MAXeaglet/commandcode-proxy
Command Code API 反代代理,兼容 OpenAI 与 Anthropic 接口 | Reverse proxy exposing Command Code API as OpenAI- and Anthropic-compatible endpoints
What it solves
This project provides a reverse proxy that allows developers to use the Command Code API through standard OpenAI and Anthropic-compatible tools. It bridges the gap between the proprietary Command Code protocol and common AI development ecosystems, enabling features like tool calling, multimodal input, and reasoning effort in environments that expect standard API formats.
How it works
The proxy functions as a translation layer. It intercepts incoming requests formatted for OpenAI or Anthropic endpoints and converts them into the specific request protocol required by Command Code. This includes handling complex lifecycle requirements like device fingerprinting, session management, and protocol-specific headers. It is built as a single-file Node.js application with zero external dependencies, making it lightweight and easy to deploy.
Who it’s for
This tool is designed for developers who want to integrate Command Code's capabilities into existing applications or workflows that are already built to support OpenAI or Anthropic SDKs. It is particularly useful for those who need to manage device identity stability or handle specific API protocol nuances without modifying their core application code.
Highlights
- Protocol Translation: Seamlessly maps OpenAI Chat Completions, Anthropic Messages, and OpenAI Responses API to the Command Code protocol.
- Device Fingerprinting: Implements deterministic device identity generation based on API keys, ensuring stable sessions and preventing unwanted account flagging.
- Resource Management: Includes built-in memory protection, such as tool screenshot budget limits and request body size caps, to prevent crashes during high-load operations.
- Zero-Dependency Design: A single-file core implementation that simplifies deployment and reduces maintenance overhead.
- Advanced Features: Supports streaming, multimodal image inputs, reasoning effort, and dynamic model list fetching.
Related
- Project
- Project
- Project
- Project
- Project