The First Generation of Open-Source AI-Native FEM

全球第一代开源 AI 原生有限元平台

AgentFEM

AgentFEM is an open-source finite-element platform that turns an engineering analysis into a readable Python workflow: define the study, model, materials, loads, solution procedure, outputs, and verification in one place. The same workflow can be understood and operated by researchers, scripts, IDEs, future GUIs, and AI agents.

AgentFEM was initiated by Haoming Luo and open-sourced on GitHub in July 2026.

Its immediate goal is practical: to become a dependable and unusually usable open-source FEM platform. Its longer-term vision is to make finite-element simulation an accessible scientific workspace connecting engineering, computation, data, and AI.

All You Need Is an Agent

[!TIP] Give this prompt to Codex, DS Harness, or another AI agent:

Bring AgentFEM to life.

Use https://github.com/haoming-luo/agentfem as the guide. Follow the
tested route in INSTALL.md, read AGENT_GUIDE.md, and run
`agentfem doctor`.

When it is ready, reply briefly with the environment, AgentFEM
version, and health-check result.

AgentFEM includes the guidance and machine-readable interfaces an agent needs. Prefer manual setup? Continue to Install.

Why AgentFEM

• AI-Native FEM — finite-element software designed from the start for agents to construct, operate, and automate naturally, without replacing deterministic mechanics and numerical computation with AI.

• Humans and Agents, Together — people and AI agents work through the same readable materials, regions, loads, solution steps, and results. AI work remains understandable, editable, and reusable by humans.

• Results You Can Check — convergence, failures, required outputs, benchmark comparisons, and applicability limits remain attached to the result instead of being separated from the simulation that produced it.

• One Run or Thousands — the same model can support an individual analysis, parameter campaigns, parallel execution, restartable studies, and reproducible data generation.

• Simulation to Learning — results can flow into scientific datasets, user-owned models, PyTorch, surrogate models, and high-fidelity fallback without rebuilding the workflow around separate glue scripts.

• Open at Every Layer — users can begin with a clear engineering workflow and still reach operators, UFL, DOLFINx, PETSc, and custom constitutive models whenever needed.

Our conviction: Open FEM for everyone. Useful simulation within reach with AI. Engineering AI grounded in physical models, observations, and verification.

Install

AgentFEM supports Linux, macOS, and Windows through WSL2.

Recommended: conda-forge

Install AgentFEM and its compatible FEniCSx/PETSc/MPI foundation together:

mamba create -n agentfem-env --override-channels -c conda-forge \
  python=3.11 fenics-dolfinx=0.11 agentfem
mamba activate agentfem-env
agentfem doctor

On Windows, run these commands inside an Ubuntu WSL2 terminal, then protect projects and results from distribution removal:

agentfem workspace --protect

mamba create -n agentfem-env --no-rc --override-channels \
  -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge \
  python=3.11 fenics-dolfinx=0.11 agentfem
mamba activate agentfem-env
agentfem doctor

No environment setup: Complete Runtime

The Complete Runtime is the easiest route for a new or offline user. It bundles AgentFEM, FEniCSx, PETSc, MPI, HDF5, and Gmsh in one verified download:

• Apple Silicon macOS: unsigned .pkg Preview
• Windows 10/11: offline WSL2 Preview

Download the latest Complete Runtime →

For Windows, extract the complete ZIP, open PowerShell in that folder, and run:

powershell -ExecutionPolicy Bypass -File .\Install-AgentFEM.ps1

The included START-HERE.txt gives the same short contract to people and AI agents. If WSL is absent, the installer explains how to enable it first. A new bundle can be tested beside the old runtime, or safely replace it with a validated backup-and-rollback transaction:

powershell -ExecutionPolicy Bypass -File .\Install-AgentFEM.ps1 -Upgrade

The installer keeps projects and results in Windows Documents\AgentFEMProjects, visible inside WSL as ~/AgentFEMProjects. Use the bundled Remove-AgentFEM.ps1 instead of manually unregistering the distribution; the safe remover verifies project custody and retains a recovery snapshot first.

Existing FEniCSx environment: PyPI

If you already maintain a compatible FEniCSx environment:

python -m pip install agentfem
agentfem doctor

In mainland China, the same Python package is also available from the TUNA PyPI mirror; use that route only inside an already compatible FEniCSx/PETSc environment.

See INSTALL.md for platform and MPI details, or the runtime guide for Preview installation and integrity checks.

python -m pip install 'agentfem[mesh-formats]'   # Abaqus/NASTRAN meshes
python -m pip install 'agentfem[gmsh]'           # Gmsh model/.msh import
python -m pip install 'agentfem[visualization]'  # ParaView-ready helpers
python -m pip install 'agentfem[ml]'             # PyTorch adapters

Gmsh is an optional, separately licensed GPL component. It is not contained in the Apache-2.0 Python package; the recommended offline Complete runtime may aggregate it with its license and corresponding source for a one-click CAD-to-mesh workflow.

AgentFEM can share a minimal anonymous reliability signal to improve the free, open-source software; models, meshes, parameters, code, paths, and results are never included. Inspect or disable it with agentfem telemetry status|off. See the feedback and privacy contract.

Run Your First Model

Create and run a complete static-solid project in any directory:

mkdir first-agentfem-model && cd first-agentfem-model
agentfem init --template static-solid .
agentfem check
agentfem run --name baseline
agentfem show latest

The generated case.py is ordinary, editable Python. Its public workflow reads like an engineering analysis:

study = studies.static_solid(dimension=2, assumption="plane_strain")
model = models.create(study=study, mesh=domain, name="cantilever")
u = model.field(fields.displacement(domain, degree=1))

model.material(elasticity.isotropic_elastic(young=210e9, poisson=0.30))
model.clamp(u, on=left)
model.traction((0.0, -1.0e6), on=right)

result = model.step(target=u, name="static_load").solve_result()
result.verify("engineering").require()

The CLI gives the same model a readable run folder such as outputs/001-baseline/, an immutable evidence identity, a concise result summary, an MPI launch path, and a machine-readable interface. Use agentfem runs to find earlier runs and --json when an agent or GUI needs the complete record. You can also run case.py directly with Python.

What Works Today

AgentFEM records capability maturity explicitly. A working material-point law, an integrated global solver, and an externally verified analysis are different levels of evidence; the software does not silently treat them as equivalent. See the capability and verification guide for the detailed scope.

Release Examples

• Static elasticity — the readable beginner workflow.
• NAFEMS LE10 3D elasticity — a public curved- solid pressure benchmark with stress, displacement, force, energy, and MPI evidence.
• Transient heat transfer — implicit time integration, progress, and field output.
• Wave propagation with an inclusion — dynamic fields, source amplitude, and boundary models.
• Abaqus C3D10H periodic cell — direct mesh/equation import, quasi-incompressible hyperelasticity, and homogenized response.
• J2 plasticity and global creep — stateful nonlinear material workflows with cutback and restart.
• Simulation-to-surrogate campaign — accepted FEM data, surrogate validation, applicability guard, and FEM fallback.

These are executable release assets with numerical contracts, not only syntax demonstrations. More examples are indexed in examples/ and on the documentation site.

Open and Extensible

AgentFEM has three visible layers:

Engineering workflow
    -> reusable FEM operators, constitutive laws, constraints, and outputs
        -> FEniCSx / DOLFINx / PETSc / MPI numerical kernel

Users can stay in the concise engineering workflow or descend to operators, UFL, DOLFINx, PETSc, and custom constitutive implementations when a research problem needs a lower layer. This is also the extension path for user materials, new elements, private domain modules, GUIs, and agent tools.

Learning follows the same rule. A laboratory-owned model can connect directly through the framework-neutral model.step(target=spec, executor=...) boundary. The optional AgentFEM-Learning companion provides maintained providers, examples, and benchmark evidence for selected scientific-learning methods; it is not required to use a user's own model.

Documentation

• Getting started
• Standard modeling workflow
• Engineering concepts
• Scientific functions and theory
• Results, campaigns, and learning
• AI-native learning contracts
• AI-agent guide
• Roadmap and release gates

The complete user and scientific reference is available at haoming-luo.github.io/agentfem.

Scope

AgentFEM is an early-stage research and engineering platform. It prioritizes depth, transparent evidence, and a coherent user workflow over claiming every analysis available in mature general-purpose CAE systems. Current maturity and known boundaries are documented per capability so users can decide what is appropriate for exploration, research, or engineering use.

Citation

If AgentFEM helps your research or engineering work, please cite the project metadata in CITATION.cff. The AgentFEM Technical Report, AgentFEM: An AI-Native Open-Source Platform for Finite-Element Computing, presents the platform's architecture, founding principles, representative workflows, and author's vision. Read the PDF.

title: "AgentFEM: An AI-native open-source platform for finite-element computing"
version: "0.3.3"
authors:
  - family-names: Luo
    given-names: Haoming
    affiliation: "Materials Department, Xi'an Thermal Power Research Institute (TPRI)"
date-released: 2026-09-10

Author

Haoming Luo is the initiator and maintainer of AgentFEM. His interests include computational mechanics, materials engineering, finite-element simulation, and AI-assisted scientific computing, with education and research experience associated with NWPU, INSA Lyon and Ecole Polytechnique.

The project is also motivated by engineering needs in materials evaluation, defect inspection, and simulation analysis for power-generation equipment.

License

AgentFEM is available under the Apache License 2.0. It can be used, modified, and extended in research, education, and commercial products under the terms of that license.