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Getting Started with Hermes Agent: A Beginner-Friendly Guide to Real AI Agent Workflows

#hermesagentchallenge #devchallenge #agents

Hermes Agent Challenge Submission: Write About Hermes Agent

Introduction

AI agents are useful when a task needs more than one reply.

A normal chatbot can answer a question, explain code, or help write text. An AI agent can go further because it can use tools, work with files, run commands, remember useful context, and continue through multi-step tasks.

Hermes Agent is an open source AI agent from Nous Research. It can run from the terminal, connect to different model providers, use tools, store memory, load skills, and work through interfaces like CLI, TUI, messaging platforms, API Server/Webhooks and Batch Runner/Cron.

This article is written for first-time users.

The goal is to explain Hermes Agent in a simple way: what it is, how to set it up, where it can be used, and what kind of real workflows make sense when you are just getting started.

A simple map for learning Hermes Agent

The easiest way to understand Hermes Agent is to not start with every feature at once.

A better path is:

  1. Install Hermes
  2. Connect a model
  3. Verify one working chat
  4. Try one small real task
  5. Add useful tools
  6. Understand memory and skills
  7. Expand where Hermes works
  8. Run it beyond one laptop if needed

This order matters.

Agents can become confusing if the first thing you do is enable every tool, connect every platform, and try to automate a large workflow. A beginner should start with one working chat and one small task. After that, the other features are easier to understand.

What is Hermes Agent?

Hermes Agent is an AI agent that connects a language model with tools, memory, skills, and different execution environments.

In simple words:

Hermes Agent is an AI assistant that can work with your environment, not just reply with text.

It can help with tasks like:

  • understanding a codebase
  • summarizing files
  • improving documentation
  • running terminal commands
  • searching the web
  • using browser automation
  • connecting to messaging platforms
  • using reusable skills
  • remembering useful context across sessions
  • running in local, remote, or container-based environments

This makes Hermes useful for work that depends on real project context.

For example, this is a normal chatbot-style question:

What should I add to my README?
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This is a better agent-style task:

Review this repository and suggest what is missing from the README for a first-time user.
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The second prompt is more useful because the agent can look at the project context and give a more specific answer.

How Hermes Agent is different from a simple chatbot

A simple chatbot usually works like this:

  1. You send a message
  2. It replies
  3. You send another message

Hermes Agent can work more like this:

  1. You give it a task
  2. It builds context
  3. It uses a configured model provider
  4. It decides whether tools are needed
  5. It may read files, search, run commands, or use integrations
  6. It can loop through tool calls before replying
  7. It gives the final result
  8. The session can be saved for future context

That does not mean the agent should be trusted blindly.

If an agent can run commands or edit files, the user still needs to review what is happening. Hermes should be treated as an assistant, not an autopilot.

The useful part is that Hermes can connect conversation with actual work.

Step 1: Install Hermes Agent

For Linux, macOS, WSL2, or Termux, the install command is:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
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After installation, reload your shell.

For bash:

source ~/.bashrc
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For zsh:

source ~/.zshrc
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Then check that Hermes is available:

hermes --version
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For Windows PowerShell, the install command is:

iex (irm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.ps1)
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This gets the CLI installed so you can start configuring Hermes.

Step 2: Connect a model

Hermes needs a model provider before it can respond.

The fast setup path is:

hermes setup --portal
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The manual setup path is:

hermes model
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This step connects Hermes to the model that will handle the language reasoning.

The model answers the language part. Hermes manages the surrounding workflow: tools, sessions, memory, skills, provider resolution, and execution.

Step 3: Verify a working chat

Before using advanced features, start with a normal chat.

Run:

hermes
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Or use the terminal UI:

hermes --tui
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Then ask a simple question:

Explain what Hermes Agent can help me do in a local development workflow.
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At this stage, the goal is not automation. The goal is just to confirm that Hermes is installed, configured, and responding.

This step sounds basic, but it saves time. If the first chat works, then you can move forward with more confidence.

Step 4: Try one small real task

After the chat works, try a task that is useful but easy to review.

Good first prompts are:

Summarize this repository in simple terms.
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Which files should I read first to understand this project?
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Review this README for beginners and tell me what is missing.
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Create a setup checklist for this project.
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These tasks are good for beginners because the output can be checked manually.

You are not asking the agent to make risky changes. You are asking it to help understand and organize information.

Step 5: Add useful toolsets

Tools are what make Hermes more practical.

A tool gives the agent an ability outside normal text generation. For example, tools can help with files, terminal execution, web search, browser automation, messaging, MCP integrations, automation, and more.

Hermes organizes tools into toolsets. This is useful because not every workflow needs every tool.

For example:

  • A documentation task may need file tools
  • A research task may need web search
  • A debugging task may need terminal access
  • A browser task may need browser automation
  • A connected workflow may need MCP integrations or messaging delivery

The best beginner habit is simple:

Enable only the tools needed for the task.

This keeps the workflow easier to understand and safer to control.

Step 6: Understand memory and skills

Two important Hermes concepts are memory and skills.

They sound similar at first, but they solve different problems.

Memory

Memory helps Hermes keep useful context across sessions.

Hermes uses memory files such as:

MEMORY.md
USER.md
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MEMORY.md is for things like project facts, environment details, conventions, and notes the agent has learned.

USER.md is for user preferences, communication style, and expectations.

A simple way to understand memory is:

Memory helps Hermes remember context.

This is useful because the agent does not have to start from zero every time.

Skills

Skills are reusable knowledge documents or workflows.

A skill can help Hermes perform a specific kind of task more consistently. Instead of explaining the same process every time, a skill gives the agent a reusable pattern.

Skills can be useful for:

  • writing documentation
  • reviewing logs
  • preparing release notes
  • creating project plans
  • working with Kubernetes
  • writing pull request summaries
  • following a checklist

Skills commonly live in:

~/.hermes/skills
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A simple way to understand skills is:

Memory remembers useful context. Skills remember useful procedures.

Together, memory and skills make Hermes more useful over time.

Step 7: Expand where Hermes works

Hermes is not limited to one terminal window.

It can be used through different entry points, including:

  • CLI
  • TUI
  • Messaging Gateway
  • API Server / Webhooks
  • Batch Runner / Cron
  • ACP adapters for editors
  • Python library

This makes Hermes flexible.

It can start as a local terminal assistant and later become part of a bigger workflow.

For example, Hermes could be used as:

  • a local developer assistant
  • a Discord or Telegram bot
  • a documentation helper
  • a scheduled research assistant
  • a repo onboarding assistant
  • a workflow assistant connected to tools

A beginner does not need all of this on day one. It is better to start with CLI or TUI first, then expand only when there is a real use case.

Step 8: Run beyond one laptop

Hermes can also run in different environments.

Examples include:

  • local machine
  • Docker
  • SSH
  • Modal
  • Daytona
  • Singularity

This matters because some agent tasks are better isolated from your main machine.

For example, if an agent needs to run commands, test scripts, or work on long-running jobs, a container or remote environment may be safer and cleaner.

This also makes Hermes useful for workflows that need to run beyond a personal laptop.

Beginner-Friendly Project Ideas

Once Hermes is installed and one chat is working, the best next step is to try a small project.

The project should be simple enough to review, but practical enough to show why an agent is useful.

Here are a few beginner-friendly ideas.

1. Repo Onboarding Assistant

A good first project with Hermes Agent is a repo onboarding assistant.

The problem is common: opening a new repository can be confusing.

A repo onboarding assistant could help answer:

  • What does this project do?
  • What technologies does it use?
  • Which files are most important?
  • How do I run it locally?
  • What setup steps are missing?
  • What should a new contributor read first?
  • What could be improved in the README?

A prompt for this workflow could be:

You are helping a new developer understand this repository.
Inspect the project structure and create a beginner-friendly onboarding guide.
Include the project summary, important files, setup steps, common commands, and missing documentation.
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This is a strong beginner use case because it is practical and easy to judge.

The agent is not just writing random text. It is using project context to create something useful.

2. README Reviewer

Another beginner-friendly use case is a README reviewer.

A prompt could be:

Review this README from the point of view of a first-time user.
Tell me what is clear, what is confusing, and what should be added before someone tries to run the project.
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The output could include:

  • missing installation steps
  • missing environment variables
  • unclear commands
  • missing screenshots
  • missing troubleshooting notes
  • missing contribution steps

This is useful because documentation is often the first thing people see before they try a project.

A better README can make a project easier to use and easier to contribute to.

3. Developer Checklist Assistant

Hermes can also help create checklists.

For example:

Create a release checklist for this project based on the files and scripts available in the repository.
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The checklist could include things like:

  • run tests
  • check build commands
  • update documentation
  • review environment variables
  • confirm version numbers
  • prepare release notes

This does not replace a developer, but it can reduce forgotten steps.

4. Research Summary Assistant

Hermes can also be useful for research tasks.

A beginner-friendly research workflow could be:

Research this topic and create a short summary with key points, useful links, and open questions I should explore next.
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This can be helpful when learning a new library, comparing tools, or preparing notes before building a project.

For this kind of task, web tools are useful, but the output should still be reviewed. Research summaries are a starting point, not a final source of truth.

5. Documentation Improvement Assistant

A documentation improvement assistant is another practical project.

A prompt could be:

Look at this project documentation and suggest improvements for a beginner.
Focus on setup steps, examples, troubleshooting, and missing context.
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This project works well because it creates a useful output without requiring risky automation.

It also shows how agents can help with work that developers often delay, even though it matters a lot.

Safe habits when using Hermes Agent

AI agents can be helpful, but they need boundaries.

This is especially important when the agent can access files, run commands, use credentials, or connect to external platforms.

Good habits include:

  • start with a small test project
  • review commands before approving them
  • use Docker or a sandbox when possible
  • avoid sharing secrets
  • enable only the tools needed for the task
  • do not give broad access on the first day
  • treat Hermes as an assistant, not autopilot

The more access an agent has, the more carefully it should be used.

A safe workflow is better than a fast but risky workflow.

When should you use Hermes Agent?

Hermes Agent is useful when a task needs context, tools, or multiple steps.

Good use cases include:

  • understanding a codebase
  • improving a README
  • creating onboarding guides
  • summarizing project structure
  • researching a topic
  • building a messaging bot
  • running scheduled workflows
  • repeating a developer checklist
  • connecting an AI assistant to tools

It may not be necessary for very small questions.

If you only need a short explanation or a small code snippet, a normal chat interface may be enough.

Hermes becomes more useful when the task depends on files, tools, sessions, memory, or repeatable workflows.

What first-time users should remember

The best path is:

  1. Install Hermes
  2. Connect a model
  3. Verify one chat works
  4. Try one small real task
  5. Add tools slowly
  6. Use memory and skills when the workflow repeats
  7. Move to messaging or remote runtimes only after the basics work

This keeps the learning process clear.

It also helps avoid the biggest beginner mistake: trying to automate too much before understanding the basics.

Final thoughts

Hermes Agent is a practical example of how AI agents can support developer workflows.

The important idea is not that the agent replaces the developer. The useful part is that it can connect conversation with tools, memory, skills, and real project context.

For a first-time user, the best use cases are simple:

  • understand a repo
  • improve documentation
  • create onboarding notes
  • review setup steps
  • build repeatable workflows

Start small, verify the output, and expand gradually.

That is the cleanest way to learn Hermes Agent without getting lost in all the features at once.

Top comments (1)

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harjjotsinghh profile image
Harjot Singh •

i appreciate how you broke down the capabilities of ai agents, especially their ability to handle multi-step tasks. it’s fascinating how they can interact with various tools and interfaces. speaking of deployment, at moonshift, you can get a full next.js + postgres + auth app up and running in about 7 minutes, and you own the code on your github. happy to offer you a free run if you're curious.