> ## 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.

# Overview

> Use Bifrost as a drop-in replacement for Anthropic API with full compatibility and enhanced features.

## Overview

Bifrost provides complete Anthropic API compatibility through protocol adaptation. The integration handles request transformation, response normalization, and error mapping between Anthropic's Messages API specification and Bifrost's internal processing pipeline.

This integration enables you to utilize Bifrost's features like governance, load balancing, semantic caching, multi-provider support, and more, all while preserving your existing Anthropic SDK-based architecture.

**Endpoint:** `/anthropic`

<Note>
  **Enabling the beta header**: Anthropic frequently uses the `anthropic-beta` header to gate access to new features.
  Clients like Vercels AI SDK use these. Bifrost will block unrecognized headers by default for security purposes.
  To enable the beta header for full compatability, add `anthropic-beta` to the AllowList under Settings -> Client Settings in the UI.
</Note>

***

## Setup

<Tabs group="anthropic-sdk">
  <Tab title="Python">
    ```python {5} theme={null}
    import anthropic

    # Configure client to use Bifrost
    client = anthropic.Anthropic(
        base_url="http://localhost:8080/anthropic",
        api_key="dummy-key"  # Keys handled by Bifrost
    )

    # Make requests as usual
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        messages=[{"role": "user", "content": "Hello!"}]
    )

    print(response.content[0].text)
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript {5} theme={null}
    import Anthropic from "@anthropic-ai/sdk";

    // Configure client to use Bifrost
    const anthropic = new Anthropic({
      baseURL: "http://localhost:8080/anthropic",
      apiKey: "dummy-key", // Keys handled by Bifrost
    });

    // Make requests as usual
    const response = await anthropic.messages.create({
      model: "claude-3-sonnet-20240229",
      max_tokens: 1000,
      messages: [{ role: "user", content: "Hello!" }],
    });

    console.log(response.content[0].text);
    ```
  </Tab>
</Tabs>

***

## Provider/Model Usage Examples

Use multiple providers through the same Anthropic SDK format by prefixing model names with the provider:

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

    client = anthropic.Anthropic(
        base_url="http://localhost:8080/anthropic",
        api_key="dummy-key"
    )

    # Anthropic models (default)
    anthropic_response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        messages=[{"role": "user", "content": "Hello from Claude!"}]
    )

    # OpenAI models via Anthropic SDK format
    openai_response = client.messages.create(
        model="openai/gpt-4o-mini",
        max_tokens=1000,
        messages=[{"role": "user", "content": "Hello from OpenAI!"}]
    )

    # Google Vertex models via Anthropic SDK format
    vertex_response = client.messages.create(
        model="vertex/gemini-pro",
        max_tokens=1000,
        messages=[{"role": "user", "content": "Hello from Gemini!"}]
    )

    # Azure models
    azure_response = client.messages.create(
        model="azure/gpt-4o",
        max_tokens=1000,
        messages=[{"role": "user", "content": "Hello from Azure!"}]
    )

    # Local Ollama models
    ollama_response = client.messages.create(
        model="ollama/llama3.1:8b",
        max_tokens=1000,
        messages=[{"role": "user", "content": "Hello from Ollama!"}]
    )
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript theme={null}
    import Anthropic from "@anthropic-ai/sdk";

    const anthropic = new Anthropic({
      baseURL: "http://localhost:8080/anthropic",
      apiKey: "dummy-key",
    });

    // Anthropic models (default)
    const anthropicResponse = await anthropic.messages.create({
      model: "claude-3-sonnet-20240229",
      max_tokens: 1000,
      messages: [{ role: "user", content: "Hello from Claude!" }],
    });

    // OpenAI models via Anthropic SDK format
    const openaiResponse = await anthropic.messages.create({
      model: "openai/gpt-4o-mini",
      max_tokens: 1000,
      messages: [{ role: "user", content: "Hello from OpenAI!" }],
    });

    // Google Vertex models via Anthropic SDK format
    const vertexResponse = await anthropic.messages.create({
      model: "vertex/gemini-pro",
      max_tokens: 1000,
      messages: [{ role: "user", content: "Hello from Gemini!" }],
    });

    // Azure models
    const azureResponse = await anthropic.messages.create({
      model: "azure/gpt-4o",
      max_tokens: 1000,
      messages: [{ role: "user", content: "Hello from Azure!" }],
    });

    // Local Ollama models
    const ollamaResponse = await anthropic.messages.create({
      model: "ollama/llama3.1:8b",
      max_tokens: 1000,
      messages: [{ role: "user", content: "Hello from Ollama!" }],
    });
    ```
  </Tab>
</Tabs>

***

## Adding Custom Headers

Pass custom headers required by Bifrost plugins (like governance, telemetry, etc.):

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

    client = anthropic.Anthropic(
        base_url="http://localhost:8080/anthropic",
        api_key="dummy-key",
        default_headers={
            "x-bf-vk": "vk_12345",  # Virtual key for governance
        }
    )

    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        messages=[{"role": "user", "content": "Hello with custom headers!"}]
    )
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript theme={null}
    import Anthropic from "@anthropic-ai/sdk";

    const anthropic = new Anthropic({
      baseURL: "http://localhost:8080/anthropic",
      apiKey: "dummy-key",
      defaultHeaders: {
        "x-bf-vk": "vk_12345", // Virtual key for governance
      },
    });

    const response = await anthropic.messages.create({
      model: "claude-3-sonnet-20240229",
      max_tokens: 1000,
      messages: [{ role: "user", content: "Hello with custom headers!" }],
    });
    ```
  </Tab>
</Tabs>

***

## Async Inference

Submit inference requests asynchronously and poll for results later using the `x-bf-async` header. This is useful for long-running requests where you don't want to hold a connection open. See [Async Inference](../../features/async-inference) for full details.

<Note>
  Async inference requires a [Logs Store](../../features/observability/default) to be configured and is not compatible with streaming.
</Note>

### Messages

<Tabs group="anthropic-sdk">
  <Tab title="Python">
    ```python theme={null}
    import anthropic
    import time

    client = anthropic.Anthropic(
        base_url="http://localhost:8080/anthropic",
        api_key="dummy-key"
    )

    # Submit async request
    initial = client.messages.create(
        model="anthropic/claude-sonnet-4-20250514",
        max_tokens=256,
        messages=[{"role": "user", "content": "Tell me a short story."}],
        extra_headers={"x-bf-async": "true"}
    )

    # If content is present, the request completed synchronously
    if initial.content:
        print(initial.content[0].text)
    else:
        # Poll until completed
        while True:
            time.sleep(2)
            poll = client.messages.create(
                model="anthropic/claude-sonnet-4-20250514",
                max_tokens=256,
                messages=[{"role": "user", "content": "Tell me a short story."}],
                extra_headers={"x-bf-async-id": initial.id}
            )
            if poll.content:
                print(poll.content[0].text)
                break
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript theme={null}
    import Anthropic from "@anthropic-ai/sdk";

    const anthropic = new Anthropic({
      baseURL: "http://localhost:8080/anthropic",
      apiKey: "dummy-key",
    });

    // Submit async request
    const initial = await anthropic.messages.create(
      {
        model: "anthropic/claude-sonnet-4-20250514",
        max_tokens: 256,
        messages: [{ role: "user", content: "Tell me a short story." }],
      },
      { headers: { "x-bf-async": "true" } }
    );

    // If content is present, the request completed synchronously
    if (initial.content?.length > 0) {
      console.log(initial.content[0].text);
    } else {
      // Poll until completed
      while (true) {
        await new Promise((r) => setTimeout(r, 2000));
        const poll = await anthropic.messages.create(
          {
            model: "anthropic/claude-sonnet-4-20250514",
            max_tokens: 256,
            messages: [{ role: "user", content: "Tell me a short story." }],
          },
          { headers: { "x-bf-async-id": initial.id } }
        );
        if (poll.content?.length > 0) {
          console.log(poll.content[0].text);
          break;
        }
      }
    }
    ```
  </Tab>
</Tabs>

### Async Headers

| Header                                 | Description                                                            |
| -------------------------------------- | ---------------------------------------------------------------------- |
| `x-bf-async: true`                     | Submit the request as an async job. Returns immediately with a job ID. |
| `x-bf-async-id: <job-id>`              | Poll for results of a previously submitted async job.                  |
| `x-bf-async-job-result-ttl: <seconds>` | Override the default result TTL (default: 3600s).                      |

***

## Supported Features

The Anthropic integration supports all features that are available in both the Anthropic SDK and Bifrost core functionality. If the Anthropic SDK supports a feature and Bifrost supports it, the integration will work seamlessly.

***

## Next Steps

* **[Files and Batch API](./files-and-batch)** - File uploads and batch processing
* **[OpenAI SDK](../openai-sdk/overview)** - GPT integration patterns
* **[Google GenAI SDK](../genai-sdk)** - Gemini integration patterns
* **[Configuration](../../quickstart/README)** - Bifrost setup and configuration
* **[Core Features](../../features/)** - Advanced Bifrost capabilities