How to run multiple AI models and agent swarms in Hermes Workspace

Most people run their AI agent with a single model and a single "brain." Hermes Workspace changes that. It gives you a central control room where you can plug in multiple AI models, switch between them, and even run agent swarms that work together on tasks.

This guide walks through how Hermes Workspace works in practice: installing it, adding multiple models, fixing the common sync issues, and using agent swarms. You’ll also see a simpler alternative setup if you don’t want to wrestle with the more technical parts.

What Hermes Workspace actually does

Hermes Workspace is a UI layer on top of the Hermes agent. Instead of being locked into one model, you can connect several providers and models, then pick the right one for each job from a single dashboard.

Once it’s running, you can:

• Switch between different models (like Grok, Mistral, Llama, OpenAI-style models, and local models).
• Manage multiple agent profiles and run them in swarms.
• Access tools like chat, file handling, terminal, and a conductor/orchestrator for agents.

If you’re already interested in unified workspaces for AI, it’s a similar idea to using an all-in-one AI hub like GenSpark AI Workspace, but focused specifically on Hermes agents.

Installing Hermes Workspace

Hermes Workspace is installed via a single terminal command. You copy the install command from the official documentation and paste it into your terminal. That kicks off the setup process and pulls in everything needed for the workspace UI and gateway.

The install can take a few minutes. While it runs, it’s useful to prepare your models and API connections so they’re ready to plug in once the UI is up.

Adding multiple AI models to Hermes

Before you worry about the workspace UI, you need to make sure Hermes itself knows about all the models you want to use.

You do this with the Hermes model command in your terminal. For each provider or model you want to add, you run a variant of this command and follow the prompts.

Examples of what you can connect:

• Mistral – already supported and easy to add.
• Grok (X) – you can use your existing X/Twitter subscription and authorize Hermes via OAuth, then select a model like Grok 4.3.
• Image/video models – for tasks like image or video generation, you can plug in specialized providers as backups or alternatives.
• Other providers – News Portal, OpenAI-style APIs, local models, and more.

Once a provider is authorized (for example, logging into X and approving access for Grok), you go back to Hermes and select the specific model you want to use. At this point, Hermes itself is aware of the models—but the workspace UI still needs to sync to see them.

Starting the Hermes gateway and UI

Hermes Workspace relies on a gateway process and the UI process running together.

The usual flow is:

1. Run hermes gateway run in your terminal to start the backend gateway.
2. Run hermes gateway ui to start the workspace interface.

By default, the UI runs on localhost:3000. If something is already using that port, you’ll need to change the configuration or free the port. This is one of the most common early issues people hit.

Dealing with bugs and sync issues

Hermes Workspace is powerful, but it can be a bit buggy and fiddly to set up. Common issues include:

• The gateway starting but not fully loading.
• The UI throwing errors when you open settings or model configuration pages.
• Models that are configured in Hermes not appearing in the Workspace dropdowns.

A practical workaround is to use an AI coding assistant (like Claude with code/terminal access) to debug locally. A typical workflow looks like this:

• Paste the GitHub documentation and error logs into your AI assistant.
• Let it suggest fixes or even run terminal commands for you (if you allow bypass or terminal control).
• Iterate until the gateway and UI both run cleanly.

This approach is especially helpful if you’re not a developer and don’t want to manually edit backend configs or deal with multiple local ports.

Syncing models into Hermes Workspace settings

Once the UI is finally running, you still need to make sure your models are visible inside Hermes Workspace itself.

In the Workspace UI:

1. Go to Settings.
2. Open Model and provider settings (or similar wording, depending on the version).
3. If you see errors here, you may need another round of backend fixes or a refresh after your AI assistant updates the configuration.

When it’s working correctly, you’ll see a list of providers and models, plus options to:

• Add new models or providers.
• Plug in additional services like News Portal.
• Connect OpenAI-style APIs, Atomic Chat (for local models), Open Claude, and Llama.
• Add or update API keys, including custom keys.

From here, you can finally switch between models in the Workspace UI. If Hermes knows about the model but it doesn’t appear here, it usually means the backend gateway configuration hasn’t fully synced, and you’ll need to fix that first.

Running multiple agents and swarms

Hermes Workspace isn’t just about swapping models; it also supports multiple agents and agent swarms.

Inside the UI you’ll find sections such as:

• Chat – talk to a single agent with a selected model.
• Files – work with documents and uploads.
• Terminal – run commands through the agent.
• Conductor – orchestrate more complex workflows.
• Office – a hub for more structured work.
• Operations > Swarm – where you can configure agent swarms.

In the swarm section, you can add different agent profiles and have them collaborate. For example, you might have:

• A research agent using a reasoning-focused model.
• A writing agent using a model tuned for text generation.
• A coding agent connected to a code-focused model.

These can run together on the same task, each using the model that suits its role best.

A simpler alternative: an agent operating system

Because Hermes Workspace can be slow to sync and occasionally fragile, some users prefer to keep Hermes running in the background and manage everything through a separate "agent operating system" (agent OS) dashboard.

With this kind of setup, you:

• Run Hermes and your models as usual in the background.
• Use a custom control room UI to manage models, agents, and workflows.
• Get prebuilt workflows for SEO, video creation, image generation, and more.
• Use Kanban boards and other productivity tools that are ready out of the box.

This can be easier to maintain because it’s designed to sync quickly and receive frequent updates, rather than relying on you to manually fix backend issues every time something changes in Hermes.

If you like the idea of a tightly integrated, multi-tool workspace, it’s a similar philosophy to what Google is doing with NotebookLM and Gemini for research workflows, as explored in this deep dive on Gemini’s NotebookLM integration.

When to use Hermes Workspace vs an agent OS

Both approaches have their place:

Use Hermes Workspace if:

• You want a native UI that’s built specifically for Hermes.
• You’re comfortable troubleshooting ports, gateway configs, and sync issues.
• You want direct access to features like agent swarms and conductor inside the same interface.

Use an agent OS if:

• You want a smoother, more opinionated setup with daily updates.
• You value prebuilt workflows (SEO, content, media, project boards) over raw flexibility.
• You don’t want to spend time debugging backend issues every time you add a new model.

Key takeaways

Hermes Workspace can be incredibly powerful once it’s fully configured. You can:

• Run multiple AI models from different providers in one place.
• Switch models per task so you always use the best tool for the job.
• Create multiple agents and run them in swarms for complex workflows.

However, expect some friction during setup: port conflicts, sync problems, and backend errors are common. Using an AI coding assistant to handle the technical fixes and considering an agent OS as a simpler control room can make the whole experience much smoother.