mlsanityData to 14 Oct 2026

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

arXiv 2508.07880 ↗First posted 11 Aug 2025cs.MAcited by 9

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 44 of its 60 references that are in the map. First posted on arXiv 11 Aug 2025.

Builds on

44 of 60 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle11 Jul
  2. 2025LLM-guided Chemical Process Optimization with a Multi-Agent Approach26 Jun
  3. 2025AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up30 May
  4. 2025A Multi-Agent Framework Integrating Large Language Models and Generative AI for Accelerated Metamaterial Design25 Mar
  5. 2024Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design8 Dec
  6. 2024ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning27 Jan

Other references · 38

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  3. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  4. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  5. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
  6. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  7. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  8. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
  9. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 631
  10. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  11. 2024Large Language Models: A Surveycited by 525
  12. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 431
26 more
  1. 2025The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Searchcited by 422
  2. 2025Accelerating scientific discovery with Co-Scientistcited by 401
  3. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 328
  4. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 316
  5. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
  6. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  7. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
  8. 2024LLM Multi-Agent Systems: Challenges and Open Problemscited by 120
  9. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
  10. 2024Let Your Graph Do the Talking: Encoding Structured Data for LLMscited by 118
  11. 2024Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimizationcited by 84
  12. 2024GRAG: Graph Retrieval-Augmented Generationcited by 78
  13. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generationcited by 64
  14. 2024Probing the limitations of multimodal language models for chemistry and materials researchcited by 32
  15. 2025AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMscited by 31
  16. 2024Text2Cypher: Bridging Natural Language and Graph Databasescited by 30
  17. 2024Autonomous Industrial Control using an Agentic Framework with Large Language Modelscited by 19
  18. 2024An Agentic Approach to Automatic Creation of P&ID Diagrams from Natural Language Descriptionscited by 12
  19. 2023Deep reinforcement learning for process design: Review and perspectivecited by 10
  20. 2024Generative AI and Process Systems Engineering: The Next Frontiercited by 9
  21. 2024Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligencecited by 5
  22. 2025Talking like Piping and Instrumentation Diagrams (P&IDs)cited by 5
  23. 2025Text2Model: Generating dynamic chemical reactor models using large language models (LLMs)cited by 5
  24. 2024Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solvingcited by 4
  25. 2024Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Modelscited by 3
  26. 2023Multimodal Deep Learning for Scientific Imaging Interpretationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 8.

9 papers.

  1. 2025Accelerating Scientific Discovery with Autonomous Goal-evolving Agentscited by 19
  2. 2026A Systematic Approach to Multi-Agent AI from Advanced Regulatory Control Theory: Safe and Auditable LLM Operator Agents for Process Controlcited by 4
  3. 2026Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Modelscited by 3
  4. 2026CeProAgents: A Hierarchical Agents System for Automated Chemical Process Developmentcited by 2
  5. 2026Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulationscited by 2
  6. 2026GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDscited by 1
  7. 2026Automating Cause-Effect Specification with Knowledge Graphs and Large Language Modelscited by 0
  8. 2026A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agentscited by 0
  9. 2026LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026From Text to Simulation: A Multi-Agent LLM Workflow for Automated Chemical Process Design
  2. 2024A Review of Large Language Models and Autonomous Agents in Chemistry
  3. 2026LLM Agents Perform Controlled Experiments Using Simulation Models
  4. 2026CRAFTS: Collaborative Role-Adaptive Fine-Tuning of LLM Agents for Chemical Process Simulation
  5. 2026AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature
  6. 2026From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
  7. 2025LLM Agents for Knowledge Discovery in Atomic Layer Processing
  8. 2026TMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving