WolfBench

WolfBench is an open-source Apache-2.0 toolkit and evaluation methodology by Wolfram Ravenwolf for collecting, curating, analyzing, and publishing AI-agent benchmark results. It evaluates agents on Terminal-Bench 2.0 through Harbor orchestration and Daytona sandboxes, preserving configuration metadata, per-task results, token usage, timing, failures, and trajectory traces. Rather than reporting only an average, WolfBench calculates five metrics: the union of tasks solved across runs (Answers), the best single-run score, the mean across valid runs, the worst single-run score, and the tasks solved in every run (Solid). Repeated runs under consistent benchmark, runner, sandbox, agent, model-service, and settings configurations expose variance and distinguish dependable performance from isolated successes. The repository includes a Marimo dashboard that scans local and remote run directories over SSH, deduplicates and classifies runs, filters incomplete or invalid results, downloads data, saves curated JSON snapshots, uploads evaluations and trajectory traces to W&B Weave, and generates interactive HTML leaderboard charts. The core collector and chart generator use only the Python standard library; the dashboard requires Python 3.12+, Marimo, Weave/W&B, and SSH access for initial collection. Benchmark data is licensed CC-BY-4.0.

Terminal Bench 4 is a benchmark for comparing coding-model performance across tasks with different difficulty levels.

CommerceAgentBench is a benchmark developed and maintained by the Accio team at Alibaba International for evaluating whether AI agents can complete long-horizon business workflows in high-fidelity, stateful, reproducible replicas of online services. Its tasks cover browser operations, native-style command-line tools, file and spreadsheet production, API and MCP workflows, public-web research, supplier analysis, product publishing, logistics, storefront configuration, and other commerce operations. Each task runs in a fresh container and is graded by a deterministic or LLM-assisted verifier. The benchmark uses local mock services representing commerce, SaaS, messaging, document, and operational systems; runs preserve the resolved configuration, agent trajectory, verifier result, artifacts, logs, and container metadata for auditing and reproducibility. The repository describes 107 tasks spanning CLI, browser, file, and API/MCP interfaces, with text-only, browser-text-capable, and vision-required capability slices. The project was previously called RealReplicaBench.

W&B Weave is an AI evaluation and observability tool that stores and displays per-run trajectories behind leaderboard results. In the WolfBench workflow, it preserves inspectable traces of commands, messages, failures, and other run evidence so reviewers can trace a score back to the conditions that produced it.

Harbor is a framework from the creators of Terminal-Bench for evaluating and optimizing AI agents and language models in container environments. It can run arbitrary agents, including Claude Code, OpenHands, and Codex CLI, against shared or custom benchmarks and environments such as Terminal-Bench, SWE-Bench, and Aider Polyglot. Harbor launches benchmark runs locally with Docker or in parallel through cloud and sandbox providers, and can generate rollouts for reinforcement-learning optimization. It is distributed as a Python package installable with uv or pip and provides command-line tools for running datasets, selecting agents and models, and listing supported benchmarks.