ragflow

• 💡 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

💡 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

1. 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
   

2. Clone the repo:

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

3. 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

Note: Prior to v0.22.0, we provided both images with embedding models and slim images without embedding models. Details as follows:

RAGFlow image tag	Image size (GB)	Has embedding models?	Stable?
v0.21.1	≈9	✔️	Stable release
v0.21.1-slim	≈2	❌	Stable release

Starting with v0.22.0, we ship only the slim edition and no longer append the -slim suffix to the image tag.

4. Check the server status after having the server up and running:

   $ docker logs -f docker-ragflow-cpu-1
   

   The following output confirms a successful launch of the system:

   
         ____   ___    ______ ______ __
        / __ \ /   |  / ____// ____// /____  _      __
       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
   
    * Running on all addresses (0.0.0.0)
   

   If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a network abnormal error because, at that moment, your RAGFlow may not be fully initialized.

5. In your web browser, enter the IP address of your server and log in to RAGFlow.

   With the default settings, you only need to enter http://IP_OF_YOUR_MACHINE (sans port number) as the default HTTP serving port 80 can be omitted when using the default configurations.

6. In service_conf.yaml.template, select the desired LLM factory in user_default_llm and update the API_KEY field with the corresponding API key.

   See llm_api_key_setup for more information.

   The show is on!

🔧 Configurations

When it comes to system configurations, you will need to manage the following files:

• .env: Keeps the fundamental setups for the system, such as SVR_HTTP_PORT, MYSQL_PASSWORD, and MINIO_PASSWORD.
• service_conf.yaml.template: Configures the back-end services. The environment variables in this file will be automaticall