ragflow

RAGFlow is a retrieval-augmented generation engine built around deep document understanding, and it has grown into an agent platform on top of that. It handles the messy parts of RAG other frameworks hand-wave past: PDFs with tables, slides, spreadsheets and scanned images, with MinerU and Docling parsing. Recent releases added knowledge compilation into wikis, graphs and mind maps, agent memory, and delivery into chat apps like Discord, Telegram and Line. Apache 2.0.

Self-hosting is a real project, not a docker run. You need 4+ CPU cores, 16+ GB RAM, 50+ GB disk, and x86 hardware, since there are no ARM64 images. The compose file brings up RAGFlow plus MySQL, Redis, object storage and a search backend: Elasticsearch by default, with Infinity, OpenSearch and OceanBase among the options. Template-based chunking is still the killer feature: you see and adjust how documents are split before they reach the index.

Solo developers and small teams that want full control can self-host for free. Everyone else can use RAGFlow Cloud, currently $29/mo for 5 seats on Starter or $129/mo for 20 seats on Pro, with Enterprise adding on-premises or bring-your-own-cloud deployment.

The catch is operational weight. Just chatting with a few documents? LlamaIndex or LangChain over a simple vector store ships faster. Note that v0.27 pulled the old GraphRAG and RAPTOR features from the UI, so check tutorials against your version.