Streamlit Components

Most Streamlit Components are distributed as Python packages on PyPI and installed via pip—for example, 'pip install streamlit-aggrid' or 'pip install streamlit-folium'. After installation, you import and call the component's function within your Streamlit app script. Some components may require a frontend build step if you are creating your own custom component from scratch.

Some of the most widely used community components include streamlit-aggrid (interactive data grids with filtering and sorting), streamlit-folium (interactive maps via Folium/Leaflet), streamlit-plotly-events (capturing click and selection events on Plotly charts), and streamlit-option-menu (custom navigation). The official Streamlit Components Gallery on their website and GitHub community tracks hundreds of community-built components.

Yes, Streamlit provides a official component template repository on GitHub that scaffolds a React frontend with a Python wrapper, allowing you to build custom interactive UI elements like specialized charts, maps, or form controls. The frontend communicates with the Streamlit Python backend through a defined data-passing API. The Streamlit documentation includes a step-by-step tutorial for building your first custom component.

Streamlit Components are designed for quick embedding of pre-built or custom UI elements with minimal boilerplate, while Plotly Dash uses explicit callback functions to wire interactivity, offering more precise control over reactivity and state management. Streamlit's top-to-bottom execution model is simpler for prototyping, whereas Dash's component-callback architecture scales better for complex, multi-page production applications. Dash has a steeper learning curve but more mature deployment tooling.