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Urban environments, characterized by their complex, multi-layered networksencompassing physical, social, economic, and environmental dimensions, facesignificant challenges in the face of rapid urbanization. These challenges,ranging from traffic congestion and pollution to social inequality, call foradvanced technological interventions. Recent developments in big data,artificial intelligence, urban computing, and digital twins have laid thegroundwork for sophisticated city modeling and simulation. However, a gappersists between these technological capabilities and their practicalimplementation in addressing urban challenges in an systemic-intelligent way.This paper proposes Urban Generative Intelligence (UGI), a novel foundationalplatform integrating Large Language Models (LLMs) into urban systems to fostera new paradigm of urban intelligence. UGI leverages CityGPT, a foundation modeltrained on city-specific multi-source data, to create embodied agents forvarious urban tasks. These agents, operating within a textual urban environmentemulated by city simulator and urban knowledge graph, interact through anatural language interface, offering an open platform for diverse intelligentand embodied agent development. This platform not only addresses specific urbanissues but also simulates complex urban systems, providing a multidisciplinaryapproach to understand and manage urban complexity. This work signifies atransformative step in city science and urban intelligence, harnessing thepower of LLMs to unravel and address the intricate dynamics of urban systems.The code repository with demonstrations will soon be released herehttps://github.com/tsinghua-fib-lab/UGI.

Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment