Learn Hermes AI Agent - Prerequisite Skills & Environment Setup

#Introduction

Welcome to the Learn Hermes AI Agent series! This series will take you from mastering the fundamentals to building production-grade agents with Hermes AI Agent — a modular agentic runtime that combines a controller, kernel, tools, memory, and environment. In total there are 23 episodes organized into six phases, starting from prerequisites and ending with production hardening.

But before writing your first agent, there are some foundational skills and software you must have. Why do these prerequisites matter? Because Hermes is not just a wrapper around a single LLM API. It is an agent: a system that receives input, decides on steps, calls tools, and then evaluates the results — over and over again. If you do not yet understand how multi-turn conversations work, or how HTTP and webhooks flow, the agent you build will feel like a black box that sometimes works and sometimes does not.

Imagine wanting to become an airplane pilot without ever learning to read the instrument panel. No matter how great the machine is — and Hermes is a truly great machine — it is still hard to fly the plane without understanding the fundamentals. Episode 0 is your roadmap: we will lay down the foundational skills, make sure the tooling is installed, and then set up the LLM provider credentials that will accompany you throughout the series.

#Foundational Skills You Must Master

#AI Agents and Conversational AI Basics

An AI agent is a system that turns a language model into an actor. The model only produces text; the agent produces actions — calling functions, reading files, running commands, and then continuing the conversation based on the results. This difference is the core of the entire series.

From now on, get used to thinking in the agent cycle: perceive (reading the input and context), decide (choosing a step), act (running a tool), and observe (evaluating the results). We will formalize this cycle as a lifecycle in episode 2. For this episode, it is enough to recognize that an AI conversation is not a single request and response, but a sequence of exchanges in which the agent is allowed to call tools along the way.

#APIs, Webhooks, and Asynchronous Workflows

Hermes calls LLM providers through the HTTP API. Every tool call is, at its core, an API request. Understand the basic shape of a conversation: a request contains a method, path, headers, and body; a response contains a status code, headers, and body.

Also get to know the status code classes: 2xx success, 4xx client errors (including 429 for rate limiting), and 5xx server errors. In episode 14 we will handle 429 with retries and backoff.

Beyond synchronous request/response, understand webhooks: a callback mechanism in which a server sends an HTTP request back to the client when an event occurs. In the Hermes ecosystem, webhooks are used, for example, by messaging gateways to receive messages from Telegram or Slack, and by cron schedulers to send job completion notifications. Also understand asynchronous workflows: jobs that do not finish immediately — the agent starts them, then reports status through polling or callbacks.

#JavaScript/TypeScript and Other Supported Languages

Hermes AI Agent provides a multi-language SDK. In this series we focus on JavaScript/TypeScript, the most approachable language for writing custom actions and tools. Make sure you are comfortable with the basic concepts: functions, objects, async/await, and modules.

export async function getWeather(city: string): Promise<string> {
  const url = `https://api.weather.example/v1/forecast?city=${encodeURIComponent(city)}`;
  const res = await fetch(url);
  const data = await res.json();
  return JSON.stringify(data);
}

Do not worry if it is not perfect yet — the example above is an ordinary function that we will register as a tool in episode 2. What matters now: you are comfortable reading TypeScript code and understand async/await flow for network operations.

#Git, Environment Management, and Basic Deployment

All of Hermes's work is code: agent profiles, tools, and configuration. That is why it must be version-controlled with Git, reviewed through pull requests, and rollback-able when something goes wrong. YAML configuration is an ideal baseline for diffing between versions.

Next is environment management: values such as API keys and model names should never be typed directly into code. Store them as environment variables in a dot-env file that is never committed, then read them from the environment. Finally, master basic deployment concepts: understanding the difference between running an agent on your laptop, on a server, or in a container — we will dig into this in phase 4 of the series.

#Software You Need to Prepare

#Node.js and a Package Manager

The Hermes AI Agent SDK runs on Node.js. Make sure the latest LTS version of Node.js is installed along with the npm package manager (or bun if you prefer something faster):

node --version
npm --version

If it is not installed yet, install it from the official Node.js website or via nvm for flexible version management.

#Editor: VS Code with Extensions

VS Code is the most recommended editor for this series. Install the extensions that support agent work:

• TypeScript from Microsoft for language support and auto-import.
• ESLint for automatic linting while writing custom actions.
• YAML for validating agent configuration files.
• An AI agent extension of your choice to help you write faster — but remember, what you are learning is how the machinery behind such a tool works.

#Hermes AI Agent SDK / CLI and LLM Provider Access Tokens

The most important step: set up access to Hermes AI Agent. The official project scaffold can be created via npm:

npm create hermes-agent@latest{:npm} my-agent

That command produces a project containing the CLI, default configuration, and an example agent. This CLI also provides commands such as hermes setup, hermes model, and hermes doctor for setup and diagnostics.

Besides the CLI, you also need access tokens for LLM providers — OpenAI, Anthropic, or OpenRouter. The agent uses these tokens to call the model. Store them as environment variables:

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Important note: the values above are only examples. Real keys are secret — store them in a secret manager or a dot-env file, and never commit them to git.

#Optional: Docker for Local Testing and Emulation

To test agents in an isolated and reproducible environment, install Docker. Hermes supports a container-based terminal backend — we will use it in episode 7 to run tools in a sandbox. Verify the Docker installation:

docker --version
docker run --rm hello-world

The second command downloads the hello-world image and runs it — if a confirmation message appears, Docker is ready to use.

#Environment Verification

Before moving on, make sure all core tools are installed by running a combined verification:

node --version
npm --version
git --version
docker --version
hermes --version

All commands must return a version number, not command not found. If hermes is not recognized, run npm create hermes-agent@latest again in your project and follow the path instructions it prints.

#Conclusion

In episode 0 you have laid the groundwork for the entire series: understanding the basics of AI agents and conversational AI, HTTP/API, webhooks, and asynchronous workflows; making sure Node.js, VS Code, the Hermes CLI, LLM provider credentials, and Docker are ready in your environment.

Key takeaways:

• An AI agent differs from a model-only setup: the agent decides on actions and calls tools within one loop.
• Master HTTP, status codes, webhooks, and asynchronous workflows — all of them are used throughout the series.
• All of an agent's work is code: version-control it with git and treat credentials as environment variables.
• Node.js, npm, git, Docker, and the Hermes CLI are the baseline tooling that must be verified.
• Scaffolding a project starts with npm create hermes-agent@latest.

In the next episode 1 we will cover the history, background, and why choose Hermes AI Agent — from the role of AI agents in modern applications and a comparison of agent architecture with model-only workflows, to Hermes's strengths: event-driven, plugin extensibility, and orchestration. Make sure your environment is ready, because the Learn Hermes AI Agent journey has only just begun!