Tools & Function Calling

This comprehensive guide shows you how to build an AI agent that can execute custom Python functions using the OpenAI Responses API.

Prerequisites

cycls installed
OpenAI API key

pip install cycls

Step 1: Define a Tool

Create a standard Python function. In this example, we’ll create a mock weather function.

import json

def get_weather(location):
    """Get current weather for a city."""
    return json.dumps({"temp": "24", "unit": "celsius"})

Step 2: Define the Schema

OpenAI needs to know what your tool does. You define this in a JSON schema format.

tools = [{
    "type": "function",
    "name": "get_weather",
    "description": "Get current temperature",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {"type": "string"}
        },
        "required": ["location"]
    }
}]

Note: The description fields are crucial—they tell the LLM when to use your tool.

Step 3: Create the Agent

import cycls
import json

@cycls.app(pip=["openai"], copy=[".env"])
async def app(context):
    from openai import OpenAI

    client = OpenAI()

    # Define the tool logic
    def get_weather(location):
        return json.dumps({"temp": "24", "unit": "celsius"})

    # Define the tool schema
    tools = [{
        "type": "function",
        "name": "get_weather",
        "description": "Get current temperature",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }]

    # First Call
    response = client.responses.create(
        model="gpt-4o",
        input=context.messages,
        tools=tools
    )

    # Update history
    context.messages.extend(response.output)

    # Handle Tool Execution
    tool_called = False
    for item in response.output:
        if item.type == "function_call":
            tool_called = True
            if item.name == "get_weather":
                args = json.loads(item.arguments)
                result = get_weather(args["location"])
                context.messages.append({
                    "type": "function_call_output",
                    "call_id": item.call_id,
                    "output": result
                })

    # Final Call
    if tool_called:
        final = client.responses.create(
            model="gpt-4o",
            input=context.messages
        )
        yield final.output_text
    else:
        yield response.output_text

app.local()

Step 4: Set Up Environment

Create a .env file with your OpenAI API key:

OPENAI_API_KEY=sk-proj-...

Step 5: Run the Agent

python app.py

Full Code

Here is the complete app.py:

import cycls
import json

@cycls.app(pip=["openai"], copy=[".env"])
async def app(context):
    from openai import OpenAI

    client = OpenAI()

    # Define the tool logic
    def get_weather(location):
        return json.dumps({"temp": "24", "unit": "celsius"})

    # Define the tool schema
    tools = [{
        "type": "function",
        "name": "get_weather",
        "description": "Get current temperature",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }]

    # First Call
    response = client.responses.create(
        model="gpt-4o",
        input=context.messages,
        tools=tools
    )

    # Update history
    context.messages.extend(response.output)

    # Handle Tool Execution
    tool_called = False
    for item in response.output:
        if item.type == "function_call":
            tool_called = True
            if item.name == "get_weather":
                args = json.loads(item.arguments)
                result = get_weather(args["location"])
                context.messages.append({
                    "type": "function_call_output",
                    "call_id": item.call_id,
                    "output": result
                })

    # Final Call
    if tool_called:
        final = client.responses.create(
            model="gpt-4o",
            input=context.messages
        )
        yield final.output_text
    else:
        yield response.output_text

app.local()

Get Started

Basics

Guide

Resources

LLMs

Tools & Function Calling

Prerequisites

Step 1: Define a Tool

Step 2: Define the Schema

Step 3: Create the Agent

Step 4: Set Up Environment

Step 5: Run the Agent

Full Code

​Prerequisites

​Step 1: Define a Tool

​Step 2: Define the Schema

​Step 3: Create the Agent

​Step 4: Set Up Environment

​Step 5: Run the Agent

​Full Code

Prerequisites

Step 1: Define a Tool

Step 2: Define the Schema

Step 3: Create the Agent

Step 4: Set Up Environment

Step 5: Run the Agent

Full Code