1
Create a Python file
external_tool_execution_toolkit.py
import subprocess
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.tools.toolkit import Toolkit
from agno.utils import pprint
class ShellTools(Toolkit):
def __init__(self, *args, **kwargs):
super().__init__(
tools=[self.list_dir],
external_execution_required_tools=["list_dir"],
*args,
**kwargs,
)
def list_dir(self, directory: str):
"""
Lists the contents of a directory.
Args:
directory: The directory to list.
Returns:
A string containing the contents of the directory.
"""
return subprocess.check_output(f"ls {directory}", shell=True).decode("utf-8")
tools = ShellTools()
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
tools=[tools],
markdown=True,
db=SqliteDb(session_table="test_session", db_file="tmp/example.db"),
)
run_response = agent.run("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 == "list_dir":
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 = tools.list_dir(**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 = agent.continue_run(
run_id=run_response.run_id,
requirements=run_response.requirements,
)
pprint.pprint_run_response(run_response)
2
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
3
Install dependencies
uv pip install -U agno openai
4
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
5
Run Agent
python external_tool_execution_toolkit.py