# Pydantic AI

URL: https://docs.hanzo.ai/docs/guides/integrations/pydantic-ai

> OpenAIProvider takes base_url. Which model class you pick decides whether you land on chat completions or responses.

`OpenAIProvider` takes **`base\_url`** and `api\_key`, and is handed to a model class as `provider=\`.

```bash
pip install pydantic-ai
```

```python
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIChatModel(
    'zen6',
    provider=OpenAIProvider(base_url='https://api.hanzo.ai/v1', api_key='sk-...'),
)

agent = Agent(model)
print(agent.run_sync('Hello').output)
```

Lands on `POST /v1/chat/completions`.

Pick the model class deliberately, because the two land on different addresses. `OpenAIChatModel`, prefix `openai-chat:`, is what backs every OpenAI-compatible provider and reaches `POST /v1/chat/completions`. `OpenAIResponsesModel`, the bare `openai:` prefix and the modern default, reaches `POST /v1/responses`. Both are served; the chat class is the safer default against a compatible endpoint.

`OPENAI_BASE_URL` and `OPENAI_API_KEY` are the environment equivalents, and `OpenAIProvider` also accepts a pre-built client as `openai_client`.

## MCP

`MCPToolset` takes the server URL as its first positional argument. A plain URL is treated as streamable HTTP; SSE only if the path ends in `/sse`.

```python
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPToolset

toolset = MCPToolset('https://api.hanzo.ai/v1/mcp')
agent = Agent(model, toolsets=[toolset])
```

It also reads a JSON config file carrying an `mcpServers` object.