feature: human input per task (#395)

* feature: human input per task

* Update executor.py

* Update executor.py

* Update executor.py

* Update executor.py

* Update executor.py

* feat: change human input for unit testing
added documentation and unit test

* Create test_agent_human_input.yaml

add yaml for test

---------

Co-authored-by: João Moura <joaomdmoura@gmail.com>

src/crewai/agents/executor.py

@@ -18,6 +18,7 @@ from crewai.utilities import I18N
 
 class CrewAgentExecutor(AgentExecutor):
     _i18n: I18N = I18N()
+    should_ask_for_human_input: bool = False
     llm: Any = None
     iterations: int = 0
     task: Any = None
@@ -54,6 +55,9 @@ class CrewAgentExecutor(AgentExecutor):
             [tool.name for tool in self.tools], excluded_colors=["green", "red"]
         )
         intermediate_steps: List[Tuple[AgentAction, str]] = []
+        # Allowing human input given task setting
+        if self.task.human_input:
+            self.should_ask_for_human_input = True
         # Let's start tracking the number of iterations and time elapsed
         self.iterations = 0
         time_elapsed = 0.0
@@ -169,8 +173,24 @@ class CrewAgentExecutor(AgentExecutor):
 
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):
-            yield output
-            return
+            if self.should_ask_for_human_input:
+                # Making sure we only ask for it once, so disabling for the next thought loop
+                self.should_ask_for_human_input = False
+                human_feedback = self._ask_human_input(output.return_values["output"])
+                action = AgentAction(
+                    tool="Human Input", tool_input=human_feedback, log=output.log
+                )
+                yield AgentStep(
+                    action=action,
+                    observation=self._i18n.slice("human_feedback").format(
+                        human_feedback=human_feedback
+                    ),
+                )
+                return
+
+            else:
+                yield output
+                return
 
         actions: List[AgentAction]
         actions = [output] if isinstance(output, AgentAction) else output
@@ -203,3 +223,9 @@ class CrewAgentExecutor(AgentExecutor):
                         tools=", ".join([tool.name for tool in self.tools]),
                     )
             yield AgentStep(action=agent_action, observation=observation)
+
+    def _ask_human_input(self, final_answer: dict) -> str:
+        """Get human input."""
+        return input(
+            self._i18n.slice("getting_input").format(final_answer=final_answer)
+        )

src/crewai/task.py

@@ -70,6 +70,10 @@ class Task(BaseModel):
         frozen=True,
         description="Unique identifier for the object, not set by user.",
     )
+    human_input: Optional[bool] = Field(
+        description="Whether the task should have a human review the final answer of the agent",
+        default=False,
+    )
 
     _original_description: str | None = None
     _original_expected_output: str | None = None

src/crewai/translations/en.json

@@ -15,7 +15,9 @@
     "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task\nYour final answer must be the great and the most complete as possible, it must be outcome described\n\n",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
-    "expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary."
+    "expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary.",
+    "human_feedback": "You got human feedback on your work, re-avaluate it and give a new Final Answer when ready.\n {human_feedback}",
+    "getting_input": "This is the agent final answer: {final_answer}\nPlease provide a feedback: "
   },
   "errors": {
     "unexpected_format": "\nSorry, I didn't use the expected format, I MUST either use a tool (use one at time) OR give my best final answer.\n",