---
title: 'Generative Agents: Interactive Simulacra of Human Behavior'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2304-03442-generative-agents.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Joon Sung Park, Joseph C. O''Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, Generative Agents: Interactive Simulacra of Human Behavior (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: agents'
tags: arxiv, agents, memory, simulation
arxiv_id: 2304.03442
alias: 2304.03442-generative-agents
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Generative Agents: Interactive Simulacra of Human Behavior'
    publisher: arXiv
    authors: 'Joon Sung Park, Joseph C. O''Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein'
    url: https://arxiv.org/abs/2304.03442
    version_read: 'v2 (6 Aug 2023)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Generative Agents: Interactive Simulacra of Human Behavior

## Summary
[Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442) (2023) showed that language-model agents with memory, planning, and social routines can sustain believable long-horizon behavior in an interactive environment. The well-known "small town" sandbox is the demonstration: agents remember events, form plans, and interact over many steps.

What it introduced: an agent architecture that stores a stream of experiences, summarizes and retrieves from that memory, and uses the result to plan, plus a human evaluation of how believable the resulting behavior is.

What it led to: a large body of work on memory-using agents and multi-agent simulations. Live notes on related designs include [ao-architecture-cognitive-agents](https://iinventedlight.com/library/notes/ao-architecture-cognitive-agents/) and [ao-memory-mechanisms-survey](https://iinventedlight.com/library/notes/ao-memory-mechanisms-survey/).

It is a good first paper if you want a concrete memory-plus-planning loop rather than a single-shot agent prompt. Where to read it: https://arxiv.org/abs/2304.03442 (v2, last revised 6 Aug 2023).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).