TaskCraft: Automated Generation of Agentic Tasks
Authors: name: Dingfeng Shi, name: Jingyi Cao, name: Qianben Chen, name: Weichen Sun, name: Weizhen Li, name: Hongxuan Lu, name: Fangchen Dong, name: Tianrui Qin, name: King Zhu, name: Minghao Yang, name: Jian Yang, name: Ge Zhang, name: Jiaheng Liu, name: Changwang Zhang, name: Jun Wang, name: Yuchen Eleanor Jiang, name: Wangchunshu Zhou

Agentic tasks, which require multi-step problem solving with autonomy, tool
use, and adaptive reasoning, are becoming increasingly central to the
advancement of NLP and AI. However, existing instruction data lacks tool
interaction, and current agentic benchmarks rely on costly human annotation,
limiting their scalability. We introduce TaskCraft, an automated
workflow for generating difficulty-scalable, multi-tool, and verifiable agentic
tasks with execution trajectories. TaskCraft expands atomic tasks using
depth-based and width-based extensions to create structurally and
hierarchically complex challenges. Empirical results show that these tasks
improve prompt optimization in the generation workflow and enhance supervised
fine-tuning of agentic foundation models. We present a large-scale synthetic
dataset of approximately 36,000 tasks with varying difficulty to support future
research on agent tuning and evaluation.