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Access NotebookLM programmatically to automate research, analysis, and document workflows.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "notebooklm-py" yet — see the docs or source repo.
Using notebooklm-py, write a Python script that uploads PDFs from the ./reports folder to NotebookLM, creates a notebook, generates a summary, key findings, and open questions for each document, then exports everything as JSON.
A runnable Python script that batch uploads documents, invokes NotebookLM analysis, and outputs structured JSON results.
Show how to use the notebooklm-py CLI to import multiple interview transcripts into one NotebookLM project, generate a research brief with themes, viewpoint comparisons, and quoted excerpts, and save it as Markdown.
A clear set of CLI commands or script examples that directly produce a Markdown research brief.
Design an agent workflow based on notebooklm-py so Claude Code or Codex can read a specified NotebookLM notebook, retrieve relevant sources for user questions, generate evidence-backed answers, and clearly state when information is insufficient.
An agent integration design or sample code showing how to connect to NotebookLM, perform retrieval-based Q&A, and return evidence-backed answers.
Connect Google NotebookLM for research, source analysis, and content generation workflows.
Connect AI agents to NotebookLM for querying, sourcing, and generating artifacts.
Automate NotebookLM to manage sources, answer questions, and generate audio overviews.
Connect to Google NotebookLM to manage notebooks, add sources, ask questions, and generate audio overviews.
Connect to Google NotebookLM for source ingestion, querying, and audio summary generation.
Process multi-source content for NotebookLM and generate podcasts, slides, mind maps, and quizzes.