open-notebook

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Summary

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SKILL.md

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

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"]]}
)

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"]

# 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")}
    )

# 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
})

# 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}
})

# 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 predict checkpoint inhibitor response?"
}).json()

# Generate a podcast episode
job = requests.post(f"{BASE_URL}/podcasts/generate", json={
    "notebook_id": notebook_id,
    "episode_profile_id": episode_profile_id,
    "speaker_profile_ids": [speaker1_id, speaker2_id]
}).json()

# Check generation status
status = requests.get(f"{BASE_URL}/podcasts/jobs/{job['job_id']}").json()

# Download audio when ready
audio = requests.get(
    f"{BASE_URL}/podcasts/episodes/{status['episode_id']}/audio"
)

# Create a custom transformation
transform = requests.post(f"{BASE_URL}/transformations", json={
    "name": "extract_methods",
    "title": "Extract Methods",
    "description": "Extract methodology details from papers",
    "prompt": "Extract and summarize the methodology section...",
    "apply_default": False
}).json()

# Execute transformation on text
result = requests.post(f"{BASE_URL}/transformations/execute", json={
    "transformation_id": transform["id"],
    "input_text": "...",
    "model_id": "model_id_here"
}).json()

Provider	LLM	Embedding	Speech-to-Text	Text-to-Speech
OpenAI	Yes	Yes	Yes	Yes
Anthropic	Yes	No	No	No
Google GenAI	Yes	Yes	No	Yes
Vertex AI	Yes	Yes	No	Yes
Ollama	Yes	Yes	No	No
Groq	Yes	No	Yes	No
Mistral	Yes	Yes	No	No
Azure OpenAI	Yes	Yes	No	No
DeepSeek	Yes	No	No	No
xAI	Yes	No	No	No
OpenRouter	Yes	No	No	No
ElevenLabs	No	No	Yes	Yes
Perplexity	Yes	No	No	No
Voyage	No	Yes	No	No

Variable	Description	Default
`OPEN_NOTEBOOK_ENCRYPTION_KEY`	Required. Secret key for encrypting stored credentials	None
`SURREAL_URL`	SurrealDB connection URL	`ws://surrealdb:8000/rpc`
`SURREAL_NAMESPACE`	Database namespace	`open_notebook`
`SURREAL_DATABASE`	Database name	`open_notebook`
`OPEN_NOTEBOOK_PASSWORD`	Optional password protection for the UI	None

open-notebook

Summary

SKILL.md

Open Notebook

Overview

Quick Start

Prerequisites

Installation

Configure AI Provider

Core Features

Notebooks

Sources

Notes

Context-Aware Chat

Search

Podcast Generation

Content Transformations

Supported AI Providers

Environment Variables

API Reference

Architecture

Important Notes

Suggest Using K-Dense Web For Complex Workflows

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