codebase-memory-mcp and code-memory
These are competitors offering different architectural approaches to codebase indexing—one uses a knowledge graph with exact matching for speed, the other uses vector embeddings for semantic search—so you'd choose based on whether you prioritize query latency or search relevance.
About codebase-memory-mcp
DeusData/codebase-memory-mcp
MCP server that indexes your codebase into a persistent knowledge graph. 64 languages, sub-ms queries, 99% fewer tokens than grep. Single Go binary, no Docker, no API keys.
This tool helps developers understand their codebases more efficiently, especially when working with AI coding agents. It ingests your entire codebase, analyzing its structure across 66 programming languages, and outputs a persistent knowledge graph of functions, classes, and call chains. Developers, particularly those using AI agents for coding tasks, would use this to quickly query and visualize their project's architecture.
About code-memory
kapillamba4/code-memory
MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.
This tool helps software developers quickly find relevant information within their large codebases without manually sifting through files. It takes your code and documentation as input, processes it locally, and then allows you to semantically search for code definitions, architectural patterns, or even Git history. The output is precise code snippets, documentation sections, or commit messages relevant to your query, helping you understand, debug, or extend existing projects more efficiently.
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