That’s where Open Notebook comes in. And it’s an open-source, self-hosted alternative that replicates Notebook LM’s core features and then some. Still, 24,600+ GitHub stars, 739 commits, 51 contributors, and a thriving community aren’t everything. I spent a full afternoon installing it, poking around, and stress-testing it against my own research docs. Here’s what I found.

TL;DR: Should You Switch?

If you care about data privacy, want to pick your own AI model, or need programmatic access — yes, Open Notebook is a solid choice. Still, it’s not a perfect 1:1 replacement (citations are weaker), but it beats Notebook LM in flexibility. And Docker setup takes about 2 minutes — you can run it with local models via Ollama for zero API costs.

What Is Open Notebook?

Open Notebook is an open-source research copilot. So you feed it documents — PDFs, web pages, videos, audio files, Office docs — and it lets you chat with your data, generate summaries, and even produce AI-hosted podcasts. Think of it as Notebook LM that you install on your own server.

The stack is TypeScript (64.6%) + Python (33.6%), running on FastAPI + Next.js + React + SurrealDB. And it’s MIT licensed. So the latest release is v1.9.0, and the last commit was two days ago — this thing is actively maintained.

Open Notebook Core Features: What Can It Actually Do?

Multi-Modal Content Management

You can create multiple notebooks for different projects. Each notebook accepts PDFs, videos, audio, web pages, and Office documents. And there’s full-text search plus vector search across everything. I dumped three research papers (PDF), a YouTube video transcript (URL), and a blog post about RAG architectures into one notebook — it indexed everything without a hitch.

AI Chat With Your Data

Look, this is the main event. So upload your materials, then ask questions. Open Notebook does RAG (Retrieval-Augmented Generation) over your content, pulling relevant chunks and citing sources. And you can fine-tune which content gets sent to the AI — granular context control.

I asked it: “What are the main challenges in RAG deployment according to these papers?” It pulled chunks from two of my three PDFs and the blog post, stitched together a coherent answer, and pointed me to the specific pages. And it worked better than I expected for a self-hosted tool.

Podcast Generation

But here’s where Open Notebook actually one-ups Google. Notebook LM gives you two fixed podcast hosts. Open Notebook lets you configure 1 to 4 hosts with custom roles and voice profiles. And you control the script content.

So want a three-way debate between a skeptic, an enthusiast, and a neutral moderator? You can set that up.

I tested this with a dense academic paper on transformer architectures. And the generated podcast actually made the material more digestible than the paper itself. Sure, the voices aren’t as polished as Google’s DeepMind audio — ElevenLabs integration helps here — but the flexibility more than makes up for it.

18+ AI Providers

You’re not locked into one ecosystem. Still, pick the cheapest, fastest, or most private option. Or run everything locally with Ollama.

REST API & MCP Integration

This is huge for power users. Open Notebook exposes a full REST API for programmatic access. And it supports the Model Context Protocol (MCP) — meaning you can connect it to Claude Desktop, VS Code, or any MCP-compatible tool. I hooked it up to Claude Desktop in about 5 minutes and was querying my research notebooks directly from the Claude interface. That workflow alone sold me.

Installation: Docker in 2 Minutes

Here’s the honest install experience. I ran this on a $20 DigitalOcean droplet (4GB RAM, 2 vCPUs), but it works just as well on a local machine.

curl -o docker-compose.yml https://raw.githubusercontent.com/lfnovo/open-notebook/main/docker-compose.yml
# Edit OPEN_NOTEBOOK_ENCRYPTION_KEY in the file
docker compose up -d

Wait 15-20 seconds, then hit http://localhost:8502. The UI loads clean — no configuration wizard, no registration. Just a settings page where you add your API keys.

One thing: you need SurrealDB as a dependency. The docker-compose.yml handles it, but it’s an extra moving part compared to something like /posts/headroom-review-2026/ that runs purely as a CLI wrapper. Still, for a web-based research tool, having a proper database makes sense.

After Docker was up, I went to Settings → API Keys, added my OpenAI key, clicked Test Connection, and it discovered available models automatically. So click Register Models and you’re ready to create notebooks.

Open Notebook vs Google Notebook LM: Comparison

What I Like

• Data ownership. Your research, your documents, your server. No Google reading your PDFs.
• Model flexibility. I swapped from OpenAI to DeepSeek mid-session just to test. Cost dropped 80% for comparable quality on my use case.
• Podcast customization. Being able to script a 3-host format for technical content is genuinely useful for learning.
• MCP integration. Connecting it to Claude Desktop changed how I work with research materials — a level of integration /posts/headroom-review-2026/ doesn’t offer for desktop tools. I’m keeping this setup.

What Could Be Better

• Citations aren’t great. Notebook LM shows you exactly which source chunk it used. Open Notebook’s citations are more basic — they point to the source but not the specific section. The devs say this is being worked on.
• SurrealDB adds complexity. Docker hides it, but if something goes wrong with the database, debugging requires SurrealDB knowledge. I hit a connection timeout on first boot and had to restart the stack.
• Resource usage. The Docker setup idles at about 1.2GB RAM. On a cheap VPS that matters.
• Frontend load times. The Next.js frontend takes 3-4 seconds to load on first visit. Not a dealbreaker, but noticeable.

Who Should Use Open Notebook

• Researchers who handle sensitive or proprietary documents and can’t trust cloud services
• Students who want a private research assistant without paying for Notebook LM Plus
• Knowledge workers dealing with large document collections daily
• Teams that need API access for research automation workflows
• Privacy-conscious users who don’t trust Google with their data
• AI enthusiasts who want to experiment with different models on the same dataset

So if you fall into any of these buckets, this is probably the self-hosted research tool you’ve been waiting for.

Skip it if: you need rock-solid source citations, you don’t want to manage Docker, or you’re happy with Notebook LM’s free tier.

The Bottom Line

Open Notebook is the most mature open-source Notebook LM alternative I’ve tested. And 24.6k stars isn’t just hype — this project has real momentum, real commits, real users. The Docker setup is straightforward, the feature set exceeds Google’s offering in several areas, and the MCP integration makes it genuinely useful beyond just a toy.

Sure, the citation system needs work, and the SurrealDB dependency adds a bit of friction. But for anyone who values data privacy or wants flexibility in AI model choice, this is the self-hosted research tool to beat.

I’m keeping my instance running and connecting it to my daily research workflow. If you’ve been looking for a reason to ditch Notebook LM, this is it.

Or get started with $100 free credits on Vultr if you prefer their global data center options.

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