There are 2 repositories under structured-output topic.
The AI framework that adds the engineering to prompt engineering (Python/TS/Ruby/Java/C#/Rust/Go compatible)
MLX Omni Server is a local inference server powered by Apple's MLX framework, specifically designed for Apple Silicon (M-series) chips. It implements OpenAI-compatible API endpoints, enabling seamless integration with existing OpenAI SDK clients while leveraging the power of local ML inference.
Unified Go interface for Language Model (LLM) providers. Simplifies LLM integration with flexible prompt management and common task functions.
A versatile workflow automation platform to create, organize, and execute AI workflows, from a single LLM to complex AI-driven workflows.
Hybrid Schema-Guided Reasoning (SGR) has agentic system design create by neuraldeep community Creator of SGR concept: https://abdullin.com/schema-guided-reasoning/demo Schema-Guided Reasoning (SGR) is a technique that guides large language models (LLMs) to produce structured, clear, and predictable outputs by enforcing reasoning through
React Native Apple LLM plugin using Foundation Models
Simplifies the retrieval, extraction, and training of structured data from various unstructured sources.
🚬 cigs are chainable Ai functions for typescript. Call functions with natural language and get a response back in a specified structure. Uses OpenAI's latest Structured Outputs.
[ti]ny [li]ttle machine learning [tool]box - Machine learning, anomaly detection, one-class classification, and structured output prediction
Making LLM Tool-Calling Simpler.
(Discontinued) Non-Pydantic, Non-JSON Schema, efficient AutoPrompting and Structured Output Library
A comprehensive Go client library for the Perplexity AI API with support for chat completions, async jobs, streaming, multimodal messages, structured outputs, and web search integration
This repository demonstrates how to leverage OpenAI's GPT-4 models with JSON Strict Mode to extract structured data from web pages. It combines web scraping capabilities from Firecrawl with OpenAI's advanced language models to create a powerful data extraction pipeline.
🔍Declarative LLM-powered analyzer for security events and system logs. Extracts, structures, and visualizes data for Kibana/Elasticsearch.
Learn how to build effective LLM-based applications with Semantic Kernel in C#
Code from the ODSC Agentic Graph RAG workshop combining vector, FTS & graph retrieval for RAG. Includes observability and guardrails for evaluating outputs.
Prompture is an API-first library for requesting structured JSON output from LLMs (or any structure), validating it against a schema, and running comparative tests between models.
Better LLMs Structured Outputs - A useful python package!
[ACL 2025] Repository for our paper "DRS: Deep Question Reformulation With Structured Output".
Schema-first AI analysis CLI that transforms messy data into structured insights. Define your output format, get guaranteed JSON results from any source. Combines OpenAI models with multi-tool orchestration (Code Interpreter, File Search, Web Search, MCP) for AI-powered data synthesis.
This is the Python backend for InsightAI
Structured Output OpenAI Showcase. A Prime Numbers Calculator that demonstrates OpenAI's structured output capabilities. This repository is public because current LLM examples often use outdated API calls, and this script aims to help users quickly experiment with structured outputs.
Python decorator to define GPT-powered functions on top of OpenAI's structured output
Open Source Deep Research
Pickup Line Generator is a fun and creative web application that helps you craft the perfect pickup line for your crush. Simply input a description of your crush and choose a style, and our AI-powered generator will create unique and creative pickup lines tailored to your preferences.
Universal Python library for Structured Outputs with any LLM provider
Enter cricket activity log (fitness, coaching, match, rest day) through voice interface. Get insights on historical data.
Develop an intuition about Large Language Models (LLMs)
This repository demonstrates how to use OpenAI's Response API (with GPT-4.1 and tool calling) to extract the main product image URL from an e-commerce product page. It provides both Python and TypeScript implementations, returning a structured output for easy integration.
A sample application to demonstrate how to use Structured Outputs in OpenAI Chat Completions API with streaming, built using Next.js.
This repository contains examples for learning Google's Agent Development Kit (ADK), a powerful framework for building LLM-powered agents.
🤖 Build powerful AI applications effortlessly with the AI SDK x OpenAI, simplifying integration and enhancing your projects with cutting-edge technology.