GPTeam is an open-source, customizable, multi-agent simulation inspired by Stanford’s "Generative Agents" paper. The project allows users to create and run simulations where each agent, characterized by unique personalities, memories, and directives, interacts to produce emergent behaviors. Users can configure simulations using a JSON file and observe agent interactions via a web interface. The simulation architecture involves a loop where agents observe events, plan actions, react to changes, act upon their plans, and reflect based on memory significance. Despite the project's success in emulating human-like social behavior, the developers note that a dialog-reliant architecture may not be optimal for productivity-focused tasks compared to hive-mind systems like AutoGPT. Future potential lies in leveraging faster language models, enhancing user interfaces, and increasing interactivity to make the system more accessible and engaging for applications in interactive entertainment, particularly in video gaming, where agents could form emotional connections with players.