Notebooklm

NotebookLM Automation

Complete programmatic access to Google NotebookLM—including capabilities not exposed in the web UI. Create notebooks, add sources (URLs, YouTube, PDFs, audio, video, images), chat with content, generate all artifact types, and download results in multiple formats.

Installation

From PyPI (Recommended):

pip install notebooklm-py

From GitHub (use latest release tag, NOT main branch):

# Get the latest release tag (using curl)
LATEST_TAG=$(curl -s https://api.github.com/repos/teng-lin/notebooklm-py/releases/latest | grep '"tag_name"' | cut -d'"' -f4)
pip install "git+https://github.com/teng-lin/notebooklm-py@${LATEST_TAG}"

⚠️ DO NOT install from main branch (pip install git+https://github.com/teng-lin/notebooklm-py). The main branch may contain unreleased/unstable changes. Always use PyPI or a specific release tag, unless you are testing unreleased features.

After installation, install the Claude Code skill:

notebooklm skill install

Prerequisites

IMPORTANT: Before using any command, you MUST authenticate:

notebooklm login          # Opens browser for Google OAuth
notebooklm list           # Verify authentication works

If commands fail with authentication errors, re-run notebooklm login.

CI/CD, Multiple Accounts, and Parallel Agents

For automated environments, multiple accounts, or parallel agent workflows:

CI/CD setup: Set NOTEBOOKLM_AUTH_JSON from a secret containing your storage_state.json contents.

Multiple accounts: Use different NOTEBOOKLM_HOME directories per account.

Parallel agents: The CLI stores notebook context in a shared file (~/.notebooklm/context.json). Multiple concurrent agents using notebooklm use can overwrite each other's context.

Solutions for parallel workflows:

1. Always use explicit notebook ID (recommended): Pass -n <notebook_id> (for wait/download commands) or --notebook <notebook_id> (for others) instead of relying on use
2. Per-agent isolation: Set unique NOTEBOOKLM_HOME per agent: export NOTEBOOKLM_HOME=/tmp/agent-$ID
3. Use full UUIDs: Avoid partial IDs in automation (they can become ambiguous)

Agent Setup Verification

Before starting workflows, verify the CLI is ready:

1. notebooklm status → Should show "Authenticated as: email@..."
2. notebooklm list --json → Should return valid JSON (even if empty notebooks list)
3. If either fails → Run notebooklm login

When This Skill Activates

Explicit: User says "/notebooklm", "use notebooklm", or mentions the tool by name

Intent detection: Recognize requests like:

• "Create a podcast about [topic]"
• "Summarize these URLs/documents"
• "Generate a quiz from my research"
• "Turn this into an audio overview"
• "Create flashcards for studying"
• "Generate a video explainer"
• "Make an infographic"
• "Create a mind map of the concepts"
• "Download the quiz as markdown"
• "Add these sources to NotebookLM"

Autonomy Rules

Run automatically (no confirmation):

• notebooklm status - check context
• notebooklm auth check - diagnose auth issues
• notebooklm list - list notebooks
• notebooklm source list - list sources
• notebooklm artifact list - list artifacts
• notebooklm language list - list supported languages
• notebooklm language get - get current language
• notebooklm language set - set language (global setting)
• notebooklm artifact wait - wait for artifact completion (in subagent context)
• notebooklm source wait - wait for source processing (in subagent context)
• notebooklm research status - check research status
• notebooklm research wait - wait for research (in subagent context)
• notebooklm use <id> - set context (⚠️ SINGLE-AGENT ONLY - use -n flag in parallel workflows)
• notebooklm create - create notebook
• notebooklm ask "..." - chat queries (without --save-as-note)
• notebooklm history - display conversation history (read-only)
• notebooklm source add - add sources

Ask before running:

• notebooklm delete - destructive
• notebooklm generate * - long-running, may fail
• notebooklm download * - writes to filesystem
• notebooklm artifact wait - long-running (when in main conversation)
• notebooklm source wait - long-running (when in main conversation)
• notebooklm research wait - long-running (when in main conversation)
• notebooklm ask "..." --save-as-note - writes a note
• notebooklm history --save - writes a note

Quick Reference

Parallel safety: Use explicit notebook IDs in parallel workflows. Commands supporting -n shorthand: artifact wait, source wait, research wait/status, download *. Download commands also support -a/--artifact. Other commands use --notebook. For chat, use -c <conversation_id> to target a specific conversation.

Partial IDs: Use first 6+ characters of UUIDs. Must be unique prefix (fails if ambiguous). Works for ID-based commands such as use, source delete, and wait. For exact source-title deletion, use source delete-by-title "Title". For automation, prefer full UUIDs to avoid ambiguity.

Command Output Formats

Commands with --json return structured data for parsing:

Create notebook:

$ notebooklm create "Research" --json
{"id": "abc123de-...", "title": "Research"}

Add source:

$ notebooklm source add "https://example.com" --json
{"source_id": "def456...", "title": "Example", "status": "processing"}

Generate artifact:

$ notebooklm generate audio "Focus on key points" --json
{"task_id": "xyz789...", "status": "pending"}

Chat with references:

$ notebooklm ask "What is X?" --json
{"answer": "X is... [1] [2]", "conversation_id": "...", "turn_number": 1, "is_follow_up": false, "references": [{"source_id": "abc123...", "citation_number": 1, "cited_text": "Relevant passage from source..."}, {"source_id": "def456...", "citation_number": 2, "cited_text": "Another passage..."}]}

Source fulltext (get indexed content):

$ notebooklm source fulltext <source_id> --json
{"source_id": "...", "title": "...", "char_count": 12345, "content": "Full indexed text..."}

Understanding citations: The cited_text in references is often a snippet or section header, not the full quoted passage. The start_char/end_char positions reference NotebookLM's internal chunked index, not the raw fulltext. Use SourceFulltext.find_citation_context() to locate citations:

fulltext = await client.sources.get_fulltext(notebook_id, ref.source_id)
matches = fulltext.find_citation_context(ref.cited_text)  # Returns list[(context, position)]
if matches:
    context, pos = matches[0]  # First match; check len(matches) > 1 for duplicates

Extract IDs: Parse the id, source_id, or task_id field from JSON output.

Generation Types

All generate commands support:

• -s, --source to use specific source(s) instead of all sources
• --language to set output language (defaults to configured language or 'en')
• --json for machine-readable output (returns task_id and status)
• --retry N to automatically retry on rate limits with exponential backoff

Features Beyond the Web UI

These capabilities are available via CLI but not in NotebookLM's web interface:

Common Workflows

Research to Podcast (Interactive)

Time: 5-10 minutes total

1. notebooklm create "Research: [topic]" — if fails: check auth with notebooklm login
2. notebooklm source add for each URL/document — if one fails: log warning, continue with others
3. Wait for sources: notebooklm source list --json until all status=READY — required before generation
4. notebooklm generate audio "Focus on [specific angle]" (confirm when asked) — if rate limited: wait 5 min, retry once
5. Note the artifact ID returned
6. Check notebooklm artifact list later for status
7. notebooklm download audio ./podcast.mp3 when complete (confirm when asked)

Research to Podcast (Automated with Subagent)

Time: 5-10 minutes, but continues in background

When user wants full automation (generate and download when ready):

1. Create notebook and add sources as usual
2. Wait for sources to be ready (use source wait or check source list --json)
3. Run notebooklm generate audio "..." --json → parse artifact_id from output
4. Spawn a background agent using Task tool:

   Task(
     prompt="Wait for artifact {artifact_id} in notebook {notebook_id} to complete, then download.
             Use: notebooklm artifact wait {artifact_id} -n {notebook_id} --timeout 600
             Then: notebooklm download audio ./podcast.mp3 -a {artifact_id} -n {notebook_id}",
     subagent_type="general-purpose"
   )
   

5. Main conversation continues while agent waits

Error handling in subagent:

• If artifact wait returns exit code 2 (timeout): Report timeout, suggest checking artifact list
• If download fails: Check if artifact status is COMPLETED first

Benefits: Non-blocking, user can do other work, automatic download on completion

Document Analysis

Time: 1-2 minutes

1. notebooklm create "Analysis: [project]"
2. notebooklm source add ./doc.pdf (or URLs)
3. notebooklm ask "Summarize the key points"
4. notebooklm ask "What are the main arguments?"
5. Continue chatting as needed

Bulk Import

Time: Varies by source count

1. notebooklm create "Collection: [name]"
2. Add multiple sources:

   notebooklm source add "https://url1.com"
   notebooklm source add "https://url2.com"
   notebooklm source add ./local-file.pdf
   

3. notebooklm source list to verify

Source limits: Varies by plan—Standard: 50, Plus: 100, Pro: 300, Ultra: 600 sources per notebook. See NotebookLM plans for details. The CLI does not enforce these limits; they are applied by your NotebookLM account. Supported types: PDFs, YouTube URLs, web URLs, Google Docs, text files, Markdown, Word docs, audio files, video files, images

Bulk Import with Source Waiting (Subagent Pattern)

Time: Varies by source count

When adding multiple sources and needing to wait for processing before chat/generation:

1. Add sources with --json to capture IDs:

   notebooklm source add "https://url1.com" --json  # → {"source_id": "abc..."}
   notebooklm source add "https://url2.com" --json  # → {"source_id": "def..."}
   

2. Spawn a background agent to wait for all sources:

   Task(
     prompt="Wait for sources {source_ids} in notebook {notebook_id} to be ready.
             For each: notebooklm source wait {id} -n {notebook_id} --timeout 120
             Report when all ready or if any fail.",
     subagent_type="general-purpose"
   )
   

3. Main conversation continues while agent waits
4. Once sources are ready, proceed with chat or generation

Why wait for sources? Sources must be indexed before chat or generation. Takes 10-60 seconds per source.

Deep Web Research (Subagent Pattern)

Time: 2-5 minutes, runs in background

Deep research finds and analyzes web sources on a topic:

1. Create notebook: notebooklm create "Research: [topic]"
2. Start deep research (non-blocking):

   notebooklm source add-research "topic query" --mode deep --no-wait
   

3. Spawn a background agent to wait and import:

   Task(
     prompt="Wait for research in notebook {notebook_id} to complete and import sources.
             Use: notebooklm research wait -n {notebook_id} --import-all --timeout 300
             Report how many sources were imported.",
     subagent_type="general-purpose"
   )
   

4. Main conversation continues while agent waits
5. When agent completes, sources are imported automatically

Alternative (blocking): For simple cases, omit --no-wait:

notebooklm source add-research "topic" --mode deep --import-all
# Blocks for up to 5 minutes

When to use each mode:

• --mode fast: Specific topic, quick overview needed (5-10 sources, seconds)
• --mode deep: Broad topic, comprehensive analysis needed (20+ sources, 2-5 min)

Research sources:

• --from web: Search the web (default)
• --from drive: Search Google Drive

Output Style

Progress updates: Brief status for each step

• "Creating notebook 'Research: AI'..."
• "Adding source: https://example.com..."
• "Starting audio generation... (task ID: abc123)"

Fire-and-forget for long operations:

• Start generation, return artifact ID immediately
• Do NOT poll or wait in main conversation - generation takes 5-45 minutes (see timing table)
• User checks status manually, OR use subagent with artifact wait

JSON output: Use --json flag for machine-readable output:

notebooklm list --json
notebooklm auth check --json
notebooklm source list --json
notebooklm artifact list --json

JSON schemas (key fields):

notebooklm list --json:

{"notebooks": [{"id": "...", "title": "...", "created_at": "..."}]}

notebooklm auth check --json:

{"checks": {"storage_exists": true, "json_valid": true, "cookies_present": true, "sid_cookie": true, "token_fetch": true}, "details": {"storage_path": "...", "auth_source": "file", "cookies_found": ["SID", "HSID", "..."], "cookie_domains": [".google.com"]}}

notebooklm source list --json:

{"sources": [{"id": "...", "title": "...", "status": "ready|processing|error"}]}

notebooklm artifact list --json:

{"artifacts": [{"id": "...", "title": "...", "type": "Audio Overview", "status": "in_progress|pending|completed|unknown"}]}

Status values:

• Sources: processing → ready (or error)
• Artifacts: pending or in_progress → completed (or unknown)

Error Handling

On failure, offer the user a choice:

1. Retry the operation
2. Skip and continue with something else
3. Investigate the error

Error decision tree:

Exit Codes

All commands use consistent exit codes:

Examples:

• source wait returns 1 if source not found or processing failed
• artifact wait returns 2 if timeout reached before completion
• generate returns 1 if rate limited (check stderr for details)

Known Limitations

Rate limiting: Audio, video, quiz, flashcards, infographic, and slide deck generation may fail due to Google's rate limits. This is an API limitation, not a bug.

Reliable operations: These always work:

• Notebooks (list, create, delete, rename)
• Sources (add, list, delete)
• Chat/queries
• Mind-map, study-guide, report, data-table generation

Unreliable operations: These may fail with rate limiting:

• Audio (podcast) generation
• Video generation
• Quiz and flashcard generation
• Infographic and slide deck generation

Workaround: If generation fails:

1. Check status: notebooklm artifact list
2. Retry after 5-10 minutes
3. Use the NotebookLM web UI as fallback

Processing times vary significantly. Use the subagent pattern for long operations:

Polling intervals: When checking status manually, poll every 15-30 seconds to avoid excessive API calls.

Language Configuration

Language setting controls the output language for generated artifacts (audio, video, etc.).

Important: Language is a GLOBAL setting that affects all notebooks in your account.

# List all 80+ supported languages with native names
notebooklm language list

# Show current language setting
notebooklm language get

# Set language for artifact generation
notebooklm language set zh_Hans  # Simplified Chinese
notebooklm language set ja       # Japanese
notebooklm language set en       # English (default)

Common language codes:

Override per command: Use --language flag on generate commands:

notebooklm generate audio --language ja   # Japanese podcast
notebooklm generate video --language zh_Hans  # Chinese video

Offline mode: Use --local flag to skip server sync:

notebooklm language set zh_Hans --local  # Save locally only
notebooklm language get --local  # Read local config only

Troubleshooting

notebooklm --help              # Main commands
notebooklm auth check          # Diagnose auth issues
notebooklm auth check --test   # Full auth validation with network test
notebooklm notebook --help     # Notebook management
notebooklm source --help       # Source management
notebooklm research --help     # Research status/wait
notebooklm generate --help     # Content generation
notebooklm artifact --help     # Artifact management
notebooklm download --help     # Download content
notebooklm language --help     # Language settings

Diagnose auth: notebooklm auth check - shows cookie domains, storage path, validation status Re-authenticate: notebooklm login Check version: notebooklm --version Update skill: notebooklm skill install

Source: wx-b/notebooklm-py — distributed by TomeVault.