> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-studio-tools-doc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# External Tool Execution Stream Async

> This example demonstrates how to execute tools outside of the agent using external tool execution with async streaming responses. It shows how to handle external tool execution in an asynchronous environment while maintaining real-time streaming.

<Steps>
  <Step title="Create a Python file">
    ```python external_tool_execution_stream_async.py theme={null}
    import asyncio
    import subprocess

    from agno.agent import Agent
    from agno.db.sqlite import SqliteDb
    from agno.models.openai import OpenAIResponses
    from agno.tools import tool


    # 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=OpenAIResponses(id="gpt-5.2"),
        tools=[execute_shell_command],
        markdown=True,
        db=SqliteDb(session_table="test_session", db_file="tmp/example.db"),
    )


    async def main():
        async for run_event in agent.arun(
            "What files do I have in my current directory?", stream=True
        ):
            if run_event.is_paused:
                for requirement in run_event.active_requirements:  # type: ignore
                    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
                            )  # 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)

                async for resp in agent.acontinue_run(  # type: ignore
                    run_id=run_event.run_id,
                    requirements=run_event.requirements,  # type: ignore
                    stream=True,
                ):
                    print(resp.content, end="")
            else:
                print(run_event.content, end="")

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


    if __name__ == "__main__":
        asyncio.run(main())

    ```
  </Step>

  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
        export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
        $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run Agent">
    ```bash theme={null}
    python external_tool_execution_stream_async.py
    ```
  </Step>
</Steps>