33rd of 15 in RAG and knowledge

RAGFlow

RAG engine with document parsing, agentic retrieval and citations

Stars
92k
License
Apache-2.0
Last commit
Oct 2026
Last release
Sep 2026
Language
Go

Overview

RAGFlow parses documents (Word, slides, Excel, TXT, images, scans, web pages) with template-based chunking, then retrieves with multiple recall and fused re-ranking to produce answers with traceable citations. Recent releases add agentic multi-step retrieval with four thinking modes and Knowledge Compilation into wikis, graphs, trees and mind maps. Models are configured by name, address and API key for the LLM, embedding and reranker.

Who it is for: Teams building document Q&A with cited answers on their own servers

Strengths

  • Chunk visualization lets you inspect and correct parsing before retrieval
  • Citations link answers back to source chunks
  • Ingests sitemaps and Google BigQuery with incremental sync
  • Apache-2.0 with prebuilt Docker Compose deployment

Weaknesses

  • Stack needs MySQL, MinIO, NATS, Kvrocks, ClickHouse and a document engine
  • Go backend not supported on macOS; Linux x86_64 host required
  • DeepDoc OCR and layout analysis run on CPU only in 1.0
  • Current release is 1.0.0-rc1, a release candidate

What it needs

  • RAM ≥ 16 GB
  • no GPU
  • Docker + Compose
  • Needs Elasticsearch or Infinity, MySQL, MinIO, NATS JetStream, Kvrocks, ClickHouse
  • Models: configurable LLM, embedding, reranker
  • port 80

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