Streamlit is an open-source Python framework for turning scripts into interactive web apps, aimed at data scientists, ML engineers, and analysts who need dashboards, reports, and chat interfaces without building front-end code.

What it is

Streamlit is a Python library, released under the Apache-2.0 licence, that lets you transform Python scripts into interactive web apps in minutes rather than weeks. You write ordinary Python — importing it as st and calling elements such as st.slider and st.write — and run the file with a single command; the framework renders it as a browser-based application with input widgets, dataframes, charts, layout controls, and multi-page apps. Live editing means the app updates instantly as you edit your script, so iteration happens in the edit-run loop rather than through a separate front-end build cycle.

The concrete problem it addresses is the gap between analysis code and something other people can actually use. Data work typically lives in notebooks or static scripts that stakeholders cannot interact with, and building a conventional web front end for it takes weeks. Streamlit closes that gap for dashboards, generated reports, and chat applications, including LLM and chatbot interfaces, and the accompanying Community Cloud platform lets you deploy, manage, and share the resulting app rather than hand-maintaining hosting yourself.

Key capabilities

• Converts a plain Python file, such as streamlit_app.py, into a running web application with streamlit run streamlit_app.py.
• Provides a widget and display API — input widgets, dataframes, charts, layout primitives, and multi-page apps — documented in the API reference at docs.streamlit.io.
• Delivers live editing, so the app refreshes instantly in the browser as the script is edited during prototyping.
• Ships a built-in example, the Streamlit Hello app, launched with streamlit hello, which demonstrates what the framework can do.
• Extends beyond the core library through Streamlit Components, including custom components (components-v2), and the community streamlit-extras repository.
• Offers Community Cloud for deploying, managing, and sharing apps for free, with sign-up at share.streamlit.io/signup.
• Supplies a GitHub badge in Markdown form ([![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](URL_TO_YOUR_APP)) to link a repository to a hosted app.

Who uses it and how

• Data scientists and ML practitioners prototype dashboards quickly and let others interact with the data to provide feedback early.
• Developers building LLM and chatbot apps, NLP and language apps, and other AI interfaces on top of Python model code.
• Analysts in finance and business producing reports and dashboards, as reflected in the gallery's finance-business category.
• Creators working on science and technology, or geography and society, applications who publish them to Community Cloud and share links via the GitHub badge.
• Contributors who cannot submit pull requests still participate by reporting bugs, requesting features, upvoting existing issues, commenting on open specs, and creating custom components.

Getting started

Install with pip install streamlit and verify the setup by running streamlit hello; then create a streamlit_app.py file and start it with streamlit run streamlit_app.py. Hosted deployment is available by signing up for Community Cloud at share.streamlit.io/signup.

How it compares

No list of paid products this project replaces is provided in the available facts, and no other specific tools are named for comparison, so Streamlit stands alone in this registry.

When to use it — and when not to

It suits Python-flavoured data and AI work where a script-based workflow and rapid interactive output matter, but the project's own documentation should temper expectations about contributing: pull requests from outside the Streamlit maintainer team are currently paused, so third-party code contributions go through issues, specs, and components instead. With 1,200 open issues, the backlog is substantial, and anyone evaluating it should check the documentation at docs.streamlit.io for current behaviour before committing to it for production use.

$ pip install streamlit
$ streamlit hello

import streamlit as st

x = st.slider("Select a value")
st.write(x, "squared is", x * x)

$ streamlit run streamlit_app.py

[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](URL_TO_YOUR_APP)