Function Calling

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Function Calling

Function calling lets models generate structured JSON arguments for functions you define, enabling integration with external tools and APIs.

What you’ll learn:

How to define tools/functions for the model to call
How to use function calling with the Responses API
How to extract structured data using JSON Schema

Basic Example

Python

from openai import OpenAI

client = OpenAI()

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "format": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use.",
                    },
                },
                "required": ["location", "format"],
            },
        },
    },
]

messages = [
    {"role": "system", "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous."},
    {"role": "user", "content": "What's the weather like today in Hamburg?"},
]

response = client.chat.completions.create(
    model="gpt-4.1",
    messages=messages,
    tools=tools,
)

print(response.choices[0].message)

Example output:

ChatCompletionMessage(
    content=None,
    role='assistant',
    tool_calls=[
        ChatCompletionMessageToolCall(
            id='call_adnFRLazqswLI1ky6FU2O40u',
            function=Function(
                arguments='{"location":"Hamburg","format":"celsius"}',
                name='get_current_weather'
            ),
            type='function'
        )
    ]
)

Function Calling with Responses API

from openai import OpenAI

client = OpenAI()

tools = [
    {
        "type": "function",
        "name": "get_weather",
        "description": "Get current temperature for a given location.",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "City and country e.g. Bogota, Colombia",
                }
            },
            "required": ["location"],
            "additionalProperties": False,
        },
    }
]

response = client.responses.create(
    model="gpt-4.1",
    input=[{"role": "user", "content": "What is the weather like in Paris today?"}],
    tools=tools,
)

print(response.output)

Structured Output (JSON Schema)

Parse text into a structured schema:

import json
from openai import OpenAI

client = OpenAI()

description = """
Our Premium Laptop Backpack features padded compartments that fit
laptops up to 15.6 inches. Available in navy blue, black, and gray.
Price: $79.99, on sale for $64.99.
"""

response = client.responses.create(
    model="gpt-4.1",
    input=f"Extract structured product information: {description}",
    text={
        "format": {
            "type": "json_schema",
            "name": "product_details",
            "schema": {
                "type": "object",
                "properties": {
                    "product_name": {"type": "string"},
                    "features": {"type": "array", "items": {"type": "string"}},
                    "colors": {"type": "array", "items": {"type": "string"}},
                    "pricing": {
                        "type": "object",
                        "properties": {
                            "regular_price": {"type": "number"},
                            "sale_price": {"type": "number"},
                            "currency": {"type": "string"},
                        },
                        "required": ["regular_price", "sale_price", "currency"],
                        "additionalProperties": False,
                    },
                },
                "required": ["product_name", "features", "colors", "pricing"],
                "additionalProperties": False,
            },
            "strict": True,
        }
    },
)

print(json.dumps(json.loads(response.output[0].content[0].text), indent=2))

Compatibility

Next Steps

Streaming — Stream function calling responses in real-time
Chat Completions — Basic chat API usage
API Endpoints — Full endpoint reference