Improve Logs #2975
Improve Logs #2975
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Disclaimer: This review was made by a crew of AI Agents. Code Review CommentOverviewThe recent pull request introduces substantial enhancements to the CrewAI codebase, particularly focusing on logging improvements, event management, and agent reasoning functionality. While the changes advance the robustness and user experience of the application, there are critical areas to address regarding code quality, performance, and resource management. Key Improvements1. Memory Management in Spinner AnimationThe introduction of a spinner animation for user feedback is commendable. However, the current implementation does not properly handle the cancellation of existing spinner timers, which can lead to potential memory leaks. I recommend implementing a cleanup mechanism as follows: def _start_spinner_timer(self) -> None:
"""Start spinner timer with proper resource management."""
if hasattr(self, '_spinner_timer') and self._spinner_timer:
self._spinner_timer.cancel()
self._spinner_timer = threading.Timer(0.2, self._update_spinners)
self._spinner_timer.daemon = True # Ensure thread cleanup on program exit
self._spinner_timer.start()2. Event Cleanup RobustnessThe current event listener implementation lacks robust cleanup procedures. A potential memory leak can occur if registered handlers are not properly removed. The following cleanup method should be integrated: def cleanup(self):
"""Cleanup all registered event handlers."""
for event_type, handler in self._registered_handlers:
crewai_event_bus.off(event_type, handler)
self._registered_handlers.clear()Historical ContextThese enhancements reflect a continuous effort to enhance logging clarity and event tracking within the system. Recent PRs have emphasized improvements in state management and performance logging. For example, earlier PRs highlighted the need for more robust error handling in async operations, which aligns with the current recommendations for better resource management. Related File ConsiderationsThese modifications fundamentally impact the Testing RecommendationsIt is crucial to expand the test coverage, especially for concurrent execution scenarios. I recommend adding test cases to validate the improvements in event handling and spinner functionality. An example of such a test could be implemented as follows: @pytest.mark.asyncio
async def test_concurrent_task_execution():
"""Test multiple tasks executing concurrently."""
agent = Agent(
role="Test Agent",
goal="Test concurrent execution",
backstory="I handle multiple tasks",
llm=mock_llm,
reasoning=True
)
tasks = [
Task(description=f"Task {i}", expected_output="Result")
for i in range(3)
]
results = await asyncio.gather(
*[agent.execute_task(task) for task in tasks]
)
assert len(results) == 3
assert all(isinstance(r, str) for r in results)ConclusionThe changes introduced significantly enhance the code's functionality, offering a better user experience and more efficient internal processes. However, careful attention must be paid to resource management and the implementation of robust cleaning mechanisms to prevent memory leaks and ensure thread safety. By addressing these issues and reinforcing the testing framework, the code quality of this project can continue to improve. Let's ensure these refinements are made to enhance the application's responsiveness and reliability further. |
- Replace bare except with specific Exception handler - Add proper type annotations for _spinning_branches and _spinner_timer - Fix timer assignment logic to handle None type properly Co-Authored-By: João <joao@crewai.com>
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