Nanbeige/ToolMind

ToolMind是一个大规模、高质量的工具体用数据集，包含160k个合成数据实例和200k个增强的开源数据实例。该数据集通过构建基于参数相关性的函数图，并利用多智能体框架模拟真实的用户-助手-工具交互来生成数据。除了轨迹级别的验证外，还采用细粒度的回合级别过滤来移除错误或次优步骤，确保仅保留高质量的推理轨迹。数据集支持文本生成任务，主要用于功能调用和工具调用场景。

ToolMind is a large-scale, high-quality tool-agentic dataset with 160k synthetic data instances generated using over 20k tools and 200k augmented open-source data instances. Our data synthesis pipeline first constructs a function graph based on parameter correlations and then uses a multi-agent framework to simulate realistic user–assistant–tool interactions. Beyond trajectory-level validation, we employ fine-grained turn-level filtering to remove erroneous or suboptimal steps, ensuring that only high-quality reasoning traces are retained. The dataset supports text-generation tasks and is primarily used for function-calling and tool-calling scenarios.