APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

Training effective AI agents for multi-turn interactions requires high-quality data that captures realistic human-agent dynamics, yet such data is scarce and expensive to collect manually. We introduce APIGen-MT, a two-phase framework that generates verifiable and diverse multi-turn agent data. In the first phase, our agentic pipeline produces detailed task blueprints with ground-truth actions, leveraging a committee of LLM reviewers and iterative feedback loops. These blueprints are then transformed into complete interaction trajectories through simulated human-agent interplay. We train a family of models -- the xLAM-2-fc-r series with sizes ranging from 1B to 70B parameters. Our models outperform frontier models such as GPT-4o and Claude 3.5 on $\tau$-bench and BFCL benchmarks, with the smaller models surpassing their larger counterparts, particularly in multi-turn settings, while maintaining superior consistency across multiple trials. Comprehensive experiments demonstrate that our verified blueprint-to-details approach yields high-quality training data, enabling the development of more reliable, efficient, and capable agents. We open-source 5K synthetic data trajectories and the trained xLAM-2-fc-r models to advance research in AI agents.Models atthis https URLDataset atthis https URLand Website atthis https URL

@article{prabhakar2025_2504.03601,
  title={ APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay },
  author={ Akshara Prabhakar and Zuxin Liu and Ming Zhu and Jianguo Zhang and Tulika Awalgaonkar and Shiyu Wang and Zhiwei Liu and Haolin Chen and Thai Hoang and Juan Carlos Niebles and Shelby Heinecke and Weiran Yao and Huan Wang and Silvio Savarese and Caiming Xiong },
  journal={arXiv preprint arXiv:2504.03601},
  year={ 2025 }
}