The article provides a detailed guide on building three foundational applications using TypeScript, LangChain.js, and Zep, showcasing the integration of LangChain's ZepMemory and ZepVectorStore classes to enhance large language model (LLM) functionalities. It explores the creation of a simple conversational bot that recalls past conversations, a Retrieval Augmented Generation (RAG) application that populates Zep's VectorStore with books for Q&A, and a REACT-type agent using Zep's memory retrieval and search tools. The guide highlights Zep's robust support for TypeScript and JavaScript, emphasizing its long-term memory capabilities that simplify document, chat history, and user data management, along with automatic chat history embedding. LangSmith's observability tools provide insights into model behavior, while Maximal Marginal Relevance reranking improves search result diversity in RAG applications. The article encourages developers to explore these approaches, offering source code in the Zep By Example Repository and emphasizing the potential to customize solutions with LangChain's documentation.