How to Turn a Python Script Into an AI Agent

mkdir website-agent cd website-agent

uv init uv add openai-agents requests

Or use pip:

pip install openai-agents requests

Set your OpenAI API key:

export OPENAI_API_KEY="your-api-key"

The Agents SDK provides a lightweight runtime for agents, tools, handoffs, sessions, and tracing.

Next, expose our existing Python function as a tool that the model can choose to call.

We can keep almost all of our existing function.

The main change is adding @function_tool:

from time import perf_counter

import requests from agents import function_tool

@function_tool def check_website(url: str) -> str: """Check a website's HTTP status and response time."""

start = perf_counter()

try: response = requests.get(url, timeout=10) latency = perf_counter() - start

return ( f"URL: {url}\n" f"Status: {response.status_code}\n" f"Response time: {latency:.2f}s" )

except requests.RequestException as error: return f"URL: {url}\nError: {error}"

The OpenAI Agents SDK automatically converts the function signature into the JSON schema required by the model. It also uses the function name and docstring to describe the tool.

We do not need to manually create a tool schema.

Now, create an Agent, define what it should do, and give it access to our check_website() tool.

from agents import Agent, Runner

agent = Agent( name="Website Monitor", model="gpt-5.6-luna", instructions=""" Monitor websites using the available tool. Compare results and explain problems clearly. """, tools=[check_website], )

Run the agent:

result = Runner.run_sync( agent, "Check python.org, github.com, and openai.com. " "Which one has the slowest response?" )

print(result.final_output)

Output:

python.org is the slowest, responding in 1.59 seconds.

• github.com: 0.83s
• openai.com: 0.49s

All returned HTTP 200.

Previously, we would have needed to write the loop and comparison logic ourselves:

for url in urls: check_website(url)

Now the model interprets the request, calls check_website() for the three websites, receives the results, compares them, and produces the answer.

Behind the scenes, the Runner manages the interaction between the model and the tools.