open-notebooklm vs podcastfy

PDF-to-podcast tools compared

open-notebooklm turns PDFs into podcast dialogue and MP3 audio through a Gradio application. podcastfy also creates conversational audio, but accepts more source types and offers package and CLI workflows for customization and automation.

open-notebooklmpodcastfy
LanguagePythonPython
LicenseApache-2.0Apache-2.0
Stars2.6k6.6k
Forks282765
Last analyzedOct 3, 2026Oct 8, 2026

Key differences

  • open-notebooklm focuses on PDFs, while podcastfy accepts websites, PDFs, images, YouTube videos, text, and topics.
  • open-notebooklm uses Llama 3.3 70B through Fireworks AI, while podcastfy supports hosted or local LLMs for transcript generation.
  • open-notebooklm uses MeloTTS and Bark; podcastfy integrates providers including OpenAI, Google, ElevenLabs, and Microsoft Edge.
  • open-notebooklm provides a Gradio interface, while podcastfy provides Python package and CLI interfaces for scripting and automation.
  • open-notebooklm is aimed at listening to PDF overviews, while podcastfy also targets developers and content teams building customized or automated workflows.
  • podcastfy lists Python 3.11 or higher and FFmpeg as prerequisites, and marks its FastAPI interface for URLs as beta.

Choose open-notebooklm if you…

  • want a Gradio app focused on turning PDFs into dialogue and MP3 audio.
  • prefer its specified Fireworks, MeloTTS, and Bark setup.
  • want an audio-style overview of a document for listening away from a screen.
Read the open-notebooklm analysis →

Choose podcastfy if you…

  • need to generate podcasts from sources beyond PDFs, including websites, images, and YouTube videos.
  • want Python package or CLI workflows for scripting and automation.
  • need to customize conversation style, transcript structure, language, or voices, or use local LLMs for transcript generation.
Read the podcastfy analysis →

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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.