The LangChain and Streamlit teams have collaborated to integrate Streamlit with LangChain, a library for building powerful applications that combine LLMs with other sources of computation or knowledge. The integration allows developers to create simpler methods for rendering and examining the thoughts and actions of an LLM agent, providing constant feedback to users through a compact expander format. This is achieved using the Streamlit Callback Handler, which displays the agent's thoughts and tool input/outputs in an expander format. Advanced usage options are available, including custom labels for expanders based on the tool name and input. Future improvements include extending the callback handler to support additional chain types, making it easier to use LangChain primitives with Streamlit chat, and adding more app examples and templates. The collaboration aims to improve the developer experience and explore deeper integrations for connecting data to apps and visualizing chain/agent state.