How Hermes Agent's Self-Improvement Works — And How to Use It

About every 15 tool calls, Hermes pauses, runs a self-evaluation, looks at what worked and what could be better — then writes the lesson into a Skill Document and stores it in its skill library. Next time it sees a similar task, it pulls that skill directly. No starting from scratch.

"About four weeks in, I stopped calling it a 'tool.'" A user who's spent three months on r/hermesagent put it this way: "It started initiating things I hadn't asked for yet. Not smart — it just knows me."

This is what separates Hermes from other agent frameworks. Not a stronger model. Not more tools. It's that the agent becomes yours through use.

The four-step agent loop

Hugging Face published an architecture breakdown of Hermes on June 17 — 63,000 views, and the most complete technical blueprint available. They broke the agent loop into four steps.

Step 1: Build context. When a message arrives, Hermes doesn't just forward it to the model. It pulls from four memory layers: working memory (the current session), core memory (who you are, what you do), skill memory (learned skills), and long-term memory (cross-session history). All four layers get assembled before the prompt reaches the model.

Once the context window hits 50%, Hermes triggers compression — summarizing existing messages rather than continuing to stack tokens. This mechanism is the key to running 24/7 without burning through your API budget.

Step 2: Tool execution. Hermes ships with 17+ tools and 90 pre-installed skills, and both numbers grow as you use it. Standard agent loop: model decides which tool to call, executes, feeds the result back into context.

Step 3: Session management. If you send another message while the agent is still processing the previous one, the session manager decides whether to interrupt or queue. Small detail, big impact on long-running agents — without it, concurrent messages would scramble the context.

Step 4: Self-evaluation — the /learn trigger. This is the real dividing line between Hermes and every other agent framework. After roughly every 15 tool calls, the agent pauses, replays what just happened, and distills effective patterns into a Skill Document. The skill gets written to ~/.hermes/skills/ and auto-loads in future sessions. After about 20 reuses of a skill, similar tasks finish roughly 40% faster.

Translation: month one, you're using Hermes. Month two, Hermes starts using you.

Three ways to teach it

You don't have to write skill files by hand. Hermes gives you three paths for feeding it knowledge.

Path 1: Just talk to it. The highest-frequency path. Tell Hermes what you want done, get it done, and it summarizes the lesson on its own. You don't need to do anything extra — provided you're actually using it, not treating it like a search engine for two days and closing the tab. Sharbel A. said it plainly in a 4,700-view video: "Installing Hermes agent is the easy part. The hard part is knowing what workflows to actually build with it." The more real, recurring tasks you give it, the more it learns.

Path 2: The /learn command. v0.18 made this one-shot. Point it at a folder, a URL, a PDF, or demonstrate a workflow, and it distills everything into a reusable skill module. Superbash pointed out an interesting pattern in his v0.18 breakdown: "You can treat that skill as a PRD, give it to a coding agent and refine it more." The skill files /learn produces are standard format — usable by Claude Code or any other tool.

Path 3: Three files to define personality. @sandy4kad shared his co-founder Hermes setup using only three files:

• ~/.hermes/SOUL.md: agent identity — "a resilient clone who executes anything in front of them."
• ~/.hermes/MEMORY.md: contextual memory — project background, current goals, constraints.
• ~/.hermes/WORKSPACE.md (optional): task board and work log.

@JulianGoldieSEO captured it well: "Most people close a chatbot and it forgets they ever existed. Hermes doesn't. Same agent in my terminal. Same brain on Telegram, Discord, Slack, WhatsApp, even email. One memory. Every surface I talk to it on. That's when it clicked: this isn't a chatbot."

Learning resources, shallow to deep

Getting started — what it can do.

• Di Decoded: Hermes AI Agent Is INSANE — Everything It Can Do + Full Setup (June 21, 2026): end-to-end install walkthrough + feature demo. $25/month complete setup. Good if you haven't installed yet.
• Greg Isenberg + Alex Finn: Hermes Agent Desktop Full Setup + Real Use Cases (June 6, 123,832 views): complete desktop app walkthrough. Alex Finn demonstrates multi-model switching — local Qwen for daily tasks, Opus 4.8 for planning. The most-viewed Hermes onboarding content.

Getting practical — making it actually do things.

• Sharbel A.: 6 Hermes Agent Workflows That Make It Actually Useful (July 1, 4,700 views): workflow design methodology, not "click this button." Key point: give the agent a system, or it "feels powerful but doesn't know what to do."
• AI LABS: Insane Ways To Make Your Hermes Agent Setup 90% Cheaper (July 1, 7,100 views): cost optimization in practice. Covers compression threshold tuning, whether the default 150 max turns is too high, and how to pair local models with cloud models.

Going deeper — understanding how it runs.

• Hugging Face: Hermes Architecture EXPLAINED: Memory, Context & Gateways (June 17, 63,331 views): the most complete technical breakdown. Four-layer memory, context building, session management, compression — not just for users, but for anyone who wants to build their own.
• Superbash (BoxminingAI): Hermes Agent Update v0.18 is HUGE! (July 2): feature-by-feature v0.18 walkthrough. Covers verification-based completion (the agent no longer says "done" without checking), MoA council, and /journey transparent memory.
• Jack Roberts: This Hermes Update Changes Everything (June 30, 29,402 views): deep dive on Mixture of Agents — "Hermes has grown a council."

Community — figuring it out together.

• r/hermesagent: main discussion hub. The July 1 v0.18 release thread, the Mac + MLX Apple Silicon megathread, the free models/APIs resource list, and the "three months in, here's what I wish I knew" reflections all live here.
• three months with Hermes Agent — what i wish i had understood earlier: the single most worth-reading user journal. "Don't treat it as a tool. Don't give it trivial tasks. It gets tired — not its tired, your budget's tired."

A judgment call

Hermes' self-improvement is not a marketing term. The four-layer memory + periodic self-evaluation + automatic skill generation is an engineering reality — the agent genuinely changes its behavior through use. But that also means the results scale with the real work you put in: the shallower and fewer the tasks, the thinner the learning.

Flip it around: if you're an indie developer or anyone whose day involves a lot of repetitive cognitive labor — Hermes might be the closest thing to a practical "personal agent" available right now. Not because it has the most features, but because it'll be better three months in than on day one. Most tools can't say that.

Don't rush to install. Answer one question first: what's the first recurring task you'll hand it? Figure that out, then install.