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11st of 15 in RAG and knowledge

LightRAG

Graph-plus-vector RAG server with web UI and Ollama-compatible API

Repository on GitHub ↗ (opens in a new tab)
Stars
40.1k
License
MIT
Last commit
Sep 2026
Last release
Sep 2026

Overview

LightRAG indexes documents into a knowledge graph plus vector store and queries both layers, as a lighter alternative to Microsoft GraphRAG. The server package ships a REST API, a web UI for inserting and visualizing the graph, and Ollama-compatible /api routes for chat frontends. Parsing runs via MinerU, Docling or a native engine; production storage goes to PostgreSQL, Neo4j, MongoDB, Milvus or OpenSearch.

Who it is for: developers wanting graph-based RAG with a ready server

Strengths

  • Dual-level graph and vector retrieval with fewer LLM calls than community-report GraphRAG
  • Incremental updates and document deletion with graph regeneration from the LLM cache
  • Three parsing engines and four chunking strategies, including paragraph-semantic
  • Separate LLM settings per role: extract, query, keywords and VLM

Weaknesses

  • Default KV, vector and graph stores are in-memory with file persistence, not for production
  • Server binds 0.0.0.0 with every endpoint public until auth is configured
  • Ollama-compatible /api routes stay open even with auth unless WHITELIST_PATHS is set
  • docx smart headings and SVG rendering need extra spaCy models and libcairo

What it needs

  • no GPU
  • Docker + Compose
  • Needs PostgreSQL (recommended for production), Neo4j (optional), MongoDB (optional), Milvus (optional), OpenSearch (optional)
  • Models: LLM and embedding providers configured in .env, tested with open models such as Qwen3-30B-A3B

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