RAGFlow: RAG Frameworks

Free open-source + enterprise/managed (contact sales)

What is RAGFlow?

RAGFlow is Infiniflow's open-source RAG engine that fuses retrieval-augmented generation with agent capabilities to create a superior context layer for LLMs. With 78,300+ GitHub stars, it's one of the leading RAG-focused projects on GitHub and is widely used for enterprise knowledge bases, compliance-heavy industries, and research assistants.

RAGFlow's parsing engine uses deep learning to understand document structure — recognizing tables, extracting text from images via OCR, preserving formatting relationships, and handling multi-language content. It supports Word, slides, Excel, txt, images, scanned copies, structured data, and web pages. Retrieval combines vector search, BM25, and custom scoring with advanced re-ranking, and every answer ships with citations pointing back to source documents and specific chunks — critical for legal, healthcare, and finance.

Released April 21, 2026, the latest version added seven prebuilt ingestion pipeline templates, lets agent apps be published, supports sandbox code execution and chart generation, and adds user-level memory storage and retrieval. Free open-source under Apache 2.0, with paid enterprise and managed offerings (contact Infiniflow).

Key features

Core capabilities this platform advertises.

Deep document understanding — tables, images, multi-language
Hybrid retrieval: vector + BM25 + custom scoring + re-ranking
Citation-backed answers pointing to source chunks
Visual agent workflow builder with RAG, tools, and MCP
April 2026: prebuilt ingestion pipelines, sandbox code, chart generation

Strengths and tradeoffs

What this tool does well, and the limitations to keep in mind.

Pros

Best document parsing in the OSS RAG space — tables and OCR done right
Citation-grade answers — every output traceable to source chunks
Hybrid retrieval (vector + BM25 + re-ranking) out of the box
Visual agent builder lowers the bar for non-engineers
78.3K stars and active 2026 development

Cons

Heavier infra than minimal RAG libraries (LlamaIndex, Haystack)
Enterprise pricing requires sales contact — opacity is a friction point
Visual builder is helpful but bypasses code-first flexibility
Best-fit team has dedicated RAG engineers, not casual builders

Plans & pricing

What's included in each plan, and how the tiers compare.

Open Source (Apache 2.0)

Forever

Full RAG engine + visual agent builder
Hybrid retrieval (vector + BM25 + re-ranking)
Self-hostable on any infrastructure
Citation-grade answers

Managed / Enterprise

Contact sales

Contact sales for a quote

Hosted RAG-as-a-service
Enterprise compliance + support
Custom integrations

View official pricing page

Common use cases

Enterprises building production RAG applications that need citation-grade answers and rich document understanding

Enterprise knowledge base with citation-grade answers
Legal and healthcare document Q&A with compliance
Multi-language document retrieval at scale
Visual agent workflows that combine RAG and tools
Research assistants with multi-source data analysis

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