OpenAI Structured Outputs Samples
https://github.com/openai/openai-structured-outputs-samples- Category
- AI Tools
- Rank
- No. 1402Tools index
Previous survey · No. 1409 ·
- Pricing
- Open Source
- Type
- TOOL
- Builder
- openai
- GitHub
- 683 stars
- Date
About
Reference apps for OpenAI's Structured Outputs feature. Patterns for getting reliable JSON out of GPT models via schemas.
What it does
A trio of runnable Next.js demonstrations shows structured model responses moving from resume extraction to generated interfaces and a conversational assistant with tool use. The applications turn constrained model output into visible, interactive workflows.
Why it's ranked here
The collection makes a strong teaching reference because its examples increase in complexity and connect model output to practical interfaces. It is less compelling as reusable infrastructure because the repository presents separate sample applications, not a shared library.
What's good
The examples cover three distinct patterns instead of repeating one basic request. Resume extraction merges streamed JSON fragments into the interface. Generative UI accumulates partial function arguments, parses incomplete JSON, and updates the rendered component while output arrives.
Tradeoffs
Setup happens separately for each application and requires an OpenAI API account and key. The repository focuses exclusively on Next.js applications. It does not present a general package, command-line tool, Python implementation, or production deployment guide.
How to use it well
Use it when learning Structured Outputs or borrowing patterns for a Next.js prototype. Start with extraction, then study streamed generative UI and the assistant workflow. Look elsewhere for a framework-neutral library, Python application, deployment recipe, or complete production architecture.
Technical notes+
README.md describes three independent Next.js apps. generative-ui/app/page.tsx handles function_arguments_delta and function_arguments_done events, accumulates argument text, and uses partial-json to parse incomplete payloads. generative-ui/lib/constants.ts selects gpt-5.2 and defines component-selection guidance. resume-extraction/app/page.tsx posts FormData to the extraction endpoint, reads the response stream, splits newline-delimited chunks, parses each JSON object, and merges fields into React state. The three next.config.mjs and tailwind.config.ts files show separate Next.js and Tailwind configurations.
Observed
- License
- MIT License
- Primary implementation language
- TypeScript and TSX in the supplied application source
- Interface
- Three browser-based Next.js sample applications
- Installation surface
- Clone the repository, enter an application folder, and follow that application's local instructions
- External requirement
- An OpenAI API account and API key are required
- Application scope
- Resume extraction, generative UI, and a conversational assistant
Read from README.md, generative-ui/next.config.mjs, generative-ui/postcss.config.mjs, generative-ui/tailwind.config.ts, resume-extraction/next.config.mjs, resume-extraction/postcss.config.mjs, resume-extraction/tailwind.config.ts, conversational-assistant/next.config.mjs, conversational-assistant/postcss.config.mjs, conversational-assistant/tailwind.config.ts, generative-ui/app/page.tsx, generative-ui/lib/utils.ts, generative-ui/app/layout.tsx, generative-ui/lib/constants.ts, resume-extraction/app/page.tsx.
What it can do
Generate structured JSON from natural language prompts
Natural language prompt and JSON schema → Valid JSON data conforming to schema
Extract structured data from unstructured text
Raw text and data schema definition → Structured JSON with extracted information
Validate JSON output against predefined schemas
JSON data and schema specification → Validation results and compliant JSON
Convert free-form responses to structured formats
Unstructured AI responses and target schema → Formatted JSON matching schema requirements
Demonstrate schema-based prompt patterns
Use case requirements and data structure needs → Working code examples and implementation patterns
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