Title: AgentRace: Benchmarking Efficiency in LLM Agent Frameworks
Primary area: datasets and benchmarks
Keywords: LLM agent, benchmark

Abstract: Large Language Model (LLM) agents are rapidly gaining traction across domains such as intelligent assistants, programming aids, and autonomous decision systems. While existing benchmarks focus primarily on evaluating the effectiveness of LLM agents, such as task success rates and reasoning correctness, the efficiency of agent frameworks remains an underexplored but critical factor for real-world deployment. In this work, we introduce AgentRace, the first benchmark specifically designed to systematically evaluate the efficiency of LLM agent frameworks across representative workloads. AgentRace enables controlled, reproducible comparisons of runtime performance, scalability, communication overhead, and tool invocation latency across popular frameworks on diverse task scenarios and workflows. Our experiments reveal 9 insights and 12 underlying mechanisms for developing efficient LLM agents. We believe AgentRace will become a valuable resource for guiding the design and optimization of next-generation efficient LLM agent systems. The platform and results are available at the anonymous website https://agent-race.github.io/.