TrueMemory vs code-graph-rag
TrueMemory: TrueMemory is a neuroscience-inspired persistent memory system for AI agents. It achieves state-of-the-art scores on LoCoMo, LongMemEval, and BEAM benchmarks while running entirely offline on a single SQLite file. The six-layer retrieval pipeline with an encoding gate filters noise and stores only meaningful information.; 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.
TL;DR
Personal AI assistant that remembers user preferences and history across conversations.
- 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.
Side-by-Side Comparison
TrueMemorycode-graph-ragFeatures
TrueMemorycode-graph-ragUse Cases
TrueMemorycode-graph-ragFAQ
Deploy on DigitalOcean
Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.