Cross Cloud ADK with Amazon Fargate, and Gemini CLI

--- title: Cross Cloud ADK with Amazon Fargate, and Gemini CLI published: true series: AWS date: 2026-03-26 19:33:51 UTC tags: googlecloudplatform,geminicli,aws,googleadk canonical_url: https://xbill999.medium.com/cross-cloud-adk-with-amazon-fargate-and-gemini-cli-cef85736a275 --- 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 Fargate 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](https://www.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](https://github.com/pyenv/pyenv) As of writing — the mainstream python version is 3.13. To validate your current Python: ```console admin@ip-172-31-70-211:~/gemini-cli-aws/mcp-lightsail-python-aws$ python --version Python 3.13.12 ``` #### Amazon Fargate AWS Fargate is a serverless, pay-as-you-go compute engine for containers that works with [Amazon Elastic Container Service (ECS)](https://aws.amazon.com/documentation-overview/fargate/) or Elastic Kubernetes Service (EKS). It eliminates the need to manage, patch, or scale underlying [EC2 virtual machines](https://www.geeksforgeeks.org/devops/introduction-to-aws-fargate/). Fargate automatically allocates, scales, and manages compute infrastructure, allowing developers to focus solely on designing and operating applications. Details are here: [Serverless Compute - AWS Fargate - AWS](https://aws.amazon.com/fargate/) #### Gemini CLI If not pre-installed you can download the Gemini CLI to interact with the source files and provide real-time assistance: ```shell 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: ```console gemini admin@ip-172-31-70-211:~/gemini-cli-aws/mcp-lightsail-python-aws$ gemini ▝▜▄ Gemini CLI v0.33.1 ▝▜▄ ▗▟▀ Logged in with Google /auth ▝▀ Gemini Code Assist Standard /upgrade ? for shortcuts ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── shift+tab to accept edits 3 GEMINI.md files | 1 MCP server ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── > Type your message or @path/to/file ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ~/.../mcp-lightsail-python-aws (main*) 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: [GitHub - nvm-sh/nvm: Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions](https://github.com/nvm-sh/nvm) #### 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: [Install](https://howtowhale.github.io/dvm/install.html) #### 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](https://docs.aws.amazon.com/lightsail/latest/userguide/amazon-lightsail-install-software.html) #### Agent Development Kit The [Google Agent Development Kit](https://www.google.com/search?q=Google+Agent+Development+Kit&rlz=1CAIWTJ_enUS1114&oq=what+is+the+adk+google&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIICAEQABgWGB4yCAgCEAAYFhgeMggIAxAAGBYYHjIICAQQABgWGB4yCAgFEAAYFhgeMggIBhAAGBYYHjIKCAcQABgKGBYYHjINCAgQABiGAxiABBiKBTIKCAkQABiABBiiBNIBCDMxODlqMGo3qAIAsAIA&sourceid=chrome&ie=UTF-8&mstk=AUtExfB5Oo7ZHHcDEHu7aqZiPBA2l1c-QGh5dB7xkkDPIiYcn8O1Imt2IHNR7bzA6JnyDCSDCUGpGWTeBW14namlN_QqzJLLI5-px1BE9jfSxwli6njPDPERjm5pRqNP3uC6HhUKiRcTJ1T8x5LHQrCkVxylw7QWg0N8B4dQDIcWpnVX9Gc&csui=3&ved=2ahUKEwjYu-G8p-uSAxXrv4kEHUbpLo0QgK4QegQIARAB) (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)](https://google.github.io/adk-docs/) #### This seems like a lot of Configuration! Getting the key tools in place is the first step to working across Cloud environments. For a deeper dive- a project with a similar setup can be found here: [MCP Development with Amazon Fargate and Gemini CLI](https://xbill999.medium.com/mcp-development-with-amazon-fargate-and-gemini-cli-519db4e47c18) #### 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](https://codelabs.developers.google.com/codelabs/create-low-code-agent-with-ADK-visual-builder#0) 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: ```shell cd ~ git clone https://github.com/xbill9/gemini-cli-aws cd adkui-fargate ``` Then run **init.sh** from the cloned directory. The script will attempt to determine your shell environment and set the correct variables: ```shell 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: ```shell 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: ```console xbill@penguin:~/gemini-cli-aws/adkui-fargate$ adk run Agent1 Log setup complete: /tmp/agents_log/agent.20260322_155748.log To access latest log: tail -F /tmp/agents_log/agent.latest.log /home/xbill/.pyenv/versions/3.13.12/lib/python3.13/site-packages/google/adk/cli/utils/agent_loader.py:248: 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/.pyenv/versions/3.13.12/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/.pyenv/versions/3.13.12/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/.pyenv/versions/3.13.12/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 the weather in ho ho kus [Agent1]: In Ho Ho Kus, New Jersey, it is currently cloudy with a temperature of 64°F (18°C), and it feels like 64°F (18°C). There is a 0% chance of precipitation at the moment, and the humidity is around 62%. For tonight, expect mostly cloudy skies with a slight chance of showers after midnight, and lows around 40°F. Northeast winds of 5 to 10 mph will become southeast after midnight, with a 20% chance of rain. Tomorrow, Sunday, March 22, 2026, the forecast indicates mostly cloudy conditions with a slight chance of showers in the morning, followed by partly sunny skies in the afternoon. Highs will be in the upper 60s, with south winds at 5 to 10 mph and a 20% chance of rain. [user]: Run the deploy version on the local system: 0.0s 0.0s ``` #### Deploying to Amazon Fargate First authenticate: ```shell aws login --remote ``` Then cache the credentials locally: ```console xbill@penguin:~/gemini-cli-aws/adkui-fargate$ source save-aws-creds.sh Exporting AWS credentials... Successfully saved credentials to .aws_creds The Makefile will now automatically use these for deployments. xbill@penguin:~/gemini-cli-aws/adkui-fargate$ ``` Then start the deployment: ```console xbill@penguin:~/gemini-cli-aws/adkui-fargate$ make deploy ./save-aws-creds.sh Exporting AWS credentials... Successfully saved credentials to .aws_creds The Makefile will now automatically use these for deployments. ./deploy-fargate.sh Loading AWS credentials from .aws_creds... Building Docker image... ``` You can validate the final result by checking the messages: ```console xbill@penguin:~/gemini-cli-aws/adkui-fargate$ make status ---------------------------------------------------------------------------------------------------------------------------------- | DescribeServices | +---------+----------------------+----------+---------+--------------------------------------------------------------------------+ | Desired | Name | Running | Status | TaskDef | +---------+----------------------+----------+---------+--------------------------------------------------------------------------+ | 0 | adk-fargate-service | 0 | ACTIVE | arn:aws:ecs:us-east-1:106059658660:task-definition/adk-fargate-task:4 | +---------+----------------------+----------+---------+--------------------------------------------------------------------------+ xbill@penguin:~/gemini-cli-aws/adkui-fargate$ ``` You can then get the endpoint: ```console xbill@penguin:~/gemini-cli-aws/adkui-fargate$ make endpoint Fargate Endpoint: http://100.54.104.31:8080 ``` The service will be visible in the AWS Fargate console: [https://us-east-1.console.aws.amazon.com/ecs/v2/clusters/adk-fargate-cluster/services?region=us-east-1](https://us-east-1.console.aws.amazon.com/ecs/v2/clusters/adk-fargate-cluster/services?region=us-east-1) The console will look similar to:  #### Running the ADK Web Interface Start a connection to the Fargate Deployed ADK: ```plaintext https://us-east-1.console.aws.amazon.com/ecs/v2/clusters/adk-fargate-cluster/services?region=us-east-1 ``` This will bring up the ADK UI. Select the sub-agent “Agent1”:  #### Run a Second Sub Agent Connect to the ADK and select “Agent2”. Give the Agent this prompt: ```plaintext Create an image of a cat. ``` The sub agent will then generate the cat image:  #### Visual Build an Agent To use the ADK visual builder- select the pencil Icon next to Agent 2. You can drill down into the Agent design:  #### Summary The Agent Development Kit was used to visually define a basic agent and added the Google Search Tool. 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 Fargate deployment in AWS. This approach validates that cross cloud tools can be used — even with more complex agents.