structured-output

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.

OpenAPI definitions, converters and LLM function calling schema composer.

🚬 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.

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.

🔍Declarative LLM-powered analyzer for security events and system logs. Extracts, structures, and visualizes data for Kibana/Elasticsearch.

Python for logic. English for intelligence.

Better LLMs Structured Outputs - A useful python package!

[ACL 2025] Repository for our paper "DRS: Deep Question Reformulation With Structured Output".