Common workflows with notebooklm-py

import asyncio
from notebooklm import NotebookLMClient, AudioFormat, AudioLength

async def research_pipeline(topic: str, seed_url: str, output_path: str):
    async with await NotebookLMClient.from_storage() as client:
        # 1. Create notebook
        nb = await client.notebooks.create(f"Research: {topic}")
        print(f"Notebook: {nb.id}")

        # 2. Add seed source
        source = await client.sources.add_url(nb.id, seed_url)
        print(f"Seed source: {source.id}")

        # 3. Start deep web research (non-blocking)
        research = await client.research.start(nb.id, topic, source="web", mode="deep")
        task_id = research["task_id"]
        print(f"Research started: {task_id}")

        # 4. Poll until research completes
        while True:
            status = await client.research.poll(nb.id)
            if status["status"] == "completed":
                print(f"Research complete. Found {len(status['sources'])} sources.")
                break
            print(f"Research in progress… ({status['status']})")
            await asyncio.sleep(15)

        # 5. Import discovered sources
        imported = await client.research.import_sources(
            nb.id, task_id, status["sources"]
        )
        print(f"Imported {len(imported)} sources")

        # 6. Chat with the sources
        result = await client.chat.ask(nb.id, f"Summarize the key findings on {topic}")
        print(result.answer[:500])

        # 7. Generate a podcast
        gen_status = await client.artifacts.generate_audio(
            nb.id,
            audio_format=AudioFormat.DEEP_DIVE,
            audio_length=AudioLength.DEFAULT,
        )
        print(f"Generation task: {gen_status.task_id}")

        # 8. Wait for completion
        final = await client.artifacts.wait_for_completion(
            nb.id, gen_status.task_id, timeout=1200, poll_interval=15
        )

        if final.is_complete:
            path = await client.artifacts.download_audio(nb.id, output_path)
            print(f"Podcast saved to: {path}")
        else:
            print(f"Generation did not complete in time: {final.status}")

asyncio.run(research_pipeline(
    topic="AI safety regulations",
    seed_url="https://en.wikipedia.org/wiki/AI_safety",
    output_path="./ai-safety-podcast.mp3",
))

# .github/workflows/weekly-podcast.yml
name: Weekly Research Podcast

on:
  schedule:
    - cron: '0 8 * * 1'  # Every Monday at 8am UTC
  workflow_dispatch:

jobs:
  generate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.12'

      - name: Install notebooklm-py
        run: pip install notebooklm-py

      - name: Create notebook and add sources
        env:
          NOTEBOOKLM_AUTH_JSON: ${{ secrets.NOTEBOOKLM_AUTH_JSON }}
        run: |
          NB_ID=$(notebooklm create "Weekly Research" --json | jq -r .notebook.id)
          echo "NB_ID=$NB_ID" >> $GITHUB_ENV
          notebooklm source add "https://example.com/weekly-report" -n $NB_ID --json

      - name: Generate podcast and wait
        env:
          NOTEBOOKLM_AUTH_JSON: ${{ secrets.NOTEBOOKLM_AUTH_JSON }}
        run: |
          TASK_ID=$(notebooklm generate audio --json -n $NB_ID | jq -r .task_id)
          notebooklm artifact wait $TASK_ID -n $NB_ID --timeout 1200

      - name: Download podcast
        env:
          NOTEBOOKLM_AUTH_JSON: ${{ secrets.NOTEBOOKLM_AUTH_JSON }}
        run: |
          notebooklm download audio ./weekly-podcast.mp3 -n $NB_ID

      - name: Upload artifact
        uses: actions/upload-artifact@v4
        with:
          name: weekly-podcast
          path: ./weekly-podcast.mp3