Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918414545584128 |
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| author | Vogel, Martin Meyer-Eschenbach, Falk Kohler, Severin Grünewald, Elias Balzer, Felix |
| author_facet | Vogel, Martin Meyer-Eschenbach, Falk Kohler, Severin Grünewald, Elias Balzer, Felix |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27277 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP Vogel, Martin Meyer-Eschenbach, Falk Kohler, Severin Grünewald, Elias Balzer, Felix Software Engineering Artificial Intelligence Programming Languages D.2.3; D.2.7; D.3.4; H.3.3; I.2.2 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. |
| title | Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP |
| topic | Software Engineering Artificial Intelligence Programming Languages D.2.3; D.2.7; D.3.4; H.3.3; I.2.2 |
| url | https://arxiv.org/abs/2603.27277 |