Most AI assistants forget everything the moment a session ends. But Hermes Agent builds a memory of who you are, creates reusable skills from your past conversations, and keeps getting more capable the longer it runs. It's open source, works on a $5 VPS, and you can talk to it from Telegram while it runs quietly in the cloud. This post walks you through what it is, why it's worth paying attention to, and how to get started.
What Exactly Is An Agent?
Before we go further, let's get one thing straight: there's a real difference between a chatbot and an agent, and it matters here.
A chatbot answers your question and moves on. Every message exists in isolation. There's no memory of what you asked yesterday, no ability to take actions beyond generating text, and no way for it to get better at helping you specifically over time.
An agent is different. Think of it like the difference between a search engine and an intern. The search engine returns results. The intern can go look something up, open a file, write a script, test it, fix it when it breaks, and come back to you with a finished result. They know your preferences. They get faster and more useful as time goes on.
Hermes Agent is the intern version.
What Is Hermes Agent?
Hermes Agent is an open-source AI agent built by Nous Research. It is not a chatbot, and it is not a coding tool tied to an IDE. It is a standalone agent with a built-in learning loop that creates skills from experience, builds memory across sessions, and gets more capable the longer you use it. You can deploy it anywhere and talk to it from the messaging apps you already use.
Let me paint a familiar picture. You spend 20 minutes setting up an AI assistant. You tell it your preferences, your project context, and how you like things done. It's helpful. Then you close the tab. The next day, you come back to a blank slate. You re-explain everything from scratch, like a first date you've already been on. If that loop sounds exhausting, Hermes Agent was built to break it.
A Built-In Learning Loop
Most AI tools are frozen. You get the same capability on day one as you get on day one hundred. Hermes is designed around the opposite idea. So, every conversation teaches the agent something. After each session, Hermes reviews what happened and decides what's worth keeping as a permanent memory. It also does something more interesting, i.e., it creates skills.
What Are Skills?
Think of skills like recipes. The first time Hermes works through something complex like setting up a project scaffold or running a multi-step research task, it writes down the steps that worked. It stores those steps as a reusable skill. The next time you ask for something similar, it doesn't start from scratch. It pulls out that recipe and executes it faster, more reliably, and without you having to re-explain the context.
These skills live outside any single conversation. They compound across sessions. The more you use Hermes, the richer its skill library gets, and because it can improve existing skills during use, the recipes get better over time.
Hermes also builds a growing model of your preferences, your recurring projects, and your working style. It calls this the memory system, and it's backed by real persistent storage that survives restarts, re-deployments, and long idle periods.
Key Takeaways
Here's something that surprised me about Hermes. Most AI tools require you to have your machine open and running, but Hermes can live on a \$5 VPS you never SSH into directly. It can run on serverless infrastructure that costs nearly nothing when idle because it hibernates between conversations. You set it up once, and it stays alive in the cloud doing work whether your laptop is open or not.
Supported environments
The supported environments include your local machine, Docker containers, remote SSH servers, and two serverless options called Daytona and Modal. Daytona and Modal are the interesting ones for beginners as they handle all the infrastructure for you, and you only pay for compute when Hermes is actively doing something.
Connecting to platforms
Since Hermes isn't tied to your laptop, you can reach it from wherever you are. It connects to over 14 messaging platforms, including Telegram, Discord, WhatsApp, Slack, and Signal.
The Telegram integration is particularly worth setting up early. Once it's running, you can send Hermes a task from your phone while you're away from your desk, and it will execute it on the cloud VM, respond with results, and keep that work in memory for your next session. No need to open a laptop. No need to keep a terminal running.
Built-in tools
Hermes ships with 47 built-in tools out of the box. These cover things like web search, file reading and writing, code execution, image generation, browser control, and more. You don't configure these individually. They're available by default, and Hermes decides which ones to reach for based on what you ask.
Browser agent
Hermes Agent can search the web and pull real information rather than guessing from training data alone. It can run code and test it in isolated sandboxes so failures don't affect anything outside the agent. It can browse websites with full vision support, meaning it can look at a page the way a human would, rather than just parsing raw text. Hermes can do is spawn sub-agents to handle work in parallel.
MCP support
On top of the built-ins, Hermes supports MCP (Model Context Protocol), which is an open standard for connecting agents to external services. If a tool you want isn't built in, there's a good chance an MCP server exists for it.
Getting Started in 60 Seconds
Hermes installs on Linux, macOS, and WSL2. Open your terminal and run the following:
curl -fsSL <https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh> | bash
Once installed, start your first session by running:
Hermes setup
Hermes will run this by default and will also look for any existing installation, like OpenClaw or any other agent.
You'll be prompted to connect an AI model provider on the first run. Hermes works with multiple providers, including OpenRouter (which gives you access to hundreds of models through one API key), OpenAI, and any provider that uses a standard API format.
After that first setup, you're in. Start talking by running:
Hermes
What to Try First
Here are a few good starting points to get a feel for what Hermes actually does:
- Have a real conversation about an ongoing project you're working on.
- Tell the agent your stack, your current challenges, and what you're trying to build. Let it respond and ask follow-up questions. At the end of that session, check what the agent stored in memory. You'll see it pulling out specific facts rather than saving the whole transcript.
- Then start a new session the next day without re-explaining anything. See what the agent already knows about you.
- After a couple of sessions, type something like "what skills have you created so far?" and see what's in its library.
The first few skills will feel basic. After a month of regular use, the skill library starts to feel like a real productivity layer.
One thing worth knowing before you dive in: Hermes is built by Nous Research, the same team behind several well-regarded open source AI models. The codebase is MIT licensed, which means you can read it, modify it, self-host it, and build on top of it without restrictions. The community around it is active, and the skills are shareable through an open standard called agentskills.io.
Where to Go From Here
If this post made you curious, the best next step is just getting it running and having a real conversation with it. The documentation at hermes-agent.nousresearch.com covers everything from basic setup to advanced topics like voice mode, scheduled automations, and research pipeline configuration.
The Discord and GitHub Discussions are genuinely useful if you hit a wall - the community is active, and the team is responsive.
Happy to answer questions in the comments if anything here was unclear. What would you want a persistent, self-improving agent to do for you? Drop it below 👇
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