Anil-matcha/awesome-generative-ai-apps

50+ open-source generative AI apps you can clone, deploy, and monetize — image generators, video tools, virtual try-ons, AI SaaS templates, and platform...

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Summary Information

Updated 15 minutes ago
Added to GitGenius on September 21st, 2026
Created on November 10th, 2023
Open Issues & Pull Requests: 14 (+0)
GitHub issues: Enabled
Number of forks: 484
Total Stargazers: 3,316 (+0)
Total Subscribers: 44 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 494.9 days
Mean response time: 463.5 days
90th percentile: 806.0 days
Tracked items: 28

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Issue Activity (beta)

Open issues: 6
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 21 days
Stale 30+ days: 2
Stale 90+ days: 0

Recent activity

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

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Detailed Description

Awesome Generative AI Apps is a curated collection of open-source generative AI applications that developers can clone, deploy, and monetize.

The collection addresses the challenge of building production-ready AI products from scratch by providing complete, working SaaS applications across multiple categories including image generation, video tools, virtual try-ons, and writing assistants. Each application comes fully configured with authentication, billing integration via Stripe, and Google OAuth, eliminating the need to wire up these infrastructure components manually. The apps are built primarily with JavaScript and Next.js, enabling one-click deployment to Vercel.

Developers should choose this collection if they want to launch an AI-powered SaaS product quickly without building core infrastructure from the ground up. It suits entrepreneurs and developers who want to rebrand and sell existing templates under their own name while retaining full revenue. The collection spans diverse use cases—from beauty and fashion AI tools to e-commerce product photography and content writing—so teams can select templates matching their target market. This approach works best for those comfortable customizing and deploying existing codebases rather than building entirely custom solutions.

The project maintains an organized, categorized structure across multiple AI application domains, with each template ready for immediate deployment. The codebase is actively maintained with recent commits and includes comprehensive documentation for getting started with individual applications.