Skip to main content
This example demonstrates human-in-the-loop functionality using toolkit-based tools that require confirmation. It shows how to handle user confirmation when working with pre-built tool collections like YFinanceTools.

​
Code

confirmation_required_toolkit.py
"""🤝 Human-in-the-Loop: Adding User Confirmation to Tool Calls

This example shows how to implement human-in-the-loop functionality in your Agno tools.
It shows how to:
- Handle user confirmation during tool execution
- Gracefully cancel operations based on user choice

Some practical applications:
- Confirming sensitive operations before execution
- Reviewing API calls before they're made
- Validating data transformations
- Approving automated actions in critical systems

Run `pip install openai httpx rich agno` to install dependencies.
"""

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools import tool
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.utils import pprint
from rich.console import Console
from rich.prompt import Prompt

console = Console()

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[DuckDuckGoTools(requires_confirmation_tools=["get_current_stock_price"])],
    markdown=True,
)

run_response = agent.run("What is the current stock price of Apple?")
if run_response.is_paused:  # Or agent.run_response.is_paused
    for tool in run_response.tools_requiring_confirmation:  # type: ignore
        # Ask for confirmation
        console.print(
            f"Tool name [bold blue]{tool.tool_name}({tool.tool_args})[/] requires confirmation."
        )
        message = (
            Prompt.ask("Do you want to continue?", choices=["y", "n"], default="y")
            .strip()
            .lower()
        )

        if message == "n":
            tool.confirmed = False
        else:
            # We update the tools in place
            tool.confirmed = True

    run_response = agent.continue_run(run_response=run_response)
    pprint.pprint_run_response(run_response)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Install libraries

pip install -U agno openai ddgs rich
3

Export your OpenAI API key

  export OPENAI_API_KEY="your_openai_api_key_here"
4

Create a Python file

Create a Python file and add the above code.
touch confirmation_required_toolkit.py
5

Run Agent

python confirmation_required_toolkit.py
6

Find All Cookbooks

Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:Agno Cookbooks on GitHub