User Experience: Switching from OpenClaw to Hermes Agent on Local LLM
A developer shared their experience switching from OpenClaw to Hermes Agent for local AI development. The setup used Qwen3.5-9B model running on an RX 9070 XT GPU with 16GB VRAM.
OpenClaw Experience
The user described OpenClaw as "a mess" and "lackluster," even after extensive debugging. They noted that while they managed to make it work locally, the experience was suboptimal.
Hermes Agent Setup and Performance
The switch to Hermes Agent provided immediate improvements:
- Setup was straightforward compared to OpenClaw
- Works well even when sandboxed to WSL2
- RAG (Retrieval-Augmented Generation) functionality works
- Tool calling works effectively
- Persistent memory works decently
The most significant performance difference appeared in complex tasks. Where OpenClaw required "50+ steps and tool callings" for a decently complex task, Hermes Agent completed the same task with "5 correct tool calls" and finished "2:30 minutes less of compute."
Practical Assessment
The user acknowledges Hermes isn't equivalent to high-end models like Opus 4.6, but for those who primarily need an AI assistant, it's sufficient. They note that with additional tools like Claude Code or Codex, developers can extend Hermes's capabilities beyond its intended scope.
The developer concluded they would only return to OpenClaw if it becomes "at least on par" with Hermes Agent's current performance and reliability.
📖 Read the full source: r/openclaw
👀 See Also
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Building a Persistent AI Knowledge Infrastructure with OpenClaw
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