Hermes Agent: The AI That Actually Remembers What You Told It Yesterday

So here’s a thing that frustrated me for months. I’d set up an AI agent, spend 20 minutes explaining my project, what tools I use, what I want it to avoid, and then the next day, nothing. It remembered nothing. I’d have to start all over again like some kind of Groundhog Day for developers.

That’s actually the problem Hermes Agent was built to fix. Released by Nous Research in February 2026, it’s an open-source AI agent that runs on your own server and keeps memory across sessions. It learns from what you ask it to do. Over time, it gets better at doing the same types of tasks for you without you having to explain them again.

I’ve been using it for about two months now and it’s genuinely one of the more practical AI tools I’ve come across. This article explains what it is, how it actually works under the hood, and how to get started without breaking anything.

What Hermes Agent Actually Is

Hermes is an open-source, MIT-licensed AI agent framework that runs 24/7 on self-hosted infrastructure. It uses a large language model as its reasoning engine, a set of tools for user interaction, and a memory system that carries context across sessions.

So basically it’s not a chatbot you open in a browser. It’s more like a background process, a worker that sits on your server (or VPS or even a home machine), waits for you to send it something via Telegram or Slack or Discord, and then goes and does the thing.

The project crossed 95,000 GitHub stars in roughly seven weeks after launch, making it one of the fastest-growing open-source agent projects this year. That’s a lot of traction for something that was barely two months old. I think the reason is simple: people have been burned by AI agents that forget everything, and Hermes actually tried to solve that instead of just putting a vector database in front of the problem and calling it “long-term memory.”

The real difference between Hermes and older frameworks is what Nous Research calls the three-layer learning loop. When you complete a task, Hermes doesn’t just log it and forget. It tries to convert that task into a reusable skill, a kind of saved procedure that it can call on automatically the next time you ask for something similar. More on this in a minute.

How It Works

When you send a task to Hermes through CLI, a messaging app, or a scheduled cron job, the agent sends that request to the configured language model along with its current memory and available tools. The model decides the next step. Then the agent calls whatever tools it needs: a terminal, file editor, web browser, or MCP servers. Each tool’s output feeds back into the loop. It keeps going until the task is done, then just waits for the next trigger without shutting down.

Think of it like this. You message your Telegram bot: “Check if the deployment pipeline passed and send me a summary.” Hermes wakes up, runs a terminal command to check your CI status, reads the output, and replies back to you on Telegram, all without you being at a computer. You could be on a bus.

The memory side is where it gets interesting.

The Memory System (This Is the Good Part)

Hermes stores memory across four layers. Two curated files, MEMORY.md and USER.md, contain environment facts and user preferences, and get loaded into every system prompt at the start of a session. A SQLite database at ~/.hermes/state.db, with FTS5 full-text search, archives each session for recall. The skills directory stores procedural memory. On top of that, pluggable providers like Mem0 or Supermemory can handle long-term user modeling.

So there’s a difference between “remembering a fact” and “remembering how to do something.” Most agents before Hermes were okay at the first one. The second one, procedural memory, is what they all dropped the ball on. A vector store can retrieve a note. It cannot convert three months of debugging sessions into a reliable, reusable workflow.

When Hermes finishes a task successfully, it tries to write a skill file describing what it did and how. Next time you ask for something similar, it pulls that skill file out automatically and uses it as the starting point instead of figuring everything out from scratch. I noticed this after about two weeks of using it, tasks that used to take the agent 3–4 back-and-forth steps were getting done in one shot because it had already “learned” what I usually want.

One thing I’ll be honest about: the skill auto-generation doesn’t always work perfectly. Sometimes it writes a skill file that’s too specific, so it only triggers in one narrow case. You can edit the skill files manually (they’re just Markdown), which helps, but it takes a bit of fiddling.

Tools and MCP Integration

Hermes is not just a prompt wrapper. It combines browser automation, terminal execution, file operations, memory, skills, and scheduling to support a wide range of real-world automation workflows.

More than 40 built-in tools cover web search, terminal execution, file operations, browser automation, vision, image generation, text-to-speech, and subagent delegation. Tools self-register at import time, so you can add your own by placing a plugin in the user, project, or pip entry point directories.

But the bigger deal is MCP, Model Context Protocol. This is an open standard that lets Hermes talk to external services in a structured way. So instead of writing custom code to connect Hermes to your calendar or your GitHub or your task manager, you just point it at an MCP server for that service and it figures out how to use it.

Skills are Markdown files that teach Hermes how to handle specific tasks. You write a skill file describing what the task is, what triggers it, and what steps to follow. Hermes reads all skill files in ~/.hermes/skills/ at startup and uses them to handle relevant requests. MCP servers give Hermes access to external services. If a service publishes an MCP server, Hermes can search products, manage carts, read calendars, create tasks, and more. You add MCP servers to your Hermes config and Hermes uses them automatically when relevant.

For example, a DigitalOcean tutorial published last week showed how to use Hermes with Swiggy Instamart’s MCP server to build a grocery tracking agent that monitors stock levels and places orders automatically. Same pattern works for any MCP-compatible service.

Model Support

Hermes is model-agnostic and supports 200+ providers via OpenRouter. You can also connect it directly to Nous Portal using OAuth, or point it at your own endpoint. So if you want to run a local model with Ollama, that works too, as long as the model has at least 64,000 tokens of context (which most of the recent ones do).

I tried it with Claude via OpenRouter first, then switched to a local Mistral 7B for some tasks just to test it. The quality difference was noticeable on complex reasoning tasks, but for simple repetitive stuff like checking build status or summarizing emails, the local model was fine. And free.

Getting Started

Installation is one line. No prerequisites. Works on Linux, macOS, and WSL2. The installer handles everything automatically, no sudo needed:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

This installs uv, Python 3.11, clones the repo, and sets up everything. After that, you connect to Nous Portal with OAuth, OpenRouter with an API key, or your own endpoint. An interactive setup wizard walks you through it.

Once that’s done, you can start it two ways:

hermes          # classic CLI
hermes --tui    # terminal UI (recommended for beginners)

The TUI shows your available tools, loaded skills, and model, nice to have when you’re just figuring things out.

Setting up messaging. After you have the basic CLI working, the next thing to do is connect a messaging platform. Hermes receives instructions through platforms like Telegram, Discord, or Slack, executes tool calls on your machine, and continues working even after you’ve closed the chat. The setup wizard walks you through connecting whichever one you prefer. Telegram is the easiest, just create a bot via BotFather, paste the token in, and you’re done.

Adding your first skill. You can use /skills inside a chat session to see what’s already loaded, or you can drop a Markdown file into ~/.hermes/skills/ and restart. A skill file looks something like this:

# Check Build Status
Trigger: when user asks about CI, build status, or pipeline
Steps:
1. Run `git log --oneline -5` to see recent commits
2. Check ~/.hermes/config for CI provider URL
3. Fetch latest run status
4. Reply with pass/fail and last 3 commit messages

That’s it. Hermes reads it at startup and uses it when you ask something that matches the trigger.

Adding MCP servers. For the MCP path, you add the server to ~/.hermes/config.yaml:

mcp_servers:
  github:
    command: npx
    args: ["-y", "@modelcontextprotocol/server-github"]
    env:
      GITHUB_PERSONAL_ACCESS_TOKEN: "ghp_xxx"

After that, Hermes can talk to GitHub directly, open issues, check PRs, whatever.

What It’s Actually Good For

Honestly, Hermes shines the most for repetitive background tasks that you’d normally have to do yourself or write a custom script for. Things like: summarizing your emails every morning and sending the summary to Telegram, monitoring a folder and doing something when new files appear, running a report and posting it to Slack every Friday.

It’s also practical for managing marketing campaigns, refactoring software code, and streamlining business operations where the task has clear steps that can be broken down.

The /goal feature is worth knowing about. You give Hermes a high-level goal and it breaks it down into sub-tasks automatically, adjusting as it goes. I used this to refactor a messy Python module and it got about 70% of the way there without any hand-holding. Good enough that the remaining 30% took me 10 minutes instead of an hour.

Limitations Worth Knowing

For most enterprises, Hermes is useful for internal automation, research pipelines, and scheduled reporting, but it still lacks RBAC, signed skill governance, and enterprise audit logging. So if you’re thinking about this for a production system at a company with compliance requirements, HIPAA, SOC 2, that kind of thing, it’s not quite there yet. There’s active discussion on GitHub about adding role-based access controls but as of May 2026 it hasn’t shipped.

Also, the skill auto-improvement system is still kind of half-baked. The idea is great but in practice Hermes sometimes writes skill files that are too narrow or occasionally contradicts an existing skill. You have to go in and clean them up manually every now and then. Not a dealbreaker but worth knowing going in.

One more thing, the documentation is honestly still catching up to the codebase. I spent maybe 45 minutes trying to figure out how to configure a custom browser route because the docs had an outdated example. The GitHub issues are more useful than the official docs right now. Hopefully that improves.

Is It Worth Setting Up?

If you do any kind of repetitive computer work, coding, reporting, monitoring, data fetching, yes, it’s worth the hour it takes to get running. The install itself is 5 minutes. The useful part is the two weeks after that, when you start building skills for your specific workflows and the agent starts to actually know what you want.

For non-technical people it’s still a bit rough. You need to be comfortable with a terminal and editing YAML files. But the gap is closing. There’s a video walkthrough by Onchain AI Garage on YouTube that’s genuinely beginner-friendly if you’d rather follow along visually.

The core idea, an agent that gets smarter the more you use it, without forgetting everything each session, is one that every other AI tool should have solved years ago. Hermes actually did it. That alone makes it stand out from most of what’s out there in 2026.