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

2025-09-17

AI Summary

CFD simulation remains inaccessible to non-experts due to its steep learning curve and labor-intensive workflow. To address this, we propose the first composable multi-agent system enabling end-to-end OpenFOAM simulation—automating geometry generation (via Gmsh), meshing, HPC script generation, and ParaView-based visualization directly from natural language instructions. Our method introduces a hierarchical multi-index RAG framework and a dependency-aware configuration generation mechanism to ensure high fidelity and consistency of input configurations, while leveraging the Model Context Protocol (MCP) to provide standardized functional interfaces. Evaluated on 110 real-world simulation tasks, our system achieves an 88.2% success rate—substantially outperforming the baseline MetaOpenFOAM (55.5%)—and is the first to fully automate complex external mesh integration and high-fidelity configuration synthesis.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Automating the entire OpenFOAM CFD workflow from natural language prompts
• Reducing the steep learning curve and complex manual setup of CFD simulations
• Providing composable service architecture for flexible integration with other systems

Innovation

• Automates entire OpenFOAM workflow from natural language
• Uses composable service architecture with MCP tools
• Ensures configuration accuracy via hierarchical RAG