Get simple retrieval-augmented generation with LightRAG, a tool for founders using large language models, backed by 36k+ GitHub stars.
36,202 stars5,114 forksPythonQuality 8/10Updated 6/5/2026100% free ยท open source
What it does
LightRAG helps developers quickly build AI applications that retrieve and use relevant information to generate more accurate and contextual responses.
When to use it
โขBuilding AI-powered research assistants that pull from specific knowledge bases
โขCreating customer support chatbots with deep, domain-specific knowledge retrieval
โขDeveloping intelligent Q&A systems that dynamically source precise information
Quick start
1Install via pip: `pip install lightrag`
2Define your knowledge base or document collection
3Configure retrieval parameters and embedding models
4Integrate retrieval logic with your generative AI model
Ready-to-paste prompt
from lightrag import Retriever, Generator
retriever = Retriever(documents=['tech_docs.txt'])
response = Generator.augment_with_retrieval(query='How does this system work?')
Saves to your device
Topics
genai
gpt
gpt-4
graphrag
knowledge-graph
large-language-models
llm
rag
retrieval-augmented-generation
What's inside โ free to inspect
No purchase needed
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.