If you’ve used Google’s NotebookLM, you remember that “wow” moment — throwing PDFs, web pages, and videos into a single workspace and having AI summarize, distill, and even generate a two-person podcast from your materials.

But if you’ve used it for real work, you’ve also run into these headaches:

• Data locked in the Google cloud — would you upload sensitive research materials?
• Tied to Gemini only — want Claude or DeepSeek? No dice.
• Accessibility issues outside the US — network hurdles to deal with.
• 50-source limit — way too small for large projects.

Today’s project solves all of these — completely.

Open Notebook: The Fully-Featured, Freedom-First NotebookLM Alternative

Open Notebook (GitHub: lfnovo/open-notebook) is a fully open-source replacement for Google NotebookLM. As of June 2026, it has amassed 30,600+ Stars on GitHub, with over 20,000 of those coming in the last three months alone.

Its core philosophy is simple: Your data, your rules.

The tech stack is modern: Next.js on the frontend, FastAPI on the backend, SurrealDB for storage, and a self-developed Esperanto library that unifies 18+ AI model providers. It’s licensed under MIT — use it, modify it, ship it.

What Makes It So Good?

1. 18+ AI Models — Use Whoever You Want

This is its biggest differentiator. OpenAI’s GPT-4o, Anthropic’s Claude, Google’s Gemini, DeepSeek, Mistral, Groq, and even fully local Ollama and LM Studio — all supported.

Think Claude writes better summaries today? Use Claude. Find DeepSeek-R1 sharper at reasoning tomorrow? One-click switch. No vendor lock-in. Maximum AI freedom.

Pair it with Ollama for fully offline operation — zero API costs, data never leaves your machine. For privacy-sensitive scenarios, this is a genuine necessity.

2. 1-4 Person Podcasts, Fully Customizable Characters

NotebookLM’s Deep Dive podcast is impressive, but it’s locked into a fixed two-person format with no control over content or roles.

Open Notebook takes podcast generation to a whole new level: 1 to 4 speakers, each with customizable personality, tone, and expertise. Script comes first — preview, edit, and only then generate audio. Want a three-person roundtable with a “Tech Expert + PM + End User”? No problem.

Podcast quality depends on the underlying model — with GPT-4o or Claude Sonnet, the output is impressively solid. Smaller models (7B and below) tend to loop repetitive phrases, so keep that in mind.

3. Fully Private Data — Deploy with Docker in Two Minutes

One command to start:

curl -o docker-compose.yml https://raw.githubusercontent.com/lfnovo/open-notebook/main/docker-compose.yml
docker compose up -d

Then open http://localhost:8502 — your private AI knowledge workspace is ready. All research materials, conversation history, and generated notes live in your local SurrealDB. Zero Google involvement.

4. Full REST API — Programmable Integration

Rarely seen in tools like this. Open Notebook provides a complete REST API (with Swagger docs), so you can integrate the entire “upload → AI conversation → podcast generation” pipeline as a backend service in your own products.

Open Notebook vs Google NotebookLM: 8 Out of 9 Dimensions

The one area where NotebookLM still wins is citation precision — NotebookLM lets you click a citation and jump directly to the highlighted passage in the original PDF. Open Notebook’s citations are still at a basic level, but the team is actively improving this.

Who It’s For — and Who It Isn’t

Recommended for:

• Researchers, lawyers, and analysts handling sensitive materials — data can’t go to the cloud, must run locally
• Users who want fully offline AI research with Ollama — zero cost, zero privacy risk
• Content creators who want high-quality multi-role AI podcasts — far more flexible than NotebookLM
• Developers integrating AI research capabilities into their own products — REST API out of the box

Not ideal for:

• Users who just need a “PDF to Markdown” pipeline — Marker or Docling is more efficient
• Users who heavily rely on precise citations — if you need “click to jump to original position”, this isn’t there yet
• Command-line purists — the core experience is in the Web UI

Final Thoughts

Honestly, for an open-source project to reach this level of completeness — multi-model support, podcast generation, REST API, Docker one-click deploy, 7-language UI — is seriously impressive. It went from zero to 30,000 Stars in under a year, not through marketing, but by solving a real problem: people don’t want to hand over their research data to a single company, nor be locked into a single company’s models.

Want to try it? Two minutes is all it takes. If you’re handling sensitive data, pair it with Ollama for a fully local pipeline.

GitHub repository: https://github.com/lfnovo/open-notebook
Official documentation: https://www.open-notebook.ai/