--- language: - en - zh license: mit size_categories: - n<1K task_categories: - other pretty_name: Claw-Eval dataset_info: features: - name: task_id dtype: string - name: query dtype: string - name: fixture list: string - name: language dtype: string - name: category dtype: string splits: - name: general num_bytes: 200118 num_examples: 161 - name: multimodal num_bytes: 72393 num_examples: 101 - name: multi_turn num_bytes: 50000 num_examples: 38 download_size: 155773 dataset_size: 322511 configs: - config_name: default data_files: - split: general path: data/general-* - split: multimodal path: data/multimodal-* - split: multi_turn path: data/multi_turn-* tags: - agent-bench - evaluation - real-world - multimodal ---

Claw-Eval

Claw-Eval Logo [![Tasks](https://img.shields.io/badge/tasks-300-blue)](#dataset-structure) [![Leaderboard](https://img.shields.io/badge/leaderboard-live-purple)](https://claw-eval.github.io) [![License](https://img.shields.io/badge/license-MIT-orange)](https://github.com/claw-eval/claw-eval/blob/main/LICENSE) **End-to-end transparent benchmark for AI agents acting in the real world.** [Paper](https://huggingface.co/papers/2604.06132) | [Leaderboard](https://claw-eval.github.io) | [Code](https://github.com/claw-eval/claw-eval)
--- ## Dataset Structure ### Splits | Split | Examples | Description | |---|---:|---| | `general` | 161 | Core agent tasks across 24 categories (communication, finance, ops, productivity, etc.) | | `multimodal` | 101 | Multimodal agentic tasks requiring perception and creation (webpage generation, video QA, document extraction, etc.) | | `multi_turn` | 38 | Multi-turn conversational tasks where the agent interacts with a simulated user persona to clarify needs and provide advice | ### Fields | Field | Type | Description | |---|---|---| | `task_id` | string | Unique task identifier | | `query` | string | Task instruction / description | | `fixture` | list[string] | Fixture files required for the task (available in `data/fixtures.tar.gz`) | | `language` | string | Task language (`en` or `zh`) | | `category` | string | Task domain | ## Usage ```python from datasets import load_dataset # Load all splits dataset = load_dataset("claw-eval/Claw-Eval") # Load a specific split general = load_dataset("claw-eval/Claw-Eval", split="general") multimodal = load_dataset("claw-eval/Claw-Eval", split="multimodal") multi_turn = load_dataset("claw-eval/Claw-Eval", split="multi_turn") # Inspect a sample print(general[0]) ``` ## Acknowledgements Our test cases are built on the work of the community. We draw from and adapt tasks contributed by OpenClaw, PinchBench, OfficeQA, OneMillion-Bench, Finance Agent, and Terminal-Bench 2.0. ## Citation If you use Claw-Eval in your research, please cite: ```bibtex @article{ye2026claw, title={Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents}, author={Ye, Bowen and Li, Rang and Yang, Qibin and Liu, Yuanxin and Yao, Linli and Lv, Hanglong and Xie, Zhihui and An, Chenxin and Li, Lei and Kong, Lingpeng and others}, journal={arXiv preprint arXiv:2604.06132}, year={2026} } ``` ## Core Contributors [Bowen Ye](https://github.com/pkuYmiracle)(PKU), [Rang Li](https://github.com/lirang04) (PKU), [Qibin Yang](https://github.com/yangqibin-caibi) (PKU), [Zhihui Xie](https://zhxie.site/)(HKU), [Yuanxin Liu](https://llyx97.github.io/)(PKU), [Linli Yao](https://yaolinli.github.io/)(PKU), [Hanglong Lyu](https://github.com/Albus2002)(PKU), [Lei Li](lilei-nlp.github.io)(HKU, project lead) ## Advisors: [Tong Yang](https://yangtonghome.github.io/) (PKU), [Zhifang Sui](https://cs.pku.edu.cn/info/1226/2014.htm) (PKU), [Lingpeng Kong](https://ikekonglp.github.io/) (HKU), [Qi Liu](https://leuchine.github.io/) (HKU) ## Contribution We welcome any kind of contribution. Let us know if you have any suggestions! ## License This dataset is released under the [MIT License](https://github.com/claw-eval/claw-eval/blob/main/LICENSE).