ragflow

RAGFlow is a retrieval-augmented generation engine built around deep document understanding, and it has grown into an agent platform sitting on top of that. It still handles the messy parts of RAG that other frameworks hand-wave past: PDFs with tables, slides, spreadsheets, scanned images, mixed-language docs, now with MinerU and Docling parsing. On top of that it added agent memory, an MCP server, and delivery into chat channels like Discord, Telegram, and Feishu. Apache 2.0.

Self-hosting is a real project, not a `docker run`. You need 4-plus CPU cores, 16-plus GB RAM, 50-plus GB disk, and Docker. The compose file spins up the RAGFlow service plus a search backend (Elasticsearch, or their in-house Infinity vector DB) and MinIO for blob storage. The template-based chunking is still the killer feature: you can see and tweak how documents are segmented before they ever hit the index.

Solo developers and small teams that want full control of their RAG pipeline can self-host this and feel good about it. Larger teams, or anyone without infra time, should look at RAGFlow Cloud, which gives you the same engine without the ops.

The catch is operational weight. If you just need to chat with a few documents, LlamaIndex or LangChain over a simple vector store ships faster. Pick RAGFlow when document parsing quality, citation grounding, and now agent workflows over your own documents are the thing you are actually buying.