EcoAgent: An Efficient Edge-Cloud Collaborative Multi-Agent Framework for Mobile Automation

Abstract: Cloud-based mobile agents powered by (multimodal) LLMs ((M)LLMs) offer strong reasoning abilities but suffer from high latency and cost. While fine-tuned (M)SLMs enable edge deployment, they often lose general capabilities and struggle with complex tasks. To address this, we propose \textbf{EcoAgent}, an \textbf{E}dge-\textbf{C}loud c\textbf{O}llaborative multi-agent framework for mobile automation. EcoAgent features a closed-loop collaboration among a cloud-based Planning Agent and two edge-based agents: the Execution Agent for action execution and the Observation Agent for verifying outcomes. The Observation Agent uses a Pre-Understanding Module to compress screen images into concise text, reducing token usage and communication overhead. In case of failure, the Planning Agent retrieves screen history through a Memory Module and replans via a Reflection Module. Experiments on AndroidWorld show that EcoAgent achieves task success rates comparable to cloud-based mobile agents while significantly reducing MLLM token consumption, enabling efficient and practical mobile automation.