

PageLm
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PageLM is a community driven version of NotebookLM & a education platform that transforms study materials into interactive resources like quizzes, flashcards, notes, and podcasts.
Cost / License
- Free
- Open Source
Platforms
- Mac
- Windows
- Linux
- Node.JS
- Docker


PageLm
2 likes
Features
- AI-Powered
Tags
- educational-tool
PageLm News & Activities
Highlights All activities
Recent activities
- justarandom added PageLm as alternative to Open Notebook
- eliasbuenosdias added PageLm
- POX updated PageLm
PageLm information
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What is PageLm?
An open source AI powered education platform that transforms study materials into interactive learning experiences, slightly inspired by NotebookLM.
Features:
PageLM converts study material into interactive resources including quizzes, flashcards, structured notes, and podcasts. The platform provides a modern interface for students, educators, and researchers to enhance learning efficiency using state-of-the-art LLMs and TTS systems.
Learning Tools
- Contextual Chat – Ask questions about uploaded documents (PDF, DOCX, Markdown, TXT)
- SmartNotes – Generate Cornell-style notes automatically from topics or uploaded content
- Flashcards – Extract non-overlapping flashcards for spaced repetition
- Quizzes – Create interactive quizzes with hints, explanations, and scoring
- AI Podcast – Convert notes and topics into engaging audio content for learning on the go
- Voice Transcribe - Convert lecture recordings and voice notes into organized, searchable study materials instantly.
- Homework Planner - Plans your Homework Smartly using AI, Assists if your stuck.
- ExamLab - Simulate any exam, get feedback, and be prepared for the exam
- Debate - Debate with AI to improve your Debate skills.
- Study Companion - A personalised AI Companion that assists you.
Supported AI Models
- Google Gemini • OpenAI GPT • Anthropic Claude • xAI Grok • Ollama (local) • OpenRouter
Embedding Providers
- OpenAI • Gemini • Ollama
Technical Highlights
- WebSocket streaming for real-time chat, notes, and podcast generation
- JSON or vector database support for embeddings and retrieval
- File-based persistent storage for generated content
- Markdown-based outputs for structured answers and notes
- Configurable multi-provider setup for LLMs and TTS engines
