Anthropic Compatible API

Overview

Bifrost provides 100% Anthropic API compatibility with enhanced features:

Zero code changes - Works with existing Anthropic SDK applications
Same request/response formats - Exact Anthropic API specification
Enhanced capabilities - Multi-provider fallbacks, MCP tools, monitoring
Full tool use support - Native Anthropic tool calling + MCP integration
Any provider under the hood - Use any configured provider (Anthropic, OpenAI, etc.)

Endpoint: POST /anthropic/v1/messages

Provider Flexibility: While using Anthropic SDK format, you can specify any model like "claude-3-sonnet-20240229" (uses Anthropic) or "openai/gpt-4o-mini" (uses OpenAI) - Bifrost will route to the appropriate provider automatically.

Quick Migration

Python (Anthropic SDK)

import anthropic

# Before - Direct Anthropic
client = anthropic.Anthropic(
    base_url="https://api.anthropic.com",
    api_key="your-anthropic-key"
)

# After - Via Bifrost
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",  # Only change this
    api_key="your-anthropic-key"
)

# Everything else stays the same
response = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Hello!"}]
)

JavaScript (Anthropic SDK)

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

// Before - Direct Anthropic
const anthropic = new Anthropic({
  baseURL: "https://api.anthropic.com",
  apiKey: process.env.ANTHROPIC_API_KEY,
});

// After - Via Bifrost
const anthropic = new Anthropic({
  baseURL: "http://localhost:8080/anthropic", // Only change this
  apiKey: process.env.ANTHROPIC_API_KEY,
});

// Everything else stays the same
const response = await anthropic.messages.create({
  model: "claude-3-sonnet-20240229",
  max_tokens: 1000,
  messages: [{ role: "user", content: "Hello!" }],
});

Supported Features

Fully Supported

Feature	Status	Notes
Messages API	✅ Full	All parameters supported
Tool Use	✅ Full	Native + MCP tools
System Messages	✅ Full	Anthropic system prompts
Vision/Images	✅ Full	Image analysis
Streaming	✅ Full	Currently returns full response
Max Tokens	✅ Full	Token limit control
Temperature	✅ Full	Sampling control
Stop Sequences	✅ Full	Custom stop tokens

Enhanced Features

Feature	Enhancement	Benefit
Multi-provider Fallbacks	Automatic failover	Higher reliability
MCP Tool Integration	External tools available	Extended capabilities
Load Balancing	Multiple API keys	Better performance
Monitoring	Prometheus metrics	Observability
Cross-provider Tools	Use with any provider	Flexibility

Request Examples

Basic Message

# Use Anthropic provider
curl -X POST http://localhost:8080/anthropic/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-sonnet-20240229",
    "max_tokens": 1000,
    "messages": [
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'

# Use OpenAI provider via Anthropic SDK format
curl -X POST http://localhost:8080/anthropic/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "max_tokens": 1000,
    "messages": [
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'

Response:

{
  "id": "msg_123",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "The capital of France is Paris."
    }
  ],
  "model": "claude-3-sonnet-20240229",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 13,
    "output_tokens": 7
  }
}

System Message

curl -X POST http://localhost:8080/anthropic/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-sonnet-20240229",
    "max_tokens": 1000,
    "system": "You are a helpful assistant that answers questions about geography.",
    "messages": [
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'

Tool Use

curl -X POST http://localhost:8080/anthropic/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-sonnet-20240229",
    "max_tokens": 1000,
    "tools": [
      {
        "name": "get_weather",
        "description": "Get weather information for a location",
        "input_schema": {
          "type": "object",
          "properties": {
            "location": {"type": "string", "description": "City name"}
          },
          "required": ["location"]
        }
      }
    ],
    "messages": [
      {"role": "user", "content": "What is the weather in Paris?"}
    ]
  }'

Response with Tool Use:

{
  "id": "msg_123",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "tool_use",
      "id": "toolu_123",
      "name": "get_weather",
      "input": {
        "location": "Paris"
      }
    }
  ],
  "model": "claude-3-sonnet-20240229",
  "stop_reason": "tool_use",
  "usage": {
    "input_tokens": 25,
    "output_tokens": 15
  }
}

Vision/Image Analysis

import anthropic

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

response = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1000,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this image?"
                },
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/jpeg",
                        "data": "/9j/4AAQSkZJRgABAQEAYABgAAD..."
                    }
                }
            ]
        }
    ]
)

Advanced Usage

Multi-turn Conversation

import anthropic

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

response = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1000,
    messages=[
        {"role": "user", "content": "What is 2+2?"},
        {"role": "assistant", "content": "2+2 equals 4."},
        {"role": "user", "content": "What about 3+3?"}
    ]
)

Tool Use with Results

import anthropic

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

# First request with tool use
response = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1000,
    tools=[
        {
            "name": "list_directory",
            "description": "List files in a directory",
            "input_schema": {
                "type": "object",
                "properties": {
                    "path": {"type": "string", "description": "Directory path"}
                },
                "required": ["path"]
            }
        }
    ],
    messages=[
        {"role": "user", "content": "List files in the current directory"}
    ]
)

# Tool was called, now provide results
if response.content[0].type == "tool_use":
    tool_use = response.content[0]

    # Continue conversation with tool result
    follow_up = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        messages=[
            {"role": "user", "content": "List files in the current directory"},
            {"role": "assistant", "content": response.content},
            {
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "tool_use_id": tool_use.id,
                        "content": "README.md\nconfig.json\nsrc/"
                    }
                ]
            }
        ]
    )

Error Handling

import anthropic
from anthropic import AnthropicError

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

try:
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        messages=[{"role": "user", "content": "Hello!"}]
    )
except AnthropicError as e:
    print(f"Anthropic API error: {e}")
except Exception as e:
    print(f"Other error: {e}")

Enhanced Features

Automatic MCP Tool Integration

MCP tools are automatically available in Anthropic-compatible requests:

# No tool definitions needed - MCP tools auto-discovered
response = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1000,
    messages=[
        {"role": "user", "content": "Read the config.json file and tell me about the providers"}
    ]
)

# Response may include automatic tool use
if response.content[0].type == "tool_use":
    print(f"Called MCP tool: {response.content[0].name}")

Load Balancing

Multiple API keys automatically load balanced:

{
  "providers": {
    "anthropic": {
      "keys": [
        {
          "value": "env.ANTHROPIC_API_KEY_1",
          "models": ["claude-3-sonnet-20240229"],
          "weight": 0.6
        },
        {
          "value": "env.ANTHROPIC_API_KEY_2",
          "models": ["claude-3-sonnet-20240229"],
          "weight": 0.4
        }
      ]
    }
  }
}

Testing & Validation

Compatibility Testing

Test your existing Anthropic code with Bifrost:

import anthropic

def test_bifrost_compatibility():
    # Test with Bifrost
    bifrost_client = anthropic.Anthropic(
        base_url="http://localhost:8080/anthropic",
        api_key=anthropic_key
    )

    # Test with direct Anthropic (for comparison)
    anthropic_client = anthropic.Anthropic(
        base_url="https://api.anthropic.com",
        api_key=anthropic_key
    )

    test_message = [{"role": "user", "content": "Hello, test!"}]

    # Both should work identically
    bifrost_response = bifrost_client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=100,
        messages=test_message
    )

    anthropic_response = anthropic_client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=100,
        messages=test_message
    )

    # Compare response structure
    assert bifrost_response.content[0].text is not None
    assert anthropic_response.content[0].text is not None

    print("✅ Bifrost Anthropic compatibility verified")

test_bifrost_compatibility()

Tool Use Testing

import anthropic

def test_tool_use():
    client = anthropic.Anthropic(
        base_url="http://localhost:8080/anthropic",
        api_key=anthropic_key
    )

    # Test tool use
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        tools=[
            {
                "name": "get_time",
                "description": "Get current time",
                "input_schema": {"type": "object", "properties": {}}
            }
        ],
        messages=[
            {"role": "user", "content": "What time is it?"}
        ]
    )

    # Should include tool use
    assert any(content.type == "tool_use" for content in response.content)
    print("✅ Tool use compatibility verified")

test_tool_use()

Multi-Provider Support

Use multiple providers with Anthropic SDK format by prefixing model names:

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="dummy"  # API keys configured in Bifrost
)

# Anthropic models (default)
response1 = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=100,
    messages=[{"role": "user", "content": "Hello!"}]
)

# OpenAI models via Anthropic SDK
response2 = client.messages.create(
    model="openai/gpt-4o-mini",
    max_tokens=100,
    messages=[{"role": "user", "content": "Hello!"}]
)

# Vertex models via Anthropic SDK
response3 = client.messages.create(
    model="vertex/gemini-pro",
    max_tokens=100,
    messages=[{"role": "user", "content": "Hello!"}]
)

Configuration

Bifrost Config for Anthropic

{
  "providers": {
    "anthropic": {
      "keys": [
        {
          "value": "env.ANTHROPIC_API_KEY",
          "models": [
            "claude-2.1",
            "claude-3-sonnet-20240229",
            "claude-3-haiku-20240307",
            "claude-3-opus-20240229",
            "claude-3-5-sonnet-20240620"
          ],
          "weight": 1.0
        }
      ],
      "network_config": {
        "default_request_timeout_in_seconds": 30,
        "max_retries": 2,
        "retry_backoff_initial_ms": 100,
        "retry_backoff_max_ms": 2000
      },
      "concurrency_and_buffer_size": {
        "concurrency": 3,
        "buffer_size": 10
      }
    }
  }
}

Environment Variables

# Required
export ANTHROPIC_API_KEY="sk-ant-..."

# Optional - for enhanced features
export OPENAI_API_KEY="sk-..."  # For fallbacks
export BIFROST_LOG_LEVEL="info"

Common Issues & Solutions

Issue: “Invalid API Key”

Problem: API key not being passed correctly Solution:

# Ensure API key is properly set
import os
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key=os.getenv("ANTHROPIC_API_KEY")  # Explicit env var
)

Issue: “Model not found”

Problem: Model not configured in Bifrost Solution: Add model to config.json:

{
  "providers": {
    "anthropic": {
      "keys": [
        {
          "value": "env.ANTHROPIC_API_KEY",
          "models": ["claude-3-sonnet-20240229", "claude-3-haiku-20240307"],
          "weight": 1.0
        }
      ]
    }
  }
}

Issue: “Missing anthropic-version header”

Problem: Required Anthropic API version header missing Solution:

# Add default headers for version
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key=anthropic_key,
    default_headers={"anthropic-version": "2023-06-01"}
)

Issue: “Tool schema validation error”

Problem: Tool schema format incorrect Solution:

# Ensure proper tool schema format
tools = [
    {
        "name": "tool_name",
        "description": "Tool description",
        "input_schema": {
            "type": "object",
            "properties": {
                "param": {"type": "string", "description": "Parameter description"}
            },
            "required": ["param"]
        }
    }
]

Architecture: For Anthropic integration implementation details, see Architecture Documentation.

Introduction

Prompt Engineering

Offline Evals

Online Evals

Tracing

Simulations

Library

Dashboards

Integrations

Settings

CI/CD

​Overview

​Quick Migration

​Python (Anthropic SDK)

​JavaScript (Anthropic SDK)

​Supported Features

​Fully Supported

​Enhanced Features

​Request Examples

​Basic Message

​System Message

​Tool Use

​Vision/Image Analysis

​Advanced Usage

​Multi-turn Conversation

​Tool Use with Results

​Error Handling

​Enhanced Features

​Automatic MCP Tool Integration

​Load Balancing

​Testing & Validation

​Compatibility Testing

​Tool Use Testing

​Multi-Provider Support

​Configuration

​Bifrost Config for Anthropic

​Environment Variables

​Common Issues & Solutions

​Issue: “Invalid API Key”

​Issue: “Model not found”

​Issue: “Missing anthropic-version header”

​Issue: “Tool schema validation error”

Overview

Quick Migration

Python (Anthropic SDK)

JavaScript (Anthropic SDK)

Supported Features

Fully Supported

Enhanced Features

Request Examples

Basic Message

System Message

Tool Use

Vision/Image Analysis

Advanced Usage

Multi-turn Conversation

Tool Use with Results

Error Handling

Enhanced Features

Automatic MCP Tool Integration

Load Balancing

Testing & Validation

Compatibility Testing

Tool Use Testing

Multi-Provider Support

Configuration

Bifrost Config for Anthropic

Environment Variables

Common Issues & Solutions

Issue: “Invalid API Key”

Issue: “Model not found”

Issue: “Missing anthropic-version header”

Issue: “Tool schema validation error”