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

Muhammad Wali; Muhammad Agha Afkar; Syafrinal Syafrinal

doi:10.59431/jda.v5i1.822

Quick Links

Journal

Home: Journal Home
Current: Current Issue
Archives: All Issues
News: Announcements
Search: Search Articles

About

About: About Journal
Team: Editorial Team
Reviewers: Reviewer Team
Indexing: Indexed In

For Authors

Guidelines: Author Guidelines
Submit: Submit Article
Fees: Publication Fees

Policies

Ethics: Publication Ethics
Review: Reviewer Guide
Access: Open Access
Contact: Contact Us

081377790601
journal@msti-indonesia.com
Mon - Sat: 08:00 - 15:00

Articles

PDF

Articles

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

Muhammad Wali, Muhammad Agha Afkar, Syafrinal Syafrinal

View Author Affiliations

Muhammad Wali: STMIK Indonesia Banda Aceh, Indonesia
Muhammad Agha Afkar: Lembaga Mitra Solusi Teknologi Informasi (L-MSTI), Indonesia
Syafrinal Syafrinal: STMIK Indonesia Banda Aceh, Indonesia

DOI:

https://doi.org/10.59431/jda.v5i1.822

Published:

June 30, 2026

Pages:

26–42

41

Views

23

Downloads

Article Metrics

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

Keywords

Desktop AI Assistant; OpenClaw; Virtual Private Server; Workflow Automation; Large Language Model Agent

Author Biographies

Muhammad Wali

STMIK Indonesia Banda Aceh

Department of Informatics Management, Faculty of Computer Science, STMIK Indonesia Banda Aceh, Banda Aceh City, Aceh Province, Indonesia

Muhammad Agha Afkar

Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Research Division, Lembaga Mitra Solusi Teknologi Informasi (L-MSTI), STMIK Indonesia Banda Aceh, Banda Aceh City, Aceh Province, Indonesia

Syafrinal Syafrinal

STMIK Indonesia Banda Aceh

Department of Computer System, Faculty of Computer Science, STMIK Indonesia Banda Aceh, Banda Aceh City, Aceh Province, Indonesia

Article Identifiers

Article Title: Design and Performance Evaluation of an OpenClaw-Based Desktop AI Assistant Deployed on Virtual Private Server: A Comparative Study with N8N Workflow Automation
DOI: 10.59431/jda.v5i1.822
Publication Date: 2026-06-30

Journal: Journal Dekstop Application (JDA)
Volume: 5
Issue: 1
Pages: 26–42

References

Alva, L., & Pandey, B. (2026). Agentic AI systems in the age of generative models: Architectures, cloud scalability, and real-world applications. Artificial Intelligence Review, 59, 88. https://doi.org/10.1007/s10462-025-11458-6
Arzo, S. T. (2021). Towards network automation: A multi-agent based intelligent networking system [Doctoral dissertation, Università degli Studi di Trento].
Barra, F. L., Rodella, G., Costa, A., Scalogna, A., Carenzo, L., Monzani, A., & Corte, F. D. (2025). From prompt to platform: An agentic AI workflow for healthcare simulation scenario design. Advances in Simulation, 10(1), 29. https://doi.org/10.1186/s41077-025-00357-z
Barua, S. (2024). Exploring autonomous agents through the lens of large language models: A review (arXiv:2404.04442). arXiv. https://doi.org/10.48550/arXiv.2404.04442
Borsci, S., Malizia, A., Schmettow, M., Van Der Velde, F., Tariverdiyeva, G., Balaji, D., & Chamberlain, A. (2022). The chatbot usability scale: The design and pilot of a usability scale for interaction with AI-based conversational agents. Personal and Ubiquitous Computing, 26(1), 95–119. https://doi.org/10.1007/s00779-021-01582-9
Broccia, G., Cirillo, R., Ferrari, A., Lelii, L., & Spagnolo, G. O. (2025). HumAInFlow: A no-code platform for modelling and simulating human-AI workflows (Technical Report 2025/011). Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo," CNR. https://doi.org/10.32079/ISTI-TR-2025/011
Cao, J. (2024). A study on deploying large language models as agents [Doctoral dissertation, Massachusetts Institute of Technology]. MIT DSpace. https://hdl.handle.net/1721.1/157177
Casaclang, H., Ionescu, B., Kim, Y., & Jo, J. Y. (2026). Self-hosted workflow automation for AI-based cybersecurity operation. Journal of The Colloquium for Information Systems Security Education, 13(1), 21. https://doi.org/10.53735/cisse.v13i1.241
Dibia, V. (2025). Designing multi-agent systems: Principles, patterns and implementation for AI agents. Victor Dibia.
Dong, H., Zhang, P., Gao, Y., Dong, X., Cheng, Y., Lu, M., & Zheng, S. (2025). Finch: Benchmarking finance & accounting across spreadsheet-centric enterprise workflows (arXiv:2512.13168). arXiv. https://doi.org/10.48550/arXiv.2512.13168
Ependi, U., Kurniawan, T. B., & Panjaitan, F. (2019). System usability scale vs heuristic evaluation: A review. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 10(1), 65–74.
Ferrag, M. A., Tihanyi, N., & Debbah, M. (2025). From LLM reasoning to autonomous AI agents: A comprehensive review (arXiv:2504.19678). arXiv. https://doi.org/10.48550/arXiv.2504.19678
Guran, N., Knauf, F., Ngo, M., Petrescu, S., & Rellermeyer, J. S. (2024). Towards a middleware for large language models (arXiv:2411.14513). arXiv. https://doi.org/10.48550/arXiv.2411.14513
Hua, Y., Li, K., Wang, R., Liu, Y., Leng, J., Wang, G., & Yan, Y. (2025). Towards proactive and collaborative industrial AI agents within the human-agent interaction (HAI): A review and perspective (SSRN 6076349). SSRN. https://dx.doi.org/10.2139/ssrn.6076349
Jeong, C. (2025). Beyond text: Implementing multimodal large language model-powered multi-agent systems using a no-code platform. Journal of Intelligence and Information Systems, 31(1). https://doi.org/10.13088/jiis.2025.31.1.191
Jin, W., Wang, N., Zhang, L., Tian, X., Shi, B., & Zhao, B. (2025). A review of AI-driven automation technologies: Latest taxonomies, existing challenges, and future prospects. Computers, Materials & Continua, 84(3). https://doi.org/10.32604/cmc.2025.060801
Joshi, S. (2025). Review of autonomous systems and collaborative AI agent frameworks (SSRN 5142205). SSRN. https://ssrn.com/abstract=5142205
Koubaa, A. (2025). Agent operating systems (Agent-OS): A blueprint architecture for real-time, secure, and scalable AI agents (Preprint). Authorea. https://doi.org/10.20944/preprints202509.0077.v1
Lanham, M. (2025). AI agents in action. Simon and Schuster.
Lee, Y. S., & Moon, P. J. (2025). A comparative study of modern automation tools: Make, n8n, and Opal. The International Journal of Advanced Smart Convergence, 14(4), 605–615. https://www.earticle.net/Article/A481228
Lei, Y., Xu, J., Liang, C. X., Bi, Z., Li, X., Zhang, D., & Yu, Z. (2025). Large language model agents: A comprehensive survey on architectures, capabilities, and applications (Preprint). Preprints. https://doi.org/10.20944/preprints202512.2119.v1
Lu, L., Gu, X., Huang, J., Du, J., Zheng, X., Liu, Y., & Pang, S. (2025). DREAM: Dynamic red-teaming across environments for AI models (arXiv:2512.19016). arXiv. https://doi.org/10.48550/arXiv.2512.19016
Luo, J., Zhang, W., Yuan, Y., Zhao, Y., Yang, J., Gu, Y., & Zhang, M. (2025). Large language model agent: A survey on methodology, applications and challenges (arXiv:2503.21460). arXiv. https://doi.org/10.48550/arXiv.2503.21460
Mahmoud, E. (2025). Enhancing hosting infrastructure management with AI-powered automation [Bachelor's thesis, Theseus]. https://www.theseus.fi/handle/10024/882571
Nazmunisha, N. (2026). Multi-agent human-AI systems with low-code platforms enabling adaptive web services and real-time anomaly remediation in distributed architectures (Preprint). Preprints. https://doi.org/10.20944/preprints202601.1877.v1
Ni, K., Hua, W., Shi, X., Guo, J., Chang, S., & Chen, T. (2026). Chimera: Latency- and performance-aware multi-agent serving for heterogeneous LLMs (arXiv:2603.22206). arXiv. https://doi.org/10.48550/arXiv.2603.22206
Park, G., Lee, S., & Park, Y. (2026). Minimizing response latency in LLM-based agent systems: A survey. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3664226
Pattnayak, P., & Bohra, H. (2025). Review of tools for zero-code LLM based application development. In World Conference on Artificial Intelligence: Advances and Applications (pp. 294–312). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-13803-3_24
Racharla, N. K. (2025). Agentic AI for end-to-end investment banking automation: A no-code approach using GPT, Zapier, and Airtable (SSRN 5263449). SSRN. https://dx.doi.org/10.2139/ssrn.5263449
Wang, X., Rosenberg, S., Michelini, J., Smith, C., Tran, H., Nyst, E., & Neubig, G. (2025). The OpenHands software agent SDK: A composable and extensible foundation for production agents (arXiv:2511.03690). arXiv. https://doi.org/10.48550/arXiv.2511.03690
Wali, A., Mahamad, S., & Sulaiman, S. (2023). Task automation intelligent agents: A review. Future Internet, 15(6), 196. https://doi.org/10.3390/fi15060196
Xu, R., & Peng, J. (2025). A comprehensive survey of deep research: Systems, methodologies, and applications (arXiv:2506.12594). arXiv. https://doi.org/10.48550/arXiv.2506.12594
Yang, T., Feng, P., Guo, Q., Zhang, J., Zhang, X., Ning, J., & Mao, Z. (2025). AutoHMA-LLM: Efficient task coordination and execution in heterogeneous multi-agent systems using hybrid large language models. IEEE Transactions on Cognitive Communications and Networking, 11(2), 987–998. https://doi.org/10.1109/TCCN.2025.3528892
Yu, C., Cheng, Z., Cui, H., Gao, Y., Luo, Z., Wang, Y., & Zhao, Y. (2025). A survey on agent workflow: Status and future. In Proceedings of the 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 770–781). IEEE. https://doi.org/10.1109/ICAIBD64986.2025.11082076
Yu, J. S., & Yao, Y. (2026a). Agents and workflows. In Intelligent language services: Theory and practice with large language models (pp. 113–141). Springer Nature Singapore. https://doi.org/10.1007/978-981-95-4632-9_5
Yu, J. S., & Yao, Y. (2026b). Designing translation agents: Principles and practice. In Intelligent language services: Theory and practice with large language models (pp. 225–250). Springer Nature Singapore. https://doi.org/10.1007/978-981-95-4632-9_8
Zhang, C., He, S., Qian, J., Li, B., Li, L., Qin, S., & Zhang, Q. (2024). Large language model-brained GUI agents: A survey (arXiv:2411.18279). arXiv. https://doi.org/10.48550/arXiv.2411.18279
Zhao, B., Foo, L. G., Hu, P., Theobalt, C., Rahmani, H., & Liu, J. (2025). LLM-based agentic reasoning frameworks: A survey from methods to scenarios (arXiv:2508.17692). arXiv. https://doi.org/10.48550/arXiv.2508.17692
Zheng, Y., Chen, Y., & Hu, Y. (2025). AudAgent: Automated auditing of privacy policy compliance in AI agents (arXiv:2511.07441). arXiv. https://doi.org/10.48550/arXiv.2511.07441.

Article Statistics

Downloads

Download data is not yet available.

Issue Information

Article Details

Volume:	5
Issue:	1
Year:	2026
Published:	2026-06-30
Pages:	26–42
Section:	Articles

View Full Issue

Additional Information

How to Cite

Wali, M., Afkar, M. A., & Syafrinal, S. (2026). Design and Performance Evaluation of an OpenClaw-Based Desktop AI Assistant Deployed on Virtual Private Server: A Comparative Study with N8N Workflow Automation. Journal Dekstop Application (JDA), 5(1), 26–42. https://doi.org/10.59431/jda.v5i1.822