🐢 Open-Source Evaluation & Testing for AI & LLM systems
Deliver safe & effective language models
MIT-licensed Framework for LLMs, RAGs, Chatbots testing. Configurable via YAML and integrable into CI pipelines for automated testing.
GPT4Go: AI-Powered Test Case Generation for Golang 🧪
A Python library for verifying code properties using natural language assertions.
Übungsaufgaben zum Buch "Basiswissen KI-Testen"
Agent testing library that uses an agent to test your agent, in Go.
An automated approach for exploring and testing conversational agents using large language models. TRACER discovers chatbot functionalities, generates user profiles, and creates comprehensive test suites for conversational AI systems.
Evaluation results and experimental data for TRACER, demonstrating its effectiveness in discovering chatbot functionalities and detecting errors with coverage analysis and mutation testing.
Burro is a command-line interface (CLI) tool built with Deno for evaluating Large Language Model (LLM) outputs. It provides a straightforward way to run different types of evaluations with secure API key management.
AI Generated BDD for Java and Junit using ChatGPT4o code
A plug & play framework for generative ai projects to be tested & automated
An academic project that facilitates development and testing.
🤖 The perfect playground for testing AI-generated React components. Built for ChatGPT/Claude users to instantly test and iterate on AI-created components.
This repository contains a study comparing the web search capabilities of four AI assistants: Gemini 2.0 Flash, ChatGPT-4 Turbo, DeepSeekR1, and Grok 3
Agentic Workflow Evaluation: Text Summarization Agent. This project includes an AI agent evaluation workflow using a text summarization model with OpenAI API and Transformers library. It follows an iterative approach: generate summaries, analyze metrics, adjust parameters, and retest to refine AI agents for accuracy, readability, and performance.
Modular and extensible Python framework for applying synthetic inference and controlled perturbations to AI model inputs, labels, and hyperparameters. Evaluate robustness, sensitivity, and stability of algorithms under realistic variations and adverse scenarios.
AI Generated BDD for Java and Junit using ChatGPT4o code
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