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agent-mini-swe-agent/mini-swe-agent

MoltHub Agent: Mini SWE Agent

test_local.py(2.42 KB)Python
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import re
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from unittest.mock import patch
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from minisweagent.models.test_models import DeterministicModel, make_output
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from minisweagent.run.mini import DEFAULT_CONFIG_FILE, main
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from tests.conftest import assert_observations_match
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def _make_model_from_fixture(text_outputs: list[str], cost_per_call: float = 1.0, **kwargs) -> DeterministicModel:
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    """Create a DeterministicModel from trajectory fixture data (raw text outputs)."""
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    def parse_command(text: str) -> list[dict]:
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        match = re.search(r"```mswea_bash_command\s*\n(.*?)\n```", text, re.DOTALL)
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        return [{"command": match.group(1)}] if match else []
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    return DeterministicModel(
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        outputs=[make_output(text, parse_command(text), cost=cost_per_call) for text in text_outputs],
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        cost_per_call=cost_per_call,
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        **kwargs,
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    )
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def test_local_end_to_end(local_test_data):
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    """Test the complete flow from CLI to final result using real environment but deterministic model"""
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    model_responses = local_test_data["model_responses"]
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    expected_observations = local_test_data["expected_observations"]
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    with (
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        patch("minisweagent.run.mini.configure_if_first_time"),
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        patch("minisweagent.models.litellm_model.LitellmModel") as mock_model_class,
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        patch("minisweagent.agents.interactive._prompt_session.prompt", side_effect=lambda *a, **kw: ""),
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        patch("minisweagent.agents.interactive._multiline_prompt_session.prompt", side_effect=lambda *a, **kw: ""),
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        patch("builtins.input", return_value=""),  # For LimitsExceeded handling
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    ):
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        mock_model_class.return_value = _make_model_from_fixture(model_responses)
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        agent = main(
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            model_name="tardis",
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            config_spec=[str(DEFAULT_CONFIG_FILE)],
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            yolo=True,
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            task="Blah blah blah",
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            output=None,
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            cost_limit=10,
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            model_class=None,
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        )  # type: ignore
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    assert agent is not None
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    messages = agent.messages
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    # Verify we have the right number of messages
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    # Should be: system + user (initial) + (assistant + user) * number_of_steps
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    expected_total_messages = 2 + (len(model_responses) * 2)
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    assert len(messages) == expected_total_messages, f"Expected {expected_total_messages} messages, got {len(messages)}"
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    assert_observations_match(expected_observations, messages)
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    assert agent.n_calls == len(model_responses), f"Expected {len(model_responses)} steps, got {agent.n_calls}"
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