Structured Outputs

FriendliAI offers structured outputs capability with two core guarantees:

Model-agnostic: Supported on all chat‑capable models on Friendli.
High schema fidelity: Generates outputs that reliably conform to your provided schemas.

What Is Structured Outputs

Structured Outputs ensures LLMs return predictable, machine‑readable results (e.g., JSON) instead of free‑form text. This is essential for workflows that require validation or downstream automation.

Structured Outputs with FriendliAI

Schema‑aligned generation: High‑accuracy adherence to your JSON Schema.
Flexible modes: Select strict or loose JSON mode, or apply regex constraints as needed.
OpenAI compatible: Use standard response_format options with OpenAI SDKs.

Structured Outputs Parameters

Type	Description	Name at OpenAI
`json_schema`	The model returns a JSON object that conforms to the given schema.	Structured Outputs
`json_object`	The model can return any JSON object.	JSON mode
`regex`	The model returns a string that conforms to the given regex schema.	N/A

Supported JSON Schemas

FriendliAI supports all seven standard JSON schema types (null, boolean, number, integer, string, object, array). The supported JSON schema keywords are listed below.

Using unsupported or unexpected JSON schema keywords may result in them being ignored, triggering an error, or causing undefined behavior.

Type-Specific Keywords

integer
- exclusiveMinimum, exclusiveMaximum, minimum, maximum (Note: these are not supported in number)
string
- pattern
- format
  - Supported values: uuid, date-time, date, time, uri
object
- properties
- additionalProperties is ignored, and is always set to False.
- required: FriendliAI supports both required and optional properties, with these limitations:
  - The sequence of the properties is fixed.
  - The first property should be required. If not, the first required property is moved to the first position.
array
- items
- minItems: FriendliAI supports only 0 or 1 for minItems.

Constant Values and Enumerated Values

const and enum only support constant values of null, boolean, number, and string.

Schema Composition

FriendliAI supports only anyOf for schema composition.

Referencing Subschemas

FriendliAI supports only references ($ref) to “internal” subschemas. These subschemas must be defined within $defs, and the value of $ref must be a valid URI pointing to a subschema.

Annotations

JSON schema annotations such as title or description are accepted but ignored.

Simple Example

json_schema
json_object
Regex

This example provides a step-by-step guide on how to create a structured output response in JSON format. This example uses Python and the pydantic library to define a schema for the output.

Define Object Schema

Define a schema that contains information about a dish.

from pydantic import BaseModel

class Result(BaseModel):
    dish: str
    cuisine: str
    calories: int

Asking the Model for Structured Output

Call structured output and use schema to structure the response.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.friendli.ai/serverless/v1",
    api_key=os.getenv("API_KEY"),
)

completion = client.chat.completions.create(
    model="zai-org/GLM-5.3",
    messages=[
        {
            "role": "user",
            "content": "Suggest a popular Italian dish in JSON format.",
        },
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "schema": Result.model_json_schema(),
        }
    }
)

curl -X POST https://api.friendli.ai/serverless/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai-org/GLM-5.3",
    "messages": [
      {
        "role": "user",
        "content": "Suggest a popular Italian dish in JSON format."
      }
    ],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "schema": {
          "type": "object",
          "properties": {
            "dish": {"type": "string"},
            "cuisine": {"type": "string"},
            "calories": {"type": "integer"}
          },
          "required": ["dish", "cuisine", "calories"]
        }
      }
    }
  }'

Using Structured Output Results

You can use the output in the following way.

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

The code output result is as follows.

Result:

{
    "dish": "Spaghetti Bolognese",
    "cuisine": "Italian",
    "calories": 540
}

This example demonstrates how to generate an arbitrary JSON object response without a predefined schema.

In json_object mode, the response may start with { or [ and can be any arbitrary JSON object (dictionary) or array. If you need predictable results, use json_schema.

curl -X POST https://api.friendli.ai/serverless/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai-org/GLM-5.3",
    "messages": [
      {"role": "system", "content": "You MUST answer with JSON."},
      {"role": "user", "content": "Generate a lasagna recipe. (very short)"}
    ],
    "response_format": {"type": "json_object"}
  }'

import os
from openai import OpenAI

client = OpenAI(
  base_url="https://api.friendli.ai/serverless/v1",
  api_key=os.getenv("API_KEY"),
)

completion = client.chat.completions.create(
  model="zai-org/GLM-5.3",
  messages=[
    {"role": "system", "content": "You MUST answer with JSON."},
    {"role": "user", "content": "Generate a lasagna recipe. (very short)"},
  ],
  response_format={"type": "json_object"},
)

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

This example shows how to generate output that matches a specific regular expression pattern.

curl -X POST https://api.friendli.ai/serverless/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai-org/GLM-5.3",
    "messages": [
      {
        "role": "user",
        "content": "조선 왕조의 첫번째 왕은 누구입니까 (Who is the first king of the Joseon Dynasty)?"
      }
    ],
    "response_format": {
      "type": "regex",
      "schema": "[\\n ,.?!0-9\\uac00-\\ud7af]*"
    }
  }'

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.friendli.ai/serverless/v1",
    api_key=os.getenv("API_KEY"),
)

completion = client.chat.completions.create(
    model="zai-org/GLM-5.3",
    messages=[
        {
            "role": "user",
            "content": "조선 왕조의 첫번째 왕은 누구입니까 (Who is the first king of the Joseon Dynasty)?",
        },
    ],
    # Korean characters and numbers are allowed in the response.
    response_format={"type": "regex", "schema": "[\n ,.?!0-9\uac00-\ud7af]*"},
)

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

Advanced Examples

For more advanced use cases, see the FriendliAI Blog: Structured Output for LLM Agents.

Introduction

Capabilities

Friendli Model APIs

Friendli Dedicated Endpoints

Friendli Suite Guide

What Is Structured Outputs

Structured Outputs with FriendliAI

Structured Outputs Parameters

Supported JSON Schemas

Type-Specific Keywords

Constant Values and Enumerated Values

Schema Composition

Referencing Subschemas

Annotations

Simple Example

Advanced Examples

​What Is Structured Outputs

​Structured Outputs with FriendliAI

​Structured Outputs Parameters

​Supported JSON Schemas

​Type-Specific Keywords

​Constant Values and Enumerated Values

​Schema Composition

​Referencing Subschemas

​Annotations

​Simple Example

​Advanced Examples

What Is Structured Outputs

Structured Outputs with FriendliAI

Structured Outputs Parameters

Supported JSON Schemas

Type-Specific Keywords

Constant Values and Enumerated Values

Schema Composition

Referencing Subschemas

Annotations

Simple Example

Advanced Examples