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Highly autonomous generative agents powered by large language models promiseto simulate intricate social behaviors in virtual societies. However, achievingreal-time interactions with humans at a low computational cost remainschallenging. Here, we introduce Lyfe Agents. They combine low-cost withreal-time responsiveness, all while remaining intelligent and goal-oriented.Key innovations include: (1) an option-action framework, reducing the cost ofhigh-level decisions; (2) asynchronous self-monitoring for betterself-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizingcritical memory items at a low cost. We evaluate Lyfe Agents' self-motivationand sociability across several multi-agent scenarios in our custom LyfeGame 3Dvirtual environment platform. When equipped with our brain-inspired techniques,Lyfe Agents can exhibit human-like self-motivated social reasoning. Forexample, the agents can solve a crime (a murder mystery) through autonomouscollaboration and information exchange. Meanwhile, our techniques enabled LyfeAgents to operate at a computational cost 10-100 times lower than existingalternatives. Our findings underscore the transformative potential ofautonomous generative agents to enrich human social experiences in virtualworlds.

Lyfe Agents: Generative agents for low-cost real-time social interactions