# Using the OpenAI Agents SDK 

This guide explains how to use the **OpenAI Agents SDK** with model endpoints deployed on the E2E Networks Inference platform using **vLLM** and **tool calling support**.

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## Step 1: Create a Model Endpoint

1. Go to the AI Platform
2. Navigate to **Model Endpoints**
3. Click **Create Endpoint**
4. Select **vLLM Framework**
5. Choose your preferred model (example: Llama‑3.3‑70B‑Instruct)
6. Choose a GPU plan and replicas

### Enable Tool Calling

Under **LLM Settings**:

* Enable **Auto Tool Choice**
* Select a **Tool Call Parser** (e.g., `llama4_pythonic`, `mistral`)

Launch the endpoint and wait for the status to show **Running**.

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## Step 2: Connect via OpenAI Agents SDK

Use a **custom model provider** to send API requests directly to your endpoint.

```python
BASE_URL = "https://infer.e2enetworks.net/project/{project_id}/endpoint/{inference_id}/v1"
API_KEY=""
MODEL_NAME = "meta-llama/Llama-3.3-70B-Instruct"

if not BASE_URL or not API_KEY or not MODEL_NAME:
    raise ValueError("Configure BASE_URL, API_KEY, MODEL_NAME appropriately.")

client = AsyncOpenAI(base_url=BASE_URL, api_key=API_KEY)
set_tracing_disabled(disabled=True)

class CustomModelProvider(ModelProvider):
    def get_model(self, model_name: str | None) -> Model:
        return OpenAIChatCompletionsModel(model=model_name or MODEL_NAME, openai_client=client)

CUSTOM_MODEL_PROVIDER = CustomModelProvider()
```

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## Example 1: Customer Service Agent

A multi-agent chatbot handling:

* Seat booking
* Airline FAQs
* Automatic delegation between agents

Tools used:

* `faq_lookup_tool`
* `update_seat`

Run example:

```bash
python customer_service_agent.py
```

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## Example 2: Research Bot Agent

Performs automated:
✅ Web search planning
✅ Parallel searching
✅ AI‑generated report writing

### Repo Setup

```bash
git clone https://github.com/openai/openai-agents-python.git
cd openai-agents-python
```

### Modify Model Configurations

Inside example files, remove existing model config lines like:

```python
# model="gpt-4o"
# model="o3-mini"
```

### Use Custom Provider in `manager.py`

```python
result = await Runner.run(
    planner_agent,
    f"Query: {query}",
    run_config=RunConfig(model_provider=CUSTOM_MODEL_PROVIDER),
)
```

To run:

```bash
python examples/research_bot/manager.py
```

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## Notes

* Works with most open‑weights models (LLaMA, Mixtral, etc.)
* Endpoint must have tool calling enabled
* Tools must be registered using `@function_tool`

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## Example Use Cases

* Airline and travel support bots
* Automated research assistants
* Multi‑agent domain‑specific workflows

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## Resources

* OpenAI Agents SDK
* vLLM Documentation

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## Tips

* Use **Pydantic** models for structured I/O
* Disable tracing in production to reduce logs
* Maintain context using `RunContextWrapper`


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