code-graph-rag vs subcog
code-graph-rag: Code-Graph-RAG is an accurate Retrieval-Augmented Generation (RAG) system designed for multi-language codebases. It uses Tree-sitter to analyze code, build comprehensive knowledge graphs, and allows natural language querying of codebase structure and relationships. The system also includes advanced editing capabilities, enabling users to modify code directly through natural language prompts.; subcog: Subcog is a persistent memory system for AI coding assistants that captures decisions, learnings, and context from coding sessions. It features a single-binary distribution, three-layer storage architecture (SQLite, FTS5, usearch HNSW), MCP server integration, and Claude Code hooks. It achieves high accuracy on factual recall benchmarks.
TL;DR
- You want to parse a multi-language monorepo with Tree-sitter into a Memgraph knowledge graph and query or edit it via natural language or Claude Code MCP.
- You want semantic code search using UniXcoder embeddings paired with Cypher graph queries.
- You want RAG over PDFs, Word files, and slides rather than source code repositories; use RAGFlow or MaxKB.
- You want a zero-dependency code context MCP server without running Docker and Memgraph; jCodeMunch or Context7 are much lighter.
Capturing and recalling technical decisions during development
Side-by-Side Comparison
code-graph-ragsubcogFeatures
code-graph-ragsubcogUse Cases
code-graph-ragsubcogFAQ
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