  Believable proxies of human behavior can empower interactive applications
ranging from immersive environments to rehearsal spaces for interpersonal
communication to prototyping tools. In this paper, we introduce generative
agents--computational software agents that simulate believable human behavior.
Generative agents wake up, cook breakfast, and head to work; artists paint,
while authors write; they form opinions, notice each other, and initiate
conversations; they remember and reflect on days past as they plan the next
day. To enable generative agents, we describe an architecture that extends a
large language model to store a complete record of the agent's experiences
using natural language, synthesize those memories over time into higher-level
reflections, and retrieve them dynamically to plan behavior. We instantiate
generative agents to populate an interactive sandbox environment inspired by
The Sims, where end users can interact with a small town of twenty five agents
using natural language. In an evaluation, these generative agents produce
believable individual and emergent social behaviors: for example, starting with
only a single user-specified notion that one agent wants to throw a Valentine's
Day party, the agents autonomously spread invitations to the party over the
next two days, make new acquaintances, ask each other out on dates to the
party, and coordinate to show up for the party together at the right time. We
demonstrate through ablation that the components of our agent
architecture--observation, planning, and reflection--each contribute critically
to the believability of agent behavior. By fusing large language models with
computational, interactive agents, this work introduces architectural and
interaction patterns for enabling believable simulations of human behavior.
