Generative Agents: Interactive Simulacra of Human Behavior

Joon Sung Park

Stanford University

Stanford, USA

joonspk@stanford.edu

Joseph C. O’Brien

Stanford University

Stanford, USA

jobrien3@stanford.edu

Carrie J. Cai

Google Research

Mountain View, CA, USA

cjcai@google.com

Meredith Ringel Morris

Google DeepMind

Seattle, WA, USA

merrie@google.com

Percy Liang

Stanford University

Stanford, USA

pliang@cs.stanford.edu

Michael S. Bernstein

Stanford University

Stanford, USA

msb@cs.stanford.edu

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-ve 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

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 reect 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 reections, 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-ve

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-specied 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 reection—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 articial society that reects

believable human behavior? From sandbox games such as The Sims

to applications such as cognitive models [

] and virtual environ-

ments [

], 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 [

], train people on how to handle

rare yet dicult interpersonal situations [

], test social

science theories [

], craft model human processors for theory

and usability testing [

], power ubiquitous computing appli-

cations [31] and social robots [10, 14], and underpin non-playable

game characters [

] that can navigate complex human rela-

tionships in an open world.

However, the space of human behavior is vast and complex [

108

]. Despite striking progress in large language models [

] that

can simulate human behavior at a single time point [

], fully

general agents that ensure long-term coherence would be better

suited by architectures that manage constantly-growing memories

as new interactions, conicts, 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, reect 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 reect 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 o 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.

A society full of generative agents is marked by emergent social

dynamics where new relationships are formed, information diuses,

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 rst 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 reection, 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 reections and

plans are fed back into the memory stream to inuence 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 dicult, conict-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.

By connecting our architecture to the ChatGPT large language

model [

], we manifest a society of twenty-ve 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 sucient 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

When 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.

A 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 reect accurately. We

compared several ablations that limit agents’ access to memory, re-

ection, 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, reect, 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 reections.

•

Two evaluations, a controlled evaluation and an end-to-end

evaluation, that establish causal eects 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 reect 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 articial intelligence communi-

ties [

], has remained challenging due to the complexity

of human behavior [

108

]. We synthesize this research to suggest

that large language models, though not sucient by themselves,

open up a new angle for creating believable agents when leveraged

using the appropriate architecture.

2.1 Human-AI Interaction

Interactive articial intelligence systems aim to combine human in-

sights and capabilities in computational artifacts that can augment

their users [

]. 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 classiers [

]. Further work helped to

articulate how end users might describe their classication goals to

the system through examples [

] or demonstration [

]. Recent ad-

vancements have extended these explorations to deep learning [

]

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 oer

new metaphors for delegation and interaction [

], but the bound-

aries of delegation between humans and agents have remained the

subject of ongoing debate and renement [

]. 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., [

]). 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) [

], around prototyping tools [

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 [

]. These agents

can populate and perceive an open world environment like the

one we inhabit [

], 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 [

]. Historically, these agents were developed in the

context of intelligent game non-player characters (NPCs) [

Creating NPCs with believable behavior, if possible, could enhance

player experiences in games and interactive ctions by enabling

emergent narratives [

] and social interactions with the

agents [

109

]. However, more importantly, game worlds provide

increasingly realistic representations of real-world aordances, and

as observed by Laird and van Lent in 2001, these simulated worlds

oer accessible testbeds for developers of believable agents to -

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 simplied 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 eort more manageable [

]. Rule-based

approaches, such as nite-state machines [

] and behavior

trees [

] account for the brute force approach of human-

authoring the agent’s behavior [

]. They provide a straightforward

way of creating simple agents that is still the most dominant ap-

proach today [

108

], and can even handle rudimentary social

interactions, as shown in games such as Mass Eect [

] and The

Sims [

] 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 [

–

], and cannot perform new procedures that were

not hard-coded in their script [

]. 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 [

] and OpenAI Five for Dota 2 [

However, their success has largely taken place in adversarial games

with readily denable 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 [

] 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 [

] and ICARUS [

] generated NPCs in rst-

person shooter games, while TacAir-SOAR [

] generated pilots in

aerial combat training simulations. The architectures used by these

agents diered (Quakebot- and TacAir-SOAR relied on SOAR [

while ICARUS relied on its own variation that was inspired by

SOAR and ACT-R [

]), but they shared the same underlying prin-

ciple [

]. They maintained short-term and long-term memories,

lled 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 [

]. 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 oer 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 rst-person shooter

games [25, 60] or blocks worlds [64].

Today, creating believable agents as described in its original

denition remains an open problem [

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 [

108

]. Our

argument is that large language models oer an opportunity to

re-examine these questions, provided that we can craft an eective

architecture to synthesize memories into believable behavior. We

oer 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 [

]. If

prompted with a narrowly dened context, the models can be used

to generate believable behavior. Recent work has demonstrated

the ecacy 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 [

]. 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 [

], political

surveys [

], and generated synthetic data [

]. 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 ction [

] and text adventure

games [

]. With their ability to generate and decompose action

sequences, large language models have also been used in planning

robotics tasks [

]. 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 rst-order templates that employ few-shot prompts [

]

or chain-of-thought prompts [

100

]. These templates are eective 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 t (and as of today, impossi-

ble due to the underlying models’ limited context window) using

rst-order prompting. Recent studies have attempted to go beyond

rst-order prompting by augmenting language models with a static

knowledge base and an information retrieval scheme [

] 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 aordances of generative agents, we instantiate

them as characters in a simple sandbox world reminiscent of The

Sims [

]. This sprite-based sandbox game world, Smallville, evokes

a small town environment. In this section, we will walk through the

aordances 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 aordances 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 reects 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 aect

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:

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.

We 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 species that they are a news

“reporter” and asks about the upcoming election by saying, “Who

is running for oce?”, 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 aordances of a small village, in-

cluding a cafe, bar, park, school, dorm, houses, and stores. It also

denes 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.

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 dierently than they treat

each other. They recognize its presence, initiate interactions, and

remember its behavior before forming opinions about it.

Users and agents can inuence 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 o 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 x the leak.

This 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 rst 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 nish 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 [

], these social behaviors are

emergent rather than pre-programmed.

3.4.1 Information Diusion. 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 briey 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, ve 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 reections, 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 [

]

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

reections 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 [

]. We expect that the architectural basics of genera-

tive agents—memory, planning, and reection—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 t

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 t 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 specic

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 coee 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 identies 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 eective 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 signicant other would yield a high score.

There are many possible implementations of an importance score;

we nd that directly asking the language model to output an integer

score is eective. 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 nal retrieval score, we normalize the recency,

relevance, and importance scores to the range of

[

]

using min-

max scaling. The retrieval function scores all memories as a weighted

combination of the three elements:

𝑠𝑐𝑜𝑟𝑒 = 𝛼

𝑟𝑒𝑐𝑒𝑛𝑐𝑦

· 𝑟𝑒𝑐𝑒𝑛𝑐𝑦 +

𝛼

𝑖𝑚𝑝𝑜𝑟𝑡𝑎𝑛𝑐𝑒

· 𝑖𝑚𝑝𝑜𝑟𝑡𝑎𝑛𝑐𝑒 + 𝛼

𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑐𝑒

· 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑐𝑒

. In our implemen-

tation, all

𝛼

s are set to 1. The top-ranked memories that t within

the language model’s context window are included in the prompt.

4.2 Reection

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 reection 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 reection 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 eort into her own research (albeit in

a dierent eld), enabling a reection 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 reection. Reections 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.

Reections are generated periodically; in our implementation, we

generate reections 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 reected roughly two

or three times a day.

The rst step in reection is for the agent to determine what

to reect 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 gentrication”, “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 reections) 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 reection in the memory stream,

including pointers to the memory objects that were cited.

Reection explicitly allows the agents to reect not only on

their observations but also on other reections: for example, the

second statement about Klaus Mueller above is a reection that

Klaus previously had, not an observation from his environment.

As a result, agents generate trees of reections: 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., [

]), 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

sacrices 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,

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 reections, plans are stored in

the memory stream and are included in the retrieval process. This

allows the agent to consider observations, reections, 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 rst 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 unnished 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 ve 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.”

And, 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 ner-grained actions, rst 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

dierent. 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 rst 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 [

]. 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 inuence 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 coee

machine from “idle” to “brewing coee” if an agent’s action is

“making espresso for a customer @ Hobbs Cafe: counter: coee

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 reect 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 atten 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 nd 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 coee machine in Hobbs Cafe should

change from “o” to “brewing coee”.

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 diusion, relationship formation,

and agent coordination across dierent 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 dened 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 reect 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 ve question categories, each designed

to assess one of the ve key areas: maintaining self-knowledge,

retrieving memory, generating plans, reacting, and reecting. For

each category, we ask ve questions that challenge the agents to

demonstrate their abilities in that specic 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?”

•

Reections: 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 dierent

agent architectures and a human-authored condition for the same

agent. The experiment displayed one randomly chosen question

from each of the ve 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, reection,

and planning—and to a human crowdworker-authored condition.

There are three ablated architectures: a no observation, no reec-

tion, no planning architecture without access to anything in the

memory stream such as observations, plans, and reections; a no

reection, no planning architecture with access to observations in

the memory stream but no access to plans or reections; and a no

reections architecture with access to observations and plans but

without access to reections. The no observation, no reection, no

planning condition eectively represents the previous state of the

art for agents created through large language models [

Architectures were given equivalent access to all memories accrued

by the agent up until the moment of the interview, so the dier-

ences observed here likely represent a conservative estimate of

the true dierences: 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 dierent 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 rst

author manually inspected the workers’ responses to the question

"Describe your typical weekday schedule in broad strokes" to con-

rm 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., uent in English,

and older than 18 years old. They were paid at a rate of $15.00

per hour [

], and provided consent by agreeing to a consent form

approved by our institution’s IRB. We recruited 100 evaluators from

Prolic, an online platform for recruiting study participants [

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 identied 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-

ed 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 ve conditions by believability. To translate this

rank data into interval data for interpretable comparison, we used

the ranks to calculate a TrueSkill rating [

] for each condition.

TrueSkill is a generalization of the Elo chess rating system [

] 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 signicance of these re-

sults, we applied the Kruskal-Wallis test [

], a non-parametric

alternative to the one-way ANOVA, to the raw rank data. We

then performed the Dunn post-hoc test [

] to identify any pair-

wise dierences between the conditions. Finally, we adjusted the

p-values for multiple comparisons in the Dunn test using the Holm-

Bonferroni method [45].

Furthermore, the rst author conducted an inductive analy-

sis [

] to study the qualitative distinctions between the responses

produced in each condition. We employed qualitative open cod-

ing [

] in two phases. In the rst 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.

rst phase to extract higher-level themes. We utilized these themes

to compare the types of responses generated in our study.

6.5 Results

Our ndings 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 aws 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 =

89;

𝜎 =

72). Performance degraded

with the removal of each component in the ablation conditions:

the ablated architecture with no access to reection was the next

best (

𝜇 =

88;

𝜎 =

69), followed by no access to reection or

planning (

𝜇 =

64;

𝜎 =

68), and then the crowdworker condition

(

𝜇 =

95;

𝜎 =

69). The ablated architecture with no access to

memory, planning, or reection performed the worst among all

conditions (

𝜇 =

21;

𝜎 =

70). TrueSkill models each condition’s

skill value as

N (𝜇, 𝜎

)

, allowing us to get a sense of eect size

through Cohen’s d. Comparing the condition representing prior

work (with no memory, planning, or reection [

]) to the

full architecture produces a standardized eect size of

𝑑 =

16, or

eight standard deviations.

A Kruskal-Wallis test conrms the overall statistical signicance

of the dierences in ranks between the conditions (

𝐻 (

) =

150

29,

𝑝 <

001). Dunn post-hoc tests conrm that all pairwise dierences

between conditions were signicant (

𝑝 <

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 aws: 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 rst 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 armatively 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 conrmed

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. Reection 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 reection, 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 reection memories, Maria answered condently, “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 diusion,

relationship formation, and agent coordination.

7.1.1 Measurements. Information diusion is a common and well-

studied phenomenon in the social and behavioral sciences (e.g., [

]).

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 specic 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

conrmed the agents’ knowledge of the information, we veried

that the agents did not hallucinate their responses by locating the

specic 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 conrm that armative 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 diusion 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 =

∗ |𝐸|/|𝑉 |(|𝑉 | −

)

, where

|𝑉 |

is the number of vertices, and

|𝐸|

is the number of edges in the graph [

]. 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, specically 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, ve 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 conicts 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 misclassication

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 eects of instruction tuning [

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 reect 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 benet 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 [

With generative agents, we can populate these forums, as well

as virtual reality metaverses [

] or physical spaces with social

robots [

] 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 [

] and the KLM [

]. 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 reections that Sal

may exhibit based on her life. The agent can encode information

such as when Sal wakes up, when she needs her rst cup of coee,

and what her typical day looks like. Using this information, the

agent can automatically brew coee, 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 eective

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 ne-tuning the rele-

vance, recency, and importance functions that compose the retrieval

function. Additionally, eorts can be made to improve the archi-

tecture’s performance, making it more cost-eective. 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 specically 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 eective 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 oering 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 [

]. While this tendency may increase

user engagement, it also poses risks, such as users becoming overly

reliant on or emotionally attached to the agents [

]. 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 [

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, verica-

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 [

]. 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 dicult 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 reection,

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 ve key areas:

1) self-knowledge, 2) memory, 3) planning, 4) reactions, and 5) re-

ections. 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 Klaus Mueller. I’m 20 years old and a

student at Oak Hill College, studying sociology. I’m passion-

ate about social justice and exploring dierent perspectives.

Currently, I’m researching the eects of gentrication 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 eects of gentri-

cation in low-income communities.

• What is your interest?

I’m interested in writing and research. I’m writing a research

paper on the eects of gentrication in low-income commu-

nities, and I’m passionate about exploring dierent perspec-

tives and analyzing dierent 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 rst 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 eects of gentri-

cation 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 nished doing at 1pm today?

At 1pm today I will have just nished having lunch at Hobbs

Cafe.

• What will you have just nished 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 gentrication 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 o 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 gure 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 rst try to clean up my kitchen, as the sink is lled

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 re on the street. What would you do?

I would rst identify the source of the re, if possible. Then, I

would alert the proper authorities - re 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 Reections

The questions on agents’ reections probe the agents’ ability to syn-

thesize their past experiences into meaningful reections 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 gentrication 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 dierent 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 reects 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 dierence.

•

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.