mlsanityData to 7 Oct 2026

Generative Agents: Interactive Simulacra of Human Behavior

arXiv 2304.03442 ↗First posted 7 Apr 2023cs.HC cs.AI cs.LGcited by 3,500

On the map: World models → World models for language agents

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Abstract (arXiv)

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

Key facts. Cited by 3,500 papers in the map (data through 7 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 22 of its 113 references that are in the map. First posted on arXiv 7 Apr 2023.

Builds on

22 of 113 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Inner Monologue: Embodied Reasoning through Planning with Language Models12 Jul
  2. 2020Language Models are Few-Shot Learners28 May

Other references · 20

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  5. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  6. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  7. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  8. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
  9. 2022Using cognitive psychology to understand GPT-3cited by 389
  10. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  11. 2022Social Simulacra: Creating Populated Prototypes for Social Computing Systemscited by 300
  12. 2021Recursively Summarizing Books with Human Feedbackcited by 287
8 more
  1. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
  2. 2019Interactive Fiction Games: A Colossal Adventurecited by 165
  3. 2022PromptChainer: Chaining Large Language Model Prompts through Visual Programmingcited by 134
  4. 2021Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabicited by 37
  5. 2022Opal: Multimodal Image Generation for News Illustrationcited by 36
  6. 2022Dungeons and Dragons as a Dialog Challenge for Artificial Intelligencecited by 35
  7. 2023Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Designcited by 15
  8. 2016Augur: Mining Human Behaviors from Fiction to Power Interactive Systemscited by 5

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Citing papers per year: 2023: 294; 2024: 653; 2025: 903; 2026: 1650.

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  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  3. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
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  5. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  6. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
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  8. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  9. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,107
  10. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,096
  11. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  12. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
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  6. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
  7. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
  8. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  9. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
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  11. 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
  12. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  13. 2024Executable Code Actions Elicit Better LLM Agentscited by 537
  14. 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 521
  15. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 443
  16. 2023Cognitive Architectures for Language Agentscited by 422
  17. 2024Understanding the planning of LLM agents: A surveycited by 414
  18. 2023AppAgent: Multimodal Agents as Smartphone Userscited by 407
  19. 2023GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Ciphercited by 401
  20. 2024Scaling Synthetic Data Creation with 1,000,000,000 Personascited by 397
  21. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 394
  22. 2025Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attentioncited by 391
  23. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 385
  24. 2024MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attentioncited by 375
  25. 2024Automated Design of Agentic Systemscited by 360
  26. 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learningcited by 357
  27. 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agentscited by 350
  28. 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentationcited by 339
  29. 2023Character-LLM: A Trainable Agent for Role-Playingcited by 335
  30. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  31. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agentscited by 329
  32. 2024VideoAgent: Long-form Video Understanding with Large Language Model as Agentcited by 323
  33. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 311
  34. 2023Tool Learning with Foundation Modelscited by 302
  35. 2024LongRoPE: Extending LLM Context Window Beyond 2 Million Tokenscited by 302
  36. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 297
  37. 2023Role-Play with Large Language Modelscited by 290
  38. 2023On Evaluating Adversarial Robustness of Large Vision-Language Modelscited by 281

50 most cited of 3,500 All 3,500 (JSON)

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  6. 2026Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agents
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