BAMAS: Structuring Budget-Aware Multi-Agent Systems

TL;DR

BAMAS introduces a budget-aware framework for multi-agent systems that optimizes LLM selection and collaboration topology, significantly reducing costs while maintaining high performance.

Contribution

It presents a novel method combining integer linear programming and reinforcement learning to structure cost-effective multi-agent systems with LLMs.

Findings

Achieves up to 86% cost reduction

Maintains comparable task performance

Outperforms existing agent construction methods

Abstract

Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks. As these systems scale in complexity, cost becomes an important consideration for practical deployment. However, existing work rarely addresses how to structure multi-agent systems under explicit budget constraints. In this paper, we propose BAMAS, a novel approach for building multi-agent systems with budget awareness. BAMAS first selects an optimal set of LLMs by formulating and solving an Integer Linear Programming problem that balances performance and cost. It then determines how these LLMs should collaborate by leveraging a reinforcement learning-based method to select the interaction topology. Finally, the system is instantiated and executed based on the selected agents and their collaboration topology. We evaluate BAMAS on three representative tasks and compare it with state-of-the-art agent construction methods. Results show that BAMAS achieves comparable performance while reducing cost by up to 86%.