During a meetup in May, Charles Packer discussed how MemoryGPT (MemGPT) aims to solve the problem of limited memory in large language models (LLMs). MemGPT introduces a virtually extended context window inspired by computer system design. It divides the LLM context into two parts: main context and external context. The main context has a limited bandwidth, while the external context is stored on persistent storage with an infinite window. MemGPT efficiently manages information flow between the two contexts, allowing for long-context memory applications like personal assistant chatbots.