<p>Vehicle routing problems (VRP) are typical NP-hard combinatorial optimization problems that have great significance in real-world applications. Recently, neural methods have shown promising abilities to solve VRP with superior quality while maintaining high efficiency. Existing neural methods still have limitations in terms of data requirements, especially for VRP variants. Furthermore, it is difficult to generalize across different types of VRPs with a unified learning framework without extensive retraining. Notably, the large language models (LLMs) have shown potential to assist in solving VRP problems. Therefore, this paper proposes a universal framework for VRP based on LLMs. This framework captures the essential characteristics of different VRPs in a unified manner and adapts to various problem specifications with less data reliance. Concretely, the core architecture is built on pre-training and fine-tuning. Low-rank adaptation and the mixture of experts are further integrated to improve the adaptation ability. Experimental results demonstrate that on multiple vehicle routing problems, such as the traveling salesman problem and the capacitated vehicle routing problem, our framework achieves promising solution quality and computational efficiency.</p>