feat: change human input for unit testing

added documentation and unit 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 Tools
-# Loading Human Tools
-human_tools = load_tools(["human"])
 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

@@ -18,7 +18,7 @@ from crewai.utilities import I18N
 
 class CrewAgentExecutor(AgentExecutor):
     _i18n: I18N = I18N()
-    _should_ask_for_human_input: bool = False
+    should_ask_for_human_input: bool = False
     llm: Any = None
     iterations: int = 0
     task: Any = None
@@ -57,7 +57,7 @@ class CrewAgentExecutor(AgentExecutor):
         intermediate_steps: List[Tuple[AgentAction, str]] = []
         # Allowing human input given task setting
         if self.task.human_input:
-            self._should_ask_for_human_input = True
+            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
@@ -173,9 +173,9 @@ 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:
+            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
+                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

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"