Ross ROSS = Recommend OSS · open-source software intelligence for agents

UKGovernmentBEIS/inspect_ai

Inspect: A framework for large language model evaluations observed · 2026-08-28

github.com/UKGovernmentBEIS/inspect_ai · homepage · Python · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

71/100

  • Activity 99
  • Release rhythm 35
  • Longevity 73

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 1023
  • days_rel: n/a
  • days_push: 7
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

2640 stars · 679 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Inspect is an open-source Python framework from the UK AI Security Institute for running large language model evaluations, with composable building blocks for datasets, solvers, tools, scorers, and agents. It ships 200+ pre-built benchmark evaluations, a web-based Inspect View for visualizing results, a VS Code extension, and sandboxing support for running untrusted model code across Docker, Kubernetes, Modal, and other backends.

Use cases

  • run LLM benchmarks on any model provider
  • evaluate coding agents like Claude Code or Codex CLI on agentic tasks
  • build custom evaluations with prompt engineering, tool use, and multi-turn dialog
  • score model outputs with model-graded and custom scorers
  • sandbox untrusted model-generated code safely during evals
  • monitor and visualize evaluation logs in a web UI
  • test agent behavior, reasoning, and safety properties
  • run MCP and custom tools inside evaluations

When to choose

  • you need a rigorous, reproducible evaluation harness for LLMs or agents
  • you want to run established benchmarks without writing them yourself
  • you need sandboxed execution of model-generated code
  • you want extensible Python tooling with a mature ecosystem and active development

When to avoid

  • you only need simple one-off model API calls without evaluation scoring
  • you need a production inference-serving framework rather than an eval harness
  • your stack is not Python-based and you cannot add Python tooling

Facets

framework · maturity active

llm-inference agent-framework testing benchmarking mcp chatbot machine-learning developer-tools cli data-visualization large-language-models artificial-intelligence machine-learning developer-tools testing python cloud cli cross-platform llm-evaluations eval-framework benchmark-suite sandboxing model-graded-evals vscode-extension inspect-view agentic-evals ai-safety prompt-engineering ai-agents command-line docker kubernetes

4 sources

Member repositories

RepositoryRoleHealth v2
UKGovernmentBEIS/inspect_aimain71

For agents

markdown · JSON · MCP: product_card(name="UKGovernmentBEIS/inspect_ai")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem