Quick Summary: 📝
Codebase-Memory-MCP is a high-performance C-based code intelligence engine that indexes entire codebases into a knowledge graph for AI coding agents. It achieves extreme indexing speeds and sub-millisecond query times, supporting 158 languages with enhanced semantic resolution for popular ones, and ships as a single, zero-dependency static binary.
Key Takeaways: 💡
✅ Lightning-fast indexing of even massive codebases (e.g., Linux kernel in 3 minutes).
✅ Creates a deep, semantic knowledge graph using Tree-sitter and Hybrid LSP across 158 languages.
✅ Significantly improves AI coding agent performance, reducing tokens and tool calls for better accuracy.
✅ Simple, zero-dependency installation as a single static binary for all major OS.
✅ Ensures privacy and security with 100% local processing of your codebase.
Project Statistics: 📊
- ⭐ Stars: 25735
- 🍴 Forks: 1907
- ❗ Open Issues: 145
Tech Stack: 💻
- ✅ C
Ever felt your AI coding assistant struggling to grasp the full context of your sprawling codebase? It's like asking someone to navigate a complex city without a map – they might get there, but it'll take a lot of detours and confused looks. That's precisely the problem codebase-memory-mcp swoops in to solve. This project is a game-changer for anyone working with AI coding agents, transforming them from hesitant explorers into confident navigators of your entire project.
At its core, codebase-memory-mcp is an incredibly fast and efficient code intelligence engine. Imagine it building a super-detailed, interactive map of your entire codebase in mere moments. For an average repository, it indexes everything in milliseconds. Even a behemoth like the Linux kernel, with its 28 million lines of code, is fully mapped out in just three minutes! This incredible speed is achieved through a smart, RAM-first pipeline, using techniques like LZ4 compression and in-memory SQLite, ensuring that while it's fast, it's also resource-conscious and releases memory after indexing.
How does it create this magical map? It leverages tree-sitter for deep Abstract Syntax Tree (AST) analysis across a staggering 158 programming languages. This gives it a foundational understanding of your code's structure. But it doesn't stop there. For popular languages like Python, TypeScript, PHP, C#, Go, C, C++, Java, Kotlin, and Rust, it goes a step further with "Hybrid LSP" semantic type resolution. This means it understands not just what your code looks like, but what it means – tracking functions, classes, call chains, HTTP routes, and even cross-service links, building a rich, persistent knowledge graph.
The real benefit for developers is how this empowers your AI coding agents. With this deep, instantaneous understanding of your codebase, your AI can answer complex structural queries in under a millisecond. This translates directly into more accurate suggestions, significantly fewer tokens consumed (saving you money and speeding up responses), and drastically fewer tool calls needed to get the job done. Instead of your agent fumbling through files, it instantly knows where everything is and how it connects. Plus, it’s incredibly easy to get started: a single static binary for macOS, Linux, and Windows, zero dependencies – just download, install, and you’re good to go. All processing happens 100% locally on your machine, so your code never leaves your environment, ensuring top-notch security and privacy.
Learn More: 🔗
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