NotebookLM-py vs notebooklm-skill: Which Python Library Should You Use in 2025?

Published on ClawList.io | Category: AI Automation | Reading time: ~6 minutes

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If you've been building AI-powered knowledge management workflows or experimenting with Google's NotebookLM through automation, you've likely encountered two competing Python libraries: notebooklm-skill and the newer NotebookLM-py. A recent discussion from AI automation engineer @servasyy_ai sparked renewed interest in comparing these tools — with a clear verdict emerging: NotebookLM-py appears to be the more powerful and complete solution.

In this post, we'll break down what each library offers, why developers are making the switch, and how you can integrate NotebookLM-py into your own AI automation pipelines.

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What Is NotebookLM and Why Automate It?

Before diving into the library comparison, let's establish why automating NotebookLM matters in the first place.

Google NotebookLM is an AI-powered research and note-taking assistant that allows users to upload documents, PDFs, and web content, then query that knowledge base using natural language. It's become a go-to tool for researchers, developers, and content creators who need to synthesize large volumes of information quickly.

However, NotebookLM's web interface has limitations for power users:

• No native API for programmatic access (at least not publicly available)
• Manual upload workflows that slow down automated pipelines
• Limited integration with existing developer toolchains
• No bulk processing capabilities out of the box

This is exactly where Python automation libraries step in. By scripting interactions with NotebookLM, developers can build powerful workflows — from automated research pipelines to knowledge base management systems — that would otherwise require tedious manual effort.

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notebooklm-skill: The Original Approach

The notebooklm-skill library was one of the first community-driven attempts to provide programmatic access to NotebookLM. It gained traction quickly among developers looking for a lightweight way to interact with the platform via Python.

What notebooklm-skill offered:

• Basic session management and authentication handling
• Simple document upload functionality
• Query execution against uploaded notebooks
• Minimal dependencies and easy setup

Here's a typical usage pattern with notebooklm-skill:

from notebooklm_skill import NotebookLMClient

# Initialize client
client = NotebookLMClient(credentials="your_google_credentials.json")

# Upload a document
client.upload_document("research_paper.pdf")

# Ask a question
response = client.query("What are the key findings in this paper?")
print(response)

While this worked well for basic use cases, developers quickly ran into friction with more complex workflows. Issues reported by the community included:

• Brittle session handling that broke frequently with UI updates
• Limited error recovery — failed requests often required manual intervention
• No support for batch operations on multiple notebooks
• Sparse documentation making it difficult to extend or customize
• Infrequent maintenance, leaving known bugs unresolved

For quick prototypes or one-off scripts, notebooklm-skill was serviceable. But for production-grade automation or anything beyond the most basic interactions, developers found themselves hacking around its limitations more than actually building.

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NotebookLM-py: A More Complete Solution

Enter NotebookLM-py — a more mature, feature-rich library that addresses many of the shortcomings of its predecessor. According to @servasyy_ai and others in the AI automation community, NotebookLM-py represents a significant step forward in both functionality and developer experience.

Key Improvements in NotebookLM-py

1. Robust Authentication and Session Management

NotebookLM-py implements a more resilient authentication layer that handles token refresh, session expiry, and credential rotation gracefully — reducing the number of unexpected failures in long-running pipelines.

from notebooklm_py import NotebookLM

# More robust initialization with retry logic
nlm = NotebookLM(
    credentials_path="credentials.json",
    auto_refresh=True,
    retry_attempts=3
)

# Session persists reliably across multiple operations
nlm.authenticate()

2. Comprehensive Notebook Management

Where notebooklm-skill focused primarily on single-notebook interactions, NotebookLM-py provides full lifecycle management:

# Create a new notebook
notebook = nlm.create_notebook(
    title="Q3 Competitive Analysis",
    description="Research on competitor landscape"
)

# Add multiple sources at once
notebook.add_sources([
    "competitor_report_2024.pdf",
    "market_analysis.docx",
    "https://example.com/industry-trends"
])

# List all notebooks
notebooks = nlm.list_notebooks()
for nb in notebooks:
    print(f"{nb.title} — {nb.source_count} sources — Created: {nb.created_at}")

3. Structured Query and Response Handling

NotebookLM-py returns structured response objects rather than raw strings, making it far easier to parse, log, and act on results programmatically:

# Rich response objects with metadata
result = notebook.query(
    "Summarize the main competitive threats identified",
    include_citations=True
)

print(result.answer)
print(result.confidence_score)
print(result.cited_sources)

# Export results directly
result.export_to_markdown("competitive_summary.md")

4. Async Support for High-Throughput Workflows

One of the standout features of NotebookLM-py is native async support, enabling developers to run multiple queries concurrently — a critical capability for production AI pipelines:

import asyncio
from notebooklm_py import AsyncNotebookLM

async def batch_research(queries):
    async with AsyncNotebookLM(credentials_path="credentials.json") as nlm:
        notebook = await nlm.get_notebook("my_research_notebook")
        
        tasks = [notebook.aquery(q) for q in queries]
        results = await asyncio.gather(*tasks)
        
        return results

queries = [
    "What is the market size?",
    "Who are the key players?",
    "What are the growth projections?"
]

results = asyncio.run(batch_research(queries))

5. OpenClaw / OpenAI Tool Integration

For developers building AI agent workflows — particularly those using OpenClaw skills or OpenAI function calling — NotebookLM-py ships with tool-compatible wrappers:

from notebooklm_py.tools import NotebookLMTool

# Ready-to-use tool definition for AI agents
tool = NotebookLMTool(notebook_id="your_notebook_id")

# Use directly in an OpenAI-style tool call
tools = [tool.as_openai_tool()]

This integration is a game-changer for anyone building autonomous research agents that need to pull from curated knowledge bases on demand.

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When to Use Each Library

| Feature | notebooklm-skill | NotebookLM-py | |---|---|---| | Easy setup | ✅ | ✅ | | Stability | ⚠️ Moderate | ✅ High | | Batch operations | ❌ | ✅ | | Async support | ❌ | ✅ | | Structured responses | ❌ | ✅ | | Agent/tool integration | ❌ | ✅ | | Active maintenance | ⚠️ | ✅ | | Documentation quality | ⚠️ Sparse | ✅ Comprehensive |

Use notebooklm-skill if:

• You have existing code built around it and migration isn't worth the effort
• You only need extremely simple, one-off query scripts
• You're running a quick proof-of-concept and setup time is the priority

Use NotebookLM-py if:

• You're building any production-grade automation pipeline
• You need reliable, long-running session management
• You're integrating NotebookLM into an AI agent or OpenClaw skill
• You require async processing or batch operations
• You want well-maintained, documented tooling you can depend on

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Conclusion: Make the Switch to NotebookLM-py

The community consensus, as highlighted by @servasyy_ai, is increasingly clear: NotebookLM-py is the more complete and capable choice for developers serious about automating Google NotebookLM interactions.

While notebooklm-skill served as a useful early solution that helped validate the use case, NotebookLM-py takes the concept significantly further — offering the robustness, flexibility, and developer-friendly design needed for real-world AI automation workflows.

Whether you're building an automated research assistant, a document intelligence pipeline, or integrating NotebookLM into a multi-step OpenClaw skill, NotebookLM-py gives you the foundation to do it properly.

The migration path from notebooklm-skill is straightforward enough that there's little reason to stay behind. If you haven't already explored NotebookLM-py, now is the time.

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Have you made the switch from notebooklm-skill to NotebookLM-py? Share your experience in the comments or tag us on X. For more AI automation tools, libraries, and OpenClaw skill guides, stay tuned to ClawList.io.

Reference: @servasyy_ai on X