TechLatest

Posted on Aug 6 • Originally published at Medium on Aug 6

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

#agents #amazonwebservices #aws #opensource

Autonomous AI agents are transforming how developers automate complex tasks by combining the reasoning capabilities of large language models with tools, APIs, code execution, and workflow orchestration. Hermes Agent is an open-source, production-ready framework designed to help you build, deploy, and scale intelligent AI agents that can plan, reason, and execute multi-step tasks with minimal human intervention.

The Hermes Agent — Build, Deploy & Scale Autonomous AI virtual machine from TechLatest provides a fully configured environment with everything you need to get started. Instead of spending time installing dependencies and configuring services, you can launch a ready-to-use instance that includes the Hermes CLI, a browser-based dashboard, and support for multiple AI model providers. Whether you’re building AI assistants, automating workflows, or experimenting with agentic applications, this VM enables you to start developing immediately.

In this tutorial, you’ll learn how to deploy the Hermes Agent VM, securely connect to the instance, access the web dashboard, configure language models, and begin building autonomous AI workflows in just a few steps.

Step-by-Step Guide

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

Open the Hermes Agent — Build, Deploy & Scale Autonomous AI VM listing on the AWS Marketplace.

Click on View purchase options.

Log in with your credentials and follow the instructions.
Review the prices and subscribe to the product by clicking on the Subscribe button located at the bottom of this page. Once you are subscribed to the offer, click on the Launch your software button.

The next page will show you the options to launch the instance: Launch through EC2 and One-click launch from AWS Marketplace. Tick the 2nd option, One-click launch from AWS Marketplace.

Select a Region where you want to launch the VM(such as US East (N.Virginia))
Optionally change the EC2 instance type. (This defaults to t2.xlarge instance type, 4 vCPUs, and 16 GB RAM.)

Please note that the VM can also be deployed using an NVIDIA GPU instance. If you want to deploy this instance with a GPU configuration, then please choose an NVIDIA GPU (e.g g4dn.xlarge) or check the available NVIDIA GPU instances on the AWS documentation page.

Optionally change the network name and subnetwork names.

Select the Security Group. Be sure that whichever Security Group you specify have ports 22 (for SSH) and 443 (for HTTPS) exposed. Or you can create the new SG by clicking on the “Create Security Group” button. Provide the name and description, and save the SG for this instance.

Be sure to download the key pair, which is available by default, or you can create a new key pair and download it.
Click on Launch.
Hermes Agent — Build, Deploy & Scale Autonomous AI will begin deploying.

A summary page displays. To see this instance on the EC2 Console, click on the View instance on EC2 link.

To connect to this instance through PuTTY, copy the IPv4 Public IP Address from the VM’s details page.

Open PuTTY, paste the IP address, and browse to the private key you downloaded while deploying the VM. Go to SSH->Auth->Credentials, click on Open. Enter ubuntu as the user ID.

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 at 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”. 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 the 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 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 context length error as shown in below screenshot, while running the chat then set the context_length and ollama_num_ctx to required value by running below commands in 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 below.

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

You have successfully learned how to deploy and access the Hermes Agent — Build, Deploy & Scale Autonomous AI virtual machine on AWS. With its pre-configured environment, browser-based dashboard, and powerful CLI, Hermes removes the complexity of setting up an agent framework from scratch, allowing you to focus on building intelligent applications instead of infrastructure.

Whether you’re creating AI assistants, automating business processes, integrating external tools and APIs, or experimenting with multi-agent workflows, Hermes provides a flexible and extensible platform to support your development needs. You can further customize your deployment by connecting your preferred AI providers, switching between supported models, or leveraging local Ollama models for on-premises inference.

As your projects grow, you can easily scale your deployment using larger EC2 instance types or GPU-enabled instances for improved performance. Explore the Hermes documentation to discover advanced features, workflow customization, and best practices for building production-ready autonomous AI agents.