Getting Started with Hermes Agent Desktop: A Quick Guide
▼ Summary
– Hermes Desktop is a cross-platform application that stores your conversation history, tool outputs, and other agent data in a local context store you control, keeping your complete working history private from the LLM provider.
– You connect Hermes to an LLM provider (such as OpenRouter, OpenAI, or Anthropic) by entering API credentials; OpenRouter is the easiest option as it provides access to multiple models through one account.
– Skills are reusable reference documents (e.g., brand voice guidelines or audience personas) that the agent consults; you can create them by using the `/learn` command in chat or by placing a Markdown file in the Skills folder.
– To run your first task, connect a model provider, enable a skill, and enter a marketing request in the chat interface; Hermes loads the relevant skill, sends the prompt to the model, and returns the output.
– Because your data lives on your system, you can switch model providers without losing your skills, conversation history, or context, and the same architecture scales to larger deployments like Docker or a remote API server.
This is the final installment of a three-part series focused on lowering costs in AI-driven marketing workflows. The first piece explored the challenge of token waste and the value of maintaining control over your context. The second broke down the technical architecture behind that idea. Now, we’ll cover installing Hermes Desktop, connecting it to an AI model, building your first skill, and executing an initial task.
Hermes Desktop runs smoothly on macOS, Windows, and Linux. To get started, head to the Hermes Agent website, grab the latest release, and install it like any other desktop app. The installer bundles both the interface and the agent runtime, so there’s nothing extra to download before you begin.
When you first launch Hermes, it asks where to store your local context. This context store holds your conversation history, tool outputs, embeddings, and other information the agent picks up over time. By default, Hermes places it in your home directory, but you can choose a different location during setup or adjust it later in the application settings.
For individual use, the default spot works fine. If you’re collaborating with a team, consider a synchronized cloud folder, a shared network drive, or dedicated local storage for easier backups and management. Unlike most AI chat tools, this data stays under your control. The LLM provider only sees what’s needed to answer each request. Your full working history remains on your own storage.
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Next, link Hermes to an LLM provider. Hermes is provider-agnostic, so it works with OpenAI, Anthropic, Google, Meta models, self-hosted setups, or any compatible API endpoint. During setup, you’ll pick a provider and enter the relevant API credentials.
The simplest route is OpenRouter. An OpenRouter API key gives you access to dozens of models through one account, letting you switch providers without changing your workflow. Create an OpenRouter account, generate an API key, and paste it into Hermes’ provider settings. The free tier is enough for testing.
If you already have API keys for OpenAI, Anthropic, Google Gemini, or a self-hosted model like LLaMA, you can use those instead. Once connected, choose the model you want Hermes to use as its default.
Now, let’s create your first skill. Skills are a core concept in Hermes. A skill is a reusable reference document the agent can consult. Typical marketing skills include brand voice guidelines, editorial style guides, messaging frameworks, campaign briefs, audience personas, competitive positioning documents, and standard operating procedures.
There are two ways to build a skill. The easiest is using the `/learn` command in the chat interface. Give Hermes the reference material you want it to learn, like uploading a brand voice guide or pasting its contents, and ask it to create a skill. For example:
`/learn Create a reusable skill from these brand voice guidelines for future email campaigns.`
Hermes analyzes the material, turns it into a structured skill, and saves it automatically to your skills folder.
Alternatively, you can craft the skill yourself. Write the reference material as a Markdown document and drop it directly into the Skills folder on your filesystem. The next time Hermes starts, or after refreshing the Skills panel, the new skill becomes available.
In the Desktop application, the Skills panel shows every skill you have. Enable the ones you want the agent to use for the current task and disable those that aren’t relevant. Over time, your skill library becomes one of your most valuable assets. Instead of copying brand guidelines or campaign instructions into every prompt, Hermes reuses the right skill whenever it applies.
With your model provider connected and at least one skill enabled, you’re ready to run your first task. Open the chat interface and enter a straightforward marketing request, like:
`Draft five email subject lines for our upcoming product launch using the Brand Voice skill.`
Hermes loads the relevant skill, combines it with your request, sends the prompt to the selected model, and returns the completed draft. You can refine the output through normal conversation. Since the conversation history is stored in your local context store, Hermes maintains context throughout the session without requiring you to repeat earlier instructions.
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Hermes can do more than generate text. Depending on the tools you’ve enabled, you can ask it to search the web, analyze CSV files, generate images, read local documents, call external APIs, execute Python code, or automate multi-step workflows. The experience stays the same regardless of the task. You interact through a single chat interface while Hermes coordinates the necessary tools behind the scenes.
One advantage of Hermes is that your working data remains accessible. Open the folder you selected as your context store, and you’ll find your saved conversations, tool outputs, embeddings, and other supporting files. Your skills folder contains the reusable reference documents you’ve created or imported. Because these assets live on your system rather than inside a provider’s application, changing model providers doesn’t affect them. If you decide to switch from OpenRouter to Anthropic or from a hosted model to a self-hosted LLaMA deployment, your skills, conversation history, and context remain intact. You only need to update the provider settings.
Hermes Desktop is designed to help individuals get started quickly, but the same architecture scales to larger deployments. The runtime can also operate through a command-line interface, inside Docker containers, or as a remote API server serving an entire team. The skills, context store, and workflows you build in the desktop application transfer directly to those environments. For most marketers, the desktop application is the easiest place to begin. Once you’ve built a useful skill library and experienced how local context reduces repeated prompting and unnecessary token usage, you’ll have a solid foundation for deciding whether a larger deployment makes sense for your organization.
(Source: MarTech)
