feat: restructure project as UV workspace with crewai in lib/

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tests/utilities/crew/test_crew_context.py

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-import uuid
-
-import pytest
-from opentelemetry import baggage
-from opentelemetry.context import attach, detach
-
-from crewai.utilities.crew.crew_context import get_crew_context
-from crewai.utilities.crew.models import CrewContext
-
-
-def test_crew_context_creation():
-    crew_id = str(uuid.uuid4())
-    context = CrewContext(id=crew_id, key="test-crew")
-    assert context.id == crew_id
-    assert context.key == "test-crew"
-
-
-def test_get_crew_context_with_baggage():
-    crew_id = str(uuid.uuid4())
-    assert get_crew_context() is None
-
-    crew_ctx = CrewContext(id=crew_id, key="test-key")
-    ctx = baggage.set_baggage("crew_context", crew_ctx)
-    token = attach(ctx)
-
-    try:
-        context = get_crew_context()
-        assert context is not None
-        assert context.id == crew_id
-        assert context.key == "test-key"
-    finally:
-        detach(token)
-
-    assert get_crew_context() is None
-
-
-def test_get_crew_context_empty():
-    assert get_crew_context() is None
-
-
-def test_baggage_nested_contexts():
-    crew_id1 = str(uuid.uuid4())
-    crew_id2 = str(uuid.uuid4())
-
-    crew_ctx1 = CrewContext(id=crew_id1, key="outer")
-    ctx1 = baggage.set_baggage("crew_context", crew_ctx1)
-    token1 = attach(ctx1)
-
-    try:
-        outer_context = get_crew_context()
-        assert outer_context.id == crew_id1
-        assert outer_context.key == "outer"
-
-        crew_ctx2 = CrewContext(id=crew_id2, key="inner")
-        ctx2 = baggage.set_baggage("crew_context", crew_ctx2)
-        token2 = attach(ctx2)
-
-        try:
-            inner_context = get_crew_context()
-            assert inner_context.id == crew_id2
-            assert inner_context.key == "inner"
-        finally:
-            detach(token2)
-
-        restored_context = get_crew_context()
-        assert restored_context.id == crew_id1
-        assert restored_context.key == "outer"
-    finally:
-        detach(token1)
-
-    assert get_crew_context() is None
-
-
-def test_baggage_exception_handling():
-    crew_id = str(uuid.uuid4())
-
-    crew_ctx = CrewContext(id=crew_id, key="test")
-    ctx = baggage.set_baggage("crew_context", crew_ctx)
-    token = attach(ctx)
-
-    with pytest.raises(ValueError):
-        try:
-            assert get_crew_context() is not None
-            raise ValueError("Test exception")
-        finally:
-            detach(token)
-
-    assert get_crew_context() is None

tests/utilities/evaluators/__init__.py

@@ -1 +0,0 @@
-"""Tests for evaluators."""

tests/utilities/evaluators/test_crew_evaluator_handler.py

@@ -1,142 +0,0 @@
-from unittest import mock
-
-import pytest
-
-from crewai.agent import Agent
-from crewai.crew import Crew
-from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
-from crewai.utilities.evaluators.crew_evaluator_handler import (
-    CrewEvaluator,
-    TaskEvaluationPydanticOutput,
-)
-
-
-class InternalCrewEvaluator:
-    @pytest.fixture
-    def crew_planner(self):
-        agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")
-        task = Task(
-            description="Task 1",
-            expected_output="Output 1",
-            agent=agent,
-        )
-        crew = Crew(agents=[agent], tasks=[task])
-
-        return CrewEvaluator(crew, openai_model_name="gpt-4o-mini")
-
-    def test_setup_for_evaluating(self, crew_planner):
-        crew_planner._setup_for_evaluating()
-        assert crew_planner.crew.tasks[0].callback == crew_planner.evaluate
-
-    def test_set_iteration(self, crew_planner):
-        crew_planner.set_iteration(1)
-        assert crew_planner.iteration == 1
-
-    def test_evaluator_agent(self, crew_planner):
-        agent = crew_planner._evaluator_agent()
-        assert agent.role == "Task Execution Evaluator"
-        assert (
-            agent.goal
-            == "Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance."
-        )
-        assert (
-            agent.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"
-        )
-        assert agent.verbose is False
-        assert agent.llm.model == "gpt-4o-mini"
-
-    def test_evaluation_task(self, crew_planner):
-        evaluator_agent = Agent(
-            role="Evaluator Agent",
-            goal="Evaluate the performance of the agents in the crew",
-            backstory="Master in Evaluation",
-        )
-        task_to_evaluate = Task(
-            description="Task 1",
-            expected_output="Output 1",
-            agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"),
-        )
-        task_output = "Task Output 1"
-        task = crew_planner._evaluation_task(
-            evaluator_agent, task_to_evaluate, task_output
-        )
-
-        assert task.description.startswith(
-            "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance."
-        )
-
-        assert task.agent == evaluator_agent
-        assert (
-            task.description
-            == "Based on the task description and the expected output, compare and evaluate "
-            "the performance of the agents in the crew based on the Task Output they have "
-            "performed using score from 1 to 10 evaluating on completion, quality, and overall "
-            "performance.task_description: Task 1 task_expected_output: Output 1 "
-            "agent: Agent 1 agent_goal: Goal 1 Task Output: Task Output 1"
-        )
-
-    @mock.patch("crewai.utilities.evaluators.crew_evaluator_handler.Console")
-    @mock.patch("crewai.utilities.evaluators.crew_evaluator_handler.Table")
-    def test_print_crew_evaluation_result(self, table, console, crew_planner):
-        # Set up task scores and execution times
-        crew_planner.tasks_scores = {
-            1: [10, 9, 8],
-            2: [9, 8, 7],
-        }
-        crew_planner.run_execution_times = {
-            1: [24, 45, 66],
-            2: [55, 33, 67],
-        }
-
-        # Mock agents and assign them to tasks
-        crew_planner.crew.agents = [
-            mock.Mock(role="Agent 1"),
-            mock.Mock(role="Agent 2"),
-        ]
-        crew_planner.crew.tasks = [
-            mock.Mock(
-                agent=crew_planner.crew.agents[0], processed_by_agents=["Agent 1"]
-            ),
-            mock.Mock(
-                agent=crew_planner.crew.agents[1], processed_by_agents=["Agent 2"]
-            ),
-        ]
-
-        # Run the method
-        crew_planner.print_crew_evaluation_result()
-
-        # Verify that the table is created with the appropriate structure and rows
-        table.assert_has_calls(
-            [
-                mock.call(
-                    title="Tasks Scores \n (1-10 Higher is better)", box=mock.ANY
-                ),  # Title and styling
-                mock.call().add_column("Tasks/Crew/Agents", style="cyan"),  # Columns
-                mock.call().add_column("Run 1", justify="center"),
-                mock.call().add_column("Run 2", justify="center"),
-                mock.call().add_column("Avg. Total", justify="center"),
-                mock.call().add_column("Agents", style="green"),
-                # Verify rows for tasks with agents
-                mock.call().add_row("Task 1", "10.0", "9.0", "9.5", "- Agent 1"),
-                mock.call().add_row("", "", "", "", "", ""),  # Blank row between tasks
-                mock.call().add_row("Task 2", "9.0", "8.0", "8.5", "- Agent 2"),
-                # Add crew averages and execution times
-                mock.call().add_row("Crew", "9.00", "8.00", "8.5", ""),
-                mock.call().add_row("Execution Time (s)", "135", "155", "145", ""),
-            ]
-        )
-
-        # Ensure the console prints the table
-        console.assert_has_calls([mock.call(), mock.call().print(table())])
-
-    def test_evaluate(self, crew_planner):
-        task_output = TaskOutput(
-            description="Task 1", agent=str(crew_planner.crew.agents[0])
-        )
-
-        with mock.patch.object(Task, "execute_sync") as execute:
-            execute().pydantic = TaskEvaluationPydanticOutput(quality=9.5)
-            crew_planner.evaluate(task_output)
-            assert crew_planner.tasks_scores[0] == [9.5]

tests/utilities/evaluators/test_task_evaluator.py

@@ -1,103 +0,0 @@
-from unittest import mock
-from unittest.mock import MagicMock, patch
-
-
-from crewai.utilities.evaluators.task_evaluator import (
-    TaskEvaluator,
-    TrainingTaskEvaluation,
-)
-from crewai.utilities.converter import ConverterError
-
-
-@patch("crewai.utilities.evaluators.task_evaluator.TrainingConverter")
-def test_evaluate_training_data(converter_mock):
-    training_data = {
-        "agent_id": {
-            "data1": {
-                "initial_output": "Initial output 1",
-                "human_feedback": "Human feedback 1",
-                "improved_output": "Improved output 1",
-            },
-            "data2": {
-                "initial_output": "Initial output 2",
-                "human_feedback": "Human feedback 2",
-                "improved_output": "Improved output 2",
-            },
-        }
-    }
-    agent_id = "agent_id"
-    original_agent = MagicMock()
-    original_agent.llm.supports_function_calling.return_value = False
-    function_return_value = TrainingTaskEvaluation(
-        suggestions=[
-            "The initial output was already good, having a detailed explanation. However, the improved output "
-            "gave similar information but in a more professional manner using better vocabulary. For future tasks, "
-            "try to implement more elaborate language and precise terminology from the beginning."
-        ],
-        quality=8.0,
-        final_summary="The agent responded well initially. However, the improved output showed that there is room "
-        "for enhancement in terms of language usage, precision, and professionalism. For future tasks, the agent "
-        "should focus more on these points from the start to increase performance.",
-    )
-    converter_mock.return_value.to_pydantic.return_value = function_return_value
-    result = TaskEvaluator(original_agent=original_agent).evaluate_training_data(
-        training_data, agent_id
-    )
-
-    assert result == function_return_value
-    converter_mock.assert_has_calls(
-        [
-            mock.call(
-                llm=original_agent.llm,
-                text="Assess the quality of the training data based on the llm output, human feedback , and llm "
-                "output improved result.\n\nIteration: data1\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
-                "1\n\nImproved Output:\nImproved output 1\n\n------------------------------------------------\n\nIteration: data2\nInitial Output:\nInitial output 2\n\nHuman "
-                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\n------------------------------------------------\n\nPlease provide:\n- Provide "
-                "a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's "
-                "performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific "
-                "action items for future tasks. Ensure all key and specificpoints from the human feedback are "
-                "incorporated into these instructions.\n- A score from 0 to 10 evaluating on completion, quality, and "
-                "overall performance from the improved output to the initial output based on the human feedback\n",
-                model=TrainingTaskEvaluation,
-                instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the "
-                "following structure, with the following keys:\n{\n    suggestions: List[str],\n    quality: float,\n    final_summary: str\n}",
-            ),
-            mock.call().to_pydantic(),
-        ]
-    )
-
-@patch("crewai.utilities.converter.Converter.to_pydantic")
-@patch("crewai.utilities.training_converter.TrainingConverter._convert_field_by_field")
-def test_training_converter_fallback_mechanism(convert_field_by_field_mock, to_pydantic_mock):
-    training_data = {
-        "agent_id": {
-            "data1": {
-                "initial_output": "Initial output 1",
-                "human_feedback": "Human feedback 1",
-                "improved_output": "Improved output 1",
-            },
-            "data2": {
-                "initial_output": "Initial output 2",
-                "human_feedback": "Human feedback 2",
-                "improved_output": "Improved output 2",
-            },
-        }
-    }
-    agent_id = "agent_id"
-    to_pydantic_mock.side_effect = ConverterError("Failed to convert directly")
-
-    expected_result = TrainingTaskEvaluation(
-        suggestions=["Fallback suggestion"],
-        quality=6.5,
-        final_summary="Fallback summary"
-    )
-    convert_field_by_field_mock.return_value = expected_result
-
-    original_agent = MagicMock()
-    result = TaskEvaluator(original_agent=original_agent).evaluate_training_data(
-        training_data, agent_id
-    )
-
-    assert result == expected_result
-    to_pydantic_mock.assert_called_once()
-    convert_field_by_field_mock.assert_called_once()

tests/utilities/events/__init__.py

@@ -1 +0,0 @@
-"""Tests for events."""

tests/utilities/events/test_crewai_event_bus.py

@@ -1,47 +0,0 @@
-from unittest.mock import Mock
-
-from crewai.events.base_events import BaseEvent
-from crewai.events.event_bus import crewai_event_bus
-
-
-class TestEvent(BaseEvent):
-    pass
-
-
-def test_specific_event_handler():
-    mock_handler = Mock()
-
-    @crewai_event_bus.on(TestEvent)
-    def handler(source, event):
-        mock_handler(source, event)
-
-    event = TestEvent(type="test_event")
-    crewai_event_bus.emit("source_object", event)
-
-    mock_handler.assert_called_once_with("source_object", event)
-
-
-def test_wildcard_event_handler():
-    mock_handler = Mock()
-
-    @crewai_event_bus.on(BaseEvent)
-    def handler(source, event):
-        mock_handler(source, event)
-
-    event = TestEvent(type="test_event")
-    crewai_event_bus.emit("source_object", event)
-
-    mock_handler.assert_called_once_with("source_object", event)
-
-
-def test_event_bus_error_handling(capfd):
-    @crewai_event_bus.on(BaseEvent)
-    def broken_handler(source, event):
-        raise ValueError("Simulated handler failure")
-
-    event = TestEvent(type="test_event")
-    crewai_event_bus.emit("source_object", event)
-
-    out, err = capfd.readouterr()
-    assert "Simulated handler failure" in out
-    assert "Handler 'broken_handler' failed" in out

tests/utilities/prompts.json

@@ -1,40 +0,0 @@
-{
-  "hierarchical_manager_agent": {
-    "role": "Lorem ipsum dolor sit amet",
-    "goal": "Lorem ipsum dolor sit amet",
-    "backstory": "Lorem ipsum dolor sit amet."
-  },
-  "planning_manager_agent": {
-    "role": "Lorem ipsum dolor sit amet",
-    "goal": "Lorem ipsum dolor sit amet",
-    "backstory": "Lorem ipsum dolor sit amet."
-  },
-  "slices": {
-    "observation": "Lorem ipsum dolor sit amet",
-    "task": "Lorem ipsum dolor sit amet",
-    "memory": "Lorem ipsum dolor sit amet",
-    "role_playing": "Lorem ipsum dolor sit amet",
-    "tools": "Lorem ipsum dolor sit amet",
-    "no_tools": "Lorem ipsum dolor sit amet",
-    "format": "Lorem ipsum dolor sit amet",
-    "final_answer_format": "Lorem ipsum dolor sit amet",
-    "format_without_tools": "Lorem ipsum dolor sit amet",
-    "task_with_context": "Lorem ipsum dolor sit amet",
-    "expected_output": "Lorem ipsum dolor sit amet",
-    "human_feedback": "Lorem ipsum dolor sit amet",
-    "getting_input": "Lorem ipsum dolor sit amet "
-  },
-  "errors": {
-    "force_final_answer": "Lorem ipsum dolor sit amet",
-    "agent_tool_unexisting_coworker": "Lorem ipsum dolor sit amet",
-    "task_repeated_usage": "Lorem ipsum dolor sit amet",
-    "tool_usage_error": "Lorem ipsum dolor sit amet",
-    "tool_arguments_error": "Lorem ipsum dolor sit amet",
-    "wrong_tool_name": "Lorem ipsum dolor sit amet",
-    "tool_usage_exception": "Lorem ipsum dolor sit amet"
-  },
-  "tools": {
-    "delegate_work": "Lorem ipsum dolor sit amet",
-    "ask_question": "Lorem ipsum dolor sit amet"
-  }
-}

tests/utilities/test_console_formatter_pause_resume.py

@@ -1,116 +0,0 @@
-from unittest.mock import MagicMock, patch
-from rich.tree import Tree
-from rich.live import Live
-from crewai.events.utils.console_formatter import ConsoleFormatter
-
-
-class TestConsoleFormatterPauseResume:
-    """Test ConsoleFormatter pause/resume functionality."""
-
-    def test_pause_live_updates_with_active_session(self):
-        """Test pausing when Live session is active."""
-        formatter = ConsoleFormatter()
-
-        mock_live = MagicMock(spec=Live)
-        formatter._live = mock_live
-        formatter._live_paused = False
-
-        formatter.pause_live_updates()
-
-        mock_live.stop.assert_called_once()
-        assert formatter._live_paused
-
-    def test_pause_live_updates_when_already_paused(self):
-        """Test pausing when already paused does nothing."""
-        formatter = ConsoleFormatter()
-
-        mock_live = MagicMock(spec=Live)
-        formatter._live = mock_live
-        formatter._live_paused = True
-
-        formatter.pause_live_updates()
-
-        mock_live.stop.assert_not_called()
-        assert formatter._live_paused
-
-    def test_pause_live_updates_with_no_session(self):
-        """Test pausing when no Live session exists."""
-        formatter = ConsoleFormatter()
-
-        formatter._live = None
-        formatter._live_paused = False
-
-        formatter.pause_live_updates()
-
-        assert formatter._live_paused
-
-    def test_resume_live_updates_when_paused(self):
-        """Test resuming when paused."""
-        formatter = ConsoleFormatter()
-
-        formatter._live_paused = True
-
-        formatter.resume_live_updates()
-
-        assert not formatter._live_paused
-
-    def test_resume_live_updates_when_not_paused(self):
-        """Test resuming when not paused does nothing."""
-        formatter = ConsoleFormatter()
-
-        formatter._live_paused = False
-
-        formatter.resume_live_updates()
-
-        assert not formatter._live_paused
-
-    def test_print_after_resume_restarts_live_session(self):
-        """Test that printing a Tree after resume creates new Live session."""
-        formatter = ConsoleFormatter()
-
-        formatter._live_paused = True
-        formatter._live = None
-
-        formatter.resume_live_updates()
-        assert not formatter._live_paused
-
-        tree = Tree("Test")
-
-        with patch("crewai.events.utils.console_formatter.Live") as mock_live_class:
-            mock_live_instance = MagicMock()
-            mock_live_class.return_value = mock_live_instance
-
-            formatter.print(tree)
-
-            mock_live_class.assert_called_once()
-            mock_live_instance.start.assert_called_once()
-            assert formatter._live == mock_live_instance
-
-    def test_multiple_pause_resume_cycles(self):
-        """Test multiple pause/resume cycles work correctly."""
-        formatter = ConsoleFormatter()
-
-        mock_live = MagicMock(spec=Live)
-        formatter._live = mock_live
-        formatter._live_paused = False
-
-        formatter.pause_live_updates()
-        assert formatter._live_paused
-        mock_live.stop.assert_called_once()
-        assert formatter._live is None  # Live session should be cleared
-
-        formatter.resume_live_updates()
-        assert not formatter._live_paused
-
-        formatter.pause_live_updates()
-        assert formatter._live_paused
-
-        formatter.resume_live_updates()
-        assert not formatter._live_paused
-
-    def test_pause_resume_state_initialization(self):
-        """Test that _live_paused is properly initialized."""
-        formatter = ConsoleFormatter()
-
-        assert hasattr(formatter, "_live_paused")
-        assert not formatter._live_paused

tests/utilities/test_converter.py

@@ -1,600 +0,0 @@
-import json
-from typing import Dict, List, Optional
-from unittest.mock import MagicMock, Mock, patch
-
-import pytest
-from pydantic import BaseModel
-
-from crewai.llm import LLM
-from crewai.utilities.converter import (
-    Converter,
-    ConverterError,
-    convert_to_model,
-    convert_with_instructions,
-    create_converter,
-    generate_model_description,
-    get_conversion_instructions,
-    handle_partial_json,
-    validate_model,
-)
-from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
-# Tests for enums
-from enum import Enum
-
-
-@pytest.fixture(scope="module")
-def vcr_config(request) -> dict:
-    return {
-        "cassette_library_dir": "tests/utilities/cassettes",
-    }
-
-
-# Sample Pydantic models for testing
-class EmailResponse(BaseModel):
-    previous_message_content: str
-
-
-class EmailResponses(BaseModel):
-    responses: list[EmailResponse]
-
-
-class SimpleModel(BaseModel):
-    name: str
-    age: int
-
-
-class NestedModel(BaseModel):
-    id: int
-    data: SimpleModel
-
-
-class Address(BaseModel):
-    street: str
-    city: str
-    zip_code: str
-
-
-class Person(BaseModel):
-    name: str
-    age: int
-    address: Address
-
-
-class CustomConverter(Converter):
-    pass
-
-
-# Fixtures
-@pytest.fixture
-def mock_agent():
-    agent = Mock()
-    agent.function_calling_llm = None
-    agent.llm = Mock()
-    return agent
-
-
-# Tests for convert_to_model
-def test_convert_to_model_with_valid_json():
-    result = '{"name": "John", "age": 30}'
-    output = convert_to_model(result, SimpleModel, None, None)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "John"
-    assert output.age == 30
-
-
-def test_convert_to_model_with_invalid_json():
-    result = '{"name": "John", "age": "thirty"}'
-    with patch("crewai.utilities.converter.handle_partial_json") as mock_handle:
-        mock_handle.return_value = "Fallback result"
-        output = convert_to_model(result, SimpleModel, None, None)
-        assert output == "Fallback result"
-
-
-def test_convert_to_model_with_no_model():
-    result = "Plain text"
-    output = convert_to_model(result, None, None, None)
-    assert output == "Plain text"
-
-
-def test_convert_to_model_with_special_characters():
-    json_string_test = """
-    {
-        "responses": [
-            {
-                "previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-            }
-        ]
-    }
-    """
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-    )
-
-
-def test_convert_to_model_with_escaped_special_characters():
-    json_string_test = json.dumps(
-        {
-            "responses": [
-                {
-                    "previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-                }
-            ]
-        }
-    )
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-    )
-
-
-def test_convert_to_model_with_multiple_special_characters():
-    json_string_test = """
-    {
-        "responses": [
-            {
-                "previous_message_content": "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
-            }
-        ]
-    }
-    """
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
-    )
-
-
-# Tests for validate_model
-def test_validate_model_pydantic_output():
-    result = '{"name": "Alice", "age": 25}'
-    output = validate_model(result, SimpleModel, False)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice"
-    assert output.age == 25
-
-
-def test_validate_model_json_output():
-    result = '{"name": "Bob", "age": 40}'
-    output = validate_model(result, SimpleModel, True)
-    assert isinstance(output, dict)
-    assert output == {"name": "Bob", "age": 40}
-
-
-# Tests for handle_partial_json
-def test_handle_partial_json_with_valid_partial():
-    result = 'Some text {"name": "Charlie", "age": 35} more text'
-    output = handle_partial_json(result, SimpleModel, False, None)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Charlie"
-    assert output.age == 35
-
-
-def test_handle_partial_json_with_invalid_partial(mock_agent):
-    result = "No valid JSON here"
-    with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert:
-        mock_convert.return_value = "Converted result"
-        output = handle_partial_json(result, SimpleModel, False, mock_agent)
-        assert output == "Converted result"
-
-
-# Tests for convert_with_instructions
-@patch("crewai.utilities.converter.create_converter")
-@patch("crewai.utilities.converter.get_conversion_instructions")
-def test_convert_with_instructions_success(
-    mock_get_instructions, mock_create_converter, mock_agent
-):
-    mock_get_instructions.return_value = "Instructions"
-    mock_converter = Mock()
-    mock_converter.to_pydantic.return_value = SimpleModel(name="David", age=50)
-    mock_create_converter.return_value = mock_converter
-
-    result = "Some text to convert"
-    output = convert_with_instructions(result, SimpleModel, False, mock_agent)
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "David"
-    assert output.age == 50
-
-
-@patch("crewai.utilities.converter.create_converter")
-@patch("crewai.utilities.converter.get_conversion_instructions")
-def test_convert_with_instructions_failure(
-    mock_get_instructions, mock_create_converter, mock_agent
-):
-    mock_get_instructions.return_value = "Instructions"
-    mock_converter = Mock()
-    mock_converter.to_pydantic.return_value = ConverterError("Conversion failed")
-    mock_create_converter.return_value = mock_converter
-
-    result = "Some text to convert"
-    with patch("crewai.utilities.converter.Printer") as mock_printer:
-        output = convert_with_instructions(result, SimpleModel, False, mock_agent)
-        assert output == result
-        mock_printer.return_value.print.assert_called_once()
-
-
-# Tests for get_conversion_instructions
-def test_get_conversion_instructions_gpt():
-    llm = LLM(model="gpt-4o-mini")
-    with patch.object(LLM, "supports_function_calling") as supports_function_calling:
-        supports_function_calling.return_value = True
-        instructions = get_conversion_instructions(SimpleModel, llm)
-        model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
-        expected_instructions = (
-            "Please convert the following text into valid JSON.\n\n"
-            "Output ONLY the valid JSON and nothing else.\n\n"
-            "The JSON must follow this schema exactly:\n```json\n"
-            f"{model_schema}\n```"
-        )
-        assert instructions == expected_instructions
-
-
-def test_get_conversion_instructions_non_gpt():
-    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
-    with patch.object(LLM, "supports_function_calling", return_value=False):
-        instructions = get_conversion_instructions(SimpleModel, llm)
-        assert '"name": str' in instructions
-        assert '"age": int' in instructions
-
-
-# Tests for is_gpt
-def test_supports_function_calling_true():
-    llm = LLM(model="gpt-4o")
-    assert llm.supports_function_calling() is True
-
-
-def test_supports_function_calling_false():
-    llm = LLM(model="non-existent-model")
-    assert llm.supports_function_calling() is False
-
-
-def test_create_converter_with_mock_agent():
-    mock_agent = MagicMock()
-    mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
-
-    converter = create_converter(
-        agent=mock_agent,
-        llm=Mock(),
-        text="Sample",
-        model=SimpleModel,
-        instructions="Convert",
-    )
-
-    assert isinstance(converter, Converter)
-    mock_agent.get_output_converter.assert_called_once()
-
-
-def test_create_converter_with_custom_converter():
-    converter = create_converter(
-        converter_cls=CustomConverter,
-        llm=LLM(model="gpt-4o-mini"),
-        text="Sample",
-        model=SimpleModel,
-        instructions="Convert",
-    )
-
-    assert isinstance(converter, CustomConverter)
-
-
-def test_create_converter_fails_without_agent_or_converter_cls():
-    with pytest.raises(
-        ValueError, match="Either agent or converter_cls must be provided"
-    ):
-        create_converter(
-            llm=Mock(), text="Sample", model=SimpleModel, instructions="Convert"
-        )
-
-
-def test_generate_model_description_simple_model():
-    description = generate_model_description(SimpleModel)
-    expected_description = '{\n  "name": str,\n  "age": int\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_nested_model():
-    description = generate_model_description(NestedModel)
-    expected_description = (
-        '{\n  "id": int,\n  "data": {\n  "name": str,\n  "age": int\n}\n}'
-    )
-    assert description == expected_description
-
-
-def test_generate_model_description_optional_field():
-    class ModelWithOptionalField(BaseModel):
-        name: Optional[str]
-        age: int
-
-    description = generate_model_description(ModelWithOptionalField)
-    expected_description = '{\n  "name": Optional[str],\n  "age": int\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_list_field():
-    class ModelWithListField(BaseModel):
-        items: List[int]
-
-    description = generate_model_description(ModelWithListField)
-    expected_description = '{\n  "items": List[int]\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_dict_field():
-    class ModelWithDictField(BaseModel):
-        attributes: Dict[str, int]
-
-    description = generate_model_description(ModelWithDictField)
-    expected_description = '{\n  "attributes": Dict[str, int]\n}'
-    assert description == expected_description
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_convert_with_instructions():
-    llm = LLM(model="gpt-4o-mini")
-    sample_text = "Name: Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    # Act
-    output = converter.to_pydantic()
-
-    # Assert
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice"
-    assert output.age == 30
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_converter_with_llama3_2_model():
-    llm = LLM(model="openrouter/meta-llama/llama-3.2-3b-instruct")
-    sample_text = "Name: Alice Llama, Age: 30"
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-    output = converter.to_pydantic()
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice Llama"
-    assert output.age == 30
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_converter_with_llama3_1_model():
-    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
-    sample_text = "Name: Alice Llama, Age: 30"
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-    output = converter.to_pydantic()
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice Llama"
-    assert output.age == 30
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_converter_with_nested_model():
-    llm = LLM(model="gpt-4o-mini")
-    sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
-
-    instructions = get_conversion_instructions(Person, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=Person,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, Person)
-    assert output.name == "John Doe"
-    assert output.age == 30
-    assert isinstance(output.address, Address)
-    assert output.address.street == "123 Main St"
-    assert output.address.city == "Anytown"
-    assert output.address.zip_code == "12345"
-
-
-# Tests for error handling
-def test_converter_error_handling():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = "Invalid JSON"
-    sample_text = "Name: Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    with pytest.raises(ConverterError) as exc_info:
-        converter.to_pydantic()
-
-    assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
-
-
-# Tests for retry logic
-def test_converter_retry_logic():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.side_effect = [
-        "Invalid JSON",
-        "Still invalid",
-        '{"name": "Retry Alice", "age": 30}',
-    ]
-    sample_text = "Name: Retry Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-        max_attempts=3,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Retry Alice"
-    assert output.age == 30
-    assert llm.call.call_count == 3
-
-
-# Tests for optional fields
-def test_converter_with_optional_fields():
-    class OptionalModel(BaseModel):
-        name: str
-        age: Optional[int]
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    # Simulate the LLM's response with 'age' explicitly set to null
-    llm.call.return_value = '{"name": "Bob", "age": null}'
-    sample_text = "Name: Bob, age: None"
-
-    instructions = get_conversion_instructions(OptionalModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=OptionalModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, OptionalModel)
-    assert output.name == "Bob"
-    assert output.age is None
-
-
-# Tests for list fields
-def test_converter_with_list_field():
-    class ListModel(BaseModel):
-        items: List[int]
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"items": [1, 2, 3]}'
-    sample_text = "Items: 1, 2, 3"
-
-    instructions = get_conversion_instructions(ListModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=ListModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, ListModel)
-    assert output.items == [1, 2, 3]
-
-
-def test_converter_with_enum():
-    class Color(Enum):
-        RED = "red"
-        GREEN = "green"
-        BLUE = "blue"
-
-    class EnumModel(BaseModel):
-        name: str
-        color: Color
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"name": "Alice", "color": "red"}'
-    sample_text = "Name: Alice, Color: Red"
-
-    instructions = get_conversion_instructions(EnumModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=EnumModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, EnumModel)
-    assert output.name == "Alice"
-    assert output.color == Color.RED
-
-
-# Tests for ambiguous input
-def test_converter_with_ambiguous_input():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"name": "Charlie", "age": "Not an age"}'
-    sample_text = "Charlie is thirty years old"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    with pytest.raises(ConverterError) as exc_info:
-        converter.to_pydantic()
-
-    assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
-
-
-# Tests for function calling support
-def test_converter_with_function_calling():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = True
-
-    instructor = Mock()
-    instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Eve, Age: 35",
-        model=SimpleModel,
-        instructions="Convert this text.",
-    )
-    converter._create_instructor = Mock(return_value=instructor)
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Eve"
-    assert output.age == 35
-    instructor.to_pydantic.assert_called_once()
-
-
-def test_generate_model_description_union_field():
-    class UnionModel(BaseModel):
-        field: int | str | None
-
-    description = generate_model_description(UnionModel)
-    expected_description = '{\n  "field": int | str | None\n}'
-    assert description == expected_description

tests/utilities/test_events.py

@@ -1,993 +0,0 @@
-from datetime import datetime
-from unittest.mock import Mock, patch
-
-import pytest
-from pydantic import Field
-
-from crewai.agent import Agent
-from crewai.agents.crew_agent_executor import CrewAgentExecutor
-from crewai.crew import Crew
-from crewai.flow.flow import Flow, listen, start
-from crewai.llm import LLM
-from crewai.task import Task
-from crewai.tools.base_tool import BaseTool
-from crewai.events.types.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-from crewai.events.types.crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestResultEvent,
-    CrewTestStartedEvent,
-)
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.event_listener import EventListener
-from crewai.events.types.tool_usage_events import ToolUsageFinishedEvent
-from crewai.events.types.flow_events import (
-    FlowCreatedEvent,
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFailedEvent,
-    MethodExecutionStartedEvent,
-)
-from crewai.events.types.llm_events import (
-    LLMCallCompletedEvent,
-    LLMCallFailedEvent,
-    LLMCallStartedEvent,
-    LLMStreamChunkEvent,
-)
-from crewai.events.types.task_events import (
-    TaskCompletedEvent,
-    TaskFailedEvent,
-    TaskStartedEvent,
-)
-from crewai.events.types.tool_usage_events import (
-    ToolUsageErrorEvent,
-)
-
-
-@pytest.fixture(scope="module")
-def vcr_config(request) -> dict:
-    return {
-        "cassette_library_dir": "tests/utilities/cassettes",
-    }
-
-
-@pytest.fixture(scope="module")
-def base_agent():
-    return Agent(
-        role="base_agent",
-        llm="gpt-4o-mini",
-        goal="Just say hi",
-        backstory="You are a helpful assistant that just says hi",
-    )
-
-
-@pytest.fixture(scope="module")
-def base_task(base_agent):
-    return Task(
-        description="Just say hi",
-        expected_output="hi",
-        agent=base_agent,
-    )
-
-
-@pytest.fixture
-def reset_event_listener_singleton():
-    """Reset EventListener singleton for clean test state."""
-    original_instance = EventListener._instance
-    original_initialized = (
-        getattr(EventListener._instance, "_initialized", False)
-        if EventListener._instance
-        else False
-    )
-
-    EventListener._instance = None
-
-    yield
-
-    EventListener._instance = original_instance
-    if original_instance and original_initialized:
-        EventListener._instance._initialized = original_initialized
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_start_kickoff_event(
-    base_agent, base_task, reset_event_listener_singleton
-):
-    received_events = []
-    mock_span = Mock()
-
-    @crewai_event_bus.on(CrewKickoffStartedEvent)
-    def handle_crew_start(source, event):
-        received_events.append(event)
-
-    mock_telemetry = Mock()
-    mock_telemetry.crew_execution_span = Mock(return_value=mock_span)
-    mock_telemetry.end_crew = Mock(return_value=mock_span)
-    mock_telemetry.set_tracer = Mock()
-    mock_telemetry.task_started = Mock(return_value=mock_span)
-    mock_telemetry.task_ended = Mock(return_value=mock_span)
-
-    # Patch the Telemetry class to return our mock
-    with patch("crewai.events.event_listener.Telemetry", return_value=mock_telemetry):
-        # Now when Crew creates EventListener, it will use our mocked telemetry
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-        crew.kickoff()
-
-    mock_telemetry.crew_execution_span.assert_called_once_with(crew, None)
-    mock_telemetry.end_crew.assert_called_once_with(crew, "hi")
-
-    assert len(received_events) == 1
-    assert received_events[0].crew_name == "TestCrew"
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "crew_kickoff_started"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_end_kickoff_event(base_agent, base_task):
-    received_events = []
-
-    @crewai_event_bus.on(CrewKickoffCompletedEvent)
-    def handle_crew_end(source, event):
-        received_events.append(event)
-
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-
-    crew.kickoff()
-
-    assert len(received_events) == 1
-    assert received_events[0].crew_name == "TestCrew"
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "crew_kickoff_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_test_kickoff_type_event(base_agent, base_task):
-    received_events = []
-
-    @crewai_event_bus.on(CrewTestStartedEvent)
-    def handle_crew_end(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(CrewTestCompletedEvent)
-    def handle_crew_test_end(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(CrewTestResultEvent)
-    def handle_crew_test_result(source, event):
-        received_events.append(event)
-
-    eval_llm = LLM(model="gpt-4o-mini")
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-    crew.test(n_iterations=1, eval_llm=eval_llm)
-
-    assert len(received_events) == 3
-    assert received_events[0].crew_name == "TestCrew"
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "crew_test_started"
-    assert received_events[1].crew_name == "TestCrew"
-    assert isinstance(received_events[1].timestamp, datetime)
-    assert received_events[1].type == "crew_test_result"
-    assert received_events[2].crew_name == "TestCrew"
-    assert isinstance(received_events[2].timestamp, datetime)
-    assert received_events[2].type == "crew_test_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_kickoff_failed_event(base_agent, base_task):
-    received_events = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(CrewKickoffFailedEvent)
-        def handle_crew_failed(source, event):
-            received_events.append(event)
-
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-
-        with patch.object(Crew, "_execute_tasks") as mock_execute:
-            error_message = "Simulated crew kickoff failure"
-            mock_execute.side_effect = Exception(error_message)
-
-            with pytest.raises(Exception):
-                crew.kickoff()
-
-        assert len(received_events) == 1
-        assert received_events[0].error == error_message
-        assert isinstance(received_events[0].timestamp, datetime)
-        assert received_events[0].type == "crew_kickoff_failed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_start_task_event(base_agent, base_task):
-    received_events = []
-
-    @crewai_event_bus.on(TaskStartedEvent)
-    def handle_task_start(source, event):
-        received_events.append(event)
-
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-
-    crew.kickoff()
-
-    assert len(received_events) == 1
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "task_started"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_end_task_event(
-    base_agent, base_task, reset_event_listener_singleton
-):
-    received_events = []
-
-    @crewai_event_bus.on(TaskCompletedEvent)
-    def handle_task_end(source, event):
-        received_events.append(event)
-
-    mock_span = Mock()
-
-    mock_telemetry = Mock()
-    mock_telemetry.task_started = Mock(return_value=mock_span)
-    mock_telemetry.task_ended = Mock(return_value=mock_span)
-    mock_telemetry.set_tracer = Mock()
-    mock_telemetry.crew_execution_span = Mock()
-    mock_telemetry.end_crew = Mock()
-
-    with patch("crewai.events.event_listener.Telemetry", return_value=mock_telemetry):
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-        crew.kickoff()
-
-        mock_telemetry.task_started.assert_called_once_with(crew=crew, task=base_task)
-        mock_telemetry.task_ended.assert_called_once_with(mock_span, base_task, crew)
-
-    assert len(received_events) == 1
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "task_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_emits_failed_event_on_execution_error(base_agent, base_task):
-    received_events = []
-    received_sources = []
-
-    @crewai_event_bus.on(TaskFailedEvent)
-    def handle_task_failed(source, event):
-        received_events.append(event)
-        received_sources.append(source)
-
-    with patch.object(
-        Task,
-        "_execute_core",
-    ) as mock_execute:
-        error_message = "Simulated task failure"
-        mock_execute.side_effect = Exception(error_message)
-        agent = Agent(
-            role="base_agent",
-            goal="Just say hi",
-            backstory="You are a helpful assistant that just says hi",
-        )
-        task = Task(
-            description="Just say hi",
-            expected_output="hi",
-            agent=agent,
-        )
-
-        with pytest.raises(Exception):
-            agent.execute_task(task=task)
-
-            assert len(received_events) == 1
-            assert received_sources[0] == task
-            assert received_events[0].error == error_message
-            assert isinstance(received_events[0].timestamp, datetime)
-            assert received_events[0].type == "task_failed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_emits_execution_started_and_completed_events(base_agent, base_task):
-    received_events = []
-
-    @crewai_event_bus.on(AgentExecutionStartedEvent)
-    def handle_agent_start(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(AgentExecutionCompletedEvent)
-    def handle_agent_completed(source, event):
-        received_events.append(event)
-
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-    crew.kickoff()
-    assert len(received_events) == 2
-    assert received_events[0].agent == base_agent
-    assert received_events[0].task == base_task
-    assert received_events[0].tools == []
-    assert isinstance(received_events[0].task_prompt, str)
-    assert (
-        received_events[0].task_prompt
-        == "Just say hi\n\nThis is the expected criteria for your final answer: hi\nyou MUST return the actual complete content as the final answer, not a summary."
-    )
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "agent_execution_started"
-    assert isinstance(received_events[1].timestamp, datetime)
-    assert received_events[1].type == "agent_execution_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_emits_execution_error_event(base_agent, base_task):
-    received_events = []
-
-    @crewai_event_bus.on(AgentExecutionErrorEvent)
-    def handle_agent_start(source, event):
-        received_events.append(event)
-
-    error_message = "Error happening while sending prompt to model."
-    base_agent.max_retry_limit = 0
-    with patch.object(
-        CrewAgentExecutor, "invoke", wraps=base_agent.agent_executor.invoke
-    ) as invoke_mock:
-        invoke_mock.side_effect = Exception(error_message)
-
-        with pytest.raises(Exception):
-            base_agent.execute_task(
-                task=base_task,
-            )
-
-        assert len(received_events) == 1
-        assert received_events[0].agent == base_agent
-        assert received_events[0].task == base_task
-        assert received_events[0].error == error_message
-        assert isinstance(received_events[0].timestamp, datetime)
-        assert received_events[0].type == "agent_execution_error"
-
-
-class SayHiTool(BaseTool):
-    name: str = Field(default="say_hi", description="The name of the tool")
-    description: str = Field(
-        default="Say hi", description="The description of the tool"
-    )
-
-    def _run(self) -> str:
-        return "hi"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_tools_emits_finished_events():
-    received_events = []
-
-    @crewai_event_bus.on(ToolUsageFinishedEvent)
-    def handle_tool_end(source, event):
-        received_events.append(event)
-
-    agent = Agent(
-        role="base_agent",
-        goal="Just say hi",
-        backstory="You are a helpful assistant that just says hi",
-        tools=[SayHiTool()],
-    )
-
-    task = Task(
-        description="Just say hi",
-        expected_output="hi",
-        agent=agent,
-    )
-    crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
-    crew.kickoff()
-    assert len(received_events) == 1
-    assert received_events[0].agent_key == agent.key
-    assert received_events[0].agent_role == agent.role
-    assert received_events[0].tool_name == SayHiTool().name
-    assert received_events[0].tool_args == "{}" or received_events[0].tool_args == {}
-    assert received_events[0].type == "tool_usage_finished"
-    assert isinstance(received_events[0].timestamp, datetime)
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_tools_emits_error_events():
-    received_events = []
-
-    @crewai_event_bus.on(ToolUsageErrorEvent)
-    def handle_tool_end(source, event):
-        received_events.append(event)
-
-    class ErrorTool(BaseTool):
-        name: str = Field(
-            default="error_tool", description="A tool that raises an error"
-        )
-        description: str = Field(
-            default="This tool always raises an error",
-            description="The description of the tool",
-        )
-
-        def _run(self) -> str:
-            raise Exception("Simulated tool error")
-
-    agent = Agent(
-        role="base_agent",
-        goal="Try to use the error tool",
-        backstory="You are an assistant that tests error handling",
-        tools=[ErrorTool()],
-        llm=LLM(model="gpt-4o-mini"),
-    )
-
-    task = Task(
-        description="Use the error tool",
-        expected_output="This should error",
-        agent=agent,
-    )
-
-    crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
-    crew.kickoff()
-
-    assert len(received_events) == 48
-    assert received_events[0].agent_key == agent.key
-    assert received_events[0].agent_role == agent.role
-    assert received_events[0].tool_name == "error_tool"
-    assert received_events[0].tool_args == "{}" or received_events[0].tool_args == {}
-    assert str(received_events[0].error) == "Simulated tool error"
-    assert received_events[0].type == "tool_usage_error"
-    assert isinstance(received_events[0].timestamp, datetime)
-
-
-def test_flow_emits_start_event(reset_event_listener_singleton):
-    received_events = []
-    mock_span = Mock()
-
-    @crewai_event_bus.on(FlowStartedEvent)
-    def handle_flow_start(source, event):
-        received_events.append(event)
-
-    class TestFlow(Flow[dict]):
-        @start()
-        def begin(self):
-            return "started"
-
-    mock_telemetry = Mock()
-    mock_telemetry.flow_execution_span = Mock(return_value=mock_span)
-    mock_telemetry.flow_creation_span = Mock()
-    mock_telemetry.set_tracer = Mock()
-
-    with patch("crewai.events.event_listener.Telemetry", return_value=mock_telemetry):
-        # Force creation of EventListener singleton with mocked telemetry
-        _ = EventListener()
-
-        flow = TestFlow()
-        flow.kickoff()
-
-    mock_telemetry.flow_execution_span.assert_called_once_with("TestFlow", ["begin"])
-    assert len(received_events) == 1
-    assert received_events[0].flow_name == "TestFlow"
-    assert received_events[0].type == "flow_started"
-
-
-def test_flow_name_emitted_to_event_bus():
-    received_events = []
-
-    class MyFlowClass(Flow):
-        name = "PRODUCTION_FLOW"
-
-        @start()
-        def start(self):
-            return "Hello, world!"
-
-    @crewai_event_bus.on(FlowStartedEvent)
-    def handle_flow_start(source, event):
-        received_events.append(event)
-
-    flow = MyFlowClass()
-    flow.kickoff()
-
-    assert len(received_events) == 1
-    assert received_events[0].flow_name == "PRODUCTION_FLOW"
-
-
-def test_flow_emits_finish_event():
-    received_events = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(FlowFinishedEvent)
-        def handle_flow_finish(source, event):
-            received_events.append(event)
-
-        class TestFlow(Flow[dict]):
-            @start()
-            def begin(self):
-                return "completed"
-
-        flow = TestFlow()
-        result = flow.kickoff()
-
-        assert len(received_events) == 1
-        assert received_events[0].flow_name == "TestFlow"
-        assert received_events[0].type == "flow_finished"
-        assert received_events[0].result == "completed"
-        assert result == "completed"
-
-
-def test_flow_emits_method_execution_started_event():
-    received_events = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(MethodExecutionStartedEvent)
-        def handle_method_start(source, event):
-            print("event in method name", event.method_name)
-            received_events.append(event)
-
-        class TestFlow(Flow[dict]):
-            @start()
-            def begin(self):
-                return "started"
-
-            @listen("begin")
-            def second_method(self):
-                return "executed"
-
-        flow = TestFlow()
-        flow.kickoff()
-
-        assert len(received_events) == 2
-
-        assert received_events[0].method_name == "begin"
-        assert received_events[0].flow_name == "TestFlow"
-        assert received_events[0].type == "method_execution_started"
-
-        assert received_events[1].method_name == "second_method"
-        assert received_events[1].flow_name == "TestFlow"
-        assert received_events[1].type == "method_execution_started"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_register_handler_adds_new_handler(base_agent, base_task):
-    received_events = []
-
-    def custom_handler(source, event):
-        received_events.append(event)
-
-    with crewai_event_bus.scoped_handlers():
-        crewai_event_bus.register_handler(CrewKickoffStartedEvent, custom_handler)
-
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-        crew.kickoff()
-
-        assert len(received_events) == 1
-        assert isinstance(received_events[0].timestamp, datetime)
-        assert received_events[0].type == "crew_kickoff_started"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_multiple_handlers_for_same_event(base_agent, base_task):
-    received_events_1 = []
-    received_events_2 = []
-
-    def handler_1(source, event):
-        received_events_1.append(event)
-
-    def handler_2(source, event):
-        received_events_2.append(event)
-
-    with crewai_event_bus.scoped_handlers():
-        crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_1)
-        crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_2)
-
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-        crew.kickoff()
-
-        assert len(received_events_1) == 1
-        assert len(received_events_2) == 1
-        assert received_events_1[0].type == "crew_kickoff_started"
-        assert received_events_2[0].type == "crew_kickoff_started"
-
-
-def test_flow_emits_created_event():
-    received_events = []
-
-    @crewai_event_bus.on(FlowCreatedEvent)
-    def handle_flow_created(source, event):
-        received_events.append(event)
-
-    class TestFlow(Flow[dict]):
-        @start()
-        def begin(self):
-            return "started"
-
-    flow = TestFlow()
-    flow.kickoff()
-
-    assert len(received_events) == 1
-    assert received_events[0].flow_name == "TestFlow"
-    assert received_events[0].type == "flow_created"
-
-
-def test_flow_emits_method_execution_failed_event():
-    received_events = []
-    error = Exception("Simulated method failure")
-
-    @crewai_event_bus.on(MethodExecutionFailedEvent)
-    def handle_method_failed(source, event):
-        received_events.append(event)
-
-    class TestFlow(Flow[dict]):
-        @start()
-        def begin(self):
-            raise error
-
-    flow = TestFlow()
-    with pytest.raises(Exception):
-        flow.kickoff()
-
-    assert len(received_events) == 1
-    assert received_events[0].method_name == "begin"
-    assert received_events[0].flow_name == "TestFlow"
-    assert received_events[0].type == "method_execution_failed"
-    assert received_events[0].error == error
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_emits_call_started_event():
-    received_events = []
-
-    @crewai_event_bus.on(LLMCallStartedEvent)
-    def handle_llm_call_started(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(LLMCallCompletedEvent)
-    def handle_llm_call_completed(source, event):
-        received_events.append(event)
-
-    llm = LLM(model="gpt-4o-mini")
-    llm.call("Hello, how are you?")
-
-    assert len(received_events) == 2
-    assert received_events[0].type == "llm_call_started"
-    assert received_events[1].type == "llm_call_completed"
-
-    assert received_events[0].task_name is None
-    assert received_events[0].agent_role is None
-    assert received_events[0].agent_id is None
-    assert received_events[0].task_id is None
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_emits_call_failed_event():
-    received_events = []
-
-    @crewai_event_bus.on(LLMCallFailedEvent)
-    def handle_llm_call_failed(source, event):
-        received_events.append(event)
-
-    error_message = "Simulated LLM call failure"
-    with patch("crewai.llm.litellm.completion", side_effect=Exception(error_message)):
-        llm = LLM(model="gpt-4o-mini")
-        with pytest.raises(Exception) as exc_info:
-            llm.call("Hello, how are you?")
-
-        assert str(exc_info.value) == error_message
-        assert len(received_events) == 1
-        assert received_events[0].type == "llm_call_failed"
-        assert received_events[0].error == error_message
-        assert received_events[0].task_name is None
-        assert received_events[0].agent_role is None
-        assert received_events[0].agent_id is None
-        assert received_events[0].task_id is None
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_emits_stream_chunk_events():
-    """Test that LLM emits stream chunk events when streaming is enabled."""
-    received_chunks = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(LLMStreamChunkEvent)
-        def handle_stream_chunk(source, event):
-            received_chunks.append(event.chunk)
-
-        # Create an LLM with streaming enabled
-        llm = LLM(model="gpt-4o", stream=True)
-
-        # Call the LLM with a simple message
-        response = llm.call("Tell me a short joke")
-
-        # Verify that we received chunks
-        assert len(received_chunks) > 0
-
-        # Verify that concatenating all chunks equals the final response
-        assert "".join(received_chunks) == response
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_no_stream_chunks_when_streaming_disabled():
-    """Test that LLM doesn't emit stream chunk events when streaming is disabled."""
-    received_chunks = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(LLMStreamChunkEvent)
-        def handle_stream_chunk(source, event):
-            received_chunks.append(event.chunk)
-
-        # Create an LLM with streaming disabled
-        llm = LLM(model="gpt-4o", stream=False)
-
-        # Call the LLM with a simple message
-        response = llm.call("Tell me a short joke")
-
-        # Verify that we didn't receive any chunks
-        assert len(received_chunks) == 0
-
-        # Verify we got a response
-        assert response and isinstance(response, str)
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_streaming_fallback_to_non_streaming():
-    """Test that streaming falls back to non-streaming when there's an error."""
-    received_chunks = []
-    fallback_called = False
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(LLMStreamChunkEvent)
-        def handle_stream_chunk(source, event):
-            received_chunks.append(event.chunk)
-
-        # Create an LLM with streaming enabled
-        llm = LLM(model="gpt-4o", stream=True)
-
-        # Store original methods
-        original_call = llm.call
-
-        # Create a mock call method that handles the streaming error
-        def mock_call(messages, tools=None, callbacks=None, available_functions=None):
-            nonlocal fallback_called
-            # Emit a couple of chunks to simulate partial streaming
-            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 1"))
-            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2"))
-
-            # Mark that fallback would be called
-            fallback_called = True
-
-            # Return a response as if fallback succeeded
-            return "Fallback response after streaming error"
-
-        # Replace the call method with our mock
-        llm.call = mock_call
-
-        try:
-            # Call the LLM
-            response = llm.call("Tell me a short joke")
-
-            # Verify that we received some chunks
-            assert len(received_chunks) == 2
-            assert received_chunks[0] == "Test chunk 1"
-            assert received_chunks[1] == "Test chunk 2"
-
-            # Verify fallback was triggered
-            assert fallback_called
-
-            # Verify we got the fallback response
-            assert response == "Fallback response after streaming error"
-
-        finally:
-            # Restore the original method
-            llm.call = original_call
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_streaming_empty_response_handling():
-    """Test that streaming handles empty responses correctly."""
-    received_chunks = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(LLMStreamChunkEvent)
-        def handle_stream_chunk(source, event):
-            received_chunks.append(event.chunk)
-
-        # Create an LLM with streaming enabled
-        llm = LLM(model="gpt-3.5-turbo", stream=True)
-
-        # Store original methods
-        original_call = llm.call
-
-        # Create a mock call method that simulates empty chunks
-        def mock_call(messages, tools=None, callbacks=None, available_functions=None):
-            # Emit a few empty chunks
-            for _ in range(3):
-                crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk=""))
-
-            # Return the default message for empty responses
-            return "I apologize, but I couldn't generate a proper response. Please try again or rephrase your request."
-
-        # Replace the call method with our mock
-        llm.call = mock_call
-
-        try:
-            # Call the LLM - this should handle empty response
-            response = llm.call("Tell me a short joke")
-
-            # Verify that we received empty chunks
-            assert len(received_chunks) == 3
-            assert all(chunk == "" for chunk in received_chunks)
-
-            # Verify the response is the default message for empty responses
-            assert "I apologize" in response and "couldn't generate" in response
-
-        finally:
-            # Restore the original method
-            llm.call = original_call
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_stream_llm_emits_event_with_task_and_agent_info():
-    completed_event = []
-    failed_event = []
-    started_event = []
-    stream_event = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(LLMCallFailedEvent)
-        def handle_llm_failed(source, event):
-            failed_event.append(event)
-
-        @crewai_event_bus.on(LLMCallStartedEvent)
-        def handle_llm_started(source, event):
-            started_event.append(event)
-
-        @crewai_event_bus.on(LLMCallCompletedEvent)
-        def handle_llm_completed(source, event):
-            completed_event.append(event)
-
-        @crewai_event_bus.on(LLMStreamChunkEvent)
-        def handle_llm_stream_chunk(source, event):
-            stream_event.append(event)
-
-        agent = Agent(
-            role="TestAgent",
-            llm=LLM(model="gpt-4o-mini", stream=True),
-            goal="Just say hi",
-            backstory="You are a helpful assistant that just says hi",
-        )
-        task = Task(
-            description="Just say hi",
-            expected_output="hi",
-            llm=LLM(model="gpt-4o-mini", stream=True),
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        crew.kickoff()
-
-    assert len(completed_event) == 1
-    assert len(failed_event) == 0
-    assert len(started_event) == 1
-    assert len(stream_event) == 12
-
-    all_events = completed_event + failed_event + started_event + stream_event
-    all_agent_roles = [event.agent_role for event in all_events]
-    all_agent_id = [event.agent_id for event in all_events]
-    all_task_id = [event.task_id for event in all_events]
-    all_task_name = [event.task_name for event in all_events]
-
-    # ensure all events have the agent + task props set
-    assert len(all_agent_roles) == 14
-    assert len(all_agent_id) == 14
-    assert len(all_task_id) == 14
-    assert len(all_task_name) == 14
-
-    assert set(all_agent_roles) == {agent.role}
-    assert set(all_agent_id) == {agent.id}
-    assert set(all_task_id) == {task.id}
-    assert set(all_task_name) == {task.name or task.description}
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task):
-    completed_event = []
-    failed_event = []
-    started_event = []
-    stream_event = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(LLMCallFailedEvent)
-        def handle_llm_failed(source, event):
-            failed_event.append(event)
-
-        @crewai_event_bus.on(LLMCallStartedEvent)
-        def handle_llm_started(source, event):
-            started_event.append(event)
-
-        @crewai_event_bus.on(LLMCallCompletedEvent)
-        def handle_llm_completed(source, event):
-            completed_event.append(event)
-
-        @crewai_event_bus.on(LLMStreamChunkEvent)
-        def handle_llm_stream_chunk(source, event):
-            stream_event.append(event)
-
-        crew = Crew(agents=[base_agent], tasks=[base_task])
-        crew.kickoff()
-
-    assert len(completed_event) == 1
-    assert len(failed_event) == 0
-    assert len(started_event) == 1
-    assert len(stream_event) == 0
-
-    all_events = completed_event + failed_event + started_event + stream_event
-    all_agent_roles = [event.agent_role for event in all_events]
-    all_agent_id = [event.agent_id for event in all_events]
-    all_task_id = [event.task_id for event in all_events]
-    all_task_name = [event.task_name for event in all_events]
-
-    # ensure all events have the agent + task props set
-    assert len(all_agent_roles) == 2
-    assert len(all_agent_id) == 2
-    assert len(all_task_id) == 2
-    assert len(all_task_name) == 2
-
-    assert set(all_agent_roles) == {base_agent.role}
-    assert set(all_agent_id) == {base_agent.id}
-    assert set(all_task_id) == {base_task.id}
-    assert set(all_task_name) == {base_task.name or base_task.description}
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_emits_event_with_lite_agent():
-    completed_event = []
-    failed_event = []
-    started_event = []
-    stream_event = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(LLMCallFailedEvent)
-        def handle_llm_failed(source, event):
-            failed_event.append(event)
-
-        @crewai_event_bus.on(LLMCallStartedEvent)
-        def handle_llm_started(source, event):
-            started_event.append(event)
-
-        @crewai_event_bus.on(LLMCallCompletedEvent)
-        def handle_llm_completed(source, event):
-            completed_event.append(event)
-
-        @crewai_event_bus.on(LLMStreamChunkEvent)
-        def handle_llm_stream_chunk(source, event):
-            stream_event.append(event)
-
-        agent = Agent(
-            role="Speaker",
-            llm=LLM(model="gpt-4o-mini", stream=True),
-            goal="Just say hi",
-            backstory="You are a helpful assistant that just says hi",
-        )
-        agent.kickoff(messages=[{"role": "user", "content": "say hi!"}])
-
-    assert len(completed_event) == 1
-    assert len(failed_event) == 0
-    assert len(started_event) == 1
-    assert len(stream_event) == 15
-
-    all_events = completed_event + failed_event + started_event + stream_event
-    all_agent_roles = [event.agent_role for event in all_events]
-    all_agent_id = [event.agent_id for event in all_events]
-    all_task_id = [event.task_id for event in all_events if event.task_id]
-    all_task_name = [event.task_name for event in all_events if event.task_name]
-
-    # ensure all events have the agent + task props set
-    assert len(all_agent_roles) == 17
-    assert len(all_agent_id) == 17
-    assert len(all_task_id) == 0
-    assert len(all_task_name) == 0
-
-    assert set(all_agent_roles) == {agent.role}
-    assert set(all_agent_id) == {agent.id}

tests/utilities/test_file_handler.py

@@ -1,50 +0,0 @@
-import os
-import unittest
-import uuid
-
-import pytest
-
-from crewai.utilities.file_handler import PickleHandler
-
-
-class TestPickleHandler(unittest.TestCase):
-    def setUp(self):
-        # Use a unique file name for each test to avoid race conditions in parallel test execution
-        unique_id = str(uuid.uuid4())
-        self.file_name = f"test_data_{unique_id}.pkl"
-        self.file_path = os.path.join(os.getcwd(), self.file_name)
-        self.handler = PickleHandler(self.file_name)
-
-    def tearDown(self):
-        if os.path.exists(self.file_path):
-            os.remove(self.file_path)
-
-    def test_initialize_file(self):
-        assert os.path.exists(self.file_path) is False
-
-        self.handler.initialize_file()
-
-        assert os.path.exists(self.file_path) is True
-        assert os.path.getsize(self.file_path) >= 0
-
-    def test_save_and_load(self):
-        data = {"key": "value"}
-        self.handler.save(data)
-        loaded_data = self.handler.load()
-        assert loaded_data == data
-
-    def test_load_empty_file(self):
-        loaded_data = self.handler.load()
-        assert loaded_data == {}
-
-    def test_load_corrupted_file(self):
-        with open(self.file_path, "wb") as file:
-            file.write(b"corrupted data")
-            file.flush()
-            os.fsync(file.fileno())  # Ensure data is written to disk
-
-        with pytest.raises(Exception) as exc:
-            self.handler.load()
-
-        assert str(exc.value) == "pickle data was truncated"
-        assert "<class '_pickle.UnpicklingError'>" == str(exc.type)

tests/utilities/test_i18n.py

@@ -1,44 +0,0 @@
-import pytest
-
-from crewai.utilities.i18n import I18N
-
-
-def test_load_prompts():
-    i18n = I18N()
-    i18n.load_prompts()
-    assert i18n._prompts is not None
-
-
-def test_slice():
-    i18n = I18N()
-    i18n.load_prompts()
-    assert isinstance(i18n.slice("role_playing"), str)
-
-
-def test_tools():
-    i18n = I18N()
-    i18n.load_prompts()
-    assert isinstance(i18n.tools("ask_question"), str)
-
-
-def test_retrieve():
-    i18n = I18N()
-    i18n.load_prompts()
-    assert isinstance(i18n.retrieve("slices", "role_playing"), str)
-
-
-def test_retrieve_not_found():
-    i18n = I18N()
-    i18n.load_prompts()
-    with pytest.raises(Exception):
-        i18n.retrieve("nonexistent_kind", "nonexistent_key")
-
-
-def test_prompt_file():
-    import os
-
-    path = os.path.join(os.path.dirname(__file__), "prompts.json")
-    i18n = I18N(prompt_file=path)
-    i18n.load_prompts()
-    assert isinstance(i18n.retrieve("slices", "role_playing"), str)
-    assert i18n.retrieve("slices", "role_playing") == "Lorem ipsum dolor sit amet"

tests/utilities/test_import_utils.py

@@ -1,189 +0,0 @@
-"""Tests for import utilities."""
-
-import sys
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from crewai.utilities.import_utils import (
-    OptionalDependencyError,
-    import_and_validate_definition,
-    require,
-    validate_import_path,
-)
-
-
-class TestRequire:
-    """Test the require function."""
-
-    def test_require_existing_module(self):
-        """Test requiring a module that exists."""
-        module = require("json", purpose="testing")
-        assert module.__name__ == "json"
-
-    def test_require_missing_module(self):
-        """Test requiring a module that doesn't exist."""
-        with pytest.raises(OptionalDependencyError) as exc_info:
-            require("nonexistent_module_xyz", purpose="testing missing module")
-
-        error_msg = str(exc_info.value)
-        assert (
-            "testing missing module requires the optional dependency 'nonexistent_module_xyz'"
-            in error_msg
-        )
-        assert "uv add nonexistent_module_xyz" in error_msg
-
-    def test_require_with_import_error(self):
-        """Test that ImportError is properly chained."""
-        with patch("importlib.import_module") as mock_import:
-            mock_import.side_effect = ImportError("Module import failed")
-
-            with pytest.raises(OptionalDependencyError) as exc_info:
-                require("some_module", purpose="testing error handling")
-
-            assert isinstance(exc_info.value.__cause__, ImportError)
-            assert str(exc_info.value.__cause__) == "Module import failed"
-
-    def test_optional_dependency_error_is_import_error(self):
-        """Test that OptionalDependencyError is a subclass of ImportError."""
-        assert issubclass(OptionalDependencyError, ImportError)
-
-    def test_require_with_attr(self):
-        """Test requiring a specific attribute from a module."""
-        loads = require("json", purpose="testing", attr="loads")
-        import json
-
-        assert loads == json.loads
-
-    def test_require_with_nonexistent_attr(self):
-        """Test requiring a nonexistent attribute raises AttributeError."""
-        with pytest.raises(AttributeError) as exc_info:
-            require("json", purpose="testing", attr="nonexistent_attr")
-
-        assert "Module 'json' has no attribute 'nonexistent_attr'" in str(
-            exc_info.value
-        )
-
-    def test_require_extracts_package_name(self):
-        """Test that require correctly extracts package name from module path."""
-        with pytest.raises(OptionalDependencyError) as exc_info:
-            require("some.nested.module.path", purpose="testing")
-
-        error_msg = str(exc_info.value)
-        assert "uv add some" in error_msg
-
-
-class TestValidateImportPath:
-    """Test the validate_import_path function."""
-
-    def test_validate_import_path_success(self):
-        """Test successful import of a class."""
-        result = validate_import_path("json.JSONDecoder")
-        import json
-
-        assert result == json.JSONDecoder
-
-    def test_validate_import_path_malformed_no_module(self):
-        """Test validation with no module path."""
-        with pytest.raises(ValueError) as exc_info:
-            validate_import_path("ClassName")
-
-        assert "import_path 'ClassName' must be of the form 'module.ClassName'" in str(
-            exc_info.value
-        )
-
-    def test_validate_import_path_empty_string(self):
-        """Test validation with empty string."""
-        with pytest.raises(ValueError) as exc_info:
-            validate_import_path("")
-
-        assert "import_path '' must be of the form 'module.ClassName'" in str(
-            exc_info.value
-        )
-
-    def test_validate_import_path_module_not_found(self):
-        """Test validation with non-existent module."""
-        with pytest.raises(ValueError) as exc_info:
-            validate_import_path("nonexistent_module.ClassName")
-
-        error_msg = str(exc_info.value)
-        assert "Package 'nonexistent_module' could not be imported" in error_msg
-        assert "uv add nonexistent_module" in error_msg
-
-    def test_validate_import_path_attribute_not_found(self):
-        """Test validation when attribute doesn't exist in module."""
-        with pytest.raises(ValueError) as exc_info:
-            validate_import_path("json.NonExistentClass")
-
-        assert "Attribute 'NonExistentClass' not found in module 'json'" in str(
-            exc_info.value
-        )
-
-    def test_validate_import_path_nested_module(self):
-        """Test validation with nested module path."""
-        result = validate_import_path("unittest.mock.MagicMock")
-        from unittest.mock import MagicMock
-
-        assert result == MagicMock
-
-    def test_validate_import_path_extracts_package_name(self):
-        """Test that package name is correctly extracted for error message."""
-        with pytest.raises(ValueError) as exc_info:
-            validate_import_path("some.nested.module.path.ClassName")
-
-        error_msg = str(exc_info.value)
-        assert "Package 'some' could not be imported" in error_msg
-        assert "uv add some" in error_msg
-
-
-class TestImportAndValidateDefinition:
-    """Test the import_and_validate_definition function."""
-
-    def test_import_and_validate_definition_success(self):
-        """Test successful import through Pydantic adapter."""
-        result = import_and_validate_definition("json.JSONEncoder")
-        import json
-
-        assert result == json.JSONEncoder
-
-    def test_import_and_validate_definition_with_function(self):
-        """Test importing a function instead of a class."""
-        result = import_and_validate_definition("json.loads")
-        import json
-
-        assert result == json.loads
-
-    def test_import_and_validate_definition_invalid(self):
-        """Test that invalid paths raise ValueError."""
-        with pytest.raises(ValueError) as exc_info:
-            import_and_validate_definition("InvalidPath")
-
-        assert "must be of the form 'module.ClassName'" in str(exc_info.value)
-
-    def test_import_and_validate_definition_module_error(self):
-        """Test error handling for missing modules."""
-        with pytest.raises(ValueError) as exc_info:
-            import_and_validate_definition("missing_package.SomeClass")
-
-        error_msg = str(exc_info.value)
-        assert "Package 'missing_package' could not be imported" in error_msg
-        assert "uv add missing_package" in error_msg
-
-    def test_import_and_validate_definition_attribute_error(self):
-        """Test error handling for missing attributes."""
-        with pytest.raises(ValueError) as exc_info:
-            import_and_validate_definition("json.MissingClass")
-
-        assert "Attribute 'MissingClass' not found in module 'json'" in str(
-            exc_info.value
-        )
-
-    def test_import_and_validate_definition_with_mock(self):
-        """Test that mocked modules work correctly."""
-        mock_module = MagicMock()
-        mock_class = MagicMock()
-        mock_module.MockClass = mock_class
-
-        with patch.dict(sys.modules, {"mocked_module": mock_module}):
-            result = import_and_validate_definition("mocked_module.MockClass")
-            assert result == mock_class

tests/utilities/test_knowledge_planning.py

@@ -1,92 +0,0 @@
-"""
-Tests for verifying the integration of knowledge sources in the planning process.
-This module ensures that agent knowledge is properly included during task planning.
-"""
-
-from unittest.mock import patch
-
-import pytest
-
-from crewai.agent import Agent
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-from crewai.task import Task
-from crewai.utilities.planning_handler import CrewPlanner
-
-
-@pytest.fixture
-def mock_knowledge_source():
-    """
-    Create a mock knowledge source with test content.
-    Returns:
-        StringKnowledgeSource:
-            A knowledge source containing AI-related test content
-    """
-    content = """
-    Important context about AI:
-    1. AI systems use machine learning algorithms
-    2. Neural networks are a key component
-    3. Training data is essential for good performance
-    """
-    return StringKnowledgeSource(content=content)
-
-
-@patch("crewai.rag.config.utils.get_rag_client")
-def test_knowledge_included_in_planning(mock_get_client):
-    """Test that verifies knowledge sources are properly included in planning."""
-    # Mock RAG client
-    mock_client = mock_get_client.return_value
-    mock_client.get_or_create_collection.return_value = None
-    mock_client.add_documents.return_value = None
-
-    # Create an agent with knowledge
-    agent = Agent(
-        role="AI Researcher",
-        goal="Research and explain AI concepts",
-        backstory="Expert in artificial intelligence",
-        knowledge_sources=[
-            StringKnowledgeSource(
-                content="AI systems require careful training and validation."
-            )
-        ],
-    )
-
-    # Create a task for the agent
-    task = Task(
-        description="Explain the basics of AI systems",
-        expected_output="A clear explanation of AI fundamentals",
-        agent=agent,
-    )
-
-    # Create a crew planner
-    planner = CrewPlanner([task], None)
-
-    # Get the task summary
-    task_summary = planner._create_tasks_summary()
-
-    # Verify that knowledge is included in planning when present
-    assert "AI systems require careful training" in task_summary, (
-        "Knowledge content should be present in task summary when knowledge exists"
-    )
-    assert '"agent_knowledge"' in task_summary, (
-        "agent_knowledge field should be present in task summary when knowledge exists"
-    )
-
-    # Verify that knowledge is properly formatted
-    assert isinstance(task.agent.knowledge_sources, list), (
-        "Knowledge sources should be stored in a list"
-    )
-    assert len(task.agent.knowledge_sources) > 0, (
-        "At least one knowledge source should be present"
-    )
-    assert task.agent.knowledge_sources[0].content in task_summary, (
-        "Knowledge source content should be included in task summary"
-    )
-
-    # Verify that other expected components are still present
-    assert task.description in task_summary, (
-        "Task description should be present in task summary"
-    )
-    assert task.expected_output in task_summary, (
-        "Expected output should be present in task summary"
-    )
-    assert agent.role in task_summary, "Agent role should be present in task summary"

tests/utilities/test_llm_utils.py

@@ -1,96 +0,0 @@
-import os
-from unittest.mock import patch
-
-import pytest
-from litellm.exceptions import BadRequestError
-
-from crewai.llm import LLM
-from crewai.utilities.llm_utils import create_llm
-
-
-def test_create_llm_with_llm_instance():
-    existing_llm = LLM(model="gpt-4o")
-    llm = create_llm(llm_value=existing_llm)
-    assert llm is existing_llm
-
-
-def test_create_llm_with_valid_model_string():
-    llm = create_llm(llm_value="gpt-4o")
-    assert isinstance(llm, LLM)
-    assert llm.model == "gpt-4o"
-
-
-def test_create_llm_with_invalid_model_string():
-    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
-        llm = create_llm(llm_value="invalid-model")
-        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
-
-
-def test_create_llm_with_unknown_object_missing_attributes():
-    class UnknownObject:
-        pass
-
-    unknown_obj = UnknownObject()
-    llm = create_llm(llm_value=unknown_obj)
-
-    # Attempt to call the LLM and expect it to raise an error due to missing attributes
-    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
-        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
-
-
-def test_create_llm_with_none_uses_default_model():
-    with patch.dict(os.environ, {}, clear=True):
-        with patch("crewai.cli.constants.DEFAULT_LLM_MODEL", "gpt-4o"):
-            llm = create_llm(llm_value=None)
-            assert isinstance(llm, LLM)
-            assert llm.model == "gpt-4o-mini"
-
-
-def test_create_llm_with_unknown_object():
-    class UnknownObject:
-        model_name = "gpt-4o"
-        temperature = 0.7
-        max_tokens = 1500
-
-    unknown_obj = UnknownObject()
-    llm = create_llm(llm_value=unknown_obj)
-    assert isinstance(llm, LLM)
-    assert llm.model == "gpt-4o"
-    assert llm.temperature == 0.7
-    assert llm.max_tokens == 1500
-
-
-def test_create_llm_from_env_with_unaccepted_attributes():
-    with patch.dict(
-        os.environ,
-        {
-            "OPENAI_MODEL_NAME": "gpt-3.5-turbo",
-            "AWS_ACCESS_KEY_ID": "fake-access-key",
-            "AWS_SECRET_ACCESS_KEY": "fake-secret-key",
-            "AWS_REGION_NAME": "us-west-2",
-        },
-    ):
-        llm = create_llm(llm_value=None)
-        assert isinstance(llm, LLM)
-        assert llm.model == "gpt-3.5-turbo"
-        assert not hasattr(llm, "AWS_ACCESS_KEY_ID")
-        assert not hasattr(llm, "AWS_SECRET_ACCESS_KEY")
-        assert not hasattr(llm, "AWS_REGION_NAME")
-
-
-def test_create_llm_with_partial_attributes():
-    class PartialAttributes:
-        model_name = "gpt-4o"
-        # temperature is missing
-
-    obj = PartialAttributes()
-    llm = create_llm(llm_value=obj)
-    assert isinstance(llm, LLM)
-    assert llm.model == "gpt-4o"
-    assert llm.temperature is None  # Should handle missing attributes gracefully
-
-
-def test_create_llm_with_invalid_type():
-    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
-        llm = create_llm(llm_value=42)
-        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])

tests/utilities/test_planning_handler.py

@@ -1,182 +0,0 @@
-from typing import Optional
-from unittest.mock import MagicMock, patch
-
-import pytest
-from pydantic import BaseModel
-
-from crewai.agent import Agent
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
-from crewai.tools.base_tool import BaseTool
-from crewai.utilities.planning_handler import (
-    CrewPlanner,
-    PlannerTaskPydanticOutput,
-    PlanPerTask,
-)
-
-
-class InternalCrewPlanner:
-    @pytest.fixture
-    def crew_planner(self):
-        tasks = [
-            Task(
-                description="Task 1",
-                expected_output="Output 1",
-                agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"),
-            ),
-            Task(
-                description="Task 2",
-                expected_output="Output 2",
-                agent=Agent(role="Agent 2", goal="Goal 2", backstory="Backstory 2"),
-            ),
-            Task(
-                description="Task 3",
-                expected_output="Output 3",
-                agent=Agent(role="Agent 3", goal="Goal 3", backstory="Backstory 3"),
-            ),
-        ]
-        return CrewPlanner(tasks, None)
-
-    @pytest.fixture
-    def crew_planner_different_llm(self):
-        tasks = [
-            Task(
-                description="Task 1",
-                expected_output="Output 1",
-                agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"),
-            )
-        ]
-        planning_agent_llm = "gpt-3.5-turbo"
-        return CrewPlanner(tasks, planning_agent_llm)
-
-    def test_handle_crew_planning(self, crew_planner):
-        list_of_plans_per_task = [
-            PlanPerTask(task="Task1", plan="Plan 1"),
-            PlanPerTask(task="Task2", plan="Plan 2"),
-            PlanPerTask(task="Task3", plan="Plan 3"),
-        ]
-        with patch.object(Task, "execute_sync") as execute:
-            execute.return_value = TaskOutput(
-                description="Description",
-                agent="agent",
-                pydantic=PlannerTaskPydanticOutput(
-                    list_of_plans_per_task=list_of_plans_per_task
-                ),
-            )
-            result = crew_planner._handle_crew_planning()
-            assert crew_planner.planning_agent_llm == "gpt-4o-mini"
-            assert isinstance(result, PlannerTaskPydanticOutput)
-            assert len(result.list_of_plans_per_task) == len(crew_planner.tasks)
-            execute.assert_called_once()
-
-    def test_create_planning_agent(self, crew_planner):
-        agent = crew_planner._create_planning_agent()
-        assert isinstance(agent, Agent)
-        assert agent.role == "Task Execution Planner"
-
-    def test_create_planner_task(self, crew_planner):
-        planning_agent = Agent(
-            role="Planning Agent",
-            goal="Plan Step by Step Plan",
-            backstory="Master in Planning",
-        )
-        tasks_summary = "Summary of tasks"
-        task = crew_planner._create_planner_task(planning_agent, tasks_summary)
-
-        assert isinstance(task, Task)
-        assert task.description.startswith("Based on these tasks summary")
-        assert task.agent == planning_agent
-        assert (
-            task.expected_output
-            == "Step by step plan on how the agents can execute their tasks using the available tools with mastery"
-        )
-
-    def test_create_tasks_summary(self, crew_planner):
-        tasks_summary = crew_planner._create_tasks_summary()
-        assert isinstance(tasks_summary, str)
-        assert tasks_summary.startswith("\n                Task Number 1 - Task 1")
-        assert '"agent_tools": "agent has no tools"' in tasks_summary
-        # Knowledge field should not be present when empty
-        assert '"agent_knowledge"' not in tasks_summary
-
-    @patch('crewai.knowledge.storage.knowledge_storage.chromadb')
-    def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma):
-        """Test task summary generation with both knowledge and tools present."""
-        # Mock ChromaDB collection
-        mock_collection = mock_chroma.return_value.get_or_create_collection.return_value
-        mock_collection.add.return_value = None
-
-        # Create mock tools with proper string descriptions and structured tool support
-        class MockTool(BaseTool):
-            name: str
-            description: str
-
-            def __init__(self, name: str, description: str):
-                tool_data = {"name": name, "description": description}
-                super().__init__(**tool_data)
-
-            def __str__(self):
-                return self.name
-
-            def __repr__(self):
-                return self.name
-
-            def to_structured_tool(self):
-                return self
-
-            def _run(self, *args, **kwargs):
-                pass
-
-            def _generate_description(self) -> str:
-                """Override _generate_description to avoid args_schema handling."""
-                return self.description
-
-        tool1 = MockTool("tool1", "Tool 1 description")
-        tool2 = MockTool("tool2", "Tool 2 description")
-
-        # Create a task with knowledge and tools
-        task = Task(
-            description="Task with knowledge and tools",
-            expected_output="Expected output",
-            agent=Agent(
-                role="Test Agent",
-                goal="Test Goal",
-                backstory="Test Backstory",
-                tools=[tool1, tool2],
-                knowledge_sources=[
-                    StringKnowledgeSource(content="Test knowledge content")
-                ]
-            )
-        )
-
-        # Create planner with the new task
-        planner = CrewPlanner([task], None)
-        tasks_summary = planner._create_tasks_summary()
-
-        # Verify task summary content
-        assert isinstance(tasks_summary, str)
-        assert task.description in tasks_summary
-        assert task.expected_output in tasks_summary
-        assert '"agent_tools": [tool1, tool2]' in tasks_summary
-        assert '"agent_knowledge": "[\\"Test knowledge content\\"]"' in tasks_summary
-        assert task.agent.role in tasks_summary
-        assert task.agent.goal in tasks_summary
-
-    def test_handle_crew_planning_different_llm(self, crew_planner_different_llm):
-        with patch.object(Task, "execute_sync") as execute:
-            execute.return_value = TaskOutput(
-                description="Description",
-                agent="agent",
-                pydantic=PlannerTaskPydanticOutput(
-                    list_of_plans_per_task=[PlanPerTask(task="Task1", plan="Plan 1")]
-                ),
-            )
-            result = crew_planner_different_llm._handle_crew_planning()
-
-            assert crew_planner_different_llm.planning_agent_llm == "gpt-3.5-turbo"
-            assert isinstance(result, PlannerTaskPydanticOutput)
-            assert len(result.list_of_plans_per_task) == len(
-                crew_planner_different_llm.tasks
-            )
-            execute.assert_called_once()

tests/utilities/test_pydantic_schema_parser.py

@@ -1,94 +0,0 @@
-from typing import Any, Dict, List, Optional, Set, Tuple, Union
-
-import pytest
-from pydantic import BaseModel, Field
-
-from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
-
-
-def test_simple_model():
-    class SimpleModel(BaseModel):
-        field1: int
-        field2: str
-
-    parser = PydanticSchemaParser(model=SimpleModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    field1: int,
-    field2: str
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_nested_model():
-    class NestedModel(BaseModel):
-        nested_field: int
-
-    class ParentModel(BaseModel):
-        parent_field: str
-        nested: NestedModel
-
-    parser = PydanticSchemaParser(model=ParentModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    parent_field: str,
-    nested: NestedModel
-    {
-        nested_field: int
-    }
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_model_with_list():
-    class ListModel(BaseModel):
-        list_field: List[int]
-
-    parser = PydanticSchemaParser(model=ListModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    list_field: List[int]
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_model_with_optional_field():
-    class OptionalModel(BaseModel):
-        optional_field: Optional[str]
-
-    parser = PydanticSchemaParser(model=OptionalModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    optional_field: Optional[str]
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_model_with_union():
-    class UnionModel(BaseModel):
-        union_field: Union[int, str]
-
-    parser = PydanticSchemaParser(model=UnionModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    union_field: Union[int, str]
-}"""
-    assert schema.strip() == expected_schema.strip()
-
-
-def test_model_with_dict():
-    class DictModel(BaseModel):
-        dict_field: Dict[str, int]
-
-    parser = PydanticSchemaParser(model=DictModel)
-    schema = parser.get_schema()
-
-    expected_schema = """{
-    dict_field: Dict[str, int]
-}"""
-    assert schema.strip() == expected_schema.strip()

tests/utilities/test_serialization.py

@@ -1,152 +0,0 @@
-from datetime import date, datetime
-from typing import List
-from unittest.mock import Mock
-
-import pytest
-from pydantic import BaseModel
-
-from crewai.utilities.serialization import to_serializable, to_string
-
-
-class Address(BaseModel):
-    street: str
-    city: str
-    country: str
-
-
-class Person(BaseModel):
-    name: str
-    age: int
-    address: Address
-    birthday: date
-    skills: List[str]
-
-
-@pytest.mark.parametrize(
-    "test_input,expected",
-    [
-        ({"text": "hello world"}, {"text": "hello world"}),
-        ({"number": 42}, {"number": 42}),
-        ({"decimal": 3.14}, {"decimal": 3.14}),
-        ({"flag": True}, {"flag": True}),
-        ({"empty": None}, {"empty": None}),
-        ({"list": [1, 2, 3]}, {"list": [1, 2, 3]}),
-        ({"tuple": (1, 2, 3)}, {"tuple": [1, 2, 3]}),
-        ({"set": {1, 2, 3}}, {"set": [1, 2, 3]}),
-        ({"nested": [1, [2, 3], {4, 5}]}, {"nested": [1, [2, 3], [4, 5]]}),
-    ],
-)
-def test_basic_serialization(test_input, expected):
-    result = to_serializable(test_input)
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "input_date,expected",
-    [
-        (date(2024, 1, 1), "2024-01-01"),
-        (datetime(2024, 1, 1, 12, 30), "2024-01-01T12:30:00"),
-    ],
-)
-def test_temporal_serialization(input_date, expected):
-    result = to_serializable({"date": input_date})
-    assert result["date"] == expected
-
-
-@pytest.mark.parametrize(
-    "key,value,expected_key_type",
-    [
-        (("tuple", "key"), "value", str),
-        (None, "value", str),
-        (123, "value", str),
-        ("normal", "value", str),
-    ],
-)
-def test_dictionary_key_serialization(key, value, expected_key_type):
-    result = to_serializable({key: value})
-    assert len(result) == 1
-    result_key = next(iter(result.keys()))
-    assert isinstance(result_key, expected_key_type)
-    assert result[result_key] == value
-
-
-@pytest.mark.parametrize(
-    "callable_obj,expected_in_result",
-    [
-        (lambda x: x * 2, "lambda"),
-        (str.upper, "upper"),
-    ],
-)
-def test_callable_serialization(callable_obj, expected_in_result):
-    result = to_serializable({"func": callable_obj})
-    assert isinstance(result["func"], str)
-    assert expected_in_result in result["func"].lower()
-
-
-def test_pydantic_model_serialization():
-    address = Address(street="123 Main St", city="Tech City", country="Pythonia")
-
-    person = Person(
-        name="John Doe",
-        age=30,
-        address=address,
-        birthday=date(1994, 1, 1),
-        skills=["Python", "Testing"],
-    )
-
-    data = {
-        "single_model": address,
-        "nested_model": person,
-        "model_list": [address, address],
-        "model_dict": {"home": address},
-    }
-
-    result = to_serializable(data)
-    assert (
-        to_string(result)
-        == '{"single_model": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, "nested_model": {"name": "John Doe", "age": 30, "address": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, "birthday": "1994-01-01", "skills": ["Python", "Testing"]}, "model_list": [{"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}], "model_dict": {"home": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}}}'
-    )
-
-
-def test_depth_limit():
-    """Test max depth handling with a deeply nested structure"""
-
-    def create_nested(depth):
-        if depth == 0:
-            return "value"
-        return {"next": create_nested(depth - 1)}
-
-    deep_structure = create_nested(10)
-    result = to_serializable(deep_structure)
-
-    assert result == {
-        "next": {
-            "next": {
-                "next": {
-                    "next": {
-                        "next": "{'next': {'next': {'next': {'next': {'next': 'value'}}}}}"
-                    }
-                }
-            }
-        }
-    }
-
-
-def test_exclude_keys():
-    result = to_serializable({"key1": "value1", "key2": "value2"}, exclude={"key1"})
-    assert result == {"key2": "value2"}
-
-    model = Person(
-        name="John Doe",
-        age=30,
-        address=Address(street="123 Main St", city="Tech City", country="Pythonia"),
-        birthday=date(1994, 1, 1),
-        skills=["Python", "Testing"],
-    )
-    result = to_serializable(model, exclude={"address"})
-    assert result == {
-        "name": "John Doe",
-        "age": 30,
-        "birthday": "1994-01-01",
-        "skills": ["Python", "Testing"],
-    }

tests/utilities/test_string_utils.py

@@ -1,187 +0,0 @@
-from typing import Any, Dict, List, Union
-
-import pytest
-
-from crewai.utilities.string_utils import interpolate_only
-
-
-class TestInterpolateOnly:
-    """Tests for the interpolate_only function in string_utils.py."""
-
-    def test_basic_variable_interpolation(self):
-        """Test basic variable interpolation works correctly."""
-        template = "Hello, {name}! Welcome to {company}."
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "name": "Alice",
-            "company": "CrewAI",
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert result == "Hello, Alice! Welcome to CrewAI."
-
-    def test_multiple_occurrences_of_same_variable(self):
-        """Test that multiple occurrences of the same variable are replaced."""
-        template = "{name} is using {name}'s account."
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "name": "Bob"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert result == "Bob is using Bob's account."
-
-    def test_json_structure_preservation(self):
-        """Test that JSON structures are preserved and not interpolated incorrectly."""
-        template = """
-        Instructions for {agent}:
-
-        Please return the following object:
-
-        {"name": "person's name", "age": 25, "skills": ["coding", "testing"]}
-        """
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "agent": "DevAgent"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert "Instructions for DevAgent:" in result
-        assert (
-            '{"name": "person\'s name", "age": 25, "skills": ["coding", "testing"]}'
-            in result
-        )
-
-    def test_complex_nested_json(self):
-        """Test with complex JSON structures containing curly braces."""
-        template = """
-        {agent} needs to process:
-        {
-          "config": {
-            "nested": {
-              "value": 42
-            },
-            "arrays": [1, 2, {"inner": "value"}]
-          }
-        }
-        """
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "agent": "DataProcessor"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert "DataProcessor needs to process:" in result
-        assert '"nested": {' in result
-        assert '"value": 42' in result
-        assert '[1, 2, {"inner": "value"}]' in result
-
-    def test_missing_variable(self):
-        """Test that an error is raised when a required variable is missing."""
-        template = "Hello, {name}!"
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "not_name": "Alice"
-        }
-
-        with pytest.raises(KeyError) as excinfo:
-            interpolate_only(template, inputs)
-
-        assert "template variable" in str(excinfo.value).lower()
-        assert "name" in str(excinfo.value)
-
-    def test_invalid_input_types(self):
-        """Test that an error is raised with invalid input types."""
-        template = "Hello, {name}!"
-        # Using Any for this test since we're intentionally testing an invalid type
-        inputs: Dict[str, Any] = {"name": object()}  # Object is not a valid input type
-
-        with pytest.raises(ValueError) as excinfo:
-            interpolate_only(template, inputs)
-
-        assert "unsupported type" in str(excinfo.value).lower()
-
-    def test_empty_input_string(self):
-        """Test handling of empty or None input string."""
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "name": "Alice"
-        }
-
-        assert interpolate_only("", inputs) == ""
-        assert interpolate_only(None, inputs) == ""
-
-    def test_no_variables_in_template(self):
-        """Test a template with no variables to replace."""
-        template = "This is a static string with no variables."
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "name": "Alice"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert result == template
-
-    def test_variable_name_starting_with_underscore(self):
-        """Test variables starting with underscore are replaced correctly."""
-        template = "Variable: {_special_var}"
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "_special_var": "Special Value"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert result == "Variable: Special Value"
-
-    def test_preserves_non_matching_braces(self):
-        """Test that non-matching braces patterns are preserved."""
-        template = (
-            "This {123} and {!var} should not be replaced but {valid_var} should."
-        )
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "valid_var": "works"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert (
-            result == "This {123} and {!var} should not be replaced but works should."
-        )
-
-    def test_complex_mixed_scenario(self):
-        """Test a complex scenario with both valid variables and JSON structures."""
-        template = """
-        {agent_name} is working on task {task_id}.
-        
-        Instructions:
-        1. Process the data
-        2. Return results as:
-        
-        {
-          "taskId": "{task_id}",
-          "results": {
-            "processed_by": "agent_name",
-            "status": "complete",
-            "values": [1, 2, 3]
-          }
-        }
-        """
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "agent_name": "AnalyticsAgent",
-            "task_id": "T-12345",
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert "AnalyticsAgent is working on task T-12345" in result
-        assert '"taskId": "T-12345"' in result
-        assert '"processed_by": "agent_name"' in result  # This shouldn't be replaced
-        assert '"values": [1, 2, 3]' in result
-
-    def test_empty_inputs_dictionary(self):
-        """Test that an error is raised with empty inputs dictionary."""
-        template = "Hello, {name}!"
-        inputs: Dict[str, Any] = {}
-
-        with pytest.raises(ValueError) as excinfo:
-            interpolate_only(template, inputs)
-
-        assert "inputs dictionary cannot be empty" in str(excinfo.value).lower()

tests/utilities/test_training_converter.py

@@ -1,97 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-from pydantic import BaseModel, Field
-from typing import List
-
-from crewai.utilities.converter import ConverterError
-from crewai.utilities.training_converter import TrainingConverter
-
-
-class TestModel(BaseModel):
-    string_field: str = Field(description="A simple string field")
-    list_field: List[str] = Field(description="A list of strings")
-    number_field: float = Field(description="A number field")
-
-
-class TestTrainingConverter:
-
-    def setup_method(self):
-        self.llm_mock = MagicMock()
-        self.test_text = "Sample text for evaluation"
-        self.test_instructions = "Convert to JSON format"
-        self.converter = TrainingConverter(
-            llm=self.llm_mock,
-            text=self.test_text,
-            model=TestModel,
-            instructions=self.test_instructions
-        )
-
-    @patch("crewai.utilities.converter.Converter.to_pydantic")
-    def test_fallback_to_field_by_field(self, parent_to_pydantic_mock):
-        parent_to_pydantic_mock.side_effect = ConverterError("Failed to convert directly")
-
-        llm_responses = {
-            "string_field": "test string value",
-            "list_field": "- item1\n- item2\n- item3",
-            "number_field": "8.5"
-        }
-
-        def llm_side_effect(messages):
-            prompt = messages[1]["content"]
-            if "string_field" in prompt:
-                return llm_responses["string_field"]
-            elif "list_field" in prompt:
-                return llm_responses["list_field"]
-            elif "number_field" in prompt:
-                return llm_responses["number_field"]
-            return "unknown field"
-
-        self.llm_mock.call.side_effect = llm_side_effect
-
-        result = self.converter.to_pydantic()
-
-        assert result.string_field == "test string value"
-        assert result.list_field == ["item1", "item2", "item3"]
-        assert result.number_field == 8.5
-
-        parent_to_pydantic_mock.assert_called_once()
-        assert self.llm_mock.call.call_count == 3
-
-    def test_ask_llm_for_field(self):
-        field_name = "test_field"
-        field_description = "This is a test field description"
-        expected_response = "Test response"
-        self.llm_mock.call.return_value = expected_response
-        response = self.converter._ask_llm_for_field(field_name, field_description)
-
-        assert response == expected_response
-        self.llm_mock.call.assert_called_once()
-
-        call_args = self.llm_mock.call.call_args[0][0]
-        assert call_args[0]["role"] == "system"
-        assert f"Extract the {field_name}" in call_args[0]["content"]
-        assert call_args[1]["role"] == "user"
-        assert field_name in call_args[1]["content"]
-        assert field_description in call_args[1]["content"]
-
-    def test_process_field_value_string(self):
-        response = "  This is a string with extra whitespace  "
-        result = self.converter._process_field_value(response, str)
-        assert result == "This is a string with extra whitespace"
-
-    def test_process_field_value_list_with_bullet_points(self):
-        response = "- Item 1\n- Item 2\n- Item 3"
-        result = self.converter._process_field_value(response, List[str])
-        assert result == ["Item 1", "Item 2", "Item 3"]
-
-    def test_process_field_value_list_with_json(self):
-        response = '["Item 1", "Item 2", "Item 3"]'
-        with patch("crewai.utilities.training_converter.json.loads") as json_mock:
-            json_mock.return_value = ["Item 1", "Item 2", "Item 3"]
-            result = self.converter._process_field_value(response, List[str])
-            assert result == ["Item 1", "Item 2", "Item 3"]
-
-    def test_process_field_value_float(self):
-        response = "The quality score is 8.5 out of 10"
-        result = self.converter._process_field_value(response, float)
-        assert result == 8.5

tests/utilities/test_training_handler.py

@@ -1,55 +0,0 @@
-import os
-import tempfile
-import unittest
-
-from crewai.utilities.training_handler import CrewTrainingHandler
-
-
-class InternalCrewTrainingHandler(unittest.TestCase):
-    def setUp(self):
-        self.temp_file = tempfile.NamedTemporaryFile(suffix=".pkl", delete=False)
-        self.temp_file.close()
-        self.handler = CrewTrainingHandler(self.temp_file.name)
-
-    def tearDown(self):
-        if os.path.exists(self.temp_file.name):
-            os.remove(self.temp_file.name)
-        del self.handler
-
-    def test_save_trained_data(self):
-        agent_id = "agent1"
-        trained_data = {"param1": 1, "param2": 2}
-        self.handler.save_trained_data(agent_id, trained_data)
-
-        # Assert that the trained data is saved correctly
-        data = self.handler.load()
-        assert data[agent_id] == trained_data
-
-    def test_append_existing_agent(self):
-        agent_id = "agent1"
-        initial_iteration = 0
-        initial_data = {"param1": 1, "param2": 2}
-
-        self.handler.append(initial_iteration, agent_id, initial_data)
-
-        train_iteration = 1
-        new_data = {"param3": 3, "param4": 4}
-        self.handler.append(train_iteration, agent_id, new_data)
-
-        # Assert that the new data is appended correctly to the existing agent
-        data = self.handler.load()
-        assert agent_id in data
-        assert initial_iteration in data[agent_id]
-        assert train_iteration in data[agent_id]
-        assert data[agent_id][initial_iteration] == initial_data
-        assert data[agent_id][train_iteration] == new_data
-
-    def test_append_new_agent(self):
-        train_iteration = 1
-        agent_id = "agent2"
-        new_data = {"param5": 5, "param6": 6}
-        self.handler.append(train_iteration, agent_id, new_data)
-
-        # Assert that the new agent and data are appended correctly
-        data = self.handler.load()
-        assert data[agent_id][train_iteration] == new_data