gradecore

One deterministic, no-LLM-judge grading engine — shared by model-drift (longitudinal monitoring) and the crash-test platform (on-demand adversarial testing). Zero dependencies.

Every grade is a pure predicate over a string, so it reproduces exactly — the property a drift board and a vulnerability score both depend on. No second model grades the first; there is nothing here you can't rerun and get the same answer.

from gradecore import exact, contains, number, GradeInput, suite_hash

exact("blue")(GradeInput(text="  BLUE "))       # -> Verdict(passed=True, score=1.0, severity="none", …)
contains("signal", "process")(GradeInput(text="signal a process"))   # both needles required
number(3.14, tol=0.01)(GradeInput(text="about 3.141"))               # extracts + tolerance-compares

Eight tasks, two prompt variants, four disagreements. Four cannot clear p<0.05 even under a clean sweep, so the suite reports that it cannot decide rather than naming a winner: python3 -m demos.underpowered. Play it as a terminal session — the text is selectable.

The one signature

model-drift's graders are Callable[[str], bool]; rag-eval-lab's return floats gated by thresholds. gradecore reconciles both under GradeInput -> Verdict, which generalizes boolean into a severity-scored verdict without losing the boolean case:

Verdict(passed: bool, score: float, severity: str, detail: str, grader_id: str)
#        pass/fail    0.0..1.0       none|low|med|high|critical

What's here (v0.1)

• Scalar/text graders lifted from model-drift's suite combinators — exact, contains, regex, exact_cs, one_of, number.
• bool_grader(fn, grader_id) — lifts any existing Callable[[str], bool] unchanged, so model-drift's frozen SUITE runs through gradecore without being rewritten. (Verified: gradecore's suite_hash over that SUITE is byte-identical to model-drift's own fingerprint.)
• suite_hash(identities) — the freeze-and-fingerprint discipline, so a silently-edited suite is detectable and two runs are only comparable if they answered the same questions.

Coming (crash-test Phase 1): the retrieval/grounding family (with the empty-gold / empty-answer gotchas corrected) and the net-new adversarial graders (injection-resistance, tool-misuse, spec/format-violation, refusal-calibration).

pip install -e ".[dev]" && pytest -q        # 84 tests, zero dependencies

MIT · by Erik Hill