This is a submission for the Hermes Agent Challenge.
I have been playing with different AI agents for a while now. Most of them feel like clever chatbots that forget everything the moment the conversation ends. Then I tried Hermes Agent from Nous Research a few weeks back. It actually feels different. It grows with you. That stuck with me.
What Hermes Agent is
Hermes is an open-source autonomous agent that runs on your own server or VPS. It is not locked to one IDE or one API. You install it once, pick any model you like, and it starts building its own memory and skills over time.
The big idea is a built-in learning loop. When it solves something useful, it can create a reusable skill in Markdown, improve it later, and pull it back when needed. It also keeps persistent memory across sessions so it slowly builds a picture of how you work and what your projects look like.
I set it up on a cheap VPS with a simple curl command. The installer is straightforward. After that I ran hermes setup and connected it to a model I already had access to. Within minutes I could chat with it from Telegram while it worked in the background on the server. That alone felt freeing.
My experience so far
I started simple. I asked it to monitor a few GitHub repos and send me a daily summary. It remembered the context from previous days without me repeating instructions. Over a week it created a couple of small skills on its own for formatting those reports nicely.
I also used it for research tasks. It can search the web, browse pages, and chain steps together. What surprised me was how it handled follow-ups. Instead of starting fresh each time, it referred back to earlier parts of our conversation. That made longer projects feel more natural.
The multi-platform support is practical. I switch between CLI on my laptop and Telegram on my phone. The agent just continues wherever I left it.
Why it matters
Most agent frameworks still feel stateless. You get good results in the moment but lose the continuity that makes an assistant truly helpful over time. Hermes tries to fix that with persistent memory and auto-generated skills. It is early days, but I can already see how this approach could change how I work with AI day to day.
It is fully open source under MIT license. You own your data and your setup. No tracking. You can run it locally, on a VPS, or even in serverless environments that sleep when idle to keep costs low.
Getting started
If you want to try it yourself:
- Run the one-line installer on Linux, macOS, or WSL.
- Do the quick setup.
- Choose your model provider. It works with many options including local ones.
- Start chatting and let it learn.
The official docs are clear and the GitHub repo is active.
I am still exploring what else I can build with it. Right now it feels like having a junior colleague who never forgets the details of our last project and quietly gets better at the things we do together.
If you have tried Hermes or any other agent framework, I would love to hear what worked for you and what did not. The space is moving fast and sharing real experiences helps everyone.
What do you think an open, self-improving agent could mean for your own workflow?