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136 lines
5.2 KiB
Plaintext
136 lines
5.2 KiB
Plaintext
---
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title: Human Input on Execution
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description: Integrating CrewAI with human input during execution in complex decision-making processes and leveraging the full capabilities of the agent's attributes and tools.
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icon: user-check
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---
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## Human input in agent execution
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Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary.
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This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
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## Using human input with CrewAI
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To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer.
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This input can provide extra context, clarify ambiguities, or validate the agent's output.
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## Customizing human input sources
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By default, human input is collected via the command line using the `input()` function. However, you can override this behavior by providing a custom function to handle human input from different sources:
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```python
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def get_input_from_api(final_answer: str) -> str:
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"""Get human feedback from an API instead of CLI with error handling."""
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try:
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# Make an API call to get feedback
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response = requests.post(
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"https://your-api.com/feedback",
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json={"answer": final_answer},
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timeout=10 # Set timeout to avoid long waits
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)
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response.raise_for_status() # Raise exception for HTTP errors
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return response.json().get("feedback", "")
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except (requests.RequestException, json.JSONDecodeError, KeyError) as e:
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print(f"Error getting feedback from API: {str(e)}")
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# Fallback to CLI input if API fails
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return input(f"API failed, please provide feedback manually:\n\n{final_answer}\n\nYour feedback: ")
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task = Task(
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description="Analyze the latest market trends",
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expected_output="A detailed analysis of market trends",
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agent=analyst,
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human_input=True,
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ask_human_input=get_input_from_api # Use the custom function
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)
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```
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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.
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The custom function should:
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- Accept a string parameter (the agent's final answer)
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- Return a string (the human feedback)
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- Return an empty string if the answer is acceptable and no further iterations are needed
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### Example:
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```shell
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pip install crewai
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```
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```python Code
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import os
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from crewai import Agent, Task, Crew
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from crewai_tools import SerperDevTool
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os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
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os.environ["OPENAI_API_KEY"] = "Your Key"
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# Loading Tools
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search_tool = SerperDevTool()
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# Define your agents with roles, goals, tools, and additional attributes
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researcher = Agent(
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role='Senior Research Analyst',
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goal='Uncover cutting-edge developments in AI and data science',
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backstory=(
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"You are a Senior Research Analyst at a leading tech think tank. "
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"Your expertise lies in identifying emerging trends and technologies in AI and data science. "
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"You have a knack for dissecting complex data and presenting actionable insights."
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),
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verbose=True,
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allow_delegation=False,
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tools=[search_tool]
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)
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writer = Agent(
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role='Tech Content Strategist',
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goal='Craft compelling content on tech advancements',
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backstory=(
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"You are a renowned Tech Content Strategist, known for your insightful and engaging articles on technology and innovation. "
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"With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
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),
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verbose=True,
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allow_delegation=True,
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tools=[search_tool],
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cache=False, # Disable cache for this agent
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)
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# Create tasks for your agents
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task1 = Task(
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description=(
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"Conduct a comprehensive analysis of the latest advancements in AI in 2025. "
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"Identify key trends, breakthrough technologies, and potential industry impacts. "
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"Compile your findings in a detailed report. "
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"Make sure to check with a human if the draft is good before finalizing your answer."
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),
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expected_output='A comprehensive full report on the latest AI advancements in 2025, leave nothing out',
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agent=researcher,
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human_input=True
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)
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task2 = Task(
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description=(
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"Using the insights from the researcher\'s report, develop an engaging blog post that highlights the most significant AI advancements. "
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"Your post should be informative yet accessible, catering to a tech-savvy audience. "
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"Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
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),
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expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2025',
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agent=writer,
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human_input=True
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)
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# Instantiate your crew with a sequential process
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crew = Crew(
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agents=[researcher, writer],
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tasks=[task1, task2],
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verbose=True,
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memory=True,
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planning=True # Enable planning feature for the crew
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)
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# Get your crew to work!
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result = crew.kickoff()
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print("######################")
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print(result)
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```
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