Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP

• URL: http://arxiv.org/abs/2603.27277v1
• Date: Sat, 28 Mar 2026 14:18:12 GMT
• Title: Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP
• Abstract summary: We present Codebase-Memory, an open-source system that constructs a persistent, Tree-Sitter-based knowledge graph via the Model Context Protocol (MCP)<n>Codebase-Memory achieves 83% answer quality versus 92% for a file-exploration agent, at ten times fewer tokens and 2.1 times fewer tool calls.<n>For graph-native queries such as hub detection and caller ranking, it matches or exceeds the explorer on 19 of 31 languages.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Evaluated across 31 real-world repositories, Codebase-Memory achieves 83% answer quality versus 92% for a file-exploration agent, at ten times fewer tokens and 2.1 times fewer tool calls. For graph-native queries such as hub detection and caller ranking, it matches or exceeds the explorer on 19 of 31 languages.

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