Key Features From Streamlit
AI-native development experience—write logic, not UI boilerplate
Live-reload mode: see changes in real time as you code
Drag-and-drop interactivity: sliders, buttons, file uploaders, and more—built-in
Smart caching (@st.cache_data, @st.cache_resource) for lightning-fast re-runs
Native compatibility with pandas, NumPy, scikit-learn, PyTorch, LlamaIndex, LangChain, and beyond
One-click sharing: deploy public or private apps directly from the CLI or Streamlit Community Cloud
Streamlit's Use Cases
Rapid prototyping of ML models and AI agents with live input/output feedback
Internal dashboards for analytics, reporting, and stakeholder demos
Educational tools and interactive tutorials for data literacy
Production-ready internal tools—from model monitoring to data annotation interfaces