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This example demonstrates how to execute tools outside of the agent using external tool execution with streaming responses. It shows how to handle external tool execution while maintaining real-time streaming capabilities.

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Code

external_tool_execution_stream.py
"""🤝 Human-in-the-Loop: Execute a tool call outside of the agent

This example shows how to implement human-in-the-loop functionality in your Agno tools.
It shows how to:
- Use external tool execution to execute a tool call outside of the agent

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

import subprocess

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


# We have to create a tool with the correct name, arguments and docstring for the agent to know what to call.
@tool(external_execution=True)
def execute_shell_command(command: str) -> str:
    """Execute a shell command.

    Args:
        command (str): The shell command to execute

    Returns:
        str: The output of the shell command
    """
    if command.startswith("ls"):
        return subprocess.check_output(command, shell=True).decode("utf-8")
    else:
        raise Exception(f"Unsupported command: {command}")


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

for run_event in agent.run(
    "What files do I have in my current directory?", stream=True
):
    if run_event.is_paused:
        for tool in run_event.tools_awaiting_external_execution:  # type: ignore
            if tool.tool_name == execute_shell_command.name:
                print(
                    f"Executing {tool.tool_name} with args {tool.tool_args} externally"
                )
                # We execute the tool ourselves. You can also execute something completely external here.
                result = execute_shell_command.entrypoint(**tool.tool_args)  # type: ignore
                # We have to set the result on the tool execution object so that the agent can continue
                tool.result = result

        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("What files do I have in my current directory?", 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
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 external_tool_execution_stream.py
5

Run Agent

python external_tool_execution_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
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