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

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Greyson LaLonde
2025-09-26 14:29:28 -04:00
parent 74b5c88834
commit daf6f679ff
763 changed files with 1181 additions and 398 deletions
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0
tests/__init__.py
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0
tests/agents/__init__.py
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tests/agents/agent_adapters/__init__.py
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"""Tests for agent adapters."""

116
tests/agents/agent_adapters/test_base_agent_adapter.py
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from typing import Any
import pytest
from pydantic import BaseModel
from crewai.agent import BaseAgent
from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter
from crewai.tools import BaseTool
from crewai.utilities.token_counter_callback import TokenProcess
# Concrete implementation for testing
class ConcreteAgentAdapter(BaseAgentAdapter):
def configure_tools(
self, tools: list[BaseTool] | None = None, **kwargs: Any
) -> None:
# Simple implementation for testing
self.tools = tools or []
def execute_task(
self,
task: Any,
context: str | None = None,
tools: list[Any] | None = None,
) -> str:
# Dummy implementation needed due to BaseAgent inheritance
return "Task executed"
def create_agent_executor(self, tools: list[BaseTool] | None = None) -> Any:
# Dummy implementation
return None
def get_delegation_tools(
self, tools: list[BaseTool], tool_map: dict[str, BaseTool] | None
) -> list[BaseTool]:
# Dummy implementation
return []
def _parse_output(self, agent_output: Any, token_process: TokenProcess):
# Dummy implementation
pass
def get_output_converter(self, tools: list[BaseTool] | None = None) -> Any:
# Dummy implementation
return None
def get_platform_tools(self, apps: Any) -> list[BaseTool]:
# Dummy implementation
return []
def test_base_agent_adapter_initialization():
"""Test initialization of the concrete agent adapter."""
adapter = ConcreteAgentAdapter(
role="test role", goal="test goal", backstory="test backstory"
)
assert isinstance(adapter, BaseAgent)
assert isinstance(adapter, BaseAgentAdapter)
assert adapter.role == "test role"
assert adapter._agent_config is None
assert adapter.adapted_structured_output is False
def test_base_agent_adapter_initialization_with_config():
"""Test initialization with agent_config."""
config = {"model": "gpt-4"}
adapter = ConcreteAgentAdapter(
agent_config=config,
role="test role",
goal="test goal",
backstory="test backstory",
)
assert adapter._agent_config == config
def test_configure_tools_method_exists():
"""Test that configure_tools method exists and can be called."""
adapter = ConcreteAgentAdapter(
role="test role", goal="test goal", backstory="test backstory"
)
# Create dummy tools if needed, or pass None
tools = []
adapter.configure_tools(tools)
assert hasattr(adapter, "tools")
assert adapter.tools == tools
def test_configure_structured_output_method_exists():
"""Test that configure_structured_output method exists and can be called."""
adapter = ConcreteAgentAdapter(
role="test role", goal="test goal", backstory="test backstory"
)
# Define a dummy structure or pass None/Any
class DummyOutput(BaseModel):
data: str
structured_output = DummyOutput
adapter.configure_structured_output(structured_output)
# Add assertions here if configure_structured_output modifies state
# For now, just ensuring it runs without error is sufficient
def test_base_agent_adapter_inherits_base_agent():
"""Test that BaseAgentAdapter inherits from BaseAgent."""
assert issubclass(BaseAgentAdapter, BaseAgent)
class ConcreteAgentAdapterWithoutRequiredMethods(BaseAgentAdapter):
pass
def test_base_agent_adapter_fails_without_required_methods():
"""Test that BaseAgentAdapter fails without required methods."""
with pytest.raises(TypeError):
ConcreteAgentAdapterWithoutRequiredMethods() # type: ignore

94
tests/agents/agent_adapters/test_base_tool_adapter.py
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from typing import Any, List
from unittest.mock import Mock
import pytest
from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter
from crewai.tools.base_tool import BaseTool
class ConcreteToolAdapter(BaseToolAdapter):
def configure_tools(self, tools: List[BaseTool]) -> None:
self.converted_tools = [f"converted_{tool.name}" for tool in tools]
@pytest.fixture
def mock_tool_1():
tool = Mock(spec=BaseTool)
tool.name = "Mock Tool 1"
return tool
@pytest.fixture
def mock_tool_2():
tool = Mock(spec=BaseTool)
tool.name = "MockTool2"
return tool
@pytest.fixture
def tools_list(mock_tool_1, mock_tool_2):
return [mock_tool_1, mock_tool_2]
def test_initialization_with_tools(tools_list):
adapter = ConcreteToolAdapter(tools=tools_list)
assert adapter.original_tools == tools_list
assert adapter.converted_tools == [] # Conversion happens in configure_tools
def test_initialization_without_tools():
adapter = ConcreteToolAdapter()
assert adapter.original_tools == []
assert adapter.converted_tools == []
def test_configure_tools(tools_list):
adapter = ConcreteToolAdapter()
adapter.configure_tools(tools_list)
assert adapter.converted_tools == ["converted_Mock Tool 1", "converted_MockTool2"]
assert adapter.original_tools == [] # original_tools is only set in init
adapter_with_init_tools = ConcreteToolAdapter(tools=tools_list)
adapter_with_init_tools.configure_tools(tools_list)
assert adapter_with_init_tools.converted_tools == [
"converted_Mock Tool 1",
"converted_MockTool2",
]
assert adapter_with_init_tools.original_tools == tools_list
def test_tools_method(tools_list):
adapter = ConcreteToolAdapter()
adapter.configure_tools(tools_list)
assert adapter.tools() == ["converted_Mock Tool 1", "converted_MockTool2"]
def test_tools_method_empty():
adapter = ConcreteToolAdapter()
assert adapter.tools() == []
def test_sanitize_tool_name_with_spaces():
adapter = ConcreteToolAdapter()
assert adapter.sanitize_tool_name("Tool With Spaces") == "Tool_With_Spaces"
def test_sanitize_tool_name_without_spaces():
adapter = ConcreteToolAdapter()
assert adapter.sanitize_tool_name("ToolWithoutSpaces") == "ToolWithoutSpaces"
def test_sanitize_tool_name_empty():
adapter = ConcreteToolAdapter()
assert adapter.sanitize_tool_name("") == ""
class ConcreteToolAdapterWithoutRequiredMethods(BaseToolAdapter):
pass
def test_tool_adapted_fails_without_required_methods():
"""Test that BaseToolAdapter fails without required methods."""
with pytest.raises(TypeError):
ConcreteToolAdapterWithoutRequiredMethods() # type: ignore

1
tests/agents/agent_builder/__init__.py
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"""Tests for agent builder."""

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tests/agents/agent_builder/test_base_agent.py
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import hashlib
from typing import Any
from pydantic import BaseModel
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.tools.base_tool import BaseTool
class MockAgent(BaseAgent):
def execute_task(
self,
task: Any,
context: str | None = None,
tools: list[BaseTool] | None = None,
) -> str:
return ""
def create_agent_executor(self, tools=None) -> None: ...
def get_delegation_tools(self, agents: list["BaseAgent"]): ...
def get_platform_tools(self, apps: list[Any]): ...
def get_output_converter(
self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
): ...
def test_key():
agent = MockAgent(
role="test role",
goal="test goal",
backstory="test backstory",
)
hash = hashlib.md5("test role|test goal|test backstory".encode(), usedforsecurity=False).hexdigest()
assert agent.key == hash

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tests/agents/test_agent.py
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tests/agents/test_agent_inject_date.py
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from datetime import datetime
from unittest.mock import patch
from crewai.agent import Agent
from crewai.task import Task
def test_agent_inject_date():
"""Test that the inject_date flag injects the current date into the task.
Tests that when inject_date=True, the current date is added to the task description.
"""
with patch("datetime.datetime") as mock_datetime:
mock_datetime.now.return_value = datetime(2025, 1, 1)
agent = Agent(
role="test_agent",
goal="test_goal",
backstory="test_backstory",
inject_date=True,
)
task = Task(
description="Test task",
expected_output="Test output",
agent=agent,
)
# Store original description
original_description = task.description
agent._inject_date_to_task(task)
assert "Current Date: 2025-01-01" in task.description
assert task.description != original_description
def test_agent_without_inject_date():
"""Test that without inject_date flag, no date is injected.
Tests that when inject_date=False (default), no date is added to the task description.
"""
agent = Agent(
role="test_agent",
goal="test_goal",
backstory="test_backstory",
# inject_date is False by default
)
task = Task(
description="Test task",
expected_output="Test output",
agent=agent,
)
original_description = task.description
agent._inject_date_to_task(task)
assert task.description == original_description
def test_agent_inject_date_custom_format():
"""Test that the inject_date flag with custom date_format works correctly.
Tests that when inject_date=True with a custom date_format, the date is formatted correctly.
"""
with patch("datetime.datetime") as mock_datetime:
mock_datetime.now.return_value = datetime(2025, 1, 1)
agent = Agent(
role="test_agent",
goal="test_goal",
backstory="test_backstory",
inject_date=True,
date_format="%d/%m/%Y",
)
task = Task(
description="Test task",
expected_output="Test output",
agent=agent,
)
# Store original description
original_description = task.description
agent._inject_date_to_task(task)
assert "Current Date: 01/01/2025" in task.description
assert task.description != original_description
def test_agent_inject_date_invalid_format():
"""Test error handling with invalid date format.
Tests that when an invalid date_format is provided, the task description remains unchanged.
"""
agent = Agent(
role="test_agent",
goal="test_goal",
backstory="test_backstory",
inject_date=True,
date_format="invalid",
)
task = Task(
description="Test task",
expected_output="Test output",
agent=agent,
)
original_description = task.description
agent._inject_date_to_task(task)
assert task.description == original_description

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tests/agents/test_agent_reasoning.py
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"""Tests for reasoning in agents."""
import json
import pytest
from crewai import Agent, Task
from crewai.llm import LLM
@pytest.fixture
def mock_llm_responses():
"""Fixture for mock LLM responses."""
return {
"ready": "I'll solve this simple math problem.\n\nREADY: I am ready to execute the task.\n\n",
"not_ready": "I need to think about derivatives.\n\nNOT READY: I need to refine my plan because I'm not sure about the derivative rules.",
"ready_after_refine": "I'll use the power rule for derivatives where d/dx(x^n) = n*x^(n-1).\n\nREADY: I am ready to execute the task.",
"execution": "4",
}
def test_agent_with_reasoning(mock_llm_responses):
"""Test agent with reasoning."""
llm = LLM("gpt-3.5-turbo")
agent = Agent(
role="Test Agent",
goal="To test the reasoning feature",
backstory="I am a test agent created to verify the reasoning feature works correctly.",
llm=llm,
reasoning=True,
verbose=True,
)
task = Task(
description="Simple math task: What's 2+2?",
expected_output="The answer should be a number.",
agent=agent,
)
agent.llm.call = lambda messages, *args, **kwargs: (
mock_llm_responses["ready"]
if any("create a detailed plan" in msg.get("content", "") for msg in messages)
else mock_llm_responses["execution"]
)
result = agent.execute_task(task)
assert result == mock_llm_responses["execution"]
assert "Reasoning Plan:" in task.description
def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
"""Test agent with reasoning that requires refinement."""
llm = LLM("gpt-3.5-turbo")
agent = Agent(
role="Test Agent",
goal="To test the reasoning feature",
backstory="I am a test agent created to verify the reasoning feature works correctly.",
llm=llm,
reasoning=True,
max_reasoning_attempts=2,
verbose=True,
)
task = Task(
description="Complex math task: What's the derivative of x²?",
expected_output="The answer should be a mathematical expression.",
agent=agent,
)
call_count = [0]
def mock_llm_call(messages, *args, **kwargs):
if any(
"create a detailed plan" in msg.get("content", "") for msg in messages
) or any("refine your plan" in msg.get("content", "") for msg in messages):
call_count[0] += 1
if call_count[0] == 1:
return mock_llm_responses["not_ready"]
return mock_llm_responses["ready_after_refine"]
return "2x"
agent.llm.call = mock_llm_call
result = agent.execute_task(task)
assert result == "2x"
assert call_count[0] == 2 # Should have made 2 reasoning calls
assert "Reasoning Plan:" in task.description
def test_agent_with_reasoning_max_attempts_reached():
"""Test agent with reasoning that reaches max attempts without being ready."""
llm = LLM("gpt-3.5-turbo")
agent = Agent(
role="Test Agent",
goal="To test the reasoning feature",
backstory="I am a test agent created to verify the reasoning feature works correctly.",
llm=llm,
reasoning=True,
max_reasoning_attempts=2,
verbose=True,
)
task = Task(
description="Complex math task: Solve the Riemann hypothesis.",
expected_output="A proof or disproof of the hypothesis.",
agent=agent,
)
call_count = [0]
def mock_llm_call(messages, *args, **kwargs):
if any(
"create a detailed plan" in msg.get("content", "") for msg in messages
) or any("refine your plan" in msg.get("content", "") for msg in messages):
call_count[0] += 1
return f"Attempt {call_count[0]}: I need more time to think.\n\nNOT READY: I need to refine my plan further."
return "This is an unsolved problem in mathematics."
agent.llm.call = mock_llm_call
result = agent.execute_task(task)
assert result == "This is an unsolved problem in mathematics."
assert (
call_count[0] == 2
) # Should have made exactly 2 reasoning calls (max_attempts)
assert "Reasoning Plan:" in task.description
def test_agent_reasoning_error_handling():
"""Test error handling during the reasoning process."""
llm = LLM("gpt-3.5-turbo")
agent = Agent(
role="Test Agent",
goal="To test the reasoning feature",
backstory="I am a test agent created to verify the reasoning feature works correctly.",
llm=llm,
reasoning=True,
)
task = Task(
description="Task that will cause an error",
expected_output="Output that will never be generated",
agent=agent,
)
call_count = [0]
def mock_llm_call_error(*args, **kwargs):
call_count[0] += 1
if call_count[0] <= 2: # First calls are for reasoning
raise Exception("LLM error during reasoning")
return "Fallback execution result" # Return a value for task execution
agent.llm.call = mock_llm_call_error
result = agent.execute_task(task)
assert result == "Fallback execution result"
assert call_count[0] > 2 # Ensure we called the mock multiple times
def test_agent_with_function_calling():
"""Test agent with reasoning using function calling."""
llm = LLM("gpt-3.5-turbo")
agent = Agent(
role="Test Agent",
goal="To test the reasoning feature",
backstory="I am a test agent created to verify the reasoning feature works correctly.",
llm=llm,
reasoning=True,
verbose=True,
)
task = Task(
description="Simple math task: What's 2+2?",
expected_output="The answer should be a number.",
agent=agent,
)
agent.llm.supports_function_calling = lambda: True
def mock_function_call(messages, *args, **kwargs):
if "tools" in kwargs:
return json.dumps(
{"plan": "I'll solve this simple math problem: 2+2=4.", "ready": True}
)
return "4"
agent.llm.call = mock_function_call
result = agent.execute_task(task)
assert result == "4"
assert "Reasoning Plan:" in task.description
assert "I'll solve this simple math problem: 2+2=4." in task.description
def test_agent_with_function_calling_fallback():
"""Test agent with reasoning using function calling that falls back to text parsing."""
llm = LLM("gpt-3.5-turbo")
agent = Agent(
role="Test Agent",
goal="To test the reasoning feature",
backstory="I am a test agent created to verify the reasoning feature works correctly.",
llm=llm,
reasoning=True,
verbose=True,
)
task = Task(
description="Simple math task: What's 2+2?",
expected_output="The answer should be a number.",
agent=agent,
)
agent.llm.supports_function_calling = lambda: True
def mock_function_call(messages, *args, **kwargs):
if "tools" in kwargs:
return "Invalid JSON that will trigger fallback. READY: I am ready to execute the task."
return "4"
agent.llm.call = mock_function_call
result = agent.execute_task(task)
assert result == "4"
assert "Reasoning Plan:" in task.description
assert "Invalid JSON that will trigger fallback" in task.description

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tests/agents/test_crew_agent_parser.py
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import pytest
from crewai.agents import parser
from crewai.agents.parser import (
AgentAction,
AgentFinish,
)
from crewai.agents.parser import (
OutputParserError as OutputParserException,
)
def test_valid_action_parsing_special_characters():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: what's the temperature in SF?"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what's the temperature in SF?"
def test_valid_action_parsing_with_json_tool_input():
text = """
Thought: Let's find the information
Action: query
Action Input: ** {"task": "What are some common challenges or barriers that you have observed or experienced when implementing AI-powered solutions in healthcare settings?", "context": "As we've discussed recent advancements in AI applications in healthcare, it's crucial to acknowledge the potential hurdles. Some possible obstacles include...", "coworker": "Senior Researcher"}
"""
result = parser.parse(text)
assert isinstance(result, AgentAction)
expected_tool_input = '{"task": "What are some common challenges or barriers that you have observed or experienced when implementing AI-powered solutions in healthcare settings?", "context": "As we\'ve discussed recent advancements in AI applications in healthcare, it\'s crucial to acknowledge the potential hurdles. Some possible obstacles include...", "coworker": "Senior Researcher"}'
assert result.tool == "query"
assert result.tool_input == expected_tool_input
def test_valid_action_parsing_with_quotes():
text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "temperature in SF"'
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "temperature in SF"
def test_valid_action_parsing_with_curly_braces():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: {temperature in SF}"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "{temperature in SF}"
def test_valid_action_parsing_with_angle_brackets():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: <temperature in SF>"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "<temperature in SF>"
def test_valid_action_parsing_with_parentheses():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: (temperature in SF)"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "(temperature in SF)"
def test_valid_action_parsing_with_mixed_brackets():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: [temperature in {SF}]"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "[temperature in {SF}]"
def test_valid_action_parsing_with_nested_quotes():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what's the temperature in 'SF'?\""
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what's the temperature in 'SF'?"
def test_valid_action_parsing_with_incomplete_json():
text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: {"query": "temperature in SF"'
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == '{"query": "temperature in SF"}'
def test_valid_action_parsing_with_special_characters():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is the temperature in SF? @$%^&*"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what is the temperature in SF? @$%^&*"
def test_valid_action_parsing_with_combination():
text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "[what is the temperature in SF?]"'
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "[what is the temperature in SF?]"
def test_valid_action_parsing_with_mixed_quotes():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what's the temperature in SF?\""
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what's the temperature in SF?"
def test_valid_action_parsing_with_newlines():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is\nthe temperature in SF?"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what is\nthe temperature in SF?"
def test_valid_action_parsing_with_escaped_characters():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is the temperature in SF? \\n"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what is the temperature in SF? \\n"
def test_valid_action_parsing_with_json_string():
text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: {"query": "temperature in SF"}'
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == '{"query": "temperature in SF"}'
def test_valid_action_parsing_with_unbalanced_quotes():
text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what is the temperature in SF?"
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what is the temperature in SF?"
def test_clean_action_no_formatting():
action = "Ask question to senior researcher"
cleaned_action = parser._clean_action(action)
assert cleaned_action == "Ask question to senior researcher"
def test_clean_action_with_leading_asterisks():
action = "** Ask question to senior researcher"
cleaned_action = parser._clean_action(action)
assert cleaned_action == "Ask question to senior researcher"
def test_clean_action_with_trailing_asterisks():
action = "Ask question to senior researcher **"
cleaned_action = parser._clean_action(action)
assert cleaned_action == "Ask question to senior researcher"
def test_clean_action_with_leading_and_trailing_asterisks():
action = "** Ask question to senior researcher **"
cleaned_action = parser._clean_action(action)
assert cleaned_action == "Ask question to senior researcher"
def test_clean_action_with_multiple_leading_asterisks():
action = "**** Ask question to senior researcher"
cleaned_action = parser._clean_action(action)
assert cleaned_action == "Ask question to senior researcher"
def test_clean_action_with_multiple_trailing_asterisks():
action = "Ask question to senior researcher ****"
cleaned_action = parser._clean_action(action)
assert cleaned_action == "Ask question to senior researcher"
def test_clean_action_with_spaces_and_asterisks():
action = " ** Ask question to senior researcher ** "
cleaned_action = parser._clean_action(action)
assert cleaned_action == "Ask question to senior researcher"
def test_clean_action_with_only_asterisks():
action = "****"
cleaned_action = parser._clean_action(action)
assert cleaned_action == ""
def test_clean_action_with_empty_string():
action = ""
cleaned_action = parser._clean_action(action)
assert cleaned_action == ""
def test_valid_final_answer_parsing():
text = (
"Thought: I found the information\nFinal Answer: The temperature is 100 degrees"
)
result = parser.parse(text)
assert isinstance(result, AgentFinish)
assert result.output == "The temperature is 100 degrees"
def test_missing_action_error():
text = "Thought: Let's find the temperature\nAction Input: what is the temperature in SF?"
with pytest.raises(OutputParserException) as exc_info:
parser.parse(text)
assert "Invalid Format: I missed the 'Action:' after 'Thought:'." in str(
exc_info.value
)
def test_missing_action_input_error():
text = "Thought: Let's find the temperature\nAction: search"
with pytest.raises(OutputParserException) as exc_info:
parser.parse(text)
assert "I missed the 'Action Input:' after 'Action:'." in str(exc_info.value)
def test_safe_repair_json():
invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": Senior Researcher'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_unrepairable():
invalid_json = "{invalid_json"
result = parser._safe_repair_json(invalid_json)
assert result == invalid_json # Should return the original if unrepairable
def test_safe_repair_json_missing_quotes():
invalid_json = (
'{task: "Research XAI", context: "Explainable AI", coworker: Senior Researcher}'
)
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_unclosed_brackets():
invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_extra_commas():
invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher",}'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_trailing_commas():
invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher",}'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_single_quotes():
invalid_json = "{'task': 'Research XAI', 'context': 'Explainable AI', 'coworker': 'Senior Researcher'}"
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_mixed_quotes():
invalid_json = "{'task': \"Research XAI\", 'context': \"Explainable AI\", 'coworker': 'Senior Researcher'}"
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_unescaped_characters():
invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher\n"}'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_missing_colon():
invalid_json = '{"task" "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_missing_comma():
invalid_json = '{"task": "Research XAI" "context": "Explainable AI", "coworker": "Senior Researcher"}'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_unexpected_trailing_characters():
invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"} random text'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_safe_repair_json_special_characters_key():
invalid_json = '{"task!@#": "Research XAI", "context$%^": "Explainable AI", "coworker&*()": "Senior Researcher"}'
expected_repaired_json = '{"task!@#": "Research XAI", "context$%^": "Explainable AI", "coworker&*()": "Senior Researcher"}'
result = parser._safe_repair_json(invalid_json)
assert result == expected_repaired_json
def test_parsing_with_whitespace():
text = " Thought: Let's find the temperature \n Action: search \n Action Input: what is the temperature in SF? "
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what is the temperature in SF?"
def test_parsing_with_special_characters():
text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "what is the temperature in SF?"'
result = parser.parse(text)
assert isinstance(result, AgentAction)
assert result.tool == "search"
assert result.tool_input == "what is the temperature in SF?"
def test_integration_valid_and_invalid():
text = """
Thought: Let's find the temperature
Action: search
Action Input: what is the temperature in SF?
Thought: I found the information
Final Answer: The temperature is 100 degrees
Thought: Missing action
Action Input: invalid
Thought: Missing action input
Action: invalid
"""
parts = text.strip().split("\n\n")
results = []
for part in parts:
try:
result = parser.parse(part.strip())
except OutputParserException as e:
result = e
results.append(result)
assert isinstance(results[0], AgentAction)
assert isinstance(results[1], AgentFinish)
assert isinstance(results[2], OutputParserException)
assert isinstance(results[3], OutputParserException)
# TODO: ADD TEST TO MAKE SURE ** REMOVAL DOESN'T MESS UP ANYTHING

539
tests/agents/test_lite_agent.py
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@@ -1,539 +0,0 @@
# mypy: ignore-errors
from collections import defaultdict
from typing import cast
from unittest.mock import Mock, patch
import pytest
from pydantic import BaseModel, Field
from crewai import LLM, Agent
from crewai.events.event_bus import crewai_event_bus
from crewai.events.types.agent_events import LiteAgentExecutionStartedEvent
from crewai.events.types.tool_usage_events import ToolUsageStartedEvent
from crewai.flow import Flow, start
from crewai.lite_agent import LiteAgent, LiteAgentOutput
from crewai.llms.base_llm import BaseLLM
from crewai.tools import BaseTool
# A simple test tool
class SecretLookupTool(BaseTool):
name: str = "secret_lookup"
description: str = "A tool to lookup secrets"
def _run(self) -> str:
return "SUPERSECRETPASSWORD123"
# Define Mock Search Tool
class WebSearchTool(BaseTool):
"""Tool for searching the web for information."""
name: str = "search_web"
description: str = "Search the web for information about a topic."
def _run(self, query: str) -> str:
"""Search the web for information about a topic."""
# This is a mock implementation
if "tokyo" in query.lower():
return "Tokyo's population in 2023 was approximately 21 million people in the city proper, and 37 million in the greater metropolitan area."
if "climate change" in query.lower() and "coral" in query.lower():
return "Climate change severely impacts coral reefs through: 1) Ocean warming causing coral bleaching, 2) Ocean acidification reducing calcification, 3) Sea level rise affecting light availability, 4) Increased storm frequency damaging reef structures. Sources: NOAA Coral Reef Conservation Program, Global Coral Reef Alliance."
return f"Found information about {query}: This is a simulated search result for demonstration purposes."
# Define Mock Calculator Tool
class CalculatorTool(BaseTool):
"""Tool for performing calculations."""
name: str = "calculate"
description: str = "Calculate the result of a mathematical expression."
def _run(self, expression: str) -> str:
"""Calculate the result of a mathematical expression."""
try:
# Using eval with restricted builtins for test purposes only
result = eval(expression, {"__builtins__": {}}) # noqa: S307
return f"The result of {expression} is {result}"
except Exception as e:
return f"Error calculating {expression}: {e!s}"
# Define a custom response format using Pydantic
class ResearchResult(BaseModel):
"""Structure for research results."""
main_findings: str = Field(description="The main findings from the research")
key_points: list[str] = Field(description="List of key points")
sources: list[str] = Field(description="List of sources used")
@pytest.mark.vcr(filter_headers=["authorization"])
@pytest.mark.parametrize("verbose", [True, False])
def test_lite_agent_created_with_correct_parameters(monkeypatch, verbose):
"""Test that LiteAgent is created with the correct parameters when Agent.kickoff() is called."""
# Create a test agent with specific parameters
llm = LLM(model="gpt-4o-mini")
custom_tools = [WebSearchTool(), CalculatorTool()]
max_iter = 10
max_execution_time = 300
agent = Agent(
role="Test Agent",
goal="Test Goal",
backstory="Test Backstory",
llm=llm,
tools=custom_tools,
max_iter=max_iter,
max_execution_time=max_execution_time,
verbose=verbose,
)
# Create a mock to capture the created LiteAgent
created_lite_agent = None
original_lite_agent = LiteAgent
# Define a mock LiteAgent class that captures its arguments
class MockLiteAgent(original_lite_agent):
def __init__(self, **kwargs):
nonlocal created_lite_agent
created_lite_agent = kwargs
super().__init__(**kwargs)
# Patch the LiteAgent class
monkeypatch.setattr("crewai.agent.LiteAgent", MockLiteAgent)
# Call kickoff to create the LiteAgent
agent.kickoff("Test query")
# Verify all parameters were passed correctly
assert created_lite_agent is not None
assert created_lite_agent["role"] == "Test Agent"
assert created_lite_agent["goal"] == "Test Goal"
assert created_lite_agent["backstory"] == "Test Backstory"
assert created_lite_agent["llm"] == llm
assert len(created_lite_agent["tools"]) == 2
assert isinstance(created_lite_agent["tools"][0], WebSearchTool)
assert isinstance(created_lite_agent["tools"][1], CalculatorTool)
assert created_lite_agent["max_iterations"] == max_iter
assert created_lite_agent["max_execution_time"] == max_execution_time
assert created_lite_agent["verbose"] == verbose
assert created_lite_agent["response_format"] is None
# Test with a response_format
monkeypatch.setattr("crewai.agent.LiteAgent", MockLiteAgent)
class TestResponse(BaseModel):
test_field: str
agent.kickoff("Test query", response_format=TestResponse)
assert created_lite_agent["response_format"] == TestResponse
@pytest.mark.vcr(filter_headers=["authorization"])
def test_lite_agent_with_tools():
"""Test that Agent can use tools."""
# Create a LiteAgent with tools
llm = LLM(model="gpt-4o-mini")
agent = Agent(
role="Research Assistant",
goal="Find information about the population of Tokyo",
backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
llm=llm,
tools=[WebSearchTool()],
verbose=True,
)
result = agent.kickoff(
"What is the population of Tokyo and how many people would that be per square kilometer if Tokyo's area is 2,194 square kilometers?"
)
assert "21 million" in result.raw or "37 million" in result.raw, (
"Agent should find Tokyo's population"
)
assert "per square kilometer" in result.raw, (
"Agent should calculate population density"
)
received_events = []
@crewai_event_bus.on(ToolUsageStartedEvent)
def event_handler(source, event):
received_events.append(event)
agent.kickoff("What are the effects of climate change on coral reefs?")
# Verify tool usage events were emitted
assert len(received_events) > 0, "Tool usage events should be emitted"
event = received_events[0]
assert isinstance(event, ToolUsageStartedEvent)
assert event.agent_role == "Research Assistant"
assert event.tool_name == "search_web"
@pytest.mark.vcr(filter_headers=["authorization"])
def test_lite_agent_structured_output():
"""Test that Agent can return a simple structured output."""
class SimpleOutput(BaseModel):
"""Simple structure for agent outputs."""
summary: str = Field(description="A brief summary of findings")
confidence: int = Field(description="Confidence level from 1-100")
web_search_tool = WebSearchTool()
llm = LLM(model="gpt-4o-mini")
agent = Agent(
role="Info Gatherer",
goal="Provide brief information",
backstory="You gather and summarize information quickly.",
llm=llm,
tools=[web_search_tool],
verbose=True,
)
result = agent.kickoff(
"What is the population of Tokyo? Return your structured output in JSON format with the following fields: summary, confidence",
response_format=SimpleOutput,
)
print(f"\n=== Agent Result Type: {type(result)}")
print(f"=== Agent Result: {result}")
print(f"=== Pydantic: {result.pydantic}")
assert result.pydantic is not None, "Should return a Pydantic model"
output = cast(SimpleOutput, result.pydantic)
assert isinstance(output.summary, str), "Summary should be a string"
assert len(output.summary) > 0, "Summary should not be empty"
assert isinstance(output.confidence, int), "Confidence should be an integer"
assert 1 <= output.confidence <= 100, "Confidence should be between 1 and 100"
assert "tokyo" in output.summary.lower() or "population" in output.summary.lower()
assert result.usage_metrics is not None
return result
@pytest.mark.vcr(filter_headers=["authorization"])
def test_lite_agent_returns_usage_metrics():
"""Test that LiteAgent returns usage metrics."""
llm = LLM(model="gpt-4o-mini")
agent = Agent(
role="Research Assistant",
goal="Find information about the population of Tokyo",
backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
llm=llm,
tools=[WebSearchTool()],
verbose=True,
)
result = agent.kickoff(
"What is the population of Tokyo? Return your structured output in JSON format with the following fields: summary, confidence"
)
assert result.usage_metrics is not None
assert result.usage_metrics["total_tokens"] > 0
@pytest.mark.vcr(filter_headers=["authorization"])
@pytest.mark.asyncio
async def test_lite_agent_returns_usage_metrics_async():
"""Test that LiteAgent returns usage metrics when run asynchronously."""
llm = LLM(model="gpt-4o-mini")
agent = Agent(
role="Research Assistant",
goal="Find information about the population of Tokyo",
backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
llm=llm,
tools=[WebSearchTool()],
verbose=True,
)
result = await agent.kickoff_async(
"What is the population of Tokyo? Return your structured output in JSON format with the following fields: summary, confidence"
)
assert isinstance(result, LiteAgentOutput)
assert "21 million" in result.raw or "37 million" in result.raw
assert result.usage_metrics is not None
assert result.usage_metrics["total_tokens"] > 0
class TestFlow(Flow):
"""A test flow that creates and runs an agent."""
def __init__(self, llm, tools):
self.llm = llm
self.tools = tools
super().__init__()
@start()
def start(self):
agent = Agent(
role="Test Agent",
goal="Test Goal",
backstory="Test Backstory",
llm=self.llm,
tools=self.tools,
)
return agent.kickoff("Test query")
def verify_agent_parent_flow(result, agent, flow):
"""Verify that both the result and agent have the correct parent flow."""
assert result.parent_flow is flow
assert agent is not None
assert agent.parent_flow is flow
def test_sets_parent_flow_when_inside_flow():
captured_agent = None
mock_llm = Mock(spec=LLM)
mock_llm.call.return_value = "Test response"
mock_llm.stop = []
class MyFlow(Flow):
@start()
def start(self):
agent = Agent(
role="Test Agent",
goal="Test Goal",
backstory="Test Backstory",
llm=mock_llm,
tools=[WebSearchTool()],
)
return agent.kickoff("Test query")
flow = MyFlow()
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(LiteAgentExecutionStartedEvent)
def capture_agent(source, event):
nonlocal captured_agent
captured_agent = source
flow.kickoff()
assert captured_agent.parent_flow is flow
@pytest.mark.vcr(filter_headers=["authorization"])
def test_guardrail_is_called_using_string():
guardrail_events = defaultdict(list)
from crewai.events.event_types import (
LLMGuardrailCompletedEvent,
LLMGuardrailStartedEvent,
)
agent = Agent(
role="Sports Analyst",
goal="Gather information about the best soccer players",
backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
guardrail="""Only include Brazilian players, both women and men""",
)
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(LLMGuardrailStartedEvent)
def capture_guardrail_started(source, event):
assert isinstance(source, LiteAgent)
assert source.original_agent == agent
guardrail_events["started"].append(event)
@crewai_event_bus.on(LLMGuardrailCompletedEvent)
def capture_guardrail_completed(source, event):
assert isinstance(source, LiteAgent)
assert source.original_agent == agent
guardrail_events["completed"].append(event)
result = agent.kickoff(messages="Top 10 best players in the world?")
assert len(guardrail_events["started"]) == 2
assert len(guardrail_events["completed"]) == 2
assert not guardrail_events["completed"][0].success
assert guardrail_events["completed"][1].success
assert (
"Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players"
in result.raw
)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_guardrail_is_called_using_callable():
guardrail_events = defaultdict(list)
from crewai.events.event_types import (
LLMGuardrailCompletedEvent,
LLMGuardrailStartedEvent,
)
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(LLMGuardrailStartedEvent)
def capture_guardrail_started(source, event):
guardrail_events["started"].append(event)
@crewai_event_bus.on(LLMGuardrailCompletedEvent)
def capture_guardrail_completed(source, event):
guardrail_events["completed"].append(event)
agent = Agent(
role="Sports Analyst",
goal="Gather information about the best soccer players",
backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
guardrail=lambda output: (True, "Pelé - Santos, 1958"),
)
result = agent.kickoff(messages="Top 1 best players in the world?")
assert len(guardrail_events["started"]) == 1
assert len(guardrail_events["completed"]) == 1
assert guardrail_events["completed"][0].success
assert "Pelé - Santos, 1958" in result.raw
@pytest.mark.vcr(filter_headers=["authorization"])
def test_guardrail_reached_attempt_limit():
guardrail_events = defaultdict(list)
from crewai.events.event_types import (
LLMGuardrailCompletedEvent,
LLMGuardrailStartedEvent,
)
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(LLMGuardrailStartedEvent)
def capture_guardrail_started(source, event):
guardrail_events["started"].append(event)
@crewai_event_bus.on(LLMGuardrailCompletedEvent)
def capture_guardrail_completed(source, event):
guardrail_events["completed"].append(event)
agent = Agent(
role="Sports Analyst",
goal="Gather information about the best soccer players",
backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
guardrail=lambda output: (
False,
"You are not allowed to include Brazilian players",
),
guardrail_max_retries=2,
)
with pytest.raises(
Exception, match="Agent's guardrail failed validation after 2 retries"
):
agent.kickoff(messages="Top 10 best players in the world?")
assert len(guardrail_events["started"]) == 3 # 2 retries + 1 initial call
assert len(guardrail_events["completed"]) == 3 # 2 retries + 1 initial call
assert not guardrail_events["completed"][0].success
assert not guardrail_events["completed"][1].success
assert not guardrail_events["completed"][2].success
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_output_when_guardrail_returns_base_model():
class Player(BaseModel):
name: str
country: str
agent = Agent(
role="Sports Analyst",
goal="Gather information about the best soccer players",
backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
guardrail=lambda output: (
True,
Player(name="Lionel Messi", country="Argentina"),
),
)
result = agent.kickoff(messages="Top 10 best players in the world?")
assert result.pydantic == Player(name="Lionel Messi", country="Argentina")
def test_lite_agent_with_custom_llm_and_guardrails():
"""Test that CustomLLM (inheriting from BaseLLM) works with guardrails."""
class CustomLLM(BaseLLM):
def __init__(self, response: str = "Custom response"):
super().__init__(model="custom-model")
self.response = response
self.call_count = 0
def call(
self,
messages,
tools=None,
callbacks=None,
available_functions=None,
from_task=None,
from_agent=None,
) -> str:
self.call_count += 1
if "valid" in str(messages) and "feedback" in str(messages):
return '{"valid": true, "feedback": null}'
if "Thought:" in str(messages):
return f"Thought: I will analyze soccer players\nFinal Answer: {self.response}"
return self.response
def supports_function_calling(self) -> bool:
return False
def supports_stop_words(self) -> bool:
return False
def get_context_window_size(self) -> int:
return 4096
custom_llm = CustomLLM(response="Brazilian soccer players are the best!")
agent = LiteAgent(
role="Sports Analyst",
goal="Analyze soccer players",
backstory="You analyze soccer players and their performance.",
llm=custom_llm,
guardrail="Only include Brazilian players",
)
result = agent.kickoff("Tell me about the best soccer players")
assert custom_llm.call_count > 0
assert "Brazilian" in result.raw
custom_llm2 = CustomLLM(response="Original response")
def test_guardrail(output):
return (True, "Modified by guardrail")
agent2 = LiteAgent(
role="Test Agent",
goal="Test goal",
backstory="Test backstory",
llm=custom_llm2,
guardrail=test_guardrail,
)
result2 = agent2.kickoff("Test message")
assert result2.raw == "Modified by guardrail"
@pytest.mark.vcr(filter_headers=["authorization"])
def test_lite_agent_with_invalid_llm():
"""Test that LiteAgent raises proper error when create_llm returns None."""
with patch("crewai.lite_agent.create_llm", return_value=None):
with pytest.raises(ValueError) as exc_info:
LiteAgent(
role="Test Agent",
goal="Test goal",
backstory="Test backstory",
llm="invalid-model",
)
assert "Expected LLM instance of type BaseLLM" in str(exc_info.value)

237
tests/cassettes/TestAgentEvaluator.test_eval_lite_agent.yaml
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status:
code: 200
message: OK
version: 1

826
tests/cassettes/test_before_kickoff_callback.yaml
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