Create test_agent_human_input.yaml

add yaml for test

docs/core-concepts/Tasks.md

@@ -23,6 +23,7 @@ Tasks in CrewAI can be designed to require collaboration between agents. For exa
 | **Output Pydantic**  *(optional)*     | Takes a pydantic model and returns the output as a pydantic object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
 | **Output File**  *(optional)*     | Takes a file path and saves the output of the task on it. |
 | **Callback**  *(optional)*  | A function to be executed after the task is completed. |
+| **Human Input** *(optional)*   |   Indicates whether the agent should ask for feedback at the end of the task |
 
 ## Creating a Task
 

docs/how-to/Human-Input-on-Execution.md

@@ -9,7 +9,7 @@ Human input plays a pivotal role in several agent execution scenarios, enabling
 
 ## Using Human Input with CrewAI
 
-Incorporating human input with CrewAI is straightforward, enhancing the agent's ability to make informed decisions. While the documentation previously mentioned using a "LangChain Tool" and a specific "DuckDuckGoSearchRun" tool from `langchain_community.tools`, it's important to clarify that the integration of such tools should align with the actual capabilities and configurations defined within your `Agent` class setup.
+Incorporating human input with CrewAI is straightforward, enhancing the agent's ability to make informed decisions. While the documentation previously mentioned using a "LangChain Tool" and a specific "DuckDuckGoSearchRun" tool from `langchain_community.tools`, it's important to clarify that the integration of such tools should align with the actual capabilities and configurations defined within your `Agent` class setup. Now it is a simple flag in the task itself that needs to be turned on.
 
 ### Example:
 
@@ -23,14 +23,10 @@ import os
 from crewai import Agent, Task, Crew
 from crewai_tools import SerperDevTool
 
-from langchain.agents import load_tools
-
 os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 os.environ["OPENAI_API_KEY"] = "Your Key"
 
-
-# Loading Human Tools
-human_tools = load_tools(["human"])
+# Loading Tools
 search_tool = SerperDevTool()
 
 # Define your agents with roles, goals, and tools
@@ -44,7 +40,7 @@ researcher = Agent(
   ),
   verbose=True,
   allow_delegation=False,
-  tools=[search_tool]+human_tools # Passing human tools to the agent
+  tools=[search_tool]
 )
 writer = Agent(
   role='Tech Content Strategist',
@@ -67,6 +63,7 @@ task1 = Task(
   ),
   expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
   agent=researcher,
+  human_input=True, # setting the flag on for human input in this task
 )
 
 task2 = Task(

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
@@ -169,6 +173,22 @@ class CrewAgentExecutor(AgentExecutor):
 
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):
+            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
 
@@ -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,
+    )
 
     def __init__(__pydantic_self__, **data):
         config = data.pop("config", {})

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",

tests/agent_test.py

@@ -680,3 +680,30 @@ def test_agent_definition_based_on_dict():
     assert agent.backstory == "test backstory"
     assert agent.verbose == True
     assert agent.tools == []
+
+
+# test for human input
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_human_input():
+    from unittest.mock import patch
+
+    config = {
+        "role": "test role",
+        "goal": "test goal",
+        "backstory": "test backstory",
+    }
+
+    agent = Agent(config=config)
+
+    task = Task(
+        agent=agent,
+        description="Say the word: Hi",
+        expected_output="The word: Hi",
+        human_input=True,
+    )
+
+    with patch.object(CrewAgentExecutor, "_ask_human_input") as mock_human_input:
+        mock_human_input.return_value = "Hello"
+        output = agent.execute_task(task)
+        mock_human_input.assert_called_once()
+        assert output == "Hello"

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