This is a submission for the Hermes Agent Challenge
I've been building with LLMs for about a year now. Wrapped a few APIs, built some chatbots, shipped a couple projects that people actually used. And through all of it, there was this one thing that kept bothering me — every tool I built was basically goldfish-brained. A user would have a great conversation, close the tab, come back tomorrow, and we'd start from zero. Context? Gone. Preferences? Forgotten. That thing they told me they struggle with? No idea what you're talking about. We kept compensating. Session IDs. Database schemas. Redis caches. RAG pipelines. Vector stores. All of this infrastructure just to give an AI a memory — which felt absurd, because memory is fundamental to usefulness. We were building the plumbing around the actual problem instead of solving it. Then I tried Hermes Agent, and something clicked. ## What Hermes Agent Actually Is (Skip This If You Already Know)
For anyone who hasn't looked at it yet: Hermes Agent is an open-source agentic system from Nous Research. You can run it on your own hardware — a $5 VPS, a GPU server, whatever you've got. It supports basically any LLM provider (OpenRouter, NVIDIA NIM, OpenAI, your own endpoint). But none of that's what makes it interesting. What makes it interesting is the self-improving learning loop. Hermes Agent creates skills from experience, improves them during use, builds a persistent model of who you're, and remembers across sessions. It's the difference between a chatbot and a colleague. Here's the stack it gives you out of the box:
- Persistent memory — survives restarts, model switches, platform changes
- Skill system — self-contained capabilities that auto-activate contextually
- Multi-platform — one build runs on Telegram, Discord, Slack, WhatsApp, CLI
- Cron scheduling — background tasks without managing infrastructure
- Tool use — web search, file access, whatever you connect
- User modeling — learns who you're over time The thing I want to focus on isn't any single feature though. It's the design philosophy — and how it exposed a blind spot I didn't know I had. ## The Statelessness Trap Here's a question: when was the last time you used a tool that remembered something about you without you explicitly telling it to? Not a recommendation algorithm. Not a cookie-based preference. I mean a tool that noticed a pattern in your behavior, learned from it, and adjusted its approach the next time you showed up — without you doing anything. For most developer tools, the answer is "never." And we've accepted this as normal. I realized this when I was building an interview preparation agent on Hermes. The idea was simple: mock interviews, scored feedback, progress tracking. I'd built something similar before — a browser-based chatbot using Gemma 4. It worked fine for single sessions. People would practice an interview question, get feedback, move on. But nobody came back. And when they did, they asked the same things. Got the same feedback. Made the same mistakes. The tool couldn't help them improve because it couldn't remember what "improve" meant for them specifically. When I rebuilt it on Hermes Agent, the first thing I noticed was I didn't need to build a persistence layer. I just wrote interview scores to Hermes's memory directory and they.. stayed. Across sessions. Across days. Across platform switches. When a user practiced on Telegram during their commute and then sat down at their laptop on CLI, their profile was already there. I hadn't built a database. I hadn't configured Redis. I hadn't written migration scripts. I just used the system as it was designed, and it solved the problem I'd spent weeks trying to engineer around in previous projects. ## What This open up (That Surprised Me) The obvious benefit of memory is continuity. But there were second-order effects I didn't anticipate. ### 1. Adaptive Difficulty Without Configuration Because Hermes remembers scores across sessions, I could implement exponential moving averages (α=0.3) that automatically tune question difficulty. Score 8+ consistently in system design? Your system design questions get harder. Plateau in behavioral? Questions shift toward your weak spots. The user doesn't configure this. They don't click a "difficulty" slider. They just practice, and the system adapts. This is only possible because the agent has history — not a chat log, but a structured understanding of the user's performance trajectory. ### 2. Company-Specific Coaching That Evolves I built in company research — when you say "I'm targeting Google," Hermes runs web searches and synthesizes interview intel. But because of persistent memory, that research gets cached and updated. If you come back a week later targeting Google again, it doesn't re-search from scratch. It uses the cached research and only refreshes what might have changed. More importantly, it starts correlating: "You've done 8 Google-targeted sessions now. Your Googleyness score (collaboration, humility) has improved from 6.2 to 7.8, but your technical depth still lags. Here's what I'd focus on." This kind of longitudinal analysis is trivial to implement when you've real memory. It's nearly impossible in a stateless system. ### 3. The Skill Self-Improvement Loop This is the one that really surprised me. When users research a new company, the discovered interview questions get added to the question bank. The skill literally grows. After 50 users each researching a different company, the question bank has organically expanded with real, company-specific questions that no one manually pick. Hermes's learning loop made this almost accidental. I wrote a function that saves research results to memory. Hermes's architecture means those results become part of the skill's knowledge base. I didn't explicitly design a "community question crowdsourcing system" — I just used memory correctly and it emerged. ## The Bigger Point I'm not trying to sell Hermes Agent. But I think there's a real insight here for anyone building AI-powered tools: Statelessness was never a feature. It was a limitation we normalized. We got comfortable building disposable interactions because the infrastructure for persistent, user-aware AI was expensive and complex. Custom databases, embedding pipelines, vector stores, prompt caching — a mountain of glue code just to give an AI a memory. Hermes Agent is one of the first systems I've used where memory isn't an add-on. It's a primitive. You don't build memory into your application — you build your application on top of memory. And that inversion changes the kind of things you can build. Here's a concrete comparison. When I built my interview coach on a stateless architecture: | Capability | Stateless (Before) | With Hermes Memory (After) | |---|---|---| | Session continuity | ❌ Starts fresh every time | ✅ Picks up where you left off | | Adaptive difficulty | ❌ Manual slider | ✅ Automatic from history | | Weakness detection | ❌ Within single session only | ✅ Across weeks of practice | | Company-specific prep | ❌ Re-research every time | ✅ Cached, updated, correlated | | Cross-platform | ❌ Separate apps | ✅ Same profile everywhere | | Progress tracking | ❌ Not possible | ✅ Reports with trends | | Question bank growth | ❌ Static | ✅ Grows from research | | Lines of code for persistence | ~400 | 0 | Zero lines of persistence code. Not because I'm lazy (well, partly), but because the right abstraction made them unnecessary. ## What I'd Tell Someone Starting With Hermes Agent If you're considering building something on Hermes Agent, here's my honest advice after spending a week with it: Start with a problem that requires memory. If your use case is a single-turn question-answer, Hermes is overkill. But if you're building something where the user's context matters across sessions — a personal tutor, a writing coach, a project manager, a health tracker — Hermes's architecture is almost unfairly good for it. Lean on the skill system. Don't try to build a monolithic application. Build a skill. It's self-contained, auto-activating, and pluggable. The SKILL.md file tells Hermes when to activate your code, and the scripts folder holds your logic. That's it. Let multi-platform be free. I didn't write any platform-specific code. Not a line. But users can interact via Telegram, Discord, Slack, or CLI. If your use case benefits from accessibility (and what doesn't?), this is a massive win you get for free. Use cron for engagement. Daily practice reminders, weekly summaries, research refreshes — all trivial with Hermes's built-in scheduler. You write a function, tell cron when to run it, done. No crontab, no infrastructure, no monitoring. ## One Honest Concern Hermes Agent is early. The community is small. Documentation exists but you'll occasionally need to read source code to understand edge cases. If you're the kind of developer who needs a mature space with Stack Overflow answers and official certifications, it might not be for you yet. But if you're the kind who likes building on powerful primitives and figuring things out — the bones are strong. Really strong. And the fact that it's open source, self-hostable, and model-agnostic means you're not building on someone else's roadmap. ## Final Thought The best tools disappear into your workflow. You stop thinking about the tool and start thinking about the problem. Hermes Agent got close to that for me — I spent most of my time designing interview rubrics and evaluation logic, not fighting infrastructure. That's the compliment I'd give it: it let me work on the interesting part. --- The project I built: Hermes Interview Coach — a self-improving interview prep agent that remembers your weak spots and adapts.
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