The creator switched from Open Claw to Hermes agent due to Hermes’ superior stability, seamless model switching, robust memory management, and active community support, which better suited complex workflows and AI experimentation. Despite initial challenges in migration, Hermes’ features like smart routing, local model support, and custom skill development made it a more reliable and flexible tool, with plans for a detailed masterclass forthcoming.
About a month ago, the creator began experimenting with the Hermes agent after initially using the Open Claw agent for over a month. Although transitioning was challenging due to the extensive workflows already established on Open Claw, the creator gradually migrated tasks to Hermes. A significant catalyst for this switch was Anthropic’s ban on Claw subscriptions in agents, which limited Open Claw’s usability. While Open Claw was suitable for simple brainstorming via Telegram bots, the creator needed a more robust, reliable, and memory-capable agent for complex workflows, leading to the full switch to Hermes.
One of the primary reasons for switching was Hermes agent’s stability. Open Claw frequently crashed two to three times a week, often closing the terminal gateway unexpectedly, which was frustrating and disruptive, especially during trips when remote restarts were impossible. In contrast, Hermes agent has proven to be much more durable, never crashing during the creator’s month-long usage. Additionally, Open Claw updates sometimes broke functionality, requiring rollbacks, whereas Hermes updates have been smooth and reliable.
Another major advantage of Hermes is its seamless model switching capability. Unlike Open Claw, which restricts model options and requires restarts or configuration changes, Hermes allows mid-chat switching between various models, including local and API-based ones, without interruption. Hermes also features smart routing, which optimizes resource use by delegating simple tasks to cheaper models and complex tasks to more powerful ones. This flexibility is especially valuable given the creator’s frequent testing of new models and architectures.
Memory management is another standout feature of Hermes. Unlike Open Claw, which struggles with session-to-session memory retention, Hermes stores memory in accessible markdown files, allowing it to remember detailed past interactions and research topics accurately over long periods. This robust memory system enhances the agent’s usefulness for ongoing projects. Furthermore, Hermes benefits from an active and growing community that contributes plugins, skills, and tools, fostering continuous improvement and innovation.
Finally, Hermes agent excels in local model support, privacy, and custom skill development, making it ideal for users experimenting with AI and machine learning research. The agent automatically creates custom skills based on user workflows and supports exporting conversations as training data for fine-tuning models. The creator also provided a step-by-step guide to migrating from Open Claw to Hermes, highlighting the ease of transferring core data like memories, skills, and configurations. Looking ahead, the creator plans to produce a comprehensive masterclass on Hermes agent, covering setup, features, and use cases.