# 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

Repository: https://github.com/infiniflow/ragflow
Canonical: https://ross.abutalabs.com/products/ragflow
Homepage: https://ragflow.io
Language: Go
License: Apache-2.0
License Family: permissive
Topics: ai, ai-agents, context-engine, llm-apps, rag, retrieval-augmented-generation, agentic-ai, agentic-retrieval, agentic-search, context-management, knowledge-compilation, agent-harness, context-engineering, harness-engineering
Last push: 2026-08-26T14:33:10+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 98, longevity 71
- inputs: {"age_days": 995, "days_push": 7, "days_rel": 14, "gap_med": 11.0, "n_releases_24m": 39}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 89328, forks 10511 (observed 2026-08-28T04:12:23.156076+00:00)

## What it is
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
- artifact type: application
- maturity: active
- function: rag, search-engine, etl, agent-framework, llm-inference, chatbot, web-scraping, ocr, pdf, self-hosted
- domain: artificial-intelligence, large-language-models, self-hosted, developer-tools
- platform: self-hosted, python, go
- tags: 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

## Member repositories
- infiniflow/ragflow (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:23.156076+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:13:52.264540+00:00, confidence not recorded.
  - readme: https://github.com/infiniflow/ragflow (fetched 2026-08-28T04:12:23.156076+00:00, sha a2ddf09726b0)
  - homepage: https://ragflow.io (fetched 2026-08-28T17:38:05.316455+00:00, sha 398767ce3be5)
  - site_page: https://ragflow.io/docs (fetched 2026-08-28T17:38:05.325220+00:00, sha 2b8ca9033dca)
  - site_page: https://ragflow.io/changelog (fetched 2026-08-28T17:38:05.327985+00:00, sha c30e8f696b26)
- Data as of 2026-08-30T08:39:29.467469+00:00.