A comprehensive survey examining efficiency in large language model-based agents, focusing on three core components: memory, tool learning, and planning. The paper reviews approaches for reducing costs such as latency and tokens through techniques like context compression, retrieval, budgeted tool use, and hierarchical planning, while evaluating efficiency through Pareto frontier analysis between effectiveness and cost.