text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksArgillaContextInfino - LangChain LLM Monitoring ExamplePromptLayerStreamlitChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsCallbacksStreamlitOn this pageStreamlitStreamlit is a faster way to build and share data apps.
  Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No frontâ€‘end experience required.
  See more examples at streamlit.io/generative-ai.In this guide we will demonstrate how to use StreamlitCallbackHandler to display the thoughts and actions of an agent in an
  interactive Streamlit app. Try it out with the running app below using the MRKL agent:Installation and Setupâ€‹pip install langchain streamlitYou can run streamlit hello to load a sample app and validate your install succeeded. See full instructions in Streamlit's
  Getting started documentation.Display thoughts and actionsâ€‹To create a StreamlitCallbackHandler, you just need to provide a parent container to render the output.from langchain.callbacks import StreamlitCallbackHandlerimport streamlit as stst_callback = StreamlitCallbackHandler(st.container())Additional keyword arguments to customize the display behavior are described in the
  API reference.Scenario 1: Using an Agent with Toolsâ€‹The primary supported use case today is visualizing the actions of an Agent with Tools (or Agent Executor). You can create an
  agent in your Streamlit app and simply pass the StreamlitCallbackHandler to agent.run() in order to visualize the