Ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge
Rating
Votes
0
score
Downloads
0
total
Price
Free
API key required
Works With
About
Document | Roadmap | Twitter | Discord | Demo
📕 Table of Contents
- 💡 What is RAGFlow?
- 🎮 Demo
- 📌 Latest Updates
- 🌟 Key Features
- 🔎 System Architecture
- 🎬 Get Started
- 🔧 Configurations
- 🔧 Build a Docker image
- 🔨 Launch service from source for development
- 📚 Documentation
- 📜 Roadmap
- 🏄 Community
- 🙌 Contributing
💡 What is 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.
🎮 Demo
Try our demo at https://cloud.ragflow.io.
🔥 Latest Updates
- 2026-03-24 RAGFlow Skill on OpenClaw — Provides an official skill to access 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.
- 2025-05-05 Supports cross-language query.
- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
🎉 Stay Tuned
⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! 🌟
🌟 Key Features
🍭 "Quality in, quality out"
- Deep document understanding-based knowledge extraction from unstructured data with complicated
formats.
- Finds "needle in a data haystack" of literally unlimited tokens.
🍱 Template-based chunking
- Intelligent and explainable.
- Plenty of template options to choose from.
🌱 Grounded citations with reduced hallucinations
- Visualization of text chunking to allow human intervention.
- Quick view of the key references and traceable citations to support grounded answers.
🍔 Compatibility with heterogeneous data sources
Don't lose this
Three weeks from now, you'll want Ragflow again. Will you remember where to find it?
Save it to your library and the next time you need Ragflow, it’s one tap away — from any AI app you use. Group it into a bench with the rest of the team for that kind of task and you can pull the whole stack at once.
⚡ Pro tip for geeks: add a-gnt 🤵🏻♂️ as a custom connector in Claude or a custom GPT in ChatGPT — one click and your library is right there in the chat. Or, if you’re in an editor, install the a-gnt MCP server and say “use my [bench name]” in Claude Code, Cursor, VS Code, or Windsurf.
a-gnt's Take
Our honest review
This plugs directly into your AI and gives it new abilities it didn't have before. RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge. Once connected, just ask your AI to use it. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.
Tips for getting started
Tap "Get" above, pick your AI app, and follow the steps. Most installs take under 30 seconds.
Heads up: this needs an API key to work. You'll get one from the service's website (usually free). The setup guide tells you exactly where.
What's New
Imported from GitHub
Ratings & Reviews
0.0
out of 5
0 ratings
No reviews yet. Be the first to share your experience.
From the Community
Free vs Paid AI Tools: When Is It Worth Paying?
An honest breakdown of when free AI tools are good enough and when paid versions are actually worth the money.
RAG vs Fine-Tuning: Two Ways to Customize AI
Understanding the difference between RAG and fine-tuning for making AI work with your specific data and use case.
RAGFlow: Build Your Own AI Knowledge Base
RAGFlow lets you create an AI assistant that actually knows your documents, with citations and source tracking built in.