A comparison of seven major Python AI frameworks for 2026: TensorFlow, PyTorch, Keras, scikit-learn, XGBoost, LangChain, and Hugging Face. Each framework is evaluated by strengths, weaknesses, and ideal use cases. Deep learning frameworks (TensorFlow, PyTorch, Keras) suit neural network tasks; classical ML frameworks (scikit-learn, XGBoost) excel on tabular data; LLM/agent frameworks (LangChain, Hugging Face) power language model applications. A decision table helps match framework choice to data type, abstraction level, deployment target, and project objective. Many teams combine multiple frameworks in production.
