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This example demonstrates human-in-the-loop functionality with streaming responses. It shows how to handle user confirmation during tool execution while maintaining real-time streaming capabilities.

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Code

confirmation_required_stream.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.
"""

import json

import httpx
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.tools import tool
from agno.utils import pprint
from rich.console import Console
from rich.prompt import Prompt

console = Console()


@tool(requires_confirmation=True)
def get_top_hackernews_stories(num_stories: int) -> str:
    """Fetch top stories from Hacker News.

    Args:
        num_stories (int): Number of stories to retrieve

    Returns:
        str: JSON string containing story details
    """
    # Fetch top story IDs
    response = httpx.get("https://hacker-news.firebaseio.com/v0/topstories.json")
    story_ids = response.json()

    # Yield story details
    all_stories = []
    for story_id in story_ids[:num_stories]:
        story_response = httpx.get(
            f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json"
        )
        story = story_response.json()
        if "text" in story:
            story.pop("text", None)
        all_stories.append(story)
    return json.dumps(all_stories)


agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    db=SqliteDb(
        db_file="tmp/example.db",
    ),
    tools=[get_top_hackernews_stories],
    markdown=True,
)

for run_event in agent.run("Fetch the top 2 hackernews stories", stream=True):
    if run_event.is_paused:
        for tool in run_event.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_id=run_event.run_id, updated_tools=run_event.tools, stream=True
        )  # type: ignore
        pprint.pprint_run_response(run_response)

# Or for simple debug flow
# agent.print_response("Fetch the top 2 hackernews stories", stream=True)

​
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 httpx 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_stream.py
5

Run Agent

python confirmation_required_stream.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