Ragflow
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. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision. Try our cloud service at https://cloud.ragflow.io. 2026-06-15 Support multiple chat channels such as Feishu, Discord, Telegram, Line, etc. 2026-04-24 Supports DeepSeek v4. 2026-03-24 RAGFlow Skill on OpenClaw, Provides an official skill for accessing RAGFlow datasets via OpenClaw. 2025-12-26 Supports 'Memory' for AI agent. 2025-11-19 Supports Gemini 3 Pro. 2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive. 2025-10-23 Supports MinerU & Docling as document parsing methods. 2025-10-15 Supports orchestrable ingestion pipeline. 2025-08-08 Supports OpenAI's latest GPT-5 series models. 2025-08-01 Supports agentic workflow and MCP. 2025-05-23 Adds a Python/JavaScript code executor component to Agent. 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files. Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! Deep document understanding-based knowledge extraction from unstructured data with complicated formats. Finds "needle in a data haystack" of literally unlimited tokens. Intelligent and explainable. Plenty of template options to choose from. Visualization of text chunking to allow human intervention. Quick view of the key references and traceable citations to support grounded answers. Supports Word, Slides, Excel, TXT, images, scanned copies, structured data, web pages, and more. Streamlined RAG orchestration catered to both personal and large businesses. Configurable LLMs as well as embedding models. Multiple recall paired with fused re-ranking. Intuitive APIs for seamless integration with business. CPU = 4 cores RAM = 16 GB Disk = 50 GB Docker = 24.0.0 & Docker Compose = v2.26.1 Python = 3.13 gVisor: Required only if you intend to use the code executor (sandbox) feature of RAGFlow.
View Ragflow on GitHub
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. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.
Try our cloud service at https://cloud.ragflow.io. 2026-06-15 Support multiple chat channels such as Feishu, Discord, Telegram, Line, etc. 2026-04-24 Supports DeepSeek v4. 2026-03-24 RAGFlow Skill on OpenClaw, Provides an official skill for accessing RAGFlow datasets via OpenClaw. 2025-12-26 Supports 'Memory' for AI agent. 2025-11-19 Supports Gemini 3 Pro. 2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive. 2025-10-23 Supports MinerU & Docling as document parsing methods. 2025-10-15 Supports orchestrable ingestion pipeline. 2025-08-08 Supports OpenAI's latest GPT-5 series models. 2025-08-01 Supports agentic workflow and MCP. 2025-05-23 Adds a Python/JavaScript code executor component to Agent. 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! Deep document understanding-based knowledge extraction from unstructured data with complicated formats. Finds "needle in a data haystack" of literally unlimited tokens. Intelligent and explainable. Plenty of template options to choose from. Visualization of text chunking to allow human intervention. Quick view of the key references and traceable citations to support grounded answers. Supports Word, Slides, Excel, TXT, images, scanned copies, structured data, web pages, and more. Streamlined RAG orchestration catered to both personal and large businesses. Configurable LLMs as well as embedding models. Multiple recall paired with fused re-ranking. Intuitive APIs for seamless integration with business. CPU = 4 cores RAM = 16 GB Disk = 50 GB Docker = 24.0.0 & Docker Compose = v2.26.1 Python = 3.13 gVisor: Required only if you intend to use the code executor (sandbox) feature of RAGFlow.
Ragflow at a glance
| Stars | 91k |
|---|---|
| Forks | 11k |
| Language | Go |
| License | Apache-2.0 |
| Last update | 2026-09-26 |
How to install Ragflow
bash git clone https://github.com/infiniflow/ragflow.git
Where Ragflow is listed
Navid Moazzez
AI business strategist & AI OS builder
Navid Moazzez helps creators and founders master AI and build their own AI Operating System (AI OS) to automate their business and life.
Navid.me is reader-supported. When you buy through links on this site, I may earn an affiliate commission. Learn more.
GitHub repos like this
More repo topics
More free tools
X
Bluesky
LinkedIn
Threads
Mastodon
Instagram
TikTok
Pinterest
YouTube
Amazon KDP
Apple Books
Amazon
Substack
Spotify
Apple Podcasts
Related MCP servers & CLIs
Free AI newsletterThe most actionable AI newsletter for founders
Every week, get proven AI strategies, curated tools, and step-by-step systems to grow your audience, create better content, and build a profitable creator business.
No fluff, no filler, no BS. Just five minutes each week that might level up your online business and life.
P.S. Sign up now to get free access to my ultimate AI tools guide for creators.
