MeterRouter: Budget-Aware Dynamic Routing for Multi-Agent Systems

Multi-agent systems (MAS) built on large language models (LLMs) outperform single-agent systems, but inter-agent communication incurs substantial token overhead. While assigning a large LLM to every agent results in inefficient resource utilization, leaving the system far from the Pareto frontier. The cost of answering a query is set jointly by how many agents are recruited, the role and LLM assigned to each, and communication topology among them. Most existing routers treat cost only implicitly rather than as an explicit optimization constraint. A fixed accuracy–cost preference also cannot express how much a caller is willing to pay for a particular query. We introduce MeterRouter, a budget-aware dynamic router that incrementally synthesizes a collaboration graph by selecting each agent's role, LLM, incoming connections, and termination conditioned on the query and a per-query monetary budget. We initialize the router by imitating successful execution graphs, then jointly refine its decisions with a variant of Group Relative Policy Optimization (GRPO) whose reward favors correct answers and penalizes spending and budget overruns. Across five benchmarks and a three-tier LLM pool, MeterRouter is (1) accurate, achieving performance comparable to the strongest MAS; (2) economical, reaching that accuracy at 82% lower cost than the strongest MAS router and extending the accuracy–cost frontier beyond the ceiling of single-agent systems; and (3) controllable, staying within the specified budget on at least 99.9% of queries.