DeusData / codebase-memory-mcp

A blazing-fast, zero-dependency MCP server that builds persistent knowledge graphs from codebases, giving AI coding agents sub-millisecond structural query capabilities with 99% token savings.

活跃维护 MIT C Tracked
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claude-code code-analysis code-intelligence developer-tools knowledge-graph mcp mcp-server model-context-protocol sqlite tree-sitter codex cursor windsurf aider gemini-cli graph-visualization kilocode opencode ast cypher

星标趋势

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AI 分析

项目摘要

A high-performance MCP server written in C that indexes codebases into a persistent knowledge graph using tree-sitter AST analysis and hybrid LSP semantic resolution. It enables AI coding agents to query code structure in sub-millisecond time with 99% fewer tokens than file-by-file exploration, supporting 158 languages and shipping as a single zero-dependency binary.

为什么值得关注

Exceptional performance (indexes Linux kernel in 3 minutes, sub-ms queries) combined with remarkable token efficiency (99% reduction) makes it a force multiplier for AI coding agents. The pure C implementation with zero dependencies, SLSA 3 compliance, and backing by peer-reviewed research (arXiv) set it apart from typical MCP servers.

优势

  • Blazing fast performance with sub-ms queries and millisecond-scale indexing
  • Zero-dependency single static binary for macOS, Linux, and Windows
  • Hybrid LSP approach combining tree-sitter parsing with semantic type resolution for 10 languages
  • Strong security posture with SLSA 3, OpenSSF Scorecard, and VirusTotal scanning
  • Comprehensive test suite with 5604 passing tests and active CI

局限性

  • No Docker support which may limit containerized deployment workflows
  • No examples directory which could slow onboarding for new users
  • Relatively new project (created Feb 2026) with 256 open issues indicating active but ongoing maturation

使用场景

  • Enhancing AI coding agents (Claude Code, Cursor, Aider) with fast codebase understanding
  • Building code navigation and exploration tools for large repositories
  • Reducing token costs when AI agents need to understand code structure
目标用户: AI coding agent users and developers who want to improve their agent's code understanding capabilities while reducing token costs
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分析模型:LongCat-2.0 | 分析时间:1 个月前