RAGFlow

What is RAGFlow?

RAGFlow is an open source RAG (retrieval-augmented generation) engine that turns your documents into a grounded knowledge base for LLMs and AI agents. It parses complex files — PDFs, Word, slides, spreadsheets, scans, and images — chunks them intelligently, and answers questions with traceable citations to cut down on hallucinations.

What is RAGFlow best for?

Teams building document-heavy AI search or chat where parsing quality is the binding constraint — think legal, finance, research, or support knowledge bases full of messy multi-column PDFs, tables, and scanned files. Its deep document understanding pipeline extracts cleaner, more usable chunks from those formats than most minimalist RAG libraries, so retrieval quality holds up on real-world data.

What can RAGFlow do?

• Parse complex, unstructured files — PDFs, DOCX, slides, Excel, images, scans, and web content — with layout-aware, multi-modal understanding
• Chunk documents with explainable, template-based segmentation you can visualize and correct by hand
• Retrieve with hybrid search — vector plus BM25 keyword — and fused re-ranking for accuracy
• Ground every answer in traceable citations back to the source text
• Orchestrate AI agents visually with RAG, tools, code components (Python/JavaScript), and MCP support
• Plug into many LLM providers and embedding models rather than locking you to one
• Connect chat to Feishu, Discord, Telegram, and Line

Where does RAGFlow fall short?

• Heavy multi-service footprint. RAGFlow isn’t one container — it orchestrates a document engine (Elasticsearch or Infinity), MySQL, Redis, and MinIO, and wants ≥16 GB RAM and ≥4 CPU cores just to start. That’s a lot more than a lightweight embed-and-query library.
• x86-only Docker images. The prebuilt images target x86 platforms; running on ARM64 (including Apple Silicon servers) means building the images yourself.
• Opinionated and tuning-heavy. The API-driven, template-based workflow is prescriptive, and getting retrieval quality right on your corpus takes hands-on chunk and re-rank tuning rather than working perfectly out of the box.

Is RAGFlow free?

Yes — RAGFlow is free and open source under the Apache-2.0 license, and self-hosting costs only your own server. InfiniFlow also runs a managed RAGFlow Cloud with a free tier (5 apps, 500 monthly credits) and paid plans — Starter at $29/mo and Pro at $129/mo — plus custom Enterprise pricing with BYOC and on-premises deployment. You pay for the managed hosting, not the engine.

What does RAGFlow replace?

RAGFlow is a self-hosted alternative to managed retrieval and enterprise-search services like Azure AI Search, Amazon Kendra, and Google Vertex AI Search. It does the same ingest-index-retrieve job for RAG, but you run it on your own infrastructure instead of paying per query or per index-hour. It sits alongside other open source LLM app builders like Dify and Flowise, but leads with document parsing rather than general agent orchestration.

FAQ

Is RAGFlow open source? Yes. RAGFlow is released under the Apache-2.0 license — a true OSI-approved open source license — so you can self-host, modify, and use it commercially without restriction.

Can I self-host RAGFlow for free? Yes. The engine is free; you only pay for the server it runs on. RAGFlow Cloud is the optional paid, managed version for teams that don’t want to run the infrastructure.

Is RAGFlow a good Azure AI Search alternative? For document-heavy RAG, yes — its parsing pipeline handles complex PDFs and scans well, and self-hosting avoids per-query cloud billing and lock-in. Managed services like Azure AI Search still win on zero-ops setup and tight integration with their own cloud.

What do I need to run RAGFlow? A Linux server with Docker (≥24.0.0) and Docker Compose (≥2.26.1), at least 4 CPU cores, 16 GB RAM, and 50 GB disk. It runs Elasticsearch or Infinity for the document engine, plus MySQL, Redis, and MinIO.