The modeling of Water Distribution Networks is a subfield of Water Engineering, with impact of billions of lives worldwide, powering communities, cities, and countries.
To handle such massive loads of data intake, software is a necessary tool for engineers, used to describe, model, analyze and interact with this infrastructure.
The de-facto standard for Water Distribution Systems modeling software is EPANET, an ODE solver library used for its suite of computational tools. The library is written in C++, with many Python programming language wrapper libraries made over it, to enable access to high-level modeling.
Even with accessible libraries available to engineers, such tools remain underutilized due to technical or expertise barriers.
In recent years, Large Language Models changed the landscape of software engineering and accessible abilities. More code than ever is AI-generated, and coding agents are used not only by software engineers, but all vocations are using automated code generation.
In this work, we introduce LLM-EPANET, an agent-based framework that enables natural language interaction with EPANET.
The framework combines retrieval-augmented generation (RAG) and multi-agent orchestration to automatically translate user queries into executable code, run simulations, and return structured results, skipping the needed technical depth to write and debug executable code.
We propose WaterAgentBench, a curated set of 69 benchmark queries to evaluate generated code performance across multiple categories relevant to WDS modeling, in tasks ranging from basic question answering on networks to multiple-command hydraulic analysis queries.
Results show that LLMs can effectively support a wide range of modeling tasks, achieving 56–81% accuracy overall and over 90% for simpler queries, highlighting the potential of LLM-based modeling to support broader accessibility of data-driven tools and advance the democratization of water systems modeling through transparent, interactive AI interfaces.
