This survey provides a comprehensive overview of tool learning with large language models (LLMs), focusing on two key aspects: why tool learning is beneficial and how it is implemented. Tool learning enhances LLMs' capabilities by enabling them to interact with external tools, thereby improving their ability to solve complex tasks. The survey explores the benefits of tool integration and the inherent advantages of the tool learning paradigm, highlighting how these contribute to better performance, accuracy, and user trust. It also systematically reviews the four stages of the tool learning workflow: task planning, tool selection, tool calling, and response generation. The survey categorizes existing benchmarks and evaluation methods based on their relevance to each stage. It discusses current challenges and outlines future directions, aiming to inspire further research and development in this promising area. The survey also provides a detailed analysis of recent advancements in tool learning, including both tuning-free and tuning-based methods, and highlights the importance of efficient tool retrieval and error handling in enhancing LLMs' capabilities. The survey concludes with a discussion on the significance of response generation, emphasizing the integration of tool outputs with internal knowledge to provide accurate and comprehensive answers. Overall, the survey aims to provide a clear understanding of tool learning with LLMs and its potential for future development.This survey provides a comprehensive overview of tool learning with large language models (LLMs), focusing on two key aspects: why tool learning is beneficial and how it is implemented. Tool learning enhances LLMs' capabilities by enabling them to interact with external tools, thereby improving their ability to solve complex tasks. The survey explores the benefits of tool integration and the inherent advantages of the tool learning paradigm, highlighting how these contribute to better performance, accuracy, and user trust. It also systematically reviews the four stages of the tool learning workflow: task planning, tool selection, tool calling, and response generation. The survey categorizes existing benchmarks and evaluation methods based on their relevance to each stage. It discusses current challenges and outlines future directions, aiming to inspire further research and development in this promising area. The survey also provides a detailed analysis of recent advancements in tool learning, including both tuning-free and tuning-based methods, and highlights the importance of efficient tool retrieval and error handling in enhancing LLMs' capabilities. The survey concludes with a discussion on the significance of response generation, emphasizing the integration of tool outputs with internal knowledge to provide accurate and comprehensive answers. Overall, the survey aims to provide a clear understanding of tool learning with LLMs and its potential for future development.