Popular GitHub Repositories for Operational Validation

structured outputs for llms

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Evidently is ??an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

53AI Hub is an open-source AI portal, which enables you to quickly build a operational-level AI portal to launch and operate AI agents, prompts, and AI tools. It supports seamless integration with development platforms like Coze, Dify, FastGPT, RAGFlow.

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

? Open-Source Evaluation & Testing library for LLM Agents

? Open-Source Evaluation & Testing for AI & LLM systems

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

A high-level machine learning and deep learning library for the PHP language.

Zero-boilerplate REST API framework for PHP 8+. Auto-generates routes, docs, validation.

Automatically find issues in image datasets and practice data-centric computer vision.

Perception toolkit for sim2real training and validation in Unity

Claude Code Skill for browser automation with Playwright. Model-invoked - Claude autonomously writes and executes custom automation for testing and validation.

? Autonomous AI development with 19 specialized sub-agents coordinated through Claude Code. Production-ready applications from natural language requests with browser validation.

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

Operational - open-source tool to track important events and receive push notifications on your device.

This methodology provides a structured approach for collaborating with AI systems on software development projects. It addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints and validation checkpoints.

A full pipeline AutoML tool for tabular data

Custom GPT Showcase, featuring advanced workflows and operational logic.

Fast, comprehensive DNS performance testing with DNSSEC validation, DoH/DoT support, and enterprise features