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POST /v1/responses Tools are declared with a flat function schema (no nested function key). The model returns function_call items inside the output array.

​
Define a tool

tools = [{
    "type": "function",
    "name": "get_weather",
    "description": "Get the weather for a city",
    "parameters": {
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"],
    },
}]

​
Full loop

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.abliteration.ai/v1", api_key=os.environ["ABLIT_KEY"])

resp = client.responses.create(
    model="abliterated-model",
    input="What's the weather in Lagos?",
    tools=tools,
)

next_input = []
for item in resp.output:
    if item.type == "function_call":
        args = json.loads(item.arguments)
        result = get_weather(args["city"])  # your function
        next_input.append({
            "type": "function_call_output",
            "call_id": item.call_id,
            "output": json.dumps(result),
        })

final = client.responses.create(
    model="abliterated-model",
    input=next_input,
    previous_response_id=resp.id,
    tools=tools,
)
print(final.output_text)

​
Streaming

Stream with stream=True. Tool arguments arrive as response.function_call.arguments.delta events; a response.function_call.done event signals the call is complete.

​
Forcing a tool

tool_choice={"type": "function", "name": "get_weather"}

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Parallel tool calls

Multiple function_call items can appear in a single output array. Produce one function_call_output per call_id.