The AI frameworks that are actually being used in production
Over 1 million GitHub stars across these 10 repositories. This isn't hype it's where developers are spending their time.
TensorFlow - 193K stars Still the enterprise standard for production ML systems https://lnkd.in/enxfpswQ
Stable Diffusion WebUI - 160K stars The framework that made generative AI accessible to non-engineers
https://lnkd.in/eW88izr8
Hugging Face Transformers - 155K stars Every LLM implementation starts here https://lnkd.in/eBGGcCCC
LangChain - 125K stars The fastest-growing framework for AI application development https://lnkd.in/edEa4rum
PyTorch - 96K stars Research teams choose this. Production teams keep it. https://lnkd.in/eJFGr-qv
Scikit-Learn - 64K stars Classical ML still powers most real-world predictions https://lnkd.in/eXFcD7Xd
Keras - 63K stars Neural networks without the complexity https://lnkd.in/eNHSZrjB
Awesome Machine Learning - 54K stars The resource list every ML engineer bookmarks https://lnkd.in/eTaYutmf
TensorFlow Tutorials - 41K stars How Google teaches TensorFlow https://lnkd.in/e2VjHAQr
ML for Beginners - 36K stars Microsoft's curriculum for learning ML fundamentals https://lnkd.in/eykzYk6R
What this data tells us:
Generative AI frameworks are now competing head-to-head with decade-old ML giants. LangChain grew from zero to 125K stars in under 2 years. The shift from research to production-ready tools is accelerating.
The question for 2026: Which of these frameworks will your team be using?
Drop a comment with your current stack. I'm curious what's working in production right now.
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