The collaboration between Streamlit and LangChain teams has resulted in the initial integration of Streamlit into LangChain to enhance the development of applications combining large language models (LLMs) with other computational or knowledge sources. Streamlit, known for transforming data scripts into shareable web apps quickly using Python, aligns with LangChain's mission to simplify the visualization and process-tracking of LLM agents. Their joint effort has led to the creation of the Streamlit Callback Handler, which provides a streamlined method for rendering and examining LLM agents' thoughts and actions, offering real-time feedback to users. This integration is showcased through examples like the MRKL app, where the callback handler displays the agent's thought process and tool interactions in an expandable format. Future plans include supporting additional chain types, enhancing the user interface and customization options, and expanding the library of app examples and templates. The teams are also exploring deeper integrations to connect data to apps and improve visualization, encouraging developers to contribute their ideas and applications.