RAGFlow
RAG that takes documents seriously
What it is
An open RAG engine centered on deep document understanding — layout-aware parsing of PDFs, tables, and scans — fused with agent capabilities to build a context layer for LLMs, with citation-grounded answers.
Why it's interesting
Genuinely Apache-2.0, which is rare among the big RAG platforms, and differentiated by serious parsing quality rather than another vector-store wrapper. Messy real-world documents are exactly where most RAG stacks fall over.
Use cases
- Enterprise document Q&A over messy PDFs
- Knowledge-base assistants with citations
- Agentic workflows grounded in documents
Who it's for
Teams with heavy document corpora; RAG engineers
Setup
Moderate. Docker Compose; 16GB+ RAM recommended; LLM API keys or local models
Limitations & cautions
Resource-hungry, with many moving parts — search engine, object store, Redis — in the full pipeline.
Editorial takeaway
The unglamorous truth of RAG is that parsing beats prompting. This project is built on that truth.