Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM
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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.
Builds on
20 of 40 references.
References · 20
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Citing papers per year: 2025: 2; 2026: 13.
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- 2026SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Modelscited by 3
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- 2026AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents7 May 2026
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- 2026A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction23 Mar 2026
- 2026PhyNiKCE: A Neurosymbolic Agentic Framework for Autonomous Computational Fluid Dynamics12 Feb 2026
3 more
- 2026SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Models12 Jan 2026
- 2025Engineering.ai: A Platform for Teams of AI Engineers in Computational Design31 Oct 2025
- 2025CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics19 Sep 2025
- 2026SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Modelscited by 3
- 2026AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agentscited by 3
- 2026PhyNiKCE: A Neurosymbolic Agentic Framework for Autonomous Computational Fluid Dynamicscited by 1
- 2026A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Predictioncited by 0
- 2026Multi-Agentic Approach for History Matching of Oil Reservoirscited by 0
- 2026AutoFOAM: The Self-Refining Autonomous OpenFOAM Agentcited by 0
- 2026What Do CAE Simulation Agents Really Need Beyond a Generic Harness?cited by 0
- 2026A Case-Bundle Operating Model for Coding Agents in OpenFOAM-Based CFDcited by 0
- 2026ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Executioncited by 0
- 2026ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Execution13 Sep 2026
- 2026What Do CAE Simulation Agents Really Need Beyond a Generic Harness?3 Sep 2026
- 2026A Case-Bundle Operating Model for Coding Agents in OpenFOAM-Based CFD15 Jul 2026
- 2026AutoFOAM: The Self-Refining Autonomous OpenFOAM Agent22 May 2026
- 2026Multi-Agentic Approach for History Matching of Oil Reservoirs14 May 2026
- 2026AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents7 May 2026
- 2026A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction23 Mar 2026
- 2026PhyNiKCE: A Neurosymbolic Agentic Framework for Autonomous Computational Fluid Dynamics12 Feb 2026
- 2026SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Models12 Jan 2026
- 2025CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamicscited by 5
- 2025Engineering.ai: A Platform for Teams of AI Engineers in Computational Designcited by 3
- 2026TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric sciencecited by 2
- 2026Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapterscited by 2
- 2026GPUPhysBench: Benchmarking Coding Agents for Correct and Efficient GPU Physics Simulationcited by 0
- 2026EngiWorld: What Can Frontier Agents Deliver in Professional Engineering Environments?cited by 0
- 2026EngiWorld: What Can Frontier Agents Deliver in Professional Engineering Environments?29 Sep 2026
- 2026GPUPhysBench: Benchmarking Coding Agents for Correct and Efficient GPU Physics Simulation28 Sep 2026
- 2026Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters8 Jun 2026
- 2026TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science29 Mar 2026
- 2025Engineering.ai: A Platform for Teams of AI Engineers in Computational Design31 Oct 2025
- 2025CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics19 Sep 2025
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