HTTP Transport Quick Start

# 🐳 Docker (fastest)
docker pull maximhq/bifrost
docker run -p 8080:8080 maximhq/bifrost

# 🔧 OR Go Binary (Make sure Go is in your PATH)
go install github.com/maximhq/bifrost/transports/bifrost-http@latest
bifrost-http -port 8080

# 🖥️ Beautiful web UI for zero-config setup
# macOS:
open http://localhost:8080
# Linux:
xdg-open http://localhost:8080
# Windows:
start http://localhost:8080
# Or simply open http://localhost:8080 manually in your browser

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

export OPENAI_API_KEY="your-openai-api-key"

# Docker with volume mount for persistence
docker run -p 8080:8080 \
  -v $(pwd):/app/data \
  -e OPENAI_API_KEY \
  maximhq/bifrost

# OR Go Binary with app directory
bifrost-http -app-dir . -port 8080

# Use current directory as app directory
bifrost-http -app-dir .

# Use specific directory as app directory
bifrost-http -app-dir /path/to/bifrost-data

# Default: current directory if no flag specified
bifrost-http -port 8080

# Map current host directory → /app/data inside container
docker run -p 8080:8080 -v $(pwd):/app/data maximhq/bifrost

# Map specific host directory → /app/data inside container
docker run -p 8080:8080 -v /host/path/bifrost-data:/app/data maximhq/bifrost

# No volume = ephemeral storage (lost when container stops)
docker run -p 8080:8080 maximhq/bifrost

# Make your first request
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello, Bifrost!"}]
  }'

# Before
from openai import OpenAI
client = OpenAI(api_key="your-key")

# After - Just change base_url!
from openai import OpenAI
client = OpenAI(
    api_key="dummy",  # Not used
    base_url="http://localhost:8080/openai"
)

# All your existing code works unchanged! ✨
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Before
from anthropic import Anthropic
client = Anthropic(api_key="your-key")

# After - Just change base_url!
from anthropic import Anthropic
client = Anthropic(
    api_key="dummy",  # Not used
    base_url="http://localhost:8080/anthropic"
)

# All your existing code works unchanged! ✨

# Before
from google import genai
client = genai.Client(api_key="your-key")

# After - Just change base_url!
from google import genai
client = genai.Client(
    api_key="dummy",  # Not used
    http_options=genai.types.HttpOptions(
        base_url="http://localhost:8080/genai"
    )
)

# All your existing code works unchanged! ✨

# Add OpenAI
curl -X POST http://localhost:8080/api/providers \
  -H "Content-Type: application/json" \
  -d '{"provider": "openai", "keys": [{"value": "env.OPENAI_API_KEY", "models": ["gpt-4o-mini"], "weight": 1.0}]}'

# Add Anthropic
curl -X POST http://localhost:8080/api/providers \
  -H "Content-Type: application/json" \
  -d '{"provider": "anthropic", "keys": [{"value": "env.ANTHROPIC_API_KEY", "models": ["claude-3-sonnet-20240229"], "weight": 1.0}]}'

# Set environment variables
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"

# Use OpenAI
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "openai/gpt-4o-mini", "messages": [{"role": "user", "content": "Hello from OpenAI!"}]}'

# Use Anthropic
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "anthropic/claude-3-sonnet-20240229", "messages": [{"role": "user", "content": "Hello from Anthropic!"}], "params":{"max_tokens": 100}}'

# Request with fallback
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello!"}],
    "fallbacks": ["anthropic/claude-3-sonnet-20240229"],
    "params": {"max_tokens": 100}
  }'

import requests

response = requests.post(
    "http://localhost:8080/v1/chat/completions",
    json={
        "model": "openai/gpt-4o-mini",
        "messages": [{"role": "user", "content": "Hello from Python!"}]
    }
)
print(response.json())

const response = await fetch("http://localhost:8080/v1/chat/completions", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    model: "openai/gpt-4o-mini",
    messages: [{ role: "user", content: "Hello from Node.js!" }],
  }),
});
console.log(await response.json());

response, err := http.Post(
    "http://localhost:8080/v1/chat/completions",
    "application/json",
    strings.NewReader(`{
        "model": "openai/gpt-4o-mini",
        "messages": [{"role": "user", "content": "Hello from Go!"}]
    }`)
)