Building a Multimodal Cross Cloud Live Agent with ADK, Azure ACA, and Gemini CLI

Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build cross cloud...

title: Building a Multimodal Cross Cloud Live Agent with ADK, Azure ACA, and Gemini CLI published: true series: Azure date: 2026-04-03 19:36:29 UTC tags: googleadk,python,geminicli,azureaca canonical_url: https://xbill999.medium.com/building-a-multimodal-cross-cloud-live-agent-with-adk-azure-aca-and-gemini-cli-d76a73daa54f

Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build cross cloud apps with the Python programming language deployed to the Azure Container App service.

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:

admin@ip-172-31-70-211:~/gemini-cli-azure$ python --version
Python 3.13.12

Azure Container App Service

Azure Container Apps (ACA) is a fully managed, serverless platform designed for running containerized applications and microservices without managing underlying infrastructure. Built on Azure Kubernetes Service (AKS), it offers built-in autoscaling (including to zero), traffic splitting for blue/green deployments, and Dapr integration, making it ideal for event-driven, API, and background processing workloads.

https://azure.microsoft.com/en-us/products/container-apps

Why would I want Gemini CLI with Azure? Isn’t that a Google Thing?

Yes- Gemini CLI leverages the Google Cloud console and Gemini models but it is also open source and platform agnostic. Many applications are already cross-cloud so this enables familiar tools to be run natively on Microsoft Azure.

Azure Container App Configuration

To configure your Azure Service with the base system tools- this article provides a reference:

MCP Development with Python, and Azure Container Apps

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

admin@ip-172-31-70-211:~/gemini-cli-azure$ 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
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
 ~/.../gemini-cli-azure (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

Docker Version Management

The Azure 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

Azure CLI

The Azure CLI provides a command line tool to directly access Azure services from your current environment. Full details on the CLI are available here:

Azure Command-Line Interface (CLI) documentation

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. For a deeper dive- a project with a similar setup can be found here:

Deploying ADK Agents on Azure ACA (Azure Container Apps)

Where do I start?

The strategy for starting multimodal real time cross cloud agent development is a incremental step by step approach.

The agents in the demo are based on the original code lab:

Way Back Home - Building an ADK Bi-Directional Streaming Agent | 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. All of the relevant code examples and documentation is available in GitHub. This repo has a wide variety of samples- but this lab will focus on the ‘level_3-lightsail’ setup.

The next step is to clone the GitHub repository to your local environment:

cd ~
git clone https://github.com/xbill9/gemini-cli-azure
cd level_3-aca

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-azure/level_3-aca/backend/app$ adk run biometric_agent
Log setup complete: /tmp/agents_log/agent.20260402_203152.log
To access latest log: tail -F /tmp/agents_log/agent.latest.log
/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 biometric_agent, type exit to exit.
[user]: 

Deploying to Azure Container Apps

The first step is to refresh the Azure credentials in the current build environment:

xbill@penguin:~/gemini-cli-azure/level_3-aca$ az login

Run the deploy version on the local system:

 > make deploy
✦ I will execute make deploy, which builds the Docker image and deploys it to Azure Container Apps, creating the
  necessary resource groups, container registry, and environment as defined in deploy.sh. 0.0s 0.0s

You can validate the final result by checking the messages:

✦ The deployment to Azure Container Apps was successful. You can access the application at the following URL:

  https://biometric-scout-app.agreeablebeach-34887419.canadaeast.azurecontainerapps.io

Once the container is deployed- you can then get the endpoint:

✦ The endpoint for your Azure Container App is:
  biometric-scout-app.agreeablebeach-34887419.canadaeast.azurecontainerapps.io

The service will be visible in the Azure console:

Running the Web Interface

Start a connection to the deployed app:

biometric-scout-app.agreeablebeach-34887419.canadaeast.azurecontainerapps.io

Then connect to the app :

Then use the Live model to process audio and video:

Finally — complete the sequence:

Summary

The Agent Development Kit was used to enable a multi-modal agent using the Gemini Live Model. This Agent was tested locally with the CLI and then deployed to Azure Container App services. This approach validates that cross cloud tools can be used — even with more complex agents.