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

Setup

Python
JavaScript

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)

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);

Provider/Model Usage Examples

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

Python
JavaScript

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!"}]
)

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!" }],
});

Adding Custom Headers

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

Python
JavaScript

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!"}]
)

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!" }],
});

Using Direct Keys

Pass API keys directly in requests to bypass Bifrost’s load balancing. You can pass any provider’s API key (OpenAI, Anthropic, Mistral, etc.) since Bifrost only looks for Authorization or x-api-key headers. This requires the Allow Direct API keys option to be enabled in Bifrost configuration.

Learn more: See Key Management for enabling direct API key usage.

Python
JavaScript

import anthropic

# Using Anthropic's API key directly
client_with_direct_key = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="sk-your-anthropic-key"  # Anthropic's API key works
)

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

# or pass different provider keys per request using headers
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="dummy-key"
)

# Use Anthropic key for Claude
anthropic_response = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Hello Claude!"}],
    extra_headers={
        "x-api-key": "sk-ant-your-anthropic-key"
    }
)

# Use OpenAI key for GPT models
openai_response = client.messages.create(
    model="openai/gpt-4o-mini",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Hello GPT!"}],
    extra_headers={
        "Authorization": "Bearer sk-your-openai-key"
    }
)

import Anthropic from "@anthropic-ai/sdk";

// Using Anthropic's API key directly
const anthropicWithDirectKey = new Anthropic({
  baseURL: "http://localhost:8080/anthropic",
  apiKey: "sk-your-anthropic-key", // Anthropic's API key works
});


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


// or pass different provider keys per request using headers
const anthropic = new Anthropic({
  baseURL: "http://localhost:8080/anthropic",
  apiKey: "dummy-key",
});

// Use Anthropic key for Claude
const anthropicResponse = await anthropic.messages.create({
  model: "claude-3-sonnet-20240229",
  max_tokens: 1000,
  messages: [{ role: "user", content: "Hello Claude!" }],
  headers: {
    "x-api-key": "sk-ant-your-anthropic-key",
  },
});

// Use OpenAI key for GPT models
const openaiResponseWithHeader = await anthropic.messages.create({
  model: "openai/gpt-4o-mini",
  max_tokens: 1000,
  messages: [{ role: "user", content: "Hello GPT!" }],
  headers: {
    "Authorization": "Bearer sk-your-openai-key",
  },
});

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 for full details.

Async inference requires a Logs Store to be configured and is not compatible with streaming.

Messages

Python
JavaScript

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

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;
    }
  }
}

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 - File uploads and batch processing
OpenAI SDK - GPT integration patterns
Google GenAI SDK - Gemini integration patterns
Configuration - Bifrost setup and configuration
Core Features - Advanced Bifrost capabilities

Overview

Quick Start

CLI Agents & Editors

Providers & Guides

SDK Integrations

MCP Gateway

Custom plugins

Open Source Features

Enterprise Features

Overview

Overview

Setup

Provider/Model Usage Examples

Adding Custom Headers

Using Direct Keys

Async Inference

Messages

Async Headers

Supported Features

Next Steps

Overview

Quick Start

CLI Agents & Editors

Providers & Guides

SDK Integrations

MCP Gateway

Custom plugins

Open Source Features

Enterprise Features

​Overview

​Setup

​Provider/Model Usage Examples

​Adding Custom Headers

​Using Direct Keys

​Async Inference

​Messages

​Async Headers

​Supported Features

​Next Steps

Overview

Setup

Provider/Model Usage Examples

Adding Custom Headers

Using Direct Keys

Async Inference

Messages

Async Headers

Supported Features

Next Steps