code-graph-rag and flexible-graphrag
These are complementary tools that operate at different scales: code-graph-rag specializes in monorepo code understanding with knowledge graphs, while flexible-graphrag provides a broader infrastructure for building graph-based RAG systems across multiple database and data source options, making it suitable as a foundation that could integrate code analysis tools like the former.
About code-graph-rag
vitali87/code-graph-rag
The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs
Uses Tree-sitter for multi-language AST parsing and Memgraph for graph-based storage of codebase structure and relationships. Supports cloud models (Gemini, OpenAI), local models (Ollama), and language-agnostic Cypher queries to retrieve code snippets and enable surgical file editing with AST-based function targeting.
About flexible-graphrag
stevereiner/flexible-graphrag
Flexible GraphRAG: Python, LlamaIndex, Docker Compose: 8 Graph dbs, 10 Vector dbs, OpenSearch, Elasticsearch, Alfresco. 13 data sources (9 auto-sync), KG auto-building, schemas, LLMs, Docling or LlamaParse doc processing, GraphRAG, RAG only, Hybrid search, AI chat. React, Vue, Angular frontends, FastAPI backend, REST API, MCP Server. Please 🌟 Star
Supports RDF-based ontologies and SPARQL queries across both property graph and triple store databases, enabling schema-guided knowledge graph extraction. Implements automatic incremental synchronization that detects changes across 13 data sources and updates vector, search, and graph databases in near real-time without full reprocessing. Built on LlamaIndex abstractions with OpenTelemetry instrumentation for production observability, plus an MCP Server integration for Claude Desktop and compatible clients.
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