# RAGFlow

> RAG engine built on deep document understanding

A retrieval-augmented generation engine focused on parsing complex documents (PDFs, tables, scans) accurately, with citations grounded in the source.

- Source: https://github.com/infiniflow/ragflow
- Homepage: https://ragflow.io
- License: Apache-2.0
- Language: Go
- Stars: 91682
- Forks: 10890
- Contributors: 847
- Last commit: 2026-10-03
- Latest release: v1.0.0-rc1 (2026-09-29)
- Purpose: AI & LLM tooling, RAG & vector DBs
- Runs on: Docker / self-host
- For: Enterprise

## Worth score: 87/100

How much DigGitHub recommends it, from activity, adoption, docs, license and security signals.

- Popularity: 25/25
- Momentum: 10/20
- Maintenance: 25/25
- Community: 15/15
- Readiness: 12/15

## Alternatives

- [AnythingLLM](https://diggithub.com/Mintplex-Labs/anything-llm.md): Chat with your documents using any LLM
- [LightRAG](https://diggithub.com/HKUDS/LightRAG.md): Simple and fast graph-based RAG
- [Pathway LLM App](https://diggithub.com/pathwaycom/llm-app.md): Ready-to-run templates for live RAG and enterprise search
- [Local Deep Research](https://diggithub.com/LearningCircuit/local-deep-research.md): Deep research with local or cloud LLMs
- [Dify](https://diggithub.com/langgenius/dify.md): Build, run and monitor LLM apps and agent workflows
- [MaxKB](https://diggithub.com/1Panel-dev/MaxKB.md): Platform for enterprise agents
- [Crawl4AI](https://diggithub.com/unclecode/crawl4ai.md): Web crawler that turns sites into LLM-ready Markdown
- [WeKnora](https://diggithub.com/Tencent/WeKnora.md): LLM knowledge platform

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Source page: https://diggithub.com/infiniflow/ragflow
Updated: 2026-10-05