Leveraging Multi-agent System Powered by Large Language Model to Improve Transparency and Reliability in Automated Supply Chain Coordination

Lack of transparency and information reliability in supply chain management have been persistent challenges.Based on the LangChain and LangGraph frameworks, this research proposed a Large Language Model (LLM)-based Multi-Agent System (MAS) specifically designed to enhance information reliability and transparency in construction supply chain coordination.A prototype system composed of multiple autonomous agents was designed and developed capable of working collaboratively, sharing information, and supporting decisionmaking.The system comprises Supplier Agents and General Contractor Agents capable of engaging in natural language interactions.These agents coordinate the supply chain by facilitating communication about material deliveries and project progress.The prototype demonstrated the potential of LLM-based MAS in improving supply chain transparency and reliability.This research not only validated the feasibility of applying large language models in automated supply chain coordination but also offered insights for the design and implementation of future systems.