infiniflow/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 observed · 2026-08-28
Health v2 · maintenance only
93/100
- Activity 99
- Release rhythm 98
- Longevity 71
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 11.0
- age_days: 995
- days_rel: 14
- days_push: 7
- n_releases_24m: 39
Adoption not part of the score
89328 stars · 10511 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that combines deep document understanding with agent orchestration to provide a context layer for LLMs. It offers ingestion pipelines for multi-format data, hybrid retrieval (vector, full-text, tensor), and visual agent workflows, deployable via Docker.
Use cases
- build a chatbot that answers questions from my own documents with citations
- parse and index PDFs and complex documents for RAG
- set up a self-hosted RAG engine with hybrid vector and BM25 search
- build AI agents that retrieve knowledge from enterprise datasets
- extract structured data from legal or financial documents
- create a knowledge base Q&A system for my company
- orchestrate multi-agent workflows with retrieval and web search
- add a context layer to LLM applications
When to choose
- you need a full-featured, self-hosted RAG platform with document parsing, retrieval, and agent orchestration in one product
- you want grounded answers with citations from complex formatted documents
- you need hybrid search (vector, full-text, tensor) with re-ranking
- you want visual workflow-based agent building with MCP and tool integration
When to avoid
- you only need a lightweight embedding or vector search library to embed in your own code
- you cannot run Docker with at least 4 CPU cores and 16 GB RAM
- you need ARM Docker images, which are not officially maintained
- you want a minimal headless API without a built-in UI and agent platform
Facets
application · maturity active
rag search-engine etl agent-framework llm-inference chatbot web-scraping ocr pdf self-hosted artificial-intelligence large-language-models self-hosted developer-tools self-hosted python go rag-engine document-understanding hybrid-search agentic-rag knowledge-compilation vector-search bm25 mcp enterprise question-answering retrieval-augmented-generation ai-agents search natural-language-processing docker linux web-server
4 sources
- readme: https://github.com/infiniflow/ragflow · fetched 2026-08-28 · a2ddf09726b0
- homepage: https://ragflow.io · fetched 2026-08-28 · 398767ce3be5
- site_page: https://ragflow.io/docs · fetched 2026-08-28 · 2b8ca9033dca
- site_page: https://ragflow.io/changelog · fetched 2026-08-28 · c30e8f696b26
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| infiniflow/ragflow | main | 93 |
For agents
markdown · JSON · MCP: product_card(name="infiniflow/ragflow")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem