RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs

A collection of practical, installable AI agent skills for disk cleanup, AI news retrieval, and project management, following the Agent Skills standard.

[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"

A modular graph-based Retrieval-Augmented Generation (RAG) system

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.

ConardLi's open-source Skills collection, featuring web design, knowledge retrieval, image generation, and more.

Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.

Open-source context retrieval layer for AI agents

Retrieval and Retrieval-augmented LLMs