agents工具调用训练数据集

训练语言模型精准调用工具是打造实用AI智能体的核心环节。本数据集以248,215条真实生产级智能体提炼的指令-工具调用样本为基石，系统性培养模型四大能力：根据任务需求智能选择适配工具、确保参数传递类型准确且必填项完备、编排4-6步多工具协同工作流解决复杂任务、将模糊用户请求精准映射为可执行调用，同时兼顾紧急情境、资源限制等真实约束条件。基于此训练的模型可输出具备实战效能的工具调用方案，实现从指令理解到多步骤执行的端到端能力。

Precisely enabling language models to invoke tools accurately is a core step in developing practical AI Agents. This dataset is grounded in 248,215 instruction-tool invocation samples extracted from real-world production-grade AI Agents, and systematically equips trained models with four core capabilities: intelligently selecting suitable tools based on task requirements, ensuring accurate parameter transfer types and completeness of mandatory fields, orchestrating 4-6-step collaborative tool workflows to solve complex tasks, and precisely mapping ambiguous user requests into executable tool invocations. It also takes into account real-world constraints such as emergency scenarios and resource limitations. Models trained on this dataset can produce practical, operationally effective tool invocation schemes, realizing end-to-end capabilities ranging from instruction understanding to multi-step execution.

搜集汇总

背景与挑战

背景概述

该数据集是一个包含248,215条指令-工具调用样本的训练集，旨在训练语言模型正确调用工具，以构建实用AI智能体。它覆盖了50个来自生产级智能体代码库的工具和12个真实业务领域，支持多步骤工作流和双语，并采用标准工具调用格式确保部署兼容性。