The paper introduces MambaAD, a novel approach for multi-class unsupervised anomaly detection using the Mamba framework. MambaAD combines a pre-trained encoder and a Mamba-based decoder with Locality-Enhanced State Space (LSS) modules at multiple scales. The LSS module integrates Hybrid State Space (HSS) blocks and parallel multi-kernel convolutions to capture both global and local information. The HSS block uses Hybrid Scanning (HS) encoders to encode feature maps into five scanning methods and eight directions, enhancing global connections through the State Space Model (SSM). The method demonstrates superior performance on six diverse anomaly detection datasets and seven metrics, achieving state-of-the-art results while maintaining low computational complexity and model parameters. The code and models are available at <https://lewandofskie.github.io/projects/MambaAD>.The paper introduces MambaAD, a novel approach for multi-class unsupervised anomaly detection using the Mamba framework. MambaAD combines a pre-trained encoder and a Mamba-based decoder with Locality-Enhanced State Space (LSS) modules at multiple scales. The LSS module integrates Hybrid State Space (HSS) blocks and parallel multi-kernel convolutions to capture both global and local information. The HSS block uses Hybrid Scanning (HS) encoders to encode feature maps into five scanning methods and eight directions, enhancing global connections through the State Space Model (SSM). The method demonstrates superior performance on six diverse anomaly detection datasets and seven metrics, achieving state-of-the-art results while maintaining low computational complexity and model parameters. The code and models are available at <https://lewandofskie.github.io/projects/MambaAD>.