pytest Plugin¶

Evalcraft ships a native pytest plugin that provides fixtures, markers, and terminal reporting for agent evaluation.

Installation¶

pip install "evalcraft[pytest]"

The plugin registers automatically via the pytest11 entry point — no configuration needed.

Fixtures¶

`capture_context`¶

Provides an active CaptureContext for the duration of the test. All MockLLM and MockTool calls inside the test are automatically recorded.

def test_agent(capture_context, mock_llm):
    mock_llm.add_response("*", "4")
    mock_llm.complete("What is 2+2?")
    assert capture_context.cassette.llm_call_count == 1

When combined with @pytest.mark.evalcraft_capture(save=True) (the default), the cassette is written to disk in evalcraft_cassette_dir after the test finishes — even on failure.

@pytest.mark.evalcraft_capture(name="math_agent_test")
def test_math(capture_context, mock_llm):
    mock_llm.add_response("*", "4")
    mock_llm.complete("What is 2+2?")
    assert capture_context.cassette.llm_call_count == 1
    # Cassette saved to tests/cassettes/math_agent_test.json

`mock_llm`¶

Returns a fresh MockLLM instance. Automatically records calls into the active capture context.

def test_agent(mock_llm):
    mock_llm.add_response("What is 2+2?", "4")
    result = mock_llm.complete("What is 2+2?")
    assert result.content == "4"

`mock_tool`¶

Factory fixture for creating MockTool instances. Call it with the tool name.

def test_agent(mock_tool):
    search = mock_tool("web_search")
    search.returns({"results": [{"title": "Python", "url": "https://python.org"}]})

    result = search(query="Python tutorial")
    assert result["results"][0]["title"] == "Python"
    search.assert_called_with(query="Python tutorial")

Multiple tools in one test:

def test_multi_tool(mock_tool, capture_context):
    weather = mock_tool("get_weather")
    weather.returns({"temp": 20, "condition": "clear"})

    email = mock_tool("send_email")
    email.returns({"sent": True})

    capture_context.record_input("Check weather and email me")
    weather_data = weather.call(city="Paris")
    email.call(to="user@example.com", body=str(weather_data))
    capture_context.record_output("Weather checked and email sent.")

    assert capture_context.cassette.tool_call_count == 2
    weather.assert_called(times=1)
    email.assert_called_with(to="user@example.com")

`cassette`¶

Load a Cassette from the path given in @pytest.mark.evalcraft_cassette. Skips the test if the cassette file is not found (in none record mode).

@pytest.mark.evalcraft_cassette("tests/cassettes/search_agent.json")
def test_replay(cassette):
    assert cassette.output_text == "Here are the results."
    assert cassette.tool_call_count == 1

`replay_engine`¶

Load a ReplayEngine from the path given in @pytest.mark.evalcraft_cassette.

@pytest.mark.evalcraft_cassette("tests/cassettes/search_agent.json")
def test_override_tool(replay_engine):
    replay_engine.override_tool_result("web_search", {"results": []})
    run = replay_engine.run()
    assert run.cassette.tool_call_count == 1

`evalcraft_cassette_dir`¶

Session-scoped fixture returning the Path to the cassette directory. Created automatically if it doesn't exist.

def test_custom_dir(evalcraft_cassette_dir):
    print(evalcraft_cassette_dir)  # Path("tests/cassettes")

Override with --cassette-dir:

pytest --cassette-dir my_cassettes/

Markers¶

`@pytest.mark.evalcraft_cassette(path)`¶

Provide the path to a cassette file for replay-based tests.

@pytest.mark.evalcraft_cassette("tests/cassettes/weather.json")
def test_weather_agent(cassette):
    assert cassette.tool_call_count == 1
    assert "Paris" in cassette.output_text

@pytest.mark.evalcraft_cassette("tests/cassettes/weather.json")
def test_with_replay_engine(replay_engine):
    engine = replay_engine
    engine.override_tool_result("get_weather", {"temp": -10, "condition": "blizzard"})
    run = engine.run()
    assert run.cassette.tool_call_count == 1

`@pytest.mark.evalcraft_capture(name=None, save=True)`¶

Auto-capture the test's agent run and optionally save the cassette.

@pytest.mark.evalcraft_capture(name="research_agent")
def test_research(capture_context, mock_llm, mock_tool):
    search = mock_tool("web_search")
    search.returns({"results": ["result 1", "result 2"]})
    mock_llm.add_response("*", "Here's my summary.")

    capture_context.record_input("Research AI trends")
    search.call(query="AI trends 2026")
    response = mock_llm.complete("Summarize: " + str(search.last_call))
    capture_context.record_output(response.content)

    assert capture_context.cassette.tool_call_count == 1
    # Cassette saved to tests/cassettes/research_agent.json

Disable saving:

@pytest.mark.evalcraft_capture(name="temp_test", save=False)
def test_no_save(capture_context, mock_llm):
    mock_llm.add_response("*", "ok")
    mock_llm.complete("ping")
    assert capture_context.cassette.llm_call_count == 1

`@pytest.mark.evalcraft_agent`¶

Informational marker — tags tests as agent evaluation tests for filtering.

@pytest.mark.evalcraft_agent
def test_weather_agent():
    run = replay("tests/cassettes/weather.json")
    assert run.cassette.tool_call_count == 1

Run only agent tests:

pytest -m evalcraft_agent

CLI options¶

`--cassette-dir DIR`¶

Override the cassette storage directory (default: tests/cassettes).

pytest --cassette-dir ci_cassettes/

`--evalcraft-record MODE`¶

Control cassette recording behavior:

Mode	Behavior
`none` (default)	Replay-only. Skip test if cassette is missing.
`new`	Record cassettes that don't exist yet.
`all`	Always re-record (overwrite existing cassettes).

# Record new cassettes for tests that don't have one yet
pytest --evalcraft-record=new

# Re-record all cassettes
pytest --evalcraft-record=all

Terminal summary¶

After each test run, Evalcraft appends a compact agent-run metrics table to the terminal output:

============================= evalcraft agent run summary =============================
  tests/test_weather.py::test_agent: tokens=135, cost=$0.0008, tools=1, llm_calls=1, latency=450ms, fingerprint=a3f1c2d4
  tests/test_search.py::test_research: tokens=820, cost=$0.0041, tools=3, llm_calls=2, latency=1200ms, fingerprint=b7e2f1a3

  TOTAL: 2 test(s) — 955 tokens, $0.0049 cost, 4 tool call(s)

This summary only appears when capture_context is used.

conftest.py patterns¶

Shared cassette directory¶

# conftest.py
import pytest

@pytest.fixture(scope="session")
def cassette_base():
    return "tests/cassettes"

Shared agent fixture¶

# conftest.py
import pytest
from evalcraft import CaptureContext, MockLLM, MockTool

@pytest.fixture
def weather_agent_mocks(mock_llm, mock_tool):
    weather = mock_tool("get_weather")
    weather.returns({"temp": 18, "condition": "cloudy"})
    mock_llm.add_response("*", "It's 18°C and cloudy.")
    return {"llm": mock_llm, "weather": weather}


# In your test file:
def test_agent(capture_context, weather_agent_mocks):
    mocks = weather_agent_mocks
    capture_context.record_input("Weather in Paris?")
    result = mocks["weather"].call(city="Paris")
    response = mocks["llm"].complete(f"Data: {result}")
    capture_context.record_output(response.content)
    assert capture_context.cassette.tool_call_count == 1

Parametrized cassette tests¶

import pytest
from evalcraft import assert_tool_called, assert_cost_under

@pytest.mark.parametrize("cassette_file", [
    "tests/cassettes/weather.json",
    "tests/cassettes/search.json",
    "tests/cassettes/planner.json",
])
def test_all_agents_within_budget(cassette_file):
    from evalcraft import replay
    run = replay(cassette_file)
    result = assert_cost_under(run, max_usd=0.10)
    assert result.passed, f"{cassette_file}: {result.message}"

pytest Plugin¶

Installation¶

Fixtures¶

capture_context¶

mock_llm¶

mock_tool¶

cassette¶

replay_engine¶

evalcraft_cassette_dir¶