promptcheck 0.1.0b2

CI-first prompt testing framework for LLMs

PromptCheck

PromptCheck is a CI-first test harness for LLM prompts. It lets you write tests for GPT/Claude outputs, and automatically checks them in CI so you can catch prompt regressions early — before they reach users.

PromptCheck is a CI-first test harness for LLM prompts.

Write tests in YAML, gate pull-requests, and see pass/fail summaries posted as comments—so your prompts don't quietly regress.

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Install & Run

pip install promptcheck
promptcheck init   # creates a config and test scaffold
promptcheck run    # runs all prompt tests

Need full example? See example/ or the Quick-Start Guide.

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Get Started

Ready to dive in?

• See a basic test example in the example/ directory.
• Follow our Quick-Start Guide for step-by-step setup instructions.

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Why Prompt Testing Matters

LLMs can break without warning — even small prompt changes or model updates can cause major regressions. PromptCheck automates prompt evaluation like unit tests automate code quality.

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What does PromptCheck do? Key Concepts in Automated LLM Evaluation

When you tweak a prompt, swap models, or refactor your agent code, PromptCheck runs a battery of tests in CI (Rouge, regex, token-cost, latency, etc.) and fails the pull-request if quality regresses or cost spikes.

Think pytest + coverage, but for LLM output.

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Key Features for Effective LLM Testing

• Easy setup — drop a YAML test file, add the GitHub Action, done.
• Multi‑provider — OpenAI, Groq, OpenRouter (more soon).
• Metrics out‑of‑the‑box — exact/regex match, ROUGE‑L, BLEU (optional), token‑count, latency, cost.
• Readable reports — Action log output and (coming soon) PR comment bot. run.json artifact produced.
• Fast to extend — write your own metric in <30 lines (standard Python).

Feature	Free	Pro
CLI & GitHub Action	✅	✅
30-day result history	✅	✅
Unlimited history & charts	—	✅
Slack alerts	—	✅

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What it looks like

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How the YAML works (tests/*.yaml)

A test file contains a list of test cases. Here's an example structure:

- id: "openrouter_greet_test_001"
  name: "OpenRouter Basic Greeting Test"
  description: "Tests a basic greeting prompt."
  type: "llm_generation"

  input_data:
    prompt: "Briefly introduce yourself and greet the user."

  expected_output:
    # For regex_match, a pattern to find in the LLM's output
    regex_pattern: ".+" # Example: matches any non-empty string

  metric_configs:
    - metric: "regex_match" 
    - metric: "token_count"
      parameters:
        count_types: ["completion", "total"]
    - metric: "latency"
      threshold: # Optional: fail if conditions aren't met
        value: 10000 # e.g., latency_ms <= 10000
    - metric: "cost" 

  model_config:
    provider: "openrouter"
    model_name: "mistralai/mistral-7b-instruct"
    parameters:
      temperature: 0.7
      max_tokens: 50
      timeout_s: 25.0 
      retry_attempts: 2

  tags: ["openrouter", "greeting"] 

Add more cases in tests/. Thresholds (like value for latency, or f_score for rouge) are defined within the threshold object of a metric_config.

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Installation Options & Development Setup

# From PyPI (once 0.1.0+ is live)
# pip install promptcheck

# With optional BLEU metric (requires NLTK)
# pip install promptcheck[bleu]

# For development:
poetry install # Installs base dependencies
poetry install --extras bleu # Installs with BLEU support

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Releasing (maintainers)

# 1. Ensure tests pass and docs are updated
# 2. Bump version in pyproject.toml
poetry version <new_version_例えば_0.1.0>  # e.g., 0.1.0, 0.2.0b1

# 3. Build the package
poetry build

# 4. Publish to TestPyPI first (configured in pyproject.toml or via poetry config)
# poetry config pypi-token.testpypi <YOUR_TESTPYPI_TOKEN>
poetry publish -r testpypi

# 5. Test TestPyPI package thoroughly (e.g., in a clean venv or CI)

# 6. Publish to PyPI (prod)
# poetry config pypi-token.pypi <YOUR_PYPI_TOKEN>
poetry publish

# 7. Tag the release in Git
git tag v<new_version_例えば_0.1.0> # e.g., v0.1.0
git push origin v<new_version_例えば_0.1.0>

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Documentation

📖 Docs: Quick-Start Guide · YAML Reference (Coming Soon!)

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Roadmap

• PR comment bot (✅/❌ matrix in‑line)
• Hosted dashboard (Supabase)
• Async runner for large test suites
• More metrics and LLM provider integrations

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Contributing

1. Fork & clone the repository.
2. Set up your development environment: poetry install --extras bleu (to include all deps).
3. Run tests locally: poetry run promptcheck run tests/ (or a specific file). Keep it green!
4. Make your changes, add tests for new features.
5. Open a Pull Request.

Feedback & Questions

Found an issue or have a question? We'd love to hear from you! Please open an issue or start a discussion.

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License

License: Business Source License 1.1
PromptCheck is free to use for evaluation and non-production use. For commercial licenses, contact us.

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End of Document – keep this file as the project's living reference; version & timestamp changes at top on each major edit.