Streamlit is an open-source Python framework that transforms Python scripts into interactive web applications. It is widely used for building AI and machine learning applications, offering chat UI components, session state management, and seamless LLM integration without requiring frontend expertise.¹⁾

Streamlit scripts execute sequentially like plain Python, but widgets trigger automatic reruns, making apps reactive.²⁾ Key characteristics:

• Python-only – no HTML, CSS, or JavaScript required
• Reactive execution – full script reruns on user interactions
• Live reload – instant previews on code changes
• Rich ecosystem – integrates with Pandas, NumPy, Matplotlib, Plotly, TensorFlow, scikit-learn

Streamlit provides dedicated components for conversational AI applications:³⁾

• st.chat_input – captures user messages as a bottom input box, returning the message string on submission
• st.chat_message – displays messages in chat bubbles styled as “user” or “assistant,” supporting markdown, images, and multimedia
• st.status – shows processing status for long-running LLM calls
• st.write_stream – streams token-by-token output for real-time LLM responses

These components enable building ChatGPT-like interfaces with minimal code.

By default, Streamlit is stateless due to full-script reruns. st.session_state persists data across interactions:⁴⁾

• Mutable dictionary persisted across reruns
• Essential for maintaining chat histories (st.session_state.messages)
• Widget state binding via key parameter
• Callback support for complex state logic

Streamlit pairs naturally with LLMs via Python libraries:⁵⁾

• Direct API calls – OpenAI, Anthropic, Google, and Hugging Face APIs
• LangChain integration – chains, agents, and memory management
• LiteLLM – multi-provider unified interface
• Local models – Ollama integration for private inference
• RAG patterns – file uploads combined with vector search for retrieval-augmented generation

Common patterns include parameter sliders for prompt tuning, file uploads for document QA, and real-time visualization of model outputs.

• Streamlit Community Cloud – free GitHub-integrated hosting with requirements.txt⁶⁾
• Docker – containerize with streamlit run app.py --server.port 8501
• Self-hosted – any server with Python
• Snowflake – managed Streamlit in Snowflake environments with data write-back
• Cloud platforms – AWS, GCP, Azure, Heroku

Both are popular, but Streamlit leads in data/AI prototyping while Gradio excels for quick model sharing on Hugging Face.

• Gradio
• Hugging Face
• Vercel AI SDK