fix: Fix linting issue in test_multi_round_dialogue.py

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/agent.py

@@ -249,6 +249,7 @@ class Agent(BaseAgent):
                     "tool_names": self.agent_executor.tools_names,
                     "tools": self.agent_executor.tools_description,
                     "ask_for_human_input": task.human_input,
+                    "max_dialogue_rounds": task.max_dialogue_rounds,
                 }
             )["output"]
         except Exception as e:

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -94,10 +94,20 @@ class CrewAgentExecutorMixin:
                 print(f"Failed to add to long term memory: {e}")
                 pass
 
-    def _ask_human_input(self, final_answer: str) -> str:
+    def _ask_human_input(self, final_answer: str, current_round: int = 1, max_rounds: int = 10) -> str:
-        """Prompt human input with mode-appropriate messaging."""
+        """Prompt human input with mode-appropriate messaging.
+        
+        Args:
+            final_answer: The final answer from the agent
+            current_round: The current dialogue round (default: 1)
+            max_rounds: Maximum number of dialogue rounds (default: 10)
+            
+        Returns:
+            str: The user's feedback
+        """
+        round_info = f"\033[1m\033[93mRound {current_round}/{max_rounds}\033[00m"
         self._printer.print(
-            content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
+            content=f"\033[1m\033[95m ## Result {round_info}:\033[00m \033[92m{final_answer}\033[00m"
         )
 
         # Training mode prompt (single iteration)
@@ -113,7 +123,7 @@ class CrewAgentExecutorMixin:
         else:
             prompt = (
                 "\n\n=====\n"
-                "## HUMAN FEEDBACK: Provide feedback on the Final Result and Agent's actions.\n"
+                f"## HUMAN FEEDBACK (Round {current_round}/{max_rounds}): Provide feedback on the Result and Agent's actions.\n"
                 "Please follow these guidelines:\n"
                 " - If you are happy with the result, simply hit Enter without typing anything.\n"
                 " - Otherwise, provide specific improvement requests.\n"

src/crewai/agents/crew_agent_executor.py

@@ -103,6 +103,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_start_logs()
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
+        max_rounds = int(inputs.get("max_dialogue_rounds", 10))
 
         try:
             formatted_answer = self._invoke_loop()
@@ -121,7 +122,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 raise e
 
         if self.ask_for_human_input:
-            formatted_answer = self._handle_human_feedback(formatted_answer)
+            formatted_answer = self._handle_human_feedback(formatted_answer, max_rounds)
 
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
@@ -524,21 +525,22 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         prompt = prompt.rstrip()
         return {"role": role, "content": prompt}
 
-    def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
+    def _handle_human_feedback(self, formatted_answer: AgentFinish, max_rounds: int = 10) -> AgentFinish:
         """Handle human feedback with different flows for training vs regular use.
 
         Args:
             formatted_answer: The initial AgentFinish result to get feedback on
+            max_rounds: Maximum number of dialogue rounds (default: 10)
 
         Returns:
             AgentFinish: The final answer after processing feedback
         """
-        human_feedback = self._ask_human_input(formatted_answer.output)
+        human_feedback = self._ask_human_input(formatted_answer.output, 1, max_rounds)
 
         if self._is_training_mode():
             return self._handle_training_feedback(formatted_answer, human_feedback)
 
-        return self._handle_regular_feedback(formatted_answer, human_feedback)
+        return self._handle_regular_feedback(formatted_answer, human_feedback, max_rounds)
 
     def _is_training_mode(self) -> bool:
         """Check if crew is in training mode."""
@@ -560,19 +562,33 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return improved_answer
 
     def _handle_regular_feedback(
-        self, current_answer: AgentFinish, initial_feedback: str
+        self, current_answer: AgentFinish, initial_feedback: str, max_rounds: int = 10
     ) -> AgentFinish:
-        """Process feedback for regular use with potential multiple iterations."""
+        """Process feedback for regular use with potential multiple iterations.
+        
+        Args:
+            current_answer: The initial AgentFinish result to get feedback on
+            initial_feedback: The initial feedback from the user
+            max_rounds: Maximum number of dialogue rounds (default: 10)
+            
+        Returns:
+            AgentFinish: The final answer after processing feedback
+        """
+        if max_rounds < 1:
+            raise ValueError("max_rounds must be positive")
+            
         feedback = initial_feedback
         answer = current_answer
+        current_round = 1
 
-        while self.ask_for_human_input:
+        while self.ask_for_human_input and current_round <= max_rounds:
             # If the user provides a blank response, assume they are happy with the result
             if feedback.strip() == "":
                 self.ask_for_human_input = False
             else:
                 answer = self._process_feedback_iteration(feedback)
-                feedback = self._ask_human_input(answer.output)
+                feedback = self._ask_human_input(answer.output, current_round, max_rounds)
+                current_round += 1
 
         return answer
 

src/crewai/task.py

@@ -125,6 +125,12 @@ class Task(BaseModel):
         description="Whether the task should have a human review the final answer of the agent",
         default=False,
     )
+    max_dialogue_rounds: int = Field(
+        default=10,
+        description="Maximum number of dialogue rounds for human input",
+        ge=1,  # Ensures positive integer
+        examples=[5, 10, 15],
+    )
     converter_cls: Optional[Type[Converter]] = Field(
         description="A converter class used to export structured output",
         default=None,

tests/agent_test.py

@@ -1206,6 +1206,7 @@ def test_agent_max_retry_limit():
                         "tool_names": "",
                         "tools": "",
                         "ask_for_human_input": True,
+                        "max_dialogue_rounds": 10,
                     }
                 ),
                 mock.call(
@@ -1214,6 +1215,7 @@ def test_agent_max_retry_limit():
                         "tool_names": "",
                         "tools": "",
                         "ask_for_human_input": True,
+                        "max_dialogue_rounds": 10,
                     }
                 ),
             ]

tests/agents/test_multi_round_dialogue.py

@@ -0,0 +1,77 @@
+import unittest
+from unittest.mock import MagicMock, patch
+
+from langchain_core.agents import AgentFinish
+
+from crewai.task import Task
+
+
+class TestMultiRoundDialogue(unittest.TestCase):
+    """Test the multi-round dialogue functionality."""
+
+    def test_task_max_dialogue_rounds_default(self):
+        """Test that Task has a default max_dialogue_rounds of 10."""
+        # Create a task with default max_dialogue_rounds
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            human_input=True
+        )
+        
+        # Verify the default value
+        self.assertEqual(task.max_dialogue_rounds, 10)
+
+    def test_task_max_dialogue_rounds_custom(self):
+        """Test that Task accepts a custom max_dialogue_rounds."""
+        # Create a task with custom max_dialogue_rounds
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            human_input=True,
+            max_dialogue_rounds=5
+        )
+        
+        # Verify the custom value
+        self.assertEqual(task.max_dialogue_rounds, 5)
+        
+    def test_task_max_dialogue_rounds_validation(self):
+        """Test that Task validates max_dialogue_rounds as a positive integer."""
+        # Create a task with invalid max_dialogue_rounds
+        with self.assertRaises(ValueError):
+            task = Task(
+                description="Test task",
+                expected_output="Test output",
+                human_input=True,
+                max_dialogue_rounds=0
+            )
+            
+    def test_handle_regular_feedback_rounds(self):
+        """Test that _handle_regular_feedback correctly handles multiple rounds."""
+        from crewai.agents.crew_agent_executor import CrewAgentExecutor
+        
+        # Create a simple mock executor
+        executor = MagicMock()
+        executor.ask_for_human_input = True
+        executor._ask_human_input = MagicMock(side_effect=["Feedback", ""])
+        executor._process_feedback_iteration = MagicMock(return_value=MagicMock())
+        
+        # Create a sample initial answer
+        initial_answer = MagicMock()
+        
+        # Call the method directly
+        CrewAgentExecutor._handle_regular_feedback(
+            executor, 
+            initial_answer, 
+            "Initial feedback", 
+            max_rounds=3
+        )
+        
+        # Verify the correct number of iterations occurred
+        # First call for initial feedback, second call for empty feedback to end loop
+        self.assertEqual(executor._ask_human_input.call_count, 2)
+        # The _process_feedback_iteration is called for the initial feedback and the first round
+        self.assertEqual(executor._process_feedback_iteration.call_count, 2)
+
+
+if __name__ == "__main__":
+    unittest.main()