This PR adds Portkey AI as a tracing provider. Portkey helps you take
your OpenAI agents from prototype to production.

Portkey turns your experimental OpenAI Agents into production-ready
systems by providing:

- Complete observability of every agent step, tool use, and interaction
- Built-in reliability with fallbacks, retries, and load balancing
- Cost tracking and optimization to manage your AI spend
- Access to 1600+ LLMs through a single integration
- Guardrails to keep agent behavior safe and compliant
- Version-controlled prompts for consistent agent performance


Towards openai#786

### Summary

Introduced the `RunErrorDetails` object to get partial results from a
run interrupted by `MaxTurnsExceeded` exception. In this proposal the
`RunErrorDetails` object contains all the fields from `RunResult` with
`final_output` set to `None` and `output_guardrail_results` set to an
empty list. We can decide to return less information.

@rm-openai At the moment the exception doesn't return the
`RunErrorDetails` object for the streaming mode. Do you have any
suggestions on how to deal with it? In the `_check_errors` function of
`agents/result.py` file.

### Test plan

I have not implemented any tests currently, but if needed I can
implement a basic test to retrieve partial data.

### Issue number

This PR is an attempt to solve issue openai#719 

### Checks

- [✅ ] I've added new tests (if relevant)
- [ ] I've added/updated the relevant documentation
- [ ✅] I've run `make lint` and `make format`
- [ ✅] I've made sure tests pass

Small fix:

Removing `import litellm.types` as its outside the try except block for
importing litellm so the import error message isn't displayed, and the
line actually isn't needed. I was reproducing a GitHub issue and came
across this in the process.

### Overview

This PR fixes a typo in the assert statement within the `handoff`
function in `handoffs.py`, changing `'on_input'` to `'on_handoff`' for
accuracy and clarity.

### Changes

- Corrected the word “on_input” to “on_handoff” in the docstring.

### Motivation

Clear and correct documentation improves code readability and reduces
confusion for users and contributors.

### Checklist

- [x] I have reviewed the docstring after making the change.
- [x] No functionality is affected.
- [x] The change follows the repository’s contribution guidelines.

The documentation in `docs/mcp.md` listed three server types (stdio,
HTTP over SSE, Streamable HTTP) but incorrectly stated "two kinds of
servers" in the heading. This PR fixes the numerical discrepancy.

**Changes:** 

- Modified from "two kinds of servers" to "three kinds of servers". 
- File: `docs/mcp.md` (line 11).

Changed the function comment as input_guardrails only deals with input
messages

)

### Overview

This PR fixes a small typo in the docstring of the
`is_strict_json_schema` abstract method of the `AgentOutputSchemaBase`
class in `agent_output.py`.

### Changes

- Corrected the word “valis” to “valid” in the docstring.

### Motivation

Clear and correct documentation improves code readability and reduces
confusion for users and contributors.

### Checklist

- [x] I have reviewed the docstring after making the change.
- [x] No functionality is affected.
- [x] The change follows the repository’s contribution guidelines.

People keep trying to fix this, but its a breaking change.

This pull request resolves openai#777; If you think we should introduce a new
item type for MCP call output, please let me know. As other hosted tools
use this event, I believe using the same should be good to go tho.

)

The EmbeddedResource from MCP tool call contains a field with type
AnyUrl that is not JSON-serializable. To avoid this exception, use
item.model_dump(mode="json") to ensure a JSON-serializable return value.

### Summary:
Towards openai#767. We were caching the list of tools for an agent, so if you
did `agent.tools.append(...)` from a tool call, the next call to the
model wouldn't include the new tool. THis is a bug.

### Test Plan:
Unit tests. Note that now MCP tools are listed each time the agent runs
(users can still cache the `list_tools` however).

Closes openai#796. Shouldn't start a busy waiting thread if there aren't any
traces.

Test plan
```
import threading
assert threading.active_count() == 1
import agents
assert threading.active_count() == 1
```

### Summary:
Allows a user to do `function_tool(is_enabled=<some_callable>)`; the
callable is called when the agent runs.

This allows you to dynamically enable/disable a tool based on the
context/env.

The meta-goal is to allow `Agent` to be effectively immutable. That
enables some nice things down the line, and this allows you to
dynamically modify the tools list without mutating the agent.

### Test Plan:
Unit tests

bump version