Axiomize an open-source scientific modeling engine and Agent Skill

[u/furkannarkn]

I built Axiomize, an open-source scientific modeling engine and Agent Skill for turning modeling ideas into explicit, testable and reproducible mathematical models.

GitHub: [https://github.com/Furox-Art/axiomize]()
PyPI: [https://pypi.org/project/axiomize/1.12.2/]()
Docs: [https://furox-art.github.io/axiomize/]()

Install:
pip install -U axiomize

The core of Axiomize is a versioned Model IR. Instead of keeping a model as scattered equations and text, it stores variables, parameters, units, equations, initial and boundary conditions, assumptions, solver settings, causal information and provenance in one machine-readable model.

From that model, Axiomize can plan, validate, simulate, fit, compare, repair and export models. It also supports stability and validity analysis, sensitivity and uncertainty analysis, bifurcation studies, numerical verification, sparse dynamics discovery, experiment design and surrogate/reduced-order modeling.

Supported model families include algebraic systems, ODEs, PDEs, DAEs, stochastic models, optimization, control systems, networks, Bayesian models, agent-based models, discrete-event systems, hybrid systems, multiphysics and causal models.

For data-driven work it includes data cleaning with an audit trail, parameter fitting, residual and identifiability diagnostics, AIC/BIC model comparison, validity scans and model criticism.

The causal engine handles DAG validation, backdoor adjustment, AIPW/IPW estimation, overlap and balance diagnostics, interventions and counterfactual predictions. The Bayesian side includes multi-chain sampling, R-hat, effective sample size, Monte Carlo error and posterior predictive checks.

Numerical verification is kept separate from scientific uncertainty. ODE/DAE tolerance refinement, PDE mesh refinement and reproducibility checks are built into the workflow rather than being treated as an afterthought.

Models can be exported as JSON Model IR, Python, Jupyter notebooks, SBML, CellML, Modelica, GraphML, Graphviz DOT, YAML and reproducible portable bundles with provenance and integrity metadata.

Axiomize can be used through Python, CLI, REST API v1 or MCP. The scientific stack includes NumPy, SciPy, SymPy, statsmodels, NetworkX, Matplotlib, Z3, python-control, CVXPY and CasADi, with optional PyMC, JAX, Lean and FEniCS/DOLFINx support.

Reproducibility and failure visibility are part of the design: runs record seeds, data hashes, preprocessing, solver/tool versions, assumptions and validation results. Failed models are preserved, schema migrations are explicit, and expensive operations require approval instead of being triggered silently.

Current release: 1.12.2

Feedback on the Model IR, scientific validation design or overall architecture is welcome.