Multi-agent systems for chemical engineering: a review and perspective

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 MASs 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.

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Publication Details

Journal
Current Opinion in Chemical Engineering
Published
2025-12-24
DOI
https://doi.org/10.1016/j.coche.2025.101209
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52
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article

Multi-agent systems for chemical engineering: a review and perspective

Tanuj Karia, Qinghe Gao, Artur M. Schweidtmann, Sophia Rupprecht
1 citations
Current Opinion in Chemical Engineering
Machine Learning in Materials Science
0.52
article

Multi-agent systems for chemical engineering: a review and perspective

Tanuj Karia, Qinghe Gao, Artur M. Schweidtmann, Sophia Rupprecht
article en
1 citations

Abstract

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 MASs 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.

Current Opinion in Chemical EngineeringVol. 51
Delft University of Technology (NL)
Openalex Percentile: Top 29%
Machine Learning in Materials Science
0.52
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Multi-agent systems for chemical engineering: a review and perspective — Tanuj Karia, Qinghe Gao, et al. · Current Opinion in Chemical Engineering (2025) | TGRS Research Map | TGRS