EvalSeal

Reproducibility receipts for LLM evals: run it N times, report the score with its noise, and seal what actually ran.

The problem

An eval score from a single run is one sample of a random process. Run the same eval again and borderline items quietly flip from PASS to FAIL, especially when an LLM judge grades them. Reports also name the model you asked for, not the one that answered. EvalSeal measures the flips, records the real provenance, and seals both into a tamper-evident ledger.

Quickstart

pip install evalseal

# The recorded demo (examples + cassette) lives in the repo.
git clone https://github.com/patibandlavenkatamanideep/evalseal && cd evalseal

# Replays the committed cassette: no API key, no network.
evalseal run \
  --dataset examples/borderline_judge/dataset.jsonl \
  --target-config examples/borderline_judge/target.json \
  --scorer-config examples/borderline_judge/scorer.json \
  --n 5

Two minutes, no API key

$ evalseal run --suite examples/borderline_judge/suite.json --show-cases --unstable-only
Aggregate: 20 cases · mean 0.92 · 15 stable / 0 borderline / 5 unstable

 case_id  runs  pass  fail  flips  flip_rate  majority  stability  judge_prompt_hash
 b01      5     3     2     2      40%        PASS      unstable   sha256:457d7c3
 b10      5     3     2     2      40%        PASS      unstable   sha256:457d7c3
 b16      5     3     2     2      40%        PASS      unstable   sha256:457d7c3
 b05      5     4     1     1      20%        PASS      unstable   sha256:457d7c3
 b19      5     4     1     1      20%        PASS      unstable   sha256:457d7c3

5 case(s) fail --fail-on unstable: b01, b05, b10, b16, b19

$ evalseal verify
Chain intact: 1 record(s).

$ evalseal gate --min-score 0.85 --max-flip-rate 0.10
FAIL 5 case(s) exceed --max-flip-rate 0.10 (worst: b01 at 40%)

Exit codes: 0 passed, 3 a gate or stability policy failed, 1 the tool failed. A ready-to-copy workflow is in docs/github-action-example.yml.

Real flip rates

Recorded 2026-09-15 and committed in tests/cassettes/run.json: gemini-2.5-flash as both the target and the judge, temperature left at the provider default, 20 arguable prompts, 5 runs each. The quickstart above replays exactly this run.

Mean score: 0.92, but 5 of 20 cases did not get the same verdict every time.

A perfect 5-for-5 case reports [0.57, 1.00], not [1.00, 1.00]: five identical runs do not establish certainty, and the interval says so.

Every case that disagreed with itself is UNSTABLE, including the two that dissented only once. Stability is decided by where the 95% Wilson interval of the pass proportion sits: 4/5 gives [0.38, 0.96], which straddles 0.5, so five runs do not establish which way that case goes. BORDERLINE cannot occur at N=5 at all - only 5/5 and 0/5 clear 0.5. Before 2.0 a single dissent in five was called BORDERLINE because the flip rate was 0.20 and the cutoff was 0.20, which reads as a 20% tolerance band. At five runs the only reachable flip rates are 0, 0.2 and 0.4, so that cutoff was never a band; it was the single outcome "one of five runs disagreed" wearing a percentage.

Treat the k-th repeat of every case as one ordinary single-run eval, and the five "single runs" of this identical eval scored 0.90, 0.95, 0.85, 0.90 and 1.00. A single run can't tell you which of those numbers you got.

The report also flagged TEMPERATURE NOT SET for both the target and the judge, which is the reason these borderline verdicts can come out differently from run to run.

To re-record with your own key, copy .env.example to .env, add a free Google AI Studio key, and run the quickstart with EVALSEAL_RECORD=1. If the free tier rate-limits you, run the same command again later; responses already recorded are kept.

A benchmark, objectively graded

The suite above is graded by an LLM judge. This one is graded by arithmetic: 40 problems sampled (seed 20260918) from the GSM8K test split, each with one verified numeric answer, same model, same N=5, answer_match scoring.

evalseal run --suite examples/gsm8k/suite.json

Accuracy 0.975, and every case STABLE - 40 stable, 0 borderline, 0 unstable. All five runs scored exactly 0.975. No flips at all, even with temperature left at the provider default.

Put the two runs side by side:

This contrast cannot tell you where the variance came from, and until 2.0 the README claimed it could. It said the irreproducibility came from the judge rather than the model. The two suites differ in the grader and in the task - arguable open-ended prompts against GSM8K arithmetic - so the comparison is confounded. A difference in flip rate is equally consistent with an unreliable judge, with open-ended prompts drawing more variable answers out of the model, or with both. The design cannot separate them, so the claim was not supported by the evidence offered for it.

evalseal decompose runs the experiment that can, on one suite with the task held fixed.

Where the variance actually comes from

Two arms over borderline_judge, same 20 cases, N=5 each. The first samples the target once and then judges that single frozen response five times, so nothing but the judge can vary. The second samples the target five times and judges each once, which is an ordinary run and contains both sources.

evalseal decompose --dataset examples/borderline_judge/dataset.jsonl \
  --target-config examples/borderline_judge/target.json \
  --scorer-config examples/borderline_judge/scorer.json \
  --cassette tests/cassettes/decompose_borderline.json --n 5

Recorded live on 2026-09-21 against gemini-2.5-flash and committed, so the replay above needs no key:

The judge, grading one unchanging string five times, disagrees with itself about as often as the whole pipeline does. That is the claim the old two-suite table was reaching for, and this time the task is held fixed while it is made.

What this does not show, stated plainly because the previous version of this section over-read its evidence:

• The arms are not ordered. judge_only came out higher than target_and_judge (6.0% vs 5.0%). They are two independent five-run samples of 20 cases, so that gap is noise, not a finding that freezing the target increases variance. The same goes for b01 flipping in one arm and b10 in the other.
• judge_only is a lower bound, not a share. It measures the judge's variance at one particular response. Another draw from the target might be easier or harder to grade consistently, so this does not average over the target's output distribution.
• Three flipping cases is below the floor for a significant paired test. At alpha 0.05 six items must move in the same direction, so nothing here is significant - it is a description of one recorded experiment, not an inference about judges generally.

One problem (gsm016) was wrong in all five runs - consistently, not randomly. Reading it shows why: it says ten stalls, then refers to "the twenty stalls". GSM8K's reference answer assumes ten; the model assumed twenty and answered 176 every time. A stable failure is a different thing from a flaky one, and worth a different response: here, fix the question.

An eval built from a real codebase

examples/codeqa/ is generated, not hand-written: build_dataset.py walks a directory of checked-out repositories, parses each Python file, and asks questions whose answers the parser already knows - a parameter's default, how many parameters a function takes, the exception it raises, its declared return type. Ground truth comes from the AST, so the suite is objectively gradable by answer_match with no judge in the loop.

python examples/codeqa/build_dataset.py ~/src/my-repos 60   # point it at your own code
evalseal run --suite examples/codeqa/suite.json

The committed run covers 60 questions drawn from a pool of 3,279 across 7 repositories, answered by gemini-2.5-flash at N=5: accuracy 1.000, all 60 cases STABLE, with every question type at 1.00.

That clean number took three corrections, and every one of them was a defect in the eval rather than in the model.

Three times the variance caught the eval, not the model

• A case failing all five runs asked which exception _call_with_retry raises, where the source says raise last_exc - a variable holding an exception, not a type. The question was unanswerable; the generator now requires a class-shaped name.
• A case flipping 40% asked which exception _parse raises. It explicitly raises ValueError, but also calls json.loads and model_validate, which propagate others. The model alternated between those two readings - both defensible. Rewording the question to "raised explicitly in its own body" took that case to PPPPP.
• Two cases flipping 20% looked like genuine model uncertainty about class names defined in the codebase. They were not. Re-running just those two at N=20 (40 API calls) showed the model answered correctly every time; it simply wrote `RetrievalResult` with backticks in some runs, and the scorer counted markdown decoration as a wrong answer. answer_match now normalizes surrounding backticks and quotes before comparing. Both cases are 20-for-20 STABLE.

evalseal run --suite examples/codeqa/suite.json --only cq043,cq052 --n 20

--only re-examines named cases; a flip at N=5 is worth a closer look, because five runs cannot tell 20% from 30% - or, as here, from 0%.

The rule that survives all three: a stable failure means your eval item is broken, and a flip means either the model is unsure or your question and scorer are. Look before concluding. Normalizing decoration did not paper over real variance - the judge-graded suite above still flips on 5 of 20 cases, because that disagreement is real.

Did the score actually change?

The same suite, same N, against a second model - gemini-3.1-flash-lite - then asking evalseal whether the difference is real:

evalseal run --suite examples/gsm8k/suite.json      --ledger .evalseal/compare.jsonl
evalseal run --suite examples/gsm8k/suite-lite.json --ledger .evalseal/compare.jsonl
evalseal diff --ledger .evalseal/compare.jsonl -- 0 1

Comparable: yes. Same grading setup, so the scores mean the same thing.

 metric              before  after  change
 ───────────────────────────────────────────────────────────
 score               0.975   0.975  no change (inconclusive)
 mean flip rate      0.0%    0.0%   +0.0%
 cases that flipped  0       0      +0

Paired test over 40 shared item(s).

 quantity                               value
 ──────────────────────────────────────────────────────────────────
 difference in mean score               +0.0000
 95% CI (cluster bootstrap over items)  [+0.0000, +0.0000]
 p-value (exact mcnemar)                1
 discordant items                       0 (0 regressed, 0 improved)
 verdict                                inconclusive

Inconclusive is not the same as no difference. The test did not reach
significance, which is a statement about this experiment's power, not about
the two runs being the same.

 • Widest per-case CI half-width (per_case_halfwidth): ±0.217. This describes
   one item at this N, not the suite mean; before 2.0 it was used as the
   threshold for a change.

Both models scored 0.975, both perfectly stable, and both missed the same problem - the broken one. On this suite the two models are indistinguishable, which says less about the models than about the suite: 40 problems this easy cannot separate them. That is a useful thing to learn before quoting a benchmark number as evidence one model beats another.

The comparison is paired, because both runs cover the same items. Every item is measured twice, so the question is which items changed verdict, not how two independent samples compare. Binary scorers get an exact McNemar test on the discordant items; the interval comes from a cluster bootstrap that resamples whole items and carries all of an item's repeats with them.

Here nothing was discordant: the two models produced identical per-item results, so there is no evidence of a difference and none of sameness either. That is what inconclusive means, and it is why the word "noise" is gone.

Before 2.0 this line was wrong in a way worth naming. diff compared the suite-level mean difference against the widest per-case Wilson half-width - 0.217 at N=5 - and called anything smaller "within noise". Those are quantities about different things: a per-item interval says how little five repeats pin down one item, while a mean over 40 items is far better determined than any item in it. The effect was to report almost every real shift as noise. Eight of forty items regressing is a difference of 0.20 with p = 0.0078, and the old rule called it noise. The number itself still has a meaning and is still reported, under the name per_case_halfwidth.

The old text here also advised raising N to narrow that floor. That advice was wrong, and evalseal power is in the tool partly to keep it from being given again - see below.

Comparability comes before the delta

The first line is the one that matters. A score delta means nothing until you know the two runs measured the same thing, so diff answers that first and the number second.

Comparability is decided by the evaluator fingerprint: scorer type, rubric, judge prompt, judge model and parameters, and the dataset. It deliberately excludes the target model, because comparing two target models is the entire point of a benchmark. Change the rubric and the score moves while the model sits still - that is the comparison EvalSeal refuses to let you make silently.

Any two sealed records work, whether they are receipt files or ledgers:

evalseal diff report-before.json report-after.json     # two receipts
evalseal diff --ledger .evalseal/runs.jsonl 0 -1       # first vs latest
evalseal diff runs-a.jsonl runs-b.jsonl                # heads of two ledgers
evalseal diff before.json after.json --json            # for CI

Pointing diff at the judge suite and the arithmetic suite, both replayed from committed cassettes, gets the refusal it should:

Not directly comparable: evaluator configuration changed.

 what changed  before                          after
 ──────────────────────────────────────────────────────────────────────────────
 scorer type   llm_judge                       answer_match
 judge model   gemini-2.5-flash                None
 judge prompt  sha256:457d7c3841ce0f2d1e2b03…  None
 rubric        sha256:8d95f8c65711cb262abcaf…  None
 dataset       sha256:1d94ddad418a97f1e39e86…  sha256:c9623577a124d8688bd81ba6…
 suite         sha256:271f373da83684958c57ef…  sha256:4af862fcb84ed2e65e635ef6…

 metric              before  after  change
 ──────────────────────────────────────────────────────────
 score               0.920   0.975  +0.055 (not comparable)
 mean flip rate      8.0%    0.0%   -8.0%
 cases that flipped  5       0      -5

 • Cases added: gsm001, gsm002, …            (40 ids, elided here)
 • Cases removed: b01, b02, …                (20 ids, elided here)

0.920 to 0.975 looks like an improvement and is not one: the grader changed from an LLM judge to arithmetic and the dataset changed with it. The two runs also share no items at all, so there is nothing to pair and no test to run. diff reports the delta because hiding it would be its own kind of lie, and marks it not comparable so it cannot be quoted as a result.

Cases that leave the dataset are reported as removed, never as "now stable". Dropping a flaky case is not the same as fixing it, and a diff that conflates them rewards deleting the hard items.

--json emits the same content as a machine-readable object (comparable, score, flip_rate, unstable_cases, cases, config_changes, provenance) for a pipeline to assert on. diff always exits 0: it reports, it does not gate. Gating is gate's job.

"Inconclusive" - so how big would the experiment need to be?

diff returns inconclusive often, and honestly. evalseal power answers the question that follows:

$ evalseal power --from-receipt 0 --ledger .evalseal/compare.jsonl --mdd 0.05
Power 0.0% to detect a 0.050 drop from 0.975, with 40 item(s) at N=5 (model:
from_receipt, 2000 simulated experiments, alpha 0.05).

A paired test needs at least 6 items to change verdict in the same direction
before any result can be significant at alpha 0.05: 2 x 0.5^6 = 0.0312. A suite
where fewer than 6 items can move cannot produce a significant result at any
number of repeats.

For 80% power you would need about 110 items at N=5.
Adding repeats does not help here. Repeats sharpen each item toward its own
majority verdict; when a shift does not move items across that boundary, more
repeats remove the disagreement the test feeds on. More items is the dial that
works.

The six-item floor is the most useful number here. Under an exact paired test, n items all moving the same way gives p = 2 x 0.5^n. That is 0.0625 at five items and 0.03125 at six, so six is the smallest count that can clear alpha 0.05 - and a 40-item suite scoring 0.975 has only one failing item to begin with. No amount of repeating will make such a suite able to separate two models.

Raising N is usually the wrong dial, and the README used to advise it. Repeats sharpen each item toward its own majority verdict. That helps only when a change pushes items across the 50% line; when it lowers every item's pass rate from 0.90 to 0.85, extra repeats remove the disagreement the test feeds on and power falls. The simulator shows both regimes, and the tests assert both.

The item model is an explicit choice rather than a hidden default, because it drives the answer more than the arithmetic does: deterministic (items pass or fail every time - what gsm8k and codeqa actually look like, and where repeats do nothing), bernoulli (every repeat an independent coin flip - the optimistic end), or --from-receipt to use a real run's observed per-item rates. Method and assumptions are in src/evalseal/power.py.

A receipt you can attach to a pull request

Terminal output is for the person who ran the eval. --html is for everyone else.

evalseal run --suite examples/gsm8k/suite.json --html receipt.html
evalseal report receipt.json --html receipt.html      # from a receipt you already have
evalseal diff 0 -1 --html drift.html                  # the drift report, same styling

Both pages are a single file with no stylesheet, script, font or image to fetch, so a report archived as a CI artifact renders the same way a year later on a machine with no network. Nothing in them reads the clock: the same record produces byte-identical HTML, which means the receipt can itself be hashed and attached to the chain. The only timestamp shown is the one sealed into the record.

The receipt leads with four numbers - mean score, per-case halfwidth, mean flip rate, case counts - then gives every case a verdict strip: one cell per repeat, in run order, red where the verdict disagreed with the majority. FPPFP tells you a case flipped; the strip tells you when, which is what you need to know before blaming the model.

Prompts and responses are never written into either page, only their hashes. A receipt is meant to be forwarded, and a file that quietly carries your dataset is a leak waiting to happen. Verify what the page claims with evalseal verify.

Commands

run takes --concurrency (default 4 requests in flight), --max-retries (default 5, on HTTP 429/408/5xx and connection errors, honouring Retry-After), --timeout, and --quiet. Concurrency never changes the result: each response is recorded against its own (case, repeat) slot, so a 16-worker replay is identical to a serial one.

One suite file instead of many flags

evalseal run --suite examples/gsm8k/suite.json          # flags still override it

{
  "dataset": "dataset.jsonl", "target": "target.json", "scorer": "scorer.json",
  "n_repeats": 5, "concurrency": 4, "fail_on": "unstable",
  "cassette": "../../tests/cassettes/gsm8k.json"
}

Paths resolve relative to the suite file, and an unknown key is an error rather than a silently ignored typo.

Signing a receipt

The hash chain proves a ledger hasn't been edited. It doesn't prove who made it: anyone who can rewrite the whole file can rebuild a consistent chain. Signing closes that gap for anyone holding your public key.

evalseal keygen                                  # evalseal.key (0600) + evalseal.pub
evalseal run --suite my-suite.json --sign-key evalseal.key
evalseal verify --public-key evalseal.pub        # chain + signatures

A signature says this key signed a ledger whose head was H, and the head commits to every record before it. It does not say the run happened as described: a signer can sign whatever they like. What it rules out is someone else altering your receipt afterwards.

Gating a pipeline with gate

gate applies CI thresholds to a sealed record and exits 3 when one is violated:

evalseal verify --ledger .evalseal/ledger.jsonl      # chain, siblings, schema
evalseal report --unstable-only                      # which cases flipped
evalseal gate --min-score 0.85 --max-flip-rate 0.10  # thresholds
evalseal gate --critical b01,b02                     # these must never flip
evalseal gate --expect-config sha256:407033d3836b8   # evaluator must not have changed

--expect-config compares the evaluator fingerprint - target model and parameters, scorer type, rubric hash, judge prompt hash, judge model and parameters, dataset and suite hashes. A change there moves the score without the model changing, so a gate that ignores it will eventually mistake evaluator drift for model drift.

A policy file instead of a wall of flags

A gate spelled out in CI flags is a gate nobody reads. --policy moves the thresholds into a file that lives in the repo, so loosening one shows up in review and git log says who did it:

# evalseal.yml
version: 1

run:
  min_score: 0.95
  max_flip_rate: 0.20        # per case, not the average
  max_unstable_cases: 1
  verify_ledger: true        # the hash chain has to check out (default)

cases:
  critical: [gsm016]         # these must not flip at all
  min_score:
    gsm001: 1.0

drift:
  baseline: baselines/main.json   # relative to this file
  require_comparable: true        # refuse to compare across a changed rubric or dataset
  max_score_regression: 0.02      # and the paired test must support a regression
  allow_new_unstable: false       # a case that starts flipping fails the build
  allow_removed_cases: false      # quietly dropping a hard case is not an improvement

$ evalseal gate --policy evalseal.yml
ok   run.verify_ledger: Chain intact: 2 record(s).
ok   run.min_score: mean score 0.975 vs floor 0.950
ok   run.max_flip_rate: 0 case(s) over 20%
ok   run.max_unstable_cases: 0 unstable vs limit 1
ok   cases.critical: 0 critical case(s) flipped
ok   cases.min_score[gsm001]: 1.000 vs floor 1.000
PASS mean 0.975 · 0 unstable case(s) · config sha256:5a5c7d55d5368...

Every rule that ran is printed, passed or failed. A gate that speaks up only on failure cannot be told apart from a gate that checked nothing, and the second one is the common case once a policy has been in a repo for a year.

Two refusals are deliberate, because the usual way a quality gate fails is not that it fails wrongly, it is that it silently checks nothing:

• An unknown key is an error. min_scor: 0.9 is refused with exit 2, not ignored. A threshold that quietly does nothing is worse than no threshold, because the build going green gets read as evidence.
• A rule that cannot run is a failure, not a skip. If drift.baseline names a file that is not there, or cases.critical names a case the dataset no longer has, the gate fails and says so.

max_score_regression fails a build only when the drop exceeds the budget and the paired test supports a regression, so a drop the experiment cannot distinguish from noise never fails one. Before 2.0 the allowance was the budget plus a per-case half-width of 0.217, which passed almost anything. Policy rules and command-line flags are additive: a flag can tighten a checked-in policy, never silently loosen it. --json emits every check for a pipeline to assert on. YAML needs pip install 'evalseal[yaml]'; JSON policies need nothing extra.

Gating a run directly

--fail-on decides which stability classes fail the run, and --junit-xml writes a report CI can display next to ordinary tests:

- name: Eval reproducibility
  run: |
    evalseal run --dataset evals/dataset.jsonl \
      --target-config evals/target.json --scorer-config evals/scorer.json \
      --n 5 --fail-on borderline --junit-xml junit.xml --quiet

Start with --fail-on none to observe flip rates without blocking merges, then tighten.

Per-case instability

An aggregate flip rate hides which cases are unstable. --show-cases prints the distribution, worst first, stamped with the judge prompt that produced it:

| case_id | runs | pass | fail | flips | flip_rate | majority | stability | judge_prompt_hash |
| b01     | 5    | 3    | 2    | 2     | 40%       | PASS     | unstable  | sha256:457d7c3    |
| b05     | 5    | 4    | 1    | 1     | 20%       | PASS     | unstable  | sha256:457d7c3    |

evalseal report --json emits the same data machine-readably, with summary.flip_rate, cases[].verdict_distribution, cases[].verdict_sequence and a provenance block including the config fingerprint.

Flip rate is non-majority verdicts / total runs - not transitions between runs. So PPFP is 1/4 = 25%, not 2 transitions out of 3. The definition was chosen because it does not depend on the order runs happened to execute in, which matters once runs are concurrent.

Stability classes follow the 95% Wilson interval of the case's pass proportion, not a flip-rate cutoff. UNSTABLE when the interval contains 0.5, so the direction of the verdict is not established at this N. STABLE when it excludes 0.5 and every run agreed. BORDERLINE in between: the majority is established, but the case did disagree with itself. At N=5 only 5/5 and 0/5 clear 0.5, so BORDERLINE is unreachable there and becomes reachable at larger N (18/20 gives [0.70, 0.97]). Each case also carries a finer stability_label: stable_pass, stable_fail, unstable, or insufficient_runs when N < 5.

Before 2.0 these were flip-rate thresholds - 0 for STABLE, up to 0.20 for BORDERLINE. At five runs the only reachable flip rates are 0, 0.2 and 0.4, so "at most 20%" was never a tolerance band; it was the single outcome "one of five runs disagreed", written as a percentage that invites reading it as a 20% error budget.

Confidence intervals use the Wilson score interval for binary verdicts and a seeded percentile bootstrap for float scores. Both are deterministic. Wilson is used because the bootstrap collapses to zero width when every run agrees, which would report certainty that five runs cannot support.

Scorers: exact, regex, answer_match (extracts the final answer and compares it to expected, numerically when both are numbers), and llm_judge (a second model, itself a tracked target).

Provenance warnings show up in the report when:

• the served model differs from the requested one (for the target or the judge),
• the endpoint isn't a canonical provider host,
• temperature was left at the provider default,
• the served model or system fingerprint changed partway through the run.

What EvalSeal guarantees

• Hash-chain integrity. Every record commits to the previous one; editing a past score breaks that record's hash, and re-hashing it breaks the next record's link.
• Linear appends under concurrent writers. The head is read and the record appended inside one file lock, so two simultaneous runs cannot create sibling records. verify reports siblings explicitly if a ledger acquires them another way.
• Repeat-run instability measurement. Per-case verdict sequences, counts, flip rates and Wilson intervals, rather than one aggregate number.
• A paired comparison between two runs. Both runs cover the same items, so diff tests them item by item: an exact McNemar test on the items that changed verdict, and a cluster bootstrap over items for the interval.
• Evaluator config fingerprinting. The receipt seals the judge prompt hash, rubric hash, scorer type, judge model and parameters, dataset and suite hashes, EvalSeal version, git commit and dirty flag, Python version and platform.

What EvalSeal does not guarantee

• It cannot prove the provider behaved the same way later. A replay reproduces recorded responses; it says nothing about what the model would return today.
• It does not make a nondeterministic judge deterministic. It measures the nondeterminism instead.
• It does not replace human review of borderline cases. A 40% flip rate tells you where to look, not what the right answer is.
• Local file locking is not distributed consensus. fcntl/msvcrt coordinate processes on one machine. On NFS or SMB the guarantee weakens or disappears.
• Hashes detect change, not incorrectness. A sealed record with a wrong rubric is sealed just as firmly as one with a right rubric.
• A paired test cannot see a shift that leaves majorities alone. McNemar works on items that change verdict. A change lowering every item's pass rate from 0.90 to 0.85 moves the suite mean and flips almost nothing, and raising N makes it harder to see rather than easier. diff reports the disagreement when the bootstrap interval excludes zero and McNemar does not agree, instead of resolving it silently.
• "Inconclusive" is not "the same". It means this experiment lacked the power to tell, which at N=5 on a small suite is the usual outcome. evalseal power says what it would take.

Two providers, two wire formats

provider in a target config picks the wire format. Left out, it means openai, so every config written before this existed keeps working.

{ "provider": "anthropic", "model": "claude-haiku-4-5-20251001", "max_tokens": 512 }

The Anthropic adapter speaks the Messages API directly rather than through an OpenAI-compatible shim, because the differences are exactly the kind a receipt is supposed to record: max_tokens is required rather than optional, the system prompt is a top-level field rather than a message, the reply is a list of content blocks rather than a string, there is no system_fingerprint to pin a backend with, and an overload is HTTP 529 rather than 503. A shim papers over each of those, and a receipt that records what a shim assumed is a receipt about the shim.

It is built on httpx rather than the SDK, so the cassette records the raw response envelope and that is also what replays. No client library sits between the recording and what a reader can verify, and the base install gains no dependency.

Two things this reports that a shim cannot:

• system_fingerprint is None, and stays None. Anthropic publishes no backend identifier. Reporting nothing is honest; deriving one from the model name would fake a pin, and diff would then claim two runs shared a backend on no evidence.
• A reply cut off at max_tokens is flagged as truncated. An incomplete answer scored naively is indistinguishable from a wrong one, which turns a configuration mistake into a finding about the model. The run warns instead. The same check now covers the OpenAI shape, where finish_reason: "length" means the same thing.

Status, stated plainly: the adapter is implemented and covered by 23 tests against the documented wire format, but unlike the suites above it is not yet backed by a recorded live run. See examples/anthropic/ to record one.

Using it as a library

from evalseal import Dataset, LocalCallableTarget, RegexScorer, run_eval

record = run_eval(
    Dataset.from_jsonl("dataset.jsonl"),
    LocalCallableTarget(my_model_fn),
    RegexScorer(r"^yes"),
    n_repeats=5,
)
print(record.aggregate.mean_score, [r.stability for r in record.results])

Comparing two sealed runs, with the same rules the CLI applies:

from evalseal import diff_records, load_receipt

result = diff_records(load_receipt("before.json"), load_receipt("after.json"))
if result.comparable and result.score_verdict == "REAL CHANGE":
    print(result.score_delta, result.newly_unstable)

load_receipt reads either shape: a report.json receipt, or a ledger, whose head it takes. DiffResult.to_dict() is what --json prints.

Policies are a library too, so a harness can apply the same rules the CLI does:

from pathlib import Path
from evalseal import evaluate, load_policy

policy = load_policy("evalseal.yml")
result = evaluate(policy, record, ledger=Path(".evalseal/ledger.jsonl"))
for check in result.checks:
    print("ok " if check.passed else "FAIL", check.rule, check.detail)

to_html(record) and diff_to_html(result) return those pages as strings, with write_html / write_diff_html to put them on disk. mean_flip_rate(record) and noise_floor(record) are the two summary statistics every surface shares, so a dashboard built on the library prints the same numbers the CLI does.

The package ships type information (PEP 561), so mypy and pyright see the annotations.

How it works

The executor sends each prompt to the target N times and scores every response. An LLM judge is itself a target, so its own randomness is measured instead of assumed away. analyze.py computes the mean, a seeded bootstrap 95% CI, and the flip rate for each case. Every request goes through a cassette, keyed by the request plus which repeat it belongs to. In record mode real responses are saved as they arrive; in replay mode, the default and what CI uses, they are served back by that key, and a missing entry fails loudly. Each run is saved as a RunRecord: its manifest (requested vs. served model, fingerprint, parameters and whether they were set explicitly, rubric hash, dataset hash) plus its results. The record is hashed and linked to the previous record's hash in an append-only JSONL ledger, so editing any past score breaks verify.

Security policy: SECURITY.md. Release notes: CHANGELOG.md. Contributing: see CONTRIBUTING.md. See DESIGN.md for what this does and does not prove.

Upgrading to 0.2: cassettes and sealed records written by 0.1.x cannot be read by 0.2, which keys entries by repeat. The demo cassette in this repo was converted in place, so the published numbers above are unchanged; your own cassettes need re-recording, and an existing ledger needs to start fresh. verify names an old record rather than calling it tampered.