You use an AI chat tool to help with work. You paste in context, get a response, and close the window. Next time you open it, the chat is fresh. Nothing carried over.
This cycle — explain, receive, forget, repeat — is the default state of every browser-based AI tool. It works for quick questions. It breaks down when your work spans multiple sessions, uses external tools, or follows a recurring process.
Every time you open a new chat, you re-explain your project. You re-state your preferences. You re-paste the document you were working on. The model might be capable, but the environment around it is disposable. There is no continuity, no tool access, and no memory of what happened last time.
The problem is not the model. The problem is the runtime.
Hermes is an . It runs on your machine — not in a browser tab that disappears when you close it. An agent in Hermes holds across sessions, uses tools on your behalf, and can operate on a schedule without you watching.
Here is what that means in concrete terms. A chat tool generates text in response to a prompt, then stops. Hermes generates a response, and if that response requires reading a file, running a search, or calling an API, the agent does it — then feeds the result back into the conversation and keeps going. When you close a chat session, everything vanishes. When a Hermes session ends, the memory, skills, and context persist for next time.
Your agent remembers what you told it last session. Preferences, prior decisions, ongoing project context — all of it survives across restarts.
Your agent can read and write files, search the web, call APIs, and run shell commands — not just generate text. Over 70 built-in tools are available.
Your agent can run tasks on a cron schedule, deliver results to your messaging platform, and wait for your approval before taking the next step.
You can run multiple agents with different roles, different instructions, and different tool access — all on the same machine. Each profile has its own memory and skills.
Suppose you need to research keywords, write content, and publish on a weekly cadence. Right now, you open a chat, paste in last week's topics, ask for keyword ideas, copy the results into a spreadsheet, and start writing.
Next week, you do it again. The chat does not remember your brand voice, does not know which keywords you already targeted, and cannot run a search on its own. You repeat the setup every time.
With a persistent agent, the workflow looks different: your agent already knows your brand voice from prior sessions, can run keyword research tools directly, stores the results in memory, and can draft content based on what it learned — without you re-explaining the context each time.
Over time, the agent builds up knowledge about what worked and what did not. It stores style rules in memory. It creates reusable research procedures as skills. The longer you work with it, the less setup you need — not because the model improved, but because the runtime persisted.
This guide uses that SEO workflow as a running example. By the end, you will understand how to build your own agent team for a similar recurring process.
SEO is our running example, but the same principles apply to any recurring workflow — client onboarding, sales follow-up, content production, operational reporting, or any process you find yourself repeating across sessions.
Note: Results from SEO work depend on many factors outside any tool's control — competition, content quality, search algorithm changes, and more. Hermes helps you run consistent workflows; it does not guarantee rankings.
If you need a quick answer to a one-off question, a chat tool works fine. There is no need to install a runtime for a single prompt.
Hermes adds value when your AI work is recurring, tool-dependent, or needs to persist across sessions. If you find yourself re-entering the same context every time you open a chat, or you wish your AI could actually do something instead of just talk about doing it, a persistent agent runtime is worth considering.
Hermes is not a website, a SaaS dashboard, or a coding assistant plugin. It is a runtime that lives on your machine — locally, on a server, or in a Docker container — and runs agents that remember, use tools, and operate on schedules. The install takes a few minutes. The setup wizard walks you through connecting a model provider and choosing which tools to enable.
When you are ready to install, the command looks like this:
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bashThis works on Linux, macOS, and WSL2. After installation, run hermes to start chatting or hermes setup for the full setup wizard.
Think about your own recurring AI workflow. What context do you re-enter every session? What would change if your AI could remember it and act on it without being asked again?