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open-notebook

open-notebook• Pinned

open-notebook: A self-hosted, multi-model research notebook for organizing sources, chatting with context, and generating podcasts.

What problem it solves

Open Notebook provides a private, self-hosted environment for organizing research material while using multiple AI providers. It supports multimodal sources, full-text and vector search, context-aware conversations, notes, content transformations, and podcast generation.

Key features

  • Multi-provider AI support — Connect language, embedding, speech-to-text, and text-to-speech models from more than 20 providers, including local services and OpenAI-compatible endpoints.

    Source: README

  • Universal research content — Organize notebooks around PDFs, videos, audio, web pages, Office documents, notes, and other source material.

    Source: README, open_notebook

  • Context-aware research workflows — Search across research content, chat with the selected material, generate notes and insights, and control exactly what context is shared with AI models.

    Source: README

  • Podcast generation — Turn research material into multi-speaker podcasts with scripts, speaker profiles, and episode profiles.

    Source: README

  • Self-hosted and API-accessible — Run the notebook through Docker Compose, protect deployments with optional password authentication, and integrate it through a comprehensive REST API.

    Source: README, docker-compose.yml

Quick start

Copied verbatim from the project's README.

mkdir open-notebook && cd open-notebook
curl -o docker-compose.yml https://raw.githubusercontent.com/lfnovo/open-notebook/main/docker-compose.yml
- OPEN_NOTEBOOK_ENCRYPTION_KEY=change-me-to-a-secret-string
docker compose up -d

Who it may suit

Inference: this may suit researchers and knowledge workers who want to keep source material private while using multiple AI providers. It may also suit developers who need a self-hosted notebook with API access and customizable research workflows.

Topics to discuss

  • Which provider and local model combinations work best for privacy-sensitive research workflows?

  • How should context selection, source citations, and transformations be improved for complex research?

  • What integrations or MCP-enabled agent workflows would add the most value?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/lfnovo/open-notebook#readme

ReadmeX opened this community for a trending project; it is not run by the project. Maintainers can claim it from the community page.

github.comhttps://github.com/lfnovo/open-notebook

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