Responses

The responses endpoint is OpenAI’s newer API format with built-in support for structured output and tool calling.

Endpoint

POST https://api.getlilac.com/v1/responses

Example

Python
JavaScript
cURL

from openai import OpenAI

client = OpenAI(
    base_url="https://api.getlilac.com/v1",
    api_key="your-lilac-api-key",
)

response = client.responses.create(
    model="moonshotai/kimi-k2.5",
    input="Explain GPU inference in two sentences.",
)

print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.getlilac.com/v1",
  apiKey: "your-lilac-api-key",
});

const response = await client.responses.create({
  model: "moonshotai/kimi-k2.5",
  input: "Explain GPU inference in two sentences.",
});

console.log(response.output_text);

curl https://api.getlilac.com/v1/responses \
  -H "Authorization: Bearer your-lilac-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/kimi-k2.5",
    "input": "Explain GPU inference in two sentences."
  }'

Request Parameters

Required

Parameter	Type	Description
`model`	`string`	Model ID (e.g., `moonshotai/kimi-k2.5`).
`input`	`string` or `array`	User prompt as a string, or conversation history as an array of message objects.

Sampling

Parameter	Type	Default	Description
`instructions`	`string`	`null`	System-level instructions for the model.
`temperature`	`float`	`1.0`	Sampling temperature (0–2).
`top_p`	`float`	`1.0`	Nucleus sampling threshold.
`max_output_tokens`	`integer`	`null`	Maximum tokens to generate (including reasoning tokens).
`stream`	`boolean`	`false`	Stream the response via SSE.

Structured Output

Parameter	Type	Default	Description
`text`	`object`	`null`	Structured output format with JSON Schema. See example below.

Tools

The responses endpoint uses a flat tool format — name, description, and parameters are top-level fields, not nested under function.

Parameter	Type	Default	Description
`tools`	`array`	`null`	List of tool definitions (see format below).

The tool format differs from /v1/chat/completions. See the tool calling example below for the correct format.

Reasoning

Models with reasoning (like Kimi K2.5 and GLM 5.1) include chain-of-thought by default. The response includes a reasoning output item containing the model’s thinking. Reasoning tokens count toward your usage.

Disabling reasoning is not currently supported on the /v1/responses endpoint. To control reasoning, use Chat Completions with chat_template_kwargs: {"thinking": false} instead.

Structured Output

Force the model to return JSON matching a schema:

Python
JavaScript
cURL

response = client.responses.create(
    model="moonshotai/kimi-k2.5",
    input="Give me a color with its name and hex code.",
    text={
        "format": {
            "type": "json_schema",
            "name": "color",
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "hex": {"type": "string"}
                },
                "required": ["name", "hex"]
            }
        }
    },
)

print(response.output_text)
# {"name": "Teal", "hex": "#008080"}

const response = await client.responses.create({
  model: "moonshotai/kimi-k2.5",
  input: "Give me a color with its name and hex code.",
  text: {
    format: {
      type: "json_schema",
      name: "color",
      schema: {
        type: "object",
        properties: {
          name: { type: "string" },
          hex: { type: "string" },
        },
        required: ["name", "hex"],
      },
    },
  },
});

console.log(response.output_text);

curl https://api.getlilac.com/v1/responses \
  -H "Authorization: Bearer your-lilac-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/kimi-k2.5",
    "input": "Give me a color with its name and hex code.",
    "text": {
      "format": {
        "type": "json_schema",
        "name": "color",
        "schema": {
          "type": "object",
          "properties": {
            "name": {"type": "string"},
            "hex": {"type": "string"}
          },
          "required": ["name", "hex"]
        }
      }
    }
  }'

Tool Calling

The responses endpoint uses a flat tool format where name, description, and parameters are at the top level:

Python
JavaScript
cURL

response = client.responses.create(
    model="moonshotai/kimi-k2.5",
    input="What's the weather in NYC?",
    tools=[
        {
            "type": "function",
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    ],
)

for item in response.output:
    if item.type == "function_call":
        print(f"{item.name}({item.arguments})")
        # get_weather({"location": "NYC"})

const response = await client.responses.create({
  model: "moonshotai/kimi-k2.5",
  input: "What's the weather in NYC?",
  tools: [
    {
      type: "function",
      name: "get_weather",
      description: "Get current weather for a location",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string" },
        },
        required: ["location"],
      },
    },
  ],
});

for (const item of response.output) {
  if (item.type === "function_call") {
    console.log(`${item.name}(${item.arguments})`);
  }
}

curl https://api.getlilac.com/v1/responses \
  -H "Authorization: Bearer your-lilac-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/kimi-k2.5",
    "input": "What is the weather in NYC?",
    "tools": [
      {
        "type": "function",
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {"type": "string"}
          },
          "required": ["location"]
        }
      }
    ]
  }'

With Instructions

Use instructions to set system-level context:

Python
cURL

response = client.responses.create(
    model="moonshotai/kimi-k2.5",
    input="Give me a color",
    instructions="Always respond in JSON with 'name' and 'hex' fields.",
    max_output_tokens=50,
)

curl https://api.getlilac.com/v1/responses \
  -H "Authorization: Bearer your-lilac-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/kimi-k2.5",
    "input": "Give me a color",
    "instructions": "Always respond in JSON with name and hex fields.",
    "max_output_tokens": 50
  }'

Differences from Chat Completions

Feature	Chat Completions	Responses
Input format	`messages` array	`input` string or array
Tool format	Nested under `function`	Flat (name/description/parameters at top level)
Max tokens param	`max_tokens`	`max_output_tokens`
Structured output	`response_format`	`text.format`
Disable reasoning	`chat_template_kwargs: {"thinking": false}`	Not supported
System prompt	`system` role message	`instructions` parameter

Getting Started

API Reference

Billing

Endpoint

Example

Request Parameters

Required

Sampling

Structured Output

Tools

Reasoning

Structured Output

Tool Calling

With Instructions

Differences from Chat Completions

Getting Started

API Reference

Billing

​Endpoint

​Example

​Request Parameters

​Required

​Sampling

​Structured Output

​Tools

​Reasoning

​Structured Output

​Tool Calling

​With Instructions

​Differences from Chat Completions

Endpoint

Example

Request Parameters

Required

Sampling

Structured Output

Tools

Reasoning

Structured Output

Tool Calling

With Instructions

Differences from Chat Completions