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
-
Updated
Apr 13, 2026 - Python
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
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.
Langchain-Chatchat(原Langchain-ChatGLM)基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
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.
"RAG-Anything: All-in-One RAG Framework"
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.
Unified framework for building enterprise RAG pipelines with small, specialized models
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Retrieval and Retrieval-augmented LLMs
Agent S: an open agentic framework that uses computers like a human
[MLsys2026]: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.
Pocket Flow: 100-line LLM framework. Let Agents build Agents!
AI memory OS for LLM and Agent systems(moltbot,clawdbot,openclaw), enabling persistent Skill memory for cross-task skill reuse and evolution.
🧑🚀 全世界最好的LLM资料总结(多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型) | Summary of the world's best LLM resources.
SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
Open-source context retrieval layer for AI agents
Add a description, image, and links to the retrieval-augmented-generation topic page so that developers can more easily learn about it.
To associate your repository with the retrieval-augmented-generation topic, visit your repo's landing page and select "manage topics."