What it does
Core capabilities at a glance
- Deep document understanding with OCR and layout parsing
- Template-based chunking strategies
- Multi-model RAG with reranking
- Knowledge graph construction from documents
- Reference citation in answers
- Web crawl and API ingestion
Deep dive
The full breakdown - performance, comparisons, and setup
RAGFlow
RAGFlow focuses on the part of RAG that most tools neglect: actually understanding the documents you're searching. While other RAG tools apply naive chunking and hope the vector search works, RAGFlow uses OCR, layout analysis, and knowledge graph extraction to understand document structure.
What it is
RAGFlow is an open-source RAG engine developed by InfiniFlow. Its core differentiator is deep document understanding - it parses PDFs, scanned documents, and complex layouts to extract structured knowledge, not just text chunks.
How it stacks up
| RAGFlow | AnythingLLM | Dify | Onyx | |
|---|---|---|---|---|
| Document parsing | ✓✓✓ | ✓ | ✓ | ✓ |
| OCR | ✓ | ✗ | ✗ | ✗ |
| Knowledge graph | ✓ | ✗ | ✗ | ✗ |
| Reranking | ✓ | ✗ | ✓ | ✓ |
| Best for | Complex docs | General RAG | LLM apps | Enterprise search |
Get started
docker run -d -p 9380:9380 infiniflow/ragflow:latestWhen to use something else
- Simple document chat: AnythingLLM is easier
- Need full LLM app platform: Dify has more features
- Enterprise connectors: Onyx has better third-party integrations
From the community
What users are saying
“Apparently "deep document understanding" refers to OCR and structured document parsing: https://github.com/infiniflow/ragflow/blob/main/deepdoc/READ... Since "deep document understanding" is not a term of art, I would have just said "OCR and document parsing". How well does it work? Please include benchmarks.”
“GraphRAG is to parse data to create a KG and retrieval the information from KB. RAGFlow is to create a Graph workflow to solve multi-hop question-answering issue.”
“RAGFlow uses Yolov8 for its OCR/layout recognition/TSR(table structure recognition). And RAGFlow uses large amount private data to train these models for them to perform well in some specialized scenarios.”
Frequently asked
Quick answers to common questions
What is RAGFlow?
RAGFlow is a rag tool for local AI workloads. Open-source RAG engine with deep document understanding for precise question answering.
Is RAGFlow free and open source?
Yes, RAGFlow has 92,435 GitHub stars and is licensed under Apache-2.0. You can self-host it for free on docker, linux.
What platforms does RAGFlow support?
RAGFlow runs on docker, linux.
What hardware do I need for RAGFlow?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. RAGFlow has 92,435 GitHub stars and an active community.
Does RAGFlow support GPU acceleration?
RAGFlow supports GPU acceleration via CUDA, Metal, or Vulkan depending on your platform. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to RAGFlow?
Popular alternatives include other rag tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does RAGFlow cost?
RAGFlow is free-open-source. It is completely free and open source to self-host.
Pairs well with
Complementary tools, models, and hardware