Alterlab Open Notebook

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 with Docker Compose v2 (OrbStack or Docker Desktop both work; docker compose must be available)
• API key for at least one AI provider (or local Ollama / LM Studio 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 (v2 syntax; OrbStack/Docker Desktop both expose this)
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 predict checkpoint inhibitor response?"
}).json()

Podcast Generation

Generate professional multi-speaker podcasts from research materials with 1-4 customizable speakers.

# 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"
)

Content Transformations

Apply custom AI-powered transformations to content for summarization, extraction, and analysis.

# 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()

Supported AI Providers

Open Notebook supports 18+ AI providers through the Esperanto library. A representative subset:

LM Studio is reached via the OpenAI-compatible provider, so it inherits the full capability set. For the full provider matrix see the upstream README.

Environment Variables

Key configuration variables for Docker deployment:

API Reference

The REST API is available at http://localhost:5055/api with interactive documentation at /docs.

Core endpoint groups:

• /api/notebooks - Notebook CRUD and source association
• /api/sources - Source ingestion, processing, and retrieval
• /api/notes - Note management
• /api/chat/sessions - Chat session management
• /api/chat/execute - Chat message execution
• /api/search - Full-text and vector search
• /api/podcasts - Podcast generation and management
• /api/transformations - Content transformation pipelines
• /api/models - AI model configuration and discovery
• /api/credentials - Provider credential management

For complete API reference with all endpoints and request/response formats, see references/api_reference.md.

Architecture

Open Notebook uses a modern stack:

• Backend: Python with FastAPI
• Database: SurrealDB (document + relational)
• AI Integration: LangChain with the Esperanto multi-provider library
• Frontend: Next.js with React
• Deployment: Docker Compose with persistent volumes

Important Notes

• Open Notebook requires Docker for deployment
• At least one AI provider must be configured for AI features to work
• For free local inference without API costs, use Ollama
• The OPEN_NOTEBOOK_ENCRYPTION_KEY must be set before first launch and kept consistent across restarts
• All data is stored locally in Docker volumes for complete data sovereignty