Axiomize

Axiomize

Reproducible scientific modeling that survives contact with reality. Axiomize turns a vague idea into an explicit, versioned mathematical model, validates it dimensionally and numerically, and exports an artifact someone else can re-run years from now.

This is not the numerical-methods library. That door is scientific-computing-system. Axiomize is the modeling layer: mandatory units, a versioned Model IR, and export to SBML, CellML, and Modelica. The MCP server is axiomize mcp.

mcp-name: io.github.Furox-Art/axiomize

[CI] [Pages] [PyPI] [PyPI downloads] [Python] [License: MIT]

Current package line: 1.12.4 (PyPI is the supported install path; see npm)

Documentation: furox-art.github.io/axiomize · Changelog: CHANGELOG.md · Roadmap: ROADMAP.md · Security: SECURITY.md · Contributing: CONTRIBUTING.md · Code of conduct: CODE_OF_CONDUCT.md · Cite: CITATION.cff

There is deliberately still no npm version badge: the fix for the npm entry point has landed in this repository but has not been published yet, so the registry and this README disagree on the version (see npm).

Why

I got tired of scientific models that live in Jupyter notebooks and die there.

Someone writes a beautiful simulation, it works on their machine, they graduate or change jobs, and six months later nobody can run it. The dependencies are broken, the data is missing, and the "documentation" is a 47-cell notebook with no explanation.

Axiomize forces models to be explicit, versioned, testable code instead of exploratory spaghetti. Every assumption is written down. Every parameter carries a unit. Every result carries enough provenance that another person, on another machine, can reproduce it.

Who it is for

Not a fit: if you want a black-box predictor with no inspectable assumptions, or if you need the engine to make scientific claims for you without a human in the loop.

Install

pip install axiomize

Optional extras: pip install "axiomize[full]" (PyMC/JAX Bayesian sampling), pip install "axiomize[playground]" (the Gradio playground).

Python in five minutes

Declare the model, then let Axiomize check it. Units are mandatory, so dimensional mistakes fail loudly instead of producing a meaningless number.

from axiomize.general_engine import simulate_modelfrom axiomize.model_ir import ModelIR
model = ModelIR.from_dict({    "schema_version": "1.0",    "name": "sir-outbreak",    "family": "ode",    "independent_variable": "t",    "independent_unit": "day",    "variables": [        {"name": "S", "unit": "person", "initial": 990.0, "bounds": [0.0, None]},        {"name": "I", "unit": "person", "initial": 10.0, "bounds": [0.0, None]},    ],    "parameters": [        {"name": "beta", "unit": "1/day", "value": 0.3},        {"name": "gamma", "unit": "1/day", "value": 0.1},        {"name": "N", "unit": "persons", "value": 1000.0},    ],    "equations": [        {"target": "S", "expression": "-beta*I*S/N", "kind": "derivative"},        {"target": "I", "expression": "beta*I*S/N - gamma*I", "kind": "derivative"},    ],    "constraints": [        {"name": "cases_nonnegative", "expression": "I", "relation": "ge",         "threshold": 0.0, "scientific_basis": "case counts cannot be negative"},    ],    "assumptions": ["closed population of 1000", "homogeneous mixing"],})
result = simulate_model(model, t_span=(0.0, 30.0), points=4)print(result["status"])print([round(v, 3) for v in result["states"]["I"]])

Real output, reproducible by running python examples/quickstart_sir.py:

status: PASSsolver: scipy / DOP853days:   [0.0, 10.0, 20.0, 30.0]infected: [10.0, 65.393, 239.869, 290.024]checks: PASS (25 of them)

CLI in five minutes

No Python required. Every command prints JSON you can pipe.

pip install axiomize
# What is actually installed, and is it usable? Backends report honestly.axiomize capabilities
# Clarify a vague idea before any numbers get committed.axiomize intake "Reduce traffic congestion in a mid-size city"
# Check a model against closed-form theory, not just vibes.axiomize-validate --model sir --beta 0.3 --gamma 0.1

axiomize-validate output on those inputs:

=== SIR validation ===horizon                = 180 days  (final-size theory is the t->infinity limit)R0                     = 3.000  (outbreak)Peak infected          = 300,465 at day 61.4Final size (simulated) = 0.9404Final size (theory)    = 0.9405Theory match           = True
--- sanity checks ---population_conserved                PASScompartments_nonnegative            PASSR_monotonic_increase                PASS

Other surfaces: axiomize solve (reference SIR), axiomize fit (calibrate from CSV), axiomize model --action {plan,validate,simulate,fit,export,numerical-verify}, axiomize serve (REST, loopback by default), axiomize mcp (MCP over stdio). See docs/integrations.md.

Adoption path

1. Try it on something you already believe. Recreate a model you trust with axiomize-validate or one axiomize model run. If the engine disagrees with a result you can defend, stop here and open an issue.
2. Move one real question onto Model IR. Declare units and constraints explicitly. The dimensional checks are where the value shows up first.
3. Gate the expensive steps. Numerical refinement, mesh refinement, and heavy fitting return APPROVAL_REQUIRED until you pass --approve-heavy. Approval authorizes compute; it never disables a resource ceiling.
4. Export something portable. axiomize model --action export emits canonical Model IR JSON plus SBML, CellML, and Modelica for supported models, so the artifact outlives this library.
5. Wire it into review. Ship the exported IR and the validation record alongside the result, not just a figure.

What it actually does

• Validates dimensional consistency, so you cannot add meters to seconds
• Enforces scientific constraints as named, justified checks rather than prose
• Separates numerical error from stochastic variability before claiming convergence
• Compares candidate model families and records why one was chosen
• Exports to JSON, Python, YAML, notebooks, SBML, CellML, Modelica, GraphML, and LaTeX
• Keeps an integrity-checked run ledger, so a stored result can be verified before use

Honest limits

• It does not make a bad model good. It makes a bad model fail loudly.
• Bayesian sampling needs the full extra (PyMC/JAX); FEM needs FEniCS/DOLFINx. Both are reported as unavailable rather than silently substituted.
• Benchmark results grade report structure in blind runs, not modeling correctness. Only the table carrying script, case-set and commit hashes is reproducible; the older waves are kept as history and cannot be rerun.
• Worked examples use illustrative parameter ranges labelled lit. / data / est.. No example cites an external source, so treat the numbers as reading material rather than literature-backed results.
• Generated-code execution and theorem elaboration are not an OS sandbox. See SECURITY.md.

Documentation

• Quickstart and workflow: furox-art.github.io/axiomize
• Worked examples: example gallery, or the 18 example files
• Domain packs (which lenses matter per field): packs/domain-packs.md
• Agent integration (MCP, REST, CLI): docs/integrations.md
• Portable export formats: docs/portable-export.md
• Trust boundaries and reporting: SECURITY.md, docs/security.md
• Agent skill pack: skills/axiomize/SKILL.md, plus the 15 perspective lenses

npm

pip install axiomize is the supported install path. Use npm only if you already depend on it.

The npm index.js syntax error is fixed on main, and package.json is at 1.12.3 in lockstep with the Python package. That fix is not published yet. The npm registry still serves 1.12.2, whose tarball carries the broken entry point, so npx axiomize still fails to load today.

The fix ships with the next release, which publishes the npm shim from the same commit as the Python distributions. Until that release lands, check registry.npmjs.org/axiomize before using npm: if the reported version is lower than the PyPI version, the registry copy is still the old one. Tracked in CHANGELOG.md.

License

MIT. Use it, break it, fix it.