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 / 2 borderline / 3 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

3 case(s) fail --fail-on unstable: b01, b10, b16

$ 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.

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:

That contrast is the finding. On this model and these tasks, the irreproducibility came from the judge, not the model. Which is exactly the kind of claim a single run cannot make, and the reason to measure rather than assume.

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 (within noise, noise floor ±0.217)
 mean flip rate      0.0%    0.0%   +0.0%
 cases that flipped  0       0      +0

Other differences: served model gemini-2.5-flash to gemini-3.1-flash-lite, sealed
hash sha256:44c37b257f73e to sha256:cf13839f41540

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.

That noise floor is why N matters. At N=5 a perfectly stable case still carries a ±0.217 interval, so a one-case difference (0.025) is correctly reported as noise rather than a finding. Raising N narrows the floor and buys the power to call smaller differences real: ±0.217 at N=5 becomes roughly ±0.08 at N=20.

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

Not directly comparable: evaluator configuration changed.

 what changed  before                            after
 ──────────────────────────────────────────────────────────────────────────────────
 dataset       sha256:b2639589bcf0b3161b5fc144…  sha256:95c3248301dd733c4e3c24cf07…

 metric              before  after  change
 ──────────────────────────────────────────────────────────────────────────────
 score               0.987   0.993  +0.007 (not comparable, noise floor ±0.400)
 mean flip rate      1.3%    0.7%   -0.7%
 cases that flipped  3       2      -1

 • Now stable: cq015
 • Still unstable: cq043, cq052

The score went up by 0.007 and EvalSeal declines to call that an improvement, because the dataset underneath it changed. What it will tell you is which cases moved: cq015 stopped flipping, cq043 and cq052 still flip. That case-level drift is the part worth acting on - it points at a specific question to go read, which is where eval defects actually live.

--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.

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, noise floor, 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      # allowance is this PLUS the noise floor of the two runs
  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 is measured against the noise floor as well as the stated budget, so a drop the runs themselves cannot distinguish from noise never fails a build. 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 are based on the flip rate, the share of a case's N verdicts that disagree with its majority: STABLE (0), BORDERLINE (≤ 20%), UNSTABLE (> 20%). Each case also carries a finer stability_label: stable_pass, stable_fail, unstable, or insufficient_runs when N < 5, because a flip rate from three runs is not worth reading.

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.
• 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.

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.