code-graph-context vs code-graph-rag
code-graph-context: Code Graph Context is an MCP server that builds a semantic graph of TypeScript codebases, enabling AI assistants like Claude to understand dependencies, perform impact analysis, and coordinate multi-file changes. It supports local or OpenAI embeddings, incremental parsing, and swarm coordination for parallel refactoring.; 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
Understand codebase dependencies and perform refactoring risk analysis
- 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
code-graph-contextcode-graph-ragFeatures
code-graph-contextcode-graph-ragUse Cases
code-graph-contextcode-graph-ragFAQ
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