refactor and more clear messages

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -3,16 +3,15 @@ from typing import TYPE_CHECKING, Optional
 
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
+from crewai.utilities import I18N
 from crewai.utilities.converter import ConverterError
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-from crewai.utilities import I18N
 from crewai.utilities.printer import Printer
 
-
 if TYPE_CHECKING:
+    from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.crew import Crew
     from crewai.task import Task
-    from crewai.agents.agent_builder.base_agent import BaseAgent
 
 
 class CrewAgentExecutorMixin:
@@ -107,7 +106,12 @@ class CrewAgentExecutorMixin:
         )
 
         self._printer.print(
-            content="\n\n=====\n## Please provide feedback on the Final Result and the Agent's actions:",
+            content=(
+                "\n\n=====\n"
+                "## Please provide feedback on the Final Result and the Agent's actions. "
+                "Respond with 'looks good' or a similar phrase when you're satisfied.\n"
+                "=====\n"
+            ),
             color="bold_yellow",
         )
         return input()

src/crewai/agents/crew_agent_executor.py

@@ -90,7 +90,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         if "system" in self.prompt:
             system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs)
             user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs)
-
             self.messages.append(self._format_msg(system_prompt, role="system"))
             self.messages.append(self._format_msg(user_prompt))
         else:
@@ -102,75 +101,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
         formatted_answer = self._invoke_loop()
 
-        while self.ask_for_human_input:
+        if self.ask_for_human_input:
-            human_feedback = self._ask_human_input(formatted_answer.output)
+            formatted_answer = self._handle_human_feedback(formatted_answer)
-
-            if self.crew and self.crew._train:
-                self._handle_crew_training_output(formatted_answer, human_feedback)
-
-            # Make an LLM call to verify if additional changes are requested based on human feedback
-            additional_changes_prompt = self._i18n.slice(
-                "human_feedback_classification"
-            ).format(feedback=human_feedback)
-
-            retry_count = 0
-            llm_call_successful = False
-            additional_changes_response = None
-
-            while retry_count < MAX_LLM_RETRY and not llm_call_successful:
-                try:
-                    additional_changes_response = (
-                        self.llm.call(
-                            [
-                                self._format_msg(
-                                    additional_changes_prompt, role="system"
-                                )
-                            ],
-                            callbacks=self.callbacks,
-                        )
-                        .strip()
-                        .lower()
-                    )
-                    llm_call_successful = True
-                except Exception as e:
-                    retry_count += 1
-                    # Log the error and retry
-                    self._logger.log(
-                        "error",
-                        f"Error during LLM call: {e}. Retrying... ({retry_count}/{max_retries})",
-                        color="red",
-                    )
-
-            if not llm_call_successful:
-                # After max retries, log error and move on
-                self._logger.log(
-                    "error",
-                    "Error processing feedback after multiple attempts.",
-                    color="red",
-                )
-                self.ask_for_human_input = False
-                break  # Exit the feedback loop
-
-            # Handle unexpected responses
-            if additional_changes_response == "false":
-                self.ask_for_human_input = False
-            elif additional_changes_response == "true":
-                self.ask_for_human_input = True
-                # Add human feedback to messages
-                self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
-                # Invoke the loop again with updated messages
-                formatted_answer = self._invoke_loop()
-
-                if self.crew and self.crew._train:
-                    self._handle_crew_training_output(formatted_answer)
-            else:
-                # Unexpected response
-                self._logger.log(
-                    "error",
-                    f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
-                    color="red",
-                )
-                self.ask_for_human_input = False
 
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
@@ -384,16 +316,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _handle_context_length(self) -> None:
         if self.respect_context_window:
-            self._logger.log(
+            self._printer.print(
-                "debug",
+                content="Context length exceeded. Summarizing content to fit the model context window.",
-                "Context length exceeded. Summarizing content to fit the model context window.",
                 color="yellow",
             )
             self._summarize_messages()
         else:
-            self._logger.log(
+            self._printer.print(
-                "debug",
+                content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
-                "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
                 color="red",
             )
             raise SystemExit(
@@ -420,15 +350,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     ] = result.output
                     training_handler.save(training_data)
                 else:
-                    self._logger.log(
+                    self._printer.print(
-                        "error",
+                        content="Invalid train iteration type or agent_id not in training data.",
-                        "Invalid train iteration type or agent_id not in training data.",
                         color="red",
                     )
             else:
-                self._logger.log(
+                self._printer.print(
-                    "error",
+                    content="Crew is None or does not have _train_iteration attribute.",
-                    "Crew is None or does not have _train_iteration attribute.",
                     color="red",
                 )
 
@@ -446,15 +374,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         train_iteration, agent_id, training_data
                     )
                 else:
-                    self._logger.log(
+                    self._printer.print(
-                        "error",
+                        content="Invalid train iteration type. Expected int.",
-                        "Invalid train iteration type. Expected int.",
                         color="red",
                     )
             else:
-                self._logger.log(
+                self._printer.print(
-                    "error",
+                    content="Crew is None or does not have _train_iteration attribute.",
-                    "Crew is None or does not have _train_iteration attribute.",
                     color="red",
                 )
 
@@ -470,3 +396,81 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
         prompt = prompt.rstrip()
         return {"role": role, "content": prompt}
+
+    def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
+        """
+        Handles the human feedback loop, allowing the user to provide feedback
+        on the agent's output and determining if additional iterations are needed.
+
+        Parameters:
+            formatted_answer (AgentFinish): The initial output from the agent.
+
+        Returns:
+            AgentFinish: The final output after incorporating human feedback.
+        """
+        while self.ask_for_human_input:
+            human_feedback = self._ask_human_input(formatted_answer.output)
+
+            if self.crew and self.crew._train:
+                self._handle_crew_training_output(formatted_answer, human_feedback)
+
+            # Make an LLM call to verify if additional changes are requested based on human feedback
+            additional_changes_prompt = self._i18n.slice(
+                "human_feedback_classification"
+            ).format(feedback=human_feedback)
+
+            retry_count = 0
+            llm_call_successful = False
+            additional_changes_response = None
+
+            while retry_count < MAX_LLM_RETRY and not llm_call_successful:
+                try:
+                    additional_changes_response = (
+                        self.llm.call(
+                            [
+                                self._format_msg(
+                                    additional_changes_prompt, role="system"
+                                )
+                            ],
+                            callbacks=self.callbacks,
+                        )
+                        .strip()
+                        .lower()
+                    )
+                    llm_call_successful = True
+                except Exception as e:
+                    retry_count += 1
+
+                    self._printer.print(
+                        content=f"Error during LLM call to classify human feedback: {e}. Retrying... ({retry_count}/{MAX_LLM_RETRY})",
+                        color="red",
+                    )
+
+            if not llm_call_successful:
+                self._printer.print(
+                    content="Error processing feedback after multiple attempts.",
+                    color="red",
+                )
+                self.ask_for_human_input = False
+                break
+
+            if additional_changes_response == "false":
+                self.ask_for_human_input = False
+            elif additional_changes_response == "true":
+                self.ask_for_human_input = True
+                # Add human feedback to messages
+                self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
+                # Invoke the loop again with updated messages
+                formatted_answer = self._invoke_loop()
+
+                if self.crew and self.crew._train:
+                    self._handle_crew_training_output(formatted_answer)
+            else:
+                # Unexpected response
+                self._printer.print(
+                    content=f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
+                    color="red",
+                )
+                self.ask_for_human_input = False
+
+        return formatted_answer