musil 0.1.2

A tiny, dependency-free explicit-state model checker for Python: safety, deadlock, reachability, liveness — and concurrency by interleaving.

musil

A tiny, dependency-free explicit-state model checker for Python. Describe a system as states + guarded transitions + invariants; musil exhaustively sweeps every reachable state — and every interleaving of concurrent actors — and hands you the shortest counterexample when something breaks.

Named for Robert Musil, the engineer-mathematician turned novelist.

from dataclasses import dataclass, replace
from musil import Action, Model, check

@dataclass(frozen=True)
class Light:
    color: str = "red"

model = Model(
    init=Light("red"),
    actions=[
        Action("go",   lambda s: s.color == "red",    lambda s: replace(s, color="green")),
        Action("slow", lambda s: s.color == "green",  lambda s: replace(s, color="yellow")),
        Action("stop", lambda s: s.color == "yellow", lambda s: replace(s, color="red")),
    ],
    invariants={"known-color": lambda s: s.color in {"red", "green", "yellow"}},
)

print(check(model))   # OK -- 3 states, no violations

Why

The expensive bugs in stateful and distributed systems are temporal and concurrent: a resource wedged forever, a race that drops data, a deadlock. Tests sample executions; a model checker proves properties over all of them. musil exists to do that in Python, as a library — no separate spec language, no external binary, no JVM. Your states are frozen dataclasses, your invariants are predicates, and the whole thing runs in pytest next to your other tests.

The key move: the model can be driven from the same data your code uses. Point transition_actions at your real allowed-transitions table and the model can't drift from the code — it is the code's table.

Install

pip install musil      # or: uv add musil

Pure standard library; Python 3.12+.

What it checks

Safety + deadlock + reachability — check(model) -> Result:

result = check(model)
result.ok                 # True if every reachable state satisfied every invariant, no deadlock
print(result)             # on failure: the broken invariant (or "deadlock") + shortest trace

A state with no enabled action is a deadlock unless you mark it terminal (Model(..., terminal=lambda s: ...)) — an absorbing sink like deleted.

Concurrency, for free — model each actor's steps as actions and hand musil all of them; it fires every enabled action from every state, so all interleavings are explored:

# two non-atomic increments race; musil finds the lost update
check(Model(init=Counter(), actions=[*actor_a, *actor_b], invariants={...}))

Liveness — check_liveness(model, goal=P) -> LivenessResult:

# "eventually P" on every run; everywhere=True checks "always eventually P" (recurrence/convergence)
check_liveness(model, goal=lambda s: s.served == s.desired, everywhere=True, fair=["reconcile"])

A liveness failure is reported as a lasso: a stem into a recurring set plus the cycle. Pass fair=[...] to assume weak fairness of those actions (an action continuously enabled along a cycle must eventually be taken), or fair_strong=[...] for strong fairness (enabled infinitely often ⇒ eventually taken — what you need for delivery over a lossy channel). Pick the weakest that makes the property hold; over-strong fairness hides real bugs.

Compose & model the network — compose(...) builds the interleaved product of independent component models, lifting each component's invariants (name-qualified) and letting you add joint invariants over the whole; the channel kit (channel_actions, send) models a message channel — reliable / lossy / duplicating, and unordered so reordering is explored for free — so you can specify each component once and check the assembled system.

Zero-drift from a transition table — transition_actions / status_field_actions:

from musil import status_field_actions, terminal_states
model = Model(
    init=Service("pending"),
    actions=status_field_actions(ALLOWED["service"]),     # generated from YOUR table
    terminal=lambda s: s.status in terminal_states(ALLOWED["service"]),
)

Visualize — to_dot(model) returns Graphviz DOT (dot -Tsvg); pass highlight=[s.state for s in result.trace] to colour a counterexample.

Verify the implementation, not just the design — simulate runs your real event-driven node code under a deterministic, fault-injecting network (loss / duplication / reordering), reproducible from a seed, holding it to a model (check_refinement) plus invariants and a convergence goal. It's the FoundationDB/TigerBeetle deterministic- simulation-testing technique as a pure-Python library — bug finding, not proof:

from musil import simulate, NetworkModel
report = simulate(node_factory, seeds=range(1000), snapshot=snapshot,
                  network=NetworkModel(loss=0.3, max_latency=3),
                  model=spec, abstraction=lift, goal=lambda w: w.applied == w.desired)
if not report.ok:
    print(report.failure)   # which seed, which step, which world — re-run seeds=[that_seed] to replay

See Verifying a distributed system and the runnable examples/route_delivery.py.

How it compares

	what it is	spec language	runs the real code?	liveness
musil	in-Python library, explicit-state	Python (frozen dataclasses)	the model can be driven from your code's tables	safety + weak/strong-fairness liveness
TLA+ / TLC	standalone checker	TLA+ (math)	no (separate model)	full temporal logic
P	DSL + systematic testing, compiles to C	P (state machines)	yes (executable model)	safety + liveness
Stateright	Rust library, model-check + run	Rust	yes (same actors on a real network)	safety + liveness
FizzBee	Go binary, Python-like DSL	.fizz	no	safety + basic liveness

musil is the smallest member of this family: pick it when your state space is bounded and small, you want the model in your test suite with zero new tooling, and the win is catching races / deadlocks / stuck states and proving convergence.

Honest limitations

• Explicit-state: it enumerates reachable states, so it's for bounded models. Big or infinite state spaces blow up (use a max_states cap; the result reports truncated). Symbolic/SMT tools (TLA+'s Apalache, etc.) scale further.
• No partial-order reduction (yet): heavy concurrency interleaving can be expensive.
• Liveness is fairness-based, not full LTL: <>P and []<>P under weak and strong fairness, not arbitrary temporal-logic formulae.
• It checks the model. Whether your implementation refines the model is a separate question — generate_traces + replay give you conformance testing (generate from the model, replay against the code) as a pragmatic bridge.

Development & CI

The toolchain is pinned with proto (.prototools: moon + uv) and every task is a moon target running through uv run. After cloning:

proto install          # installs the pinned moon + uv
moon run :ci           # lint + typecheck + test + build + docs (one task graph)
moon run :test         # or run a single target
make install-hooks     # pre-commit auto-bump + pre-push checks (one-time)

CI is a single GitLab job: proto install brings up the toolchain, then one moon run resolves the whole graph — there are no per-language jobs or hand-wired stages. (make targets still work locally; they call uv run directly and don't need moon.)

Releasing

The version in pyproject.toml is the single source of truth, and releases are automated:

1. pre-commit auto-bumps the patch version whenever a commit touches src/ (run make install-hooks once after cloning). Doc/test/example/config-only commits don't bump. For an intentional minor/major release, bump deliberately: make bump TYPE=minor.
2. version-guard (the moon run :version-guard task, part of :ci) enforces the same rule non-bypassably in CI: a push or MR that changes src/ without a version bump fails the pipeline, catching --no-verify and unhooked clones.
3. :release runs on a green main pipeline (after the full :ci graph as deps): if pyproject's version isn't on PyPI yet, it publishes via OIDC Trusted Publishing. So merging a version bump to main releases itself — no second pipeline, no tag required.

One-time setup

PyPI — OIDC Trusted Publishing (no API token is stored anywhere). Account → Publishing → add a pending GitLab publisher (or add it to the project after the first manual upload):

Field	Value
PyPI Project Name	musil
Namespace	jorgeecardona
Project name (repo)	musil
Top-level pipeline file	.gitlab-ci.yml
Environment name	pypi

Publishing uses OIDC Trusted Publishing, so no token is stored anywhere. On a green main pipeline, :release ships to PyPI whenever pyproject's version isn't published yet — there is no git tag step.

Releases are automatic on main — there is no manual tag or publish step. Pushes and MRs run lint + typecheck + test + build + docs only.

License

MIT © Jorge Cardona. See LICENSE.