An AI agent that gets better the longer you use it, writes its own skills, and actually remembers you across sessions. Here is what Hermes is, how it works, and why it matters if you want to build real automations with AI.
If you have spent any time with AI tools, you have probably noticed a pattern: you set something up, it works great for a week, and then it starts to feel repetitive. You explain yourself again. You re-enter context it should already know. You fix something it broke.
Hermes Agent, built by a research group called Nous Research, is trying to solve that problem from the ground up. The pitch is simple: an AI agent that is better on day 30 than it was on day one. ↗ 0:00
A regular AI tool like ChatGPT waits for you to ask it something, answers, and then forgets everything when you close the tab. An AI agent is different. It has memory across sessions, it can take actions (search the web, run code, control apps), and it can work on tasks without you supervising every step. Think of it less like a calculator and more like a junior employee you can delegate to. *** What is a Neural Network, Really? (coming soon)
Hermes is what is called an agent harness. The harness is not the brain. It is the body. You plug in whatever AI model you want as the brain, whether that is GPT-5.5 from OpenAI, a local model running on your own computer, or something else entirely. The harness gives that brain memory, tools, messaging connections, and the ability to build skills over time. ↗ 5:45
The most popular comparable tool before Hermes was OpenClaw. You may have heard of it. Hermes is a direct alternative, and as of May 2026, Hermes just passed OpenClaw in token usage on OpenRouter, which is the clearest signal of how quickly adoption is growing. ↗ 0:12
OpenRouter is a service that lets you access many different AI models through a single connection. Instead of signing up for OpenAI, Anthropic, Google, and Meta separately, you go through OpenRouter and choose which model to use for each task. Token usage on OpenRouter is a real-world proxy for how much people are actually using a tool.
You can run Hermes on a cloud server, on your own computer, or on a Raspberry Pi. It is open source, which means the code is public and free to use. No subscription required, though you will still pay for whatever AI model you choose as the brain.
Every serious AI agent has some form of memory. The question is not whether it has memory, it is how it manages that memory over time. This is where Hermes pulls ahead in a way that is easy to feel but hard to articulate until someone explains what is happening under the hood. ↗ 9:46
Hermes keeps two core memory files for every agent:
Both files get loaded into the start of every conversation, so the agent always has relevant context without you having to re-explain yourself. This part is not unique to Hermes. OpenClaw does this too. Here is what Hermes does differently. ↗ 12:55
Hermes puts strict size limits on those memory files. The USER.md file can only be 1,375 characters. The MEMORY.md file can only hold 2,200 characters. When they fill up, the agent has to decide what to keep and what to remove. This sounds like a constraint, but it is actually a deliberate design choice. It forces the agent to distill what actually matters about you, rather than accumulating a pile of loosely related notes that eventually becomes noise.
The second difference is a background check that runs every 10 turns of conversation. Hermes quietly asks itself: has anything in this conversation changed what I should know about this person or this environment? If yes, it updates the memory files mid-session. Most agent tools, including OpenClaw, only do this at the end of a session or when you manually trigger it. Hermes does it continuously, which means it stays accurate rather than drifting. ↗ 13:54
Imagine an assistant who takes notes throughout your meeting rather than trying to reconstruct everything afterward from memory. Small difference in process, huge difference in accuracy over time.
There is also an optional add-on called Honcho that takes this even further. Honcho runs alongside Hermes as a separate service, observing every message you send and building a detailed picture of who you are over time. Your habits, your communication style, your preferences. When you send a message, Honcho injects relevant context about you into the agent's thinking in real time. The result is an agent that genuinely knows you rather than just having access to your notes. ↗ 14:43
Nous Research started as a group of people on a Discord server with one goal: build open source AI that is not controlled by any single company. They were training their own models, also named Hermes, long before the agent harness existed. ↗ 17:17
The agent harness grew out of an internal tool they built for their own workflow. When OpenClaw launched and became popular, the Nous Research team looked at it and thought: we already have something better internally. So they released it.
A lot of AI tools are built as products first and used internally second, if at all. Hermes Agent was built by AI researchers who needed it to actually work for their own daily tasks. That origin shows up in how the tool feels to use. Their stated philosophy: get out of the agent's way and let the model be smart.
Their mission statement is worth knowing: they are building humanistic, censorship-free, and democratic AI. Whether that resonates with you or not, it explains many of the decisions they make about what to include and what to leave out.
This is the headline feature, and it is the hardest one to believe until you see it happen. ↗ 19:20
When you ask a Hermes agent to do something new, it figures out how to do it. That is not unusual. What is unusual is what happens next: if the task involved steps it had to work through carefully, Hermes automatically creates a skill file. A skill is a saved, reusable procedure. Next time you ask for something similar, it reaches for that skill instead of starting from scratch.
A new employee figures out how to process a refund the first time by reading the manual and asking questions. The second time, they just do it. Hermes works the same way, except it writes the manual itself after the first time so it never has to figure out the same thing twice.
In the video, NetworkChuck demonstrates this live. He tells his agent, Ron, to set up a VPN connection to his studio network. Ron works through it step by step. Then, without being asked, Ron creates a skill called "Twingate client operations" and saves it. The next time anyone asks Ron to handle that VPN, he has a ready-made procedure ready to go. ↗ 20:56
As skills accumulate over time, Hermes runs a background process called the Curator. It periodically reviews all your agent's skills, removes ones that are outdated or no longer useful, and improves ones that could be better. Skills move through three states: active, stale, and archived. This prevents the skill library from becoming a junk drawer and keeps the agent focused on what is actually relevant to how you work today. ↗ 22:20
The combination of self-written skills and automatic curation is what creates the "better on day 30" effect. The agent is not just learning about you. It is building a library of procedures specifically tuned to your environment and your way of working.
This might sound like a low bar, but anyone who has spent time with OpenClaw or similar tools knows it is not. Updates break things. Agents stop responding. You end up spending more time maintaining the tool than using it. ↗ 27:17
NetworkChuck ran Hermes for a full month before publishing this video specifically to test this. His conclusion: he had zero issues he did not cause himself. His wife has been using her own Hermes agent for daily tasks including homeschool planning, diet management, and household coordination for a family of eight, with no technical problems.
The distinction NetworkChuck draws is between something that feels like a project and something that feels like a product. OpenClaw felt like a project: impressive, powerful, but requiring ongoing maintenance and tolerance for breakage. Hermes feels like a product: it does what it says it does, consistently, without demanding that you also be an IT person to keep it running.
The Nous Research team's philosophy explains this. They are not trying to add every possible feature. They would rather make a smaller set of things work extremely well. When a new feature ships, it has already been used internally on real work first. That approach tends to produce more reliable software.
If you are reading this site, you are probably interested in using AI to actually do things, not just chat. That might mean automating repetitive tasks, building workflows that run without you, or connecting different tools together so they talk to each other. This is where Hermes starts to get genuinely exciting.
Hermes connects to messaging platforms out of the box: Telegram, Discord, Slack, WhatsApp, Signal, and email. That means your agent is reachable from your phone. You do not need to open a browser or a terminal to tell it to do something. You send a message the same way you would text a friend. ↗ 6:44
n8n (pronounced "n-eight-n") is an open source automation tool. Think of it like a plumbing system for software: you connect different apps and services and define what should happen when something triggers. For example: when a new email arrives with an invoice attached, extract the data, add it to a spreadsheet, and send a Slack message. Hermes can act as the intelligent decision-maker inside those workflows, handling the parts that require judgment rather than just data movement. We will have a full breakdown on n8n here soon. *** What is n8n and how do I use it? (coming soon)
The combination of Hermes and n8n represents a genuinely powerful setup for building AI-assisted workflows. Hermes handles the agentic parts: the memory, the skills, the judgment calls. n8n handles the plumbing: the triggers, the data routing, the connections. Together they can automate workflows that would previously have required either a developer or an expensive SaaS subscription.
This site will be documenting exactly this kind of setup as it gets built in practice. If you want to follow along as we figure out how to connect Hermes, n8n, Cloudflare Workers, and Claude into practical workflows, the newsletter is the best way to stay updated.
For getting started with Hermes itself, the installation is a single command run in a terminal. You can run it on a cloud server (a VPS costs around $5-10 per month), on your local machine, or eventually on Windows natively. The Hermes GitHub repository has the latest installation instructions. If you are moving from OpenClaw, there is a built-in migration path that carries over your memory files. ↗ 1:24
1. Agent harnesses are how most people will actually use AI going forward. Not chatbots. Persistent agents with memory and tools that live in your Telegram or Discord and just handle things. Hermes is the most mature open source version of this idea right now.
2. The skill system changes what long-term AI use looks like. An agent that builds its own procedures from your actual work is qualitatively different from one you have to manually configure. The compounding effect over weeks and months is real.
3. You do not need to be a developer to get started. The installation is one command. The messaging interface is Telegram or Discord. If you can follow a tutorial and run a few commands in a terminal, you can have this running. We will walk through it step by step in an upcoming article. *** How to install Hermes Agent step by step (coming soon)
The bigger picture here is that the tools are good enough now. The question is whether you know how to use them. That is what this site is for.