What does the Semantic Kernel Structured Outputs skill do?

This skill enables developers to generate structured JSON outputs and typed responses from large language models using Microsoft's Semantic Kernel framework. It provides two Python implementation patterns: direct JSON structured output generation and function-calling-based structured output generation, ensuring LLM responses conform to predefined schemas and data types.

When should I use this skill?

Use this skill when you need LLM responses in a specific JSON format for downstream processing, API integrations, or database storage where schema compliance is critical. It's ideal for applications requiring consistent, parseable data structures from natural language interactions, such as form extraction, entity recognition pipelines, or structured data collection workflows.

What are the two implementation patterns provided?

The skill provides `json_structured_outputs.py` for direct structured generation and `json_structured_outputs_function_calling.py` for function-calling-based approaches to generating structured outputs.

Who is this skill designed for?

This skill serves Python developers building AI applications that require reliable data extraction and structured responses, including backend engineers integrating LLMs into data pipelines, application developers building form-processing systems, and AI engineers who need deterministic output formats for testing and validation.

Skill

Generate Structured JSON Outputs from LLMs

Name: Semantic Kernel - Structured Outputs
Availability: OnlineOnly
Author: Semantic Kernel

Documentation for using OpenAI Structured Outputs with GPT-4o models on Azure OpenAI and OpenAI platforms, supporting strict JSON schema enforcement.

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Semantic Kernel

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Updated yesterday

Source checked Sep 20, 2026

Version dotnet-1.80.1

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Why it matters

Leverage Large Language Models (LLMs) to reliably generate structured JSON outputs and typed responses, enabling seamless integration with downstream applications and data processing pipelines.

Outcomes

What it gets done

Generate JSON data from unstructured LLM responses.

Implement function calling for precise data extraction.

Ensure LLM outputs conform to predefined schemas.

Process and utilize LLM-generated structured data.

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/sk-concept-structuredoutputs | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

Semantic Kernel - Structured Outputs

This documentation covers OpenAI's structured outputs feature for GPT-4o models, which enables strict JSON schema enforcement for AI-generated responses. The feature works with both Azure OpenAI and OpenAI platforms using specific model versions that support structured outputs. Use structured outputs when building systems that require schema compliance for AI responses, such as applications that parse outputs programmatically or feed AI-generated data into databases and APIs.

What it does

This documentation covers OpenAI's structured outputs feature for GPT-4o models, which enables strict JSON schema compliance in AI-generated responses. The feature works with specific model versions on both Azure OpenAI and OpenAI platforms to ensure responses conform to predefined schemas.

When to use - and when NOT to

Use structured outputs when you need schema compliance for applications that parse AI responses programmatically, such as extracting structured data from unstructured text, generating API payloads, or populating databases with AI-derived information.

Do not use this feature if you are working with older GPT models not listed in the supported versions, or if your application requires flexible, free-form responses where strict schema adherence would limit the model's expressiveness.

Inputs and outputs

Users provide a JSON schema definition that specifies the exact structure, field types, and constraints for the desired output. The feature processes prompts through supported GPT-4o models configured to respect the schema.

The output is a JSON object that conforms to the provided schema.

Integrations

Azure OpenAI: Requires access to gpt-4o-2024-08-06 or later models with the 2024-08-01-preview API version. When using token-based authentication instead of API keys, the Cognitive Services OpenAI Contributor role must be assigned to your Azure AD user.

OpenAI: Compatible with gpt-4o-mini-2024-07-18 and later, as well as gpt-4o-2024-08-06 and later models.

Who it's for

This documentation is relevant for developers building systems that consume AI-generated content programmatically and need to work with structured JSON outputs from GPT-4o models.

Source README

OpenAI Structured Outputs

Supported Models

Azure OpenAI:

Access to gpt-4o-2024-08-06 or later
The 2024-08-01-preview API version
If using a token instead of an API key, you must have the Cognitive Services OpenAI Contributor role assigned to your Azure AD user.
See more information here

OpenAI:

The OpenAI models supported are:
- gpt-4o-mini-2024-07-18 and later
- gpt-4o-2024-08-06 and later

FAQ

Common questions

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