Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliarydata to detect anomalies without any training sample in a target dataset. Itis a crucial task when training data is not accessible due to various concerns, e.g.,data privacy, yet it is challenging since the models need to generalize to anomaliesacross different domains where the appearance of foreground objects, abnormalregions, and background features, such as defects/tumors on different products/organs, can vary significantly. Recently large pre-trained vision-languagemodels (VLMs), such as CLIP, have demonstrated strong zero-shot recognitionability in various vision tasks, including anomaly detection. However, their ZSADperformance is weak since the VLMs focus more on modeling the class semanticsof the foreground objects rather than the abnormality/normality in the images. Inthis paper we introduce a novel approach, namely AnomalyCLIP, to adapt CLIPfor accurate ZSAD across different domains. The key insight of AnomalyCLIPis to learn object-agnostic text prompts that capture generic normality and abnormalityin an image regardless of its foreground objects. This allows our model tofocus on the abnormal image regions rather than the object semantics, enablinggeneralized normality and abnormality recognition on diverse types of objects.Large-scale experiments on 17 real-world anomaly detection datasets show thatAnomalyCLIP achieves superior zero-shot performance of detecting and segmentinganomalies in datasets of highly diverse class semantics from various defectinspection and medical imaging domains.