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codebase-memory-mcp

Code intelligence MCP in pure C: tree-sitter knowledge graph over 158 languages, average repo indexed in milliseconds, sub-ms queries, 10x fewer tokens. Single static binary, zero deps.

34,159 2,621 C MITupdated yesterday
Curator's take

The performance ceiling of the code-context-server category: pure C, single static binary, the Linux kernel indexed in 3 minutes, structural queries under a millisecond — with a peer-reviewed preprint (83% answer quality, 10x fewer tokens across 31 repos) instead of vibes. Hybrid LSP adds real type resolution for the 12 languages that matter most, and 43 client surfaces means it plugs into whatever agent you run. NOT semantic search — it answers structural questions (call chains, routes, blast radius), not 'where's the code that does X'; pair it with an embedding tool for that. And note its own disclosure: it writes to your agent config files by design — audit posture is unusually good (SLSA 3, OpenSSF, per-release VirusTotal), use it.

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README.md

codebase-memory-mcp

GitHub Release License CI Tests Languages Hybrid LSP Agents Pure C Platform OpenSSF Scorecard SLSA 3 VirusTotal arXiv

The fastest and most efficient code intelligence engine for AI coding agents. Full-indexes an average repository in milliseconds, the Linux kernel (28M LOC, 75K files) in 3 minutes. Answers structural queries in under 1ms. Ships as a single static binary for macOS, Linux, and Windows — download, run install, done.

High-quality parsing through tree-sitter AST analysis across all 158 languages, enhanced with Hybrid LSP semantic type resolution for Python, TypeScript / JavaScript / JSX / TSX, PHP, C#, Go, C, C++, Java, Kotlin, Rust, and Perl — producing a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. 15 MCP tools. Zero dependencies. Plug and play across 43 supported automatic/conditional client surfaces.

Research — The design and benchmarks behind this project are described in the preprint Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP (arXiv:2603.27277). Evaluated across 31 real-world repositories: 83% answer quality, 10× fewer tokens, 2.1× fewer tool calls vs. file-by-file exploration.

Security & Trust — This tool reads your codebase and writes to your agent configuration files. That is what it is designed to do. If you prefer to audit before running, the full source is here — every release binary is signed, checksummed, and scanned by 70+ antivirus engines. All processing happens 100% locally; your code never leaves your machine. Found a security issue? We want to know — see SECURITY.md. Security is Priority #1 for us.

Graph visualization UI showing the codebase-memory-mcp knowledge graph
Built-in 3D graph visualization (UI variant) — explore your knowledge graph at localhost:9749

Why codebase-memory-mcp

  • Extreme indexing speed — Linux kernel (28M LOC, 75K files) in 3 minutes. RAM-first pipeline: LZ4 compression, in-memory SQLite, fused Aho-Corasick pattern matching. Memory released after indexing.
  • Plug and play — single static binary for macOS (a

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