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The CAE tools an AI agent can actually call

Open-source simulation, CAD & meshing tools for agentic / LLM-driven engineering — driveable headless via MCP, Python, or CLI (no GUI-only tools). The only CAE list with a weekly agent-callability ranking. Ranked by callability, not stars.

110+ tools · 3 MCP servers · 2 AI-Native · machine-readable JSON / CSV · weekly-regenerated ranking

Scope: agent-callable CAE/CAD/CAM tools, plus a small set of adjacent Datasets & Learning Resources for context.

🚀 Quickstart · 🏆 Index · 📊 Methodology

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Contents

Quickstart — give your agent a CAE tool

Three tools here ship a Model Context Protocol server, so an agent (Claude Desktop, Cursor, Cline…) drives them with zero glue code. Add one to your MCP client config:

{
  "mcpServers": {
    "viznoir": { "command": "uvx", "args": ["viznoir"] }
  }
}

Exact launch command lives in each server's README. No MCP? Every other tool is Python/CLI-scriptable — your agent calls it the same way you would.

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AI-Readiness Index

The headline metric: tools ranked by agent-callability — MCP, Python API, CLI, maintenance — not stars. Auto-updated weekly by readiness-score.py. Full table: READINESS.md · machine-readable: data/readiness.json.

🟢 2 AI-Native · 🔵 63 Agent-Ready · 🟡 26 Scriptable · ⚪ 23 Experimental — across 114 ranked tools (updated 2026-07-20). ✅ = install + import execution-verified. Full ranking →

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How the Score Works

The AI-Readiness Score (0–100) ranks tools by how directly an autonomous agent can drive them — callability over popularity.

The five base signals (MCP + Python + CLI + Maintained + Adoption) total 100; pip is an additive bonus, and the final score is capped at 100. So a tool can reach 100 several ways, but only MCP servers clear the AI-Native bar.

Grades: AI-Native 75+ Agent-Ready 50-74 Scriptable 30-49 Experimental under 30

Scores regenerate weekly from README.md via readiness-score.py — fully reproducible, no hand-tuning. Open a PR adding a tool and a bot scores it automatically.

Verified vs declared

Honest about what's checked.

Verified (objective, reproducible) — live GitHub stars/activity; PyPI availability; and an install + import smoke-test (verify_install.py → data/verified.json) that spins up an isolated uv venv, runs pip install + import, and records the result. Tools that pass are marked ✅ in the Index. Current run: 8/10 flagship tools pass; the 2 misses are recorded honestly — Gmsh needs a system GL lib, DeepXDE needs a chosen backend.

Declared (from the entry's tags) — MCP and CLI/API. We link the server/CLI; we don't yet replay an end-to-end agent call.

Roadmap (deepening the moat) — execution-verified MCP handshakes and headless runs, plus a per-tool agent-call transcript, so the score reflects tools an agent has actually driven, not just ones that expose an interface.

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Core Engine Readiness

A hand-curated editorial deep-dive on 17 foundational solvers — capability columns (Python binding, headless, Docker, AI-native) reflect maintainer judgment, ⭐ is live. This complements the auto-generated Index above, which stays the single source of truth for scores. Only 2 engines have MCP integration today.

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MCP Servers

AI agents call these directly via Model Context Protocol.

  • kimimgo/viznoir Python MCP - Cinema-quality science visualization. 22 tools for rendering, slicing, contouring, volume rendering, and animating OpenFOAM/VTK/CGNS data via VTK. Headless EGL/OSMesa.
  • llnl/paraview_mcp Python MCP - Natural language control of ParaView via MCP. Multimodal LLM observes viewport for visual feedback (LLNL).
  • webworn/openfoam-mcp-server C++ MCP - OpenFOAM MCP server with Socratic questioning for CFD education and expert error resolution.

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CFD — Computational Fluid Dynamics

Open-source solvers for fluid flow, heat transfer, and multiphysics.

  • OpenFOAM/OpenFOAM-dev C++ - The open source CFD toolbox. Finite volume solvers for incompressible/compressible flow, multiphase, combustion, heat transfer.
  • su2code/SU2 C++ Python - Multiphysics simulation and design optimization. Compressible/incompressible flow, structural analysis, adjoint-based design.
  • LLNL/Nek5000 Fortran - High-order spectral element CFD solver. DNS/LES of turbulent flows. Scalable to millions of cores.
  • Nek5000/nekRS C++ CUDA - GPU-accelerated spectral element CFD. Successor to Nek5000 with native CUDA/HIP/OpenCL support.
  • precice/precice C++ Python - Coupling library for multi-physics simulations. Fluid-structure interaction, conjugate heat transfer.
  • ProjectPhysX/FluidX3D C++ CUDA - GPU-accelerated Lattice Boltzmann fluid simulator. Real-time 3D visualization, scriptable via Python subprocess, supports multi-GPU.
  • PyFR/PyFR Python - High-order flux reconstruction CFD on mixed unstructured grids. GPU-accelerated (CUDA/OpenCL/HIP).

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FEA — Finite Element Analysis

Structural, thermal, and multiphysics FEM solvers.

  • CalculiX Fortran C - Free 3D structural FEM. Linear/nonlinear static, dynamic, thermal analysis. Abaqus INP compatible.
  • dealii/dealii C++ - Adaptive finite elements. Supports hp-refinement, multigrid, and parallel distributed computing.
  • ElmerCSC/elmerfem Fortran C++ - Multiphysics FEM solver. Fluid dynamics, structural mechanics, electromagnetics, heat transfer. CSC Finland.
  • FEniCS/dolfinx C++ Python - Next-generation FEniCS. Automated PDE solving with high-level Python/C++ interface. Parallel, scalable.
  • firedrakeproject/firedrake Python - Automated FEM with code generation from high-level problem descriptions. UFL domain-specific language.
  • FreeFem/FreeFem-sources C++ - Partial differential equation solver using finite element method. High-level scripting language for 2D/3D problems.
  • idaholab/moose C++ Python - Multiphysics Object-Oriented Simulation Environment. Coupled physics FEM framework from Idaho National Lab.
  • KratosMultiphysics/Kratos C++ Python - Framework for multi-physics FEM. Structural, fluid, thermal, contact, FSI.
  • mfem/mfem C++ - High-order finite element library. Supports GPU acceleration, AMR, and dozens of physics applications.
  • OpenSees/OpenSees C++ - Open system for earthquake engineering simulation. Structural and geotechnical response analysis. Berkeley.
  • sfepy/sfepy Python - Simple Finite Elements in Python. Solve PDEs by FEM in 1D, 2D, and 3D with plain Python scripting.

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SPH — Smoothed Particle Hydrodynamics

Meshless particle methods for free-surface flows and fluid-structure interaction.

  • DualSPHysics/DualSPHysics C++ CUDA - GPU-accelerated SPH solver. Free-surface flows, wave generation, fluid-structure interaction, floating bodies.
  • InteractiveComputerGraphics/SPlisHSPlasH C++ - Physically-based SPH fluid simulation. DFSPH, IISPH, PBF pressure solvers. Viscosity, surface tension.
  • pypr/pysph Python Cython - SPH framework in Python. Compressible/incompressible flows, solid mechanics, coupled problems.

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DEM — Discrete Element Method

Particle-based simulation of granular materials, powders, and coupled particle-fluid systems.

  • CFDEMproject/LIGGGHTS-PUBLIC C++ - Industry-standard open-source DEM for granular materials. LAMMPS-based with heat transfer and CFD coupling.
  • lammps/lammps C++ Python - Large-scale Atomic/Molecular Massively Parallel Simulator. Classical MD and DEM with granular package. Sandia National Labs.
  • MercuryDPM C++ - Open-source DEM for granular and particle-laden flows. Coarse-graining, contact models, and the MercuryCG analysis toolkit.
  • SudoDEM/SudoDEM C++ Python - DEM for non-spherical particles. Polyhedra, super-ellipsoids, and cylinders for realistic granular simulations.
  • Yade C++ Python - Extensible open-source DEM framework. Python scripting, deformable particles, coupled DEM-FEM and DEM-fluid problems.

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Visualization & Post-processing

Rendering, plotting, and interactive exploration of simulation results.

  • fury-gl/fury Python - Free Unified Rendering in Python. VTK-based scientific visualization, 3D animations, and streamline rendering with a NumPy-friendly API.
  • InsightSoftwareConsortium/itkwidgets Python - Interactive Jupyter widgets for 3D visualization of images, point sets, and meshes. Built on ITK and vtk.js.
  • kimimgo/viznoir Python MCP - Cinema-quality science visualization MCP server. 22 tools, EGL/OSMesa headless, cinematic lighting, physics animations.
  • Kitware/ParaView C++ Python - Multi-platform data analysis and visualization. VTK-based GUI + Python scripting + client-server architecture.
  • Kitware/trame Python - Build interactive scientific web applications purely in Python. Integrates VTK and ParaView for server-side or local 3D rendering.
  • Kitware/VolView TypeScript - Browser-based 3D radiological viewer for DICOM. Volume rendering, annotations, and measurements that run fully client-side.
  • Kitware/VTK C++ Python - The Visualization Toolkit. 3D computer graphics, image processing, scientific visualization. Industry standard.
  • Kitware/vtk-js JavaScript - Visualization Toolkit for the Web. WebGL/WebGPU scientific visualization and volume rendering entirely in the browser.
  • marcomusy/vedo Python - Scientific analysis and visualization of 3D objects and point clouds. VTK-based with simple API.
  • napari/napari Python - Fast n-dimensional image viewer. Plugin ecosystem for biomedical and scientific imaging.
  • nmwsharp/polyscope C++ Python - Lightweight 3D viewer for meshes, point clouds, and scalar fields. One-line visualization for geometry processing.
  • plotly/plotly.py Python - Interactive, publication-quality graphs. 3D scatter, surface, mesh, volume. Web-based rendering.
  • pyvista/pyvista Python - Pythonic VTK. Streamlined 3D plotting, mesh analysis, and interactive visualization.
  • rerun-io/rerun Rust Python - Multi-modal data logging and visualization SDK. Stream, store, and replay simulation data with Python API.

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CAD & Geometry

Parametric modeling, geometry processing, and CAD data exchange.

  • CadQuery/cadquery Python - Parametric 3D CAD scripting. Build models with Python, export STEP/STL/IGES. OpenCASCADE kernel.
  • CadQuery/OCP C++ Python - Python wrapper for OpenCASCADE via pybind11. Low-level foundation for CadQuery and build123d.
  • FreeCAD/FreeCAD C++ Python - Open-source parametric 3D CAD modeler. Part design, FEM workbench, BIM, path (CAM).
  • gumyr/build123d Python - Modern Python CAD with algebraic geometry API. Successor to CadQuery with cleaner builder pattern.
  • mikedh/trimesh Python - Load and manipulate triangular meshes. Boolean operations, ray tracing, convex hulls, format conversion.
  • nschloe/pygmsh Python - Python interface for Gmsh. Scripted geometry + mesh generation with parametric control.
  • Open-Cascade-SAS/OCCT C++ - Open CASCADE Technology. Kernel for 3D surface and solid modeling, CAD data exchange (STEP/IGES).
  • SolidCode/SolidPython Python - Python frontend for OpenSCAD. Generate 3D models programmatically with CSG operations.

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Mesh Generation

Structured, unstructured, and AI-driven mesh generation for simulation preprocessing.

  • buaacyw/MeshAnything Python - Artist-quality mesh generation with autoregressive transformers. Any 3D input to mesh (ICLR 2025 spotlight).
  • buaacyw/MeshAnythingV2 Python - Adjacent Mesh Tokenization for efficient artist-quality mesh generation. Faster and higher-quality than V1 (ICCV 2025).
  • CGAL/cgal C++ - Computational Geometry Algorithms Library. Mesh generation, triangulation, Boolean operations, convex hulls.
  • Gmsh C++ Python - Full-featured 3D finite element mesh generator. CAD engine, structured/unstructured meshing, built-in post-processing.
  • libigl/libigl C++ Python - Header-only geometry processing library. Mesh parameterization, deformation, Boolean ops. Eurographics award winner.
  • MmgTools/mmg C - Anisotropic mesh adaptation for 2D/3D surface and volume remeshing. Metric-based automatic refinement.
  • NGSolve/netgen C++ Python - Automatic 3D tetrahedral mesh generator. CAD (OCC) integration, mesh optimization, parallel meshing.
  • nmwsharp/geometry-central C++ - Applied geometry algorithms for surfaces and volumes. Geodesics, vector fields, intrinsic triangulations.
  • OpenMeshLab/MeshXL Python - Foundation model for 3D mesh generation. Pre-trained on Objaverse, text-to-mesh capable (NeurIPS 2024).
  • PyMesh/PyMesh Python C++ - Geometry processing library. Boolean, convex hull, remeshing, self-intersection repair.
  • pyvista/tetgen C++ Python - Python interface to TetGen tetrahedral mesh generator. Constrained Delaunay tetrahedralization with quality control.
  • wildmeshing/fTetWild C++ - Fast and robust tetrahedral meshing. Handles self-intersections and degenerate input. Ten times faster than TetWild.

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Differentiable Simulation

GPU-native frameworks for gradient-based optimization through physics.

  • Autodesk/XLB Python JAX - Differentiable Lattice Boltzmann for physics-ML. Scales to billions of cells on multi-GPU.
  • google/brax Python JAX - Massively parallel rigidbody physics on accelerator hardware. Millions of steps/second on TPU.
  • jax-md/jax-md Python JAX - Differentiable, hardware-accelerated molecular dynamics. Runs on CPU/GPU/TPU via XLA.
  • gbionics/jaxsim Python JAX - Differentiable multibody dynamics engine. Hardware-accelerated robot learning and control via JAX.
  • google-deepmind/mujoco C++ Python - Multi-joint dynamics with contact. General-purpose physics engine for robotics, biomechanics, and control.
  • NVIDIA/warp Python CUDA - Differentiable simulation and spatial computing. Reverse-mode AD, PyTorch/JAX interop.
  • rtqichen/torchdiffeq Python PyTorch - ODE and SDE solvers with automatic differentiation. Adjoint-based backpropagation through continuous-time dynamics.
  • taichi-dev/taichi Python CUDA - Productive GPU programming with automatic differentiation. DiffTaichi for differentiable physics.
  • tumaer/JAXFLUIDS Python JAX - Fully-differentiable CFD solver for 3D compressible single-phase and two-phase flows.

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AI/ML for Simulation

Neural operators, LLM agents, and foundation models for computational engineering.

  • csml-rpi/Foam-Agent Python API - AI agent for automated CFD workflows. LLM-driven OpenFOAM simulation setup and execution.
  • deepmodeling/deepmd-kit Python C++ - Deep learning for molecular dynamics. Neural network potentials for large-scale atomistic simulations.
  • dynamicslab/pykoopman Python - Data-driven Koopman operator approximation. Dynamical system analysis and prediction from time series.
  • dynamicslab/pysindy Python - Sparse Identification of Nonlinear Dynamics. Data-driven discovery of governing equations from measurements.
  • google-deepmind/graphcast Python - Graph neural network for medium-range weather forecasting. Ten-day forecasts in under a minute (Nature 2023).
  • google/jax-cfd Python - JAX-based CFD. Differentiable Navier-Stokes solvers. GPU-accelerated, auto-differentiable.
  • Koopman-Laboratory/KoopmanLab Python - Koopman Neural Operator for mesh-free nonlinear PDE solving. Multi-scale decomposition.
  • lululxvi/deepxde Python - Deep learning library for PDEs. PINNs, DeepONet. Backends: TensorFlow, PyTorch, JAX, PaddlePaddle.
  • microsoft/aurora Python - Foundation model for Earth system prediction. Atmosphere, ocean, air quality. Pre-trained on ERA5 and CMIP6.
  • microsoft/ClimaX Python - Foundation model for weather and climate. Pre-trained on CMIP6, fine-tunable for downstream tasks.
  • microsoft/mattergen Python - Generative model for novel inorganic materials design. Diffusion-based crystal structure generation with target property conditioning.
  • NeuralOperator/neuraloperator Python - Neural operators in PyTorch. FNO, SFNO, UNO for learning PDE solution operators.
  • NVIDIA/earth2studio Python - AI-driven Earth system forecasting framework. Built-in model zoo (FourCastNet, Pangu-Weather, CorrDiff, GraphCast).
  • NVIDIA/physicsnemo Python CUDA - Physics-ML framework (formerly Modulus). PINNs, neural operators, GNNs, diffusion models. Apache 2.0.
  • Terry-cyx/MetaOpenFOAM Python API - LLM-based multi-agent framework for CFD. Automated simulation pipeline from natural language.
  • tum-pbs/PhiFlow Python - Differentiable PDE simulations. Fluid dynamics with TF/PyTorch/JAX. ML-physics hybrid workflows.

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Surrogate Models & PINNs

Physics-informed neural networks and data-driven reduced-order models for fast PDE solving.

  • camlab-ethz/poseidon Python - Scalable foundation model for PDEs. Pre-trained on diverse physics domains; few-shot generalization via in-context operator learning.
  • lululxvi/deepxde Python - Physics-informed neural networks for PDEs. Multi-backend (TF, PyTorch, JAX). Inverse problems, fractional PDEs.
  • mathLab/PINA Python - Physics-Informed Neural networks for Advanced modeling. PyTorch Lightning-based with multi-device training.
  • mathLab/PyDMD Python - Dynamic Mode Decomposition. Data-driven reduced-order modeling for fluid dynamics and beyond.
  • NeuroDiffGym/neurodiffeq Python - Neural network solver for ODEs and PDEs. Flexible architecture with native boundary condition handling.
  • NVIDIA/physicsnemo-sym Python - Symbolic AI for physics. Physics-informed neural networks with symbolic equation definition.
  • rezaakb/pinns-torch Python PyTorch - Production-ready PINNs in PyTorch. Multi-physics support, inverse problems, uncertainty quantification.
  • sciann/sciann Python - Neural networks for scientific computing. Keras-based PINNs with custom loss and constraints.
  • thuml/Neural-Solver-Library Python - Library for advanced neural PDE solvers. Benchmarking Transolver, FNO, and variants on diverse PDE families.

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Optimization

Bayesian, topology, and multidisciplinary design optimization.

  • meta-pytorch/botorch Python PyTorch - Bayesian optimization in PyTorch. Sequential decision making, multi-objective optimization, batch acquisition.
  • OpenMDAO/dymos Python - Open-source optimal control of dynamic systems. Gradient-based trajectory optimization with Gauss-Lobatto and Radau collocation on OpenMDAO.
  • OpenMDAO/OpenMDAO Python - Multidisciplinary design optimization. NASA-developed. Gradient-based + surrogate-assisted optimization.
  • anyoptimization/pymoo Python - Multi-objective optimization. NSGA-II/III, reference directions, constraint handling, parallelization.
  • dl4to/dl4to Python PyTorch - Deep learning for 3D topology optimization. Autograd + adjoint method for efficient neural optimization.
  • williamhunter/topy Python - Topology optimization with Python. Minimum compliance, heat conduction, mechanism design.
  • mdolab/OpenAeroStruct Python - Aerostructural optimization. VLM aerodynamics + beam FEM structures + ply-level composites.

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Data Formats & I/O

Libraries for reading, writing, and converting simulation data across mesh and field formats.

  • nschloe/meshio Python - I/O for mesh formats. Abaqus, CGNS, Gmsh, VTK, XDMF, Exodus, and 30+ more.
  • h5py/h5py Python - Pythonic interface to HDF5. Read/write large numerical datasets efficiently.
  • Unidata/netcdf4-python Python - Python/NumPy interface to NetCDF. Climate, ocean, atmospheric simulation data.
  • CGNS/CGNS C Fortran - CFD General Notation System. Standard for CFD data storage and exchange. HDF5-based.
  • pyvista/pyvista Python - Read/write VTK formats (VTI, VTP, VTU, VTS, VTR), STL, OBJ, PLY, glTF, and more.

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Datasets & Benchmarks

Standardized datasets and benchmarks for training and evaluating scientific ML models.

  • divelab/AIRS Python - AI for science benchmarks. Molecular, protein, climate, physics datasets.
  • Extrality/AirfRANS Python - RANS simulation dataset for airfoils. 1000 simulations with Reynolds-averaged fields (NeurIPS 2022).
  • Mohamedelrefaie/DrivAerNet Python - Large-scale automotive CFD dataset. 4000+ car designs with drag coefficients and surface fields.
  • i207M/PINNacle Python - Comprehensive PINN benchmark with 20 PDE problems across difficulty levels (NeurIPS 2024).
  • NASA TMR - Turbulence Modeling Resource. Validation cases for CFD turbulence models with experimental data.
  • pdebench/PDEBench Python - Benchmarks for scientific ML. Standardized PDE datasets with baseline models.
  • PolymathicAI/the_well Python - Large-scale collection of diverse physics simulations for ML. Fifteen-plus PDE systems (NeurIPS 2024).

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Learning Resources

Tutorials, courses, and curated reference lists for computational engineering and AI for science.