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The learning loop
This is what makes Hermes fundamentally different from a chatbot: a closed loop where the agent gets smarter over time.
The loop
text
┌──────────────┐
│ You give │
│ a task │
└──────┬───────┘
▼
┌──────────────┐
│ Hermes acts │ ← tools, files, shell
└──────┬───────┘
▼
┌──────────────┐
│ Outcome │
└──────┬───────┘
▼
┌───────────────────────┐
│ Memory updated │ ← facts, preferences persist
│ Skill created/refined│ ← procedures packaged for reuse
│ User model deepens │ ← learns how you work
└───────────────────────┘
│
▼
Next task starts smarterFour components
- Agent-curated memory: Hermes decides what's worth remembering and stores it in
~/.hermes/memory/. - Autonomous skill creation: after repeated tasks, Hermes packages the procedure as a reusable skill.
- Skill self-improvement: skills get refined during use, not just at creation time.
- Cross-session recall: FTS5 search + LLM summarization pulls relevant past context into new sessions.
What this means in practice
Week 1: You explain your project structure every session.
Week 3: Hermes remembers your stack, your conventions, and how you like files organized. You skip the briefing.
Week 6: Hermes has a skill for "update the VitePress sidebar when I add a new page." You say "add a page about X" and it handles config + content + sidebar.
How to accelerate the loop
- Be consistent. Work from the same project directories so context files and memory align.
- Correct mistakes. When Hermes gets something wrong, tell it explicitly. Good corrections become memory.
- Repeat tasks deliberately. The second and third time you do something, Hermes is more likely to skill-ify it.
- Use SOUL.md and context files. Static context frees memory for dynamic learning.
Offline advantage
With local models, the learning loop is entirely on your hardware. Memory and skills in ~/.hermes/ never touch a cloud API. Your agent's "brain growth" stays private.
Next: Memory system and Skills system.