ToolGym: an Open-world Tool-using Environment for Scalable Agent Testing and Data Curation

• URL: http://arxiv.org/abs/2601.06328v1
• Date: Fri, 09 Jan 2026 21:59:31 GMT
• Title: ToolGym: an Open-world Tool-using Environment for Scalable Agent Testing and Data Curation
• Authors: Ziqiao Xi, Shuang Liang, Qi Liu, Jiaqing Zhang, Letian Peng, Fang Nan, Meshal Nayim, Tianhui Zhang, Rishika Mundada, Lianhui Qin, Biwei Huang, Kun Zhou,
• Abstract summary: We introduce an open-world tool-using environment, built on 5,571 format unified tools across 204 commonly used apps.<n>It includes a task creation engine that synthesizes longhorizon, multi-tool with wild constraints, and a state controller that injects interruptions and failures to stress-test robustness.<n> Comprehensive evaluation of state-of-the-art LLMs reveals the misalignment between tool planning and execution abilities, the constraint following weakness of existing LLMs, and DeepSeek-v3.2's strongest robustness.
• Score: 42.479399507055454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tool-using LLM agents still struggle in open-world settings with large tool pools, long-horizon objectives, wild constraints, and unreliable tool states. For scalable and realistic training and testing, we introduce an open-world tool-using environment, built on 5,571 format unified tools across 204 commonly used apps. It includes a task creation engine that synthesizes long-horizon, multi-tool workflows with wild constraints, and a state controller that injects interruptions and failures to stress-test robustness. On top of this environment, we develop a tool select-then-execute agent framework with a planner-actor decomposition to separate deliberate reasoning and self-correction from step-wise execution. Comprehensive evaluation of state-of-the-art LLMs reveals the misalignment between tool planning and execution abilities, the constraint following weakness of existing LLMs, and DeepSeek-v3.2's strongest robustness. Finally, we collect 1,170 trajectories from our environment to fine-tune LLMs, achieving superior performance to baselines using 119k samples, indicating the environment's value as both a realistic benchmark and a data engine for tool-using agents. Our code and data will be publicly released.

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