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✦ Free & open sourceStudents

Open Notebook

Self-hosted research notebook where you chat with your sources, make notes and generate multi-speaker podcasts.

Your own private NotebookLM

Chat with PDFs, videos and web pages using the AI models you choose.

Free forever · open source · MIT
✓ Needs IT✓ Browser-based✓ Real-time collaboration
7.9
ZEKAI
Private NotebookLM rival
Free & open source · Students
Ease of setup5
Usefulness8
Maintenance9.5
Community9.2
★ 41,912
GITHUB
Nov 2026
UPDATED
MIT
LICENSE
🛠 Don't want to self-host? We can set it up →
⚡ Quick answer

Open Notebook is a self-hosted alternative to Google's NotebookLM. It suits researchers and students who want to ask questions across their sources, with citations, while choosing which AI provider sees their material.

Runs on
Web
Hosting
Self-host
Languages
TypeScript
License
MIT

What it is

You add PDFs, videos, audio, web pages and Office documents to notebooks, then chat with them, search by full text and vector, and write AI-assisted notes. It can turn a notebook into a podcast with one to four speakers, and it exposes a full REST API.

Who it's for

Suits researchers, students and privacy-minded professionals who are comfortable running Docker and supplying their own AI keys or a local model server. It does not suit anyone who wants a hosted app with nothing to install.

Key features

Choice of 20+ AI providers

Connect OpenAI, Anthropic, Google, Mistral, Ollama, LM Studio and others, and assign separate language and embedding models.

Podcast generation

Build podcasts with 1 to 4 speakers and custom Episode Profiles, where NotebookLM offers two speakers.

Cited chat

Answers cite the sources, notes and insights they drew on.

Custom transformations

Define your own reusable actions to summarise or extract insights from a source.

Fine-grained context control

Choose exactly what each chat shares with the AI model.

REST API and MCP

A full REST API and an MCP integration let other tools drive your notebooks.

How to start

Up and running in 3 steps

1
Install Docker, then download the official docker-compose.yml with curl from the repo.
2
Edit the file to set OPEN_NOTEBOOK_ENCRYPTION_KEY (for example from `openssl rand -hex 32`) and, on shared networks, bind ports to 127.0.0.1 or set a password.
3
Run `docker compose up -d`, open http://localhost:8502, add a provider under Models, set default models, then create a notebook.
Real talk

Our honest verdict

What's great

  • You pick the model per task, including fully local ones via Ollama, so sensitive research need not leave your machine.
  • Podcasts are not capped at two voices and you control the script through episode profiles.
  • The REST API gives you programmatic access that the README says NotebookLM does not offer.
  • The interface is offered in 13 languages, including Turkish, German, Japanese, Polish, Catalan and Bengali.

Where it falls short

  • You run it yourself: Docker with two services (SurrealDB and the app), and you must set an encryption key before first start.
  • The compose file publishes the UI and API on all network interfaces with no password by default, so on a shared network you must bind to localhost or set a password.
  • You pay your AI provider per use, and Anthropic and Groq have no embedding models, so you need a second provider for embeddings.
  • Running local models for chat and podcasts needs your own capable hardware, which the README does not size for you.
Cost

Pricing & license

Open Notebook is MIT-licensed and free to run. The README says you pay only for AI usage from the provider you choose, or nothing extra if you use local models. The project shows no paid hosted plan on its site.

Paid work

Open remote AI-training roles

870 open remote AI-training roles, updated November 14, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.

See all open roles →
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📄 Free PDF: Open Notebook for Students
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Questions

FAQ

Does Open Notebook send my documents to the cloud?

Only to the AI providers you configure. With a local model server such as Ollama, the README says nothing leaves your machine.

Do I need to pay for anything?

The software is MIT. You pay whichever AI provider you connect for usage, or run local models on your own hardware.

How does it compare with NotebookLM?

The README claims more provider choice, 1 to 4 podcast speakers instead of 2, a REST API and self-hosting, versus Google's hosted-only service.

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