llm-evalgate

Deterministic eval gates and reliability primitives for LLM pipelines.

---

Most LLM eval tooling is either heavy enterprise SaaS or a pile of one-off scripts. llm-evalgate is a small, composable library that covers the full eval surface and runs in CI.

It gives you four things:

• Eval gates: code-only quality dimensions that run the same way every time. Drop them into any pipeline, run them in CI, get a pass/fail with a reason. No tokens burned, no model drift.
• LLM-as-judge and jury: when a check needs semantic judgment a regex cannot give, grade with a model, or a panel of models, and read the agreement back. Same Dimension interface, so judges compose with gates in one harness.
• Agentic evals: score agent traces, not just final text. Tool selection, argument validity, step efficiency, goal completion, and judge-backed trajectory coherence for long-horizon reasoning.
• Benchmarking and a real gate: measure your eval against labeled data — accuracy, precision/recall, Cohen's kappa, and regression-catch-rate, each with a bootstrap confidence interval — then fail CI on a statistically significant regression against a saved baseline. Power helpers tell you when your dataset is too small to trust.

Plus reliability primitives (retry with backoff, model fallback chains, circuit breaker) for pipelines that hold up in production.

Design rule: deterministic gates are the cheap, CI-friendly backbone; reach for a judge only where semantic nuance demands it; benchmark both so the eval is trustworthy rather than assumed.

Install

pip install llm-evalgate          # core: gates, agentic evals, benchmarking
pip install "llm-evalgate[judge]" # adds the Anthropic adapter for live LLM-as-judge

The judge and jury work with any callable that maps a prompt string to a response string, so the core package needs no model SDK and the test suite runs fully offline. The [judge] extra only adds a convenience adapter for live calls.

Quickstart

Eval gates

from llm_evalgate import EvalHarness
from llm_evalgate.eval.dimensions import BlocklistDimension, ReadabilityDimension, SchemaComplianceDimension

harness = EvalHarness([
    BlocklistDimension(terms=["confidential", "internal use only"]),
    ReadabilityDimension(threshold=0.3),
    SchemaComplianceDimension(required_fields=["title:", "summary:"]),
])

report = harness.run(llm_output)

if not report.passed:
    print(report)
    # EvalReport: FAIL
    #   FAIL [blocklist] score=0.000 - prohibited terms found: ['confidential']
    #   PASS [readability] score=0.612 - Flesch ease=61.2, FK grade=8.4
    #   PASS [schema_compliance] score=1.000 - all 2 required fields present

Custom dimension

from llm_evalgate import Dimension

class JsonDimension(Dimension):
    def evaluate(self, text: str) -> tuple[float, str]:
        import json
        try:
            json.loads(text)
            return 1.0, "valid JSON"
        except json.JSONDecodeError as e:
            return 0.0, f"invalid JSON: {e}"

harness = EvalHarness([JsonDimension(threshold=1.0)])
report = harness.run('{"key": "value"}')
assert report.passed

Retry

from llm_evalgate.reliable import retry

@retry(max_attempts=3, backoff=2.0)
def call_llm(prompt: str) -> str:
    return client.messages.create(...)

Fallback chain

from llm_evalgate.reliable import with_fallback, with_fallback_chain

# two-model fallback
result = with_fallback(
    primary=lambda: call_model("claude-opus-4-8", prompt),
    fallback=lambda: call_model("claude-sonnet-4-6", prompt),
)

# ordered chain - first success wins
result = with_fallback_chain([
    lambda: call_model("claude-opus-4-8", prompt),
    lambda: call_model("claude-sonnet-4-6", prompt),
    lambda: call_model("claude-haiku-4-5", prompt),
])

Circuit breaker

from llm_evalgate.reliable import CircuitBreaker, CircuitOpenError

breaker = CircuitBreaker(failure_threshold=5, recovery_timeout=60)

try:
    with breaker:
        result = call_llm(prompt)
except CircuitOpenError:
    result = cached_response  # serve from cache while circuit is open

Built-in dimensions

Dimension	What it checks	Default threshold
BlocklistDimension	No prohibited terms in output	1.0 (zero tolerance)
ReadabilityDimension	Flesch Reading Ease score	0.3 (college-level prose)
SchemaComplianceDimension	Required fields are present	1.0 (all fields)
FactualGroundingDimension	Numeric claims traceable to evidence	0.85

All dimensions follow the same interface: evaluate(text) -> (score, detail). Writing a new one is ten lines.

LLM-as-judge and jury

Some quality dimensions cannot be checked with code: tone, helpfulness, faithfulness to a brief. For those, grade with a model. JudgeDimension is a regular Dimension, so it drops into the same harness next to your deterministic gates.

from llm_evalgate import JudgeDimension

# `complete` is any callable mapping a prompt to a response string.
# Inject your own, or use the optional Anthropic adapter (see below).
judge = JudgeDimension(
    complete=my_model_call,
    rubric="Is the answer factually correct and on-topic for the question?",
    scale=5,            # model grades 1..5; normalized to [0, 1]
    threshold=0.6,
)

report = judge.run("The capital of France is Paris.")
# PASS [judge] score=1.000 - clear and correct

Want a panel instead of a single grader? JuryDimension runs several judges and aggregates the verdicts, and reports how much they agreed (a tight spread means the eval is stable; a wide one means the dimension is ambiguous).

from llm_evalgate import JudgeDimension, JuryDimension

jury = JuryDimension(
    judges=[
        JudgeDimension(complete=opus_call, rubric=rubric, name="opus"),
        JudgeDimension(complete=sonnet_call, rubric=rubric, name="sonnet"),
        JudgeDimension(complete=haiku_call, rubric=rubric, name="haiku"),
    ],
    aggregate="mean",   # or "median", "majority"
)
report = jury.run(answer)

For live grading without writing the adapter yourself, install llm-evalgate[judge] and use anthropic_judge:

from llm_evalgate.judge import anthropic_judge
from llm_evalgate import JudgeDimension

judge = JudgeDimension(complete=anthropic_judge("claude-sonnet-4-6"), rubric=rubric)

The judge never raises into your pipeline: a model error or an unparseable response scores 0.0 with the reason recorded, so a flaky grader fails closed instead of crashing the run.

Judge reliability: measure and de-bias your judge

An LLM judge is itself a model, with all the variance and bias that implies. Trusting it blind is how a bad eval ships. These three tools measure and correct for that. See examples/judge_calibration.py for a runnable, offline demo.

Uncertainty (self-consistency)

A judge at temperature > 0 returns a different score each call, and a single number hides that. SelfConsistencyJudge samples the judge N times and reports the spread and a 95% confidence interval, so you know whether a score of 0.62 is solid or a coin flip.

from llm_evalgate import JudgeDimension, SelfConsistencyJudge

judge = JudgeDimension(complete=my_model_call, rubric="Is the answer faithful to the source?")
robust = SelfConsistencyJudge(judge, samples=5)

report = robust.run(answer)
# PASS [self_consistency] score=0.640 - mean=0.640; stdev=0.080; 95% CI=[0.570, 0.710]; n=5
dist = robust.distribution(answer)   # ScoreDistribution(mean, stdev, n, ci_low, ci_high, scores)

Position bias (pairwise, order-swapped)

When a judge compares two answers, it tends to favor whichever is shown first. PairwiseJudge runs the comparison in both orders and only trusts a verdict that survives the swap; if the judge flips, the result is reported as a tie with consistent=False. position_bias_rate measures how often a judge flips across a set of pairs, so you can quantify the bias before you rely on the judge.

from llm_evalgate import PairwiseJudge, position_bias_rate

pj = PairwiseJudge(complete=my_model_call, criteria="Which answer is more helpful?")
result = pj.compare(answer_a, answer_b)
# result.winner in {"A", "B", "tie"}; result.consistent is False when order flipped the call

bias = position_bias_rate(pj, [(a1, b1), (a2, b2), ...])   # 0.0 = unbiased, 1.0 = pure position bias

Calibration (judge vs human)

The only real test of a judge is agreement with human labels. calibrate_judge scores a labeled set and reports the correlation (Pearson, Spearman) and error (MAE) against human scores, plus accuracy and Cohen's kappa against human pass/fail. verbosity_bias checks the failure mode where a judge just rewards length.

from llm_evalgate import JudgeDimension, CalibrationSample, calibrate_judge, verbosity_bias

judge = JudgeDimension(complete=my_model_call, rubric="Grade the answer.")
report = calibrate_judge(judge, [
    CalibrationSample(text=a1, human_score=0.9, human_label=True),
    CalibrationSample(text=a2, human_score=0.2, human_label=False),
    # ...
])
print(report.table())   # pearson / spearman / mae / accuracy / cohen_kappa

length_corr = verbosity_bias(judge, [a1, a2, a3])   # high = the judge is rewarding length, not quality

A judge you have calibrated, sampled for uncertainty, and checked for position and verbosity bias is one you can defend in a design review. An uncalibrated judge is a vibe with an API bill.

Agentic evaluation

Agents are not just their final answer, they are a trajectory of tool calls. llm-evalgate evaluates the whole trace.

from llm_evalgate import (
    AgentTrace, AgentStep, ToolCall, AgentEvalHarness,
    ToolSelectionDimension, ToolArgValidityDimension,
    StepEfficiencyDimension, GoalCompletionDimension,
)

trace = AgentTrace(
    goal="Find the weather in Paris and convert it to Fahrenheit.",
    steps=[
        AgentStep(
            thought="Look up the weather.",
            tool_calls=[ToolCall(name="get_weather", args={"city": "Paris"}, result="18C")],
        ),
        AgentStep(
            thought="Convert to Fahrenheit.",
            tool_calls=[ToolCall(name="celsius_to_f", args={"c": 18}, result="64.4F")],
        ),
    ],
    final_answer="It is about 64.4F in Paris.",
)

harness = AgentEvalHarness([
    ToolSelectionDimension(expected_tools=["get_weather", "celsius_to_f"], mode="order"),
    ToolArgValidityDimension(validators={"celsius_to_f": lambda a: "c" in a}),
    StepEfficiencyDimension(max_steps=3),
    GoalCompletionDimension(required_substrings=["F"]),
])

report = harness.run(trace)
if not report.passed:
    print(report)

Trace dimension	What it scores
ToolSelectionDimension	Did the agent call the expected tools (as a subset, exact set, or in order)?
ToolArgValidityDimension	Were tool arguments well-formed, and did any call error?
ToolHallucinationDimension	Did it call a tool that exists, with arguments the schema allows?
StepEfficiencyDimension	Did it finish within a step budget, without looping on repeated calls?
GoalCompletionDimension	Did the final answer satisfy a checker or required content?
StepProgressDimension	Judge-backed: does each step make progress, and which step is the weakest?
TrajectoryCoherenceDimension	Judge-backed: does the reasoning path hang together over long horizons?

TrajectoryCoherenceDimension and StepProgressDimension wrap a JudgeDimension, so reasoning quality is scored by a model while the structural checks above stay deterministic. ToolHallucinationDimension takes a tool registry and catches phantom tools and bad arguments — build the registry straight from your existing tool definitions with ToolSchema.from_json_schema(tool_def). StepProgressDimension scores every step against the history before it and names the weakest one, so a failure is localized rather than reported as a single trajectory number.

You do not have to hand-build traces. If your agent is instrumented with the OpenTelemetry GenAI semantic conventions (LangSmith, MLflow, and most OTel exporters emit them), import the spans directly:

from llm_evalgate import AgentTrace

trace = AgentTrace.from_otel(exported_spans)   # plain span dicts; no OTel SDK needed
report = harness.run(trace)

Benchmarking: is your eval any good?

An eval you have not measured is a guess. Point a BenchmarkRunner at a labeled dataset and it tells you how well your gate matches human judgment, including the metric that matters most in production: the fraction of real regressions it actually catches.

from llm_evalgate import BenchmarkRunner, EvalHarness, load_golden
from llm_evalgate.eval.dimensions import (
    BlocklistDimension, SchemaComplianceDimension, ReadabilityDimension,
)

harness = EvalHarness([
    BlocklistDimension(terms=["confidential", "internal use only", "[REDACTED]"]),
    SchemaComplianceDimension(required_fields=["title:", "summary:"]),
    ReadabilityDimension(threshold=0.2),
])

result = BenchmarkRunner(harness).run(load_golden())
print(result.table())

Run against the bundled 60-sample stratified golden set (see examples/benchmark.py), the deterministic harness alone scores (95% bootstrap CIs shown):

n=60
accuracy               0.850  [0.750, 0.933]
precision              0.775  [0.641, 0.900]
recall                 1.000  [1.000, 1.000]
f1                     0.873  [0.781, 0.947]
cohen_kappa            0.697  [0.516, 0.864]
regression_catch_rate  0.690  [0.500, 0.857]

It catches every formatting, policy, and readability violation but misses all 9 semantic regressions, because those failures are semantic (a fluent, well-formatted answer that is simply wrong). Add an LLM judge to the same harness and the catch rate closes. That is the whole thesis: deterministic gates are necessary but not sufficient, and the benchmark is how you prove where the line is.

Those [low, high] columns are 95% bootstrap confidence intervals, on by default. They are not decoration: at n=60 the minimum detectable effect is about 0.145, so a 2-point regression is inside the noise. The library tells you that rather than letting you pretend a point estimate is precise — see confidence intervals and the regression gate below.

Confidence intervals, power, and the regression gate

A benchmark number with no error bar is a guess with a decimal point. Every BenchmarkRunner metric ships with a percentile bootstrap CI, and two power helpers tell you whether your dataset can even resolve the regression you want to gate on.

from llm_evalgate.bench import min_detectable_effect, required_sample_size

min_detectable_effect(60)        # ~0.145 — a 60-sample set can't see a 2pp drop
required_sample_size(0.02)       # ~3100  — that's what a 2pp gate actually needs

The regression gate is the piece that makes the package name true. Save a known-good run as a baseline, then fail the build when a metric regresses past a threshold — but only when the drop clears the noise floor, computed with a paired bootstrap on the same resampled rows.

from llm_evalgate import BenchmarkRunner, RegressionGate

baseline = BenchmarkRunner(harness).run(golden)
baseline.save("baseline.json")               # commit this

# ... later, on a PR ...
current = BenchmarkRunner(new_harness).run(golden)
gate = RegressionGate(metrics="all", threshold=0.02, require_significance=True)
report = gate.check(current, BenchmarkResult.load("baseline.json"))
if not report.passed:
    raise SystemExit(report.table())

A dataset fingerprint guards against the classic mistake of diffing two runs that were not on the same data. A drop past the threshold whose delta CI still includes zero is reported as a WARN, not a hard FAIL, so a small eval set does not block merges on noise — and a power warning fires when the dataset is too small for the threshold. Run it in CI with the bundled entry point:

python -m llm_evalgate.gate current.json baseline.json --threshold 0.02   # exit 1 on regression

Correct the verdict, not just the judge

Calibration tells you the judge's sensitivity and specificity against human labels. The Rogan–Gladen estimator then uses them: it converts the judge's raw pass rate into a bias-corrected estimate of the true pass rate, with a CI that propagates uncertainty from both the eval set and the finite calibration set.

from llm_evalgate import calibrate_judge, corrected_pass_rate

cal = calibrate_judge(judge, labeled_samples)   # now carries sensitivity/specificity
judge_labels = [judge.run(t).passed for t in production_outputs]
rate = corrected_pass_rate(judge_labels, cal)
# observed=0.690; corrected=0.704 [0.631, 0.770] (n_eval=200, n_calibration=120)

A judge at 95% sensitivity and 60% specificity reporting a 78% pass rate is really seeing about 64% — an 8-point bias an uncorrected number hides.

Claim-level faithfulness

FactualGroundingDimension traces numbers; ClaimFaithfulnessDimension traces every claim. It decomposes an answer into atomic claims and grades each as supported, unsupported, or contradicted against the evidence — the canonical RAG faithfulness check, and a regular Dimension so it drops into the same harness.

from llm_evalgate import ClaimFaithfulnessDimension

faith = ClaimFaithfulnessDimension(complete=my_model_call, evidence=retrieved_chunks, threshold=0.9)
report = faith.run(answer)
# claims=7: 5 supported, 1 unsupported, 1 contradicted; score=0.714
#   CONTRADICTED: "the contract allows early termination" (evidence says 90-day notice)

Routing uncertain verdicts to a human

An unstable judge verdict should go to a person, not be trusted as pass/fail. SelfConsistencyJudge, JuryDimension, and PairwiseJudge can now flag a result as needs_review — when the score spread is too wide, the CI straddles the threshold, the jury disagrees, or a pairwise verdict flips on order swap. The flag is orthogonal to pass/fail; the report surfaces it so you decide what to route.

from llm_evalgate import SelfConsistencyJudge

robust = SelfConsistencyJudge(judge, samples=5, max_stdev=0.15, review_margin=0.1)
result = robust.run(answer)
if result.needs_review:
    send_to_human_queue(answer)

Deterministic gates first, judge when you need to

Reach for the cheapest check that works:

• Deterministic gates (BlocklistDimension, SchemaComplianceDimension, and your own) are pure functions: same input, same pass/fail, every run. No model calls, no network, no randomness. Run them on every commit in CI without burning tokens, and keep an audit trail that does not depend on a model that may drift.
• Judges and juries are the opt-in layer for the semantic checks code cannot express. They cost tokens and carry variance, so use them where they earn it, and benchmark them so the variance is known rather than assumed.

Composing with a pipeline

from llm_evalgate import EvalHarness
from llm_evalgate.eval.dimensions import BlocklistDimension, ReadabilityDimension
from llm_evalgate.reliable import retry, with_fallback

harness = EvalHarness([
    BlocklistDimension(terms=["[REDACTED]", "TODO"]),
    ReadabilityDimension(threshold=0.2),
])

@retry(max_attempts=3, backoff=2.0)
def generate(prompt: str) -> str:
    return with_fallback(
        primary=lambda: call_model("claude-opus-4-8", prompt),
        fallback=lambda: call_model("claude-sonnet-4-6", prompt),
    )

output = generate(prompt)
report = harness.run(output)
if not report.passed:
    raise ValueError(f"Output failed eval gate:\n{report}")

License

MIT