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

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Install / Use

npx skills add infiniflow/ragflow

Installs into whichever agent you are using.

About this skill

Quality Score

0/100

README

<div align="center"> <a href="https://cloud.ragflow.io/"> <img src="https://raw.githubusercontent.com/infiniflow/ragflow/main/web/src/assets/logo-with-text.svg" width="520" alt="ragflow logo"> </a> </div> <p align="center"> <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-DBEDFA"></a> <a href="./README_zh.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DFE0E5"></a> <a href="./README_tzh.md"><img alt="繁體版中文自述文件" src="https://img.shields.io/badge/繁體中文-DFE0E5"></a> <a href="./README_ja.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-DFE0E5"></a> <a href="./README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-DFE0E5"></a> <a href="./README_fr.md"><img alt="README en Français" src="https://img.shields.io/badge/Français-DFE0E5"></a> <a href="./README_id.md"><img alt="Bahasa Indonesia" src="https://img.shields.io/badge/Bahasa Indonesia-DFE0E5"></a> <a href="./README_pt_br.md"><img alt="Português(Brasil)" src="https://img.shields.io/badge/Português(Brasil)-DFE0E5"></a> <a href="./README_ar.md"><img alt="README in Arabic" src="https://img.shields.io/badge/Arabic-DFE0E5"></a> <a href="./README_tr.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-DFE0E5"></a> <a href="./README_ru.md"><img alt="Русская версия README" src="https://img.shields.io/badge/Русский-DFE0E5"></a> </p> <p align="center"> <a href="https://x.com/intent/follow?screen_name=infiniflowai" target="_blank"> <img src="https://img.shields.io/twitter/follow/infiniflow?logo=X&color=%20%23f5f5f5" alt="follow on X(Twitter)"> </a> <a href="https://cloud.ragflow.io" target="_blank"> <img alt="Static Badge" src="https://img.shields.io/badge/Get-Started-4e6b99"> </a> <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank"> <img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/infiniflow/ragflow-stats/main/badges/docker-pulls.json&style=flat-square&logo=docker&logoColor=white" alt="docker pull infiniflow/ragflow:v0.26.4"> </a> <a href="https://github.com/infiniflow/ragflow/releases/latest"> <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release"> </a> <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE"> <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license"> </a> <a href="https://deepwiki.com/infiniflow/ragflow"> <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg"> </a> </p> <h4 align="center"> <a href="https://cloud.ragflow.io">Cloud</a> | <a href="https://ragflow.io/docs/dev/">Documentation</a> | <a href="https://github.com/infiniflow/ragflow/issues/12241">Roadmap</a> | <a href="https://discord.gg/NjYzJD3GM3">Discord</a> </h4> <div align="center" style="margin-top:20px;margin-bottom:20px;"> <img alt="RAGFlow in the GitHub Octoverse" src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/> </div> <div align="center"> <a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </div> <details open> <summary><b>📕 Table of Contents</b></summary>

💡 What is RAGFlow?
🎮 Get Started
🔥 Latest Updates
🌟 Key Features
🔎 System Architecture
🎬 Self-Hosting
🔧 Configurations
🔧 Build a Docker Image
🔨 Launch Service from Source for Development
📚 Documentation
📜 Roadmap
🏄 Community
🙌 Contributing

</details>

💡 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.

🎮 Get Started

Try our cloud service at https://cloud.ragflow.io.

🔥 Latest Updates

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.

🎉 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

Supports Word, Slides, Excel, TXT, images, scanned copies, structured data, web pages, and more.

🛀 Automated and effortless RAG workflow

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.

🔎 System Architecture

🎬 Self-Hosting

📝 Prerequisites

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.

[!TIP] If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine.

🚀 Start up the server

Ensure vm.max_map_count >= 262144:
To check the value of vm.max_map_count:
```
sysctl vm.max_map_count
```
Reset vm.max_map_count to a value at least 262144 if it is not.
```
# In this case, we set it to 262144:
sudo sysctl -w vm.max_map_count=262144
```
This change will be reset after a system reboot. To ensure your change remains permanent, add or update the vm.max_map_count value in /etc/sysctl.conf accordingly:
```
vm.max_map_count=262144
```

Clone the repo:

git clone https://github.com/infiniflow/ragflow.git

Start up the server using the pre-built Docker images:

[!CAUTION] All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64. If you are on an ARM64 platform, follow this guide to build a Docker image compatible with your system.

The command below downloads the v0.26.4 edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from v0.26.4, update the RAGFLOW_IMAGE variable accordingly in docker/.env before using docker compose to start the server.

   cd ragflow/docker

   git checkout v0.26.4
   # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
   # This step ensures the **entrypoint.sh** file in the code matches the Docker image version.

   # Use CPU for DeepDoc tasks:
   docker compose -f docker-compose.yml up -d

   # To use GPU to accelerate DeepDoc tasks:
   # sed -i '1i DEVICE=gpu' .env
   # docker compose -f docker-compose.yml up -d