Integrating Google NotebookLM into Your AI Development Pipeline with Python

teng-lin/notebooklm-py

Since you're building out a technical documentation library, notebooklm-py is a potential game-changer for automating how you process and synthesize information.

While Google's NotebookLM is famous for its "Audio Overviews" and grounded RAG (Retrieval-Augmented Generation) capabilities, it doesn't have an official public API yet.

notebooklm-py is an unofficial Python SDK and CLI tool that bridges this gap. It allows you to interact with NotebookLM programmatically. For a software engineer, this means you can move away from manual file uploads and "point-and-click" interactions, moving instead toward automated pipelines.

Massive Context Handling
You can feed it entire repositories, documentation sets, or research papers and query them via script.

Agentic Integration
It’s designed to work with AI agents (like Claude Code). You can give your agent the "skill" to read and summarize your notebooks.

Hidden Features
It often exposes internal parameters or batch processing capabilities that the web UI hides to keep things simple for casual users.

You can install the library directly via pip

pip install notebooklm-py

Since this is unofficial, it typically uses your browser's session cookies to authenticate. You'll need to grab the __Secure-1PSID and __Secure-1PSIDTS cookies from your Google account while logged into NotebookLM.

Here is a clean example of how you might use this to automate the creation of a technical "source" for your documentation project.

from notebooklm import NotebookLM

# Initialize the client with your session cookies
# (Keep these secret and out of version control!)
client = NotebookLM(
    sb_1psid="your_cookie_here",
    sb_1psidts="your_timestamp_cookie_here"
)

def build_tech_notebook(notebook_title, file_paths):
    # 1. Create a new notebook
    notebook = client.create_notebook(title=notebook_title)
    print(f"Created Notebook: {notebook.id}")

    # 2. Upload source files (PDFs, Markdown, or Text)
    for path in file_paths:
        source = notebook.upload_source(path)
        print(f"Uploaded: {source.title}")

    # 3. Ask a technical question based on the sources
    response = notebook.ask("Summarize the architectural patterns found in these files.")
    print("\n--- Summary ---\n")
    print(response.answer)

# Usage
my_docs = ["api_spec.md", "system_design.pdf"]
build_tech_notebook("Project Architecture Deep-Dive", my_docs)

As a content creator, you could build a script that

Watches a folder for new technical drafts.

Uploads them to a "Knowledge Base" notebook.

Generates a set of titles (formatted in your preferred <pre> tags!) or a "Creative Story" summary automatically using the NotebookLM engine.

It effectively turns NotebookLM into a specialized RAG-as-a-Service that you control through Python.