OpenAI Compatible API

Overview

Bifrost provides 100% OpenAI API compatibility with enhanced features:

Zero code changes - Works with existing OpenAI SDK applications
Same request/response formats - Exact OpenAI API specification
Enhanced capabilities - Multi-provider fallbacks, MCP tools, monitoring
All endpoints supported - Chat completions, text completions, function calling
Any provider under the hood - Use any configured provider (OpenAI, Anthropic, etc.)

Endpoint: POST /openai/v1/chat/completions

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

Quick Migration

Python (OpenAI SDK)

import openai

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

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

# Everything else stays the same
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

JavaScript (OpenAI SDK)

import OpenAI from "openai";

// Before - Direct OpenAI
const openai = new OpenAI({
  baseURL: "https://api.openai.com",
  apiKey: process.env.OPENAI_API_KEY,
});

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

// Everything else stays the same
const response = await openai.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [{ role: "user", content: "Hello!" }],
});

Supported Features

Fully Supported

Feature	Status	Notes
Chat Completions	✅ Full	All parameters supported
Function Calling	✅ Full	Original + MCP tools
Vision/Multimodal	✅ Full	Images, documents, etc.
System Messages	✅ Full	All message types
Temperature/Top-p	✅ Full	All sampling parameters
Stop Sequences	✅ Full	Custom stop tokens
Max Tokens	✅ Full	Token limit control
Presence/Frequency Penalty	✅ Full	Repetition control

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
Rate Limiting	Built-in throttling	Cost control

Request Examples

Basic Chat Completion

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

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

Response:

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "gpt-4o-mini",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 13,
    "completion_tokens": 7,
    "total_tokens": 20
  }
}

Function Calling

curl -X POST http://localhost:8080/openai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      {"role": "user", "content": "What files are in the current directory?"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "list_directory",
          "description": "List files in a directory",
          "parameters": {
            "type": "object",
            "properties": {
              "path": {"type": "string", "description": "Directory path"}
            },
            "required": ["path"]
          }
        }
      }
    ]
  }'

Response with Tool Call:

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": null,
        "tool_calls": [
          {
            "id": "call_123",
            "type": "function",
            "function": {
              "name": "list_directory",
              "arguments": "{\"path\": \".\"}"
            }
          }
        ]
      }
    }
  ]
}

Vision/Multimodal

import openai

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

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this image?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD..."
                    }
                }
            ]
        }
    ]
)

Advanced Usage

Streaming Responses

import openai

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

# Note: Streaming not yet supported
# This will work but return complete response
stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Custom Headers

import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/openai",
    api_key=openai_key,
    default_headers={
        "X-Organization": "your-org-id",
        "X-Environment": "production"
    }
)

Error Handling

import openai
from openai import OpenAIError

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

try:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello!"}]
    )
except OpenAIError as e:
    print(f"OpenAI API error: {e}")
except Exception as e:
    print(f"Other error: {e}")

Enhanced Features

Automatic MCP Tool Integration

MCP tools are automatically available in OpenAI-compatible requests:

# No tool definitions needed - MCP tools auto-discovered
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Read the config.json file and tell me about the providers"}
    ]
)

# Response may include automatic tool calls
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        print(f"Called: {tool_call.function.name}")

Load Balancing

Multiple API keys automatically load balanced:

{
  "providers": {
    "openai": {
      "keys": [
        {
          "value": "env.OPENAI_API_KEY_1",
          "models": ["gpt-4o-mini"],
          "weight": 0.7
        },
        {
          "value": "env.OPENAI_API_KEY_2",
          "models": ["gpt-4o-mini"],
          "weight": 0.3
        }
      ]
    }
  }
}

Testing & Validation

Compatibility Testing

Test your existing OpenAI code with Bifrost:

import openai

def test_bifrost_compatibility():
    # Test with Bifrost
    bifrost_client = openai.OpenAI(
        base_url="http://localhost:8080/openai",
        api_key=openai_key
    )

    # Test with direct OpenAI (for comparison)
    openai_client = openai.OpenAI(
        base_url="https://api.openai.com",
        api_key=openai_key
    )

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

    # Both should work identically
    bifrost_response = bifrost_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=test_message
    )

    openai_response = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=test_message
    )

    # Compare response structure
    assert bifrost_response.choices[0].message.content is not None
    assert openai_response.choices[0].message.content is not None

    print("✅ Bifrost OpenAI compatibility verified")

test_bifrost_compatibility()

Performance Comparison

import time
import openai

def benchmark_response_time(client, name):
    start_time = time.time()

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello!"}]
    )

    end_time = time.time()
    print(f"{name} response time: {end_time - start_time:.2f}s")
    return response

# Compare Bifrost vs Direct OpenAI
bifrost_client = openai.OpenAI(base_url="http://localhost:8080/openai", api_key=key)
openai_client = openai.OpenAI(base_url="https://api.openai.com", api_key=key)

benchmark_response_time(bifrost_client, "Bifrost")
benchmark_response_time(openai_client, "Direct OpenAI")

Multi-Provider Support

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

import openai

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

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

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

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

Configuration

Bifrost Config for OpenAI

{
  "providers": {
    "openai": {
      "keys": [
        {
          "value": "env.OPENAI_API_KEY",
          "models": [
            "gpt-3.5-turbo",
            "gpt-4",
            "gpt-4o",
            "gpt-4o-mini",
            "gpt-4-turbo",
            "gpt-4-vision-preview"
          ],
          "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": 5,
        "buffer_size": 20
      }
    }
  }
}

Environment Variables

# Required
export OPENAI_API_KEY="sk-..."

# Optional - for enhanced features
export ANTHROPIC_API_KEY="sk-ant-..."  # 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 = openai.OpenAI(
    base_url="http://localhost:8080/openai",
    api_key=os.getenv("OPENAI_API_KEY")  # Explicit env var
)

Issue: “Model not found”

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

{
  "providers": {
    "openai": {
      "keys": [
        {
          "value": "env.OPENAI_API_KEY",
          "models": ["gpt-4o-mini", "gpt-4o", "gpt-4-turbo"], // Add your model
          "weight": 1.0
        }
      ]
    }
  }
}

Issue: “Connection refused”

Problem: Bifrost not running or wrong port Solution:

# Check Bifrost is running
curl http://localhost:8080/metrics

# If not running, start it
docker run -p 8080:8080 maximhq/bifrost

Issue: “Timeout errors”

Problem: Network timeout too low Solution: Increase timeout in config.json:

{
  "providers": {
    "openai": {
      "network_config": {
        "default_request_timeout_in_seconds": 60 // Increase from 30
      }
    }
  }
}

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

​Overview

​Quick Migration

​Python (OpenAI SDK)

​JavaScript (OpenAI SDK)

​Supported Features

​Fully Supported

​Enhanced Features

​Request Examples

​Basic Chat Completion

​Function Calling

​Vision/Multimodal

​Advanced Usage

​Streaming Responses

​Custom Headers

​Error Handling

​Enhanced Features

​Automatic MCP Tool Integration

​Load Balancing

​Testing & Validation

​Compatibility Testing

​Performance Comparison

​Multi-Provider Support

​Configuration

​Bifrost Config for OpenAI

​Environment Variables

​Common Issues & Solutions

​Issue: “Invalid API Key”

​Issue: “Model not found”

​Issue: “Connection refused”

​Issue: “Timeout errors”

Overview

Quick Migration

Python (OpenAI SDK)

JavaScript (OpenAI SDK)

Supported Features

Fully Supported

Enhanced Features

Request Examples

Basic Chat Completion

Function Calling

Vision/Multimodal

Advanced Usage

Streaming Responses

Custom Headers

Error Handling

Enhanced Features

Automatic MCP Tool Integration

Load Balancing

Testing & Validation

Compatibility Testing

Performance Comparison

Multi-Provider Support

Configuration

Bifrost Config for OpenAI

Environment Variables

Common Issues & Solutions

Issue: “Invalid API Key”

Issue: “Model not found”

Issue: “Connection refused”

Issue: “Timeout errors”