AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914547793657856 |
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| author | Chen, Yulang Peng, Haoxuan Liu, Jinyan Wen, Zichen Liu, Dongrui Zhang, Linfeng |
| author_facet | Chen, Yulang Peng, Haoxuan Liu, Jinyan Wen, Zichen Liu, Dongrui Zhang, Linfeng |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08813 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems Chen, Yulang Peng, Haoxuan Liu, Jinyan Wen, Zichen Liu, Dongrui Zhang, Linfeng Machine Learning 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 |
| title | AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.08813 |