/open-notebook

name: open-notebook
description: Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting.
license: MIT
metadata:
  version: "1.3"
  skill-author: K-Dense Inc.
  openclaw:
    envVars:
    - name: OPEN_NOTEBOOK_URL
      required: true
      description: Open Notebook server URL.
    - name: OPEN_NOTEBOOK_PASSWORD
      required: false
      description: Open Notebook password, if auth is enabled.
    - name: OPEN_NOTEBOOK_ENCRYPTION_KEY
      required: false
      description: Encryption key for stored content, if configured.

Open Notebook

Overview

Open Notebook is an open-source, self-hosted alternative to Google's NotebookLM that enables researchers to organize materials, generate AI-powered insights, create podcasts, and have context-aware conversations with their documents — all while maintaining complete data privacy.

Unlike Google's Notebook LM, which has no publicly available API outside of the Enterprise version, Open Notebook provides a comprehensive REST API, supports 16+ AI providers, and runs entirely on your own infrastructure.

**Key advantages over NotebookLM:**

• Full REST API for programmatic access and automation
• Choice of 16+ AI providers (not locked to Google models)
• Multi-speaker podcast generation with 1-4 customizable speakers (vs. 2-speaker limit)
• Complete data sovereignty through self-hosting
• Open source and fully extensible (MIT license)

**Repository:** https://github.com/lfnovo/open-notebook

Quick Start

Prerequisites

• Docker Desktop installed
• API key for at least one AI provider (or local Ollama for free local inference)

Installation

Deploy Open Notebook using Docker Compose:

# Download the docker-compose file
curl -o docker-compose.yml https://raw.githubusercontent.com/lfnovo/open-notebook/main/docker-compose.yml

# Set the required encryption key
export OPEN_NOTEBOOK_ENCRYPTION_KEY="your-secret-key-here"

# Launch the services
docker-compose up -d

Access the application:

• **Frontend UI:** http://localhost:8502
• **REST API:** http://localhost:5055
• **API Documentation:** http://localhost:5055/docs

Configure AI Provider

After startup, configure at least one AI provider:

1. Navigate to **Settings > API Keys** in the UI 2. Add credentials for your preferred provider (OpenAI, Anthropic, etc.) 3. Test the connection and discover available models 4. Register models for use across the platform

Or configure via the REST API:

import requests

BASE_URL = "http://localhost:5055/api"

# Add a credential for an AI provider
response = requests.post(f"{BASE_URL}/credentials", json={
    "provider": "openai",
    "name": "My OpenAI Key",
    "api_key": "sk-..."
})
credential = response.json()

# Discover available models
response = requests.post(
    f"{BASE_URL}/credentials/{credential['id']}/discover"
)
discovered = response.json()

# Register discovered models
requests.post(
    f"{BASE_URL}/credentials/{credential['id']}/register-models",
    json={"model_ids": [m["id"] for m in discovered["models"]]}
)

Core Features

Notebooks

Organize research into separate notebooks, each containing sources, notes, and chat sessions.

import requests

BASE_URL = "http://localhost:5055/api"

# Create a notebook
response = requests.post(f"{BASE_URL}/notebooks", json={
    "name": "Cancer Genomics Research",
    "description": "Literature review on tumor mutational burden"
})
notebook = response.json()
notebook_id = notebook["id"]

Sources

Ingest diverse content types including PDFs, videos, audio files, web pages, and Office documents. Sources are processed for full-text and vector search.

# Add a web URL source
response = requests.post(f"{BASE_URL}/sources", data={
    "url": "https://arxiv.org/abs/2301.00001",
    "notebook_id": notebook_id,
    "process_async": "true"
})
source = response.json()

# Upload a PDF file
with open("paper.pdf", "rb") as f:
    response = requests.post(
        f"{BASE_URL}/sources",
        data={"notebook_id": notebook_id},
        files={"file": ("paper.pdf", f, "application/pdf")}
    )

Notes

Create and manage notes (human or AI-generated) associated with notebooks.

# Create a human note
response = requests.post(f"{BASE_URL}/notes", json={
    "title": "Key Findings",
    "content": "TMB correlates with immunotherapy response in NSCLC...",
    "note_type": "human",
    "notebook_id": notebook_id
})

Context-Aware Chat

Chat with your research materials using AI that cites sources.

# Create a chat session
session = requests.post(f"{BASE_URL}/chat/sessions", json={
    "notebook_id": notebook_id,
    "title": "TMB Discussion"
}).json()

# Send a message with context from sources
response = requests.post(f"{BASE_URL}/chat/execute", json={
    "session_id": session["id"],
    "message": "What are the key biomarkers for immunotherapy response?",
    "context": {"include_sources": True, "include_notes": True}
})

Search

Search across all materials using full-text or vector (semantic) search.

# Vector search across the knowledge base
results = requests.post(f"{BASE_URL}/search", json={
    "query": "tumor mutational burden immunotherapy",
    "search_type": "vector",
    "limit": 10
}).json()

# Ask a question with AI-powered answer
answer = requests.post(f"{BASE_URL}/search/ask/simple", json={
    "query": "How does TMB predic