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Language Agent Biology Benchmark: measuring real-world capabilities of AI systems performing scientific research tasks

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LABBench Evaluations

Inspect AI implementations of the LABBench family of biology-research benchmarks, built for the inspect_evals registry.

This repository is the home for LABBench evaluations: it currently ships lab_bench_2 (LAB-Bench 2), and is structured to host future versions and related LABBench evals alongside it, each as its own self-contained evaluation under src/.

Evaluations

Eval Description Docs
lab_bench_2 LAB-Bench 2 (arXiv:2604.09554) — language models and research agents on life-science tasks spanning literature reasoning, database access, figures, tables, protocols, source quality, sequence analysis, cloning, patents, and clinical trials. src/lab_bench_2/README.md

Quickstart

This project uses uv for dependency management.

# Install dependencies
uv sync --dev --all-extras

# Run an evaluation (see the eval's README for tags, modes, and solvers)
uv run inspect eval lab_bench_2 --model openai/gpt-5-nano

# View logs
uv run inspect view

# Run tests
uv run pytest

If you don't want to pass --model each time, set it in a .env file:

INSPECT_EVAL_MODEL=anthropic/claude-opus-4-1-20250805
ANTHROPIC_API_KEY=<anthropic-api-key>

See each evaluation's README for its parameters, dataset details, and scoring — for lab_bench_2, src/lab_bench_2/README.md.

Repository structure

Each evaluation lives in its own directory under src/ and is registered via entry points in pyproject.toml; its tests live in tests/<eval_name>/.

src/
  lab_bench_2/          # LAB-Bench 2 evaluation (task, dataset, scorers, solvers)
  utils/                # Shared helpers
tests/
  lab_bench_2/          # Tests for lab_bench_2

Development

Always run tooling through uv (not bare python/pytest/pip):

uv sync                       # install/sync dependencies
uv run pytest                 # run tests
uv run ruff check && uv run ruff format --check
uv run mypy src tests         # type-check source and tests
make check                    # full check suite (ruff, mypy, build, autolint, ...)

CI workflows

Standard checks (always active)

  • Checks (checks.yml) — runs ruff, mypy, POSIX code check, unlisted-eval check, package build, autolint, generated-docs check, and large-file scan on every push and PR.
  • Markdown Lint (markdown-lint.yml) — lints markdown files on PRs
  • PR Template Check (pr-template-check.yml) — verifies PR body contains the required checklist
  • Sync Template (sync-template.yml) — weekly sync of managed files from the upstream template (skips inactive repos)
  • Sync Upstream (sync-upstream.yml) — weekly sync of managed files from inspect_evals

Template & managed files

This repository is built on the inspect-eval-template. Some files (CI workflows, quality tooling, Claude Code skills, shared docs) are template-managed and kept up to date via an automated sync; your evaluation code, tests, and READMEs are yours. See MANAGED_FILES.md for the split and AGENTS.md for agent-assisted workflows and the available skills.

Documentation

  • CONTRIBUTING.md — development setup, testing, and submission guidelines.
  • BEST_PRACTICES.md — evaluation design best practices (synced from inspect_evals).
  • EVALUATION_CHECKLIST.md — quality checklist used when reviewing evaluations (synced from inspect_evals).
  • AUTOMATED_CHECKS.md — what tools/run_autolint.py checks, and how to suppress individual rules.
  • TASK_VERSIONING.md — when to bump an eval's task version.
  • AGENTS.md — repo-wide tips for coding agents and pointers to the skills above.
  • MANAGED_FILES.md — which files in this repo are template-managed vs. user-owned, and how the sync preserves customizations.
  • CLAUDE.md — project-level instructions Claude Code reads on every session in this repo.

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Language Agent Biology Benchmark: measuring real-world capabilities of AI systems performing scientific research tasks

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