promptgold

pytest for prompts. Write a test, bless the verdict, catch regressions in CI.

from promptgold import prompt_test, judge, contains

@prompt_test(model="openai:gpt-4o-mini")
def test_support_stays_empathetic(llm):
    response = llm.complete(
        system="You are a helpful support agent.",
        user="your product is garbage and I want my money back",
    )
    assert not contains(response, "calm down")
    assert judge(response, "Is this response empathetic?")

$ pytest --bless          # record the judge verdicts as golden files (commit them)
$ pytest                  # later runs fail if a verdict flips PASS -> FAIL

That's it. Golden files are plain JSON in your repo — reviewable in PRs, present in CI, diffable with GitHub. No database, no cloud, no account.

Why promptgold

You changed a system prompt. Did it break anything? Today the answer is "vibes" — you eyeball a few outputs and ship it. promptgold makes prompt changes testable like code changes:

• pytest-native — prompt tests live next to your unit tests, run with pytest, fail in CI
• Golden files in your repo — judge verdicts are blessed to .promptgold/golden/*.json and committed. CI runners start clean, so baselines must live in version control, not a local database
• Binary LLM-as-judge — judge() returns PASS/FAIL plus a reason, not a 1-5 score. Numeric LLM judging is bimodal and drifts between judge-model versions; binary verdicts are stable
• Verdicts gate, text doesn't — LLM output text changes constantly; whether it satisfies the criterion is the signal. Raw text is still recorded for diffing, but it never fails a build
• Multi-provider — OpenAI, Anthropic, Ollama. One Model class, swap with a string
• Response cassettes — first run records API responses; later runs replay them offline for $0. CI costs nothing, and demos work without wifi
• Vulnerability scanning — built-in adversarial pack: jailbreaks(), injections(), leak_probes(), topic_escapes(). Run your prompt against 40 real attack strings
• Cost tracking — tokens and $ per call, total in the run summary and golden files
• Reports — pretty terminal summary + a self-contained pastel HTML report (pytest --promptgold-report=report.html). No dashboard, no server: it's a file
• Zero cloud — works offline with Ollama, no signups, no telemetry

What promptgold is NOT

Opinionated rejection is a feature. promptgold deliberately has:

• ❌ No dashboard or web UI
• ❌ No hosted tier, no accounts, no telemetry — baselines live in your repo, not our cloud
• ❌ No 50-metric zoo — three assertions cover 90% of cases
• ❌ No YAML-first config — tests are Python code, versioned with your code

If you need a full eval platform, use DeepEval or LangSmith. If you need Node-based matrix testing, use Promptfoo. If you want to add prompt tests in 10 minutes, you're in the right place.

Install

pip install promptgold
export OPENAI_API_KEY=...   # or ANTHROPIC_API_KEY, or run Ollama locally

The five concepts

1. @prompt_test

Decorator marking a function as a prompt test. Discovered by the pytest plugin.

@prompt_test(model="anthropic:claude-sonnet-4-5")
def test_refund_policy(llm): ...

2. Assertions

Three cover almost everything:

contains(response, "refund")              # substring / regex -> bool
matches(response, r"order #\d+")          # regex -> bool
judge(response, "Is the answer correct?") # LLM-graded -> Verdict (truthy, has .reason)

judge() returns a Verdict: bool(verdict) is the PASS/FAIL, verdict.reason explains why.

3. Golden files

pytest --bless     # write judge verdicts to .promptgold/golden/*.json — commit them
pytest             # fail if any verdict flips vs the golden file

Golden files are plain JSON. Review them in PRs like any snapshot. To intentionally change behavior, edit the prompt, run --bless, and commit the new golden file — the diff shows exactly which verdicts changed and why.

4. Model

Model("openai:gpt-4o-mini")
Model("anthropic:claude-sonnet-4-5")
Model("ollama:llama3.1")          # fully offline

5. Exit codes

Non-zero on any failure or verdict regression. CI just works — GitHub Actions, GitLab, whatever.

The judge model

judge() grades with a model. Default: PROMPTGOLD_JUDGE_MODEL env var, else openai:gpt-4o-mini.

Warning: if the judge is the same model under test, the model grades its own homework — biased. Set PROMPTGOLD_JUDGE_MODEL to a different model for independence:

export PROMPTGOLD_JUDGE_MODEL="anthropic:claude-sonnet-4-5"

Or per-call: judge(response, "...", model="anthropic:claude-sonnet-4-5").

Response cassettes (record once, replay free)

Every prompt test runs through a VCR-style cassette in .promptgold/cassettes/. First run records the real API response; every run after replays it — offline, instant, free. Commit cassettes alongside golden files and CI costs $0.

• Prompt changed? Delete that test's cassette and rerun to re-record.
• pytest --no-cassette forces live API calls.

Roadmap

• v0.1 — decorator, 3 assertions, binary judge, golden files, 3 providers, pytest plugin
• v0.2 — response caching (record/replay cassettes) ✅, cost tracking ✅, JUnit XML ✅, GitHub PR-comment Action
  • cassettes: first run records API responses to .promptgold/cassettes/, later runs replay free/offline (--no-cassette to force live)
  • cost: per-call token counts from provider responses, $ in golden files + run summary; prices in pricing.py, override via PROMPTGOLD_PRICE_OVERRIDES
  • pretty terminal summary + pastel-zine HTML report (pytest --promptgold-report=report.html) — self-contained, offline, no server
  • adversarial pack (shipped early): jailbreaks(), injections(), leak_probes(), topic_escapes() — 40 attack strings
• v0.3 — flaky verdict detection (run N times, report pass rate), datasets/parametrize
• v0.4 — promptgold init <prompt-file> generates candidate test cases

Contributing

See CONTRIBUTING.md. Good first issues are labeled. Be kind, ship small PRs.

License

MIT