Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP
📰 ArXiv cs.AI
Codebase-Memory uses Tree-Sitter-based knowledge graphs for efficient LLM code exploration via MCP
Action Steps
- Construct a knowledge graph using Tree-Sitter parser
- Implement a multi-phase pipeline with parallel worker pools
- Perform call-graph traversal to understand code structure
- Integrate with Model Context Protocol (MCP) for LLM code exploration
Who Needs to Know This
AI engineers and researchers can benefit from this system as it enables more efficient code exploration and understanding, while software engineers can utilize it to improve their coding workflows
Key Insight
💡 Using a knowledge graph approach can significantly improve the efficiency of LLM code exploration
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🚀 Codebase-Memory: Efficient LLM code exploration via Tree-Sitter-based knowledge graphs!
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