Getting Started with Hermes: The Complete Guide to the Hermes AI Agent

There is an AI project adding tens of thousands of GitHub stars a month, and most professionals have never heard of it. It is called Hermes, built by Nous Research, and it sits in a different category from the chat assistants most people mean when they say "AI." It is not a chatbot you open and close. It is a persistent agent that remembers you, writes its own skills, and orchestrates other systems to get work done.

This guide is for the person who has heard the name, seen the star count, and wants to know what getting started with Hermes actually involves. No hype. We run our own operations on an agent stack shaped exactly like this, so what follows is the practitioner version: what Hermes is, who it is for, the honest prerequisites, and a step-by-step path from zero to a working agent.

What is Hermes?

Hermes is an open-source AI agent framework from Nous Research. Unlike a standard chatbot, it runs persistently, maintains long-term memory of your interactions, generates its own skills as patterns emerge in how you use it, and acts as an orchestration layer that triggers downstream tools and platforms to execute work. It is typically operated through a messaging surface like Telegram, Discord, or Slack, which makes it usable from a phone as easily as a laptop.

Why Hermes Is Getting So Much Attention

The headline number is the adoption curve. As of mid-2026, Hermes has crossed 170,000 GitHub stars, with tens of thousands of forks and thousands of open pull requests. That puts it in the top tier of open-source AI projects, rivaling the trajectory of the most successful agent frameworks while exceeding many of the heavily marketed ones. It got there largely through organic developer adoption rather than a funded marketing push.

But the star count is not the interesting part. The interesting part is the category. Most people, when they hear "AI," picture a chat window: you ask, it answers, the session ends, and nothing carries forward. Hermes is a different shape:

• It persists. It runs continuously rather than spinning up only when you open a tab.
• It remembers. It maintains memory across sessions and updates itself based on how you actually work with it.
• It learns skills. As repeatable patterns emerge in your usage, it can generate its own skills to handle them.
• It orchestrates. It is a controller layer. The work often gets executed by downstream platforms and tools that Hermes coordinates, not by Hermes alone.

That last point is the one most newcomers miss. The mental model is not "a smarter ChatGPT." It is "an operator you delegate to, who then fans the work out to the right tools." If you have read our piece on building AI agent teams, Hermes is the surface that sits in front of a team like that.

What You Can Actually Do With Hermes

Before you install anything, it helps to know what the payoff is. The capabilities that make Hermes worth the setup effort:

Persistent memory that compounds

A normal model is stateless. Every conversation starts cold, and you re-explain who you are, what you decided last week, and what you are working on. Hermes maintains memory over time and updates it as you interact. The practical effect is that the agent gets more useful the longer you use it, instead of resetting to zero every session. This is the same principle behind durable knowledge for AI systems: the value is in the foundation that persists, not the single response.

Self-created skills

When you find yourself asking for the same kind of task repeatedly, Hermes can generate a reusable skill for it rather than re-deriving the steps each time. Over weeks, this turns a general assistant into something shaped specifically around your work.

Cross-device, messaging-first operation

Because Hermes is commonly run through Telegram, Discord, Slack, WhatsApp, or Teams, the same agent thread is available on your phone and your desktop. You can hand off a task while walking out the door and pick up the result later from a different device. For a lot of operators, this is the feature that replaces "bring the laptop."

Orchestration of real work

Hermes is the controller. You say what you want in plain language, and it triggers the downstream systems that actually execute, for example: "draft and schedule a post using my marketing organization." The agent coordinates; the specialized tools do the heavy lifting.

Before You Start: An Honest Prerequisites Check

This is where we differ from most "getting started" content. Hermes is powerful, but it is a technical setup. It is not a download-and-go consumer app yet, and pretending otherwise sets people up to fail. Be honest with yourself about the following before you begin.

• Comfort with a terminal. You will be running commands, editing config files, and managing environment variables. If that sentence reads as a foreign language, start with a managed assistant first and come back to Hermes later. If you prefer a native graphical interface, check out the new Hermes Desktop App Guide: same agent, no terminal required.
• A model provider. Hermes needs a language model behind it. You will need API access to a provider (or a locally hosted model) and the keys to go with it.
• A place to run it. Hermes runs on Linux, macOS, and WSL2 on Windows, with additional options for a VPS, a GPU box, or serverless platforms. For a persistent agent, an always-on host (a small VPS) is the more reliable choice than your laptop.
• A messaging surface. Most people connect Telegram first because the bot setup is straightforward, but Discord and Slack are supported too.
• A little patience for week one. The first session is setup. The value shows up over the following weeks as memory and skills accumulate.

Is Hermes hard to set up?

Yes, relative to a consumer chatbot. Getting started with Hermes requires comfort with a command line, a model provider API key, and a host to run it on. It is well within reach for a technically comfortable operator or anyone who has set up a self-hosted tool before, but it is not yet a default recommendation for non-technical users.

How to Get Started With Hermes: A Step-by-Step Path

The exact commands change as the project moves fast, so always defer to the official documentation for the current syntax. What follows is the durable sequence, the steps that do not change even when the specific commands do.

Step 1: Decide where it will run

Choose your deployment target first, because it shapes everything after it.

• Local (macOS / Linux / WSL2): Fastest way to try it. The tradeoff is that the agent is only "alive" when your machine is on and awake.
• VPS: The most common production choice. A small always-on server keeps the agent persistent so it can act on schedules and respond when you are away from your desk.
• Serverless / GPU host: For heavier or local-model setups. More involved, worth it only once you know you need it.

If you are evaluating, start local. If you already know you want a real assistant, start on a cheap VPS so you only set it up once.

Step 2: Clone and install

Pull the project from the official repository at github.com/NousResearch/hermes-agent and follow the install instructions in the README for your platform. Read the documentation page before running anything; the project is under frenetic development, and the current setup flow is the source of truth, not a six-month-old blog post (including this one).

Step 3: Connect a model

Add your model provider credentials to the configuration. This is the brain behind the agent. Pick a capable model to start; you can tune cost and speed later once you understand your usage patterns. Keep your API keys in environment variables or a secrets file, never hard-coded into anything you might share.

Step 4: Connect a messaging surface

Wire up Telegram (or Discord/Slack). For Telegram, this means creating a bot, getting its token, and adding that token to your Hermes config. Once connected, you talk to your agent the same way you would message a person. This step is what turns Hermes from "a process running on a server" into "an assistant in your pocket."

Step 5: Give it memory and context

Point Hermes at a persistent store and, ideally, a knowledge source it can read, for example a notes vault or a folder of foundational documents. This is the single highest-leverage step. An agent with no durable context is just a chat client with extra steps. An agent that can read your positioning, your decisions, and your in-flight work answers questions in your context instead of generic ones. We have written separately on how to structure that foundation in durable knowledge for AI systems.

Step 6: Let it build skills

Use it for real tasks and watch for repetition. When you notice yourself asking for the same workflow more than a couple of times, that is the signal to let Hermes capture it as a skill. Over a few weeks, these accumulate into a toolkit shaped around how you actually work, not how a generic template assumes you work.

Step 7: Wire it into downstream execution

This is where Hermes earns the "orchestrator" label. Connect it to the tools and platforms that do your real work, your content pipeline, your code environment, your task system, so that a plain-language request from your phone fans out into actual execution. This is the same architecture we use to run an entire marketing operation through a single agent surface; see how we built the Viable Edge AI platform for a concrete example of the orchestrator-plus-specialists pattern.

What a Realistic First Week Looks Like

Set expectations correctly and you will not bounce off it.

• Day 1: Setup. Install, connect a model, connect Telegram, send your first messages. It feels like a slightly clunky chatbot. That is normal.
• Days 2 to 4: Give it context and start using it for actual tasks. Begin noticing what you repeat.
• Days 5 to 7: Capture a couple of skills, connect one downstream tool, and feel the first "oh, it remembered that" moment. That is the hook.
• Weeks 2 to 4: The compounding starts. The agent's memory and skill set begin reflecting your work, and you find yourself reaching for it before you reach for a browser tab.

The mistake to avoid is judging Hermes on day one. A stateless chatbot is at its best on first contact. A persistent agent is at its worst on first contact and gets better from there. Different curve, different category.

Hermes vs. Other AI Agent Frameworks

People usually ask how Hermes compares to the better-known frameworks. The short version:

• vs. orchestration libraries (e.g. LangChain): Those are toolkits you assemble into an application. Hermes is closer to a ready-to-run agent you operate, with memory, skills, and messaging built in rather than wired up by hand.
• vs. autonomous-agent experiments (e.g. AutoGPT-style projects): Hermes leans into being a reliable daily operator with persistent memory and a human in the loop via chat, rather than a fully autonomous loop you set and forget.
• vs. consumer assistants (ChatGPT, Claude apps): Those are polished and require zero setup, but they are session-bound and do not persist or orchestrate your other tools the way Hermes does. The tradeoff is convenience versus capability.

None of these are strictly better. They are different points on a curve from "zero setup, session-bound" to "real setup, persistent and orchestrating." Hermes lives firmly at the capable end.

Common Mistakes When Getting Started

• Running it only on your laptop and expecting persistence. If the host sleeps, the agent sleeps. Use an always-on host if you want it to feel alive.
• Skipping the memory and context step. Without durable context, you lose the single biggest advantage Hermes has over a normal chatbot.
• Judging it on day one. The value is in the compounding curve, not the first conversation.
• Hard-coding secrets. Keep model and bot tokens in environment variables. Treat the config like production from the start.
• Trying to do everything at once. Connect one model, one messaging surface, and one downstream tool first. Expand after it is stable.

Who Should Start With Hermes (And Who Should Wait)

Start now if you are technically comfortable, you want a persistent assistant that compounds, and you are willing to invest a setup day for weeks of payoff. Builders, technical founders, and operators who already self-host tools will feel at home.

Wait if you have never opened a terminal, you need something usable in the next ten minutes, or you are not in a position to manage a model provider and a host. The good news is that the agent paradigm Hermes represents is moving toward easier on-ramps quickly. If it is too much today, the underlying shift, persistent, memory-backed, orchestrating agents, is the direction everything is heading, so the conceptual investment is not wasted.

If you want more practitioner notes on building agent systems that compound, the newsletter goes out weekly. If you would rather have someone architect this layer for your business, our partner page has the entry point.