ragflow

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<details open>
<summary><b>📕 Table of Contents</b></summary>

- 💡 [What is RAGFlow?](#-what-is-ragflow)
- 🎮 [Demo](#-demo)
- 📌 [Latest Updates](#-latest-updates)
- 🌟 [Key Features](#-key-features)
- 🔎 [System Architecture](#-system-architecture)
- 🎬 [Get Started](#-get-started)
- 🔧 [Configurations](#-configurations)
- 🔧 [Build a Docker image](#-build-a-docker-image)
- 🔨 [Launch service from source for development](#-launch-service-from-source-for-development)
- 📚 [Documentation](#-documentation)
- 📜 [Roadmap](#-roadmap)
- 🏄 [Community](#-community)
- 🙌 [Contributing](#-contributing)

</details>

## 💡 What is RAGFlow?

[RAGFlow](https://ragflow.io/) is a leading open-source Retrieval-Augmented Generation ([RAG](https://ragflow.io/basics/what-is-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](https://ragflow.io/basics/what-is-agent-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](https://cloud.ragflow.io).

<div align="center" style="margin-top:20px;margin-bottom:20px;">
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## 🔥 Latest Updates

- 2026-03-24 [RAGFlow Skill on OpenClaw](https://clawhub.ai/yingfeng/ragflow-skill) — 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.
- 2025-05-05 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! 🌟

<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
</div>

## 🌟 Key Features

### 🍭 **"Quality in, quality out"**

- [Deep document understanding](./deepdoc/README.md)-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

<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://github.com/user-attachments/assets/31b0dd6f-ca4f-445a-9457-70cb44a381b2" width="1000"/>
</div>

## 🎬 Get Started

### 📝 Prerequisites

- CPU >= 4 cores
- RAM >= 16 GB
- Disk >= 50 GB
- Docker >= 24.0.0 & Docker Compose >= v2.26.1
- [gVisor](https://gvisor.dev/docs/user_guide/install/): 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](https://docs.docker.com/engine/install/).

### 🚀 Start up the server

1. Ensure `vm.max_map_count` >= 262144:

   > To check the value of `vm.max_map_count`:
   >
   > ```bash
   > $ sysctl vm.max_map_count
   > ```
   >
   > Reset `vm.max_map_count` to a value at least 262144 if it is not.
   >
   > ```bash
   > # 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:
   >
   > ```bash
   > vm.max_map_count=262144
   > ```
   >
2. Clone the repo:

   ```bash
   $ 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](https://ragflow.io/docs/dev/build_docker_image) to build a Docker image compatible with your system.

> The command below downloads the `v0.24.0` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.24.0`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.

```bash
   $ cd ragflow/docker

   # git checkout v0.24.0
   # 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           | &approx;9       | ✔️                    | Stable release |
| v0.21.1-slim      | &approx;2       | ❌                     | Stable release |

> Starting with `v0.