NotebookLM CLI Wrapper (Python)

Required parameters

• python3 available.
• notebooklm-py installed (CLI binary: notebooklm).
• NotebookLM authenticated (login).

Quick start

• Wrapper script: scripts/notebooklm.py.
• Command form: python3 {baseDir}/scripts/notebooklm.py <command> [args...].

python3 {baseDir}/scripts/notebooklm.py login
python3 {baseDir}/scripts/notebooklm.py list
python3 {baseDir}/scripts/notebooklm.py use <notebook_id>
python3 {baseDir}/scripts/notebooklm.py status
python3 {baseDir}/scripts/notebooklm.py ask "Summarize the key takeaways" --notebook <notebook_id>

Output guidance

• Prefer --json for machine-readable output where supported.
• Long-running waits are handled by native commands like:
  • source wait
  • artifact wait
  • research wait

⚡ Sub-Agent Delegation (Anti-Blocking)

Problem

NotebookLM operations like source wait, artifact wait, research wait, generate slide-deck, and source add-research can take minutes to complete. Running them in the main session blocks the conversation.

Strategy

For any operation expected to take >30 seconds, delegate to a sub-agent via sessions_spawn:

1. Main session: Acknowledge the user's request, then spawn a sub-agent with a clear task description.
2. Sub-agent: Executes the long-running NotebookLM commands, waits for completion, and reports back.
3. Main session: Remains responsive. The sub-agent auto-announces completion.

Which operations to delegate

Operation	Delegate?	Reason
login, status, list, use, clear	❌ No	Fast (<5s)
ask (chat)	❌ No	Usually fast (~10s)
source list, source get, note list	❌ No	Fast reads
source add (URL/text)	⚠️ Maybe	Fast to submit, but source wait after is slow
source add-research	✅ Yes	Deep research can take 2-5 min
source wait	✅ Yes	Polling wait, unpredictable duration
generate slide-deck + artifact wait	✅ Yes	Generation takes 1-5 min
research wait	✅ Yes	Can take several minutes
download slide-deck	⚠️ Maybe	Usually fast, but can be slow for large files
Multi-step workflows (add sources → wait → generate → wait → download)	✅ Yes	Compound long tasks

How to spawn

sessions_spawn:
  task: |
    You are a NotebookLM task runner. Execute the following NotebookLM operations
    and report results when done.

    Notebook ID: <notebook_id>
    Commands to run (in order):
    1. <command 1>
    2. <command 2>
    ...

    Use the CLI wrapper: python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py
    Prefer --json output where supported.
    If any step fails, report the error and stop.
    When complete, summarize what was accomplished and any output files created.
  mode: run
  label: notebooklm-<short-description>

Example: Generate slide deck

User: "帮我用 notebook X 生成一个 PPT"

Main session response:

好的，我派了一个后台任务去生成 PPT，完成后会通知你 ✧

Spawn:

sessions_spawn:
  task: |
    NotebookLM task: Generate a slide deck from notebook.

    Steps:
    1. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py generate slide-deck "Create a comprehensive slide deck" --notebook <id>
    2. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py artifact wait <artifact_id> --notebook <id> --timeout 600 --json
    3. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py download slide-deck ./output.pptx --notebook <id> --latest --format pptx

    Report: artifact details, file path, any errors.
  mode: run
  label: notebooklm-slide-deck

Example: Add research source

User: "在 notebook Y 里加一个关于碳足迹的深度研究"

Spawn:

sessions_spawn:
  task: |
    NotebookLM task: Add deep research source.

    Steps:
    1. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py source add-research "碳足迹最新研究进展" --mode deep --notebook <id>
    2. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py research wait --notebook <id> --timeout 600
    3. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py source list --notebook <id> --json

    Report: research status, new sources added, any errors.
  mode: run
  label: notebooklm-research

Guidelines

• Always tell the user you're delegating to a background task before spawning.
• Use mode: run (one-shot) — no need for persistent sessions.
• Use descriptive labels like notebooklm-slide-deck, notebooklm-research-carbon for easy tracking.
• Include all context in the task — the sub-agent has no conversation history.
• Error handling: Instruct the sub-agent to report errors clearly so you can relay them.
• File paths: Use absolute paths for output files so the main session can find them.
• Compound workflows: Bundle related steps (add → wait → generate → wait → download) into a single sub-agent task rather than spawning multiple.

PPT generation policy

• A single generated slide deck should target at most 15 pages.
• If user requirements exceed 15 pages, split into multiple decks (e.g., Part 1/2/3) and generate separately.
• After generation, provide downloadable .pptx output when possible:
  • download slide-deck ... --format pptx

References

• README.md (installation, requirements, troubleshooting)
• QUICKSTART_CN.md（中文快速上手）
• references/cli-commands.md

Assets

• None.