Tool Calling

Several local models support OpenAI-compatible tool calling, allowing the model to invoke functions you define.

Supported models

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Example

The model can’t check the weather on its own — but with a tool, it can call a real API. This example uses Open-Meteo (free, no API key needed):

import json
import requests
from openai import OpenAI

client = OpenAI(
    base_url="https://maki.uni-mannheim.de/v1",
    api_key="your-api-key",
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "City name, e.g. 'Mannheim'",
                    }
                },
                "required": ["city"],
            },
        },
    }
]


def get_weather(city: str) -> str:
    """Call the Open-Meteo API to get current weather."""
    geo = requests.get(
        "https://geocoding-api.open-meteo.com/v1/search",
        params={"name": city, "count": 1},
    ).json()
    loc = geo["results"][0]

    weather = requests.get(
        "https://api.open-meteo.com/v1/forecast",
        params={
            "latitude": loc["latitude"],
            "longitude": loc["longitude"],
            "current": "temperature_2m,wind_speed_10m",
        },
    ).json()["current"]

    return json.dumps({
        "city": loc["name"],
        "temperature_c": weather["temperature_2m"],
        "wind_speed_kmh": weather["wind_speed_10m"],
    })


messages = [
    {"role": "user", "content": "What's the weather like in Mannheim?"}
]

# Step 1: The model decides it needs the weather tool
response = client.chat.completions.create(
    model="gemma4-26b",
    messages=messages,
    tools=tools,
)

message = response.choices[0].message

# Step 2: Execute the tool call and send the result back
if message.tool_calls:
    call = message.tool_calls[0]
    args = json.loads(call.function.arguments)

    # Call the real API
    result = get_weather(args["city"])

    # Send tool result back to the model
    messages.append(message)
    messages.append({
        "role": "tool",
        "tool_call_id": call.id,
        "content": result,
    })

    # Step 3: The model uses the result to answer
    final = client.chat.completions.create(
        model="gemma4-26b",
        messages=messages,
        tools=tools,
    )
    print(final.choices[0].message.content)
    # "It's currently 18.3 °C in Mannheim with a wind speed of 12.5 km/h."

With PydanticAI

PydanticAI handles the tool-calling loop for you — you just decorate a function and the framework takes care of the rest:

import json
import requests
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIChatModel(
    "gemma4-26b",
    provider=OpenAIProvider(
        base_url="https://maki.uni-mannheim.de/v1",
        api_key="your-api-key",
    ),
)

agent = Agent(model)


@agent.tool_plain
def get_weather(city: str) -> str:
    """Get the current weather for a city.

    Args:
        city: City name, e.g. 'Mannheim'.
    """
    geo = requests.get(
        "https://geocoding-api.open-meteo.com/v1/search",
        params={"name": city, "count": 1},
    ).json()
    loc = geo["results"][0]

    weather = requests.get(
        "https://api.open-meteo.com/v1/forecast",
        params={
            "latitude": loc["latitude"],
            "longitude": loc["longitude"],
            "current": "temperature_2m,wind_speed_10m",
        },
    ).json()["current"]

    return json.dumps({
        "city": loc["name"],
        "temperature_c": weather["temperature_2m"],
        "wind_speed_kmh": weather["wind_speed_10m"],
    })


result = agent.run_sync("What's the weather like in Mannheim?")
print(result.output)
# "It's currently 18.3 °C in Mannheim with a wind speed of 12.5 km/h."