Datasets:

wanlilll
/

WeaveBench

	---
	license: mit
	language:
	- en
	size_categories:
	- n<1K
	task_categories:
	- other
	tags:
	- benchmark
	- computer-use-agent
	- gui
	- cli
	- hybrid-interface
	- long-horizon
	- agent-as-judge
	pretty_name: WeaveBench
	---

	# WeaveBench

	> A long-horizon, real-world benchmark for computer-use agents with hybrid GUI + CLI + code interfaces.

	📄 Paper: [github.com/weavebench/WeaveBench (paper.pdf)](https://github.com/weavebench/WeaveBench/blob/main/paper.pdf)
	💻 Code: [github.com/weavebench/WeaveBench](https://github.com/weavebench/WeaveBench)
	🌐 Website: [weavebench.github.io](https://weavebench.github.io)

	114 long-horizon, real-world tasks across 8 work domains, where every task requires the agent to interleave GUI clicks with shell/code in one trajectory. Scored by a trajectory-aware Agent-as-Judge that reads chat trace + deliverables and emits per-clause evidence — much harder to spoof than file-existence checks. Best observed pairing in the paper: Claude Opus 4.7 + Claude Code at 41.2 % PassRate — far from saturation.

	This HF repository hosts everything needed to run the benchmark end-to-end:

	1. `tasks/` — 114 paper-final tasks + workspace assets (~207 MB)
	2. `runtime_assets/` — pre-built bootstrap tarballs for the 4 in-VM agent harnesses (~852 MB)
	3. `vm/Ubuntu.qcow2` — the paper-canonical Ubuntu VM image (28.46 GB)
	4. `judge/judge_template.tar.gz` — host-side OpenClaw judge profile template (~7 KB)

	> 🇨🇳 Users in China: `export HF_ENDPOINT=https://hf-mirror.com` — all `weavebench-download-*` commands honor it automatically.

	---

	## 1. Layout

	```
	WeaveBench/
	├── tasks/ ← 207 MB
	│ ├── DAV/ DES/ DOC/ DSK/ GAM/ OPS/ SPA/ WEB/ ← 8 domains
	│ │ └── <DOMAIN>_task_<NN>_<slug>.md ← 114 task .md files
	│ └── workspace/<DOMAIN>/<task_dir>/ ← 951 supporting files (gt/, exec/, …)
	│
	├── runtime_assets/ ← 852 MB total
	│ ├── openclaw.tar.gz 514 MB (reference harness)
	│ ├── codex.tar.gz 131 MB (OpenAI Codex CLI)
	│ ├── claudecode.tar.gz 72 MB (Anthropic Claude Code)
	│ ├── hermes.tar.gz 127 MB (Nous Research Hermes)
	│ └── hermes_mcp_wheels.tar.gz 9 MB (offline mcp wheels for Hermes)
	│
	├── vm/
	│ └── Ubuntu.qcow2 28.46 GB (v3_eyeson_apps, paper-canonical)
	│
	└── judge/
	└── judge_template.tar.gz ~7 KB (OpenClaw judge profile)
	```

	Total: ~29.5 GB. See the [README §Resource budget](https://github.com/weavebench/WeaveBench#resource-budget-read-before-you-commit) on the code repo for what to allocate.

	## 2. One-command download + run

	```bash
	git clone https://github.com/weavebench/WeaveBench.git && cd WeaveBench

	export OPENROUTER_API_KEY=sk-or-v1-...
	# Optional: export HF_ENDPOINT=https://hf-mirror.com # if in China

	# bash scripts/setup.sh does all four downloads + npm install -g openclaw + judge bootstrap
	bash scripts/setup.sh

	# Smoke test (default model: openai/gpt-5.5)
	weavebench-run \
	--harness openclaw \
	--model openai/gpt-5.5 \
	--tasks_root ./cache/tasks \
	--domains WEB --task_filter task_1_ \
	--result_dir ./results/smoke
	```

	Or fetch individual assets:

	```bash
	weavebench-download-dataset --dest ./cache # tasks/ 207 MB
	weavebench-download-assets --dest ./cache # runtime_assets/ 852 MB total
	weavebench-download-vm --dest ./cache # vm/Ubuntu.qcow2 28.46 GB
	weavebench-download-judge # judge/ → ~/judge_agent_test/
	```

	## 3. Try it in 30 seconds (no VM, no API key)

	If you just want to see what `score.json` from the Agent-as-Judge looks like:

	```bash
	git clone https://github.com/weavebench/WeaveBench.git && cd WeaveBench
	pip install -e .
	weavebench-demo
	```

	This prints a real paper-canonical Claude Opus 4.7 rollout's score with per-artifact / per-clause judge evidence in <1 second — no network calls, no tokens.

	## 4. Task `.md` schema

	Each task file has these sections in order:

	```markdown
	# <Human title>

	## Goal — short user request (what the agent reads as `instruction`)
	## Setup — preconditions assumed true in the VM
	## Warmup — bash commands the orchestrator runs before the agent starts
	## Expected Output — files the agent must produce in /tmp_workspace/results/
	## Grader — Python `def grade(workspace_path, transcript) -> dict`
	```

	The embedded `grade(...)` function returns `{"score": float ∈ [0, 1], "scores": {sub_rubric: float, ...}, "msg": "..."}` — but in the trajectory-aware judge pipeline (the only scoring path in `weavebench-run`), this is documentation only. Scoring is done by a host-side OpenClaw judge ([`weavebench/eval/agent_judge`](https://github.com/weavebench/WeaveBench/tree/main/weavebench/eval/agent_judge)) that reads chat trace + deliverables and emits per-clause evidence-based scores. See [docs/AGENT_JUDGE.md](https://github.com/weavebench/WeaveBench/blob/main/docs/AGENT_JUDGE.md).

	## 5. Pinned revision for paper reproduction

	OpenRouter aliases drift over time. To reproduce paper numbers, pin both the dataset SHA and the model snapshot ids — see [`docs/REPRODUCE.md`](https://github.com/weavebench/WeaveBench/blob/main/docs/REPRODUCE.md) in the code repo for the canonical SHA, per-table model ids, and per-table commands.

	```bash
	export WEAVEBENCH_DATASET_REVISION=<full sha from docs/REPRODUCE.md>
	```

	## 6. Citation

	```bibtex
	@article{li2026weavebench,
	title = {WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces},
	author = {Li, Wanli and Zhou, Bowen and Yang, Yifan and Yu, Yunyao and Li, Dongsheng and Xu, Zhou and Shan, Caihua},
	year = {2026},
	}
	```

	## 7. License

	- Tasks: MIT.
	- Runtime tarballs: each tarball repackages third-party software (Codex CLI is Apache-2.0; Claude Code, Hermes, OpenClaw retain their upstream terms). See [NOTICE](https://github.com/weavebench/WeaveBench/blob/main/NOTICE) in the code repo for full attribution.
	- VM image: derived from OSWorld upstream Ubuntu image (MIT), patched to bake in app snapshots for paper reproducibility — full provenance in [docs/REPRODUCE.md](https://github.com/weavebench/WeaveBench/blob/main/docs/REPRODUCE.md).