LogiCode is a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings. Unlike traditional methods that focus on structural inconsistencies, LogiCode uses LLMs for logical reasoning to autonomously generate Python code to detect anomalies such as incorrect component quantities or missing elements. The framework introduces a custom dataset, "LOCO-Annotations," and a benchmark, "LogiBench," to evaluate LogiCode's performance across various metrics, including binary classification accuracy, code generation success rate, and reasoning precision. Findings show that LogiCode significantly improves the accuracy of logical anomaly detection and provides detailed explanations for identified anomalies, marking a shift towards more intelligent, LLM-driven approaches in industrial anomaly detection. LogiCode is structured into three main modules: Code Prompt, Code Generation, and Code Execution. The Code Prompt module formulates user-defined tasks and logical rules by analyzing normal and abnormal images. The Code Generation module uses an LLM to parse these rules into executable Python code, selecting appropriate APIs for image analysis. The Code Execution module applies logical and visual parsing to detect and report anomalies, providing rule-based explanations for detected issues. The LOCO-Annotations dataset is a specialized extension of the MVTec LOCO dataset, focusing on logical anomalies with detailed explanations for each anomaly. It includes 2908 annotated images across various categories, categorized into four types of logical anomalies: Quantity, Size, Position, and Matching Anomalies. This dataset provides essential data for testing and refining models like LogiCode. LogiBench is a benchmark designed to evaluate the LogiCode framework, focusing on binary classification accuracy, code generation success rate, and reasoning accuracy. The benchmark includes human and LLM evaluations to assess the framework's performance. Results show that LogiCode achieves high accuracy in detecting logical anomalies, with a code generation success rate around 60%, and reasoning accuracy exceeding 90%. The framework's integration of LLMs for code generation and logical reasoning sets a new benchmark in the field, demonstrating remarkable adaptability and interpretability across various industrial scenarios. LogiCode's ability to provide detailed explanations for detected anomalies enhances its effectiveness in industrial quality control. Future research could explore integrating reinforcement learning with LLMs to improve autonomy and adaptability in new scenarios.LogiCode is a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings. Unlike traditional methods that focus on structural inconsistencies, LogiCode uses LLMs for logical reasoning to autonomously generate Python code to detect anomalies such as incorrect component quantities or missing elements. The framework introduces a custom dataset, "LOCO-Annotations," and a benchmark, "LogiBench," to evaluate LogiCode's performance across various metrics, including binary classification accuracy, code generation success rate, and reasoning precision. Findings show that LogiCode significantly improves the accuracy of logical anomaly detection and provides detailed explanations for identified anomalies, marking a shift towards more intelligent, LLM-driven approaches in industrial anomaly detection. LogiCode is structured into three main modules: Code Prompt, Code Generation, and Code Execution. The Code Prompt module formulates user-defined tasks and logical rules by analyzing normal and abnormal images. The Code Generation module uses an LLM to parse these rules into executable Python code, selecting appropriate APIs for image analysis. The Code Execution module applies logical and visual parsing to detect and report anomalies, providing rule-based explanations for detected issues. The LOCO-Annotations dataset is a specialized extension of the MVTec LOCO dataset, focusing on logical anomalies with detailed explanations for each anomaly. It includes 2908 annotated images across various categories, categorized into four types of logical anomalies: Quantity, Size, Position, and Matching Anomalies. This dataset provides essential data for testing and refining models like LogiCode. LogiBench is a benchmark designed to evaluate the LogiCode framework, focusing on binary classification accuracy, code generation success rate, and reasoning accuracy. The benchmark includes human and LLM evaluations to assess the framework's performance. Results show that LogiCode achieves high accuracy in detecting logical anomalies, with a code generation success rate around 60%, and reasoning accuracy exceeding 90%. The framework's integration of LLMs for code generation and logical reasoning sets a new benchmark in the field, demonstrating remarkable adaptability and interpretability across various industrial scenarios. LogiCode's ability to provide detailed explanations for detected anomalies enhances its effectiveness in industrial quality control. Future research could explore integrating reinforcement learning with LLMs to improve autonomy and adaptability in new scenarios.