Automatically tracking and segmenting every video entity remains a significant challenge. Despite rapid advancements in video segmentation, even state-of-the-art models like SAM 2 struggle to consistently track all entities across a video—a task we refer to as Video Entity Segmentation.We propose EntitySAM, a framework for zero-shot video entity segmentation. EntitySAM extends SAM 2 by removing the need for explicit prompts, allowing automatic discovery and tracking of all entities, including those appearing in later frames. We incorporate query-based entity discovery and association into SAM 2, inspired by transformer-based object detectors. Specifically, we introduce an entity decoder to facilitate inter-object communication and an automatic prompt generator using learnable object queries. Additionally, we add a semantic encoder to enhance SAM 2's semantic awareness, improving segmentation quality. Trained on image-level mask annotations without category information from the COCO dataset, EntitySAM demonstrates strong generalization on four zero-shot video segmentation tasks: Video Entity, Panoptic, Instance, and Semantic Segmentation. Results on six popular benchmarks show that EntitySAM outperforms previous unified video segmentation methods and strong baselines, setting new standards for zero-shot video segmentation.