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 :

• Main session: Acknowledge the user's request, then spawn a sub-agent with a clear task description.

• Sub-agent: Executes the long-running NotebookLM commands, waits for completion, and reports back.

• 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.