Large Language Model-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex tasks.However, manually designing optimal communication topologies is labor-intensive, while automated expansion methods often result in bloated structures with redundant agents, leading to excessive token consumption.To address this problem, we introduce AgentSlimming, a plug-and-play compression framework for graph-structured multiagent workflows.Motivated by pruning and quantization in neural networks, AgentSlimming compresses workflows by first estimating the importance score of each agent with a hybrid mechanism, and then removes redundant agents or replaces them with low-cost ones, where each operation is validated using a baseline-anchored acceptance rule to prevent performance collapse.Experiments show that AgentSlimming reduces average token cost by up to 78.9% with negligible performance degradation, and sometimes even improves accuracy, achieving a strong Pareto-optimal tradeoff between cost and quality.Our code is publicly available at https://github.com/ CitrusYL/AgentSlimming.