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Fetch the complete documentation index at: https://docs.orxhestra.com/llms.txt

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Create an ask_human tool that lets the agent ask the user questions interactively: 
from langchain_core.tools import tool

@tool
def ask_human(question: str) -> str:
    """Ask the human user a question and return their response."""
    print(f"\n  Agent asks: {question}")
    return input("  Your answer: ").strip()

agent = LlmAgent(
    name="Assistant",
    model=model,
    tools=[ask_human, book_meeting, send_email],
    instructions=(
        "When you don't have enough information to complete a task, "
        "use the ask_human tool to ask the user for the missing details."
    ),
)
The agent will call ask_human whenever it needs clarification, then use the response to proceed.
Full example - See examples/human_in_the_loop.py for a complete runnable demo.