pytest-assay is a framework for the evaluation of Pydantic AI agents. By adding the @pytest.mark.assay decorator to a test, you can run an assay resulting in a readout report which contains the evaluation. The assay compares the current agent responses against pre-recorded baseline responses, e.g. from the main branch. The implementation is using pytest hooks which capture Agent.run() responses inside the test. Below is a minimal example which evaluates the creativity of a search query generation.
def generate_evaluation_cases():
...
return Dataset(cases)
@pytest.mark.assay(
generator=generate_evaluation_cases,
evaluator=PairwiseEvaluator(
model="openai:gpt-4o",
criterion="Which of the two search queries shows more genuine curiosity and creativity?",
),
)
@pytest.mark.asyncio
async def test_query_generation(context: AssayContext):
agent = Agent(
model="openai:gpt-4o",
system_prompt="Generate a concise web search query for the given research topic.",
)
for case in context.dataset.cases:
async with agent:
result = await agent.run(user_prompt=f"Generate a search query for the following topic: <TOPIC>{case.inputs['topic']}</TOPIC>")
Both baseline responses and the final readout report are stored in the assays/ subfolder.
tests
├── assays
│ └── test_evaluation
│ ├── test_query_generation.json # Baseline data
│ └── test_query_generation.readout.json # Readout report
└── test_evaluation.py
The baseline data are generated in new_baseline mode, and the readout report is generated in default evaluate mode.
uv run pytest tests/test_evaluation.py --assay-mode=new_baseline # Generates baseline data test_query_generation.json
uv run pytest tests/test_evaluation.py --assay-mode=evaluate # Generates readout report test_query_generation.readout.json
For full examples, please run the integration tests in tests/test_plugin_integration.py.
uv run pytest --log-cli-level=DEBUG tests/test_plugin_integration.py | grep pytest_assay
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