HKUDS/LightRAG

Health v2 · maintenance only

Adoption not part of the score

39204 stars · 5510 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LightRAG is a Python framework for retrieval-augmented generation that integrates graph structures into text indexing and retrieval, using a dual-level retrieval system combining knowledge graphs with vector representations. It supports incremental data updates and offers improved retrieval accuracy and efficiency over flat-representation RAG systems.

Use cases

• build a RAG pipeline over my documents
• question answering over a knowledge base with graph-based retrieval
• index large document collections for LLM retrieval
• graph-based RAG instead of flat vector search
• incrementally update a RAG index as new documents arrive
• chat with my PDFs using a knowledge graph

When to choose

• you need RAG that captures entity relationships and complex inter-dependencies between documents
• you want fast, graph-enhanced retrieval with incremental index updates
• you want a simple, well-adopted Python RAG library with an active community

When to avoid

• you need only simple flat vector similarity search without graph overhead
• you want a fully managed hosted RAG service rather than a self-run library
• your project is not Python-based

Facets

library · maturity active

rag search-engine llm-inference nlp large-language-models artificial-intelligence python cross-platform knowledge-graph graphrag graph-based-retrieval vector-search document-indexing llm retrieval-augmented-generation natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="HKUDS/LightRAG")