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.
Authors
- Tanuj Karia
- Qinghe Gao (ORCID: https://orcid.org/0000-0002-2350-4776)
- Artur M. Schweidtmann (ORCID: https://orcid.org/0000-0001-8885-6847)
- Sophia Rupprecht
Institutions
- Delft University of Technology (NL)
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