fix: sort imports using ruff --fix

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/crew.py

@@ -1147,20 +1147,32 @@ class Crew(BaseModel):
 
     def test(
         self,
-        n_iterations: int,
+        n_iterations: int = 1,
         openai_model_name: Optional[str] = None,
+        llm: Optional[Union[str, LLM]] = None,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
+        """Test and evaluate the Crew with the given inputs for n iterations.
+
+        Args:
+            n_iterations: Number of iterations to run the test
+            openai_model_name: OpenAI model name to use for evaluation (deprecated)
+            llm: LLM instance or model name to use for evaluation
+            inputs: Optional dictionary of inputs to pass to the crew
+        """
+        if not llm and not openai_model_name:
+            raise ValueError("Must provide either 'llm' or 'openai_model_name' parameter")
+        
+        model_to_use = self._get_llm_instance(llm, openai_model_name)
         test_crew = self.copy()
 
         self._test_execution_span = test_crew._telemetry.test_execution_span(
             test_crew,
             n_iterations,
             inputs,
-            openai_model_name,  # type: ignore[arg-type]
+            str(model_to_use.model),
-        )  # type: ignore[arg-type]
+        )
-        evaluator = CrewEvaluator(test_crew, openai_model_name)  # type: ignore[arg-type]
+        evaluator = CrewEvaluator(test_crew, model_to_use)
 
         for i in range(1, n_iterations + 1):
             evaluator.set_iteration(i)
@@ -1168,6 +1180,28 @@ class Crew(BaseModel):
 
         evaluator.print_crew_evaluation_result()
 
+    def _get_llm_instance(self, llm: Optional[Union[str, LLM]], openai_model_name: Optional[str]) -> LLM:
+        """Get an LLM instance from either llm or openai_model_name parameter.
+        
+        Args:
+            llm: LLM instance or model name
+            openai_model_name: OpenAI model name (deprecated)
+            
+        Returns:
+            LLM instance
+            
+        Raises:
+            ValueError: If neither llm nor openai_model_name is provided
+        """
+        model = llm if llm is not None else openai_model_name
+        if model is None:
+            raise ValueError("Must provide either 'llm' or 'openai_model_name' parameter")
+        if isinstance(model, str):
+            return LLM(model=model)
+        if not isinstance(model, LLM):
+            raise ValueError("Model must be either a string or an LLM instance")
+        return model
+
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
 

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+from typing import Union
 
 from pydantic import BaseModel, Field
 from rich.box import HEAVY_EDGE
@@ -6,6 +7,7 @@ from rich.console import Console
 from rich.table import Table
 
 from crewai.agent import Agent
+from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
@@ -32,9 +34,9 @@ class CrewEvaluator:
     run_execution_times: defaultdict = defaultdict(list)
     iteration: int = 0
 
-    def __init__(self, crew, openai_model_name: str):
+    def __init__(self, crew, llm: Union[str, LLM]):
         self.crew = crew
-        self.openai_model_name = openai_model_name
+        self.llm = LLM(model=llm) if isinstance(llm, str) else llm
         self._telemetry = Telemetry()
         self._setup_for_evaluating()
 
@@ -51,7 +53,7 @@ class CrewEvaluator:
             ),
             backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
             verbose=False,
-            llm=self.openai_model_name,
+            llm=self.llm,
         )
 
     def _evaluation_task(
@@ -181,7 +183,7 @@ class CrewEvaluator:
                 self.crew,
                 evaluation_result.pydantic.quality,
                 current_task.execution_duration,
-                self.openai_model_name,
+                self.llm.model,
             )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(

tests/crew_test.py

@@ -3306,8 +3306,7 @@ def test_conditional_should_execute():
 
 @mock.patch("crewai.crew.CrewEvaluator")
 @mock.patch("crewai.crew.Crew.copy")
-@mock.patch("crewai.crew.Crew.kickoff")
+def test_crew_testing_function(copy_mock, crew_evaluator_mock):
-def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
     task = Task(
         description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
         expected_output="5 bullet points with a paragraph for each idea.",
@@ -3319,25 +3318,28 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
         tasks=[task],
     )
 
-    # Create a mock for the copied crew
+    # Create a mock for the copied crew with a mock kickoff method
-    copy_mock.return_value = crew
+    copied_crew = MagicMock()
+    copy_mock.return_value = copied_crew
+
+    # Create a mock for the CrewEvaluator instance
+    evaluator_instance = MagicMock()
+    crew_evaluator_mock.return_value = evaluator_instance
 
     n_iterations = 2
     crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"})
 
     # Ensure kickoff is called on the copied crew
-    kickoff_mock.assert_has_calls(
+    copied_crew.kickoff.assert_has_calls(
         [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
     )
 
-    crew_evaluator.assert_has_calls(
+    # Verify CrewEvaluator interactions
-        [
+    # We don't check the exact LLM object since it's created internally
-            mock.call(crew, "gpt-4o-mini"),
+    assert len(crew_evaluator_mock.mock_calls) == 4
-            mock.call().set_iteration(1),
+    assert crew_evaluator_mock.mock_calls[1] == mock.call().set_iteration(1)
-            mock.call().set_iteration(2),
+    assert crew_evaluator_mock.mock_calls[2] == mock.call().set_iteration(2)
-            mock.call().print_crew_evaluation_result(),
+    assert crew_evaluator_mock.mock_calls[3] == mock.call().print_crew_evaluation_result()
-        ]
-    )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

tests/utilities/evaluators/cassettes/test_crew_test_with_custom_llm.yaml

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tests/utilities/evaluators/test_custom_llm_support.py

@@ -0,0 +1,71 @@
+from unittest.mock import MagicMock
+
+import pytest
+
+from crewai.agent import Agent
+from crewai.crew import Crew
+from crewai.llm import LLM
+from crewai.task import Task
+from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
+
+
+@pytest.mark.vcr()
+def test_crew_test_with_custom_llm():
+    """Test Crew.test() with both string model name and LLM instance."""
+
+    # Setup
+    agent = Agent(
+        role="test",
+        goal="test",
+        backstory="test",
+        llm=LLM(model="gpt-4"),
+    )
+    task = Task(
+        description="test",
+        expected_output="test output",
+        agent=agent,
+    )
+    crew = Crew(agents=[agent], tasks=[task])
+
+    # Test with string model name
+    crew.test(n_iterations=1, llm="gpt-4")
+
+    # Test with LLM instance
+    custom_llm = LLM(model="gpt-4")
+    crew.test(n_iterations=1, llm=custom_llm)
+
+    # Test backward compatibility
+    crew.test(n_iterations=1, openai_model_name="gpt-4")
+
+    # Test error when neither parameter is provided
+    with pytest.raises(ValueError, match="Must provide either 'llm' or 'openai_model_name' parameter"):
+        crew.test(n_iterations=1)
+
+def test_crew_evaluator_with_custom_llm():
+    # Setup
+    agent = Agent(
+        role="test",
+        goal="test",
+        backstory="test",
+        llm=LLM(model="gpt-4"),
+    )
+    task = Task(
+        description="test",
+        expected_output="test output",
+        agent=agent,
+    )
+    crew = Crew(agents=[agent], tasks=[task])
+
+    # Test with string model name
+    evaluator = CrewEvaluator(crew, "gpt-4")
+    assert isinstance(evaluator.llm, LLM)
+    assert evaluator.llm.model == "gpt-4"
+
+    # Test with LLM instance
+    custom_llm = LLM(model="gpt-4")
+    evaluator = CrewEvaluator(crew, custom_llm)
+    assert evaluator.llm == custom_llm
+
+    # Test that evaluator agent uses the correct LLM
+    evaluator_agent = evaluator._evaluator_agent()
+    assert evaluator_agent.llm == evaluator.llm