Agent Data Protocol: Unifying Datasets for Diverse, Effective
  Fine-tuning of LLM Agents
Authors: name: Yueqi Song, name: Ketan Ramaneti, name: Zaid Sheikh, name: Ziru Chen, name: Boyu Gou, name: Tianbao Xie, name: Yiheng Xu, name: Danyang Zhang, name: Apurva Gandhi, name: Fan Yang, name: Joseph Liu, name: Tianyue Ou, name: Zhihao Yuan, name: Frank Xu, name: Shuyan Zhou, name: Xingyao Wang, name: Xiang Yue, name: Tao Yu, name: Huan Sun, name: Yu Su, name: Graham Neubig

Public research results on large-scale supervised finetuning of AI agents
remain relatively rare, since the collection of agent training data presents
unique challenges. In this work, we argue that the bottleneck is not a lack of
underlying data sources, but that a large variety of data is fragmented across
heterogeneous formats, tools, and interfaces. To this end, we introduce the
agent data protocol (ADP), a light-weight representation language that serves
as an "interlingua" between agent datasets in diverse formats and unified agent
training pipelines downstream. The design of ADP is expressive enough to
capture a large variety of tasks, including API/tool use, browsing, coding,
software engineering, and general agentic workflows, while remaining simple to
parse and train on without engineering at a per-dataset level. In experiments,
we unified a broad collection of 13 existing agent training datasets into ADP
format, and converted the standardized ADP data into training-ready formats for
multiple agent frameworks. We performed SFT on these data, and demonstrated an
average performance gain of ~20% over corresponding base models, and delivers
state-of-the-art or near-SOTA performance on standard coding, browsing, tool
use, and research benchmarks, without domain-specific tuning. All code and data
are released publicly, in the hope that ADP could help lower the barrier to
standardized, scalable, and reproducible agent training.