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open-notebook: Self-Hosted NotebookLM Alternative

Author: NotebookLM.link Editorial

Google NotebookLM is great, but data stays on Google, models are locked to Gemini, and there is no API. open-notebook (34.8K stars) is the self-hosted alternative with 18+ providers and Docker deploy.

Google NotebookLM is excellent — but if you need data off the cloud, model freedom, and an API, it delivers none of the three. That gap is exactly what open-notebook fills.

NotebookLM is a genuinely good product. It reads PDFs, generates podcasts, organizes literature, and builds slide decks — Google has done strong work in source-grounded AI Q&A. By late 2025, NotebookLM was seeing over 103 million monthly visits, with an estimated 15–25 million daily active users worldwide.

The more you use it, though, the more friction you notice. Then I found open-notebook — an open-source project with 34.8K GitHub stars.

open-notebook GitHub repository homepage showing 34.8K stars and latest release version
open-notebook: 34.8K stars, MIT license, v1.10.0

1. Why more people are leaving NotebookLM

NotebookLM has a few pain points that grow over time:

First, your data is not in your hands. Every PDF and audio clip you upload lives on Google servers. Fine for public content — but project docs, client files, unpublished drafts? Would you really dump all of that in?

Second, you are locked to Google models. Gemini is capable. But if you want Claude for deep analysis, GPT-4o for writing, or DeepSeek for cheaper batch work — no dice. NotebookLM keeps you inside the Google ecosystem.

Third, no public API. In 2026, an AI product without an open API is a bottleneck. Bulk imports, workflow integration, automation? You are stuck uploading page by page.

Smaller annoyances add up: podcasts limited to two speakers, rigid citation formats, no custom processing logic.

2. What is open-notebook?

In one line: an open-source NotebookLM alternative — and in several ways, a stronger one.

34.8K stars on GitHub, 800+ commits, latest release v1.10.0, MIT license. Built by lfnovo with community momentum since October 2024 — under two years of rapid iteration.

open-notebook web interface showing notebook list and source upload panel
Web UI: notebook list and source upload panel

3. Where it beats NotebookLM

3.1 Data stays 100% on your machine

This is the core difference. open-notebook is self-hosted — Docker one-liner, data in local SurrealDB. Sensitive docs, client files, internal reports — as much as you want, without touching third-party servers.

Granular context control too: let the AI see summaries only, full content, or exclude specific sources — not NotebookLM's all-or-nothing switch.

Deployment is trivial: grab docker-compose.yml, set an encryption key, run docker compose up -d — done in about two minutes.

3.2 18+ models, your choice

open-notebook supports 18+ AI providers: OpenAI, Anthropic, Google, Groq, Ollama, DeepSeek, xAI, Mistral, Qwen, and more.

Crucially — different tasks, different models. Claude for chat, GPT-4o-mini for cheap summaries, Voyage for embeddings, ElevenLabs for speech-to-text. Mix and match freely.

Reasoning models work too — DeepSeek-R1, Qwen3 thinking models, and full local runs via Ollama. Model and data on your machine; works offline.

3.3 Podcasts with far more control

NotebookLM's podcast feature is good, but locked to two speakers with limited script control.

open-notebook supports 1–4 custom speakers, each with persona and voice tuning, plus Episode Profiles for fine-grained scripts.

Three-person panel? Two debaters plus a narrator? All supported — quality comparable to NotebookLM, flexibility much higher.

open-notebook podcast configuration showing multiple speakers voice customization and episode profile settings
Podcast setup: 1–4 speakers, personas, and Episode Profiles

3.4 Full REST API

The most practical gap. open-notebook ships a complete REST API for any workflow — bulk imports, automation, integration into your own stack.

4. Who should use open-notebook?

  • Privacy-conscious users — project docs, client data, internal reports that must not leave your infra
  • Model freedom seekers — tired of being locked to Gemini
  • Automation builders — API access for real workflows
  • Cost savers — run fully local at zero API spend
  • Technical tinkerers — Docker, self-hosting, fork and customize

If you are a casual user, NotebookLM free tier is probably enough. But if you care about data sovereignty, model choice, and automation — open-notebook is worth a two-minute deploy.

Project: github.com/lfnovo/open-notebook