Open Notebook Review

The README presents Open Notebook as a private, multi-model, 100% local, full-featured alternative to NotebookLM. You control your data, choose among more than 20 providers (OpenAI, Anthropic, Google, Mistral, Ollama, LM Studio, vLLM, and more), organize multi-modal content, generate multi-speaker podcasts, search intelligently across everything, and chat with context from your research; answers cite the sources, notes, and insights they used. The UI is available in English plus Bengali, Catalan, Chinese (Simplified and Traditional), French, German, Italian, Japanese, Polish, Portuguese, Russian, Spanish, and Turkish. The README includes a self-authored comparison table against NotebookLM: self-hosted versus Google cloud only, 20-plus providers versus Google models only, one to four podcast speakers with custom profiles versus two, custom content transformations, a full REST API versus none, and pay-only-for-AI-usage cost. That table is the project's own marketing, so treat it as claims rather than benchmarks.

The prerequisites are an API key from an AI provider or a local model server such as Ollama, which you add in the UI rather than in config files. Quick start is to download docker-compose.yml with curl into an open-notebook folder and bring it up with Docker. The project also describes cloud and local deployment options. We did not run it.

A browser UI organizes sources, notes, and chats in notebooks, with transformations that process content (for example summaries) and a podcast generator with speaker profiles. As with the rest of this review, we are going by the README and website, not hands-on testing.

• Notebook-style organization of PDFs, video, audio, and web pages
• Full-text and vector search across all sources
• Chat with cited answers
• Podcast generation with one to four speakers
• 20-plus AI providers, including Ollama and OpenAI-compatible servers
• Full REST API
• Multi-language UI

• Gives NotebookLM-style workflows without sending data to Google
• Provider flexibility lets you balance cost, quality, and privacy
• More podcast speaker control than the two-host format it compares itself to
• Rapid adoption: about 40,200 stars

• Output quality depends entirely on the models you plug in
• Setup needs Docker and provider keys, unlike NotebookLM's zero-setup experience
• The comparison table in the README is promotional and not independently verified
• Still a young project compared to established note and research tools

Open Notebook is free under the MIT license. You pay your AI providers for API usage, or nothing beyond hardware if you run local models.

NotebookLM is Google's free-to-start hosted research notebook, polished and zero-setup but cloud-only and limited to Google's models. Open Notebook gives you control over data and providers, an API, and flexible podcast generation, in return for setting it up yourself.

• Researchers who want NotebookLM-style features with private documents
• Teams that want to choose or mix AI providers
• Developers who want an API around a notebook-and-RAG tool

• Users who want a one-click hosted experience
• Anyone without access to capable models for good summaries and podcasts
• Teams needing proven enterprise support