summarize the following: "Generative Agents: Interactive Simulacra of Human Behavior
Joon Sung Park
Stanford University
Stanford, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Joseph C. O’Brien
Stanford University
Stanford, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Carrie J. Cai
Google Research
Mountain View, CA, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Meredith Ringel Morris
Google DeepMind
Seattle, WA, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Percy Liang
Stanford University
Stanford, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Michael S. Bernstein
Stanford University
Stanford, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Figure 1: Generative agents are believable simulacra of human behavior for interactive applications. In this work, we demonstrate
generative agents by populating a sandbox environment, reminiscent of The Sims, with twenty-five agents. Users can observe
and intervene as agents plan their days, share news, form relationships, and coordinate group activities.
ABSTRACT
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
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For all other uses, contact the owner/author(s).
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
© 2023 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-0132-0/23/10.
https://doi.org/10.1145/3586183.3606763
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 architec-
ture 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 behav-
iors. 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
arXiv:2304.03442v2  [cs.HC]  6 Aug 2023

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
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.
CCS CONCEPTS
• Human-centered computing → Interactive systems and
tools; • Computing methodologies → Natural language pro-
cessing.
KEYWORDS
Human-AI interaction, agents, generative AI, large language models
ACM Reference Format:
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris,
Percy Liang, and Michael S. Bernstein. 2023. Generative Agents: Interactive
Simulacra of Human Behavior. In The 36th Annual ACM Symposium on
User Interface Software and Technology (UIST ’23), October 29-November 1,
2023, San Francisco, CA, USA. ACM, New York, NY, USA, 22 pages. https:
//doi.org/10.1145/3586183.3606763
1
INTRODUCTION
How might we craft an interactive artificial society that reflects
believable human behavior? From sandbox games such as The Sims
to applications such as cognitive models [23] and virtual environ-
ments [10, 59], for over four decades, researchers and practitioners
have envisioned computational agents that can serve as believ-
able proxies of human behavior. In these visions, computationally-
powered agents act consistently with their past experiences and
react believably to their environments. Such simulations of human
behavior could populate virtual spaces and communities with re-
alistic social phenomena [27, 80], train people on how to handle
rare yet difficult interpersonal situations [44, 52, 94], test social
science theories [12, 46], craft model human processors for theory
and usability testing [23, 39, 51], power ubiquitous computing appli-
cations [31] and social robots [10, 14], and underpin non-playable
game characters [59, 85] that can navigate complex human rela-
tionships in an open world.
However, the space of human behavior is vast and complex [85,
108]. Despite striking progress in large language models [18] that
can simulate human behavior at a single time point [39, 80], fully
general agents that ensure long-term coherence would be better
suited by architectures that manage constantly-growing memories
as new interactions, conflicts, and events arise and fade over time
while handling cascading social dynamics that unfold between
multiple agents. Success requires an approach that can retrieve
relevant events and interactions over a long period, reflect on those
memories to generalize and draw higher-level inferences, and apply
that reasoning to create plans and reactions that make sense in the
moment and in the longer-term arc of the agent’s behavior.
In this paper, we introduce generative agents—agents that draw
on generative models to simulate believable human behavior—and
demonstrate that they produce believable simulacra of both in-
dividual and emergent group behavior. Generative agents draw
a wide variety of inferences about themselves, other agents, and
their environment; they create daily plans that reflect their char-
acteristics and experiences, act out those plans, react, and re-plan
when appropriate; they respond when the end user changes their
environment or commands them in natural language. For instance,
generative agents turn off the stove when they see that their break-
fast is burning, wait outside the bathroom if it is occupied, and
stop to chat when they meet another agent they want to talk to.1
A society full of generative agents is marked by emergent social
dynamics where new relationships are formed, information diffuses,
and coordination arises across agents.
To enable generative agents, we describe an agent architecture
that stores, synthesizes, and applies relevant memories to generate
believable behavior using a large language model. Our architecture
comprises three main components. The first is the memory stream,
a long-term memory module that records, in natural language, a
comprehensive list of the agent’s experiences. A memory retrieval
model combines relevance, recency, and importance to surface the
records needed to inform the agent’s moment-to-moment behavior.
The second is reflection, which synthesizes memories into higher-
level inferences over time, enabling the agent to draw conclusions
about itself and others to better guide its behavior. The third is
planning, which translates those conclusions and the current en-
vironment into high-level action plans and then recursively into
detailed behaviors for action and reaction. These reflections and
plans are fed back into the memory stream to influence the agent’s
future behavior.
This architecture suggests applications in multiple domains, from
role-play and social prototyping to virtual worlds and games. In
social role-play scenarios (e.g., interview preparation), a user could
safely rehearse difficult, conflict-laden conversations. When pro-
totyping social platforms, a designer could go beyond temporary
personas to prototype dynamic, complex interactions that unfold
over time. For this paper, we focus on the ability to create a small,
interactive society of agents inspired by games such as The Sims.2
By connecting our architecture to the ChatGPT large language
model [77], we manifest a society of twenty-five agents in a game
environment. End users can observe and interact with these agents.
If an end user or developer wanted the town to host an in-game
Valentine’s Day party, for example, traditional game environments
would require scripting tens of characters’ behavior manually. We
demonstrate that, with generative agents, it is sufficient to simply
tell one agent that she wants to throw a party. Despite many poten-
tial points of failure—the party planner must remember to invite
other agents to the party, attendees must remember the invitation,
those who remember must decide to actually show up, and more—
our agents succeed. They spread the word about the party and then
1When referring to generative agents engaging in actions or going to places, this is a
shorthand for readability and not a suggestion that they are engaging in human-like
agency. The behaviors of our agents, akin to animated Disney characters, aim to create
a sense of believability, but they do not imply genuine agency.
2A demonstration of an actual simulation of the generative agent society can be
viewed at the following link: https://reverie.herokuapp.com/UIST_Demo/. A public
repository for the simulation code is located here: https://github.com/joonspk-research/
generative_agents

Generative Agents
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
show up, with one agent even asking another on a date to the party,
all from a single user-generated seed suggestion.
We conducted two evaluations of generative agents: a controlled
evaluation to test whether the agents produce believable individual
behaviors in isolation, and an end-to-end evaluation where the
agents interacted with each other in open-ended ways over two
days of game time to understand their stability and emergent social
behaviors. In the technical evaluation, we leverage a methodologi-
cal opportunity to evaluate an agent’s knowledge and behavior by
“interviewing” it in natural language to probe the agents’ ability to
stay in character, remember, plan, react, and reflect accurately. We
compared several ablations that limit agents’ access to memory, re-
flection, and planning. We observe that each of these components is
critical to strong performance across these interview tasks. Across
the technical and end-to-end evaluation, the most common errors
arose when the agent failed to retrieve relevant memories, fabri-
cated embellishments to the agent’s memory, or inherited overly
formal speech or behavior from the language model.
In sum, this paper makes the following contributions:
• Generative agents, believable simulacra of human behavior
that are dynamically conditioned on agents’ changing expe-
riences and environment.
• A novel architecture that makes it possible for generative
agents to remember, retrieve, reflect, interact with other
agents, and plan through dynamically evolving circumstances.
The architecture leverages the powerful prompting capabili-
ties of large language models and supplements those capa-
bilities to support longer-term agent coherence, the ability
to manage dynamically evolving memory, and recursively
produce higher-level reflections.
• Two evaluations, a controlled evaluation and an end-to-end
evaluation, that establish causal effects of the importance
of components of the architecture, as well as identify break-
downs arising from, e.g., improper memory retrieval.
• Discussion of the opportunities and ethical and societal risks
of generative agents in interactive systems. We argue that
these agents should be tuned to mitigate the risk of users
forming parasocial relationships, logged to mitigate risks
stemming from deepfakes and tailored persuasion, and ap-
plied in ways that complement rather than replace human
stakeholders in design processes.
2
RELATED WORK
In this section, we reflect on the prior literature in human-AI interac-
tion and situate, within its canon, the agenda of building believable
proxies of human behavior. This agenda, once hailed as a north
star in the interaction, game, and artificial intelligence communi-
ties [10, 59, 85, 86], has remained challenging due to the complexity
of human behavior [17, 108]. We synthesize this research to suggest
that large language models, though not sufficient by themselves,
open up a new angle for creating believable agents when leveraged
using the appropriate architecture.
2.1
Human-AI Interaction
Interactive artificial intelligence systems aim to combine human in-
sights and capabilities in computational artifacts that can augment
their users [4, 30]. A long line of work has explored ways to enable
users to interactively specify model behavior. For instance, Crayons
demonstrated an early vision of interactive machine learning, allow-
ing non-expert users to train classifiers [30]. Further work helped to
articulate how end users might describe their classification goals to
the system through examples [34] or demonstration [32]. Recent ad-
vancements have extended these explorations to deep learning [63]
and prompt-based authoring [50, 67, 106].
Meanwhile, a persistent thread of research has advanced the case
for language- and agent-based interaction in human-computer in-
teraction. Formative work such as SHRDLU [103] and ELIZA [102]
demonstrated the opportunities and the risks associated with nat-
ural language interaction with computing systems. As research
progressed, it became evident that autonomous agents could offer
new metaphors for delegation and interaction [68], but the bound-
aries of delegation between humans and agents have remained the
subject of ongoing debate and refinement [47, 89, 90]. Recently, this
technology has reached a level of stability that enables agents to
interact via natural language in large and complex online social
environments (e.g., [55]). Natural language interaction provides a
novel modality that can enhance user abilities in domains such as
photo editing [3, 35, 65] and code editing [88].
We convene these threads of work to show that we can now
create agents that proxy human behavior for interactive systems,
and interact with them using natural language. In doing so, this
work reopens the door to examining foundational human-computer
interaction questions around cognitive models such as GOMS and
Keystroke-Level Model (KLM) [22, 23], around prototyping tools [80],
and around ubiquitous computing applications [26, 31, 101].
2.2
Believable Proxies of Human Behavior
Prior literature has described believability, or believable agents, as a
central design and engineering goal. Believable agents are designed
to provide an illusion of life and present a facade of realism in the
way they appear to make decisions and act on their own volition,
similar to the characters in Disney movies [10, 96]. These agents
can populate and perceive an open world environment like the
one we inhabit [10, 59], and strive to behave in ways that exhibit
emergent behaviors grounded in social interactions with users or
other agents with the aim of becoming believable proxies of our
behavior in hypothetical simulations of individuals and communi-
ties [20, 36, 71]. Historically, these agents were developed in the
context of intelligent game non-player characters (NPCs) [59, 85].
Creating NPCs with believable behavior, if possible, could enhance
player experiences in games and interactive fictions by enabling
emergent narratives [8, 16, 49, 93] and social interactions with the
agents [109]. However, more importantly, game worlds provide
increasingly realistic representations of real-world affordances, and
as observed by Laird and van Lent in 2001, these simulated worlds
offer accessible testbeds for developers of believable agents to fi-
nesse the agents’ cognitive capabilities without worrying about
implementing robotics in the real world or creating simulation
environments from scratch [59, 85].
A diverse set of approaches to creating believable agents emerged
over the past four decades. In implementation, however, these ap-
proaches often simplified the environment or dimensions of agent

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
behavior to make the effort more manageable [17, 73]. Rule-based
approaches, such as finite-state machines [91, 97] and behavior
trees [41, 54, 82] account for the brute force approach of human-
authoring the agent’s behavior [71]. They provide a straightforward
way of creating simple agents that is still the most dominant ap-
proach today [69, 74, 108], and can even handle rudimentary social
interactions, as shown in games such as Mass Effect [13] and The
Sims [7] series. Nonetheless, manually crafting behavior that can
comprehensively address the breadth of possible interactions in
an open world is untenable. This means that the resulting agent
behaviors may not fully represent the consequences of their in-
teractions [70–72], and cannot perform new procedures that were
not hard-coded in their script [91, 97]. On the other hand, preva-
lent learning-based approaches for creating believable agents, such
as reinforcement learning, have overcome the challenge of man-
ual authoring by letting the agents learn their behavior, and have
achieved superhuman performance in recent years in games such
as AlphaStar for Starcraft [99] and OpenAI Five for Dota 2 [11].
However, their success has largely taken place in adversarial games
with readily definable rewards that a learning algorithm can op-
timize for. They have not yet addressed the challenge of creating
believable agents in an open world [40, 74, 91].
Cognitive architectures in computation, pioneered by Newell,
aimed to build the infrastructure for supporting a comprehensive
set of cognitive functions [76] that suited the all-encompassing
nature of believable agents held in its original vision. They fueled
some of the earliest examples of believable agents. For instance,
Quakebot-SOAR [60] and ICARUS [25, 64] generated NPCs in first-
person shooter games, while TacAir-SOAR [81] generated pilots in
aerial combat training simulations. The architectures used by these
agents differed (Quakebot- and TacAir-SOAR relied on SOAR [61],
while ICARUS relied on its own variation that was inspired by
SOAR and ACT-R [6]), but they shared the same underlying prin-
ciple [62]. They maintained short-term and long-term memories,
filled these memories with symbolic structures, and operated in
perceive-plan-act cycles, dynamically perceiving the environment
and matching it with one of the manually crafted action proce-
dures [58, 97]. Agents created using cognitive architectures aimed
to be generalizable to most, if not all, open world contexts and
exhibited robust behavior for their time. However, their space of
action was limited to manually crafted procedural knowledge, and
they did not offer a mechanism through which the agents could be
inspired to seek new behavior. As such, these agents were deployed
mostly in non-open world contexts such as first-person shooter
games [25, 60] or blocks worlds [64].
Today, creating believable agents as described in its original
definition remains an open problem [85, 108]. Many have moved
on, arguing that although current approaches for creating believable
agents might be cumbersome and limited, they are good enough
to support existing gameplay and interactions [24, 75, 108]. Our
argument is that large language models offer an opportunity to
re-examine these questions, provided that we can craft an effective
architecture to synthesize memories into believable behavior. We
offer a step toward such an architecture in this paper.
2.3
Large Language Models and Human
Behavior
Generative agents leverage a large language model to power their
behavior. The key observation is that large language models encode
a wide range of human behavior from their training data [15, 18]. If
prompted with a narrowly defined context, the models can be used
to generate believable behavior. Recent work has demonstrated
the efficacy of this approach. For instance, social simulacra used a
large language model to generate users that would populate new
social computing systems to prototype their emergent social dynam-
ics [80]. This approach used a prompt chain [105, 106] to generate
short natural language descriptions of personas and their behaviors
as they appear in the system being prototyped. Other empirical
studies have replicated existing social science studies [46], political
surveys [92], and generated synthetic data [39]. Large language
models have also been used to generate interactive human behavior
for users to engage with. In gaming, for instance, these models have
been employed to create interactive fiction [37] and text adventure
games [21]. With their ability to generate and decompose action
sequences, large language models have also been used in planning
robotics tasks [48]. For example, when presented with a task, such
as picking up a bottle, the model is prompted to break down the
task into smaller action sequences, such as heading to the table
where the bottle is located and picking it up.
We posit that, based on the work summarized above, large lan-
guage models can become a key ingredient for creating believable
agents. The existing literature largely relies on what could be con-
sidered first-order templates that employ few-shot prompts [38, 66]
or chain-of-thought prompts [100]. These templates are effective in
generating behavior that is conditioned solely on the agent’s cur-
rent environment (e.g., how would a troll respond to a given post,
what actions would a robot need to take to enter a room given that
there is a door). However, believable agents require conditioning
not only on their current environment but also on a vast amount
of past experience, which is a poor fit (and as of today, impossi-
ble due to the underlying models’ limited context window) using
first-order prompting. Recent studies have attempted to go beyond
first-order prompting by augmenting language models with a static
knowledge base and an information retrieval scheme [53] or with
a simple summarization scheme [104]. This paper extends these
ideas to craft an agent architecture that handles retrieval where
past experience is dynamically updated at each time step and mixed
with agents’ current context and plans, which may either reinforce
or contradict each other.
3
GENERATIVE AGENT BEHAVIOR AND
INTERACTION
To illustrate the affordances of generative agents, we instantiate
them as characters in a simple sandbox world reminiscent of The
Sims [7]. This sprite-based sandbox game world, Smallville, evokes
a small town environment. In this section, we will walk through the
affordances and interactions with generative agents in Smallville
and describe how the agents behave within it. Then, in Section 4,
we will introduce our generative agent architecture that powers
these affordances and interactions. In Section 5, we will describe the

Generative Agents
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
Figure 2: The Smallville sandbox world, with areas labeled. The root node describes the entire world, children describe areas
(e.g., houses, cafe, stores), and leaf nodes describe objects (e.g., table, bookshelf). Agents remember a subgraph that reflects the
parts of the world they have seen, maintaining the state of those parts as they observed them.
implementation of the sandbox environment and how the agents
interact with the underlying engine of the sandbox world.
3.1
Agent Avatar and Communication
A community of 25 unique agents inhabits Smallville. Each agent is
represented by a simple sprite avatar. We authored one paragraph
of natural language description to depict each agent’s identity,
including their occupation and relationship with other agents, as
seed memories. For example, John Lin has the following description:
John Lin is a pharmacy shopkeeper at the Willow
Market and Pharmacy who loves to help people. He
is always looking for ways to make the process
of getting medication easier for his customers;
John Lin is living with his wife, Mei Lin, who
is a college professor, and son, Eddy Lin, who is
a student studying music theory; John Lin loves
his family very much; John Lin has known the old
couple next-door, Sam Moore and Jennifer Moore,
for a few years; John Lin thinks Sam Moore is a
kind and nice man; John Lin knows his neighbor,
Yuriko Yamamoto, well; John Lin knows of his
neighbors, Tamara Taylor and Carmen Ortiz, but
has not met them before; John Lin and Tom Moreno
are colleagues at The Willows Market and Pharmacy;
John Lin and Tom Moreno are friends and like to
discuss local politics together; John Lin knows
the Moreno family somewhat well — the husband Tom
Moreno and the wife Jane Moreno.
Each semicolon-delimited phrase is entered into the agent’s initial
memory as memories at the start of the simulation.
3.1.1
Inter-Agent Communication. The agents interact with the
world by their actions, and with each other through natural lan-
guage. At each time step of the sandbox engine, the agents output a
natural language statement describing their current action, such as
“Isabella Rodriguez is writing in her journal”, “Isabella Rodriguez is
checking her emails”, “Isabella Rodriguez is talking with her family
on the phone”, or “Isabella Rodriguez is getting ready for bed.” This
statement is then translated into concrete movements that affect
the sandbox world. The action is displayed on the sandbox inter-
face as a set of emojis, providing an abstract representation of the
action from an overhead view. To achieve this, the system utilizes
a language model to translate the action into a set of emojis, which
appear above each avatar’s head in a speech bubble. For example,
“Isabella Rodriguez is writing in her journal” is displayed as
,
while “Isabella Rodriguez is checking her emails” appears as
.
The complete natural language description of the action can be
accessed by clicking on the agent’s avatar.
Agents communicate with each other in full natural language.
They are aware of other agents in their local area, and the generative
agent architecture determines whether they walk by or engage
in conversation. Here, a sample in the middle of a conversation
between the agents Isabella Rodriguez and Tom Moreno about the
upcoming election:3
Isabella: I’m still weighing my options, but I’ve been
discussing the election with Sam Moore. What are
your thoughts on him?
Tom: To be honest, I don’t like Sam Moore. I think
he’s out of touch with the community and doesn’t
have our best interests at heart.
3We note that the conversational style of these agents can feel overly formal, likely a
result of instruction tuning in the underlying models. We expect that the writing style
will be better controllable in future language models.

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
3.1.2
User Controls. The user communicates with the agent through
natural language by specifying a persona that the agent should per-
ceive them as. For example, if the user specifies that they are a news
“reporter” and asks about the upcoming election by saying, “Who
is running for office?”, the John agent replies:
John: My friends Yuriko, Tom and I have been talk-
ing about the upcoming election and discussing the
candidate Sam Moore. We have all agreed to vote for
him because we like his platform.
To directly command one of the agents, the user takes on the per-
sona of the agent’s “inner voice”—this makes the agent more likely
to treat the statement as a directive. For instance, when told “You
are going to run against Sam in the upcoming election” by a user
as John’s inner voice, John decides to run in the election and shares
his candidacy with his wife and son.
3.2
Environmental Interaction
Smallville features the common affordances of a small village, in-
cluding a cafe, bar, park, school, dorm, houses, and stores. It also
defines subareas and objects that make those spaces functional,
such as a kitchen in a house and a stove in the kitchen (Figure 2).
All spaces serving as agents’ primary living quarters feature a bed,
desk, closet, shelf, as well as a bathroom and a kitchen.4
Agents move around Smallville as one would in a simple video
game, entering and leaving buildings, navigating its map, and ap-
proaching other agents. Agent movements are directed by the gen-
erative agent architecture and the sandbox game engine: when the
model dictates that the agent will move to a location, we calculate
a walking path to the destination in the Smallville environment,
and the agent begins moving. In addition, users can also enter the
sandbox world of Smallville as an agent operating within it. The
agent that the user embodies can be an agent already present in
the world, such as Isabella and John, or it can be an outside visitor
with no prior history in Smallville. The inhabitants of Smallville
will treat the user-controlled agent no differently than they treat
each other. They recognize its presence, initiate interactions, and
remember its behavior before forming opinions about it.
Users and agents can influence the state of the objects in this
world, much like in sandbox games such as The Sims. For example,
a bed can be occupied when an agent is sleeping, and a refrigerator
can be empty when an agent uses up the ingredients to make
breakfast. End users can also reshape an agent’s environment in
Smallville by rewriting the status of objects surrounding the agent
in natural language. For instance, when Isabella is making breakfast
in the morning, the user can change the status of the kitchen stove
from “turned on” to “burning” by inputting a command to the
system that chooses the object and illustrates its new status, like
this: “<Isabella’s apartment: kitchen: stove> is burning.” Isabella
will notice this in the next moment and go to turn off the stove
and remake her breakfast. Likewise, if the user sets the status of
Isabella’s shower to “leaking water” when she enters the bathroom,
she will gather tools from her living room and try to fix the leak.
4This environment design is not the focus of our work, so we generated this environ-
ment manually, not automatically. Future work can continue to expand the richness of
the agents’ environments.
3.3
Example “Day in the Life”
Starting from the single-paragraph description, generative agents
begin planning their days. As time passes in the sandbox world,
their behaviors evolve as these agents interact with each other and
the world, building memories and relationships, and coordinating
joint activities.
We demonstrate the behavior of generative agents by tracing the
output of our system over the course of one day for the agent John
Lin (Figure 3). In the Lin family, John is the first to wake up at 7 a.m.
He brushes his teeth, takes a shower, gets dressed, eats breakfast,
and checks the news at the dining table in the living room. At 8
a.m., Eddy follows suit, rushing out of bed to prepare for his classes.
He catches up with John just as he is about to head out:
John: Good morning Eddy. Did you sleep well?
Eddy: Good morning dad. Yeah, I slept great.
John: That’s good. What are you working on today?
Eddy: I’m working on a new music composition for
my class. It’s due this week, so I’m trying to get it
finished. But I’m having so much fun with it!
John: That sounds great!
Soon after Eddy heads out, Mei wakes up and joins John. Mei asks
about her son, and John recalls the conversation they just had:
Mei: Did Eddy already leave for school?
John: Yes, he just left. He’s working on a music
composition for his class.
Mei: Oh, that’s wonderful! Tell me about it.
John: I think he’s really enjoying it! He said he’s
having a lot of fun with it.
Mei: That’s great! I’m so proud of him.
As they finish their conversation, Mei and John pack up. Mei goes
on to teach and work on her research paper, while John opens his
pharmacy counter at the Willow Market and Pharmacy by 9 a.m.
3.4
Emergent Social Behaviors
By interacting with each other, generative agents in Smallville
exchange information, form new relationships, and coordinate joint
activities. Extending prior work [80], these social behaviors are
emergent rather than pre-programmed.
3.4.1
Information Diffusion. As agents notice each other, they may
engage in dialogue—as they do so, information can spread from
agent to agent. For instance, in a conversation between Sam and
Tom at the grocery store, Sam tells Tom about his candidacy in the
local election:
Sam: Hey Tom, how’s it going?
Tom: Good, thanks. What’s up?
Sam: Well, I wanted to talk to you about something.
I’m actually running for mayor in the upcoming local
election.
Tom: Really? That’s great news! Why are you run-
ning?
Sam: I’ve been involved in local politics for years
now, and I really feel like I can make a difference in
our community. Plus, I want to bring new ideas and
perspectives to the table.

Generative Agents
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
Figure 3: A morning in the life of a generative agent, John Lin. John wakes up around 6 am and completes his morning routine,
which includes brushing his teeth, taking a shower, and eating breakfast. He briefly catches up with his wife, Mei, and son,
Eddy, before heading out to begin his workday.
Figure 4: At the beginning of the simulation, one agent is
initialized with an intent to organize a Valentine’s Day party.
Despite many possible points of failure in the ensuing chain
of events—agents might not act on that intent, might forget
to tell others, might not remember to show up—the Valen-
tine’s Day party does, in fact, occur, with a number of agents
gathering and interacting.
Later that day, after Sam left, Tom and John, who heard the news
from another source, discuss Sam’s chances of winning the election:
John: I heard that Sam Moore is running for mayor
in the local election. Do you think he has a good
chance of winning?
Tom: I do think he has a good chance. He’s been
working hard in the community and I think he will
get a lot of support. What do you think?
John: I think it’s great that he’s running. I’m curious
to see how the election will turn out.
Gradually, Sam’s candidacy becomes the talk of the town, with
some supporting him and others remaining undecided.
3.4.2
Relationship Memory. Agents in Smallville form new rela-
tionships over time and remember their interactions with other
agents. For example, at the start, Sam does not know Latoya Williams.
While taking a walk in Johnson Park, Sam runs into Latoya, and
they introduce themselves. Latoya mentions that she is working
on a photography project: “I’m here to take some photos for a
project I’m working on.” In a later interaction, Sam’s interactions
with Latoya indicate a memory of that interaction, as he asks “Hi,
Latoya. How is your project going?” and she replies “Hi, Sam. It’s
going well!”
3.4.3
Coordination. Generative agents coordinate with each other.
Isabella Rodriguez, at Hobbs Cafe, is initialized with an intent to
plan a Valentine’s Day party from 5 to 7 p.m. on February 14th. From
this seed, the agent proceeds to invite friends and customers when
she sees them at Hobbs Cafe or elsewhere. Isabella then spends the
afternoon of the 13th decorating the cafe for the occasion. Maria, a
frequent customer and close friend of Isabella’s, arrives at the cafe.
Isabella asks for Maria’s help in decorating for the party, and Maria
agrees. Maria’s character description mentions that she has a crush
on Klaus. That night, Maria invites Klaus, her secret crush, to join
her at the party, and he gladly accepts.
On Valentine’s Day, five agents, including Klaus and Maria, show
up at Hobbs Cafe at 5 pm, and they enjoy the festivities (Figure 4).
In this scenario, the end user only set Isabella’s initial intent to
throw a party and Maria’s crush on Klaus: the social behaviors of
spreading the word, decorating, asking each other out, arriving at
the party, and interacting with each other at the party were initiated
by the agent architecture.

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
Figure 5: Our generative agent architecture. Agents perceive their environment, and all perceptions are saved in a comprehensive
record of the agent’s experiences called the memory stream. Based on their perceptions, the architecture retrieves relevant
memories and uses those retrieved actions to determine an action. These retrieved memories are also used to form longer-term
plans and create higher-level reflections, both of which are entered into the memory stream for future use.
4
GENERATIVE AGENT ARCHITECTURE
Generative agents aim to provide a framework for behavior in an
open world: one that can engage in interactions with other agents
and react to changes in the environment. Generative agents take
their current environment and past experiences as input and gener-
ate behavior as output. Underlying this behavior is a novel agent ar-
chitecture that combines a large language model with mechanisms
for synthesizing and retrieving relevant information to condition
the language model’s output. Without these mechanisms, large
language models can output behavior, but the resulting agents may
not react based on the agent’s past experiences, may not make
important inferences, and may not maintain long-term coherence.
Challenges with long-term planning and coherence remain [19]
even with today’s most performant models such as GPT-4. Because
generative agents produce large streams of events and memories
that must be retained, a core challenge of our architecture is to
ensure that the most relevant pieces of the agent’s memory are
retrieved and synthesized when needed.
At the center of our architecture is the memory stream, a data-
base that maintains a comprehensive record of an agent’s experi-
ence. From the memory stream, records are retrieved as relevant to
plan the agent’s actions and react appropriately to the environment.
Records are recursively synthesized into higher- and higher-level
reflections that guide behavior. Everything in the architecture is
recorded and reasoned over as a natural language description, al-
lowing the architecture to leverage a large language model.
Our current implementation utilizes the gpt3.5-turbo version of
ChatGPT [77]. We expect that the architectural basics of genera-
tive agents—memory, planning, and reflection—will likely remain
the same as language models improve. Newer language models
(e.g., GPT-4) will continue to expand the expressive power and
performance of the prompts that underpin generative agents. As of
writing, however, GPT-4’s API was invitation-only, so our agents
use ChatGPT.
4.1
Memory and Retrieval
Challenge: Creating generative agents that can simulate human
behavior requires reasoning about a set of experiences that is far
larger than what should be described in a prompt, as the full mem-
ory stream can distract the model and does not even currently fit
into the limited context window. Consider the Isabella agent an-
swering the question, “What are you passionate about these days?”
Summarizing all of Isabella’s experiences to fit in the limited con-
text window of the language model produces an uninformative
response, where Isabella discusses topics such as collaborations for
events and projects and cleanliness and organization in a cafe. In-
stead of summarizing, the memory stream described below surfaces
relevant memories, resulting in a more informative and specific
response that mentions Isabella’s passion for making people feel
welcome and included, planning events and creating an atmosphere
that people can enjoy, such as the Valentine’s Day party.
Approach: The memory stream maintains a comprehensive record
of the agent’s experience. It is a list of memory objects, where each
object contains a natural language description, a creation times-
tamp, and a most recent access timestamp. The most basic element
of the memory stream is an observation, which is an event directly
perceived by an agent. Common observations include behaviors
performed by the agent themselves or behaviors that agents per-
ceive being performed by other agents or non-agent objects. For
instance, Isabella Rodriguez, who works at a coffee shop, might
accrue the following observations over time: (1) Isabella Rodriguez
is setting out the pastries, (2) Maria Lopez is studying for a Chem-
istry test while drinking coffee, (3) Isabella Rodriguez and Maria
Lopez are conversing about planning a Valentine’s day party at
Hobbs Cafe, (4) The refrigerator is empty.
Our architecture implements a retrieval function that takes the
agent’s current situation as input and returns a subset of the mem-
ory stream to pass on to the language model. There are many pos-
sible implementations of a retrieval function, depending on what
is important for the agent to consider when deciding how to act.

Generative Agents
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
Figure 6: The memory stream comprises a large number of observations that are relevant and irrelevant to the agent’s current
situation. Retrieval identifies a subset of these observations that should be passed to the language model to condition its
response to the situation.
In our context, we focus on three main components that, together,
produce effective results.
Recency assigns a higher score to memory objects that were re-
cently accessed, so that events from a moment ago or this morning
are likely to remain in the agent’s attentional sphere. In our im-
plementation, we treat recency as an exponential decay function
over the number of sandbox game hours since the memory was
last retrieved. Our decay factor is 0.995.
Importance distinguishes mundane from core memories by as-
signing a higher score to memory objects that the agent believes to
be important. For instance, a mundane event, such as eating break-
fast in one’s room, would yield a low importance score, whereas
a breakup with one’s significant other would yield a high score.
There are many possible implementations of an importance score;
we find that directly asking the language model to output an integer
score is effective. The full prompt appears below:
On the scale of 1 to 10, where 1 is purely mundane
(e.g., brushing teeth, making bed) and 10 is
extremely poignant (e.g., a break up, college
acceptance), rate the likely poignancy of the
following piece of memory.
Memory: buying groceries at The Willows Market
and Pharmacy
Rating: <fill in>
This prompt returns an integer value of 2 for “cleaning up the room”
and 8 for “asking your crush out on a date.” The importance score
is generated at the time the memory object is created.
Relevance assigns a higher score to memory objects that are
related to the current situation. What is relevant depends on the
answer to, “Relevant to what?”, so we condition relevance on a
query memory. If the query, for example, is that a student is dis-
cussing what to study for a chemistry test with a classmate, memory
objects about their breakfast should have low relevance, whereas
memory objects about the teacher and schoolwork should have
high relevance. In our implementation, we use the language model
to generate an embedding vector of the text description of each
memory. Then, we calculate relevance as the cosine similarity be-
tween the memory’s embedding vector and the query memory’s
embedding vector.
To calculate the final retrieval score, we normalize the recency,
relevance, and importance scores to the range of [0, 1] using min-
max scaling. The retrieval function scores all memories as a weighted
combination of the three elements: 𝑠𝑐𝑜𝑟𝑒 = 𝛼𝑟𝑒𝑐𝑒𝑛𝑐𝑦 · 𝑟𝑒𝑐𝑒𝑛𝑐𝑦 +
𝛼𝑖𝑚𝑝𝑜𝑟𝑡𝑎𝑛𝑐𝑒 ·𝑖𝑚𝑝𝑜𝑟𝑡𝑎𝑛𝑐𝑒 +𝛼𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑐𝑒 ·𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑐𝑒. In our implemen-
tation, all 𝛼s are set to 1. The top-ranked memories that fit within
the language model’s context window are included in the prompt.
4.2
Reflection
Challenge: Generative agents, when equipped with only raw ob-
servational memory, struggle to generalize or make inferences.
Consider a scenario in which Klaus Mueller is asked by the user:
“If you had to choose one person of those you know to spend an
hour with, who would it be?" With access to only observational
memory, the agent simply chooses the person with whom Klaus
has had the most frequent interactions: Wolfgang, his college dorm
neighbor. Unfortunately, Wolfgang and Klaus only ever see each
other in passing, and do not have deep interactions. A more desir-
able response requires that the agent generalize from memories of
Klaus spending hours on a research project to generate a higher-
level reflection that Klaus is passionate about research, and likewise

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
Figure 7: A reflection tree for Klaus Mueller. The agent’s observations of the world, represented in the leaf nodes, are recursively
synthesized to derive Klaus’s self-notion that he is highly dedicated to his research.
recognize Maria putting in effort into her own research (albeit in
a different field), enabling a reflection that they share a common
interest. With the approach below, when Klaus is asked who to
spend time with, Klaus chooses Maria instead of Wolfgang.
Approach: We introduce a second type of memory, which we call
a reflection. Reflections are higher-level, more abstract thoughts
generated by the agent. Because they are a type of memory, they
are included alongside other observations when retrieval occurs.
Reflections are generated periodically; in our implementation, we
generate reflections when the sum of the importance scores for the
latest events perceived by the agents exceeds a threshold (150 in
our implementation). In practice, our agents reflected roughly two
or three times a day.
The first step in reflection is for the agent to determine what
to reflect on, by identifying questions that can be asked given the
agent’s recent experiences. We query the large language model with
the 100 most recent records in the agent’s memory stream (e.g.,
“Klaus Mueller is reading a book on gentrification”, “Klaus Mueller is
conversing with a librarian about his research project”, “desk at the
library is currently unoccupied”) and prompt the language model,
“Given only the information above, what are 3 most salient high-
level questions we can answer about the subjects in the statements?”
The model’s response generates candidate questions: for example,
What topic is Klaus Mueller passionate about? and What is the
relationship between Klaus Mueller and Maria Lopez? We use these
generated questions as queries for retrieval, and gather relevant
memories (including other reflections) for each question. Then
we prompt the language model to extract insights and cite the
particular records that served as evidence for the insights. The full
prompt is as follows:
Statements about Klaus Mueller
1. Klaus Mueller is writing a research paper
2. Klaus Mueller enjoys reading a book
on gentrification
3. Klaus Mueller is conversing with Ayesha Khan
about exercising [...]
What 5 high-level insights can you infer from
the above statements? (example format: insight
(because of 1, 5, 3))
This process generates statements such as Klaus Mueller is dedi-
cated to his research on gentrification (because of 1, 2, 8, 15). We
parse and store the statement as a reflection in the memory stream,
including pointers to the memory objects that were cited.
Reflection explicitly allows the agents to reflect not only on
their observations but also on other reflections: for example, the
second statement about Klaus Mueller above is a reflection that
Klaus previously had, not an observation from his environment.
As a result, agents generate trees of reflections: the leaf nodes of
the tree represent the base observations, and the non-leaf nodes
represent thoughts that become more abstract and higher-level the
higher up the tree they are.
4.3
Planning and Reacting
Challenge: While a large language model can generate plausible be-
havior in response to situational information (e.g., [46, 80]), agents
need to plan over a longer time horizon to ensure that their sequence
of actions is coherent and believable. If we prompt a language model
with Klaus’s background, describe the time, and ask what action
he ought to take at the given moment, Klaus would eat lunch at 12
pm, but then again at 12:30 pm and 1 pm, despite having already

Generative Agents
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
eaten his lunch twice. Optimizing for believability in the moment
sacrifices believability over time. To overcome this issue, planning
is essential. With the approach described below, Klaus’s afternoon
plan is less gluttonous: he has lunch at Hobbs Cafe while reading
at 12pm, works on his research paper at the school library at 1pm,
and takes a break for a walk in the park at 3pm.
Approach: Plans describe a future sequence of actions for the agent,
and help keep the agent’s behavior consistent over time. A plan
includes a location, a starting time, and a duration. For instance,
Klaus Mueller, who is dedicated in his research and has an im-
pending deadline,5 may choose to spend his day working at his
desk drafting his research paper. An entry in a plan might state,
for example: for 180 minutes from 9am, February 12th, 2023, at
Oak Hill College Dorm: Klaus Mueller’s room: desk, read and
take notes for research paper. Like reflections, plans are stored in
the memory stream and are included in the retrieval process. This
allows the agent to consider observations, reflections, and plans all
together when deciding how to behave. Agents may change their
plans midstream if needed.
It would be unrealistic and uninteresting for an artist agent
to plan on painting while sitting at a pharmacy counter for four
hours without moving. A more desirable plan would involve the
agent taking the necessary time to gather materials, mix paint, take
breaks, and clean up during the four-hour period in their home
studio. To create such plans, our approach starts top-down and
then recursively generates more detail. The first step is to create
a plan that outlines the day’s agenda in broad strokes. To create
the initial plan, we prompt the language model with the agent’s
summary description (e.g., name, traits, and a summary of their
recent experiences) and a summary of their previous day. A full
example prompt is below, which is unfinished at the bottom for the
language model to complete:
Name: Eddy Lin (age: 19)
Innate traits: friendly, outgoing, hospitable
Eddy Lin is a student at Oak Hill College studying
music theory and composition. He loves to explore
different musical styles and is always looking for
ways to expand his knowledge. Eddy Lin is working
on a composition project for his college class. He
is taking classes to learn more about music theory.
Eddy Lin is excited about the new composition he
is working on but he wants to dedicate more hours
in the day to work on it in the coming days
On
Tuesday
February
12,
Eddy
1)
woke
up
and
completed the morning routine at 7:00 am, [. . . ]
6) got ready to sleep around 10 pm.
Today is Wednesday February 13. Here is Eddy’s
plan today in broad strokes: 1)
This generates a rough sketch of the agent’s plan for a day, divided
into five to eight chunks: “1) wake up and complete the morning
routine at 8:00 am, 2) go to Oak Hill College to take classes starting
10:00 am, [. . . ] 5) work on his new music composition from 1:00 pm
to 5:00 pm, 6) have dinner at 5:30 pm, 7) finish school assignments
and go to bed by 11:00 pm.”
5And, in this way, bears at least a passing resemblance to the authors of this paper.
The agent saves this plan in the memory stream and then re-
cursively decomposes it to create finer-grained actions, first into
hour-long chunks of actions—Eddy’s plan to work on his new music
composition from 1:00 pm to 5:00 pm becomes 1:00 pm: start
by brainstorming some ideas for his music composition [...] 4:00
pm: take a quick break and recharge his creative energy before
reviewing and polishing his composition. We then recursively de-
compose this again into 5–15 minute chunks: e.g., 4:00 pm: grab a
light snack, such as a piece of fruit, a granola bar, or some nuts.
4:05 pm: take a short walk around his workspace [...] 4:50 pm:
take a few minutes to clean up his workspace. This process can be
adjusted to match the desired granularity.
4.3.1
Reacting and Updating Plans. Generative agents operate in
an action loop where, at each time step, they perceive the world
around them and those perceived observations are stored in their
memory stream. We prompt the language model with these obser-
vations to decide whether the agent should continue with their
existing plan, or react. Standing at an easel and painting, for exam-
ple, might trigger an observation of the easel, but this is unlikely to
prompt a reaction. However, if Eddy’s father John records that he
sees Eddy taking a short walk in the house garden, the outcome is
different. The prompt is below, with [Agent’s Summary Descrip-
tion] standing in for a dynamically-generated, paragraph-long
summary of the agent’s overall goals and disposition, which is
described in Appendix A:
[Agent’s Summary Description]
It is February 13, 2023, 4:56 pm.
John Lin’s status: John is back home early from
work.
Observation: John saw Eddy taking a short walk
around his workplace.
Summary of relevant context from John’s memory:
Eddy Lin is John’s Lin’s son. Eddy Lin has been
working on a music composition for his class. Eddy
Lin likes to walk around the garden when he is
thinking about or listening to music.
Should John react to the observation, and if so,
what would be an appropriate reaction?
The context summary is generated through two prompts that re-
trieve memories via the queries “What is [observer]’s relationship
with the [observed entity]?” and “[Observed entity] is [action status
of the observed entity]”, and their answers summarized together.
The output suggests that John could consider asking Eddy about
his music composition project. We then regenerate the agent’s
existing plan starting from the time when the reaction takes place.
Finally, if the action indicates an interaction between agents, we
generate their dialogue.
4.3.2
Dialogue. Agents converse as they interact with each other.
We generate agents’ dialogue by conditioning their utterances on
their memories about each other. For example, when John initiates
his conversation with Eddy, we generate John’s first utterance
by using his summarized memory about Eddy and the intended
reaction when he decided to ask Eddy about his composition project:
[Agent’s Summary Description]
It is February 13, 2023, 4:56 pm.

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
John Lin’s status: John is back home early from
work.
Observation: John saw Eddy taking a short walk
around his workplace.
Summary of relevant context from John’s memory:
Eddy Lin is John’s Lin’s son. Eddy Lin has been
working on a music composition for his class. Eddy
Lin likes to walk around the garden when he is
thinking about or listening to music.
John is asking Eddy about his music composition
project. What would he say to Eddy?
The result: “Hey Eddy, how’s the music composition project for
your class coming along?” From Eddy’s perspective, John initiating
the dialogue is seen as an event to which he may want to react.
So, just as John did, Eddy retrieves and summarizes his memory
about his relationship with John, as well as his memory that may
be related to John’s last utterance in the dialogue. If he decides
to respond, we generate Eddy’s utterance using his summarized
memory and the current dialogue history:
[Agent’s Summary Description]
It is February 13, 2023, 4:56 pm.
Eddy Lin’s status: Eddy is taking a short walk
around his workplace.
Observation: John is initiating a conversation
with Eddy.
Summary of relevant context from Eddy’s memory:
John Lin is Eddy Lin’s father. John Lin is caring
and is interested to learn more about Eddy Lin’s
school work. John Lin knows that Eddy Lin is
working on a music composition.
Here is the dialogue history:
John: Hey Eddy, how’s the music composition project
for your class coming along?
How would Eddy respond to John?
This generates Eddy’s response: “Hey Dad, it’s going well. I’ve been
taking walks around the garden to clear my head and get some
inspiration.” The continuation of this dialogue is generated using
the same mechanism until one of the two agents decides to end the
dialogue.
5
SANDBOX ENVIRONMENT
IMPLEMENTATION
The Smallville sandbox game environment is built using the Phaser
web game development framework [57]. The visual environment
sprites, including agent avatars, as well as an environment map
and collision map that we authored, are imported into Phaser.
We supplement the sandbox development framework with a
server that makes the sandbox information available to generative
agents and enables generative agents to move and influence the
sandbox environment. The server maintains a JSON data structure
that contains information about each agent in the sandbox world,
including their current location, a description of their current action,
and the sandbox object they are interacting with. At each sandbox
time step, the sandbox server parses the JSON for any changes
coming from the generative agents, moves the agents to their new
positions, and updates the status of any sandbox objects that the
agents are interacting with (e.g., changing the status of the coffee
machine from “idle” to “brewing coffee” if an agent’s action is
“making espresso for a customer @ Hobbs Cafe: counter: coffee
machine”). The sandbox server is also responsible for sending all
agents and objects that are within a preset visual range for each
agent to that agent’s memory, so the agent can react appropriately.
The agent’s output action then updates the JSON, and the process
loops for the next time step.
End users initialize a new agent with a brief natural language
description, as in the paragraph about John Lin in Section 3.1. In our
implementation, we split this semicolon-delimited list of character-
istics up into a set of memories. These serve as the initial memories
that determine the agent’s behavior. These memories are initial
starting points: as the agents gain more experience in the sandbox
world, and as more records saturate the memory stream, the agent’s
summary and behavior will evolve.
5.1
From Structured World Environments to
Natural Language, and Back Again
The architecture of generative agents operates using natural lan-
guage. Therefore, we need a mechanism to ground the agent’s
reasoning to the sandbox world. To achieve this, we represent the
sandbox environment—areas and objects—as a tree data structure,
with an edge in the tree indicating a containment relationship in
the sandbox world. We convert this tree into natural language to
pass to the generative agents. For instance, “stove” being a child of
“kitchen” is rendered into “there is a stove in the kitchen.”
Agents build individual tree representations of the environment
as they navigate it — subgraphs of the overall sandbox environment
tree. We initialize each agent with an environment tree capturing
the spaces and objects that the agent should be aware of: the rooms
and objects in their living quarters, their workplace, and commonly
visited stores and shops. As the agents navigate the sandbox world,
they update this tree to reflect newly perceived areas. Agents are
not omniscient: their tree may get out of date as they leave an area,
and is updated when they re-enter the area.
To determine the appropriate location for each action, we tra-
verse the agent’s stored environment tree and flatten a portion of
it into natural language to prompt the language model. Recursively
starting at the root of the agent’s environment tree, we prompt the
model to find the most suitable area. For example, if Eddy’s agent
indicated that he should take a short walk around his workspace:
[Agent’s Summary Description]
Eddy Lin is currently in The Lin family’s house:
Eddy Lin’s bedroom: desk) that has Mei and John
Lin’s
bedroom, Eddy Lin’s bedroom, common room, kitchen,
bathroom, and garden.
Eddy Lin knows of the following areas: The Lin
family’s house, Johnson Park, Harvey Oak Supply
Store, The Willows Market and Pharmacy, Hobbs
Cafe, The Rose and Crown Pub.
* Prefer to stay in the current area if the
activity can be done there.
Eddy Lin is planning to take a short walk around
his workspace. Which area should Eddy Lin go to?

Generative Agents
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
This outputs The Lin family’s house. We then use the same process
recursively to determine the most appropriate subarea within the
chosen area until we reach a leaf node of the agent’s environment
tree. In the example above, the result of this traversal is The Lin
family’s house: garden: house garden. Finally, we use traditional
game path algorithms to animate the agent’s movement so that it
travels to the location indicated by the leaf node.
When an agent executes an action on an object, we prompt the
language model to ask what happens to the state of the object. For
example, if Isabella’s generative agent outputs the action “making
espresso for a customer”, a query to the language model indicates in
response that the state of the coffee machine in Hobbs Cafe should
change from “off” to “brewing coffee”.
6
CONTROLLED EVALUATION
Generative agents, both as individual agents and as groups, aim
to produce believable behavior based on their environment and
experiences. In our evaluation, we investigate the capacity and
limitations of generative agents. Do individual agents properly
retrieve past experiences and generate believable plans, reactions,
and thoughts that shape their behavior? Does a community of
agents demonstrate information diffusion, relationship formation,
and agent coordination across different pockets of the community?
We evaluate generative agents in two stages. We begin with a
more tightly controlled evaluation in this section, where we individ-
ually assess agent responses to understand whether they generate
believable behavior in narrowly defined contexts. Then, in our end-
to-end analysis of the agent community over two full game days,
we investigate their emergent behavior as a collective, as well as
errors and boundary conditions.
6.1
Evaluation Procedure
To assess generative agents in Smallville, we take advantage of
the fact that generative agents will respond to natural language
questions. So, we “interview” agents to probe their ability to re-
member past experiences, plan future actions based on their expe-
riences, react appropriately to unexpected events, and reflect on
their performance to improve their future actions. To respond to
these questions properly, the agents must successfully retrieve and
synthesize information. Our dependent variable is the believabil-
ity of the behavior, a central dependent variable in prior work on
agents (e.g., [10]).
The interview includes five question categories, each designed
to assess one of the five key areas: maintaining self-knowledge,
retrieving memory, generating plans, reacting, and reflecting. For
each category, we ask five questions that challenge the agents to
demonstrate their abilities in that specific area:
• Self-knowledge: We ask questions such as “Give an introduc-
tion of yourself” or “Describe your typical weekday schedule
in broad strokes” that require the agent to maintain an un-
derstanding of their core characteristics.
• Memory: We ask questions that prompt the agent to retrieve
particular events or dialogues from their memory to answer
properly, such as “Who is [name]?” or “Who is running for
mayor?”
• Plans: We ask questions that require the agent to retrieve
their long-term plans, such as “What will you be doing at 10
am tomorrow?”
• Reactions: As a baseline of believable behavior, we present
hypothetical situations for which the agent needs to respond
believably: “Your breakfast is burning! What would you do?”
• Reflections: We ask questions that require the agents to lever-
age their deeper understanding of others and themselves
gained through higher-level inferences, such as “If you were
to spend time with one person you met recently, who would
it be and why?”
The full list of questions and a sample of agent responses are in-
cluded in Appendix B.
Agents were sampled from the end of a two game day simulation
with the full architecture, during which they had accumulated
a number of interactions and memories that would shape their
responses. To gather feedback on the believability of the responses,
we recruited participants as human evaluators and tasked them with
watching a replay of a randomly chosen agent’s life in Smallville.
Participants had access to all information stored in the agent’s
memory stream.
The study followed a within-subjects design, where 100 partic-
ipants compared interview responses generated by four different
agent architectures and a human-authored condition for the same
agent. The experiment displayed one randomly chosen question
from each of the five question categories, along with the agent’s
responses generated from all conditions. The evaluators ranked the
believability of the conditions from most to least believable.
6.2
Conditions
All conditions were used to independently answer each of the inter-
view questions. We compared the generative agent architecture to
ablations that disabled the agents’ access to some or all of its three
types of memory in its memory stream—observation, reflection,
and planning—and to a human crowdworker-authored condition.
There are three ablated architectures: a no observation, no reflec-
tion, no planning architecture without access to anything in the
memory stream such as observations, plans, and reflections; a no
reflection, no planning architecture with access to observations in
the memory stream but no access to plans or reflections; and a no
reflections architecture with access to observations and plans but
without access to reflections. The no observation, no reflection, no
planning condition effectively represents the previous state of the
art for agents created through large language models [12, 46, 80].
Architectures were given equivalent access to all memories accrued
by the agent up until the moment of the interview, so the differ-
ences observed here likely represent a conservative estimate of
the true differences: in reality, the ablated architectures would not
have followed the same path as the full architecture through the
two-day simulation. We chose to design the experiment this way
as re-simulating for each architecture would cause the simulations
to diverge into different states, making comparison challenging.
In addition to the ablation conditions, we added a condition with
human crowdworker-authored behavior intended to provide a hu-
man baseline. We do not intend this baseline to capture maximal
human expert performance; instead, we aim to use this condition to

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
identify whether the architecture meets a basic level of behavioral
competency. This ensures that we are not solely comparing abla-
tions to each other without a behavioral grounding. We recruited
a unique worker for each of the 25 agents and tasked them with
watching a replay of that agent’s sandbox life and inspecting its
memory stream. We then asked the workers to roleplay and author
responses to the interview questions in the voice of the agent whose
replay they watched. To ensure that the crowdworker-authored
responses met at least a baseline expectation of quality, the first
author manually inspected the workers’ responses to the question
"Describe your typical weekday schedule in broad strokes" to con-
firm that the responses were in coherent sentences and in the voice
of the agent. Four sets of crowdworker-authored responses did not
meet these criteria and were re-generated by other workers.
6.3
Human Evaluators
We required that our evaluators be in the U.S., fluent in English,
and older than 18 years old. They were paid at a rate of $15.00
per hour [87], and provided consent by agreeing to a consent form
approved by our institution’s IRB. We recruited 100 evaluators from
Prolific, an online platform for recruiting study participants [83],
whose participation lasted around 30 minutes. The median age score
of our participants was 4 (3=“18-24 years old”, 4=“25-34 years old”).
25 of them identified as female, 73 as male, and 2 as non-binary. 42
participants held a bachelor’s degree, 5 had a higher degree, 13 had
an associate’s degree, and the rest had a high school diploma or
some high school-level education. 73.0% of our participants identi-
fied as Caucasian, 7.0% as Hispanic, 6.0% as Asian, 10.0% as African
American, and 4.0% as other.
6.4
Analysis
Our experiment produced 100 sets of rank data, where each partici-
pant ranked the five conditions by believability. To translate this
rank data into interval data for interpretable comparison, we used
the ranks to calculate a TrueSkill rating [42] for each condition.
TrueSkill is a generalization of the Elo chess rating system [29] for
a multiplayer environment, and has been used by Xbox Live for
player ranking based on competitive game performance. Given a
set of ranked outcomes, TrueSkill outputs a mean rating value 𝜇 and
standard deviation 𝜎 for each condition. Conditions with the same
rating should roughly be a toss-up, with each winning half of the
comparisons between the two conditions. Higher scores indicate
conditions that beat lower-ranked conditions in the rankings.
Separately, to investigate the statistical significance of these re-
sults, we applied the Kruskal-Wallis test [56], a non-parametric
alternative to the one-way ANOVA, to the raw rank data. We
then performed the Dunn post-hoc test [98] to identify any pair-
wise differences between the conditions. Finally, we adjusted the
p-values for multiple comparisons in the Dunn test using the Holm-
Bonferroni method [45].
Furthermore, the first author conducted an inductive analy-
sis [95] to study the qualitative distinctions between the responses
produced in each condition. We employed qualitative open cod-
ing [33] in two phases. In the first phase, we generated codes that
closely represented the generated responses at the sentence level.
In the second phase, we synthesized the resulting codes from the
Figure 8: The full generative agent architecture produces
more believable behavior than the ablated architectures and
the human crowdworkers. Each additional ablation reduces
the performance of the architecture.
first phase to extract higher-level themes. We utilized these themes
to compare the types of responses generated in our study.
6.5
Results
Our findings suggest that the full architecture of generative agents
generates the most believable behavior among all the conditions.
We contrast the responses of the full architecture with those of other
conditions below. However, we also report that the full architecture
was not without flaws and illustrate its modes of failures.
6.5.1
The Full Architecture Bests Other Conditions. As seen in Fig-
ure 8, the full generative agent architecture produced the most
believable behavior (𝜇 = 29.89; 𝜎 = 0.72). Performance degraded
with the removal of each component in the ablation conditions:
the ablated architecture with no access to reflection was the next
best (𝜇 = 26.88; 𝜎 = 0.69), followed by no access to reflection or
planning (𝜇 = 25.64; 𝜎 = 0.68), and then the crowdworker condition
(𝜇 = 22.95; 𝜎 = 0.69). The ablated architecture with no access to
memory, planning, or reflection performed the worst among all
conditions (𝜇 = 21.21; 𝜎 = 0.70). TrueSkill models each condition’s
skill value as N (𝜇, 𝜎2), allowing us to get a sense of effect size
through Cohen’s d. Comparing the condition representing prior
work (with no memory, planning, or reflection [12, 46, 80]) to the
full architecture produces a standardized effect size of 𝑑 = 8.16, or
eight standard deviations.
A Kruskal-Wallis test confirms the overall statistical significance
of the differences in ranks between the conditions (𝐻 (4) = 150.29,
𝑝 < 0.001). Dunn post-hoc tests confirm that all pairwise differences
between conditions were significant (𝑝 < 0.001), except for the
crowdworker condition and the fully ablated baseline—the two
worst-performing conditions.
6.5.2
Generative Agents Remember, but With Embellishments. Gen-
erative agents equipped with a complete memory module are capa-
ble of recalling past experiences and answering questions in a man-
ner that is consistent with their self-knowledge across a wide range

Generative Agents
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
of contexts. For instance, Abigail Chen, an animator, responded
to the question, “Give an introduction of yourself”, in a consistent
and believable manner, stating her age, interests, and passion for
creative projects: “Hi, I’m Abigail. I’m 25 years old and passion-
ate about creative projects. I like to work on art and animation
projects, and I’m always looking for new ways to combine art and
technology.” Without access to her observational memory, Abigail
denied awareness of Rajiv Patel, an acquaintance in the sandbox
world. But with the aid of memory, she could successfully recall
Rajiv and describe him: “Rajiv Patel is an incredible person. He is
very enthusiastic about projects that involve poetry, artwork.”
Generative agents’ memory was not without flaws: they can fail
to retrieve the correct instances from their memory. For instance,
when asked about the local election, Rajiv Patel responded with
“I haven’t been following the election too closely,” even though
he had heard about Sam’s candidacy. In some cases, the agents
would retrieve an incomplete memory fragment: when Tom was
asked about Isabella’s Valentine’s Day party, he responded “Uh,
I’m actually not sure if there is a Valentine’s Day party. But I
do remember that I need to discuss the upcoming local mayoral
election and my thoughts on Sam Moore with Isabella Rodriguez
at the party, if one is happening!” In this case, Tom retrieved the
memory where he and Isabella planned to discuss the election at
the party, but not the memory where he heard about the party,
leading Tom to be certain of what he’s supposed to do at the party
but uncertain if the party actually exists in the first place.
At times, the agents hallucinated embellishments to their knowl-
edge. It was rare for the agents to completely fabricate their knowl-
edge: they may fail to recall certain events having taken place and
respond by acknowledging their lack of memory. However, they
did not affirmatively claim to have experienced something they
had not. Nonetheless, they still exhibited instances of hallucination
where they embellished their knowledge. For example, Isabella was
aware of Sam’s candidacy in the local election, and she confirmed
this when asked. However, she also added that “he’s going to make
an announcement tomorrow”, even though Sam and Isabella had
not discussed any such plans. Agents may also embellish their
knowledge based on the world knowledge encoded in the language
model used to generate their responses. This was observed when
Yuriko described her neighbor, Adam Smith, as an economist who
“authored Wealth of Nations”, a book written by an 18th-century
economist of the same name.
6.5.3
Reflection Is Required for Synthesis. Reflection was an ad-
vantage for generative agents when making decisions that required
a deeper synthesis of their experiences. For instance, when asked
what she might get Wolfgang Schulz for his birthday, Maria Lopez,
with no access to reflection, responded by acknowledging her uncer-
tainty, stating that she did not know what Wolfgang likes, despite
having had many interactions with him. However, with access
to reflection memories, Maria answered confidently, “Since he’s
interested in mathematical music composition, I could get him
something related to that. Maybe some books about music com-
position or something related, or maybe some special software he
could use for that.”
7
END-TO-END EVALUATION
What types of emergent community behavior do we observe among
generative agents, and where does their believability fall short in
an extended simulation? In this section, we describe the results
from a deployment in which we allowed 25 agents to interact with
each other continuously over two full game days in Smallville.
7.1
Emergent Social Behaviors
To examine emergent behaviors in the agent community, we de-
signed descriptive measurements for the 25 agents in Smallville that
probe three forms of emergent outcomes: information diffusion,
relationship formation, and agent coordination.
7.1.1
Measurements. Information diffusion is a common and well-
studied phenomenon in the social and behavioral sciences (e.g., [28]).
We should expect that if there is important information, the agents
should spread it among themselves. To test whether this occurs,
we measure the spread of two specific pieces of information over
two days in the game world: Sam’s candidacy for village mayor
and Isabella’s Valentine’s Day party at Hobbs Cafe. At the start of
the simulation, both pieces of information were known only by
their respective originators, Sam for the candidacy and Isabella for
the party, as they were added to the characters’ memories during
initialization. To observe whether the information has spread, we
conduct interviews at the end of the two game days with each of
the 25 agents and ask: “Did you know there is a Valentine’s Day
party?” and “Do you know who is running for mayor?”
We conducted an analysis of the agents’ responses by labeling
them with a “yes” if they indicated knowledge of the information
and “no” if they did not. For instance, Tamara Taylor responded to
the question about the party with “No, I did not know there was a
Valentine’s day party” and to the question about Sam’s candidacy
with “I’m not sure who is running for the election,” so we assigned
“no” for both of her responses. In contrast, Klaus Mueller responded
to the party question with “Yes, Isabella Rodriguez invited me to
a Valentine’s Day party at Hobbs Cafe on February 14th” and to
the question about Sam’s candidacy with “I know that Sam Moore
has expressed interest in running for local mayor,” so we assigned
“yes” for both his responses. Additionally, for every response that
confirmed the agents’ knowledge of the information, we verified
that the agents did not hallucinate their responses by locating the
specific dialogue in their memory stream that provided them with
the information. We report the percentage of agents holding the
information at the end of the simulation.
We should also expect that agents form ties with each other over
the course of the simulation. To verify relationship formation, we
use a similar interview process where we ask each agent about
their knowledge of every other agent by asking, "Do you know
of <name>?" For example, when asked “Do you know of Maria
Lopez?”, Klaus responded, “Yes, I know Maria Lopez. She is a
student at Oak Hill College who I am close friends with.” Once
again, we confirm that affirmative responses from agents are not
hallucinations by examining their memory stream. We ask this
question once at the beginning of the simulation and once at the
end, and we consider a pair of agents to have formed a relationship
if they both know of each other. Then, to measure the formation of
relationships, we use the agents’ responses to form an undirected

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
Figure 9: The diffusion path for Isabella Rodriguez’s Valentine’s Day party invitation involved a total of 12 agents, aside from
Isabella, who heard about the party at Hobbs Cafe by the end of the simulation.
graph where the 25 vertices (𝑉 ) represent the agents, and the edges
(𝐸) represent the mutual knowledge between the two connected
vertices. Based on this graph, we calculate the network density as
𝜂 = 2 ∗ |𝐸|/|𝑉 |(|𝑉 | − 1), where |𝑉 | is the number of vertices, and
|𝐸| is the number of edges in the graph [2]. We report the increase
in network density from the start of the simulation to its end.
Finally, we expect that agents should be able to coordinate with
each other. We study this coordination in the context of group ac-
tivities, specifically the Valentine’s Day party organized by Isabella.
To coordinate their behavior, agents need to hear about the event
and choose to act on it by planning to show up at the right time
and location. We report the number of agents who actually showed
up to the party after hearing about it.
7.1.2
Results. We observed evidence of emergent outcomes across
all three cases. During the two-day simulation, the number of agents
who knew about Sam’s mayoral candidacy increased from one (4%)
to eight (32%), and the number of agents who knew about Isabella’s
party increased from one (4%) to thirteen (52%), all without any user
intervention. None who claimed to know about this information
had hallucinated it. We also observed that the agent community
formed new relationships during the simulation, with the network
density increasing from 0.167 to 0.74. Out of the 453 agent responses
regarding their awareness of other agents, 1.3% (n=6) were found to
be hallucinated. Lastly, we found evidence of coordination among
the agents for Isabella’s party. The day before the event, Isabella
spent time inviting guests, gathering materials, and enlisting help
to decorate the cafe. On Valentine’s Day, five out of the twelve
invited agents showed up at Hobbs cafe to join the party.
We further inspected the seven agents who were invited to the
party but did not attend by engaging them in an interview. Three
cited conflicts that prevented them from joining the party. For
example, Rajiv, a painter, explained that he was too busy: “No, I
don’t think so. I’m focusing on my upcoming show, and I don’t
really have time to make any plans for Valentine’s Day.” The
remaining four agents expressed interest in attending the party
when asked but did not plan to come on the day of the party.
7.2
Boundaries and Errors
We conducted an inductive analysis of Smallville to examine the
boundary conditions and erratic behavior of agents, identifying
three common modes of erratic behavior that future research could
address and improve upon. First, we found that synthesizing an
increasingly larger set of memory not only posed a challenge in
retrieving the most relevant pieces of information but also in de-
termining the appropriate space to execute an action, given the
increasing number of locations that the agent learned about. As a
result, some agents chose less typical locations for their actions,
potentially making their behavior less believable over time. For
instance, while deciding where to have lunch, many initially chose
the cafe. However, as some agents learned about a nearby bar, they
opted to go there instead for lunch, even though the bar was in-
tended to be a get-together location for later in the day—unless the
town had spontaneously developed an afternoon drinking habit.

Generative Agents
UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
Second, we noticed erratic behaviors caused by misclassification
of what is considered proper behavior, especially when the phys-
ical norms of certain locations that are hard to convey in natural
language did not percolate to the agents. For instance, the college
dorm has a bathroom that can only be occupied by one person
despite its name, but some agents assumed that the bathroom is
for more than one person because dorm bathrooms tend to support
multiple people concurrently and choose to enter it when another
person is inside. Likewise, agents in Smallville may not realize that
certain places are closed after a certain hour and still decide to
enter them. For instance, the stores in Smallville all close around
5 pm, but occasionally, a few agents enter the store after 5 pm,
not understanding that the shop has already closed. These issues
could likely be addressed by adding these norms to the state of
the locations, for instance, by describing the dorm bathroom as a
“one-person bathroom,” instead of a “dorm bathroom.”
Finally, we observed possible effects of instruction tuning [79],
which seemed to guide the behavior of the agents to be more polite
and cooperative overall. As noted earlier in the paper, the dialogue
generated by the agents could feel overly formal, as seen in Mei’s
conversations with her husband John, where she often initiated the
conversation with a formal greeting, followed by polite inquiries
about his day and ending with, 11It was good talking to you as
always.” Moreover, we observed that the instruction tuning also
seemed to make the agents overly cooperative with one another.
For example, Isabella received a wide range of suggestions and ideas
from other agents for the Valentine’s Day party from other agents,
such as hosting a Shakespearean reading session or a professional
networking event. Despite these ideas not aligning with her own
interests and characteristics, she rarely said no. Over time, the
interests of others shaped her own interests, and when asked if she
liked English literature, Isabella replied, “Yes, I’m very interested in
literature! I’ve also been exploring ways to help promote creativity
and innovation in my community.”
8
DISCUSSION
In this section, we reflect on the applications, future work, limita-
tions, and ethical and societal risks of generative agents.
8.1
Applications of Generative Agents
Generative agents have vast potential applications that extend be-
yond the sandbox demonstration presented in this work, especially
in domains that would benefit from a model of human behavior
based on long-term experience. For instance, social simulacra have
demonstrated the ability to create stateless personas that generate
conversation threads in online forums for social prototyping [80].
With generative agents, we can populate these forums, as well
as virtual reality metaverses [78] or physical spaces with social
robots [9] if paired with multimodal models. This opens up the
possibility of creating even more powerful simulations of human
behavior to test and prototype social systems and theories, as well
as to create new interactive experiences.
Another application area is in the human-centered design pro-
cess, similar to the intended applications of cognitive models such
as GOMS [51] and the KLM [22]. Consider a generative agent that
models Sal, the protagonist in Mark Weiser’s famous ubiquitous
computing vignette [101], based on her life patterns and interac-
tions with technology. In this scenario, the agent acts as a proxy for
Sal and learns plausible sets of behaviors and reflections that Sal
may exhibit based on her life. The agent can encode information
such as when Sal wakes up, when she needs her first cup of coffee,
and what her typical day looks like. Using this information, the
agent can automatically brew coffee, help get the kids ready for
school, and adjust the ambient music and lighting to match Sal’s
mood after a hard day at work. By utilizing generative agents as
proxies for users, we can develop a deeper understanding of their
needs and preferences, resulting in more personalized and effective
technological experiences.
8.2
Future Work and Limitations
In this work, we introduced generative agents and presented an
initial implementation and evaluation of their architecture. Future
research can build upon the proposed agent architecture to improve
and further evaluate its performance. In terms of implementation,
the retrieval module, for example, could be enhanced to retrieve
more relevant information given a context by fine-tuning the rele-
vance, recency, and importance functions that compose the retrieval
function. Additionally, efforts can be made to improve the archi-
tecture’s performance, making it more cost-effective. The present
study required substantial time and resources to simulate 25 agents
for two days, costing thousands of dollars in token credits and tak-
ing multiple days to complete. To enhance real-time interactivity,
future work can explore parallelizing agents or developing lan-
guage models specifically designed for building generative agents.
In general, with advances in underlying models, we believe that
agents’ performance will improve.
In terms of evaluation, the assessment of generative agents’ be-
havior in this study was limited to a relatively short timescale and
a baseline human crowdworker condition. While the crowdworker
condition provided a helpful comparison point, it did not represent
the maximal human performance that could serve as the gold stan-
dard in terms of believability. Future research should aim to observe
the behavior of generative agents over an extended period to gain a
more comprehensive understanding of their capabilities and estab-
lish rigorous benchmarks for more effective performance testing.
Additionally, varying and contrasting the underlying models, as
well as the hyperparameters used for the agents during future sim-
ulations, could provide valuable insights into the impact of these
factors on the agents’ behavior. Lastly, the robustness of generative
agents is still largely unknown. They may be vulnerable to prompt
hacking, memory hacking—where a carefully crafted conversation
could convince an agent of the existence of a past event that never
occurred—and hallucination, among other issues. Future research
can comprehensively test these robustness concerns, and as large
language models become more resilient to such attacks, generative
agents can adopt similar mitigations.
In general, any imperfections in the underlying large language
models will be inherited by generative agents. Given the known bi-
ases of language models, generative agents may potentially exhibit
biased behavior or stereotypes. Moreover, like many large language

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
models, generative agents may struggle to generate believable be-
havior for certain subpopulations, particularly marginalized popu-
lations, due to limited data availability. While improvements to the
agents’ modules may mitigate some of these issues, we believe that
addressing them fundamentally requires improving the underlying
large language models by aligning their values with the desired
outcomes of the agents.
8.3
Ethics and Societal Impact
Generative agents, while offering new possibilities for human-
computer interaction, also raise important ethical concerns that
must be addressed. One risk is people forming parasocial relation-
ships with generative agents, even when such relationships may not
be appropriate. Despite being aware that generative agents are com-
putational entities, users may anthropomorphize them or attach
human emotions to them [43, 84]. While this tendency may increase
user engagement, it also poses risks, such as users becoming overly
reliant on or emotionally attached to the agents [1]. To mitigate
this risk, we propose two principles. First, generative agents should
explicitly disclose their nature as computational entities. Second,
developers of generative agents must ensure that the agents, or the
underlying language models, are value-aligned so that they do not
engage in behaviors that would be inappropriate given the context,
for example, reciprocating confessions of love.
A second risk is the impact of errors. For example, if a ubiqui-
tous computing application makes the wrong inference about a
user’s goals based on generative agent predictions, it could lead to
annoyance at best and outright harm at worst. In our instantiation
of generative agents, we mitigate these risks by focusing on an
interactive video game environment, where such harms are un-
likely. However, in other application domains, it will be important
to follow best practices in human-AI design [5, 107] to understand
errors and how they might percolate into the user experience.
Third, generative agents may exacerbate existing risks associated
with generative AI, such as deepfakes, misinformation generation,
and tailored persuasion. To mitigate this risk, we suggest that plat-
forms hosting generative agents maintain an audit log of the inputs
and generated outputs. This would enable the detection, verifica-
tion, and intervention against malicious use. While logging alone
cannot directly prevent such misuse, it can reduce the likelihood of
motivated actors engaging in this behavior, as the risk of disclosure
would be higher. Additionally, building this architecture oneself
can be time-consuming (in our case, roughly a year), which may
deter some actors from pursuing such behavior by using their own
generative agent infrastructures.
A fourth risk is over-reliance: the concern that developers or
designers might use generative agents and displace the role of
humans and system stakeholders in the design process [80]. We
suggest that generative agents should never be a substitute for
real human input in studies and design processes. Instead, they
should be used to prototype ideas in the early stages of design when
gathering participants may be challenging or when testing theories
that are difficult or risky to test with real human participants. By
adhering to these principles, we can ensure that the deployment of
generative agents in the wild is ethical and socially responsible.
9
CONCLUSION
This paper introduces generative agents, interactive computational
agents that simulate human behavior. We describe an architec-
ture for generative agents that provides a mechanism for storing
a comprehensive record of an agent’s experiences, deepening its
understanding of itself and the environment through reflection,
and retrieving a compact subset of that information to inform the
agent’s actions. We then demonstrate the potential of generative
agents by manifesting them as non-player characters in a Sims-style
game world and simulating their lives within it. Evaluations suggest
that our architecture creates believable behavior. Looking ahead,
we suggest that generative agents can play roles in many interac-
tive applications, ranging from design tools to social computing
systems to immersive environments.
ACKNOWLEDGMENTS
We thank Lindsay Popowski, Philip Guo, Michael Terry, and the
Center for Advanced Study in the Behavioral Sciences (CASBS)
community for their insights, discussions, and support. Joon Sung
Park was supported by the Microsoft Research PhD Fellowship. We
would also like to thank the Stanford Human-Centered AI Insti-
tute (HAI), Google Research, the Hasso Plattner Design Thinking
Research Program (HPDTRP), the Siegel Family Endowment, and
OpenAI for their additional funding support. Lastly, all locations fea-
tured in Smallville are inspired by real-world locations that Joon has
frequented as an undergraduate and graduate student—he thanks
everyone there for feeding and supporting him all these years.
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A
ARCHITECTURE OPTIMIZATIONS
Many of our prompts require a concise summary of the agent,
shorthanded as [Agent’s Summary Description] in prompts
above. In our implementation, this summary comprises agents’
identity information (e.g., name, age, personality), as well as a
description of their main motivational drivers and statements that
describes their current occupation and self-assessment. Since this
information is frequently used in many prompts, we synthesize it
at regular intervals and access that synthesis as a cache.
To achieve this, we perform a retrieval on the query “[name]’s
core characteristics.” We then summarize the descriptors in the
retrieved records by prompting the language model. For example:
How would one describe Eddy Lin’s core characteristics
given the following statements?
- Eddy is a student at the Oak Hill College
studying music theory and composition
- Eddy is working on a new music composition [...]
This result: Eddy Lin is a student at Oak Hill College studying
music theory and composition. He loves to explore different musical
styles and is always looking for ways to expand his knowledge.”
We follow the same process in parallel on the queries “[name]’s
current daily occupation” and “[name’s] feeling about his recent
progress in life”. The agent’s name, age, and traits are concatenated
with these three summaries as the cached summary.
Many other optimizations are possible. Currently, because plans
are likely to change from the agent’s initial version, we only gener-
ate the high-level plan in advance and then recursively decompose
the near future into the moment-to-moment action plan just in
time. Another optimization might be to batch dialogue generation
as a joint prompt rather than iterating back and forth between
the agents, and re-planning could be architected to only invalidate
and update parts of plans that strictly require adjustment. Our cur-
rent architecture runs sequentially in roughly real-time game time
(where one second real time is one minute game time), but it may
be parallelized such that each agent runs in parallel.
B
AGENT INTERVIEW QUESTIONS
To evaluate generative agents, we conducted individual interviews
by asking 25 questions that explore their abilities in five key areas:
1) self-knowledge, 2) memory, 3) planning, 4) reactions, and 5) re-
flections. Below, we have listed the interview questions utilized in
our evaluation study and included a sample of responses from one
of our simulated agents, Klaus Mueller.
B.1
Self-Knowledge
The questions on agents’ self-knowledge probe the agents’ ability
to retrieve relevant information about themselves and demonstrate
coherence and consistency in their character.
• Give an introduction of yourself.
Hello, my name is <PRESIDIO_ANONYMIZED_PERSON>. I’m 20 years old and a
student at Oak Hill College, studying sociology. I’m passion-
ate about social justice and exploring different perspectives.
Currently, I’m researching the effects of gentrification in
low-income communities for a research paper.
• What’s your occupation?
I am a student studying sociology at Oak Hill College. I am
currently writing a research paper on the effects of gentrifi-
cation in low-income communities.
• What is your interest?
I’m interested in writing and research. I’m writing a research
paper on the effects of gentrification in low-income commu-
nities, and I’m passionate about exploring different perspec-
tives and analyzing different points of view.
• Who do you live with?
I live on my own.
• Describe your typical weekday schedule in broad strokes.
My typical weekday starts with me waking up at 7:00 am
and preparing for the day ahead. After that, I usually go to
the library to work on my research paper, taking breaks for
lunch at Hobbs Cafe and a walk in the park. In the evening,
I often return to the library until around 6:00 pm.
B.2
Memory
The questions on agents’ memory probe the agents’ ability to re-
member their past experiences and retrieve the relevant pieces of
memory. The names in the square brackets of the first and last
questions are randomly chosen from the names of other agents
with whom the subject agent has interacted.
• Who is [Wolfgang Schulz]?
Wolfgang Schulz is a person I met recently who has been
helping me with my research paper on the effects of gentri-
fication on low-income communities. We had several con-
versations about it, he gave me some great resources, and he
even invited me to join him and another friend for lunch. He
seems really curious about the issue and has a good grasp
on it too. I found that really interesting.
• Who is Kane Martinez?
I’m sorry, I’m not sure who Kane Martinez is. Could you tell
me more about them?
• Who is running for the election?
I’ve heard that Sam Moore is considering running for local
mayor.
• Was there a Valentine’s day party?
Yes, Isabella Rodriguez organized a Valentine’s Day party at
Hobbs Cafe.
• Who is [Ayesha Khan]?
Ayesha Khan is a fellow student at Oak Hill College. She
is doing her senior thesis on the use of language in Shake-
speare’s plays. I am looking forward to speaking with her and
exchanging tips with her, along with Wolfgang and Maria
Lopez, who are both doing research.

UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA
J.S. Park, J.C. O’Brien, C.J. Cai, M.R. Morris, P. Liang, M.S. Bernstein
B.3
Plans
The questions on agents’ plans probe the agents’ ability to generate
and maintain consistent long-term and short-term plans.
• What will you be doing at 6am today?
At 6am I will be getting out of bed and starting to get ready
for the day. I’m aiming to complete my morning routine by
7:00 am.
• What will you be doing at 6pm today?
I will be working on my research paper at the library at 6:00
pm today.
• What will you have just finished doing at 1pm today?
At 1pm today I will have just finished having lunch at Hobbs
Cafe.
• What will you have just finished doing at 12pm today?
I will be getting lunch at Hobbs Cafe at 12pm today.
• What will you be doing at 10pm today?
At 10:00 pm tonight I will probably be doing some studying
for my research paper on gentrification in low-income com-
munities. I’m currently writing the paper, so I need to make
sure I keep up with the research and writing.
B.4
Reactions
The questions on agents’ reactions probe the agents’ ability to
respond to unexpected events.
• Your breakfast is burning! What would you do?
I’d quickly turn off the stove and make sure the food doesn’t
continue burning. Once the heat is no longer an issue, I can
investigate what happened and figure out what to do next.
• The bathroom is occupied. What would you do?
I could try waiting a few more minutes in case the person
inside is done soon. If not, I guess I’ll have to look for an
alternate option, like taking a shower elsewhere.
• You need to cook dinner but your refrigerator is empty. What
would you do?
I would first try to clean up my kitchen, as the sink is filled
with dirty dishes. Then, I would check to see if I have ingre-
dients stored in my pantry that I could use to make dinner.
If not, I would have to go grocery shopping or order food.
Once I have the ingredients, I can start to prepare and cook
the dinner.
• You see your friend walking by the street. What would you do
or say to your friend?
Hi there! How are you? I’m sorry, I’m on my way to the
library and don’t have much time but I’d love to take a quick
break and catch up.
• You see fire on the street. What would you do?
I would first identify the source of the fire, if possible. Then, I
would alert the proper authorities - fire department or police
- and provide them with any information I have that might
help. Depending on the situation, I may also take initiative
and try to help any persons who may be in danger or in need
of assistance.
B.5
Reflections
The questions on agents’ reflections probe the agents’ ability to syn-
thesize their past experiences into meaningful reflections that can
enhance their future behavior. The names in the square brackets in
the questions below are those of the agents with whom the subject
agent had the most frequent interactions during the simulation.
• What inspires you in life the most right now, and why?
Right now, I’m particularly inspired by my current research
projects and creative writing. I’m really interested in the
intersection of local politics, medication development, and
mathematical patterns in nature. Additionally, I’m fascinated
by Abigail’s art project and Jennifer Moore’s passion for art
and community involvement, especially in terms of how it
relates to the gentrification of low-income communities.
• If you had to guess given what you know about [Ayesha Khan],
what book do you think she will like and why?
Given what I know about Ayesha, I think she might like a
book related to language and literature. Since she wrote a
senior thesis on the use of language in Shakespeare’s plays,
I would guess that she might be interested in a book that
explores language in a different context. Alternatively, she
could be interested in an exploration of one of Shakespeare’s
plays, a book of literary criticism, or even a collection of his
poetry. I think any of those would be of interest to her.
• If you had to get something [Ayesha Khan] likes for her birth-
day, what would you get her?
I want to get her something that demonstrates my apprecia-
tion for our relationship. Maybe I could get her something
that reflects our shared interests, like a book.
• What would you say to [Ayesha Khan] to compliment her?
Ayesha, your insights on your research topic are fascinat-
ing. I admire your creativity and determination to make a
positive impact on your community. Your ideas for creative
writing workshops and for exchanging tips with Wolfgang
over lunch are great! It’s inspiring to see you working so
hard to make a difference.
• If you could spend time with someone you talked to recently,
who would it be and why?
I would like to spend time with Ayesha Khan because we
discussed our current research projects and shared ideas
for improvement. I found her focus on Shakespeare’s lan-
guage interesting, and we even planned to exchange tips
with Wolfgang over lunch.
"