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Posted on Aug 8 • Originally published at Medium on Aug 7

How to Deploy and Access Hermes Agent on GCP Marketplace: A Step-by-Step Guide

#agents #googlecloudplatform #opensource #gcp

Building and deploying autonomous AI agents doesn’t have to involve lengthy installations or complex infrastructure setup. Hermes Agent is an open-source, production-ready framework that enables developers to create AI agents capable of reasoning, planning, and executing multi-step tasks using tools, APIs, web browsing, code execution, and workflow automation.

The Hermes Agent — Build, Deploy & Scale Autonomous AI virtual machine on Google Cloud Marketplace provides a fully configured environment with everything pre-installed, allowing you to start building immediately. Whether you want to run local Ollama models, connect to cloud-based LLM providers, or develop production-ready AI workflows, the VM eliminates manual configuration so you can focus on development.

In this tutorial, you’ll learn how to deploy the Hermes Agent VM from Google Cloud Marketplace, connect to the instance through the browser-based SSH console, access the Hermes Web Interface, configure AI models, and start building autonomous AI agents in just a few minutes.

Step-by-Step Guide

This section describes how to provision and connect to the ‘Hermes Agent — Build, Deploy & Scale Autonomous AI’ VM solution on GCP.

Open the Hermes Agent — Build, Deploy & Scale Autonomous AI listing on GCP Marketplace.
Click Get Started.

It will ask you to enable the API’s if they are not enabled already for your account. Please click on Enable as shown in the screenshot.

It will take you to the agreement page. On this page, you can change the project from the project selector on the top navigation bar as shown in the screenshot below.
Accept the Terms and agreements by ticking the checkbox and clicking on the AGREE button.

It will show you the successfully agreed popup page. Click on Deploy.

On the deployment page, give a name to your deployment.
In the Deployment Service Account section, click on the Existing radio button and choose a service account from the Select a Service Account dropdown.

If you don’t see any service account in the dropdown, then change the radio button to New Account and create the new service account here.

If, after selecting the New Account option, you get the permission error message, then please reach out to your GCP admin to create a service account by following the step-by-step guide to create a GCP Service Account, and then refresh this deployment page once the service account is created; it should be available in the dropdown.

Select a zone where you want to launch the VM(such as us-east1-a)
Optionally change the number of cores and amount of memory.

Minimum VM Specs: 15GB RAM /4vCPU

This VM can also be deployed using an NVIDIA T4 GPU instance for faster inference. To deploy the VM with a GPU, click on the GPU tab as shown in the screenshot and select an NVIDIA T4 GPU instance. Please note that GPU availability is limited to specific regions, zones, and machine types. If you do not see a GPU option for your selected region, zone, or machine type, try adjusting those settings to find available configurations.

Optionally change the boot disk type and size. (This defaults to ‘Standard Persistent Disk’ and 50GB respectively)
Optionally change the network name and subnetwork names. Be sure that whichever network you specify has ports 22 (for SSH) and 443 (for HTTPS) exposed.
Click Deploy when you are done.
Hermes Agent — Build, Deploy & Scale Autonomous AI will begin deploying.

A summary page displays when the compute engine is successfully deployed. Click on the Instance link to go to the instance page.
On the instance page, click on the “SSH” button, select “Open in browser window”.

This will open an SSH window in a browser. Switch to the ubuntu user and navigate to the ubuntu home directory.

sudo su ubuntu

cd /home/ubuntu/

The VM will generate a random password to log in to Hermes Web Interface. To get the password, connect via SSH terminal as shown in the above step and run the command.

cat /home/ubuntu/.hermes/.env | grep HERMES_DASHBOARD_BASIC_AUTH

To access the Hermes Web Interface, copy the public IP address of the VM and paste it into your local browser as https://public_ip_of_vm. Make sure to use https and not http.

The browser will display an SSL certificate warning message. Expand the warning message, accept the certificate warning, and click Continue.

It will open a login page. Provide the password we got in the above step and click Sign In.

Now you are connected to the out-of-box Hermes Web Interface.

You can use the Hermes chat feature to run tasks or ask questions.

By default, the LLM model set is “deepseek-r1:8b”h. You can pull other Ollama models.

ollama pull <model_name>

e.g ollama pull gemma2:9b

Once your model is pulled, you can set it to default from the web interface as well as from the terminal. To switch models from the web interface, simply click on the model dropdown from the top right of your chat window. Choose the model you want to set and click Switch

Or from the terminal, you can run,

hermes config set model <provider_name>/<model_name>

e.g. hermes config set model ollama/gemma2:9b

To change the LLM provider and set the API Keys, please run the command.

hermes model

Choose your provider of choice and follow the on-screen instructions. Once the process is complete, go back to the web interface and refresh the page to see the changes.

If for any Ollama model you are getting a context length error as shown in the screenshot below while running the chat, then set the context_length and ollama_num_ctx to the required value by running the commands in the terminal, then refresh the WebUI.

Note: This is specific to Ollama; if you want to do it for other providers, then make the appropriate changes in the commands.

hermes config set model.ollama_num_ctx 65536

hermes config set model.context_length 65536

For more details, please visit the Official Documentation page

Conclusion

Congratulations! You have successfully deployed Hermes Agent on Google Cloud and configured your environment for autonomous AI development. With its pre-configured virtual machine, browser-based dashboard, and powerful Hermes CLI, you can start building intelligent AI agents without spending time on manual installation and dependency management.

Hermes supports multiple LLM providers, local Ollama models, persistent memory, and workflow automation, making it suitable for everything from AI assistants and internal automation tools to complex agentic applications. As your workloads grow, you can easily scale your deployment by upgrading your Compute Engine instance or adding an NVIDIA T4 GPU for faster inference and improved performance.

Now that your Hermes Agent environment is up and running, you can begin experimenting with different language models, automate complex workflows, and build production-ready autonomous AI applications on Google Cloud. For advanced configuration options, additional integrations, and best practices, refer to the official Hermes documentation.