Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build low code apps with the Python programming language deployed to the EKS service on AWS.
Aren’t There a Billion Python MCP Demos?
Yes there are.
Python has traditionally been the main coding language for ML and AI tools. The goal of this article is to provide a minimal viable basic working MCP stdio server that can be run locally without any unneeded extra code or extensions.
What Is Python?
Python is an interpreted language that allows for rapid development and testing and has deep libraries for working with ML and AI:
Python Version Management
One of the downsides of the wide deployment of Python has been managing the language versions across platforms and maintaining a supported version.
The pyenv tool enables deploying consistent versions of Python:
GitHub - pyenv/pyenv: Simple Python version management
As of writing — the mainstream python version is 3.13. To validate your current Python:
admin@ip-172-31-70-211:~/gemini-cli-aws/mcp-lightsail-python-aws$ python --version
Python 3.13.12
Amazon EKS
Amazon Elastic Kubernetes Service (EKS) is a fully managed service from Amazon Web Services (AWS) that makes it easy to run Kubernetes on AWS without needing to install, operate, or maintain your own Kubernetes control plane. It automates cluster management, security, and scaling, supporting applications on both Amazon EC2 and AWS Fargate.
More information is available here:
Gemini CLI
If not pre-installed you can download the Gemini CLI to interact with the source files and provide real-time assistance:
npm install -g @google/gemini-cli
Testing the Gemini CLI Environment
Once you have all the tools and the correct Node.js version in place- you can test the startup of Gemini CLI. You will need to authenticate with a Key or your Google Account:
▝▜▄ Gemini CLI v0.33.1
▝▜▄
▗▟▀ Logged in with Google /auth
▝▀ Gemini Code Assist Standard /upgrade no sandbox (see /docs) /model Auto (Gemini 3) | 239.8 MB
Node Version Management
Gemini CLI needs a consistent, up to date version of Node. The nvm command can be used to get a standard Node environment:
Docker Version Management
The AWS Cli tools and Lightsail extensions need current version of Docker. If your environment does not provide a recent docker tool- the Docker Version Manager can be used to downlaod the latest supported Docker:
AWS CLI
The AWS CLI provides a command line tool to directly access AWS services from your current environment. Full details on the CLI are available here:
Install Docker, AWS CLI, and the Lightsail Control plugin for containers
Agent Development Kit
The Google Agent Development Kit (ADK) is an open-source, Python-based framework designed to streamline the creation, deployment, and orchestration of sophisticated, multi-agent AI systems. It treats agent development like software engineering, offering modularity, state management, and built-in tools (like Google Search) to build autonomous agents.
The ADK can be installed from here:
This seems like a lot of Configuration!
Getting the key tools in place is the first step to working across Cloud environments.
Where do I start?
The strategy for starting low code agent development is a incremental step by step approach.
The agents in the demo are based on the original code lab:
First, the basic development environment is setup with the required system variables, and a working Gemini CLI configuration.
Then, a minimal ADK Agent is built with the visual builder. Next — the entire solution is deployed to Google Cloud Run.
Setup the Basic Environment
At this point you should have a working Python environment and a working Gemini CLI installation. The next step is to clone the GitHub samples repository with support scripts:
cd ~
git clone https://github.com/xbill9/gemini-cli-aws
cd adkui-eks
Then run init.sh from the cloned directory.
The script will attempt to determine your shell environment and set the correct variables:
source init.sh
If your session times out or you need to re-authenticate- you can run the set_env.sh script to reset your environment variables:
source set_env.sh
Variables like PROJECT_ID need to be setup for use in the various build scripts- so the set_env script can be used to reset the environment if you time-out.
Verify The ADK Installation
To verify the setup, run the ADK CLI locally with Agent1:
xbill@penguin:~/gemini-cli-aws/adkui-eks$ adk run Agent1
Log setup complete: /tmp/agents_log/agent.20260404_211625.log
To access latest log: tail -F /tmp/agents_log/agent.latest.log
/home/xbill/.local/lib/python3.13/site-packages/google/adk/cli/utils/agent_loader.py:277: UserWarning: [EXPERIMENTAL] _load_from_yaml_config: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
if root_agent := self._load_from_yaml_config(actual_agent_name, agents_dir):
/home/xbill/.local/lib/python3.13/site-packages/google/adk/features/_feature_decorator.py:81: UserWarning: [EXPERIMENTAL] feature FeatureName.AGENT_CONFIG is enabled.
check_feature_enabled()
/home/xbill/.local/lib/python3.13/site-packages/google/adk/cli/cli.py:204: UserWarning: [EXPERIMENTAL] InMemoryCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
credential_service = InMemoryCredentialService()
/home/xbill/.local/lib/python3.13/site-packages/google/adk/auth/credential_service/in_memory_credential_service.py:33: UserWarning: [EXPERIMENTAL] BaseCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
super(). __init__ ()
Running agent Agent1, type exit to exit.
[user]: what is Amazon EKS
[Agent1]: Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed Kubernetes service offered by Amazon Web Services (AWS). It simplifies the process of deploying, managing, and scaling containerized applications using Kubernetes on AWS.
Key aspects of Amazon EKS include:
* **Fully Managed Control Plane** AWS takes on the responsibility of managing the Kubernetes control plane, which includes tasks like scheduling applications, managing their lifecycle, and storing the cluster's state. This means AWS handles the infrastructure, scaling, patching, and updates for the control plane, freeing users to focus on their applications.
* **Kubernetes Conformance** Amazon EKS is a certified Kubernetes conformant service, allowing you to use standard Kubernetes tools and plugins. This also means you can easily migrate existing Kubernetes applications without modification.
* **High Availability and Scalability** The EKS control plane is automatically distributed across multiple Availability Zones to ensure high availability and resilience. EKS also integrates with AWS services for scaling worker nodes and pods.
* **Integration with AWS Services** EKS seamlessly integrates with other AWS services such as Amazon VPC for networking, AWS Identity and Access Management (IAM) for authentication, Amazon CloudWatch for monitoring, and Auto Scaling Groups for scaling.
* **Deployment Options** While primarily for running Kubernetes on the AWS cloud, Amazon EKS also offers deployment options for on-premises and edge environments through Amazon EKS Anywhere and Amazon EKS on AWS Outposts. These options allow for consistent Kubernetes management across various infrastructures.
Essentially, Amazon EKS reduces the operational complexity of running Kubernetes, allowing organizations to leverage the benefits of container orchestration without the overhead of managing the underlying infrastructure themselves.Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed Kubernetes service provided by Amazon Web Services (AWS). It is designed to simplify the deployment, management, and scaling of containerized applications using Kubernetes on the AWS cloud, and also offers options for on-premises and edge environments. 0.0s 0.0s
Deploying to Amazon EKS
First authenticate:
aws login --remote
Then cache the credentials locally:
xbill@penguin:~/gemini-cli-aws/adkui-eks$ source save-aws-creds.sh
Exporting AWS credentials...
Successfully saved credentials to .aws_creds
The Makefile will now automatically use these for deployments.
Then start the deployment:
✦ Deployment to Amazon EKS was successful.
Deployment Summary
- EKS Cluster: adkui-eks-cluster (Status: ACTIVE)
- Image: 106059658660.dkr.ecr.us-east-1.amazonaws.com/adk-comic-image:latest
- Pod Status: Running (1/1 READY)
- Service Endpoint: http://af62eb56d13b74cefb372550e726efaa-1528063823.us-east-1.elb.amazonaws.com
The make deploy command completed the following steps:
1. Updated kubeconfig for the EKS cluster.
2. Built the Docker image based on the Dockerfile.
3. Logged in to Amazon ECR and pushed the image.
4. Generated k8s-deployment.yaml and applied it to the cluster.
You can now access the ADK Web UI at the endpoint listed above.
You can validate the final result by checking the messages:
✦ The EKS LoadBalancer endpoint is:
http://af62eb56d13b74cefb372550e726efaa-1528063823.us-east-1.elb.amazonaws.com
You can then get the endpoint:
✦ The EKS LoadBalancer endpoint is:
http://af62eb56d13b74cefb372550e726efaa-1528063823.us-east-1.elb.amazonaws.com
The service will be visible in the AWS console. The console will look similar to:
Running the ADK Web Interface
Start a connection to the EKS Deployed ADK:
http://af62eb56d13b74cefb372550e726efaa-1528063823.us-east-1.elb.amazonaws.com
This will bring up the ADK UI. Select the sub-agent “Agent3”:
This will generate the Comic by using a multi-agent pipeline:
Once the multi Agent system is complete:
Visual Edit Agent Pipeline
The version of the ADK Deployed includes a visual builder:
Run the Online Viewer Agent
Once Agent3 has completed — go to the ADK agent selector and select “Agent4”. This agent will allow you to browse your online comic:
View the Final Artifacts
You can use Agent4 to visualize the results of the agent pipeline:
and the final panels:
Summary
The Agent Development Kit was used to visually define a multi Agent pipeline to generate comic book style HTML. This Agent was tested locally with the CLI and then with the ADK web tool. Then, several sample ADK agents were run directly from the EKS deployment in AWS. This approach validates that cross cloud tools can be used — even with more complex agents.
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