an ambient intelligence library

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.

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.

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.

Agentic Workflow Platform

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.

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.

Single-binary GGUF model runtime in C — CPU/CUDA/Metal, OpenAI-compatible. Serves, scores, and trains LoRA directly through the quantized weights it deploys, with byte-reproducible adapters. Tool calls survive the token limit; sparse MoE and schema-constrained decoding included.

Score the trustworthiness of outputs from any LLM in real-time

Kotlin Multiplatform engine for running Gemma LLMs on-device on Android via LiteRT-LM — stateful KV-cache chat sessions, resumable model management, function calling. Includes NativeLM, a private Local AI chat app. AGPL-3.0 / commercial.

This project demonstrates how to get structured outputs from Deepseek R1 reasoning model and do tool calling using an OpenAI summarization step. It's made using Next.js serverless routes and uses shadcn/ui for the frontend.

Local Codex CLI prompt refinery: ingest JSON/JSONL histories, dedupe, embed (OpenAI), cluster (sqlite-vec), synthesize atomic/workflow prompts via Responses API (Structured Outputs). FTS5+vector search; Streamlit UI; Typer CLI.

Ultra-low-latency reverse proxy that repairs truncated & malformed JSON in LLM streaming responses (OpenAI, Anthropic, Vertex AI, Bedrock) — fixes JSONDecodeError / serde_json EOF on truncated tool calls.

Experiment using JsonSchemaExporter in .NET 9 to improve developer experience with OpenAI's Structured Outputs

Extract structured fields from a document at any scale, from a handful to n number.