Podcastfy: Transform Multimodal Content into AI-Generated Multilingual Podcasts
This repository profile is provided by osrepos.com, an open source repository discovery platform.

Summary
Podcastfy is an open-source Python package that transforms diverse multimodal content, such as text, images, and videos, into engaging multilingual audio conversations. Utilizing generative AI, it offers a flexible and programmatic alternative to tools like NotebookLM, focusing on customization and scalability. This makes it an excellent solution for content creators, educators, and researchers aiming to broaden their audience reach and improve content accessibility.
Repository Information
Topics
Click on any tag to explore related repositories
Use at your own risk
OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.
Introduction
Podcastfy is an innovative open-source Python package that transforms diverse multimodal content into captivating multilingual audio conversations using Generative AI. Positioned as a flexible alternative to tools like NotebookLM, Podcastfy emphasizes programmatic control, extensive customization, and scalability for generating engaging audio content. It can process a wide array of input sources, including websites, PDFs, images, YouTube videos, and user-provided topics, generating both short (2-5 minutes) and longform (30+ minutes) podcasts.
Installation
Getting started with Podcastfy is straightforward.
Prerequisites
Ensure you have Python 3.11 or higher installed. You will also need ffmpeg for audio processing, which can typically be installed via pip:
pip install ffmpeg
Setup
- Install from PyPI:
pip install podcastfy - Set up your API keys:
Refer to the official documentation for detailed instructions on configuring your API keys for various services.
Examples
Podcastfy offers both Python and CLI interfaces for generating podcasts.
Python
from podcastfy.client import generate_podcast
audio_file = generate_podcast(urls=["<url1>", "<url2>"])
CLI
python -m podcastfy.client --url <url1> --url <url2>
Podcastfy supports generating audio from images, text, and even multi-lingual content, providing versatile options for your content creation needs.
Why Use Podcastfy?
- Versatile Content Input: Convert content from websites, PDFs, images, YouTube videos, and custom topics into engaging audio.
- AI-Powered Conversations: Leverage advanced Generative AI models to create natural and dynamic podcast-style audio.
- Multilingual Support: Reach a global audience by generating podcasts in multiple languages.
- Extensive Customization: Tailor every aspect of your podcast, including conversation format, style, voices, and even integrate local LLMs for enhanced privacy and control.
- Enhanced Accessibility: Transform written and visual content into auditory formats, making information more accessible to individuals with visual impairments or those who prefer listening.
- Open Source and Community-Driven: Benefit from a transparent, flexible, and community-supported platform that encourages contributions and continuous improvement.
Links
- GitHub Repository: https://github.com/souzatharsis/podcastfy
- PyPI Package: https://pypi.org/project/podcastfy/
- Web App (OpenPod): https://openpod.fly.dev/
- Documentation: https://podcastfy.readthedocs.io/en/latest/
- Colab Notebook: https://colab.research.google.com/github/souzatharsis/podcastfy/blob/main/podcastfy.ipynb
Related repositories
Similar repositories that may be relevant next.

A-MEM: Self-Evolving Memory for Coding Agents
August 17, 2026
A-MEM is an innovative self-evolving memory system designed for coding agents, organizing knowledge into a dynamic Zettelkasten-style graph. It allows memories to evolve and connect over time, enhancing an agent's ability to recall and utilize information effectively. This system offers both semantic and structural search capabilities for a richer knowledge base.

Agent Sandbox: Secure Local Development for AI Coding Agents
August 17, 2026
Agent Sandbox provides a robust and secure local development environment specifically designed for collaborating with AI coding agents. It ensures minimal filesystem access, configurable network egress policies, and secure secret injection, protecting your local machine from potentially risky agent operations. This project supports various AI agents and integrates seamlessly with both CLI and popular IDE devcontainer setups.

AMD Skills: Empowering AI Agents with AMD's Optimized Software Stack
August 16, 2026
AMD Skills is the official catalog of AI agent skills from AMD, designed to empower AI agents with optimized software for AMD hardware. This repository provides knowledge, scripts, and conventions for working with AMD's stack, enabling seamless integration with major coding agents like Cursor, Claude Code, OpenAI Codex, and Gemini CLI.

agent-tackle-box: A Terminal Debugger for LangGraph & LangChain Agents
August 15, 2026
agent-tackle-box is a comprehensive toolkit for developing AI agents, featuring the powerful `agent-debugger`. This terminal debugger provides deep insights into LangGraph and LangChain agents. It allows developers to inspect state, monitor tool calls, and step through Python code, all within a unified Textual UI.
Source repository
Open the original repository on GitHub.
18 counted GitHub visits