关于
RagCode is a privacy-first Model Context Protocol (MCP) server designed to make any codebase instantly AI-ready. It bridges the gap between your code and Large Language Models (LLMs) by providing semantic vector search, allowing AI assistants like GitHub Copilot, Cursor, Windsurf, and Claude to deeply understand your projects. Built with a focus on privacy, RagCode operates entirely locally, utilizing Ollama for LLMs and embeddings, and Qdrant for vector storage, ensuring your code never leaves your machine. This not only guarantees 100% data control and zero cloud dependencies but also results in significant performance benefits, offering 5-10x faster code understanding and substantial token savings for AI interactions.
主要功能
- Comprehensive MCP Toolset: Offers 9 powerful tools including semantic search, hybrid search, function detail retrieval, type definition lookup, and implementation finding.
- Zero-Configuration IDE Integration: Automatically configures with popular AI-powered IDEs like Cursor, Windsurf, VS Code (Copilot), and Claude Desktop upon installation.
- 2 GitHub stars
- Enhanced AI Performance: Delivers 5-10x faster code understanding and up to 98% token savings for AI assistants by providing precise, context-rich code snippets.
- 100% Local and Privacy-First AI: Operates entirely on local infrastructure, ensuring zero cloud dependencies, no API costs, and offline capability.
- Multi-Language Code Intelligence: Provides full semantic support for Go, PHP (including Laravel), and Python, with planned support for JavaScript/TypeScript.
使用案例
- Equipping AI assistants with deep codebase understanding for enhanced navigation, Q&A, and refactoring within private or proprietary projects.
- Accelerating developer workflows by providing AI with instant, semantic access to code context, reducing manual search and comprehension time.
- Enabling cost-free, offline AI-powered code assistance by eliminating reliance on cloud APIs and ensuring data privacy for security-conscious teams.
Sponsored