This survey examines the burgeoning field of tool learning with large language models (LLMs), a paradigm where LLMs enhance their capabilities by using external tools to solve complex problems. The authors systematically explore why tool learning is beneficial, detailing advantages like improved knowledge acquisition and robustness, and how it is implemented, outlining a four-stage workflow of task planning, tool selection, tool calling, and response generation. The paper also provides an overview of existing benchmarks and evaluation methods for this area, alongside a discussion of current challenges and future research directions, aiming to guide both researchers and industry professionals in this rapidly evolving domain.