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How digline compares

How digline compares

The space around "testing LLM applications" is crowded. The useful question is not which tool to rank first, but which question each tool answers. There are five.

"Did the output change?" — snapshot and replay testing

Tools like proveai and EvalView record your application's outputs and detect when they differ from a recorded snapshot, typically via content hashing or diffing against a golden baseline. This works well for deterministic pipelines: if the output should be identical every time, any difference is a signal.

LLM outputs are rarely identical every time. With sampling, a hash tells you that something changed — which you already knew — not whether it got worse. And a diff between two long generated texts tells you where the words differ, not whether the new answer still meets the bar you approved. Change detection answers a question that sampled outputs make nearly meaningless.

"How is it going in production?" — observability

Langfuse, LangSmith, Arize Phoenix and similar platforms trace your application in production: latency, cost, user feedback, judge scores over live traffic. This is valuable — it tells you how the system is doing now, on real inputs, after deploy.

What observability cannot tell you is whether the change you are about to ship makes things worse, because it only sees traffic after the change is live. It is a rear-view mirror — an essential one — while regression testing is the check before you pull out.

"Did it get worse than what I approved?" — regression testing

digline answers the pre-deploy question, and takes the statistics of LLM outputs seriously:

  • Your cases and assertions live in a suite.py; scores come from checks and, where judgment is needed, an LLM judge.
  • When results are good, you promote them: the approved scores — together with the prompt and the commit that produced them — become a versioned baseline in your repo. Not a hash: numbers, with tolerances.
  • On every change, digline compare tells you which case got worse, and by how much. "Was 0.91, now 0.78, still above threshold" is a first-class verdict — not a passed check, not a wall of diff.
  • Because an LLM judge is itself noisy (a judge flips its verdict on roughly 1 case in 20, measured on two suites), digline is built to separate signal from noise: sampled runs, aggregate scores, tolerances — so a flip doesn't fail your build and a real degradation doesn't hide in the variance.

Two things digline will never do, by design: no hosted service that receives your payloads, and no data collection. The baseline lives in your repo; the runs happen on your machines. For teams whose prompts and outputs cannot leave their perimeter, this is not a feature toggle — it is the architecture.

"Which of these should I ship?" — exploration

Exploration frameworks — promptfoo and DeepEval among them — put prompts, models and configurations side by side, and give you a wide library of metrics to score them with. When the question is which variant wins, that layout is the answer. It is a different job from holding the one you chose: a sweep tells you what is best today, not whether today is worse than the day someone approved.

"Is this model good enough for the job?" — benchmarking

Inspect AI and the harnesses around it evaluate a model or a capability against a target: benchmarks, comparisons across providers, agentic tasks that need a sandbox, safety and security evaluations. The sample carries the right answer, and the question is how often the model reaches it.

What that measures is the model, not your application. A benchmark tells you whether a model is good enough to build on; it does not know what you built, what you approved, or that this morning the same model answered your case differently.

How digline compares to specific tools

One page per tool. Each one says what that tool does well, in its own vocabulary and with its own feature names, before it says what digline does instead — and what is written there about another tool is checked against that tool's documentation first. Several of them answer more than one of these questions — the platforms especially, which run from tracing to experiments to dashboards — so each is tagged with the families it covers, and its page says where the weight sits.

  • promptfoo — the prompt and provider matrix, the assertion library, promptfoo redteam — exploration
  • DeepEval — the metric library, assert_test in pytest, DeepTeam, the official run on Confident AI — exploration
  • Braintrust — Eval(), experiments against a persistent baseline, Loop, autoevals — regression testing, exploration, observability
  • LangSmith — tracing, datasets and experiments, evaluate(), annotation queues, online evaluation — observability, exploration
  • Langfuse — OpenTelemetry tracing, prompt management, batch evaluation, an MIT self-hosted core — observability
  • Inspect AI — Task, solvers and scorers, sandboxing, epochs and reducers — benchmarking
  • Opik — Apache-2.0 tracing, experiments, guardrails, the Agent Optimizer — observability, exploration

Using them together

Three of these five families fit together in one pipeline. A reasonable production setup is an exploration framework while you develop, digline as the gate in CI before deploy, and observability watching live traffic after. Two families sit outside that line. Benchmarking comes earlier, when the question is still which model to build on. Snapshot and replay testing is the one the first section argues against: when outputs are sampled, a hash answers a question nobody asked.

digline only has a strong opinion about its own layer: the verdict on regressions belongs in your repo, next to your code, before the deploy. Not in someone else's cloud.