PromptProof

Snapshot & regression testing for LLM prompts.

Write your prompts and test cases as YAML, run one command against any OpenAI-compatible endpoint, and get a CI-friendly pass/fail report. When a case fails, your build goes red — before your users notice the model started behaving differently.

$ promptproof run examples/basic.yaml --mock

PromptProof — suite: sentiment-classifier
provider: mock   model: mock
------------------------------------------------------------------------
CASE            RESULT      TIME  TOKENS
----------------------------------------
glowing review  PASS         0ms      66
angry review    PASS         0ms      63
factual review  PASS         0ms      59
----------------------------------------
3 passed, 0 failed, 3 total — 188 tokens (estimated)

Why PromptProof?

Prompts are code, but they fail like weather. You swap a model version, tweak one sentence of a system prompt, or your provider silently updates a model — and the output format your parser depends on quietly changes. Nobody notices until production does.

PromptProof treats a prompt the way unit tests treat a function:

• Suites are files. Prompts, variables and expectations live in YAML/JSON next to your code, reviewed in pull requests like everything else.
• Assertions, not vibes. contains, regex, json_schema, max_chars — deterministic checks that mean the same thing on every run. For the things patterns can't judge (tone, helpfulness, "did it actually answer the question?"), an llm_judge assertion lets a second model grade the response against a criterion you state in plain language.
• Built for CI. Plain-text table for humans, JSON report for machines, a self-contained HTML report for sharing (--html report.html, no assets needed), exit code 1 when any case fails so GitHub Actions turns red.
• No key needed to try it. A built-in mock provider runs any suite offline, so a fresh clone works in 30 seconds and CI never needs secrets just to test the harness.

Quick start (offline demo)

pip install promptproof-cli        # or: pip install -e . from a clone
promptproof run examples/basic.yaml --mock
promptproof run examples/basic.yaml --mock --json report.json --html report.html

That runs the bundled sentiment-classifier suite end-to-end — rendering, "model" calls, assertions, report — with zero API keys and zero network.

Running against a real endpoint

export OPENAI_API_KEY=sk-...
promptproof run examples/basic.yaml

PromptProof speaks the OpenAI chat-completions API, so base_url can point at OpenAI, Azure OpenAI, OpenRouter, Together, Groq, or a local server (Ollama, LM Studio, …) — anything compatible. The API key is read from the environment variable named by api_key_env in the suite; keys are never written into suite files or reports.

Exit codes: 0 all cases passed · 1 a case failed · 2 the suite or configuration is invalid.

Cost & token budgets

Declare your model's per-1k-token prices and hard budget gates at the top of a suite. When the run's token total or estimated cost crosses a limit, the whole run fails — before the bill surprises you:

pricing:
  input_per_1k: 0.15    # USD per 1k input tokens
  output_per_1k: 0.60   # USD per 1k output tokens

limits:
  max_tokens: 5000      # hard gate on the run's token total
  max_usd: 0.50         # hard gate on the run's estimated cost (USD)

The estimated cost is printed in the console summary and recorded in the JSON report (summary.estimated_cost_usd). On --mock runs, usage and cost are estimated exactly like everywhere else (see "No key needed to try it"). A breached gate fails the run with exit code 1 and a BUDGET EXCEEDED: … line in the report — examples/budgets.yaml ships with a deliberately tiny budget so you can watch it fire:

$ promptproof run examples/budgets.yaml --mock
…
1 passed, 0 failed, 1 total — 66 tokens (estimated)
estimated cost: $0.0131 (estimated)
BUDGET EXCEEDED: token budget exceeded: 66 tokens > max_tokens 50

Golden-file snapshots

For the drift that patterns can't catch, mark a case with snapshot: true and record its real response once — then every later run diffs the fresh response against the recorded golden value:

$ promptproof run examples/snapshots.yaml --record    # needs the real model once
$ promptproof run examples/snapshots.yaml             # diffs against the golden file

Goldens are stored as JSON next to the suite file (<suite-name>.golden.json, overridable with a top-level snapshot_file: key), keyed by case name. A changed response fails the case with a snapshot assertion result ("re-run with --record to update"). A snapshot: true case with no recorded golden fails with a hint to record one first, so the workflow never silently skips.

Suite files

name: sentiment-classifier

model:
  base_url: https://api.openai.com/v1   # any OpenAI-compatible endpoint
  name: gpt-4o-mini
  api_key_env: OPENAI_API_KEY           # env var holding the key
  temperature: 0

prompt: |
  Classify the sentiment of this review. Respond with JSON only.
  Review: {{review}}

# Only used by --mock runs: deterministic offline responses.
mock:
  default: '{"sentiment": "neutral"}'
  rules:
    - match: "absolutely loved"        # substring, or /regex/
      response: '{"sentiment": "positive"}'

cases:
  - name: glowing review
    vars: {review: "I absolutely loved this keyboard."}
    assertions:
      - contains: '"positive"'
      - max_chars: 100
      - json_schema:
          type: object
          required: [sentiment]
          properties:
            sentiment: {type: string, enum: [positive, negative, neutral]}

Templates support {{variable}} placeholders only — a suite is data, not code. A case may set mock_response to override the mock rules for that case. Suites can also be plain JSON (same structure).

Assertions

json_schema uses a small built-in validator covering the subset prompt tests actually use — type, required, properties, items, enum, minimum/maximum, minLength/maxLength, pattern — so PromptProof needs no heavy dependencies. Assertions may also be written in the explicit form {type: contains, value: "x"}.

llm_judge: grading with a second model

Some expectations can't be a pattern: the tone is friendly, the answer addresses the question that was asked, no medical advice is given. State them as a criterion and let a judge model grade the response:

cases:
  - name: support reply
    assertions:
      - llm_judge: "the response is polite and actually answers the question"
      # mapping form, with a per-assertion model override:
      - llm_judge: {criterion: "the response gives no medical advice", model: gpt-4o}

The judge endpoint is configured with environment variables only (keys never go in suite files):

export PROMPTPROOF_JUDGE_BASE_URL=https://api.openai.com/v1  # any compatible endpoint
export PROMPTPROOF_JUDGE_MODEL=gpt-4o-mini
export PROMPTPROOF_JUDGE_API_KEY=sk-...

The judge's verdict and its one-sentence reason land in the report like any other assertion ("judge: polite and on-topic"). If the endpoint is not configured, llm_judge assertions report as skipped instead of failing — suites keep running offline, under --mock, and in CI without secrets. A configured judge that errors or answers unparseably fails the assertion with the reason shown; it never crashes the run.

GitHub Actions

- name: Prompt regression tests
  env:
    OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
  run: |
    pip install promptproof-cli
    promptproof run tests/prompts/checkout.yaml --json prompt-report.json

A failing case exits 1 and fails the step. For pull requests that only touch prompt files, run with --mock to validate the suite itself without spending tokens or requiring secrets from forks.

Roadmap

• Golden-file snapshots — done: snapshot: true cases + --record writes <suite-name>.golden.json; later runs diff against it
• Multi-turn conversation suites
• Cost budgets per suite (max_tokens / max_usd gates) — done: suite-level pricing + limits gates, cost estimate in every report
• HTML report output — done: promptproof run suite.yaml --html report.html

Contributing

Issues and pull requests are welcome — see CONTRIBUTING.md. Please keep the dependency footprint tiny and the offline demo working.

License

MIT © 2026 Dylan XM — see LICENSE.