Hermes Agent vient de tuer Openclaw ?

Hermes Agent vient de tuer Openclaw ?

by Shubham SHARMA

Applied Sciences & Engineering AI — News & Applications UYComputer scienceUYQArtificial intelligence (AI)
🎙 Shubham SHARMA 👥 313K 📅 May 27, 2026 ⏱ 23 min 👁 118K 📄 expert opinion 🧭 2026-08-24
Available in: English (current) Français

Keywords

Hermes AgentOpenClawAI agentsDeepSeek V4self-improvement

Summary

The video compares Hermes Agent and OpenClaw, two autonomous AI agents. The creator, Shubham Sharma, argues that Hermes Agent is superior due to its self-improvement loop, which automatically creates and refines skills based on user interactions. He demonstrates this with a live example of analyzing YouTube comments via WhatsApp, where Hermes creates a new skill and a cron job without explicit instruction. The video also highlights Hermes’s persistent memory, which stores user notes and profile information, and its user-friendly web UI with Kanban board for multi-agent orchestration. Sharma discusses the cost of tokens, recommending DeepSeek V4 for its low cost (40 cents over 3 days) compared to Claude (17 dollars in a few conversations). He provides installation guidance via Hostinger, which offers a one-click setup for Hermes and its web UI. The video concludes with a verdict that Hermes is a significant upgrade over OpenClaw, especially for users who want a more autonomous and self-improving agent, but he advises testing rather than following hype.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides valuable practical insights into the capabilities of Hermes Agent, particularly its self-improvement loop and persistent memory. The live demonstrations are compelling and illustrate the agent’s autonomy. The cost comparison between DeepSeek V4 and Claude is useful for users concerned about token expenses. However, the argumentation is largely anecdotal and promotional, lacking rigorous benchmarks or independent verification. The creator’s enthusiasm for Hermes is evident, but he does not critically evaluate potential drawbacks or limitations beyond mentioning installation difficulties. The comparison with OpenClaw is somewhat superficial, focusing on perceived weaknesses without a balanced assessment.

Scientific Rigor, Source Quality, Title Accuracy

The video’s scientific rigor is limited; it is an opinion piece based on personal experience rather than a systematic study. The sources cited are primarily the creator’s own links (Hostinger, newsletter, etc.) and no external research or documentation is referenced. The title is catchy but somewhat sensationalist (‘killed OpenClaw’), though the content does support the claim of Hermes’s advantages. The video includes a sponsored segment for Hostinger, which is disclosed but may bias the installation recommendation. Overall, the information is presented clearly but lacks depth in terms of evidence and source quality.

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Title / Content Match

The title accurately reflects the content, which compares Hermes Agent and OpenClaw and argues for migration.

Quality & Reliability

6/10

The video is a practical demonstration and opinion piece by a tech influencer. It provides hands-on comparisons and cost data, but relies on anecdotal evidence and promotional content. The claims about Hermes Agent's superiority are not independently verified, and the video includes a sponsored segment.

Chapters

Cited Sources

Concurring Sources

Dissenting Sources

  • Community feedback on OpenClaw — Some users in the comments report positive experiences with OpenClaw, contradicting the video's negative portrayal.

Contribution & Novelties

The video offers a practical, hands-on comparison of two AI agents, highlighting Hermes’s self-improvement loop as a key differentiator. It provides concrete examples of skill creation and cron job automation, which are valuable for users considering migration. The cost analysis with DeepSeek V4 is a practical tip for managing token expenses.

Pour aller plus loin :

  • AI agent — Background on autonomous agents.
  • Self-improving AI — Concept of agents improving their own capabilities.
  • Persistent memory in AI — Overview of memory systems in AI.
  • DeepSeek — Information on the DeepSeek model family.

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Radar Profile

The radar profile shows moderate scores across all dimensions, with a slight peak in quantity of information and a dip in reliability. This reflects a video that is informative and practical but lacks rigorous sourcing and independent verification.

Reliability 5/10

💬 Positif. Sur les 30 commentaires analysés, la majorité exprime de l'intérêt et de la gratitude pour la vidéo, avec quelques questions techniques et des retours d'expérience personnels, mais sans critiques virulentes.