enhance logging llm model used by agent

[orcema]

enhance logging by adding llm model used by agent on processing logs and finished logs

[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment for PR #2405

Overview

This PR introduces modifications to the crew_agent_executor.py file, enhancing our logging capability by including details about the LLM (Large Language Model) utilized during agent execution. This is particularly reflected in the changes made to the _show_logs method, adding important context to the logs that facilitate debugging and understanding of the model being employed.

Positive Aspects

1. Consistency: The coding style remains consistent with the existing codebase, which is crucial for readability.
2. Color Coding: The visual clarity of the terminal output is preserved through effective color coding, improving user experience.
3. Valuable Information: The addition of LLM model information enriches the logs, allowing developers to trace back the executions related to specific models.

Issues and Suggestions

1. Code Duplication

There is noticeable code duplication within the logging statements for the LLM model in both branches of the conditional logic within the _show_logs method.

Recommendation:
Refactor the duplicated logging logic into a dedicated method. This will enhance maintainability and reduce redundancy. Here’s a suggested implementation:

def _log_agent_header(self, agent_role: str):
    self._printer.print(
        content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
    )
    if self.llm and hasattr(self.llm, 'model'):
        self._printer.print(
            content=f"\033[1m\033[95m# LLM:\033[00m \033[1m\033[92m{self.llm.model}\033[00m"
        )

Invoke this method in both places where LLM is referenced:

self._log_agent_header(agent_role)

2. Null Check Enhancement

The existing if self.llm.model: presents a risk for raising an AttributeError if self.llm is None.

Recommendation:
Enhance the null check to:

if self.llm and hasattr(self.llm, 'model') and self.llm.model:

3. Type Hints

To improve code clarity, consider adding type hints for class attributes such as self.llm.

Recommendation:
Update the class definition as follows:

class CrewAgentExecutor:
    llm: Optional[BaseLLM]  # Define expected type for clarity

4. Documentation Improvement

The newly implemented functionality lacks sufficient documentation.

Recommendation:
Improve the docstring for the _show_logs method to reflect the new logging behavior:

def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
    """Displays agent execution logs, including thoughts, actions, and LLM model information.
    
    Args:
        formatted_answer (Union[AgentAction, AgentFinish]): The agent's response
        
    This method now includes:
    - Agent role identification
    - LLM model being utilized
    - Thought process or final answer
    """

Performance Impact

The modifications introduce minimal performance impact, primarily involving string formatting and conditional checks during logging processes.

Security Considerations

There are no identified security concerns as the changes are strictly related to logging functionality. However, logging sensitive model information should be performed cautiously to prevent disclosure of confidential data.

Testing Recommendations

1. Implement unit tests to ensure the accurate display of LLM model information.
2. Create scenarios where LLM model information may be unavailable to test system robustness.
3. Verify ANSI color codes in various terminal environments to ensure consistent output formatting.

Overall Assessment

The changes enhance the logging system, providing critical insight into which LLM is actively employed, thereby facilitating easier debugging. The suggested changes aim to enhance code maintainability and prevent potential bugs, primarily focusing on reducing duplication, improving null checks, and ensuring comprehensive documentation.

By addressing these concerns, the code can significantly improve in robustness and clarity, enhancing future development efforts. Thank you for your contributions!