Co-Saving: Resource Aware Multi-Agent Collaboration for Software Development

• URL: http://arxiv.org/abs/2505.21898v1
• Date: Wed, 28 May 2025 02:23:53 GMT
• Title: Co-Saving: Resource Aware Multi-Agent Collaboration for Software Development
• Authors: Rennai Qiu, Chen Qian, Ran Li, Yufan Dang, Weize Chen, Cheng Yang, Yingli Zhang, Ye Tian, Xuantang Xiong, Lei Han, Zhiyuan Liu, Maosong Sun,
• Abstract summary: We propose a resource-aware multi-agent system -- Co-Saving.<n>Our key innovation is the introduction of "shortcuts"<n>Compared to the state-of-the-art MAS ChatDev, our method achieves an average reduction of 50.85% in token usage.
• Score: 65.94639060883475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Large Language Models (LLMs) and autonomous agents have demonstrated remarkable capabilities across various domains. However, standalone agents frequently encounter limitations when handling complex tasks that demand extensive interactions and substantial computational resources. Although Multi-Agent Systems (MAS) alleviate some of these limitations through collaborative mechanisms like task decomposition, iterative communication, and role specialization, they typically remain resource-unaware, incurring significant inefficiencies due to high token consumption and excessive execution time. To address these limitations, we propose a resource-aware multi-agent system -- Co-Saving (meaning that multiple agents collaboratively engage in resource-saving activities), which leverages experiential knowledge to enhance operational efficiency and solution quality. Our key innovation is the introduction of "shortcuts" -- instructional transitions learned from historically successful trajectories -- which allows to bypass redundant reasoning agents and expedite the collective problem-solving process. Experiments for software development tasks demonstrate significant advantages over existing methods. Specifically, compared to the state-of-the-art MAS ChatDev, our method achieves an average reduction of 50.85% in token usage, and improves the overall code quality by 10.06%.

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