AI RESEARCH
Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP
arXiv CS.AI
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ArXi:2603.27277v1 Announce Type: cross Large Language Model (LLM) coding agents typically explore codebases through repeated file-reading and grep-searching, consuming thousands of tokens per query without structural understanding. We present Codebase-Memory, an open-source system that constructs a persistent, Tree-Sitter-based knowledge graph via the Model Context Protocol (MCP), parsing 66 languages through a multi-phase pipeline with parallel worker pools, call-graph traversal, impact analysis, and community discovery.