# Techwavedev Agi Agent Kit Notebooklm RAG > NotebookLM Deep RAG - Skill: `tomevault-io/techwavedev-agi-agent-kit-notebooklm-rag` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/techwavedev-agi-agent-kit-notebooklm-rag` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/techwavedev-agi-agent-kit-notebooklm-rag/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/techwavedev-agi-agent-kit-notebooklm-rag --- # NotebookLM Deep RAG > **This is a Deep RAG tool.** The agent uses NotebookLM as an autonomous knowledge backend via MCP tools. It handles auth, notebooks, queries, follow-ups, and caching — fully hands-free. > > **Opt-in:** Requires a Google account with NotebookLM. Default RAG uses `qdrant-memory` (local, offline, no account needed). ## Architecture ``` User question ↓ Agent checks Qdrant cache → hit? → return cached answer (0 cost) ↓ miss Agent checks NotebookLM auth → not authenticated? → setup_auth (opens browser) ↓ authenticated Agent resolves notebook → list_notebooks / search_notebooks / select_notebook ↓ Agent asks question → ask_question (browser automation, Gemini-grounded answer) ↓ Agent evaluates answer → gaps? → ask follow-up automatically ↓ complete Agent stores in Qdrant → cache for future use ↓ Agent responds to user with synthesized answer ``` ## Quick Start > [!IMPORTANT] > **Step 1: MCP Server Required.** The NotebookLM MCP server must be configured in your AI host. It is bundled with many setups, but verify it's running. ### 1. Check if MCP is configured The agent should call `get_health`. If the tool exists, the MCP server is active. - ✅ `status: "ok"` → MCP is running - ❌ Tool not found → Add the MCP server to your host config (see [MCP Server Setup](#mcp-server-setup)) ### 2. Authenticate (one-time) ``` Agent calls: get_health If authenticated: false → Agent calls: setup_auth (opens a browser window) User logs into Google account Agent calls: get_health to verify → authenticated: true ✅ ``` > [!TIP] > Auth is saved to disk. You only need to log in once. If it expires, the agent will detect it and propose `re_auth`. ### 3. Add a Notebook ``` User: "Here is my NotebookLM: https://notebooklm.google.com/notebook/..." Agent calls: ask_question(notebook_url=URL, question="What is the content? What topics?") Agent uses answer to fill: name, description, topics Agent calls: add_notebook(url, name, description, topics) ``` ### 4. Query ``` User: "Research [topic] from my notebook" Agent calls: ask_question(notebook_id="my-notebook", question="...") ``` That's it. The agent handles everything else autonomously. ## MCP Tools Reference The agent has direct access to these tools. Use them autonomously. ### Authentication | Tool | When | | ------------ | --------------------------------------------- | | `get_health` | First — always check auth status | | `setup_auth` | One-time Google login (opens visible browser) | | `re_auth` | Switch account or fix expired session | ### Library Management | Tool | When | | ------------------- | ----------------------------------------------------------------- | | `list_notebooks` | See all registered notebooks | | `add_notebook` | Register a new notebook (url, name, description, topics required) | | `remove_notebook` | Remove a notebook from library | | `update_notebook` | Update notebook metadata | | `search_notebooks` | Find notebooks by topic/keyword | | `select_notebook` | Set active notebook (used as default for queries) | | `get_notebook` | Get details of a specific notebook | | `get_library_stats` | Library overview | ### Querying | Tool | When | | --------------- | ------------------------------------- | | `ask_question` | Query a notebook — core research tool | | `list_sessions` | Check active browser sessions | | `close_session` | Close a session when done | | `reset_session` | Clear session history | ### Maintenance | Tool | When | | -------------- | ------------------------------ | | `cleanup_data` | Clean browser data, fix issues | ## Autonomous Workflow ### Auth Gate (Mandatory First Step) > [!CAUTION] > **ALWAYS check auth before any NotebookLM operation.** If `authenticated: false`, propose `setup_auth` to the user before proceeding. Never silently fail. ``` get_health → authenticated? → true: proceed to step 1 → false: tell user "NotebookLM needs authentication. A browser will open for Google login." → setup_auth → get_health → verify authenticated: true → if still false: propose cleanup_data(preserve_library=true) + setup_auth ``` ### On Any Research Request: 1. **Check Qdrant first** — `memory_manager.py auto --query "..."`. If cache hit, return immediately. 2. **Auth gate** — `get_health`. If not authenticated, run `setup_auth` and tell user a browser will open. **Do not proceed without auth.** 3. **Resolve notebook** — `list_notebooks`. If user mentions a topic, `search_notebooks`. If no notebooks exist, ask user for a NotebookLM URL and `add_notebook`. 4. **Ask the question** — `ask_question` with the resolved notebook. Can pass `notebook_id` (from library) or `notebook_url` (direct URL). 5. **Follow up** — Every answer ends with "Is that ALL you need to know?" The agent MUST: - Compare answer to original request - Identify gaps - Ask follow-up questions automatically (include full context — each question is a new browser session) - Repeat until information is complete 6. **Cache in Qdrant** — Store result with `memory_manager.py store` and `cache-store`. 7. **Respond** — Synthesize all answers into a cohesive response. ### On "Add a notebook": **Smart Add** — If user provides a URL but no details: 1. `ask_question` with `notebook_url` and question: "What is the content of this notebook? What topics are covered? Provide a brief overview." 2. Use the answer to fill in `name`, `description`, `topics` 3. `add_notebook` with discovered metadata **Manual Add** — If user provides all details directly, just `add_notebook`. ### On Notebook Management: - "List my notebooks" → `list_notebooks` - "Remove X" → Confirm with user → `remove_notebook` - "Switch to X" → `select_notebook` - "Update X description" → `update_notebook` - "Search for X" → `search_notebooks` ## Qdrant Integration (Context Keeping) NotebookLM answers are cached in Qdrant. Prior research is recalled automatically. ### Before Query ```bash python3 execution/memory_manager.py auto --query "" ``` - `cache_hit: true` → Skip browser, return cached answer - `source: memory` → Inject prior context into the question - `source: none` → Proceed with NotebookLM query ### After Query ```bash python3 execution/memory_manager.py store \ --content "Q: [question] A: [answer]" \ --type technical \ --project notebooklm-research \ --tags notebooklm [notebook-name] [topic] python3 execution/memory_manager.py cache-store \ --query "[question]" \ --response "[synthesized answer]" ``` ### Context Keeping - Prior research on same topic is auto-recalled - Previous findings enrich follow-up questions - Each session builds compound knowledge ## MCP Server Setup The NotebookLM MCP server must be configured in the AI host: ### Claude Desktop / Claude Code ```json { "mcpServers": { "notebooklm": { "command": "npx", "args": ["-y", "@anthropic/notebooklm-mcp"] } } } ``` ### Opencode ```json { "mcpServers": { "notebooklm": { "command": "npx", "args": ["-y", "@anthropic/notebooklm-mcp"] } } } ``` If MCP is not configured, fall back to the Python scripts in `scripts/` (see Fallback section below). ## Fallback: Python Scripts When MCP is not available, use the bundled scripts via `run.py`: ```bash python scripts/run.py auth_manager.py status # Check auth python scripts/run.py auth_manager.py setup # Authenticate python scripts/run.py notebook_manager.py list # List notebooks python scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS python scripts/run.py ask_question.py --question "..." # Query python scripts/run.py ask_question.py --question "..." --notebook-url "https://..." python scripts/run.py cleanup_manager.py --confirm # Cleanup ``` The `run.py` wrapper auto-creates `.venv`, installs dependencies (patchright, python-dotenv), and installs Chrome. ## Troubleshooting | Problem | Solution | | ------------------------ | ------------------------------------------------------- | | Not authenticated | `setup_auth` (browser opens for Google login) | | Rate limit (50/day free) | Wait 24h or `re_auth` with different Google account | | Browser crashes | `cleanup_data(preserve_library=true)` then `setup_auth` | | Stale cached answer | Re-query or clear Qdrant cache | | Notebook not found | `list_notebooks`, then `add_notebook` if missing | | MCP not available | Use fallback Python scripts via `run.py` | ## Limitations - Rate limits: 50 queries/day (free), 250/day (Google AI Pro) - Manual upload: User must add documents to NotebookLM first - Browser overhead: Few seconds per query - No live notebook discovery: User must provide URLs to register notebooks ## Credits MCP server: [PleasePrompto/notebooklm-mcp](https://github.com/PleasePrompto/notebooklm-mcp) Browser automation: [PleasePrompto/notebooklm-skill](https://github.com/PleasePrompto/notebooklm-skill) (MIT License) Adapted for the Agi Agent Framework with Qdrant memory integration. ## AGI Framework Integration ### Qdrant Memory Integration Before executing complex tasks with this skill: ```bash python3 execution/memory_manager.py auto --query "" ``` **Decision Tree:** - **Cache hit?** Use cached response directly — no need to re-process. - **Memory match?** Inject `context_chunks` into your reasoning. - **No match?** Proceed normally, then store results: ```bash python3 execution/memory_manager.py store \ --content "Description of what was decided/solved" \ --type decision \ --tags notebooklm-rag ``` > **Note:** Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices. ### Agent Team Collaboration - **Strategy**: This skill communicates via the shared memory system. - **Orchestration**: Invoked by `orchestrator` via intelligent routing. - **Context Sharing**: Always read previous agent outputs from memory before starting. ### Local LLM Support When available, use local Ollama models for embedding and lightweight inference: - Embeddings: `nomic-embed-text` via Qdrant memory system - Lightweight analysis: Local models reduce API costs for repetitive patterns --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/techwavedev) — claim your Tome and manage your conversions.