cai/examples/cai/agent_patterns/agent_as_tool.py

90 lines
3.0 KiB
Python

"""
Agents as Tools Example.
This module demonstrates how to embed one agent as a tool within another agent's workflow.
An agent-as-tool operates independently on its provided input —without access to prior conversation history or "taking over"
the conversation — completes its specific task, and returns the result to the calling (parent) agent.
"""
from __future__ import annotations
import os
import json
import random
import asyncio
from openai import AsyncOpenAI
from cai.sdk.agents import function_tool
from cai.tools.common import run_command
from cai.sdk.agents import Agent, OpenAIChatCompletionsModel, HandoffInputData, Runner, function_tool, handoff, trace, ItemHelpers, MessageOutputItem
from cai.sdk.agents.extensions import handoff_filters
@function_tool
def execute_cli_command(command: str) -> str:
return run_command(command)
flag_discriminator = Agent(
name="Flag discriminator",
description="Agent focused on extracting the flag from the output",
instructions="You are an agent tailored to extract the flag from a given output.",
model=OpenAIChatCompletionsModel(
model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
openai_client=AsyncOpenAI(),
),
handoff_description = "Agent focused on extracting the flag from the output"
)
# We'll use this variable to check if the flag_discriminator agent is used
flag_discriminator_used = False
# Wrap the as_tool method to set the flag when used
def flag_discriminator_as_tool(*args, **kwargs):
global flag_discriminator_used
flag_discriminator_used = True
return flag_discriminator._as_tool(*args, **kwargs)
# Patch the as_tool method
flag_discriminator._as_tool = flag_discriminator.as_tool
flag_discriminator.as_tool = flag_discriminator_as_tool
ctf_agent = Agent(
name="CTF agent",
description="Agent focused on conquering security challenges",
instructions="You are a Cybersecurity expert Leader facing a CTF",
tools=[
execute_cli_command,
flag_discriminator.as_tool(
tool_name="find_flag",
tool_description ="Find flag in output text"
)
],
model=OpenAIChatCompletionsModel(
model= os.getenv('CAI_MODEL', "qwen2.5:14b"),
openai_client=AsyncOpenAI(),
)
)
# Main function to execute the workflow
async def main():
result = await Runner.run(
ctf_agent,
input= [
{"content": "Here is some output from a task. Find the flag: nhwitm flag{1234} mlsk. And returns only the flag", "role": "user"}
],
)
for item in result.new_items:
if isinstance(item, MessageOutputItem):
text = ItemHelpers.text_message_output(item)
if text:
print(f"Final step: {text}")
# Print whether the flag_discriminator agent was used
if flag_discriminator_used:
print("Flag discriminator agent was used.")
else:
print("Flag discriminator agent was NOT used.")
if __name__ == "__main__":
asyncio.run(main())