{
    "app": {
        "name": "lightrag-graph-based-retrieval-augmented-generation-framework",
        "description": "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.",
        "mode": "advanced-chat",
        "model_config": {
            "provider": "deepseek",
            "model": "deepseek-chat",
            "parameters": {
                "temperature": 0.7,
                "max_tokens": 4096
            }
        }
    },
    "instructions": "name LightRAG Graph-Based Retrieval-Augmented Generation Framework slug lightrag-graph-rag-framework description 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. github_stars 33160 verification security_reviewed source https://github.com/HKUDS/LightRAG category Data Extraction & Transformation framework Multi-Framework tool_ecosystem {\"github_repo\":\"hkuds/lightrag\",\"github_stars\":33160} 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: [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",
    "variables": [],
    "opening_statement": "你好，我是 lightrag-graph-based-retrieval-augmented-generation-framework，LightRAG is a Python-based retrieval-augmented gen...",
    "suggested_questions": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=agentskillexchange-skills-skills-lightrag-graph-rag-framework-skill-md"
}