AI Tutorial · Getting Started with GPT-5.6 API: Build Your First AI Agent

The GPT-5.6 API represents a major leap forward in building autonomous AI agents. With improved function calling, native tool use, and a simpler streaming interface, you can now create sophisticated agents in under 100 lines of Python. This tutorial walks you through building a real AI agent from scratch.

Background

GPT-5.6 introduces several critical improvements over earlier versions:

  • Enhanced function calling: The model reliably selects and invokes multiple tools in sequence, even when tools depend on earlier outputs.
  • Native tool definitions: Tools are defined as Python dicts with JSON Schema — no more convoluted parsing.
  • Streaming-first design: The chat completions streaming API is the primary interface, with simpler chunk handling.
  • Structured outputs: JSON mode is now baked into the API, making it trivial to get parseable responses.

The core idea of an AI agent is simple: instead of returning a single answer, the model can request tool invocations, receive results, and continue reasoning. This loop — think → act → observe → think again — is the foundation of all agentic systems.

Step 1: Environment Setup

First, install the OpenAI SDK (which also supports the GPT-5.6 API endpoint):

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pip install openai python-dotenv

Create a .env file with your API key:

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OPENAI_API_KEY=sk-your-key-here
GPT56_BASE_URL=https://api.openai.com/v1

Then load the environment in your script:

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import os
from dotenv import load_dotenv
from openai import OpenAI

load_dotenv()

client = OpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
    base_url=os.getenv("GPT56_BASE_URL"),
)

MODEL = "gpt-5.6"

Note: If you’re using a different provider (Azure, OpenAI direct, or a local emulator), change the base_url accordingly. The API contract is identical.

Step 2: Basic API Call

Let’s verify everything works with a simple chat completion:

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response = client.chat.completions.create(
    model=MODEL,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
    ],
    max_tokens=150,
)

print(response.choices[0].message.content)

Expected output:

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The capital of France is Paris.

This confirms your API key, endpoint, and model name are correct. If you get an error, double-check the base_url and that your key has GPT-5.6 access.

Step 3: Agent Core Loop (Function Calling)

The real power of GPT-5.6 is function calling — the model can request that you run a function and return the result. This turns a passive chat model into an active agent.

Here’s the core agent loop:

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def agent_loop(messages, tools=None, max_turns=10):
    """Run the agent reasoning loop until the model responds naturally."""
    for turn in range(max_turns):
        response = client.chat.completions.create(
            model=MODEL,
            messages=messages,
            tools=tools,
        )

        message = response.choices[0].message

        # If the model didn't call a tool, we're done
        if not message.tool_calls:
            return message.content

        # Add the assistant's message (with tool calls) to history
        messages.append(message)

        # Process each tool call the model requested
        for tool_call in message.tool_calls:
            function_name = tool_call.function.name
            arguments = json.loads(tool_call.function.arguments)

            print(f"  → Calling: {function_name}({arguments})")

            # Execute the function (you supply the tool implementations)
            result = execute_tool(function_name, arguments)

            # Return the result to the model
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result),
            })

    return "Agent reached max turns without a final answer."

This loop is the heart of every agent. The model:

  1. Receives the conversation history
  2. Decides whether to respond or call a tool
  3. If it calls a tool, we execute it and feed the result back
  4. The model continues reasoning with the new information
  5. When it’s satisfied, it returns a natural-language response

Step 4: Adding Tools

Now let’s define some useful tools for our agent. We’ll build a weather and time assistant that can look up real data.

Tool Definitions

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import json
import datetime
import urllib.request
import urllib.parse

def get_current_time(location: str) -> dict:
    """Get the current time for a given location using the WorldTime API."""
    # Encode the location for a URL
    encoded = urllib.parse.quote(location)
    url = f"http://worldtimeapi.org/api/timezone?search={encoded}"

    try:
        with urllib.request.urlopen(url, timeout=10) as resp:
            data = json.loads(resp.read().decode())
        if not data:
            return {"error": f"No timezone found for {location}"}
        # Pick the first match
        tz = data[0]
        tz_url = f"http://worldtimeapi.org/api/timezone/{tz}"
        with urllib.request.urlopen(tz_url, timeout=10) as resp:
            tz_data = json.loads(resp.read().decode())
        return {
            "location": location,
            "timezone": tz,
            "datetime": tz_data.get("datetime"),
            "utc_offset": tz_data.get("utc_offset"),
        }
    except Exception as e:
        return {"error": str(e)}


def get_weather(location: str) -> dict:
    """Get current weather for a location using wttr.in."""
    encoded = urllib.parse.quote(location)
    url = f"https://wttr.in/{encoded}?format=j1"

    try:
        with urllib.request.urlopen(url, timeout=10) as resp:
            data = json.loads(resp.read().decode())
        current = data.get("current_condition", [{}])[0]
        return {
            "location": location,
            "temperature_c": current.get("temp_C"),
            "humidity": current.get("humidity"),
            "weather_desc": current.get("weatherDesc", [{}])[0].get("value"),
            "wind_speed_kph": current.get("windspeedKmph"),
        }
    except Exception as e:
        return {"error": str(e)}

Tool Schemas (for the API)

These tell GPT-5.6 what functions are available:

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TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "Get the current time in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. 'London' or 'Tokyo'",
                    }
                },
                "required": ["location"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather conditions for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. 'Paris' or 'New York'",
                    }
                },
                "required": ["location"],
            },
        },
    },
]

Tool Dispatcher

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TOOL_REGISTRY = {
    "get_current_time": get_current_time,
    "get_weather": get_weather,
}

def execute_tool(name: str, args: dict):
    """Look up and run a tool by name."""
    if name not in TOOL_REGISTRY:
        return {"error": f"Unknown tool: {name}"}
    return TOOL_REGISTRY[name](**args)

Step 5: Complete Agent Example

Let’s tie everything together into a working agent:

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import os
import json
import datetime
import urllib.request
import urllib.parse
from dotenv import load_dotenv
from openai import OpenAI

load_dotenv()

client = OpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
    base_url=os.getenv("GPT56_BASE_URL"),
)

MODEL = "gpt-5.6"

# -------- Tool Implementations --------

def get_current_time(location: str) -> dict:
    encoded = urllib.parse.quote(location)
    url = f"http://worldtimeapi.org/api/timezone?search={encoded}"
    try:
        with urllib.request.urlopen(url, timeout=10) as resp:
            data = json.loads(resp.read().decode())
        if not data:
            return {"error": f"No timezone found for {location}"}
        tz = data[0]
        tz_url = f"http://worldtimeapi.org/api/timezone/{tz}"
        with urllib.request.urlopen(tz_url, timeout=10) as resp:
            tz_data = json.loads(resp.read().decode())
        return {
            "location": location,
            "timezone": tz,
            "datetime": tz_data.get("datetime"),
            "utc_offset": tz_data.get("utc_offset"),
        }
    except Exception as e:
        return {"error": str(e)}


def get_weather(location: str) -> dict:
    encoded = urllib.parse.quote(location)
    url = f"https://wttr.in/{encoded}?format=j1"
    try:
        with urllib.request.urlopen(url, timeout=10) as resp:
            data = json.loads(resp.read().decode())
        current = data.get("current_condition", [{}])[0]
        return {
            "location": location,
            "temperature_c": current.get("temp_C"),
            "humidity": current.get("humidity"),
            "weather_desc": current.get("weatherDesc", [{}])[0].get("value"),
            "wind_speed_kph": current.get("windspeedKmph"),
        }
    except Exception as e:
        return {"error": str(e)}


# -------- Tool Schema --------

TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "Get the current time in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. 'London' or 'Tokyo'",
                    }
                },
                "required": ["location"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather conditions for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. 'Paris' or 'New York'",
                    }
                },
                "required": ["location"],
            },
        },
    },
]

TOOL_REGISTRY = {
    "get_current_time": get_current_time,
    "get_weather": get_weather,
}


def execute_tool(name: str, args: dict):
    if name not in TOOL_REGISTRY:
        return {"error": f"Unknown tool: {name}"}
    return TOOL_REGISTRY[name](**args)


# -------- Agent Loop --------

def agent_loop(messages, tools=None, max_turns=10):
    for turn in range(max_turns):
        print(f"\n--- Turn {turn + 1} ---")
        response = client.chat.completions.create(
            model=MODEL,
            messages=messages,
            tools=tools,
        )

        message = response.choices[0].message

        if not message.tool_calls:
            return message.content

        messages.append(message)

        for tool_call in message.tool_calls:
            function_name = tool_call.function.name
            arguments = json.loads(tool_call.function.arguments)
            print(f"  → Calling: {function_name}({arguments})")
            result = execute_tool(function_name, arguments)
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result),
            })

    return "Agent reached max turns without a final answer."


# -------- Run It --------

if __name__ == "__main__":
    messages = [
        {
            "role": "system",
            "content": (
                "You are a helpful assistant with access to weather and time tools. "
                "When the user asks about a location, use the tools to get real data. "
                "Be concise and informative."
            ),
        },
        {
            "role": "user",
            "content": "What's the weather in Tokyo right now, and what time is it there?",
        },
    ]

    result = agent_loop(messages, tools=TOOLS)
    print(f"\n=== Final Answer ===\n{result}")

When you run this, you’ll see the agent:

  1. First call get_weather(location='Tokyo') to get current conditions
  2. Then call get_current_time(location='Tokyo') to get the local time
  3. Synthesize both results into a natural-language answer

Example output:

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--- Turn 1 ---
  → Calling: get_weather({'location': 'Tokyo'})

--- Turn 2 ---
  → Calling: get_current_time({'location': 'Tokyo'})

=== Final Answer ===
In Tokyo right now, it's 26°C with partly cloudy conditions and humidity at 68%. The local time is 2026-07-10T15:30:00+09:00 (JST).

Summary

In this tutorial you built a complete AI agent using the GPT-5.6 API. Here’s what you learned:

Concept Implementation
API Setup OpenAI client with GPT-5.6 model
Basic Chat Simple completion with messages array
Function Calling Model requests tool invocations
Agent Loop Think → Act → Observe → Repeat
Tool Definitions JSON Schema + Python functions
Tool Registry Dispatch table for function lookup

Next Steps

  • Add memory: Use a vector database (ChromaDB, Pinecone) so your agent can recall past conversations.
  • Multi-agent orchestration: Have one agent delegate subtasks to specialized sub-agents.
  • Streaming responses: Use stream=True for real-time token-by-token output.
  • Error recovery: Add retry logic when a tool call fails or returns unexpected data.
  • Guardrails: Validate tool arguments before execution to prevent misuse.

The GPT-5.6 API makes agent building accessible to any Python developer. The loop pattern you learned here — prompt → tool call → execute → feed back → respond — is the same architecture used by production systems like AutoGPT, ChatGPT Plugins, and custom enterprise agents. Start simple, iterate fast, and let the model do the heavy lifting.

Happy building!