> ## 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.

# LiteLLM SDK

> Use Bifrost as a drop-in proxy for LiteLLM applications with zero code changes.

Since LiteLLM already provides multi-provider abstraction, Bifrost adds enterprise features like governance, semantic caching, MCP tools, observability, etc, on top of your existing setup.

**Endpoint:** `/litellm`

<Warning>
  **Provider Compatibility:** This integration only works for AI providers that both LiteLLM and Bifrost support. If you're using a provider specific to LiteLLM that Bifrost doesn't support (or vice versa), those requests will fail.
</Warning>

***

## Setup

<Tabs group="litellm-sdk">
  <Tab title="Python">
    ```python {7} theme={null}
    from litellm import completion

    # Configure client to use Bifrost
    response = completion(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello!"}],
        base_url="http://localhost:8080/litellm"  # Point to Bifrost
    )

    print(response.choices[0].message.content)
    ```
  </Tab>
</Tabs>

***

## Provider/Model Usage Examples

Your existing LiteLLM provider switching works unchanged through Bifrost:

<Tabs group="litellm-sdk">
  <Tab title="Python">
    ```python {4} theme={null}
    from litellm import completion

    # All your existing LiteLLM patterns work the same
    base_url = "http://localhost:8080/litellm"

    # OpenAI models
    openai_response = completion(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello GPT!"}],
        base_url=base_url
    )

    # Anthropic models  
    anthropic_response = completion(
        model="claude-3-sonnet-20240229",
        messages=[{"role": "user", "content": "Hello Claude!"}],
        base_url=base_url
    )

    # Google models
    google_response = completion(
        model="gemini/gemini-1.5-flash",
        messages=[{"role": "user", "content": "Hello Gemini!"}],
        base_url=base_url
    )

    # Azure models
    azure_response = completion(
        model="azure/gpt-4o",
        messages=[{"role": "user", "content": "Hello Azure!"}],
        base_url=base_url
    )
    ```
  </Tab>
</Tabs>

***

## Adding Custom Headers

Add Bifrost-specific headers for governance and tracking:

<Tabs group="litellm-sdk">
  <Tab title="Python">
    ```python theme={null}
    from litellm import completion

    # Add custom headers for Bifrost features
    response = completion(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello!"}],
        base_url="http://localhost:8080/litellm",
        extra_headers={
            "x-bf-vk": "your-virtual-key",          # Virtual key for governance
        }
    )

    print(response.choices[0].message.content)
    ```
  </Tab>
</Tabs>

***

## Supported Features

The LiteLLM integration supports all features that are available in both the LiteLLM SDK and Bifrost core functionality. Your existing LiteLLM code works 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