The integration of Large Language Models (LLMs) into engineering workflows presents new opportunities for making computational tools more accessible. Especially where such tools remain underutilized due to technical or expertise barriers, such as water distribution system (WDS) management. This study introduces LLM-EPANET, an agent-based framework that enables natural language interaction with EPANET, the benchmark WDS simulator. The framework combines retrieval-augmented generation and multi-agent orchestration to automatically translate user queries into executable code, run simulations, and return structured results. A curated set of 69 benchmark queries is introduced to evaluate performance across state-of-the-art LLMs. Results show that LLMs can effectively support a wide range of modeling tasks, achieving 56-81% accuracy overall, and over 90% for simpler queries. These findings highlight the potential of LLM-based modeling to democratize data-driven decision-making in the water sector through transparent, interactive AI interfaces. The framework code and benchmark queries are shared as an open resource: https://github.com/yinon-gold/LLMs-in-WDS-Modeling.

翻译：将大型语言模型（LLMs）集成到工程工作流中，为提升计算工具的可及性提供了新的机遇。尤其是在因技术或专业知识壁垒导致工具利用不足的领域，例如水分配系统（WDS）管理。本研究介绍了LLM-EPANET，一个基于智能体的框架，支持通过自然语言与基准WDS模拟器EPANET进行交互。该框架结合了检索增强生成与多智能体协同机制，能够自动将用户查询转换为可执行代码、运行模拟并返回结构化结果。研究引入了一套包含69个基准查询的精选集，用于评估当前先进LLMs的性能。结果表明，LLMs能够有效支持广泛的建模任务，总体准确率达到56-81%，对于较简单的查询准确率超过90%。这些发现凸显了基于LLM的建模技术通过透明、交互式人工智能界面，在水务领域推动数据驱动决策民主化的潜力。框架代码与基准查询已作为开放资源发布：https://github.com/yinon-gold/LLMs-in-WDS-Modeling。