Addresses openai#614

When an input image is given as input, the code tries to access the
'detail' key, that may not be present as noted in openai#159.

With this pull request, now it tries to access the key, otherwise set
the value to `None`.
@pakrym-oai  or @rm-openai let me know if you want any changes.

**Purpose**  
Allow arbitrary `extra_body` parameters (e.g. `cached_content`) to be
forwarded into the LiteLLM call. Useful for context caching in Gemini
models
([docs](https://ai.google.dev/gemini-api/docs/caching?lang=python)).

**Example usage**  
```python
import os
from agents import Agent, ModelSettings
from agents.extensions.models.litellm_model import LitellmModel

cache_name = "cachedContents/34jopukfx5di"  # previously stored context

gemini_model = LitellmModel(
    model="gemini/gemini-1.5-flash-002",
    api_key=os.getenv("GOOGLE_API_KEY")
)

agent = Agent(
    name="Cached Gemini Agent",
    model=gemini_model,
    model_settings=ModelSettings(
        extra_body={"cached_content": cache_name}
    )
)

Added missing word "be" in prompt instructions.

This is unlikely to change the agent functionality in most cases, but
optimal clarity in prompt language is a best practice.

Co-authored-by: aagarwal25 <[email protected]>

Adding an AGENTS.md file for Codex use

Added the `instructions` attribute to the MCP servers to solve openai#704 .

Let me know if you want to add an example to the documentation.

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