This paper introduces LogiCode, a novel framework that leverages Large Language Models (LLMs) to identify logical anomalies in industrial settings. Unlike traditional methods focusing on structural inconsistencies, LogiCode uses LLMs for logical reasoning to generate Python codes that pinpoint anomalies such as incorrect component quantities or missing elements. The framework includes a custom dataset, "LOCO-Annotations," and a benchmark, "LogiBench," to evaluate performance across metrics like binary classification accuracy, code generation success rate, and reasoning precision. LogiCode demonstrates enhanced interpretability and significantly improves the accuracy of logical anomaly detection, offering detailed explanations for identified anomalies. This approach represents a significant advancement in industrial anomaly detection, promising substantial impacts on industry-specific applications. The paper also discusses the limitations and future directions of LLMs in this context, emphasizing the need for further research to improve model autonomy and generalizability.This paper introduces LogiCode, a novel framework that leverages Large Language Models (LLMs) to identify logical anomalies in industrial settings. Unlike traditional methods focusing on structural inconsistencies, LogiCode uses LLMs for logical reasoning to generate Python codes that pinpoint anomalies such as incorrect component quantities or missing elements. The framework includes a custom dataset, "LOCO-Annotations," and a benchmark, "LogiBench," to evaluate performance across metrics like binary classification accuracy, code generation success rate, and reasoning precision. LogiCode demonstrates enhanced interpretability and significantly improves the accuracy of logical anomaly detection, offering detailed explanations for identified anomalies. This approach represents a significant advancement in industrial anomaly detection, promising substantial impacts on industry-specific applications. The paper also discusses the limitations and future directions of LLMs in this context, emphasizing the need for further research to improve model autonomy and generalizability.