MemGPT is an open-source Python package designed to address the limitations of traditional large language models (LLMs) with restricted context windows by implementing a hierarchical memory system, inspired by operating systems, to optimize data movement between fast and slow memory. This system allows LLMs to manage their own memory, enhancing their ability to handle tasks requiring extended conversations and complex reasoning by enabling them to remember and process more information than usual. MemGPT facilitates this through a tiered memory structure, using function calls to manage data between main and external contexts, and supports external data sources via vectorized storage, with LanceDB as the default archival storage. It enables conversational agents to maintain coherence and engage users by leveraging memory, customizing interactions with various LLMs and supporting diverse data sources, offering a scalable solution that can be configured and expanded according to user needs. The authors evaluate MemGPT's effectiveness in improving conversation consistency and engagement by assessing its ability to remember past interactions and incorporate long-range user information into dialogues, with a focus on its potential for personalization and deep memory retrieval.