Address PR feedback: Add validation, error handling, refactoring, and tests

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

docs/how-to/human-input-on-execution.mdx

@@ -20,10 +20,20 @@ By default, human input is collected via the command line using the `input()` fu
 
 ```python
 def get_input_from_api(final_answer: str) -> str:
-    """Get human feedback from an API instead of CLI."""
-    # Make an API call to get feedback
-    response = requests.post("https://your-api.com/feedback", json={"answer": final_answer})
-    return response.json()["feedback"]
+    """Get human feedback from an API instead of CLI with error handling."""
+    try:
+        # Make an API call to get feedback
+        response = requests.post(
+            "https://your-api.com/feedback", 
+            json={"answer": final_answer},
+            timeout=10  # Set timeout to avoid long waits
+        )
+        response.raise_for_status()  # Raise exception for HTTP errors
+        return response.json().get("feedback", "")
+    except (requests.RequestException, json.JSONDecodeError, KeyError) as e:
+        print(f"Error getting feedback from API: {str(e)}")
+        # Fallback to CLI input if API fails
+        return input(f"API failed, please provide feedback manually:\n\n{final_answer}\n\nYour feedback: ")
 
 task = Task(
     description="Analyze the latest market trends",
@@ -34,6 +44,8 @@ task = Task(
 )
 ```
 
+Note: CrewAI will automatically fallback to the default input method if your custom function raises an exception, but implementing your own fallback gives you more control over the user experience.
+
 The custom function should:
 - Accept a string parameter (the agent's final answer)
 - Return a string (the human feedback)

src/crewai/agents/crew_agent_executor.py

@@ -81,7 +81,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.respect_context_window = respect_context_window
         self.request_within_rpm_limit = request_within_rpm_limit
         self.ask_for_human_input = False
-        self.ask_human_input_function = None
+        self.ask_human_input_function: Optional[Callable[[str], str]] = None
         self.messages: List[Dict[str, str]] = []
         self.iterations = 0
         self.log_error_after = 3
@@ -535,17 +535,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             AgentFinish: The final answer after processing feedback
         """
-        # Get output from either return_values dict or output attribute
-        output = ""
-        if hasattr(formatted_answer, "return_values") and formatted_answer.return_values:
-            output = formatted_answer.return_values.get("output", "")
-        elif hasattr(formatted_answer, "output"):
-            output = formatted_answer.output
+        output = self._extract_output_from_agent_finish(formatted_answer)
             
         # Use custom function if provided, otherwise use default
-        if self.ask_human_input_function and callable(self.ask_human_input_function):
-            human_feedback = self.ask_human_input_function(output)
-        else:
+        try:
+            if self.ask_human_input_function and callable(self.ask_human_input_function):
+                human_feedback = self.ask_human_input_function(output)
+            else:
+                human_feedback = self._ask_human_input(output)
+        except Exception as e:
+            # Fallback to default method if custom method fails
+            print(f"Error using custom input function: {str(e)}. Falling back to default.")
             human_feedback = self._ask_human_input(output)
 
         if self._is_training_mode():
@@ -585,21 +585,30 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self.ask_for_human_input = False
             else:
                 answer = self._process_feedback_iteration(feedback)
-                # Get output from either return_values dict or output attribute
-                output = ""
-                if hasattr(answer, "return_values") and answer.return_values:
-                    output = answer.return_values.get("output", "")
-                elif hasattr(answer, "output"):
-                    output = answer.output
-                    
+                output = self._extract_output_from_agent_finish(answer)
+                
                 # Use custom function if provided, otherwise use default
-                if self.ask_human_input_function and callable(self.ask_human_input_function):
-                    feedback = self.ask_human_input_function(output)
-                else:
+                try:
+                    if self.ask_human_input_function and callable(self.ask_human_input_function):
+                        feedback = self.ask_human_input_function(output)
+                    else:
+                        feedback = self._ask_human_input(output)
+                except Exception as e:
+                    # Fallback to default method if custom method fails
+                    print(f"Error using custom input function: {str(e)}. Falling back to default.")
                     feedback = self._ask_human_input(output)
 
         return answer
 
+    def _extract_output_from_agent_finish(self, agent_finish: AgentFinish) -> str:
+        """Extract output from an AgentFinish object."""
+        output = ""
+        if hasattr(agent_finish, "return_values") and agent_finish.return_values:
+            output = agent_finish.return_values.get("output", "")
+        elif hasattr(agent_finish, "output"):
+            output = agent_finish.output
+        return output
+
     def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
         """Process a single feedback iteration."""
         self.messages.append(

src/crewai/task.py

@@ -196,6 +196,14 @@ class Task(BaseModel):
                         "If return type is annotated, it must be Tuple[bool, Any]"
                     )
         return v
+        
+    @field_validator("ask_human_input")
+    @classmethod
+    def validate_ask_human_input(cls, v: Optional[Callable]) -> Optional[Callable]:
+        """Validate that the ask_human_input function is callable."""
+        if v is not None and not callable(v):
+            raise ValueError("ask_human_input must be a callable function")
+        return v
 
     _original_description: Optional[str] = PrivateAttr(default=None)
     _original_expected_output: Optional[str] = PrivateAttr(default=None)

tests/test_custom_human_input.py

@@ -1,5 +1,6 @@
+from unittest.mock import MagicMock, patch
+
 import pytest
-from unittest.mock import patch, MagicMock
 
 from crewai.task import Task
 
@@ -21,3 +22,54 @@ def test_task_custom_human_input_parameter():
     # Verify the parameter was stored correctly
     assert task.ask_human_input == custom_input_func
     assert callable(task.ask_human_input)
+
+
+def test_task_invalid_human_input_parameter():
+    """Test that non-callable input raises validation error."""
+    with pytest.raises(ValueError) as exc_info:
+        Task(
+            description="Test task",
+            expected_output="Test output",
+            human_input=True,
+            ask_human_input="not_a_function"
+        )
+    
+    assert "Input should be callable" in str(exc_info.value)
+
+
+def test_custom_input_function_error_handling():
+    """Test handling of errors in custom input function."""
+    def failing_input(_):
+        raise Exception("API Error")
+    
+    # Create a simplified test for error handling
+    # We'll directly test the error handling in the _handle_human_feedback method
+    
+    # Create a mock agent finish object with a simple output
+    agent_finish = MagicMock()
+    agent_finish.output = "Test output"
+    
+    # Create a mock executor with our failing function
+    executor = MagicMock()
+    executor.ask_human_input_function = failing_input
+    
+    # Set up the default input method mock
+    executor._ask_human_input = MagicMock(return_value="Default input used")
+    
+    # Add the extract method that returns the output directly
+    executor._extract_output_from_agent_finish = MagicMock(return_value="Test output")
+    
+    # Test the error handling by calling the method directly
+    from crewai.agents.crew_agent_executor import CrewAgentExecutor
+    
+    # Capture print output to verify error message
+    with patch('builtins.print') as mock_print:
+        # Call the method we're testing
+        CrewAgentExecutor._handle_human_feedback(executor, agent_finish)
+        
+        # Verify error was printed
+        mock_print.assert_called_once()
+        assert "Error using custom input function" in mock_print.call_args[0][0]
+        
+    # Verify fallback to default method occurred
+    executor._ask_human_input.assert_called_once_with("Test output")