---
license: apache-2.0
pretty_name: Pi-Bench Tasks
task_categories:
- text-generation
language:
- en
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/tasks.jsonl
tags:
- Proactive
- Agent
- OpenClaw
- Benchmark
---

# Pi-Bench Tasks

**Code and full benchmark:** [GitHub repository](https://github.com/Simplified-Reasoning/Pi-Bench)  
**Paper:** [arXiv:2605.14678](https://arxiv.org/abs/2605.14678)  
**Project page:** [simplified-reasoning.github.io/Pi-Bench](https://simplified-reasoning.github.io/Pi-Bench/)

This lightweight dataset exposes only the `task.yaml` files from Pi-Bench so
people can quickly inspect the benchmark tasks in the Hugging Face Dataset
Viewer.

Pi-Bench evaluates proactive personal assistant agents in long-horizon
workflows. It contains 100 multi-turn tasks across 5 domain-specific personas:
`researcher`, `marketer`, `pharmacist`, `law_trainee`, and `financier`.

Each row corresponds to one `data/<role>/tasks/<task_id>/task.yaml` file. The
`yaml` column preserves the original YAML text, while the other columns extract
common fields for filtering and browsing.

## Contents

- Rows: 100
- Rows with objectives: 62

## Roles

- Financier: 20
- law_trainee: 20
- marketer: 20
- pharmacist: 20
- researcher: 20

## Difficulty

- easy: 29
- hard: 20
- medium: 51

## Columns

- `role`, `task_id`, `user_id`, `environment_id`
- `title`, `display_title`, `description`, `task_type`, `difficulty`
- `initial_input`, `hidden_intents`, `hidden_intent_count`
- `has_objectives`, `objectives_json`, `metadata_json`
- `yaml_path`, `yaml`

## Citation

If you use Pi-Bench, please cite:

```bibtex
@misc{zhang2026pibenchevaluatingproactivepersonal,
  title={${\pi}$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows},
  author={Haoran Zhang and Luxin Xu and Zhilin Wang and Runquan Gui and Shunkai Zhang and Haodi Lei and Zihao He and Bingsu He and Chicheng Qin and Tong Zhu and Xiaoye Qu and Yang Yang and Yu Cheng and Yafu Li},
  year={2026},
  eprint={2605.14678},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2605.14678}
}
```