# MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation

arXiv: https://arxiv.org/abs/2504.08621 · HTML: https://mlsanity.com/paper/2504.08621/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 11 of 11)

- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAI](https://mlsanity.com/paper/2411.18241/) (2024)
- [MatterChat: A Multi-Modal LLM for Material Science](https://mlsanity.com/paper/2502.13107/) (2025)
- [Generative Hierarchical Materials Search](https://mlsanity.com/paper/2409.06762/) (2024)
- [MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for Automating CFD Simulation and Post-Processing](https://mlsanity.com/paper/2502.00498/) (2025)
- [LLM Agent for Fire Dynamics Simulations](https://mlsanity.com/paper/2412.17146/) (2024)
- [AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA](https://mlsanity.com/paper/2410.15222/) (2024)

## Built on by (most cited first, 17 of 17)

- [Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM](https://mlsanity.com/paper/2509.18178/) (2025)
- [GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis](https://mlsanity.com/paper/2507.21035/) (2025)
- [VASP Agent: An Agentic Framework for Autonomous First-principles Calculations](https://mlsanity.com/paper/2512.19458/) (2025)
- [Agentic Scientific Simulation: Execution-Grounded Model Construction and Reconstruction](https://mlsanity.com/paper/2603.00214/) (2026)
- [COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration](https://mlsanity.com/paper/2604.05547/) (2026)
- [MOOSEnger: A Simulation-Aware AI Agent Framework for the MOOSE Ecosystem](https://mlsanity.com/paper/2603.04756/) (2026)
- [Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters](https://mlsanity.com/paper/2606.09774/) (2026)
- [Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards](https://mlsanity.com/paper/2607.10474/) (2026)
- [An Agentic Evaluation Framework for AI-Generated Scientific Code in PETSc](https://mlsanity.com/paper/2603.15976/) (2026)
- [AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design](https://mlsanity.com/paper/2603.27195/) (2026)
- [A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems](https://mlsanity.com/paper/2606.00138/) (2026)
- [Multi-Agentic Approach for History Matching of Oil Reservoirs](https://mlsanity.com/paper/2605.15028/) (2026)
- [Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration](https://mlsanity.com/paper/2605.20190/) (2026)
- [VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis](https://mlsanity.com/paper/2605.28978/) (2026)
- [PHITSBench: an execution-scored benchmark for AI-assisted PHITS radiation-transport input generation using natural language](https://mlsanity.com/paper/2607.09789/) (2026)
- [CP-Agent: A Harness-Engineered Agent for Crystal Plasticity Simulation Workflows](https://mlsanity.com/paper/2609.31790/) (2026)
- [NeutronGym: Physics-Graded Neutron Instrument Design for LLM Agents](https://mlsanity.com/paper/2610.03631/) (2026)

## Abstract (arXiv)

The Finite Element Method (FEM) is widely used in engineering and scientific computing, but its pre-processing, solver configuration, and post-processing stages are often time-consuming and require specialized knowledge. This paper proposes an automated solution framework, MooseAgent, for the multi-physics simulation framework MOOSE, which combines large-scale pre-trained language models (LLMs) with a multi-agent system. The framework uses LLMs to understand user-described simulation requirements in natural language and employs task decomposition and multi-round iterative verification strategies to automatically generate MOOSE input files. To improve accuracy and reduce model hallucinations, the system builds and utilizes a vector database containing annotated MOOSE input cards and function documentation. We conducted experimental evaluations on several typical cases, including heat transfer, mechanics, phase field, and multi-physics coupling. The results show that MooseAgent can automate the MOOSE simulation process to a certain extent, especially demonstrating a high success rate when dealing with relatively simple single-physics problems. The main contribution of this research is the proposal of a multi-agent automated framework for MOOSE, which validates its potential in simplifying finite element simulation processes and lowering the user barrier, providing new ideas for the development of intelligent finite element simulation software. The code for the MooseAgent framework proposed in this paper has been open-sourced and is available at https://github.com/taozhan18/MooseAgent