Documentation Index
Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
Use this file to discover all available pages before exploring further.
Code
cookbook/agent_concepts/user_control_flows/external_tool_execution_stream.py
import subprocess
from agno.agent import Agent
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,
)
for run_response in agent.run(
"What files do I have in my current directory?", stream=True
):
if run_response.is_paused:
for tool in run_response.tools_awaiting_external_execution:
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)
# 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=agent.run_response.run_id,
updated_tools=agent.run_response.tools,
stream=True,
)
pprint.pprint_run_response(run_response)
Usage
Create a virtual environment
Open the Terminal and create a python virtual environment.python3 -m venv .venv
source .venv/bin/activate
Set your API key
export OPENAI_API_KEY=xxx
Install libraries
pip install -U agno openai
Run Example
python cookbook/agent_concepts/user_control_flows/external_tool_execution_stream.py
Key Features
- Uses
agent.run(stream=True) for streaming responses
- Implements streaming continuation with
agent.continue_run(stream=True)
- Maintains real-time interaction with external tool execution
- Demonstrates how to handle streaming responses with external tools
Use Cases
- Real-time external tool execution
- Streaming applications with external service calls
- Interactive interfaces with external tool execution
- Progressive response generation with external tools