Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents

TL;DR

This paper introduces the agent data protocol (ADP), a unified data format that consolidates diverse agent training datasets, enabling more effective fine-tuning of large language model agents and improving performance across multiple benchmarks.

Contribution

The paper presents ADP, a lightweight, expressive data representation language that unifies heterogeneous datasets for agent training, facilitating scalable and reproducible fine-tuning of LLM agents.

Findings

Unified 13 datasets into ADP format

Achieved ~20% performance improvement on base models

Attained state-of-the-art results on multiple benchmarks

Abstract

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.