Inspect AI

UK AI Security Institute framework for large language model evaluations and benchmarks.

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About

Inspect is an open source framework for large language model evaluations created by the UK AI Security Institute. Evaluations are composed from datasets, solvers, and scorers in Python, with built-in components for prompt engineering, multi-turn dialog, tool usage, and model-graded scoring, so tasks range from simple question-answering benchmarks to agentic evaluations in which models operate tools. A companion collection provides more than 200 prebuilt evaluations that run against supported model providers without modification, and the framework is extensible through ordinary Python packages that add new elicitation and scoring techniques. Tooling includes a log viewer for examining transcripts of evaluation runs. Released under the MIT license with documentation at inspect.aisi.org.uk, Inspect is used by safety researchers, government evaluators, and engineering teams that need reproducible, auditable measurements of model capability and behavior.

Should you use Inspect AI?

Pick it when

Pick Inspect when you build reproducible capability or safety evaluations, especially agentic tasks where the model operates tools, and want 200+ prebuilt evals plus a log viewer for auditing transcripts.

Look elsewhere when

Skip it if you just need leaderboard-style accuracy numbers on open checkpoints, where lm-evaluation-harness or LightEval are the common reference, or if you are testing an app's prompts in CI, where promptfoo or DeepEval fit.

Alternatives to Inspect AI

  • LM Evaluation Harness

    Standard prompt formats for comparable accuracy numbers on local checkpoints; not designed for agentic tasks that need tool use.

  • LightEval

    Broad 1,000+ task catalog across many inference backends and can run Inspect tasks; centers on benchmark suites, not custom agent scaffolds.

  • HELM

    Holistic multi-metric leaderboards with full prompt transparency, but in maintenance mode since June 2026.

  • PyRIT

    Focused on adversarial red-team campaigns against deployed endpoints rather than scored capability benchmarks.

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