In an era of rapid transformation within the automotive industry, the demand for personalized and adaptive vehicle recommendations is growing. This thesis presents the development of a Dynamic Basket Recommendation System tailored for the automotive market, leveraging cutting-edge technologies in Language Models (LMs) and Multi-Agent Systems (MAS), and exploiting a traditional similarity-based approach. The system is designed to ingest real-time data and respond to evolving user preferences and market offerings, addressing the challenges posed by frequent updates in vehicle models, trims, and features. The foundation of the system lies in the integration of GPT-based models, specifically AzureChatOpenAI, orchestrated through LangChain and LangGraph, two useful Python libraries that enable modular agent-based architectures.