Run Tests with pytest

Configure pytest to run async Giskard Checks tests with pytest-asyncio.

1. Install dependencies

To get started, install the three packages needed to run async tests in pytest. pytest-asyncio is what bridges the gap between pytest’s synchronous test runner and Giskard’s async Scenario.run() method.

pip install pytest pytest-asyncio giskard-checks

2. Configure asyncio_mode

Add asyncio_mode = auto so every async def test_* function runs automatically without a per-test decorator.

pytest.ini:

[pytest]
asyncio_mode = auto

pyproject.toml:

[tool.pytest.ini_options]
asyncio_mode = "auto"

3. Write your first async test

With the configuration in place, your test functions can now use async def and await directly. Notice that the assert result.passed at the end is what turns a Giskard result into a pytest failure — without it, pytest would consider the test passed regardless of the scenario outcome.

test_chatbot.py

from giskard.checks import Scenario, RegexMatching


async def test_greeting_response():
    scenario = (
        Scenario("greeting")
        .interact(
            inputs="Hello!",
            outputs=lambda inputs: my_chatbot(inputs),
        )
        .check(RegexMatching(pattern=r"hi|hello|hey", name="has_greeting"))
    )

    result = await scenario.run()
    assert result.passed

Output

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4. Share generator config with a conftest.py fixture

Next, we’ll avoid duplicating LLM configuration across test files by moving it into a shared conftest.py. The scope="session" setting means the generator is configured once per test run, not once per test — important when your test suite has dozens of LLM-backed checks.

Avoid repeating set_default_generator() in every test file by calling it once in a session-scoped fixture.

conftest.py

import pytest
from giskard.checks import set_default_generator


@pytest.fixture(scope="session", autouse=True)
def configure_generator():
    set_default_generator("openai/gpt-5-mini")

With autouse=True the fixture runs before any test in the session without requiring an explicit parameter.

5. Parametrize for data-driven tests

With the generator configured, we can now scale up to data-driven tests. Each parametrized case gets its own entry in the pytest output, so when one question fails you can see exactly which input caused it without digging through a combined result object.

Use @pytest.mark.parametrize to run the same scenario against multiple inputs.

import pytest
from giskard.checks import Scenario, StringMatching

test_cases = [
    ("What is the capital of France?", r"Paris"),
    ("What is 2 + 2?", r"4"),
    ("Who wrote Hamlet?", r"Shakespeare"),
]


@pytest.mark.parametrize("question,pattern", test_cases)
async def test_factual_answers(question, pattern):
    scenario = (
        Scenario(f"factual_{question[:20]}")
        .interact(
            inputs=question,
            outputs=lambda inputs: my_agent(inputs),
        )
        .check(StringMatching(keyword=pattern, name="correct_answer"))
    )

    result = await scenario.run()
    assert result.passed, f"Failed for question: {question}"

6. Run the tests

With everything wired up, you can now execute your suite with a single command. The -v flag prints each test name and its result individually, making it easy to spot which parametrized case failed.

pytest -v

Expected output:

test_chatbot.py::test_greeting_response PASSED
test_factual.py::test_factual_answers[What is the capital of France?-Paris] PASSED
test_factual.py::test_factual_answers[What is 2 + 2?-4] PASSED
test_factual.py::test_factual_answers[Who wrote Hamlet?-Shakespeare] PASSED

As shown above, each parametrized case appears on its own line so failures are immediately identifiable. Run a single file or test by name:

pytest test_chatbot.py -v
pytest -k "factual" -v

Next steps

• Async design & pytest — why Scenario.run() is async
• Single-turn testing tutorial — the scenario patterns used above
• Simulate Users — drive multi-turn tests with LLM-generated inputs