I
infiniflow/ragflow
RAGFlow is an agent-native database: a Retrieval-Augmented Generation (RAG) engine that grew into context infrastructure.
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
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- 94,126 ★ · infiniflow/ragflow · read from the GitHub search API · (checked 2026-11-13 20:00:50)
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- read directly from its GitHub page · type: open-source project
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- first seen 2026-10-26 · last seen 2026-11-11 · seen 11× · listed by 1 sources
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infiniflow/ragflow；first catalogued 2026-10-26；last seen 2026-11-11。There is no official certification or quality rating here — we record sources and dates only.Cloud |
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Documentation |
Roadmap |
Discord
📕 Table of Contents
💡 What is RAGFlow?
🎮 Get Started
🔥 Latest Updates
🌟 Key Features
🔎 System Architecture
🏠 Local Deployment
📚 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.
For local deployment, see Local Deployment.
## 🔥 Latest Updates
2026-09-29 RAGFlow 1.0.0-rc1 released.
2026-09-10 Added website content ingestion through sitemaps.
2026-08-19 Introduced Knowledge Compilation to generate Wikis, Graphs, Trees, PageIndex, Mind Maps, Timelines, and Skills at the document and dataset levels.
2026-08-19 Introduced Agentic RAG with Low, Medium, High, and Ultra thinking modes.
2026-07-02 Added Google BigQuery data source ingestion and incremental synchronization.
See the full release notes for more updates.
## 🎉 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.
### 🧩 Knowledge Compilation
Compile content at the document and dataset levels into structured knowledge artifacts.
Use compilation templates to generate Wikis, Graphs, Trees, PageIndex, Mind Maps, Timelines, and Skills for different knowledge organization and reuse needs.
Configure compilation models and processing rules, and view, update, and regenerate knowledge artifacts.
### 🧠 Agentic Retrieval
Perform multi-step retrieval for complex questions: the model analyzes the question and, when needed, breaks it down, retrieves knowledge, and verifies evidence.
Gather more complete context through multiple rounds of retrieval and reasoning to help generate well-grounded answers.
Choose Low, Medium, High, or Ultra thinking modes to control retrieval and reasoning depth according to question complexity.
### 🌱 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 documents, PowerPoint presentations, Excel spreadsheets, TXT files, images, PDFs, 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.
Roadmap |
Discord
📕 Table of Contents
💡 What is RAGFlow?
🎮 Get Started
🔥 Latest Updates
🌟 Key Features
🔎 System Architecture
🏠 Local Deployment
📚 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.
For local deployment, see Local Deployment.
## 🔥 Latest Updates
2026-09-29 RAGFlow 1.0.0-rc1 released.
2026-09-10 Added website content ingestion through sitemaps.
2026-08-19 Introduced Knowledge Compilation to generate Wikis, Graphs, Trees, PageIndex, Mind Maps, Timelines, and Skills at the document and dataset levels.
2026-08-19 Introduced Agentic RAG with Low, Medium, High, and Ultra thinking modes.
2026-07-02 Added Google BigQuery data source ingestion and incremental synchronization.
See the full release notes for more updates.
## 🎉 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.
### 🧩 Knowledge Compilation
Compile content at the document and dataset levels into structured knowledge artifacts.
Use compilation templates to generate Wikis, Graphs, Trees, PageIndex, Mind Maps, Timelines, and Skills for different knowledge organization and reuse needs.
Configure compilation models and processing rules, and view, update, and regenerate knowledge artifacts.
### 🧠 Agentic Retrieval
Perform multi-step retrieval for complex questions: the model analyzes the question and, when needed, breaks it down, retrieves knowledge, and verifies evidence.
Gather more complete context through multiple rounds of retrieval and reasoning to help generate well-grounded answers.
Choose Low, Medium, High, or Ultra thinking modes to control retrieval and reasoning depth according to question complexity.
### 🌱 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 documents, PowerPoint presentations, Excel spreadsheets, TXT files, images, PDFs, 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.
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- github · https://github.com/infiniflow/ragflow
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