Hermes Agent: The Self-Improving AI on Your Own

There's a particular kind of AI demo that circulates every few weeks—the one that makes you think, "okay, this is the thing that changes everything." Usually it doesn't. But occasionally you encounter a project that is genuinely different in its architecture, even if the demo itself is imperfect. Hermes Agent sits somewhere in that interesting middle ground.

In a recent walkthrough, AI automation creator Nate Herk spent nearly an hour demonstrating Hermes Agent—an MIT-licensed, open-source AI assistant from Noose Research that currently sits at 140,000 GitHub stars and climbing. The pitch: an AI that doesn't just respond to you, but actively builds its own operational memory, writes reusable procedures, and runs scheduled tasks while you're not even at your keyboard. It's a meaningful architectural departure from how most people currently interact with AI tools.

What Makes This Different From the Chatbot You're Already Using

The core distinction isn't capability—it's persistence. Most AI interactions are, as Herk puts it, "stateless." The model wakes up every session knowing nothing about you. Hermes is designed to fight that from the ground up.

The project is organized around five pillars: memory, skills, soul, cron jobs, and a self-improving loop. Memory is handled through structured markdown files—user.md captures your preferences and working style, while memory.md holds environmental context like current projects and business information. These load at session start, giving the agent something to work with before you've typed a single word.

Skills are essentially reusable procedures—what Herk calls "recipes." If you ask Hermes to do something once, it can convert that interaction into a saved skill file that gets invoked consistently the next time a similar task comes up. The analogy is apt: the difference between a chef cooking from memory and one cooking from a written recipe shows up in the consistency of the output.

There's also a "soul" file—a personality configuration that shapes how the agent communicates. Herk mentions he configured his comment-responding Hermes to be "very sarcastic, but not rude," which is either charming or slightly unnerving depending on your perspective on AI agents impersonating your online persona.

The fourth pillar, cron jobs, is where things get genuinely interesting for productivity use cases. Herk describes being able to simply tell Hermes, in plain language, "every morning at 6 a.m., do X, Y, and Z"—and the agent creates the scheduled automation without any manual configuration. His current setup includes a daily AI news briefing posted to his community, YouTube comment monitoring, morning business summaries, and server health checks.

"Crons turn Hermes from reactive into a proactive scheduled automation, and you're still getting that full agentic loop if you want it."

That's a real shift in how AI assistants typically work. Most AI tools require you to initiate every interaction. A system that fires off tasks independently—and reports back to your Telegram—is a different beast.

The Honest Comparison

Herk positions Hermes in relation to two other tools he uses: Claude Code (Anthropic's terminal-based coding assistant) and OpenClaw (an open-source project with over 350,000 GitHub stars, originally built by Peter Steinberger before he joined OpenAI).

His framing is worth taking seriously rather than dismissing as product categorization. Claude Code, in his workflow, is for focused desk-based knowledge work. Hermes and OpenClaw are for being on his phone, on a walk, spinning up automations conversationally. They're not substitutes; they're complements with different interaction modes.

His practical reason for gravitating toward Hermes over OpenClaw is less flattering to the competitive landscape: OpenClaw "was just kind of breaking a lot" after frequent updates, requiring manual fixes. Hermes has been more stable—"fingers crossed," as he adds, which is the kind of honest hedging that's more useful than polished marketing language.

The infrastructure angle is notable too. Hermes runs on a VPS (virtual private server)—in this case a Hostinger instance—meaning your data and your agent's memory live on your own server, not inside a third-party cloud product. For anyone paying attention to where AI data actually lives, this distinction matters. The tradeoff is that you're now responsible for maintaining that infrastructure, keeping it backed up (Herk covers GitHub backup in the walkthrough), and handling security.

Where the Seams Show

The demo isn't all clean. Herk shows Hermes attempting to produce a video using a tool called HyperFrames. The first pass fails—the agent doesn't even use the right tool, and the output has misaligned spacing. When pushed, Hermes researches the correct approach, asks permission to install HyperFrames, and produces a notably better second version. That's a reasonable illustration of the self-correcting loop, but it also illustrates something worth holding in mind: these systems still require significant human oversight and course-correction. The "natural language request" that produces good output usually has several rounds of iteration behind it that demos tend to compress.

The skills library claim—684 total skills, 91 built-in—is harder to independently verify from the video, but the existence of a community skills hub with over 520 contributed skills does suggest genuine ecosystem activity around the project. Herk notes that Anthropic has contributed 16 official skills, which is an interesting data point about how the lines between proprietary AI companies and open-source infrastructure are blurring.

There's also an implicit assumption running through the whole setup: that you want an AI agent that knows a lot about you, your workflows, your preferences, your business context. The user.md and memory.md files accumulate this information over time. Herk's advice to not store API keys or secrets in those files is sound, but it underscores that the memory system is only as trustworthy as your server setup. The upside of running on your own infrastructure is privacy; the downside is that you're now the security team.

The Bigger Pattern This Fits Into

Hermes isn't emerging in a vacuum. The broader "personal AI agent" space—tools that live on your own infrastructure, maintain persistent memory, and take autonomous action—is maturing quickly. OpenClaw, Hermes, Claude Code's dispatch features, and various self-hosted alternatives represent a category of tooling that's moving from hobbyist experiment toward something more durable.

"I'm not just a chatbot in a browser. I can use tools, remember preferences, write reusable skills, run scheduled automations, search past conversations, work through Telegram, and help manage real workflows."

That's Hermes describing itself in Herk's demo, which is a neat trick—but it also pretty cleanly articulates the value proposition that this entire category of tools is chasing. The question isn't whether these systems can do impressive things in demos. They clearly can. The question is whether the self-improvement loop actually compounds in meaningful ways over months of real use, or whether the agent slowly accumulates a memory file full of half-baked context and outdated preferences that nobody thinks to clean up.

That's genuinely unknown right now. Herk is enthusiastic, but he's also only a few months into using Hermes in production. The architecture is elegant. The execution, at this stage, is somewhere between promising and requiring significant personal investment to maintain.

What Hermes represents, at minimum, is a coherent answer to a real problem: AI tools that forget everything the moment a session ends are fundamentally limited for anything that requires continuity. Whether this particular implementation is the one that solves it, or whether it's a useful step toward something better, probably depends on how much you enjoy being an early adopter of infrastructure you didn't write.

By Marcus Chen-Ramirez, Senior Technology Correspondent