PR to enhance the `Usage` object and related logic, to support more
granular token accounting, matching the details available in the [OpenAI
Responses API](https://platform.openai.com/docs/api-reference/responses)
. Specifically, it:

- Adds `input_tokens_details` and `output_tokens_details` fields to the
`Usage` dataclass, storing detailed token breakdowns (e.g.,
`cached_tokens`, `reasoning_tokens`).
- Flows this change through
- Updates and extends tests to match
- Adds a test for the Usage.add method

### Motivation
- Aligns the SDK’s usage with the latest OpenAI responses API Usage
object
- Supports downstream use cases that require fine-grained token usage
data (e.g., billing, analytics, optimization) requested by startups

---------

Co-authored-by: Wulfie Bain <[email protected]>

---
[//]: # (BEGIN SAPLING FOOTER)
* openai#732
* openai#731
* __->__ openai#730

---
[//]: # (BEGIN SAPLING FOOTER)
* openai#732
* __->__ openai#731

…penai#732)

Fix for openai#734

## Summary
- avoid infinite recursion in visualization by tracking visited agents
- test cycle detection in graph utility

## Testing
- `make mypy`
- `make tests` 

Resolves openai#668

## Summary
- mention MCPServerStreamableHttp in MCP server docs
- document CodeInterpreterTool, HostedMCPTool, ImageGenerationTool and
LocalShellTool
- update Japanese translations

## Summary
- avoid AttributeError when Gemini API returns `None` for chat message
- return empty output if message is filtered
- add regression test

## Testing
- `make format`
- `make lint`
- `make mypy`
- `make tests`

Towards openai#744

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