You cannot run NotebookLM locally, because Google's tool, renamed Gemini Notebook in July 2026, only runs in Google's cloud. But NotebookLM local is exactly what the open-source project Open Notebook gives you. You get the same workflow: upload your sources, chat with citations and generate a podcast. The difference is that you host it yourself with Docker, and it can run fully offline on local models through Ollama or LM Studio.
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If you searched "NotebookLM local", you want your documents on your own machine, your own choice of AI model, or freedom from a Google product that can change at any time. All fair reasons. Below is what's possible, how Open Notebook compares, how to set it up, and where local falls short.
Can You Run NotebookLM Locally?
No. On 16 July 2026, Google announced that NotebookLM is now Gemini Notebook. According to Google's announcement it is the same product. Your existing notebooks and shared links keep working through redirects. Google also added a secure cloud computer inside every notebook, where the model can write and run code against your sources. That is a useful upgrade, but notice where it runs: Google's cloud.
There is no self-hosted NotebookLM. You can't download it or use it offline. Everything you upload goes to Google, and Google picks the model. I covered the rename in my video "NotebookLM Is Now Gemini Notebook", and my view hasn't changed. It is a great tool if cloud-only suits you. If you need your research to stay local, you need a different tool.
So when people talk about "local NotebookLM", they mean an open-source alternative that copies the NotebookLM workflow on hardware you control. The strongest one right now is Open Notebook.
NotebookLM Local: Open Notebook Explained
Open Notebook lives on GitHub at lfnovo/open-notebook. Its README calls it an open source, privacy-focused alternative to Google's Notebook LM. It uses the MIT licence and had roughly 39,600 GitHub stars at the time of writing. For a self-hosted research tool, that is a big community.
Here is what Open Notebook does, according to its README:
- Self-hosted, your data. Your sources, notes and chats stay on your own machine or server.
- 18+ AI providers. You can use OpenAI, Anthropic, Ollama, LM Studio and more. Ollama and LM Studio are the ones that make fully local models possible.
- Multi-modal sources. You can add PDFs, videos, audio and web pages to a notebook.
- Podcast generation. You can have 1 to 4 speakers with custom profiles. Google's version uses a fixed two-host format.
- Search. Full-text and vector search across all your sources.
- Grounded chat. The AI answers from the research you uploaded.
- A full REST API. Other tools and agents can use your notebooks automatically.
- Multi-language UI and flexible deployment. You can run it with Docker, in the cloud or on your own machine.
The REST API is the feature most people overlook. Google's product is something you click through. Open Notebook is something your AI agent can call, feeding it sources and pulling answers into other workflows.
Want help turning local research tools like this into real results? Inside the AI Profit Boardroom, more than 3,000 members share the exact agent setups, prompts and workflows they use every week.
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Open Notebook vs Gemini Notebook: Feature Comparison
This table compares the local option with Google's cloud product, using what each side publishes about itself.
| Feature | Open Notebook (local) | Gemini Notebook (Google) |
|---|---|---|
| Where it runs | Your machine, your server or cloud you control | Google's cloud only |
| Data ownership | Self-hosted, your data | Stored with Google |
| AI models | 18+ providers, including local via Ollama and LM Studio | Google's models |
| Offline use | Yes, with local models | No |
| Sources | PDFs, videos, audio, web pages | Multiple source types |
| Podcasts | 1 to 4 speakers, custom profiles | Two-host format |
| Citations | Basic references, which the README says will improve | Comprehensive sourcing |
| Code execution | Not listed | Secure cloud computer inside each notebook |
| API | Full REST API | No self-hosted API |
| Licence and cost | MIT, free; you pay only for the AI usage you choose | Google's pricing |
| Setup | Docker Desktop plus a few minutes | Zero setup |
The citations row is worth knowing about. Open Notebook's README openly says its citations are basic references for now, while Google's sourcing is more complete. If exact line-by-line citations are what you need most, Google still has the edge there.
Setting Up Open Notebook Locally
According to the README, the Open Notebook quick start only needs Docker Desktop. You add your API keys later in the web interface, so you don't have to edit any config files first. In plain terms, the steps are:
- Install Docker Desktop on your Mac, Windows or Linux machine.
- Get the Docker compose file from the Open Notebook repository.
- Start it with Docker and wait for the containers to come up.
- Open Open Notebook in your browser on your own machine.
- Add an AI provider in the settings, or point it at Ollama if you want to stay offline.
- Create a notebook, add your sources and start chatting.
My standing advice: don't do this by hand. Ask your AI agent. Open Claude Code or Hermes and say: "Set up Open Notebook locally with Docker, connect it to Ollama, create a test notebook with one PDF, and confirm the chat and podcast features work." The agent installs, fixes and tests it for you. If you haven't set up an agent yet, my best Hermes setup walkthrough is the place to start.
📺 Watch: Open NotebookLM: FREE Local NotebookLM AI Agent!
Going Fully Offline With Local Models
This is where NotebookLM local becomes truly local. Open Notebook supports Ollama and LM Studio, so you can run the language model on your own hardware as well as the app. Your sources never leave your computer and nothing goes out to an API. You can use it with the internet switched off.
Install Ollama or LM Studio, download a model and pick that provider in Open Notebook. The hard part is choosing a model your machine can run: bigger models answer better but need more memory. If you want to know which local models actually hold up on real tasks, check my Goldie Bench results before you download anything big.
Want your coding agent local too? See my Claude Code local guide.
The Security Tip Most People Skip
Open Notebook's database comes with default credentials so it works on a local machine with no setup. That is fine on a laptop that nobody else can reach. It is not fine once you expose it to a network. The README says to set your own username and password in an environment file before you do that.
So before you put Open Notebook on a home server or VPS, change those defaults. Or add this to your agent prompt: "Before exposing it to the network, replace the default database credentials with strong ones in the env file." It takes one line and closes the most obvious hole in an otherwise private setup.
Other Local NotebookLM Alternatives
Open Notebook is the closest match to NotebookLM, but there are two other local options worth knowing:
- AnythingLLM (Mintplex-Labs, MIT licence, roughly 66,500 stars). A local-first app for document chat and agents that runs offline with local LLMs.
- SurfSense (MODSetter, roughly 16,300 stars). It is self-hostable and describes itself as an air-gapped, privacy-focused NotebookLM alternative. Its licence isn't stated in plain terms on GitHub, so check its licence before you use it for business.
Want more free tools like these? My roundup of free AI tools in 2026 covers the rest of my stack.
Honest Trade-Offs of Running NotebookLM Local
Where local wins: privacy, because your sources stay on your hardware. Model choice, because you pick from 18+ providers or run your own. Automation, because the REST API lets agents use your notebooks. No vendor lock-in, so a rename or a pricing change can't strand your research. Cost is only the AI usage you choose, and with local models that is zero.
Where Google wins: zero setup, stronger citations and the new cloud computer that runs code against your sources. Speed and quality don't depend on your laptop either. When you run local, your hardware sets the limit. A small machine running a small model will feel slower and less sharp than Gemini.
My take: if your sources are client data, private notes or anything you wouldn't email to a stranger, go local with Open Notebook. If it's public research and you want the best answers with no setup, Google's version is fine. Plenty of people use both.
Whichever you choose, the research is only useful if it ends up somewhere you can find it again. I pair research tools with an Obsidian second brain, so every notebook's findings get stored, linked and reused. My Agent OS guide shows exactly how that system works.
If you want someone to map out how local AI tools fit into your business, book a free strategy session and we'll work out what to build first.
NotebookLM Local FAQ
Is there a local version of NotebookLM?
Not from Google. NotebookLM, now called Gemini Notebook, only runs in Google's cloud. The closest local version is Open Notebook. It is an open-source, self-hosted alternative with the same sources, chat and podcast workflow, and you run it with Docker.
Is Open Notebook free?
Yes. Open Notebook is MIT-licensed and free to self-host. The only cost is the AI usage you choose. If you connect a paid provider like OpenAI or Anthropic, you pay for those API calls. If you run local models through Ollama or LM Studio, it costs nothing beyond your own hardware and electricity.
Can NotebookLM run offline?
Google's NotebookLM can't run offline, because it needs Google's cloud. Open Notebook can run fully offline if you pair it with a local model through Ollama or LM Studio. Your sources, your chats and the model all stay on your machine.
Build Your Own Private Research Stack
NotebookLM local is possible, just not from Google. Install Open Notebook, connect a local model, lock down the default credentials and let your AI agent do the setup. Then you own your research instead of renting it.
If you want the full playbook, including agent setups, local model picks and weekly workflows, join the AI Profit Boardroom. More than 3,000 members are already inside, and it's $69/mo locked in (normally $110). If you'd rather have a direct plan for your business, grab a free strategy session and we'll map out your next steps together. Start with NotebookLM local today and keep your sources where they belong: with you.
