Everything you need to understand and customize Hermes Agent. Self-evolving skills, three-tier memory, GEPA optimization, and going from 1 to 10 agents that work for you 24/7.
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Hermes Agent crossed 90,000 GitHub stars in two months. Developers are quietly building personal AI agents that learn their workflow, remember their context, and run 24/7.
Every AI agent you've used has the same problem: it forgets everything the moment your session ends.
Your coding preferences, the project conventions you corrected it on three times, the fix it spent 10 minutes figuring out yesterday. All gone. Next session, you start from scratch.
Hermes Agent by Nous Research takes a fundamentally different approach. It ships with a learning loop that:
• Remembers across sessions
• Writes its own reusable skills
• Prunes them in the background
• And validates them offline through an evolutionary engine called GEPA
No other open-source agent combines all three. Not even OpenClaw.
This guide covers how this learning loop works, what each memory layer does, and how to configure everything from scratch.
By the end, you'll have three fully isolated agents running on your machine: a programmer (who uses your Claude code), a deep researcher, and a designer, each with its own personality, memory, skills, and Telegram bot.
Check this out:
The whole setup takes minutes and everything here is reproducible on your own hardware.
Note: All the illustrations in this guide were designed by Pixel, one of the Hermes agents you'll learn to build by the end. Watch for them as you read.
Let's get started!
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