Fix issue #2237: Properly handle LLM output with both Action and Final Answer

[devin-ai-integration]

This PR fixes issue #2237 where an agent tries to both perform an Action and give a Final Answer at the same time, causing an error. The fix improves the error handling in the _process_llm_response method to better handle this case.

Link to Devin run: https://app.devin.ai/sessions/4179745b470946018f18c9e2928de962

[devin-ai-integration]

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[joaomdmoura]

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

Code Review for PR #2238

Overview

This PR introduces significant enhancements to the _process_llm_response method in the CrewAgentExecutor class, aimed at improving how the class handles mixed outputs from the language model. The proposed changes ensure that 'Action' responses take precedence over 'Final Answer' outputs, thereby refining the decisions made by the agent.

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Key Code Changes

1. File: crew_agent_executor.py

   • Change: The logic for processing LLM responses now prioritizes 'Action' responses over 'Final Answer' when both are present.
   • Example:

     if "Action" in output and "Final Answer" in output:
         # Prioritize Action handling
         result = process_action_output(output['Action'])

2. File: test_action_final_answer_error.py

   • Change: A new test suite has been added to validate the updated processing method.
   • Example:

     def test_action_final_answer_handling():
         output = {"Action": "move", "Final Answer": "continue"}
         result = executor._process_llm_response(output)
         assert isinstance(result, AgentAction)

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General Recommendations

1. Type Hints: Introduce consistent type hints throughout the code to enhance clarity and maintainability.
2. Error Logging: Implement logging mechanisms for response handling errors, providing improved debugging capabilities in production environments.
3. Documentation: Clearly document parsing logic and rules in the class docstring. This enhances future maintainability and reduces ambiguity for new developers.
4. Performance Monitoring: Consider embedding performance metrics to identify bottlenecks in the response processing.

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Conclusion

The enhancement made in PR #2238 provides a robust solution for processing outputs from the language model more effectively. The focus on prioritizing 'Action' over 'Final Answer' will lead to more apt responses from the agent. Furthermore, the addition of extensive unit tests reflects a commitment to quality and reliability.

I recommend proceeding with merging this PR once the outlined suggestions have been implemented, particularly regarding type annotations and enhanced error handling. This will bolster the overall quality and robustness of the codebase while ensuring that the improvements yield their intended benefits effectively.

Related PR Insights

To gain more clarity on previous discussions and iteration strategies, consider reviewing past PRs that focused on similar areas of parsing logic and error handling enhancements. These discussions may provide valuable insights into ongoing efforts toward optimizing agent behavior.

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Thank you for your diligent work on this PR, looking forward to the implementation of these suggestions!

[YDS854394028]

This PR fixes issue #2237 where an agent tries to both perform an Action and give a Final Answer at the same time, causing an error. The fix improves the error handling in the _process_llm_response method to better handle this case.

Link to Devin run: https://app.devin.ai/sessions/4179745b470946018f18c9e2928de962

It seems I've found the reason. In version 0.95, there is a truncation operation for non-compliant responses. However, in my large model implementation, the supports_stop_words function returns true, so no processing is done, leading to an error.

  if not self.use_stop_words:
                        try:
                            self._format_answer(answer)
                        except OutputParserException as e:
                            if (
                                FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE
                                in e.error
                            ):
                                answer = answer.split("Observation:")[0].strip()

                    self.iterations += 1
                    formatted_answer = self._format_answer(answer)