Popular GitHub Repositories for Component Testing

Curso para aprender el lenguaje de programación Python desde cero y para principiantes. 100 clases, 44 horas en vídeo, código, proyectos y grupo de chat. Fundamentos, frontend, backend, testing, IA...

This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more.

Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view

Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

A GPT-empowered penetration testing tool

The Generative AI Landscape - A Collection of Awesome Generative AI Applications

Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Qodo-Cover: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement! ????

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

The Abstraction and Reasoning Corpus

??AutoDev: The AI-powered coding wizard（AI 驱动编程助手）with multilingual support ?, auto code generation ??, and a helpful bug-slaying assistant ?! Customizable prompts ? and a magic Auto Dev/Testing/Document/Agent feature ? included! ?

Open source data anonymization and synthetic data platform for developers. Anonymize your production data and sync it across your environments so that developers can safely use it.

This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced AI models developed by Microsoft. Phi models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks

Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR

Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

An open-source visual programming environment for battle-testing prompts to LLMs.

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Fully customizable AI chatbot component for your website

Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet