Kotaemon

Open-source RAG UI for chatting with your own documents, locally or self-hosted.

Free· Free, open-source (MIT-style); self-hosted infrastructure costs onlyRAGMulti-model (OpenAI, LlamaCPP, any OpenAI-compatible endpoint)7.0 / 10

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Best for

Pick Kotaemon if you want a hackable, self-hosted RAG UI over your own documents with citation support and full control of the LLM and vector store.

Skip if

Skip it if you need a polished SaaS product, enterprise SSO out of the box, or a managed RAG service you don't have to deploy yourself.

Kotaemon is an open-source document question-answering application built by Cinnamon AI that wraps a full RAG pipeline behind a usable web interface. It handles ingestion of PDFs, DOCX, Excel, HTML and plain text, embeds them into a vector store of your choice (Chroma, Qdrant, Milvus, LanceDB, or Elasticsearch), and serves grounded answers with inline citations. The UI ships with admin authentication, multi-user support, and configurable retrieval, re-ranking, and embedding pipelines out of the box.

It's aimed at two audiences: end users who want a private ChatGPT-for-my-files without piping data to a SaaS vendor, and developers who want a hackable RAG starter they can extend rather than build from scratch. Pricing is effectively free — you host it yourself, either via a HuggingFace Spaces template (~10 minutes) or with the Windows/macOS/Linux installer scripts (~20 minutes). Costs are whatever your chosen LLM and vector backend charge.

Kotaemon is model-agnostic: plug in OpenAI, local LlamaCPP models, or any OpenAI-compatible endpoint (Ollama, vLLM, LM Studio, OpenRouter). Optional web-search retrieval via Jina or Tavily extends it beyond local corpora. It's a Gradio-based app, so the UI is functional rather than polished, and production-scale deployments will need work around auth, observability, and concurrency.

Editor's take

Kotaemon is one of the better open-source RAG frontends to land in the last couple of years — it strikes a workable balance between batteries-included and pluggable. Treat it as a strong starting point for an internal knowledge bot rather than a finished product. You will end up customizing the UI and auth before anyone serious uses it.

— The AI Tool Bible editorial team

Pros

✅ Genuinely model- and vector-store-agnostic; swap backends without touching code
✅ Citations with source highlights, not just naked LLM answers
✅ One-click HuggingFace Spaces deploy or local installer scripts
✅ Active GitHub project with clear extension hooks for developers