structured-outputs

an ambient intelligence library

🔥🔥 Awesome Jev: open-source models, projects, benchmarks, and independent evaluations for Jev and System One models.

A high-performance API server that provides OpenAI-compatible endpoints for MLX models. Developed using Python and powered by the FastAPI framework, it provides an efficient, scalable, and user-friendly solution for running MLX-based vision and language models locally with an OpenAI-compatible interface.

An agentic extractor for messaging platforms. Noria receives messages & URLs via chat; scrapes web data to evaluate the content against custom scoring matrices (e.g., jobs, scholarships); and sends a structured summary with a calculated match score directly to the assigned messenger number.

Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE)

OpenAI's Structured Outputs with Logprobs

Structured Data Extractor for AI Agents. Search your documents or the web for specific data and get it back in JSON or Markdown in a single tool call.

Non-autoregressive decision engine on ModernBERT (151M) with calibrated uncertainty (RLCD), TypeSafe AI Jev benchmark audit, and in-browser WebGPU playground

Open reproduction of TypeSafe Jev: a 150M typed decision engine (noul/choice/score in one non-autoregressive pass, calibrated confidence). 0.697 vs Jev's 0.727, 2.5x better calibrated, 4x faster, free. Trains on a Colab T4 in 30 min.

Agentic Workflow Platform

A custom coding harness for TypeSafe AI's Jev: an LLM proposes, Jev answers narrow questions, code decides, every step leaves a receipt.

Local inference you can prove. A single-binary GGUF engine in C: serves, scores, and trains LoRA through the quantized weights it deploys, byte-reproducibly. Tool calls survive the token limit; every run replays as a signed receipt. CPU/CUDA/Metal, OpenAI- and Anthropic-compatible.

Declare the outcome, skip the logic. The developer-first infrastructure engine for structured, multi-turn AI conversations with built-in safety, async follow-ups, and native compliance

Python command-line tool for interacting with AI models through the OpenRouter API/Cloudflare AI Gateway, or local self-hosted Ollama. Optionally support Microsoft LLMLingua prompt token compression

OpenAi.JsonSchema is a lightweight library for generating valid JSON Schema for OpenAI models' Structured Outputs feature. It supports a wide range of types, ensures compatibility with OpenAI's JSON Schema format, and leverages C# descriptions and attributes for schema generation.

🧰 Some (very) simple helper functions for writing concise JSON Schema — perfect for OpenAI Structured Outputs.

Production grade Document AI workflows run on Tensorlake

Personal finance agent that decides whether an expense is safe — pay in full, split, use installments, or wait — from a 90-day balance forecast over real transaction history, with a deterministic decision core and a narrow multimodal evidence layer for receipts and messages.

Build typed agent workflows and operate every run from authoritative evidence — OpenAI Build Week 2026

This repository demonstrates structured data extraction using various language models and frameworks. It includes examples of generating JSON outputs for name and age extraction from text prompts. The project leverages models like Qwen and frameworks such as LangChain, vLLM, and Outlines for Transformers models.