Capabilities

Tool calling

Describe your functions, let the model decide when to call them, run them yourself and send back the results.

Tool calling uses the OpenAI format. The model never runs anything: it returns the function name and JSON arguments, your code runs the function, and you continue the conversation with the result.

The loop

Send your messages with a tools array.
When the reply has finish_reason: "tool_calls", run each call in message.tool_calls.
Append the assistant message, then one role: "tool" message per call with its tool_call_id and result.
Send the conversation again. Repeat until the model answers in content.

main.py

import json
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://lyber-paid-acceptance-gateway-production.up.railway.app/v1",
    api_key=os.environ["LYBER_API_KEY"],
)

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Current weather for a city.",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    },
}]
messages = [{"role": "user", "content": "What's the weather in Buenos Aires?"}]

first = client.chat.completions.create(
    model="minimax-m3.1-flash-abliterated", messages=messages, tools=tools, max_tokens=2048,
)
reply = first.choices[0].message
messages.append(reply)

for call in reply.tool_calls or []:
    args = json.loads(call.function.arguments)
    result = {"city": args["city"], "temperature_c": 21}  # your code runs here
    messages.append({"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)})

final = client.chat.completions.create(
    model="minimax-m3.1-flash-abliterated", messages=messages, tools=tools, max_tokens=2048,
)
print(final.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://lyber-paid-acceptance-gateway-production.up.railway.app/v1",
  apiKey: process.env.LYBER_API_KEY,
});

const tools = [{
  type: "function" as const,
  function: {
    name: "get_weather",
    description: "Current weather for a city.",
    parameters: {
      type: "object",
      properties: { city: { type: "string" } },
      required: ["city"],
    },
  },
}];
const messages: OpenAI.ChatCompletionMessageParam[] = [
  { role: "user", content: "What's the weather in Buenos Aires?" },
];

const first = await client.chat.completions.create({
  model: "minimax-m3.1-flash-abliterated", messages, tools, max_tokens: 2048,
});
const reply = first.choices[0].message;
messages.push(reply);

for (const call of reply.tool_calls ?? []) {
  if (call.type !== "function") continue;
  const args = JSON.parse(call.function.arguments);
  const result = { city: args.city, temperature_c: 21 }; // your code runs here
  messages.push({ role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
}

const final = await client.chat.completions.create({
  model: "minimax-m3.1-flash-abliterated", messages, tools, max_tokens: 2048,
});
console.log(final.choices[0].message.content);

Choosing tools

tool_choice	Effect
`"auto"`	The model decides. This is the default when you send tools.
`"none"`	The model answers in text and calls nothing.
`"required"`	The model must call at least one tool.
`{"type": "function", "function": {"name": "get_weather"}}`	The model must call this function.

Streaming tool calls

With stream: true, each tool call arrives in pieces in delta.tool_calls. The first piece carries the call's id and function.name; later pieces with the same index carry fragments of function.arguments. Concatenate them and parse the JSON when the stream finishes with finish_reason: "tool_calls".

Limits

Up to 32 tools per request, all type: "function". Names are 1–128 characters and must be unique; parameters must be a JSON Schema object.
Up to 32 tool calls in one assistant message.
parallel_tool_calls is not accepted on Chat Completions and returns 400. The model may still return several calls at once; handle every entry in tool_calls.
Server-side tools such as web search or code execution are not available. Run them in your own code and pass the results back as tool messages.