This is a submission for the Hermes Agent Challenge: Write About Hermes Agent
There's a version of AI agent development that most people are building toward: a capable agent you fully control, that runs on infrastructure you own, that gets better the longer it runs, and that you can actually understand from the inside.
For a while, that version felt like it was always six months away.
Hermes Agent by Nous Research makes a serious case that we're there now.
The "Not Tied to Your Laptop" Insight
Most AI agent tools are built around the assumption that you're sitting at a computer, running the agent locally, in an IDE or a browser tab. The interaction model is: you prompt, it responds, you wait, you prompt again.
Hermes is designed around a different assumption: the agent should be running somewhere, doing things, while you get on with your life.
The infrastructure flexibility here is real, not marketing. It supports six terminal backends:
- Local — your machine, direct execution
- Docker — containerized, isolated
- SSH — remote server you already have
- Daytona — cloud dev environments
- Singularity — HPC/research clusters
- Modal — serverless, costs nearly nothing when idle
That last two matter a lot. Serverless persistence means your environment hibernates when idle. A pipeline that runs twice a day doesn't need to cost you $50/month on a dedicated VM. You spin it up on Modal, it runs when triggered, it sleeps the rest of the time.
And the messaging gateway makes the "while you get on with your life" part literal. Hermes connects to 20+ platforms: Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Teams, Email, SMS — and more. You kick off a research task from Telegram while commuting. The agent runs on a cloud VM. You get the results in the same Telegram chat when it's done.
This is not a trivial architecture decision. It's a fundamentally different relationship between you and the agent.
Open Source Without the Asterisks
"Open source AI" has a credibility problem right now. It often means: open weights for the model, closed system around it, rate-limited API you're dependent on, no way to actually inspect what's happening.
Hermes is MIT licensed. It works with any OpenAI-compatible endpoint — Nous Portal, OpenRouter, OpenAI, your own local model. The skills system is based on the agentskills.io open standard, meaning skills are portable across compatible agents.
More importantly: the agent's knowledge is inspectable. Every skill it creates, every procedure it encodes, lives in markdown files under ~/.hermes/skills/. You can read them. Edit them. Delete them. Version control them. The agent's growing competence isn't locked in a proprietary database somewhere — it's files on a filesystem.
This is what open infrastructure for AI agents should look like. Not just open weights, but open runtime behavior.
The Self-Improvement Loop: Genuine or Hype?
The phrase "self-improving AI" triggers justified skepticism. Let me be specific about what Hermes actually does and what it doesn't claim to do.
What it does:
After completing a complex multi-step task, Hermes writes a skill — a structured markdown document encoding the procedure it followed, the pitfalls it encountered, and how to verify success. When it hits errors and finds the working path, it updates or creates skills. When you correct its approach, it captures the correction.
Next time a similar task comes up, it loads the relevant skill and works from encoded experience rather than reasoning from scratch.
This is not model fine-tuning. The underlying model weights don't change. What changes is the agent's procedural knowledge about your specific environment, workflows, and preferences.
What it doesn't do:
It doesn't make the model fundamentally smarter. It doesn't guarantee skills are always correct. The quality of auto-generated skills depends on the quality of the underlying task execution.
What it does well:
For anyone running repetitive agentic workflows — deployment pipelines, research tasks, code review processes — the compound effect over weeks of use is real. The skill library fills with your actual procedures. The agent stops reinventing your wheel every session.
The Skills Hub: A Bet on Community Infrastructure
Beyond self-created skills, Hermes integrates with a growing ecosystem of community skills:
- skills.sh — Vercel's public skills directory
-
Well-known endpoints — sites publishing
/.well-known/skills/index.json(Mintlify does this) -
GitHub taps — install from any public repo with a
skills/directory - browse.sh — 200+ site-specific browser automation skills for Amazon, arXiv, Airbnb, and more
- ClawHub, LobeHub — community marketplaces
Publishing your own skills is as simple as pushing to a GitHub repo. Other Hermes users add it as a tap with one command.
This is a bet that skills are a valuable unit of shareable knowledge for AI agents — that the right abstraction isn't sharing prompts or sharing models, but sharing encoded workflows that agents can discover, install, inspect, and build on.
That bet makes sense to me. A well-written skill is higher-signal than a prompt, more portable than a fine-tuned model, and more readable than a code library. If the ecosystem grows, there's real compounding value here.
What This Means for Developers
If you're building AI-powered workflows right now, Hermes offers something most options don't: a path from prototype to production that doesn't require you to bet on a single vendor's API staying available and affordable.
Run it on Daytona or Modal when you need serverless scale. Run it on a $5 VPS when you need persistent, always-on access. Switch model providers when prices change. Keep your skills and memory — they're just files.
The messaging gateway means you can interact with your agent from wherever you actually are, not just from a laptop with the right tab open.
And the open architecture means you can inspect, audit, and control the agent's behavior in ways that closed systems simply don't allow.
The Honest Caveats
Hermes is not the finished version of this vision — it's a serious, working implementation of the early version. Some things to know:
Setup has friction. The one-liner installer is smooth, but configuring SSH backends, Telegram gateways, and custom skill directories takes time and comfort with terminal tooling. This is not a consumer product yet.
Native Windows support is early beta. The docs are honest about this. Linux and macOS are the primary targets.
The skills system compounds slowly. You won't see dramatic value on day one. The payoff comes from sustained use on real tasks, not from kicking the tires once.
Model quality still matters. Hermes is the runtime, not the model. The quality of its reasoning depends on which model you point it at.
The Larger Point
We're at an interesting inflection point in AI agent development. The question is no longer "can agents do complex tasks?" They can. The question is "who controls the agent infrastructure?"
Hermes is a clear answer: you do. The agent runs where you put it, talks through the platforms you choose, learns procedures in formats you can read and edit, and doesn't require a subscription to a specific cloud to keep working.
For developers who care about infrastructure ownership and long-term capability building, that matters. Not because the alternatives are bad — some are excellent — but because having a capable open option shapes the entire ecosystem.
That's what Hermes Agent actually is. Not just another capable agent, but a demonstration that capable agents and open infrastructure aren't in tension.
Try it yourself:
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
hermes setup --portal
hermes chat
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