Teknolohiya

May Skill na ang NotebookLM — 17.2K-star open source para sa research automation

May-akda: NotebookLM.link Editorial Na-publish 2026-07-10

Ginagawang CLI at Python API ng notebooklm-py ang NotebookLM: batch import/export, Claude Code Skill at MCP Server integration, at buong automation ng research workflow.

Nag-a-upload pa rin ng docs isa-isa at nagda-download gamit ang click sa web? Ginagawang CLI ng notebooklm-py ang NotebookLM: batch import/export at AI Agent integration, fully automated.

Useful talaga ang NotebookLM: PDF, podcast, literature, slides. Pero manual lang ang web version — awkward reality pag matagal na.

Batch import? Isa-isa lang. I-download ang results? Click per click. I-integrate sa Claude Code o Codex? Walang ready path. I-automate ang research? Hindi supported ng web.

Diyan pumapasok ang notebooklm-py.

Infographic: manual NotebookLM web vs automated notebooklm-py CLI workflow — Web: click per click. CLI: isang command para sa buong pipeline

1. Ano ang notebooklm-py?

Ang notebooklm-py ay unofficial Python API at Skill layer para sa NotebookLM. Binalot ang lahat ng capabilities sa API at CLI — mula terminal o AI Agents.

Lampas na sa 17,200 stars ang open-source project sa GitHub. Para sa researchers at creators: hindi na file-per-file clicking.

Terminal na nag-i-import ng URL, PDF, YouTube at gumagawa ng podcast gamit ang notebooklm-py — Isang command: import, analyze, generate podcast

2. Apat na core capabilities

Buong NotebookLM coverage — Notebooks, Sources, Chat, Notes, Research, Sharing: may API at CLI sa bawat stage. URL, YouTube, PDF, Google Drive at dose+ sources, batch-ready.

AI Agent integration — May Claude Code Skill, Codex, AGENTS.md. MCP Server para sa Claude Desktop, Cursor, at Windsurf.

Malakas na content generation — Audio Overview: 4 formats, 3 lengths, 50+ languages. Video Overview: 3 formats, 9 visual styles. Slides, infographics, quiz, flashcards, reports, mind maps, at data tables.

Wala sa web — Batch download ng artifacts, export quiz/flashcards sa JSON/Markdown/HTML, mind map JSON, slide revision sa natural language, multi Google account profiles.

3. Tunay na halimbawa: isang command para sa research + podcast

I-compare ang LangChain, AutoGen, at CrewAI gamit ang tatlong official docs, dalawang tech blog PDF, at isang YouTube video. Walang oras magbasa? Gumawa ng podcast.

Isang command sa terminal:

Hakbang 1: Gumawa ng notebook at i-import ang 6 sources (3 URL, 2 PDF, 1 YouTube)
Hakbang 2: I-analyze ang sources at i-extract ang key points
Hakbang 3: Comparison na may citations — LangChain chaining, AutoGen multi-agent, CrewAI task orchestration
Hakbang 4: Sampung minutong podcast ready na pakinggan

AI podcast player na may cover, waveform, at controls — Auto-generated podcast: cover, wave, at playback ready

4. Installation sa 2 minuto

CLI at agents: uv tool. Python API: uv add. Agent integration: skill install:

uv tool install notebooklm-py[browser]
notebooklm login
otebooklm auth check --test --json

Repo: github.com/teng-lin/notebooklm-py

Bottom line

Malakas na ang NotebookLM, pero nililimitahan ng manual web ang potential. Tinatapos ng notebooklm-py ang huling piyesa: batch, agents, research automation.

Sobrang daming sources, mabagal mag-organize, sobrang daming clicks — worth subukan ang 2 minutong install.

Magsimula: github.com/teng-lin/notebooklm-py

Bumalik sa blog Gamitin ang NotebookLM