Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex workflows into teams of collaborative agents with specialized knowledge and tools. This review surveys the state-of-the-art of MAS within chemical engineering. While early studies demonstrate promising results, scientific challenges remain, including the design of tailored architectures, integration of heterogeneous data modalities, development of foundation models with domain-specific modalities, and strategies for ensuring transparency, safety, and environmental impact. As a young but fast-moving field, MASs offer exciting opportunities to rethink chemical engineering workflows.
@article{arxiv.2508.07880,
title = {Multi-agent systems for chemical engineering: A review and perspective},
author = {Sophia Rupprecht and Qinghe Gao and Tanuj Karia and Artur M. Schweidtmann},
journal= {arXiv preprint arXiv:2508.07880},
year = {2025}
}