: + raise ValueError(f"Unknown runner method: {runner_method}") + + +# Parametrized tests for different runner methods +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_basic_functionality_parametrized(runner_method): + """Test basic session memory functionality with SQLite backend across all runner methods.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_memory.db" + session_id = "test_session_123" + session = SQLiteSession(session_id, db_path) + + model = FakeModel() + agent = Agent(name="test", model=model) + + # First turn + model.set_next_output([get_text_message("San Francisco")]) + result1 = await run_agent_async( + runner_method, + agent, + "What city is the Golden Gate Bridge in?", + session=session, + ) + assert result1.final_output == "San Francisco" + + # Second turn - should have conversation history + model.set_next_output([get_text_message("California")]) + result2 = await run_agent_async( + runner_method, + agent, + "What state is it in?", + session=session, + ) + assert result2.final_output == "California" + + # Verify that the input to the second turn includes the previous conversation + # The model should have received the full conversation history + last_input = model.last_turn_args["input"] + assert len(last_input) > 1 # Should have more than just the current message + + session.close() + + +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_with_explicit_instance_parametrized(runner_method): + """Test session memory with an explicit SQLiteSession instance across all runner methods.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_memory.db" + session_id = "test_session_456" + session = SQLiteSession(session_id, db_path) + + model = FakeModel() + agent = Agent(name="test", model=model) + + # First turn + model.set_next_output([get_text_message("Hello")]) + result1 = await run_agent_async(runner_method, agent, "Hi there", session=session) + assert result1.final_output == "Hello" + + # Second turn + model.set_next_output([get_text_message("I remember you said hi")]) + result2 = await run_agent_async( + runner_method, + agent, + "Do you remember what I said?", + session=session, + ) + assert result2.final_output == "I remember you said hi" + + session.close() + + +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_disabled_parametrized(runner_method): + """Test that session memory is disabled when session=None across all runner methods.""" + model = FakeModel() + agent = Agent(name="test", model=model) + + # First turn (no session parameters = disabled) + model.set_next_output([get_text_message("Hello")]) + result1 = await run_agent_async(runner_method, agent, "Hi there") + assert result1.final_output == "Hello" + + # Second turn - should NOT have conversation history + model.set_next_output([get_text_message("I don't remember")]) + result2 = await run_agent_async(runner_method, agent, "Do you remember what I said?") + assert result2.final_output == "I don't remember" + + # Verify that the input to the second turn is just the current message + last_input = model.last_turn_args["input"] + assert len(last_input) == 1 # Should only have the current message + + +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_different_sessions_parametrized(runner_method): + """Test that different session IDs maintain separate conversation histories across all runner + methods.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_memory.db" + + model = FakeModel() + agent = Agent(name="test", model=model) + + # Session 1 + session_id_1 = "session_1" + session_1 = SQLiteSession(session_id_1, db_path) + + model.set_next_output([get_text_message("I like cats")]) + result1 = await run_agent_async(runner_method, agent, "I like cats", session=session_1) + assert result1.final_output == "I like cats" + + # Session 2 - different session + session_id_2 = "session_2" + session_2 = SQLiteSession(session_id_2, db_path) + + model.set_next_output([get_text_message("I like dogs")]) + result2 = await run_agent_async(runner_method, agent, "I like dogs", session=session_2) + assert result2.final_output == "I like dogs" + + # Back to Session 1 - should remember cats, not dogs + model.set_next_output([get_text_message("Yes, you mentioned cats")]) + result3 = await run_agent_async( + runner_method, + agent, + "What did I say I like?", + session=session_1, + ) + assert result3.final_output == "Yes, you mentioned cats" + + session_1.close() + session_2.close() + + +@pytest.mark.asyncio +async def test_sqlite_session_memory_direct(): + """Test SQLiteSession class directly.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_direct.db" + session_id = "direct_test" + session = SQLiteSession(session_id, db_path) + + # Test adding and retrieving items + items: list[TResponseInputItem] = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + ] + + await session.add_items(items) + retrieved = await session.get_items() + + assert len(retrieved) == 2 + assert retrieved[0].get("role") == "user" + assert retrieved[0].get("content") == "Hello" + assert retrieved[1].get("role") == "assistant" + assert retrieved[1].get("content") == "Hi there!" + + # Test clearing session + await session.clear_session() + retrieved_after_clear = await session.get_items() + assert len(retrieved_after_clear) == 0 + + session.close() + + +@pytest.mark.asyncio +async def test_sqlite_session_memory_pop_item(): + """Test SQLiteSession pop_item functionality.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_pop.db" + session_id = "pop_test" + session = SQLiteSession(session_id, db_path) + + # Test popping from empty session + popped = await session.pop_item() + assert popped is None + + # Add items + items: list[TResponseInputItem] = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + {"role": "user", "content": "How are you?"}, + ] + + await session.add_items(items) + + # Verify all items are there + retrieved = await session.get_items() + assert len(retrieved) == 3 + + # Pop the most recent item + popped = await session.pop_item() + assert popped is not None + assert popped.get("role") == "user" + assert popped.get("content") == "How are you?" + + # Verify item was removed + retrieved_after_pop = await session.get_items() + assert len(retrieved_after_pop) == 2 + assert retrieved_after_pop[-1].get("content") == "Hi there!" + + # Pop another item + popped2 = await session.pop_item() + assert popped2 is not None + assert popped2.get("role") == "assistant" + assert popped2.get("content") == "Hi there!" + + # Pop the last item + popped3 = await session.pop_item() + assert popped3 is not None + assert popped3.get("role") == "user" + assert popped3.get("content") == "Hello" + + # Try to pop from empty session again + popped4 = await session.pop_item() + assert popped4 is None + + # Verify session is empty + final_items = await session.get_items() + assert len(final_items) == 0 + + session.close() + + +@pytest.mark.asyncio +async def test_session_memory_pop_different_sessions(): + """Test that pop_item only affects the specified session.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_pop_sessions.db" + + session_1_id = "session_1" + session_2_id = "session_2" + session_1 = SQLiteSession(session_1_id, db_path) + session_2 = SQLiteSession(session_2_id, db_path) + + # Add items to both sessions + items_1: list[TResponseInputItem] = [ + {"role": "user", "content": "Session 1 message"}, + ] + items_2: list[TResponseInputItem] = [ + {"role": "user", "content": "Session 2 message 1"}, + {"role": "user", "content": "Session 2 message 2"}, + ] + + await session_1.add_items(items_1) + await session_2.add_items(items_2) + + # Pop from session 2 + popped = await session_2.pop_item() + assert popped is not None + assert popped.get("content") == "Session 2 message 2" + + # Verify session 1 is unaffected + session_1_items = await session_1.get_items() + assert len(session_1_items) == 1 + assert session_1_items[0].get("content") == "Session 1 message" + + # Verify session 2 has one item left + session_2_items = await session_2.get_items() + assert len(session_2_items) == 1 + assert session_2_items[0].get("content") == "Session 2 message 1" + + session_1.close() + session_2.close() + + +@pytest.mark.asyncio +async def test_sqlite_session_get_items_with_limit(): + """Test SQLiteSession get_items with limit parameter.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_count.db" + session_id = "count_test" + session = SQLiteSession(session_id, db_path) + + # Add multiple items + items: list[TResponseInputItem] = [ + {"role": "user", "content": "Message 1"}, + {"role": "assistant", "content": "Response 1"}, + {"role": "user", "content": "Message 2"}, + {"role": "assistant", "content": "Response 2"}, + {"role": "user", "content": "Message 3"}, + {"role": "assistant", "content": "Response 3"}, + ] + + await session.add_items(items) + + # Test getting all items (default behavior) + all_items = await session.get_items() + assert len(all_items) == 6 + assert all_items[0].get("content") == "Message 1" + assert all_items[-1].get("content") == "Response 3" + + # Test getting latest 2 items + latest_2 = await session.get_items(limit=2) + assert len(latest_2) == 2 + assert latest_2[0].get("content") == "Message 3" + assert latest_2[1].get("content") == "Response 3" + + # Test getting latest 4 items + latest_4 = await session.get_items(limit=4) + assert len(latest_4) == 4 + assert latest_4[0].get("content") == "Message 2" + assert latest_4[1].get("content") == "Response 2" + assert latest_4[2].get("content") == "Message 3" + assert latest_4[3].get("content") == "Response 3" + + # Test getting more items than available + latest_10 = await session.get_items(limit=10) + assert len(latest_10) == 6 # Should return all available items + assert latest_10[0].get("content") == "Message 1" + assert latest_10[-1].get("content") == "Response 3" + + # Test getting 0 items + latest_0 = await session.get_items(limit=0) + assert len(latest_0) == 0 + + session.close() + + +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_rejects_both_session_and_list_input(runner_method): + """Test that passing both a session and list input raises a UserError across all runner + methods. + """ + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_validation.db" + session_id = "test_validation_parametrized" + session = SQLiteSession(session_id, db_path) + + model = FakeModel() + agent = Agent(name="test", model=model) + + # Test that providing both a session and a list input raises a UserError + model.set_next_output([get_text_message("This shouldn't run")]) + + list_input = [ + {"role": "user", "content": "Test message"}, + ] + + with pytest.raises(UserError) as exc_info: + await run_agent_async(runner_method, agent, list_input, session=session) + + # Verify the error message explains the issue + assert "Cannot provide both a session and a list of input items" in str(exc_info.value) + assert "manually manage conversation history" in str(exc_info.value) + + session.close() diff --git a/tests/test_tracing_errors_streamed.py b/tests/test_tracing_errors_streamed.py index 416793e70..40efef3fa 100644 --- a/tests/test_tracing_errors_streamed.py +++ b/tests/test_tracing_errors_streamed.py @@ -168,10 +168,6 @@ async def test_tool_call_error(): "children": [ { "type": "agent", - "error": { - "message": "Error in agent run", - "data": {"error": "Invalid JSON input for tool foo: bad_json"}, - }, "data": { "name": "test_agent", "handoffs": [], diff --git a/tests/test_usage.py b/tests/test_usage.py new file mode 100644 index 000000000..405f99ddf --- /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 index 6aa867743..8bce897e9 100644 --- a/tests/test_visualization.py +++ b/tests/test_visualization.py @@ -134,3 +134,18 @@ def test_draw_graph(mock_agent): '"Handoff1" [label="Handoff1", shape=box, style=filled, style=rounded, ' "fillcolor=lightyellow, width=1.5, height=0.8];" in graph.source ) + + +def test_cycle_detection(): + agent_a = Agent(name="A") + agent_b = Agent(name="B") + agent_a.handoffs.append(agent_b) + agent_b.handoffs.append(agent_a) + + nodes = get_all_nodes(agent_a) + edges = get_all_edges(agent_a) + + assert nodes.count('"A" [label="A"') == 1 + assert nodes.count('"B" [label="B"') == 1 + assert '"A" -> "B"' in edges + assert '"B" -> "A"' in edges diff --git a/tests/voice/test_workflow.py b/tests/voice/test_workflow.py index 2bdf2a657..e99810be1 100644 --- a/tests/voice/test_workflow.py +++ b/tests/voice/test_workflow.py @@ -2,6 +2,7 @@ import json from collections.abc import AsyncIterator +from typing import Any import pytest from inline_snapshot import snapshot @@ -18,11 +19,12 @@ TResponseStreamEvent, ) +from ..fake_model import get_response_obj +from ..test_responses import get_function_tool, get_function_tool_call, get_text_message + 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 @@ -53,6 +55,7 @@ async def get_response( tracing: ModelTracing, *, previous_response_id: str | None, + prompt: Any | None, ) -> ModelResponse: raise NotImplementedError("Not implemented") @@ -67,6 +70,7 @@ async def stream_response( tracing: ModelTracing, *, previous_response_id: str | None, + prompt: Any | None, ) -> AsyncIterator[TResponseStreamEvent]: output = self.get_next_output() for item in output: @@ -81,11 +85,13 @@ async def stream_response( 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, ) diff --git a/uv.lock b/uv.lock index c6824a082..c6d04a424 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 2 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.10'", @@ -10,9 +10,9 @@ resolution-markers = [ name = "aiohappyeyeballs" version = "2.6.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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