Abstract: We introduce UnrealZoo, a rich collection of 100 photo-realistic 3D virtual worlds built on Unreal Engine, designed to reflect the complexity and variability of open worlds with scales up to $16 km^2$ landscapes. Additionally, we offer a rich variety of playable entities including humans, animals, robots, and vehicles for embodied AI. We extend UnrealCV with optimized Python APIs and tools for data collection, environment augmentation, distributed training, and benchmarking, achieving significant improvements in the efficiency of rendering and communication, to support advanced applications, such as multi-agent interactions. Our experimental evaluation across complex navigation and tracking tasks reveals two key insights: first, the substantial benefits of the diversity of environments for developing generalizable reinforcement learning (RL) agents; second, the persistent challenges that current embodied agents face in open-world settings. These challenges include transferring to a new embodiment at test time, managing latency in closed-loop control systems for dynamic environments, and effectively reasoning about complex 3D spatial structures in unstructured terrain. UnrealZoo thus provides both a powerful testing ground and a pathway toward more capable embodied AI systems for real-world deployment.