RayVentura/ShortGPT

🚀🎬 ShortGPT - Experimental AI framework for youtube shorts / tiktok channel automation

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Summary Information

Updated 47 minutes ago
Added to GitGenius on October 2nd, 2026
Created on June 27th, 2023
Open Issues & Pull Requests: 86 (+0)
GitHub issues: Enabled
Number of forks: 1,159
Total Stargazers: 8,010 (+0)
Total Subscribers: 83 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 49.9 days
Mean response time: 371.7 days
90th percentile: 1042.7 days
Tracked items: 78

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Issue Activity (beta)

Open issues: 75
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 966 days
Stale 30+ days: 75
Stale 90+ days: 74

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (29)
  • question (25)

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Detailed Description

ShortGPT is an AI framework for automating the creation of short-form video content for platforms like YouTube Shorts and TikTok.

The tool addresses the labor-intensive process of producing short videos by automating multiple stages of content creation. It uses large language models to drive an editing framework that handles script generation, voiceover production, caption generation, and asset sourcing. The system connects to external APIs like Pexels for video and image retrieval, supports text-to-speech across more than thirty languages, and persists editing state using TinyDB to maintain consistency across automated workflows.

ShortGPT suits creators and teams looking to scale short-form content production without manual editing work. It works best for projects where you can define content patterns through prompts and scripts, since the automation relies on LLM-driven editing instructions rather than traditional video editing interfaces. The tool is positioned as experimental, so adopters should expect an evolving codebase rather than a stable, production-hardened system. The project offers a Google Colab notebook as an entry point, removing the need for local installation to test the framework.

Development activity shows consistent engagement with the codebase through regular updates and maintenance. The project maintains active community channels and documentation resources to support users. Pull requests are reviewed and merged to incorporate improvements and fixes. The maintainers respond to issues and incorporate feedback from the user base into the tool's direction.