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

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.

---

Setup

Python

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)

JavaScript

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

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!"}]
)

JavaScript

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

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!"}]
)

JavaScript

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!" }],
});

---

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

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

JavaScript

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