Design and Performance Evaluation of an OpenClaw-Based Desktop AI Assistant Deployed on Virtual Private Server: A Comparative Study with N8N Workflow Automation

Abstract

The escalating demand for more intelligent workflow automation has driven organizations to move beyond traditional rule-based platforms. This study designs, develops, and evaluates a Desktop AI Assistant system built on OpenClaw and deployed on a Virtual Private Server (VPS), and benchmarks its performance against N8N, a widely adopted open-source workflow automation platform. Employing a Design and Development Research (DDR) framework combined with a comparative experimental method, the research assesses both systems across three increasingly complex workflow automation scenarios: message management, task scheduling, and multi-service integration. Six evaluation metrics were recorded: response time, memory usage, CPU utilization, task completion accuracy, setup complexity, and user satisfaction as measured by the System Usability Scale (SUS). Results indicate that OpenClaw outperforms N8N on five of the six parameters. It achieves an average response time 75.5% faster (16.294 ms vs. 66.534 ms) with markedly greater consistency—standard deviation of 1.024 ms in Scenario 2 compared to N8N’s 116.228 ms. Memory usage averaged 27.7% lower (573 MB vs. 793 MB), CPU utilization 34.5% lower (28.1% vs. 42.9%), and task completion accuracy 5.6% higher (90.0% vs. 84.4%). The SUS score also favored OpenClaw at 78.4 versus 72.6. N8N holds an advantage only in setup complexity, scoring 2.18 compared with 3.25 on a 1–5 Likert scale, reflecting the benefit of its visual node-based interface for initial configuration. These findings suggest that LLM-based autonomous agent systems deployed on VPS represent a meaningful advancement over rule-based automation platforms and merit serious consideration as next-generation workflow infrastructure for organizations requiring high performance and the capacity to manage complex, multi-step tasks