Most RAG demos fall apart the moment they meet real documents — scanned PDFs, nested tables, 300-page reports. The bet here is that the hard part was never the LLM call but everything around it: parsing, indexing, re-ranking, and serving. So Docling (document parsing), OpenSearch (vector + keyword search), and Langflow (visual orchestration) are pre-wired into a single package that runs with one command.
What Sets It Apart
- Bundled, not glued — instead of a tutorial stitching five services together, the core pieces arrive pre-connected: install and run.
- Agentic retrieval — multi-agent coordination and re-ranking rather than single-shot vector lookup, so answers hold up on noisy corpora.
- Docling ingestion — parses messy real-world data (tables, layout, scans) instead of naive text splitting that drops structure.
- Visual and programmable — a drag-and-drop Langflow builder for fast iteration, plus Python/TypeScript SDKs and a built-in MCP server so assistants like Cursor or Claude Desktop can query your knowledge base directly.
Who It's For
Great fit if you want a self-hosted RAG stack you can stand up quickly and later extend with enterprise add-ons and OpenSearch-scale indexing. Look elsewhere if you need a fully managed SaaS with zero infrastructure, or a lightweight library to embed rather than a full platform — FastAPI + Next.js + OpenSearch is a real footprint to operate and keep running.
