# Evaluation & Test

## Articles

- [Put your AI to the Test with Microsoft.Extensions.AI.Evaluation](https://developer.microsoft.com/blog/put-your-ai-to-the-test-with-microsoft-extensions-ai-evaluation)
- [The Microsoft.Extensions.AI.Evaluation libraries](https://learn.microsoft.com/en-us/dotnet/ai/evaluation/libraries)
- [Evaluating LLM Models in GitHub Copilot. A Practical Scoring and Assessment Guide](https://dev.to/pwd9000/evaluating-llm-models-in-github-copilot-a-practical-scoring-and-assessment-guide-1f23)

## libraries

- [vibrantlabsai/ragas](https://github.com/vibrantlabsai/ragas) - Supercharge Your LLM Application Evaluations
- [promptfoo/promptfoo](https://github.com/promptfoo/promptfoo) - Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI
- [confident-ai/deepeval](https://github.com/confident-ai/deepeval) - The LLM Evaluation Framework
- [AgentEvalHQ/AgentEval](https://github.com/AgentEvalHQ/AgentEval) - AgentEval is the comprehensive .NET toolkit for AI agent evaluation—tool usage validation, RAG quality metrics, stochastic evaluation, and model comparison—built first for Microsoft Agent Framework (MAF) and Microsoft.Extensions.AI. What RAGAS, PromptFoo and DeepEval do for Python, AgentEval does for .NET
- [elbruno/elbruno-ai-evaluation](https://github.com/elbruno/elbruno-ai-evaluation) - AI Testing & Observability Toolkit for .NET - deterministic evaluators, synthetic data, golden datasets, regression detection
- [SWE-bench/SWE-bench](https://github.com/SWE-bench/SWE-bench) - SWE-bench: Can Language Models Resolve Real-world Github Issues?
- [harbor-framework/terminal-bench](https://github.com/harbor-framework/terminal-bench) - A benchmark for LLMs on complicated tasks in the terminal
- [harbor-framework/terminal-bench-2](https://github.com/harbor-framework/terminal-bench-2) - Terminal-Bench is a popular benchmark for measuring the capabilities of agents and language models to perform valuable work in containerized environments. 
- [harbor-framework/terminal-bench-2-1](https://github.com/harbor-framework/terminal-bench-2-1) - Terminal-Bench 2.1
- [Aider-AI/aider/benchmark](https://github.com/Aider-AI/aider/tree/main/benchmark)
- [openai/human-eval](https://github.com/openai/human-eval) - Code for the paper "Evaluating Large Language Models Trained on Code"
- [microsoft/waza](https://github.com/microsoft/waza) - CLI / Framework for Agent Skills - create, test, measure and improve skill quality and effectiveness
- [microsoft/vally](https://microsoft.github.io/vally/) - The extensible eval platform
- [UiPath/coder_eval](https://github.com/UiPath/coder_eval) - Test that your Claude Code skills, MCP servers, and CLIs actually work when an agent uses them — sandboxed YAML suites, activation checks, A/B experiments, CI gates.
- [scaleapi/SWE-bench_Pro-os](https://github.com/scaleapi/SWE-bench_Pro-os) - SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
- [harbor-framework/harbor](https://github.com/harbor-framework/harbor) - Framework for evaluating and improving agents