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Cross Cloud Multi Agent Comic Builder with ADK, Amazon ECS Express, and Gemini CLI

#google #gemini #ecsexpress #python

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 ECS express 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:

Welcome to Python.org

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:

Python 3.13.12
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Amazon ECS Express Configuration

Amazon ECS Express Mode (announced Nov 2025) is a simplified deployment feature for Amazon Elastic Container Service (ECS) designed to rapidly launch containerized applications, APIs, and web services on AWS Fargate. It automates infrastructure setup — including load balancing, networking, scaling, and HTTPS endpoints — allowing developers to deploy from container image to production in a single step.

More details are available here:

Amazon ECS Express Mode

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
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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

▝▜▄ 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
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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:

GitHub - nvm-sh/nvm: Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

Docker Version Management

The AWS CLI tools 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:

Install

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:

Agent Development Kit (ADK)

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:

Create and deploy low code ADK (Agent Deployment Kit) agents using ADK Visual Builder | Google Codelabs

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-ecsexpress
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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
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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
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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-ecsexpress$ adk run Agent1
Log setup complete: /tmp/agents_log/agent.20260404_202121.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 ecs express
[Agent1]: Amazon ECS Express Mode is a new feature for Amazon Elastic Container Service (ECS) that simplifies and accelerates the deployment and management of containerized applications, particularly web applications and APIs, on AWS. It aims to reduce the operational overhead for developers by automating much of the infrastructure setup that typically accompanies deploying containerized applications to production. 0.0s 0.0s
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Deploying to Amazon ECS Express

First authenticate:

aws login --remote
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Then cache the credentials locally:

xbill@penguin:~/gemini-cli-aws/adkui-ecsexpress$ source save-aws-creds.sh
Exporting AWS credentials...
Successfully saved credentials to .aws_creds
The Makefile will now automatically use these for deployments.
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Then start the deployment:

 > make deploy
✦ I will execute make deploy to initiate the full ECS Express Mode deployment cycle, including building the Docker
  image, pushing it to ECR, and deploying to ECS.
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You can validate the final result by checking the messages:

✦ The ECS service adkui-ecsexpress is currently ACTIVE.

   * Service Name: adkui-ecsexpress
   * Status: ACTIVE
   * Endpoint: http://ad-27f169e1d3994ae3a8fd357bc014bbd2.ecs.us-east-1.on.aws
     (http://ad-27f169e1d3994ae3a8fd357bc014bbd2.ecs.us-east-1.on.aws)
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You can then get the endpoint:

   * Endpoint: http://ad-27f169e1d3994ae3a8fd357bc014bbd2.ecs.us-east-1.on.aws
     (http://ad-27f169e1d3994ae3a8fd357bc014bbd2.ecs.us-east-1.on.aws)
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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 AWS Deployed ADK:

http://ad-27f169e1d3994ae3a8fd357bc014bbd2.ecs.us-east-1.on.aws
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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 ECS express deployment in AWS. This approach validates that cross cloud tools can be used — even with more complex agents.

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