{"ID":2843602,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09572","arxiv_id":"2511.09572","title":"SynthTools: A Framework for Scaling Synthetic Tools for Agent Development","abstract":"For agentic systems to use external tools to solve complex, long-horizon tasks, we need a large set of diverse and controllable tool-use environments. We introduce SynthTools, a fully LLM-based pipeline spanning the entire lifecycle: environment generation, simulation, validation and task construction. By operating end-to-end through LLMs, our framework complements other tool-use environments bottlenecked by the complexity of real APIs, and ensures scalability and controllability by design. The framework consists of three components: top-down environment generation, which hierarchically constructs diverse, domain-grounded tool environments; environment simulation and validation, which ensures tools can be reliably emulated and filters out those that cannot; and bottom-up task and trajectory generation, which produces solvable and verifiable tasks together with multi-step trajectories, exposing control over difficulty, length, trajectory composition, and domain focus to guarantee flexibility. As a concrete instantiation, we release the dataset comprising $73{,}883$ validated tools across $6{,}800$ environments and $100$ fields, $79{,}925$ verifiable tasks as well as the pipeline to generate trajectories at scale. Training Qwen3 models of various sizes on a corpus of trajectories generated from these tasks yields gains across multiple tool-use benchmarks, including real APIs, indicating tool-use capabilities trained on synthetic data may transfer to some real environments. Together, these results suggest that SynthTools can serve as a useful infrastructure for large-scale training of tool-use agents.","short_abstract":"For agentic systems to use external tools to solve complex, long-horizon tasks, we need a large set of diverse and controllable tool-use environments. We introduce SynthTools, a fully LLM-based pipeline spanning the entire lifecycle: environment generation, simulation, validation and task construction. By operating end...","url_abs":"https://arxiv.org/abs/2511.09572","url_pdf":"https://arxiv.org/pdf/2511.09572v2","authors":"[\"Tommaso Castellani\",\"Naimeng Ye\",\"Daksh Mittal\",\"Thomson Yen\",\"Emmanouil Koukoumidis\",\"William Zeng\",\"Hongseok Namkoong\"]","published":"2025-11-11T19:26:40Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.LG\",\"cs.SE\"]","methods":"[\"Large Language Model\"]","has_code":false}