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
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that integrates RAG with Agent capabilities. It provides a superior context layer for LLMs and offers a streamlined RAG workflow adaptable to enterprises of any scale.
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- Shawn LeeApr 14, 2026
Evaluated RAGFlow against LlamaIndex for enterprise doc search. RAGFlow's deep document understanding handles PDFs with complex layouts far better. Switched after a 2-week POC.
- SSage HarrisApr 5, 2026
The agent + RAG combination is where it gets interesting. Built a multi-step research assistant that handles 50+ concurrent users without issues.
- PParker BrownMar 31, 2026
Building a knowledge base product on top of this. The chunking strategies alone would've taken weeks to implement from scratch.
- JJesse MartinezMar 8, 2026
Works well in a multi-container setup with separate Elasticsearch and MinIO instances. The monitoring dashboard could use improvement.
- CCameron JohnsonJan 22, 2026
got a basic RAG pipeline working in an afternoon. the UI makes it easy to actually test retrieval quality
- Justice LeeJan 14, 2026
setup took about an hour including Docker. the chunk visualization is a genuinely useful debugging tool
- DDakota NguyenJan 5, 2026
Wrote a comparison of open-source RAG frameworks — RAGFlow's hybrid search (keyword + vector) is the standout feature for production workloads.
- DDakota HarrisDec 30, 2025
Our team uses this to search through internal docs. Finds relevant stuff even when you don't use exact keywords.
- BBlake WilsonDec 24, 2025
Replaced a paid vector DB service with this. Performance is comparable, running costs are way down.
- QQuinn DavisDec 7, 2025
Docker deployment is straightforward. Elasticsearch dependency adds overhead but the search quality justifies it. Memory usage is higher than alternatives though.