LangChain's LangSmith service is designed to help developers identify and address latency issues in language model (LLM) applications, particularly in chatbot systems. By using LangSmith, developers can easily diagnose where latency originates without the need for extensive manual instrumentation. The article details an example of a slow chatbot app using LangChain, where latency was primarily caused by the ConversationSummaryBufferMemory's summarization process. By switching to Zep, an open-source long-term memory store that processes data asynchronously and supports stateless architectures, the latency was significantly reduced. The experiment conducted showed that Zep could decrease runtime by an order of magnitude, with most latency now attributed to the OpenAI API. LangSmith proved effective for performance analysis, with Zep offering a faster alternative for managing chat history and memory in LLM applications.