Claude Code API

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A high-performance Rust implementation of an OpenAI-compatible API gateway for Claude Code CLI. This project provides a RESTful API interface that allows you to interact with Claude Code using the familiar OpenAI API format.

✨ Features

• 🔌 OpenAI API Compatibility - Drop-in replacement for OpenAI API, works with existing OpenAI client libraries
• 🚀 High Performance - Built with Rust, Axum, and Tokio for exceptional performance
• 💬 Conversation Management - Built-in session support for multi-turn conversations
• 🖼️ Multimodal Support - Process images alongside text in your requests
• ⚡ Response Caching - Intelligent caching system to reduce latency and costs
• 🔧 MCP Support - Model Context Protocol integration for accessing external tools and services
• 📁 File Access Control - Configurable file system permissions for secure operations
• 🌊 Streaming Responses - Real-time streaming support for long-form content
• 🛡️ Robust Error Handling - Comprehensive error handling with automatic retries
• 📊 Statistics API - Monitor usage and performance metrics

🚀 Quick Start

Prerequisites

• Rust 1.75 or higher
• Claude CLI installed and configured
• (Optional) MCP servers for extended functionality

Installation

1. Clone the repository:

git clone https://github.com/yourusername/claude-code-api.git
cd claude-code-api/rust-claude-code-api

2. Build the project:

cargo build --release

3. Start the server:

./target/release/claude-code-api

The API server will start on http://localhost:8080 by default.

Quick Test

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-20250514",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'

📖 Core Features

1. OpenAI-Compatible Chat API

import openai

# Configure the client to use Claude Code API
client = openai.OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed"  # API key is not required
)

response = client.chat.completions.create(
    model="claude-opus-4-20250514",
    messages=[
        {"role": "user", "content": "Write a hello world in Python"}
    ]
)

print(response.choices[0].message.content)

2. Conversation Management

Maintain context across multiple requests:

# First request - creates a new conversation
response = client.chat.completions.create(
    model="claude-opus-4-20250514",
    messages=[
        {"role": "user", "content": "My name is Alice"}
    ]
)
conversation_id = response.conversation_id

# Subsequent request - continues the conversation
response = client.chat.completions.create(
    model="claude-opus-4-20250514",
    conversation_id=conversation_id,
    messages=[
        {"role": "user", "content": "What's my name?"}
    ]
)
# Claude will remember: "Your name is Alice"

3. Multimodal Support

Process images with text:

response = client.chat.completions.create(
    model="claude-opus-4-20250514",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "image_url", "image_url": {"url": "/path/to/image.png"}}
        ]
    }]
)

Supported image formats:

• Local file paths
• HTTP/HTTPS URLs
• Base64 encoded data URLs

4. Streaming Responses

stream = client.chat.completions.create(
    model="claude-opus-4-20250514",
    messages=[{"role": "user", "content": "Write a long story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

5. MCP (Model Context Protocol)

Enable Claude to access external tools and services:

# Create MCP configuration
cat > mcp_config.json << EOF
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/allowed/path"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-token"
      }
    }
  }
}
EOF

# Start with MCP support
export CLAUDE_CODE__MCP__ENABLED=true
export CLAUDE_CODE__MCP__CONFIG_FILE="./mcp_config.json"
./target/release/claude-code-api

🔧 Configuration

Environment Variables

# Server configuration
CLAUDE_CODE__SERVER__HOST=0.0.0.0
CLAUDE_CODE__SERVER__PORT=8080

# Claude CLI configuration
CLAUDE_CODE__CLAUDE__COMMAND=claude
CLAUDE_CODE__CLAUDE__TIMEOUT_SECONDS=300
CLAUDE_CODE__CLAUDE__MAX_CONCURRENT_SESSIONS=10

# File access permissions
CLAUDE_CODE__FILE_ACCESS__SKIP_PERMISSIONS=false
CLAUDE_CODE__FILE_ACCESS__ADDITIONAL_DIRS='["/path1", "/path2"]'

# MCP configuration
CLAUDE_CODE__MCP__ENABLED=true
CLAUDE_CODE__MCP__CONFIG_FILE="./mcp_config.json"
CLAUDE_CODE__MCP__STRICT=false
CLAUDE_CODE__MCP__DEBUG=false

# Cache configuration
CLAUDE_CODE__CACHE__ENABLED=true
CLAUDE_CODE__CACHE__MAX_ENTRIES=1000
CLAUDE_CODE__CACHE__TTL_SECONDS=3600

# Conversation management
CLAUDE_CODE__CONVERSATION__MAX_HISTORY_MESSAGES=20
CLAUDE_CODE__CONVERSATION__SESSION_TIMEOUT_MINUTES=30

Configuration File

Create config/local.toml:

[server]
host = "0.0.0.0"
port = 8080

[claude]
command = "claude"
timeout_seconds = 300
max_concurrent_sessions = 10

[file_access]
skip_permissions = false
additional_dirs = ["/Users/me/projects", "/tmp"]

[mcp]
enabled = true
config_file = "./mcp_config.json"
strict = false
debug = false

📚 API Endpoints

Chat Completions

• POST /v1/chat/completions - Create a chat completion

Models

• GET /v1/models - List available models

Conversations

• POST /v1/conversations - Create a new conversation
• GET /v1/conversations - List active conversations
• GET /v1/conversations/:id - Get conversation details

Statistics

• GET /stats - Get API usage statistics

Health Check

• GET /health - Check service health

🛠️ Advanced Usage

Using with LangChain

from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed",
    model="claude-opus-4-20250514"
)

response = llm.invoke("Explain quantum computing")
print(response.content)

Using with Node.js

const OpenAI = require('openai');

const client = new OpenAI({
  baseURL: 'http://localhost:8080/v1',
  apiKey: 'not-needed'
});

async function chat() {
  const response = await client.chat.completions.create({
    model: 'claude-opus-4-20250514',
    messages: [{ role: 'user', content: 'Hello!' }]
  });
  
  console.log(response.choices[0].message.content);
}

Using with curl

# Basic request
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-20250514",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# With conversation ID
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-20250514",
    "conversation_id": "uuid-here",
    "messages": [{"role": "user", "content": "Continue our chat"}]
  }'

# With image
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-20250514",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "What is this?"},
        {"type": "image_url", "image_url": {"url": "/path/to/image.png"}}
      ]
    }]
  }'

🔒 Security

• File access is controlled through configurable permissions
• MCP servers run in isolated processes
• No API key required (relies on Claude CLI authentication)
• Supports CORS for web applications
• Request ID tracking for audit trails

🐛 Troubleshooting

Common Issues

1. "Permission denied" errors

   # Enable file permissions
   export CLAUDE_CODE__FILE_ACCESS__SKIP_PERMISSIONS=true
   # Or use the startup script
   ./start_with_permissions.sh
   

2. MCP servers not working

   # Enable debug mode
   export CLAUDE_CODE__MCP__DEBUG=true
   # Check MCP server installation
   npx -y @modelcontextprotocol/server-filesystem --version
   

3. High latency on first request

   • This is normal as Claude CLI needs to start up
   • Subsequent requests will be faster due to process reuse

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built on top of Claude Code CLI
• Inspired by OpenAI's API design
• Powered by Axum web framework

📞 Support

• Report Issues
• Documentation
• Discussions

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Made with ❤️ by the Claude Code API team