Exploring Emergence: Building a 100-Agent City Simulation with Local LLMs

What happens when you give 100 autonomous AI agents a few dollars, place them on a real-world city map, and give them complete free will? This is the question that drove the creation of Emergent City , an open-source, AI-driven city simulation project. Rather than scripting predefined behaviors, this project aims to explore emergence —how macro-economic and geographical patterns naturally arise from simple, localized AI decisions. In this technical breakdown, I'll explain how I built a buttery smooth, 100-agent simulation running entirely on local LLMs, and how you can fork the project to build your own world. The Challenge Running a true multi-agent simulation is notoriously resource-intensive. If you want 100 agents to "think" simultaneously, relying on external APIs (like OpenAI or Anthropic) becomes prohibitively expensive and inevitably hits rate limits. The goal for Emergent City was strict: 100% Local : Powered by local, small-parameter models (like qwen2.5:3b via Ollama). Real-