LightRAG Graph-Based Retrieval-Augmented Generation Framework

LightRAG is a Python-based retrieval-augmented generation framework that builds knowledge graphs from documents for more connected, contextual retrieval. Published at EMNLP 2025, it enables graph-powered RAG with support for multiple storage backends and LLM providers.

Installation

Use the upstream install or setup path that matches your environment:

• Note: You can also use pip if you prefer, but uv is recommended for better performance and more reliable dependency management.
• uv tool install "lightrag-hku[api]"
• git clone https://github.com/HKUDS/LightRAG.git
• make dev

Requirements and caveats from upstream:

• <img src="https://img.shields.io/badge/🐍Python-3.10-4ecdc4?style=for-the-badge&logo=python&logoColor=white&labelColor=1a1a2e">
• [2026.03]🎯[New Feature]: Introduced a setup wizard. Support for local deployment of embedding, reranking, and storage backends via Docker.

• python -m venv .venv

Basic usage or getting-started notes:

• 📦 Offline Deployment: For offline or air-gapped environments, see the Offline Deployment Guide for instructions on pre-installing all dependencies and cache files.

• The LightRAG Server is designed to provide Web UI and API support. The Web UI facilitates document indexing, knowledge graph exploration, and a simple RAG query interface. LightRAG Server also provide an Ollama compat...

• Install from PyPI

• Source: https://github.com/HKUDS/LightRAG

• Extracted from upstream docs: https://raw.githubusercontent.com/HKUDS/LightRAG/HEAD/README.md

Source

• Agent Skill Exchange