OpenGPTs, an open-source implementation of OpenAI GPTs and the Assistant API, facilitates the creation of conversational agents with a focus on memory, particularly long-term memory, which is often underexplored in current implementations. The blog discusses various types of memory, including conversational and semantic memory, highlighting the limitations of stateless large language models (LLMs) that require users to manage state externally. It also explores the concept of generative agents, which utilize recency, relevancy, and reflection to enhance memory capabilities. A specific application is demonstrated through a Dungeons and Dragons chatbot, which employs a custom memory system to track character and quest information, utilizing a state machine architecture to transition between gathering character information and leading quests. The blog emphasizes the importance of application-specific memory in enhancing reliability and performance and introduces tools like LangChain and LangSmith to assist in building such systems, acknowledging the complexity involved in developing long-term memory solutions tailored to specific applications.