Independent by design · no pay-to-rank placements  · How we evaluate tools →
Anclum Submit
H

haoming-luo/agentfem

AI-native open-source finite-element platform connecting engineering, computation, data, and AI

Before you install: no copy-paste command · license Apache-2.0 · link reachable

First step: python -m pip install agentfem "scipy>=1.11" matplotlib "gmsh>=4.11"

View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
Install & config
Install command from the repository
pip install agentfem

Parsed from the README we fetched on 2026-11-05; we have not executed it.

This is the install method the repository states, not an MCP client config, so no JSON is generated.
Key names differ by client: Clients & CLI
Star count
38 ★ · haoming-luo/agentfem · read from the GitHub search API · (checked 2026-11-13 20:00:50)
Source
read directly from its GitHub page · type: open-source project
Harvest history
first seen 2026-11-06 · last seen 2026-11-14 · seen 9× · listed by 2 sources
Score
score 203 · 38 ★ · 2 sources · repo
Install
pip install agentfem
FAQ (answers come from our records)
How do I install it?
The repository provides this install command: pip install agentfem. It comes from the repository itself; we cannot guarantee it runs as-is in your client.
Who is it for?
We filed it under these tags: Agent 技能. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository haoming-luo/agentfem;first catalogued 2026-11-06;last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.

The First Generation of Open-Source AI-Native FEM

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

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.
Explore before installing: Browser demos, datasets & models
· Six classroom exercises
(中文 · Français).
The exercises include prompts, reference results, and downloadable handouts;
running your own FEM cases requires the AgentFEM runtime below.
## All You Need Is an Agent
> [!TIP]
> Give this prompt to Codex, DS Harness, or another AI agent:
text
Bring AgentFEM to life.
Use https://haoming-luo.github.io/agentfem/agentfem.json as the guide.
Detect my platform and reuse a working environment. If I say
"走镜像通道 / use the mirror channel", retain that choice for later installs.
On Windows use the linked short WSL/Ubuntu route, without cloning the repo.
Do every permitted step; if one fails, give me one simple manual action and
continue after it is resolved. Finish after checking the installed environment:
report the version, environment location and readiness. Ask whether I want to
try a first example; do not create a project or run a simulation until I agree.
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.
Already installed? Go straight to your first model.
Listed badge (optional)
Copy this line into the README if you want the listing mark. Optional, and it does not affect being listed.
[![Listed on Anclum](https://anclum.com/badge/listed.svg)](https://anclum.com/en/s/haoming-luo-agentfem/)
Source
github · https://github.com/haoming-luo/agentfem
Browse by field
Agent skills
Traction
38 ★
Listed by
2 independent sources
added
today

More like this

All 420 →

Filed under

Every tag opens the full list for that filter. Counts are the live catalogue figures.

Browse all fields →

← Back to index