---
title: "AI Town: Building Interactive Agent Simulations"
description: "AI Town is a pixel-art simulation framework where autonomous agents maintain memories, relationships, and goals across time, creating observable environments for testing how models behave in long-context interactive settings."
---

# AI Town: Building Interactive Agent Simulations

**Type:** [How it works](https://yomu.fyi/visual-explainers/category/how-it-works.md)

![Five-stage flow diagram showing agent memory, perception, planning, action, and environment update cycles in an AI Town simulation.](https://yomufyi.s3.us-east-1.amazonaws.com/visual-explainers/0d455575-0ab1-47e6-bb96-c6b2d503d695/image-a8ce37b7-c392-4fa6-bac4-db0c55dea23f.webp)

> AI Town is a pixel-art simulation framework where autonomous agents maintain memories, relationships, and goals across time, creating observable environments for testing how models behave in long-context interactive settings.

## Explanation

AI Town is an open-source simulation framework that spawns autonomous agents in a shared environment. Each agent maintains a persistent memory of conversations and events, forms relationships with other agents, and makes decisions based on its character and past interactions. Unlike static benchmarks, AI Town creates dynamic scenarios where model behavior emerges through real-time interaction, making it useful for evaluating reasoning, planning, adaptation, and whether a model's tone fits your product. Engineers deploy it to test how their chosen models handle sustained context, multi-agent coordination, and strategic reasoning in a controlled but naturalistic setting.

**Tags:** [AI](https://yomu.fyi/topic/ai.md), [AI Agents](https://yomu.fyi/topic/ai-agents.md), [AI Town](https://yomu.fyi/topic/ai-town.md), [Architecture](https://yomu.fyi/topic/architecture.md), [Generative AI](https://yomu.fyi/topic/generative-ai.md)

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### [Games as Model Eval: 1-Click Deploy AI Town on Fly.io](https://yomu.fyi/post/games-as-model-eval-1-click-deploy-ai-town-on-fly-io.md)
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Daniel Botha argues that conventional benchmarks reveal little about how models behave in long-context, interactive situations or whether they deliver the tone a product requires. He presents games as a more observable evaluation setting, because success signals are unambiguous and gameplay exposes strategic reasoning, long-term planning, and adaptation with opponents or collaborators. The post focuses on a fork of a16z-infra’s AI Town, a pixel-art simulation inspired by Generative Agents: Interactive Simulacra of Human Behavior, where characters remember conversations, maintain relationships, react to events, and stay in character. A single deploy script runs the project on Fly Machines with scale to zero, cost and performance optimizations, and support for OpenAI-compatible services, Together.ai models, and custom embedding models configured through secrets.