AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems

• URL: http://arxiv.org/abs/2605.08813v1
• Date: Sat, 09 May 2026 09:03:54 GMT
• Title: AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems
• Abstract summary: AgentSlimming is a plug-and-play compression framework for graph-structured multi-agent systems.<n>It reduces average token cost by up to 78.9% with negligible performance degradation, and sometimes even improves accuracy.
• Score: 19.373173948984626
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 \textbf{AgentSlimming}, a plug-and-play compression framework for graph-structured multi-agent 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 trade-off between cost and quality. \textit{Our code is publicly available at https://github.com/CitrusYL/AgentSlimming

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