Discover / RAG & Knowledge

RAGFlow

Name: RAGFlow
Author: infiniflow

by infiniflowGo

Open-source RAG engine based on deep document understanding.

Toolexperimental

Maturity: experimental because latest release v0.26.4 is pre 1.0. Derived from release and commit history, not a rating.

Stars: 87k
Forks: 10k
Downloads / mo: 363
Last commit: 2026-08-03
License: Apache-2.0
Open issues: 2.0k

Market and trust evidence

Edition not yet matched

No exact skills.sh identity match is available for this repository. Repository adoption and freshness remain visible above; install momentum is not inferred.

Trust analysis is a screening signal, not a security warranty. Read the ranking and trust methodology.

In practice

Written by AI from this repository’s README · medium confidence

Turning messy enterprise documents into a reliable retrieval layer for an LLM requires building parsing and chunking yourself.

Use it when

Use it when you need document ingestion, chunking and agentic retrieval as one deployable engine.

Not the right pick when

The README is mostly a changelog and links, so configuration and self hosting detail lives in external docs.

Capabilities

streamlined RAG workflow adaptable to enterprises of any scale
converged context engine with pre-built agent templates
orchestrable ingestion pipeline
MinerU and Docling as document parsing methods
data synchronization from Confluence, S3, Notion, Discord and Google Drive
agentic workflow and MCP support

Cost: Free and open source

Install

Derived from the published package name in the repository, not from a model.

Compare Repository ↗Homepage ↗PyPI ↗

Video walkthroughs

Install RAGFlow Locally - Deep Document Understanding

Fahd Mirza · 9K views · ≈Aug 2025

Breaking RAG Barriers: RAGFlow's Technical Breakthroughs & Infinity's AI-Native Design

InHand Studio · 465 views · ≈Aug 2025

RAGFlow: Free Open Source Generative AI Platform

Elestio · 32K views · ≈Aug 2025

Third-party YouTube uploads matched to this tool by title, channel and repository name on 2026-08-03. Not made, reviewed or endorsed by SkillPilot. View counts and publish months are as of the match date and the month is approximate. Nothing loads from YouTube until you press play.

What the repository ships

Ships CLAUDE.mdHas testsHas docsHas examplesDocker imageSecurity policyCI configured

Detected from the actual files in the repository root.

Latest release v0.26.4

Published 2026-07-07

Summary

Released on July 7, 2026.

New features

NLP/Tokenization: Adds a language-aware Snowball stemmer supporting 16 languages, integrates the dataset language parameter across the tokenization pipeline, and adds Dutch to the frontend. #14140

Bug fixes

The system crashed with a ValueError when parsing LM-Studio model names containing an '@' symbol. #16467
The MCP server crashed because the list_chats function expected a list from the /chats API but received a paginated dictionary instead. #16639
The Docling parser silently dropped mathematical formulas from documents instead of extracting them. #16645
The system failed to persist inline edits made to metadata values to the backend. #16655
The system removed existing links when bulk-linking files to datasets. #16587
The filter failed to use Chinese. #16673
The system failed to enforce that tag weights must be greater than zero. #16657
The layout of the agent prompt dropdown menu rendered incorrectly. #16653
The Laws chunker incorrectly classified dotted-number cross-references as headings. #16626
The system failed to prevent users from saving duplicate MCP server names during the editing process. #16588
The system allowed users to export an empty MCP server selection. #16589

What's Changed

Go: unify three services into one binary by @JinHai-CN in https://github.com/infiniflow/ragflow/pull/16462
Feat: Add knowledge compilation workflows by @KevinHuSh in https://github.com/infiniflow/ragflow/pull/16515
Go: file syncer service framework by @JinHai-CN in https://github.com/infiniflow/ragflow/pull/16579
fix(agent/tools): port AkShare to ToolBase so it works as an Agent tool by @immuhammadfurqan in https://github.com/infiniflow/ragflow/pull/16417
fix: prevent duplicate 'skills' and '.knowledgebase' folders caused by race conditions by @euvre in https://github.com/infiniflow/ragflow/pull/16568
Fix harness DAG slow-branch test cased by nil initialization of pregel engine by @yingfeng in https://github.com/infiniflow/ragflow/pull/16591
Refactor: reformat all code for lefthook using ruff and gofmt by @wangq8 in https://github.com/infiniflow/ragflow/pull/16585
fix: preserve existing links when bulk linking files to knowledge bases by @euvre in https://github.com/infiniflow/ragflow/pull/16587
fix: agent chat completions can not use by @Haruko386 in https://github.com/infiniflow/ragflow/pull/16570
fix: unable to import mcp from local by @Haruko386 in https://github.com/infiniflow/ragflow/pull/16590
fix: unable to fetch tools for MCP by @Haruko386 in https://github.com/infiniflow/ragflow/pull/16583
fix(go-file): KB counter drift when deleting files with linked documents by @Hz-186 in https://github.com/infiniflow/ragflow/pull/16584
fix: prevent exporting empty MCP server selection by @euvre in https://github.com/infiniflow/ragflow/pull/16589
fix: prevent duplicate MCP server name when editing by @euvre in https://github.com/infiniflow/ragflow/pull/16588
Go: fix 'list services' by @JinHai-CN in https://github.com/infiniflow/ragflow/pull/16598
i18n: localize visual input file label in agent form by @euvre in https://github.com/infiniflow/ragflow/pull/16594
Fix: optimize folder data handling in MoveDialog component by @dcc123456 in https://github.com/infiniflo

README

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<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>

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<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.

</div>

🔥 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! 🌟

</div>

🌟 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

</div>

🎬 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:

```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

```

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

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

|-------------------|-----------------|-----------------------|----------------|

| 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.

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.

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.

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 automatically populated when the Docker container starts. Any environment variables set within the Docker container will be available for use, allowing you to customize service behavior based on the deployment environment.
docker-compose.yml: The system relies on docker-compose.yml to start up.

The ./docker/README file provides a detailed description of the environment settings and service

configurations which can be used as ${ENV_VARS} in the service_conf.yaml.template file.

To update the default HTTP serving port (80), go to docker-compose.yml and change 80:80

to <YOUR_SERVING_PORT>:80.

Updates to the above configurations require a reboot of all containers to take effect:

```bash

docker compose -f docker-compose.yml up -d

```

Switch doc engine from Elasticsearch to Infinity

RAGFlow uses Elasticsearch by default for storing full text and vectors. To switch to Infinity, follow these steps:

Stop all running containers:


   docker compose -f docker/docker-compose.yml down -v

[!WARNING]

-v will delete the docker container volumes, and the existing data will be cleared.

Set DOC_ENGINE in docker/.env to infinity.
Start the containers:


   docker compose -f docker/docker-compose.yml up -d

[!WARNING]

Switching to Infinity on a Linux/arm64 machine is not yet officially supported.

🔧 Build a Docker Image

This image is approximately 2 GB in size and relies on external LLM and embedding services.


git clone https://git

Truncated. Read the full README on GitHub ↗

RAGFlow

Market and trust evidence

In practice

Use it when

Not the right pick when

Capabilities

Install

Video walkthroughs

Install RAGFlow Locally - Deep Document Understanding

Breaking RAG Barriers: RAGFlow's Technical Breakthroughs & Infinity's AI-Native Design

RAGFlow: Free Open Source Generative AI Platform

What the repository ships

Latest release v0.26.4

Summary

New features

Bug fixes

What's Changed

Tags

README

💡 What is RAGFlow?

🎮 Get Started

🔥 Latest Updates

🎉 Stay Tuned

🌟 Key Features

🍭 "Quality in, quality out"

🍱 Template-based chunking

🌱 Grounded citations with reduced hallucinations

🍔 Compatibility with heterogeneous data sources

🛀 Automated and effortless RAG workflow

🔎 System Architecture

🎬 Self-Hosting

📝 Prerequisites

🚀 Start up the server

🔧 Configurations

Switch doc engine from Elasticsearch to Infinity

🔧 Build a Docker Image

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