mlsanityData to 14 Oct 2026

Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM

arXiv 2509.18178 ↗First posted 17 Sep 2025cs.AI cs.CE cs.LGcited by 15

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Abstract (arXiv)

Computational Fluid Dynamics (CFD) is an essential simulation tool in engineering, yet its steep learning curve and complex manual setup create significant barriers. To address these challenges, we introduce Foam-Agent, a multi-agent framework that automates the entire end-to-end OpenFOAM workflow from a single natural language prompt. Our key innovations address critical gaps in existing systems: 1. An Comprehensive End-to-End Simulation Automation: Foam-Agent is the first system to manage the full simulation pipeline, including advanced pre-processing with a versatile Meshing Agent capable of handling external mesh files and generating new geometries via Gmsh, automatic generation of HPC submission scripts, and post-simulation visualization via ParaView. 2. Composable Service Architecture: Going beyond a monolithic agent, the framework uses Model Context Protocol (MCP) to expose its core functions as discrete, callable tools. This allows for flexible integration and use by other agentic systems, such as Claude-code, for more exploratory workflows. 3. High-Fidelity Configuration Generation: We achieve superior accuracy through a Hierarchical Multi-Index RAG for precise context retrieval and a dependency-aware generation process that ensures configuration consistency. Evaluated on a benchmark of 110 simulation tasks, Foam-Agent achieves an 88.2% success rate with Claude 3.5 Sonnet, significantly outperforming existing frameworks (55.5% for MetaOpenFOAM). Foam-Agent dramatically lowers the expertise barrier for CFD, demonstrating how specialized multi-agent systems can democratize complex scientific computing. The code is public at https://github.com/csml-rpi/Foam-Agent.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 17 Sep 2025.

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20 of 40 references.

References · 20

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  4. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  5. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  6. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
  7. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 631
  8. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 316
  9. 2023Self-collaboration Code Generation via ChatGPTcited by 258
  10. 2023Auto-GPT for Online Decision Making: Benchmarks and Additional Opinionscited by 255
  11. 2023Emergent autonomous scientific research capabilities of large language modelscited by 132
  12. 2024HoneyComb: A Flexible LLM-Based Agent System for Materials Sciencecited by 57
8 more
  1. 2025El Agente: An Autonomous Agent for Quantum Chemistrycited by 44
  2. 2025SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integrationcited by 43
  3. 2024MetaOpenFOAM: an LLM-based multi-agent framework for CFDcited by 39
  4. 2023MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalitiescited by 38
  5. 2025FEABench: Evaluating Language Models on Multiphysics Reasoning Abilitycited by 22
  6. 2025MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulationcited by 17
  7. 2024Collaboration Dynamics and Reliability Challenges of Multi-Agent LLM Systems in Finite Element Analysiscited by 6
  8. 2024Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systemscited by 6

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Citing papers per year: 2025: 2; 2026: 13.

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  1. 2025Foam-Agent: A Large Language Model-Based Multi-Agent Framework for Automating Computational Fluid Dynamics Workflows
  2. 2025ChatCFD: An LLM-Driven Agent for End-to-End CFD Automation with Structured Knowledge and Reasoning
  3. 2024LLM Agent for Fire Dynamics Simulations
  4. 2025CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation
  5. 2025Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
  6. 2025MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for Automating CFD Simulation and Post-Processing
  7. 2025A Status Quo Investigation of Large Language Models towards Cost-Effective CFD Automation with OpenFOAMGPT: ChatGPT vs. Qwen vs. Deepseek
  8. 2026Towards LLM-enabled autonomous combustion research: A literature-aware agent for self-corrective modeling workflows