Hermes: The AI Agent That Learns From Itself
Here is a strange thing about most AI agents: they are brilliant and they have amnesia at the same time.
You spend Monday teaching one how your project is laid out, which commands to run, the quirks of your setup. It nails the task. Then Tuesday comes, you open a fresh session, and it is a blank slate again. Everything you taught it, gone. You are onboarding the same new hire every single morning, forever. It is exhausting, and it is the quiet ceiling on how useful these things have been.
Hermes, an open-source agent from Nous Research, is one of the first serious attempts to knock that ceiling out. Its whole pitch is in the tagline: the agent that grows with you. Instead of forgetting, it learns, it writes down what worked as a reusable skill, remembers it across sessions, and gets faster and more reliable at the things you actually do. Lately it has been the agent everyone’s arguing about, and it’s usually argued about next to a rival called OpenClaw. So let me walk you through what it is, why it matters, how the two differ, and, honestly, where Hermes still stumbles. No hype, just what the research shows.
The core idea: an agent with a memory that compounds
A normal agent
Hermes
If you’ve read my post on Skills, this will feel familiar, and that’s the point. A Skill is a folder with instructions an agent can pull in when a task matches. The twist Hermes adds is: the agent writes its own Skills. You don’t author them by hand. When Hermes finishes something tricky, it distills what worked into a reusable skill file and files it away. Next time a similar task shows up, it reaches for that skill instead of figuring it all out again.
How the learning loop actually works
Under the hood it’s a loop, and once you see the shape it’s not mysterious at all. It sits around the normal agent loop (think, act, observe) and adds a fourth beat: learn.
So when you come back tomorrow, the skills are already there. Run something similar and it leans on what it learned, and executes faster. That “come back the next day and it remembers” behaviour is the headline feature, and it’s baked into the architecture rather than bolted on.
Why this matters now (and why it hit #1)
Watch what compounding does over even a short week. This is the intuition, a rough sketch, not a benchmark, of why a self-improving agent pulls ahead:
Hermes vs OpenClaw: two philosophies, not two products
You cannot read about Hermes without bumping into OpenClaw, its main rival, and the comparison is genuinely useful because they disagree at a deep level. They’re not two versions of the same thing; they’re two different bets about what a personal AI agent should be.
- Self-improving loop: writes its own skills
- Persistent, curated memory across sessions
- Gets faster at your recurring work
- Bet: a private assistant that compounds
- Central gateway wiring 50+ messaging channels
- Human-authored skills, batteries included
- Fast to deploy, tooling out of the box
- Bet: a hub that reaches every channel
Put plainly: if you want an agent that reaches you on twenty-five messaging channels and works out of the box, OpenClaw’s breadth wins. If you want a private assistant that quietly gets better at your specific recurring work, Hermes’s depth wins. The clean way to choose is to ask whether you value reach or learning more for your use.
| Dimension | Hermes | OpenClaw |
|---|---|---|
| Core idea | Self-improving skill loop | Central gateway to many channels |
| Skills | Agent writes its own | Human-authored |
| Memory | Persistent, curated, cross-session | Session / gateway-centric |
| Strength | Depth: learns your work | Breadth: 50+ channels, fast setup |
| Setup time | Longer (2 to 4 hours) | Shorter (under 30 min) |
| License | Open source (MIT) | Open source |
What it looks like in real life: three examples
Abstract talk of “self-improving” only lands with concrete scenes. Here’s the same capability across three very different users:
It runs about anywhere too, a $5 VPS, a GPU box, or serverless, and you reach it through the channels you already use (Telegram, Discord, Slack and more). It’s model-agnostic (200+ models via Nous Portal, OpenRouter, OpenAI-compatible endpoints, or local Ollama), ships with 40-plus built-in tools, and speaks MCP, so it plugs into the same tool ecosystem I wrote about in the MCP post. In other words, it isn’t an island; it’s a learning loop wrapped around the agent ideas you already know.
The honest part: where Hermes falls short
A teaching post that only sells you the upside isn’t teaching, it’s advertising. So here’s the balanced ledger, straight from what practitioners report.
Genuine strengths
Real limitations
That last limitation is worth sitting with, because it’s the real intellectual catch of self-improving agents in general. Learning from your own experience is powerful when there’s a clear signal for “did that work?”, tests passed, the deploy succeeded, the user said yes. In domains without that clear signal, a self-reinforcing agent can happily get more efficient at a mistake. Speed is not the same as correctness. Any agent that trains on itself inherits this, and it’s exactly the kind of thing worth designing guardrails around, the same “keep a human in the loop for the ambiguous, irreversible calls” instinct that good agent design already demands.
The takeaway
Hermes is a bet that the next leap in agents isn’t a smarter model, it’s a better memory. Make the agent write down what it learns, keep it across sessions, and let competence compound instead of resetting to zero every morning. That’s a genuinely different shape from the forgetful assistants we’ve lived with, and it’s why it climbed to the top of the usage charts.
It’s not magic, and it’s not free. It asks for setup patience, a flipped-on config switch most people miss, and a clear-eyed awareness that an agent improving on its own can drift confidently wrong where there’s no ground truth. But the core idea, an agent that grows with you instead of forgetting you, is the right direction, and Hermes is one of the clearest, most open expressions of it yet. Whether you pick it or OpenClaw comes down to one honest question: do you want reach, or do you want an agent that learns you?