Open-source LLM eval frameworks (HELM, lm-eval-harness, BIG-Bench) — pick one

Quick guide. EleutherAI lm-evaluation-harness: the de facto research standard, hundreds of tasks, supports HuggingFace, OpenAI, Anthropic, vLLM; pick this for academic-benchmark coverage on local or API models. Stanford HELM: holistic, multi-metric (accuracy, calibration, robustness, fairness, efficiency, bias, toxicity); pick when you need a multi-dimensional report card, especially for procurement. Google BIG-Bench: 200+ creative reasoning tasks; pick for stress-testing novel capabilities. OpenAI Evals: lightweight Python framework, model-graded evals; pick for quick custom suites. DeepEval / Promptfoo / Inspect: app-side eval frameworks, friendlier for product teams.

None of these run continuously against provider APIs with alerting — they are libraries, not services. ModelWatch is the continuous-monitoring service on top, and it uses lm-evaluation-harness under the hood for the academic-benchmark slice so scores are directly comparable to published numbers.