External Tool Execution Async

import asyncio
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-4o-mini"),
    tools=[execute_shell_command],
    markdown=True,
    db=SqliteDb(session_table="test_session", db_file="tmp/example.db"),
)

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

run_response = asyncio.run(
    agent.acontinue_run(
        run_id=run_response.run_id,
        requirements=run_response.requirements,
    )
)
pprint.pprint_run_response(run_response)


# Or for simple debug flow
# agent.print_response("What files do I have in my current directory?")