LightRAG

Open-source Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone, providing automatic chunking, embedding, chat history management, and query optimization.

Open Source RAG Framework designed to be 'Conventional but Configurable', streamlining the creation of RAG applications with efficient data management, embeddings generation, and vector storage.

Python toolkit for efficient RAG research providing 36 pre-processed benchmark datasets and 23 state-of-the-art RAG algorithms in a unified, modular framework for reproduction and development.

Retrieval-augmented generation framework that utilizes small, specialized models instead of large language models, significantly reducing computational and financial costs while offering cost-effective RAG solutions that can run on standard hardware.

Open-source LLM app development platform with an intuitive interface that combines AI workflow, RAG pipeline, agent capabilities, model management, and observability features for rapid prototyping and production deployment.

Knowledge engine for AI agent memory and memory layer for AI agents. Replaces complex RAG pipelines with serverless, single-file memory supporting instant retrieval and long-term memory.