RepoBench

Benchmark the coding agents you actually use on the code you actually work on.

Your repo. Your agents. Your benchmark.

RepoBench turns a repository's real engineering history into a private, reproducible benchmark suite, then runs the coding agent CLIs already installed and configured on your machine — Claude Code, Codex CLI, OpenCode, Gemini CLI, or any custom command — against it, verifying every attempt with hidden tests.

Public benchmarks answer "which model scores highest on a fixed suite?". That is not the question your team faces. You need to answer:

Which agent stack works best for the work that actually happens in this codebase?

RepoBench compares whole Execution Targets — harness + model + provider + configuration + repository instructions — on tasks mined from your own merged PRs. It never manages API keys, never implements an agent loop, and never calls a model API: it runs the CLIs you already have, the way you already have them configured.

$ repobench init

1,842 PRs analyzed (last 180 days)
43 high-confidence eval candidates

$ repobench benchmark build

24 representative tasks
Benchmark Health: 86/100

$ repobench run claude codex glm

Running 72 local trials...
  [01/72] t_482_1a2b · claude · SOLVED · 8m41s
  [02/72] t_482_1a2b · codex  · SOLVED · 7m02s
  ...

$ repobench report

Target                     Solve      Time   $/Solve

claude                     86%       9m18      $1.49
codex                      82%       8m42      $0.71
glm                        75%      11m03      $0.18

claude vs codex
Observed difference: +4pp
95% CI: -7pp → +14pp
No conclusive quality difference.

Cost-effective recommendation:
codex

That is the entire product: your history in, a defensible comparison out.

Install

Requires Python 3.12+ and uv:

# run inside a checkout of this repository
uv sync

# or install as a tool
uv tool install .

Quickstart

cd your-repository

repobench doctor            # git, project tooling, installed harnesses
repobench init              # detect project environment, write repobench.yml
repobench analyze           # mine merged PRs into eval candidates (no tokens used)
repobench candidates        # inspect what was found and what was filtered
repobench benchmark build   # reconstruct + validate tasks, sample a benchmark
repobench run claude codex glm   # or: repobench run --all
repobench report            # text; --format json for CI/dashboards

repobench.yml starts from detected suggestions — test commands, package manager, benchmark size — and is yours to edit:

version: 1

project:
  language: python
  install_command: uv sync --frozen
  test_command: python -m pytest
  regression_command: python -m pytest

benchmark:
  size: 24
  dimensions:
    task_type: 0.30
    subsystem: 0.40
    complexity: 0.30

execution:
  jobs: 1
  timeout_minutes: 20

targets:
  claude:
    harness: claude
    model: opus
  codex:
    harness: codex
    model: gpt-x
  glm:
    harness: opencode
    model: zai/glm-x

How it works

• Mining — every merged PR (GitHub merge-commit convention) is assessed as a potential retrospective task: base/gold SHAs, implementation vs test changes, task type, subsystem and complexity. Hard filters (no test change, no instruction provenance, too small/too large) reject candidates with stable codes — repobench candidates shows exactly what was filtered and why.
• Validation — each candidate is reconstructed into a task package (base.tar, instruction.md, gold.patch, verifier.patch, metadata.json) and proven with real test runs: the baseline must pass, the hidden verifier must fail on the untouched base, must pass with the gold solution, and must pass deterministically. Tasks that fail are rejected, never silently kept.
• Benchmark sampling — validated tasks are sampled greedily so the benchmark's task-type/subsystem/complexity distribution matches your Workload Universe. Coverage and a composite Benchmark Health score (with honest warnings) are computed and stored; benchmarks are immutable and versioned.
• Local execution — each trial materializes the base tree into a fresh workspace with a synthetic git repository (a single "RepoBench benchmark base" commit, no remotes, no history, no gold), runs the target's harness CLI with a timeout and process-group cleanup, then captures the final tree diff.
• Hidden verifier + stats — after the agent finishes, the verifier patch is applied on a copy and the hidden tests decide SOLVED/UNSOLVED (exit codes never do). Results are aggregated with Wilson confidence intervals and paired bootstrap comparisons; a "winner" is only declared when the difference is statistically conclusive, and the cost-effective recommendation never invents cost data it does not have.

Requirements

• git — history mining and workspace materialization.
• Python 3.12+ with uv.
• gh (optional) — GitHub CLI for PR/issue enrichment when your repository has a GitHub origin. Without it, RepoBench degrades to local history metadata.
• Harnesses — installed and authenticated by you, exactly as you normally use them (claude, codex, opencode, gemini, or a custom command). RepoBench detects binaries and versions in repobench doctor but never probes auth, never runs inference outside a benchmark run, and never stores credentials.

Security model (read this)

RepoBench is not a security sandbox. Trials execute host-native on your machine. A coding agent with broad permissions can access the filesystem, the network and processes. What RepoBench does is reduce solution leakage:

• synthetic git repository: one commit, no remotes, no original history or branches;
• gold patch and hidden verifier kept outside the workspace until verification;
• GH_TOKEN / GITHUB_TOKEN scrubbed, GH_CONFIG_DIR pointed at an empty temp dir, SSH_AUTH_SOCK removed, git credential prompts disabled;
• no original repository path in the prompt.

Network isolation: none. Without a network sandbox an agent could search the web, and for public repositories the model may have seen the code before. Benchmark Health reports this limitation explicitly instead of hiding it. Private repositories are the methodologically stronger setting.

Any harness, including yours

The generic command adapter (PRD §25) runs any local CLI. Placeholders: {workspace} {prompt} {prompt_file} {task_id} {target_id} — substituted into a plain argv list, never a shell:

targets:
  my-agent:
    harness: command
    command:
      - my-agent
      - run
      - --model
      - my-model
      - --prompt-file
      - "{prompt_file}"
    output: text

Documentation

The full product spec — methodology, statistics, security model, acceptance criteria, roadmap — lives in docs/PRD.md.

Development

uv sync
uv run pytest -q        # unit + integration + end-to-end (hermetic, no network)
uv run repobench --help

License

Apache License 2.0 — see LICENSE.