LangChain Streamlit

LangChain Streamlit is a coding pattern that embeds LangChain-based Large Language Model (LLM) chains or agents into a Streamlit web application to deliver interactive AI dashboards with zero UI boilerplate. Developers import streamlit as st, load a LangChain object — an LLMChain, RetrievalQA, or ReAct agent — then bind widgets like st.text_input, st.file_uploader, and st.button to the chain’s invoke call. Responses are streamed in real-time via st.chat_message or st.write, while StreamlitCallbackHandler routes token-level traces, costs, and intermediate instrument outputs into extensible dashboards for transparency. A tight Python-only loop means changes are loaded in milliseconds with hot reloading and deployed to Streamlit Community Cloud or Docker without build steps. Use cases range from customer support chatbots that link to knowledge base articles, to pandas-agent data explorers, to multimodal RAG applications that view images and PDFs. By combining LangChain’s modular LLM toolkit with Streamlit’s fast UI framework, LangChain Streamlit turns prototypes into publicly available, production-ready AI applications in under 100 lines of code.