Hermes Agent :-What I Understood as a Beginner

Recently I started learning about AI Agents, and while going through different tools I came across something called Hermes Agent.

At first I was little confused.

Because I was thinking, okay, another AI chatbot?

But after spending some time understanding it, I realised that Hermes is not really just a chatbot. It is more like an AI assistant which can actually do things using tools, remember things from previous sessions, work with files, use terminal, search web and even delegate some work to other agents.

So in this blog I am going to share what I understood about Hermes Agent as a beginner.

Not very deep. Just the basic concepts which helped me understand what Hermes is actually doing.

First, What is Hermes Agent?

Hermes Agent is an open-source AI agent built by Nous Research.

The easiest way I understood it is:

A normal LLM mostly gives you an answer. An AI Agent can decide what actions it needs to take to complete a task.

And Hermes is built around this idea. For example, if I ask a normal chatbot:

“Tell me what files are inside my project.

It can tell me how to run ls. But Hermes can actually have access to the terminal and execute the command.

So instead of only telling me how to do something, the agent can actually do it.

This difference is very important when we talk about AI agents.

Hermes currently supports things like terminal execution, web search, browser automation, file editing, memory, skills and delegation.

Chatbot vs AI Agent

This was probably the first concept I needed to understand. Let’s take a simple example.

Chatbot

I ask:

Find all TODO comments in my project.

A normal chatbot might give me a command like:

grep -r "TODO" .

Now I need to copy that command and execute it.

Agent

With an agent, the flow can be more like:

User
  ↓
Agent understands task
  ↓
Agent decides it needs terminal
  ↓
Runs command
  ↓
Reads result
  ↓
Analyzes result
  ↓
Gives answer

This is what makes an agent more interesting. The LLM is not only generating text. It is also participating in a loop where it can:

think → use tool → observe result → continue → answer

Obviously the exact internal implementation can be more complicated, but this mental model helped me understand it.

So Where Does the LLM Come In?

Hermes itself is not the intelligence in the same way an LLM is.

Think about it like this.

Hermes Agent
     |
     |---- LLM
     |
     |---- Tools
     |
     |---- Memory
     |
     |---- Skills
     |
     |---- Terminal
     |
     |---- Browser
     |
     |---- Subagents

The LLM is basically the reasoning engine. Hermes provides the environment around it.

The agent can receive a task, decide which tool is useful, execute that tool and then continue based on the result.

And one thing I found interesting is that Hermes is not locked to only one model provider. Its documentation lists support for providers including Nous Portal, OpenRouter, OpenAI and other endpoints.

So Hermes is more like an agent framework/runtime around models, rather than just another model.

What are Tools?

This is another important concept.

An LLM by itself cannot magically access everything on my computer. It needs tools. A tool is basically a function that the agent can call when it needs to perform some action.

For example:

User
 ↓
"Search the web for latest Node.js news"
 ↓
Agent
 ↓
Web Search Tool
 ↓
Search results
 ↓
Agent understands results
 ↓
Response

Another example:

User
 ↓
"Check my project files"
 ↓
Agent
 ↓
Terminal Tool
 ↓
File system
 ↓
Result
 ↓
Agent
 ↓
Response

Hermes has a tool/toolset system where capabilities such as web search, terminal execution, file editing, memory and delegation can be enabled and used by the agent.

And this is where I started feeling that this is actually different from just chatting with ChatGPT.

What is Memory in an AI Agent?

This was one of the concepts I found most interesting.

Normally when I start a new chat, the model doesn’t automatically know everything from my previous conversations.

But an agent can have persistent memory.

In simple words:

Session 1
   ↓
Learn something about user/project
   ↓
Store useful information
   ↓
Session ends

Session 2
   ↓
Retrieve useful information
   ↓
Continue from previous knowledge

Hermes has persistent memory that can survive across sessions. Its documentation describes curated memory and cross-session recall.

For example, imagine I am working on a backend project.

I tell Hermes:

My backend uses Node.js,
PostgreSQL and Redis.
We use Prisma for database access.

Later I can potentially ask something related to the project without explaining everything again.

This is where an agent starts becoming more like a personal assistant for a developer.

Then What are Skills?

At first I thought skills and tools are same. They are not exactly same. A simple way I understood it:

Tool = something the agent can do. Skill = knowledge/instructions about how to do something.

For example:

Tool
 ↓
Terminal

The terminal lets the agent execute commands.

But a skill can contain knowledge or procedures for doing a specific type of work.

Hermes has a skills system where knowledge can be loaded when required, instead of putting everything into the context all the time. The documentation describes this as progressive disclosure.

This is actually a nice idea because if we put every instruction into every prompt, the context will become huge.

Instead:

Need skill?
   ↓
Load skill
   ↓
Use knowledge
   ↓
Complete task

What are Subagents?

Now this is where the concept gets more interesting.

Imagine I give one big task:

Research Node.js performance improvements
and compare Redis caching strategies
and prepare a summary.

Instead of one agent doing everything in one context, an agent can delegate work.

Something like:

Main Agent
    |
    |---- Subagent 1
    |     Research Node.js
    |
    |---- Subagent 2
    |     Research Redis
    |
    |---- Subagent 3
          Compare approaches

Then the main agent can combine the results.

Hermes supports isolated subagents, with their own conversations and execution environments, for delegation workflows.

This concept is generally called multi-agent orchestration.

And honestly this is one area I want to explore more.

Because it changes the question from:

“What can one AI do?”

to:

“How can multiple AI agents work together?”

Hermes Can Also Work Outside the Terminal

Another thing I found interesting is that Hermes is not limited to a CLI.

The same agent can be used through different interfaces and messaging platforms.

The current Hermes documentation mentions CLI, desktop and messaging integrations including Telegram, Discord, Slack, WhatsApp, Signal and others.

So the idea can be something like:

Hermes
                    |
       -------------------------
       |          |            |
      CLI      Telegram      Discord
       |          |            |
       -------------------------
                    |
              Same Agent Core

This means I don’t necessarily have to sit in front of my terminal all the time.

I can interact with the agent from another interface while the agent is working somewhere else.

That is a pretty powerful concept for automation.

Hermes + Terminal

As a backend developer, this is probably one of the things I find most useful.

An agent with terminal access can work with actual development environments.

For example:

"Check why my tests are failing."

The agent could potentially:

1. Inspect the project
2. Find the test files
3. Run tests
4. Read errors
5. Inspect related code
6. Suggest or make changes
7. Run tests again

So instead of:

Human
 ↓
Ask AI
 ↓
Copy answer
 ↓
Run command
 ↓
Copy error
 ↓
Ask AI again

we can move towards:

Human
 ↓
Give task
 ↓
Agent
 ↓
Tools + reasoning
 ↓
Result

Obviously, we still need to review what the agent is doing.

AI agents are not magic and they can make mistakes.

Context is Still Very Important

One thing I am learning while studying AI agents is that context matters a lot.

If the agent doesn’t know:

• how the project is structured
• what conventions we follow
• what commands we use
• what should not be changed
• how the application works

then even a powerful model can make wrong decisions.

Hermes supports project context files such as AGENTS.md, which can contain project-specific instructions and conventions.

So we can think about it like:

LLM
+
Tools
+
Project Context
+
Memory
+
Skills
=
More useful Agent

This is probably one of the biggest differences between simply asking an LLM a question and actually building an agent workflow.

Is Hermes an AI Model?

No.

This was another thing I wanted to make clear for myself.

Hermes Agent and an LLM are different things.

A simplified view is:

AI Model
                   |
          Reasoning / Generation
                   |
                   ↓
             Hermes Agent
                   |
       -------------------------
       |      |       |        |
     Tools  Memory  Skills  Terminal

The model provides the intelligence/reasoning.

Hermes provides the agent environment and capabilities around it.

And because Hermes can work with different model providers, we don’t have to think of Hermes itself as the model.

Why I am Learning Hermes

I am still learning it, so I don’t want to say that I already understand everything.

But the reason I started looking into Hermes is because I think AI development is moving from chat-based AI towards agent-based workflows.

Earlier we mostly asked AI:

"How do I solve this?"

Now we are moving towards:

"Here is the task.
Go and work on it."

That is a very different way of using AI.

And as developers, I think understanding this shift is important.

Not only for using AI coding tools, but also for building our own AI systems.

My Current Mental Model

After learning the basics, this is how I currently understand Hermes:

USER
                   |
                   ↓
              HERMES AGENT
                   |
        -----------------------
        |          |          |
       LLM       MEMORY     SKILLS
        |
        ↓
     Reasoning
        |
   -------------------------
   |      |       |        |
Tools  Terminal  Browser  Delegation
   |      |       |        |
   -------------------------
              |
              ↓
            RESULT

The important thing is that the agent is sitting in the middle.

It connects the LLM’s reasoning with the real world through tools. And that is what makes an AI Agent different from a simple chatbot.

What I Want to Learn Next

This is just my starting point with Hermes.

There are still many things I want to understand properly:

• How the agent loop actually works
• How tool calling works internally
• How memory is stored and retrieved
• How skills are created
• How subagents communicate
• How agent permissions and security work
• How browser automation works
• How to run Hermes on a server
• How to build real automation with it
• And finally, how to build my own AI agent

I think the best way to learn these things is not by only reading documentation.

I want to actually build something with it.

Because for me, that is when these concepts really start making sense.

Final Thoughts

I am still at the beginning of learning Hermes Agent.But one thing is already clear to me.

AI agents are not just about generating better answers.

They are about giving AI the ability to work with tools, understand context, remember useful information and perform multiple steps to complete a task.

Hermes is one interesting example of this direction.

And maybe the next generation of developer tools will not just be:

“AI that helps me write code.”

Maybe it will be:

“AI that can actually work with me on the project.”

That’s the part I am interested in learning next.