What is an integration?

Protocol adapters that translate between Bifrost’s unified API and provider-specific API formats like OpenAI, Anthropic, and Google GenAI.

Overview

An integration is a protocol adapter that translates between Bifrost’s unified API and provider-specific API formats. Each integration handles request transformation, response normalization, and error mapping between the external API contract and Bifrost’s internal processing pipeline.

Integrations enable you to utilize Bifrost’s features like governance, MCP tools, load balancing, semantic caching, multi-provider support, and more, all while preserving your existing SDK-based architecture. Bifrost handles all the overhead of structure conversion, requiring only a single URL change to switch from direct provider APIs to Bifrost’s gateway.

Bifrost converts the request/response format of the provider API to the Bifrost API format based on the integration used, so you don’t have to.

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Quick Migration

Before (Direct Provider)

import openai

client = openai.OpenAI(
    api_key="your-openai-key"
)

After (Bifrost)

import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/openai",  # Point to Bifrost
    api_key="dummy-key" # Keys are handled in Bifrost now
)

That’s it! Your application now benefits from Bifrost’s features with no other changes.

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Supported Integrations

1. OpenAI
2. Anthropic
3. Google GenAI
4. LiteLLM
5. Langchain
6. AWS Bedrock

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Provider-Prefixed Models

Use multiple providers seamlessly by prefixing model names with the provider:

OpenAI

import openai

# Single client, multiple providers
client = openai.OpenAI(
    base_url="http://localhost:8080/openai",
    api_key="dummy"  # API keys configured in Bifrost
)

# OpenAI models
response1 = client.chat.completions.create(
    model="gpt-4o-mini", # (default OpenAI since it's OpenAI's SDK)
    messages=[{"role": "user", "content": "Hello!"}]
)

Anthropic

import openai

# Anthropic models using OpenAI SDK format
response2 = client.chat.completions.create(
    model="anthropic/claude-3-sonnet-20240229",
    messages=[{"role": "user", "content": "Hello!"}]
)

Azure

import openai

# Azure models
response4 = client.chat.completions.create(
    model="azure/gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Vertex

import openai

# Google Vertex models
response3 = client.chat.completions.create(
    model="vertex/gemini-pro",
    messages=[{"role": "user", "content": "Hello!"}]
)

Ollama

import openai

# Local Ollama models
response5 = client.chat.completions.create(
    model="ollama/llama3.1:8b",
    messages=[{"role": "user", "content": "Hello!"}]
)

---

Direct API Usage

For custom HTTP clients or when you have existing provider-specific setup and want to use Bifrost gateway without restructuring your codebase:

import requests

# Fully OpenAI compatible endpoint
response = requests.post(
    "http://localhost:8080/openai/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {openai_key}",
        "Content-Type": "application/json"
    },
    json={
        "model": "gpt-4o-mini",
        "messages": [{"role": "user", "content": "Hello!"}]
    }
)

# Fully Anthropic compatible endpoint
response = requests.post(
    "http://localhost:8080/anthropic/v1/messages",
    headers={
        "Content-Type": "application/json",
    },
    json={
        "model": "claude-3-sonnet-20240229",
        "max_tokens": 1000,
        "messages": [{"role": "user", "content": "Hello!"}]
    }
)

# Fully Google GenAI compatible endpoint
response = requests.post(
    "http://localhost:8080/genai/v1beta/models/gemini-1.5-flash/generateContent",
    headers={
        "Content-Type": "application/json",
    },
    json={
        "contents": [
            {"parts": [{"text": "Hello!"}]}
        ],
        "generation_config": {
            "max_output_tokens": 1000,
            "temperature": 1
        }
    }
)

---

Listing Models

All integrations support listing available models through their respective list models endpoints (e.g., /openai/v1/models, /anthropic/v1/models). By default, list models requests return models from all configured providers in Bifrost.

Filtering by Provider

You can control which provider’s models to list using the x-bf-list-models-provider header:

Python

import openai

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

# List models from all providers (default behavior)
all_models = client.models.list()

# List models from a specific provider only
openai_models = client.models.list(
    extra_headers={
        "x-bf-list-models-provider": "openai"
    }
)

anthropic_models = client.models.list(
    extra_headers={
        "x-bf-list-models-provider": "anthropic"
    }
)

JavaScript

import OpenAI from "openai";

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

// List models from all providers (default behavior)
const allModels = await openai.models.list();

// List models from a specific provider only
const openaiModels = await openai.models.list({
  headers: {
    "x-bf-list-models-provider": "openai",
  },
});

const anthropicModels = await openai.models.list({
  headers: {
    "x-bf-list-models-provider": "anthropic",
  },
});

cURL

# List models from all providers (default)
curl http://localhost:8080/openai/v1/models

# List models from specific provider
curl http://localhost:8080/openai/v1/models \
  -H "x-bf-list-models-provider: openai"

# Explicitly request all providers
curl http://localhost:8080/openai/v1/models \
  -H "x-bf-list-models-provider: all"

Header Behavior

Header Value	Behavior
Not set (default)	Lists models from all configured providers
all	Lists models from all configured providers
openai	Lists models from OpenAI provider only
anthropic	Lists models from Anthropic provider only
vertex	Lists models from Vertex AI provider only
Any valid provider	Lists models from that specific provider

Response Fields

When listing models from all providers, some provider-specific fields may be empty or contain default values if the information is not available from all providers. This is normal behavior as different providers expose different model metadata.

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Migration Strategies

Gradual Migration

1. Start with development - Test Bifrost in dev environment
2. Canary deployment - Route 5% of traffic through Bifrost
3. Feature-by-feature - Migrate specific endpoints gradually
4. Full migration - Switch all traffic to Bifrost

Blue-Green Migration

import os
import random

# Route traffic based on feature flag
def get_base_url(provider: str) -> str:
    if os.getenv("USE_BIFROST", "false") == "true":
        return f"http://bifrost:8080/{provider}"
    else:
        return f"https://api.{provider}.com"

# Gradual rollout
def should_use_bifrost() -> bool:
    rollout_percentage = int(os.getenv("BIFROST_ROLLOUT", "0"))
    return random.randint(1, 100) <= rollout_percentage

Feature Flag Integration

# Using feature flags for safe migration
import openai
from feature_flags import get_flag

def create_client():
    if get_flag("use_bifrost_openai"):
        base_url = "http://bifrost:8080/openai"
    else:
        base_url = "https://api.openai.com"

    return openai.OpenAI(
        base_url=base_url,
        api_key=os.getenv("OPENAI_API_KEY")
    )

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Next Steps

• HTTP Transport Overview - Main HTTP transport guide
• Endpoints - Complete API reference
• Configuration - Provider setup and config