Agent Memory & Multi-Agent Systems (MemGPT/AutoGen/CAMEL)

Editorial record

Plain-language summary

MemGPT borrows virtual-memory ideas, treating the context window as limited RAM and external storage as disk, so the agent pages information in and out under its own control to maintain long-running conversations and documents beyond the token limit. AutoGen provides a framework for defining multiple conversable agents that message each other and call tools to solve a task jointly, and CAMEL uses role-playing prompts to make two agents cooperate through dialogue with minimal human steering. The shared contribution is infrastructure for memory that outlives a single context and for coordinating several agents, enabling longer and more complex workflows than a single stateless call.