digline

Regression testing for LLM applications — with the baseline in your repository, not on someone's server.

Your prompt worked on Tuesday. On Thursday it works a little less — not enough to break, enough for a user to notice in two weeks. No ordinary test catches it, because there is no correct output to compare against, only a better or a worse one.

digline gives you an approved reference — the baseline — and on every change tells you whether you are below it: which case, which check, by how much. The baseline is a JSON file in your repository, so it goes through code review and it rolls back with git. No server, no account, no network call you have not configured yourself.

$ digline compare --suite suite.py --run latest
2 checks got worse compared with the reference. Every case could be judged. No case is suspended. The suite is unchanged from the reference.

how-do-i-return · llm_rubric · Score fell from 1.000000 to 0.700000.
how-do-i-return · contains · Went from passing to failing (1.000000 → 0.000000).

Why digline

Most evaluation tools tell you whether an output is below a threshold. digline also tells you whether it is worse than it was — the drift from 0.91 to 0.78 that trips no threshold and is the first thing a user feels.

The suite is Python, not YAML: a judge is an object, a target is a function, and what may leave a perimeter is declared in code — none of which a configuration file expresses without reinventing a language. Built for teams shipping LLM features for someone else, who have to show a customer what was tested, when, under which commit, and who approved it.

Wondering how digline differs from promptfoo, DeepEval, or observability platforms? See How digline compares.

Quickstart

With uv (recommended):

uv init && uv add digline

or with pip in an existing environment: pip install digline.

Requires Python 3.12+, which uv fetches for you if you do not have it. On the pip path an older interpreter says ERROR: No matching distribution found with from versions: none, which does not say why — that is what it means.

suite.py — complete and runnable, no API key:

"""suite.py — complete and runnable: no API key, nothing else to install."""

from digline.core import Contains, CostBudget, JudgeReply, LlmRubric
from digline.run import Case, Response, Suite

ANSWERS = {
    "where-is-my-order": "Order 4821 ships Thursday. — Northwind Support",
    "how-do-i-return": "Any item, within 30 days, unused. — Northwind Support",
}


def judge(prompt: str) -> JudgeReply:
    """Your judge. digline composes `prompt` from the rubric, the question and
    the answer; it wants a score in [0, 1] and a reason back."""
    signed = "Northwind Support" in prompt
    concise = len(prompt.split()) <= 60
    return JudgeReply(
        score=0.4 + 0.3 * signed + 0.3 * concise,
        reason=f"signed={signed}, concise={concise}",
    )


def target(case: Case) -> Response:
    """Your application, called once per case. Canned here so this runs as is."""
    text = ANSWERS[case.id]
    return Response(output=text, cost_usd=0.004 + 0.001 * len(text) / 100)


suite = Suite(
    tenant="northwind",
    environment="staging",
    name="support",
    assertions=[
        Contains(needle="Northwind Support"),
        LlmRubric(
            rubric="Does the reply answer the question in at most three sentences?",
            judge=judge,
            threshold=0.7,
            tolerance=0.05,
        ),
        CostBudget(max_usd=0.02, tolerance=0.05),
    ],
    cases=[Case(id="where-is-my-order"), Case(id="how-do-i-return")],
)

When your judge is a real model, add a provider plugin: uv add digline-anthropic (or pip install digline-anthropic), likewise digline-openai and digline-bedrock.

$ digline run --suite suite.py
2026-08-26T15-44-09-282929-00-00-e7421ec503ccefe8

$ digline promote --suite suite.py --run latest
support baseline set to 2026-08-26T15-44-09-282929-00-00-e7421ec503ccefe8

Now make it worse — delete — Northwind Support from the second answer — and ask again:

$ digline run --suite suite.py
2026-08-26T15-44-09-492722-00-00-e7421ec503ccefe8

$ digline compare --suite suite.py --run latest
2 checks got worse compared with the reference. Every case could be judged. No case is suspended. The suite is unchanged from the reference.

how-do-i-return · llm_rubric · Score fell from 1.000000 to 0.700000.
how-do-i-return · contains · Went from passing to failing (1.000000 → 0.000000).

$ echo $?
1

The exit code is the answer: 0 fine, 1 got worse, 2 could not be judged. Everything lands in .digline/<tenant>/ — baselines/ committed, runs/ git-ignored through a .gitignore digline writes for you.

What it checks

Per case — pure functions (inputs) -> Verdict, no I/O, callable on their own:

Per run — one verdict on the whole suite, the kind that goes in a contract:

Every assertion carries a threshold that can fail — there is no default that passes vacuously, and Contains("") is a ValueError when the suite loads rather than a green run — and a tolerance below which a difference from the baseline is noise. Where a number is really "k out of n", write it as one: min_agreement="2/3", and a float no k/n can produce is refused at construction.

One card each — parameters, typical values, what to watch out for — in docs/metrics.md. Custom assertion? Subclass AssertionBase, or RunAssertionBase for an aggregate: docs/api.md.

How it thinks

• The judge is yours. digline never calls a model API: you inject a function, and in your tests you inject a deterministic one.
• Three states, not two — pass, fail, error. An error is neither green nor a regression: it means could not judge, and a run containing one cannot become the baseline.
• Two kinds of noise, two answers. Suite.samples asks the target more than once — the same input answered differently. Repeated grades the same output more than once — the judge changing its mind. min_agreement becomes mandatory as soon as you sample.
• A tolerance is declared; a noise floor is measured. A sampled run records the interval its own samples spanned, and a drop that stays inside the baseline's interval is reported as unchanged rather than as a regression — a tool that cries wolf on its own measurement error teaches people to promote past it. It never rescues a flip, and it never invents an interval it does not have.
• Set the threshold where the system measurably is, not where you want it: the gate protects against getting worse, and raising the bar is a visible change in a pull request.
• Promote the median of several runs, not the first green one — digline view is the table you pick it from. Cases diagnose, aggregates gate.

Worked through with real numbers in docs/guide.md; the reasoning behind every fixed decision is in docs/adr/.

Commands

Examples

Six projects in examples/, each answering a question somebody actually arrives with. Every one runs with no API key, carries its committed report.html, and is a standalone project: copy the directory anywhere and uv sync works.

• I have a classifier: how do I keep it under control? — labelled cases, an agreement check, Precision and Accuracy as the gate
• I'm writing a prompt and have no application yet — a prompt in a file, and the report showing its diff next to what it moved
• I have a RAG: how do I check it doesn't make things up? — frozen retrieval, Faithfulness, PiiAbsent
• My application is Java: can I use this? — HttpTarget against a service digline cannot import
• My app is LangChain4j: what do I put in my repo? — the walkthrough: one endpoint, three files, the CI gate
• My pipeline is LangChain: what changed when I upgraded it? — the chain called in process, FakeListChatModel in CI, one line to a real model

What digline is not

• Not an observability platform. Dashboards over production traces are a served market. What is designed and not yet built is narrower: evaluating production responses inside your perimeter, and turning a failure into a committed test case.
• Not a red-teaming tool. digline generates no attacks. Once one is found, it becomes a Case, and the suite makes sure it never works again.
• Not YAML. Cases are data and may come from files; the suite is Python.
• Not a funnel. Two commitments, by design and for good: no hosted service that receives your payloads, and no data collection. The baseline lives in your repo; the runs happen on your machines. If digline ever grows paid features, they will run inside your perimeter too.

Status

0.3.0, alpha. The offline cycle — write the suite, run, promote, compare, report — is complete, covered by tests, and used daily on a real project. The production store, the bridge from production failures back to committed cases, and the reactive side are designed in ADR 0002 and not written yet.

Python 3.12+. One runtime dependency: jsonschema.

Docs

• docs/guide.md — how to reason with digline, in eight chapters and the order the problems arrive: baseline, judge noise, sampling, tolerance, threshold, which run to promote, what to gate on, what to maintain
• docs/metrics.md — a card per assertion and aggregate: when to reach for it, what it produces, what it will do to you if you are not looking
• docs/api.md — what is imported from where, every assertion and its parameters, custom assertions, and the complete example in examples/quickstart/, which a test runs on every build
• docs/view.md · docs/migrate.md — the two commands with a surface of their own
• docs/adr/ — the architectural decisions, numbered, with the reasoning

License

Apache-2.0.