ghc-api 1.0.8

GitHub Copilot API Proxy - A Flask application serving as a proxy server for GitHub Copilot API

GitHub Copilot API Proxy (ghc-api)

A Python Flask application that serves as a proxy server for GitHub Copilot API, providing OpenAI and Anthropic API compatibility with caching and monitoring capabilities.

Features

• OpenAI API Compatibility: /v1/chat/completions endpoint
• Anthropic API Compatibility: /v1/messages endpoint with automatic request/response translation
• Model Listing: /v1/models endpoint listing available models
• Model Name Mapping: Translate model names with exact and prefix-based matching
• Token Management: Automatic GitHub Copilot token refresh
• Vision Support: Handle image inputs and enable vision capabilities
• Memory Caching: Cache all requests and responses (up to 1000 entries)
• Web Dashboard: Real-time statistics and request browser
• Request Details: View full request/response bodies with JSON formatting
• Export/Import: Export and import request history as JSON Lines files
• Content Filtering: Remove or add content from system prompts and tool results

Installation

Install the package using pip:

pip install ghc-api

Or install from source:

pip install .

Usage

Start the server with the ghc-api command:

ghc-api

By default, the server will start on http://localhost:8313.

Command Line Options

• -p PORT or --port PORT: Specify the port to listen on (default: 8313)
• -a ADDRESS or --address ADDRESS: Specify the address to listen on (default: localhost)
• -c or --config: Generate a YAML config file in ~/.ghc-api/config.yaml
• --help: Show help message

Configuration

The application looks for a configuration file at ~/.ghc-api/config.yaml. You can generate this file using:

ghc-api --config

The config file contains:

# Server Settings
address: localhost
port: 8313
debug: false

# GitHub Copilot Account Type
# Options: "individual", "business", "enterprise"
account_type: individual

# Version settings (used to build request headers)
vscode_version: "1.93.0"
api_version: "2025-04-01"
copilot_version: "0.26.7"

# Model Name Mappings
model_mappings:
  # Exact match mappings
  exact:
    opus: claude-opus-4.5
    sonnet: claude-sonnet-4.5
    haiku: claude-haiku-4.5
  # Prefix match mappings
  prefix:
    claude-sonnet-4-: claude-sonnet-4
    claude-opus-4.5-: claude-opus-4.5

# Content Filtering
system_prompt_remove: []    # Strings to remove from system prompts
system_prompt_add: []       # Strings to append to system prompts
tool_result_suffix_remove: [] # Strings to remove from end of tool results

Token Management

The application follows this priority for getting the GitHub token:

1. GITHUB_TOKEN environment variable
2. Token file at ~/.ghc-api/github_token.txt
3. Interactive GitHub Device Flow authentication

API Endpoints

OpenAI Compatible

• POST /v1/chat/completions - Chat completions
• POST /chat/completions - Chat completions (without v1 prefix)
• GET /v1/models - List available models
• GET /models - List available models (without v1 prefix)

Anthropic Compatible

• POST /v1/messages - Messages API (Anthropic format)

Dashboard & Monitoring

• GET / - Web dashboard with statistics
• GET /requests - Request browser page
• GET /api/stats - JSON statistics endpoint
• GET /api/requests - Paginated list of requests
• GET /api/requests/search - Full-text search in request/response bodies
• GET /api/requests/export - Export all requests as JSON Lines file
• POST /api/requests/import - Import requests from JSON Lines file
• GET /api/request/<id> - Individual request details
• GET /api/request/<id>/request-body - Request body only
• GET /api/request/<id>/response-body - Response body only

Example Usage

With OpenAI Python SDK

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8313/v1",
    api_key="not-needed"  # Token is managed by the proxy
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

With Anthropic Python SDK

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8313",
    api_key="not-needed"  # Token is managed by the proxy
)

message = client.messages.create(
    model="claude-sonnet-4",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)
print(message.content[0].text)

With cURL

# Chat completions
curl http://localhost:8313/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

# List models
curl http://localhost:8313/v1/models

Dashboard

Access the web dashboard at http://localhost:8313/ to:

• View overall statistics (total requests, data transfer)
• See per-model usage statistics
• See per-endpoint analytics
• Browse recent requests
• View detailed request/response bodies

Architecture

• Modular Design: Organized into separate modules for maintainability
  • main.py - Entry point and configuration loading
  • app.py - Flask application factory
  • config.py - Configuration constants and model mappings
  • cache.py - Request caching and statistics
  • translator.py - OpenAI/Anthropic format translation
  • streaming.py - Streaming response handling
  • token_manager.py - GitHub token management
  • routes/ - API endpoint handlers (openai, anthropic, dashboard)
• Thread-Safe Caching: Uses threading locks for concurrent access
• Memory-Based Storage: No external database dependencies
• RESTful API Design: Follows REST conventions

License

MIT License