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Open Notebook

Open-source, self-hosted AI research notebook with podcast generation and full model control

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open-sourceFree and open source, self-hosted. No paid tier or hosted plan is advertised; model costs are whatever you incur with the providers you configure, and local models incur none. Support runs through GitHub and Discord.View full pricing →

About Open Notebook

Open Notebook is an open-source, self-hostable AI research and note-taking platform positioned as a privacy-controlled alternative to hosted notebook assistants. The differentiating feature is not the note-taking but the control surface: you choose which models run, where they run, and precisely what information the AI is allowed to see, rather than accepting a vendor's default that everything you upload becomes context. For researchers handling unpublished work, professionals under client confidentiality, or anyone in a regulated field, that distinction determines whether the tool is usable at all. Functionally it ingests links, PDFs, TXT, PPT and YouTube content, summarises and generates insights across a corpus, and includes a podcast generator that turns notes into audio episodes with customisable voices, speakers and episode structure — the feature that made hosted equivalents popular, here running against your own model choices. Because it is open source and self-hosted, model routing can point at local inference, so a full research workflow can run with nothing leaving the machine. The project is community-driven with development on GitHub and discussion on Discord, and it targets researchers, students and professionals who want the capability without ceding the corpus. There is no paid tier and no hosted offering to upsell, which keeps the privacy claim structurally credible.

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Key Features

✓Granular control over what information the AI can access
✓Podcast generator with customisable voices, speakers and episodes
✓Ingests links, PDFs, TXT, PPT and YouTube
✓Bring your own models, including fully local inference
✓AI summarisation and insight generation across a corpus

Open Notebook Pros & Cons

✅ Pros

  • +No hosted tier to upsell, so the privacy positioning is structurally honest
  • +Local model routing means a full workflow can run with nothing leaving the machine
  • +Matches the podcast-generation feature that drove adoption of hosted rivals

⚠️ Cons

  • −Self-hosting is a real setup and maintenance cost
  • −No mobile app or hosted sync
  • −Community support only — no SLA of any kind

Tags

open-sourceself-hostedprivacyresearchpodcast
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