This episode delivers an opinionated architectural shootout of the four major LLM evaluation harnesses: Inspect AI from the UK AI Safety Institute, Promptfoo, DeepEval, and Braintrust. We break down each framework's core abstraction and design philosophy — Inspect's solver-scorer pattern, Promptfoo's matrix-style YAML configs, DeepEval's pytest-style assertions, and Braintrust's hosted experiment-tracking and dataset-versioning model. Then we stress-test each one: multi-turn conversations, tool-using agents, async execution at scale, dataset versioning, and CI integration. No equal-time hedging — we pick winners for specific use cases, from research labs running safety evals to startups needing CI regression tests to enterprise teams wanting hosted dashboards.
