GPT-powered Multi-agent System for Facility Management: Knowledge Graph-based Data Integration

Abstract: Facility management involves processing structured sensor data and unstructured maintenance records to monitor building performance and support decision-making. This study presents a graph-enhanced, multi-agent system that integrates a structured data storage system, a knowledge graph, and a GPT-powered LLM execution framework. A web-based interface enables users to submit natural language queries and obtain actionable insights. A case study on the Galbraith Building at UofT demonstrates how the system links historical maintenance records with sensor data. The results highlight its potential for predictive and prescriptive maintenance, while also reducing manual data processing, decreasing reliance on technical expertise, and improving operational efficiency.