> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getbifrost.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Pydantic AI SDK

> Use Bifrost as a drop-in proxy for Pydantic AI agents with zero code changes.

Pydantic AI is a Python agent framework that brings FastAPI-like ergonomics to GenAI development. Since Pydantic AI uses standard provider SDKs under the hood, Bifrost adds enterprise features like governance, semantic caching, MCP tools, observability, etc, on top of your existing agent setup.

**Endpoint:** `/pydanticai`

<Warning>
  **Provider Compatibility:** This integration only works for AI providers that both Pydantic AI and Bifrost support. Currently supported: OpenAI, Anthropic, and Google Gemini.
</Warning>

***

## Setup

<Tabs group="pydanticai-sdk">
  <Tab title="Python">
    ```python {7-8} theme={null}
    from pydantic_ai import Agent
    from pydantic_ai.models.openai import OpenAIChatModel
    from pydantic_ai.providers.openai import OpenAIProvider

    # Configure provider to use Bifrost
    provider = OpenAIProvider(
        base_url="http://localhost:8080/pydanticai/v1",  # Point to Bifrost
        api_key="dummy-key"  # Keys managed by Bifrost, Or add virtual key
    )
    model = OpenAIChatModel("gpt-4o-mini", provider=provider)

    # Create agent with Bifrost-routed model
    agent = Agent(model, instructions="Be concise and helpful.")

    result = agent.run_sync("Hello! How are you?")
    print(result.output)
    ```
  </Tab>
</Tabs>

***

## Provider/Model Usage Examples

Your existing Pydantic AI provider switching works unchanged through Bifrost:

<Tabs group="pydanticai-sdk">
  <Tab title="Python">
    ```python {7,10,14} theme={null}
    from pydantic_ai import Agent
    from pydantic_ai.models.openai import OpenAIChatModel
    from pydantic_ai.models.anthropic import AnthropicModel
    from pydantic_ai.models.google import GoogleModel
    from pydantic_ai.providers.openai import OpenAIProvider
    from pydantic_ai.providers.anthropic import AnthropicProvider
    from pydantic_ai.providers.google import GoogleProvider

    base_url = "http://localhost:8080/pydanticai"

    # OpenAI models via Pydantic AI
    openai_provider = OpenAIProvider(base_url=f"{base_url}/v1")
    openai_model = OpenAIChatModel("gpt-4o-mini", provider=openai_provider)
    openai_agent = Agent(openai_model)

    # Anthropic models via Pydantic AI
    # Note: Anthropic SDK adds /v1 internally, so we don't append it here
    anthropic_provider = AnthropicProvider(base_url=base_url)
    anthropic_model = AnthropicModel("claude-3-haiku-20240307", provider=anthropic_provider)
    anthropic_agent = Agent(anthropic_model)

    # Google Gemini models via Pydantic AI
    google_provider = GoogleProvider(base_url=base_url, api_key="dummy-key")
    google_model = GoogleModel("gemini-2.0-flash", provider=google_provider)
    google_agent = Agent(google_model)

    # All work the same way
    openai_result = openai_agent.run_sync("Hello GPT!")
    anthropic_result = anthropic_agent.run_sync("Hello Claude!")
    gemini_result = google_agent.run_sync("Hello Gemini!")

    print(openai_result.output)
    print(anthropic_result.output)
    print(gemini_result.output)
    ```
  </Tab>
</Tabs>

***

## Tool Calling

Pydantic AI's powerful tool system works seamlessly through Bifrost:

<Tabs group="pydanticai-sdk">
  <Tab title="Python">
    ```python {7} theme={null}
    from pydantic_ai import Agent, RunContext, Tool
    from pydantic_ai.models.openai import OpenAIChatModel
    from pydantic_ai.providers.openai import OpenAIProvider
    from dataclasses import dataclass

    # Configure Bifrost
    provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
    model = OpenAIChatModel("gpt-4o-mini", provider=provider)

    # Define tools as functions
    def get_weather(location: str) -> str:
        """Get the current weather for a location."""
        return f"The weather in {location} is 72°F and sunny."

    def calculate(expression: str) -> str:
        """Perform a mathematical calculation."""
        result = eval(expression)  # Use safe evaluation in production
        return f"The result is {result}"

    # Create agent with tools
    agent = Agent(
        model,
        tools=[get_weather, calculate],
        instructions="You can check weather and do calculations."
    )

    result = agent.run_sync("What's the weather in Boston?")
    print(result.output)
    ```
  </Tab>
</Tabs>

***

## Tools with Dependency Injection

Use `RunContext` to pass dependencies to your tools:

<Tabs group="pydanticai-sdk">
  <Tab title="Python">
    ```python {12} theme={null}
    from pydantic_ai import Agent, RunContext, Tool
    from pydantic_ai.models.openai import OpenAIChatModel
    from pydantic_ai.providers.openai import OpenAIProvider
    from dataclasses import dataclass

    @dataclass
    class UserContext:
        user_id: int
        user_name: str

    # Configure Bifrost
    provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
    model = OpenAIChatModel("gpt-4o-mini", provider=provider)

    def get_user_info(ctx: RunContext[UserContext]) -> str:
        """Get information about the current user."""
        return f"User: {ctx.deps.user_name} (ID: {ctx.deps.user_id})"

    agent = Agent(
        model,
        deps_type=UserContext,
        tools=[Tool(get_user_info, takes_ctx=True)],
        instructions="You can look up user information."
    )

    # Pass dependencies at runtime
    deps = UserContext(user_id=123, user_name="Alice")
    result = agent.run_sync("What is my user information?", deps=deps)
    print(result.output)
    ```
  </Tab>
</Tabs>

***

## Structured Output

Define response types using Pydantic models:

<Tabs group="pydanticai-sdk">
  <Tab title="Python">
    ```python {13} theme={null}
    from pydantic import BaseModel, Field
    from pydantic_ai import Agent
    from pydantic_ai.models.openai import OpenAIChatModel
    from pydantic_ai.providers.openai import OpenAIProvider

    # Define structured output type
    class CityInfo(BaseModel):
        city: str = Field(description="Name of the city")
        country: str = Field(description="Country where the city is located")
        population: int = Field(description="Approximate population")

    # Configure Bifrost
    provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
    model = OpenAIChatModel("gpt-4o-mini", provider=provider)

    # Agent with typed output
    agent = Agent(
        model,
        output_type=CityInfo,
        instructions="Extract city information from user queries."
    )

    result = agent.run_sync("Tell me about Tokyo, Japan")

    # result.output is typed as CityInfo
    print(f"City: {result.output.city}")
    print(f"Country: {result.output.country}")
    print(f"Population: {result.output.population}")
    ```
  </Tab>
</Tabs>

***

## Streaming Responses

Stream responses in real-time for better UX:

<Tabs group="pydanticai-sdk">
  <Tab title="Python">
    ```python {7} theme={null}
    import asyncio
    from pydantic_ai import Agent
    from pydantic_ai.models.openai import OpenAIChatModel
    from pydantic_ai.providers.openai import OpenAIProvider

    # Configure Bifrost
    provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
    model = OpenAIChatModel("gpt-4o-mini", provider=provider)

    agent = Agent(model, instructions="Tell engaging stories.")

    async def stream_story():
        async with agent.run_stream("Tell me a short story about a robot.") as response:
            async for chunk in response.stream_text():
                print(chunk, end="", flush=True)
        print()  # Newline at end

    asyncio.run(stream_story())
    ```
  </Tab>
</Tabs>

***

## Adding Custom Headers

Add Bifrost-specific headers for governance and tracking:

<Tabs group="pydanticai-sdk">
  <Tab title="Python">
    ```python {15} theme={null}
    from httpx import AsyncClient
    from pydantic_ai import Agent
    from pydantic_ai.models.openai import OpenAIChatModel
    from pydantic_ai.providers.openai import OpenAIProvider

    # Create HTTP client with custom headers
    http_client = AsyncClient(
        headers={
            "x-bf-vk": "your-virtual-key",  # Virtual key for governance
        }
    )

    # Configure provider with custom client
    provider = OpenAIProvider(
        base_url="http://localhost:8080/pydanticai/v1",
        http_client=http_client
    )
    model = OpenAIChatModel("gpt-4o-mini", provider=provider)

    agent = Agent(model)
    result = agent.run_sync("Hello!")
    print(result.output)
    ```
  </Tab>
</Tabs>

***

## Multi-turn Conversations

Maintain conversation history across multiple turns:

<Tabs group="pydanticai-sdk">
  <Tab title="Python">
    ```python {6} theme={null}
    from pydantic_ai import Agent
    from pydantic_ai.models.openai import OpenAIChatModel
    from pydantic_ai.providers.openai import OpenAIProvider

    # Configure Bifrost
    provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
    model = OpenAIChatModel("gpt-4o-mini", provider=provider)

    agent = Agent(model, instructions="Remember context from previous messages.")

    # First turn
    result1 = agent.run_sync("My name is Alice and I live in Paris.")

    # Second turn - pass message history to maintain context
    result2 = agent.run_sync(
        "What is my name and where do I live?",
        message_history=result1.all_messages()
    )

    print(result2.output)  # Should mention Alice and Paris
    ```
  </Tab>
</Tabs>

***

## Supported Features

The Pydantic AI integration supports all features available in both the Pydantic AI SDK and Bifrost core functionality:

| Feature                  | Supported |
| ------------------------ | --------- |
| Chat Completions         | ✅         |
| Tool/Function Calling    | ✅         |
| Structured Output        | ✅         |
| Streaming                | ✅         |
| Multi-turn Conversations | ✅         |
| Dependency Injection     | ✅         |
| OpenAI Models            | ✅         |
| Anthropic Models         | ✅         |
| Google Gemini Models     | ✅         |
| Embeddings               | ✅         |
| Speech/TTS               | ✅         |
| Transcription            | ✅         |

Your existing Pydantic AI agents work seamlessly with Bifrost's enterprise features. 😄

***

## Next Steps

* **[Governance Features](../features/governance)** - Virtual keys and team management
* **[Semantic Caching](../features/semantic-caching)** - Intelligent response caching
* **[Configuration](../quickstart/README)** - Provider setup and API key management