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Deploying ADK Agents on the Azure App Service

#azureappservice #python #imagen #googleadk

Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build Agents in the Python programming language deployed securely on the Azure Cloud platform.

What is Gemini CLI?

The Gemini CLI is an open-source, terminal-based AI agent from Google that allows developers to interact directly with Gemini models, such as Gemini 2.5 Pro, for coding, content creation, and workflow automation. It supports file operations, shell commands, and connects to external tools via the Model Context Protocol (MCP).

The full details on Gemini CLI are available here:

Build, debug & deploy with AI

Azure App Service

Azure App Service is a fully managed Platform-as-a-Service (PaaS) that enables developers to build, deploy, and scale web applications, APIs, and mobile backends quickly. It supports multiple languages (.NET, Java, Node.js, Python, PHP) on Windows or Linux, offering built-in CI/CD, auto-scaling, and high security.

https://azure.microsoft.com/en-us/products/app-service

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 App Service Configuration

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

MCP Development with Python, and the Azure App Service

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:

xbill@penguin:~/gemini-cli-azure/adkui-appservice$ python --version
Python 3.13.12
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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:

azureuser@azure-new:~/gemini-cli-azure$ gemini

▝▜▄ Gemini CLI v0.33.1
    ▝▜▄
   ▗▟▀ Logged in with Google /auth
  ▝▀ Gemini Code Assist Standard /upgrade
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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

To verify the Node version:

xbill@penguin:~/gemini-cli-azure/adkui-appservice$ nvm --version
0.40.3
xbill@penguin:~/gemini-cli-azure/adkui-appservice$ node --version
v25.8.2
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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)

Where do I start?

The strategy for starting 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-azure
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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.

Azure Login

All of the deployment is done with the az CLI tool- but it also needs an active login on the Azure environment. To check the version:

xbill@penguin:~/gemini-cli-azure/adkui-appservice$ which az
/usr/bin/az
xbill@penguin:~/gemini-cli-azure/adkui-appservice$ az --version
azure-cli 2.84.0

core 2.84.0
telemetry 1.1.0

Dependencies:
msal 1.35.0b1
azure-mgmt-resource 24.0.0

Python location '/opt/az/bin/python3'
Config directory '/home/xbill/.azure'
Extensions directory '/home/xbill/.azure/cliextensions'

Python (Linux) 3.13.11 (main, Feb 25 2026, 02:29:30) [GCC 12.2.0]

Legal docs and information: aka.ms/AzureCliLegal


Your CLI is up-to-date.
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To login to Azure:

az login
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Then you will see a message after successful authentication:

You have logged into Microsoft Azure!
You can close this window, or we will redirect you to the Azure CLI documentation in 1 minute.
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Verify The ADK Installation

To verify the setup, run the ADK CLI with Agent3:

xbill@penguin:~/gemini-cli-azure/adkui-appservice$ adk run Agent3
Log setup complete: /tmp/agents_log/agent.20260330_114427.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: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/.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 studio_director, type exit to exit.
[user]: what do you do
[studio_director]: I am the Studio Director. My objective is to manage a linear pipeline of four ADK Sequential Agents to transform a user's seed idea into a fully rendered, responsive HTML5 comic book. If the user's request starts with "Create me a comic of...", I delegate the task to my sub-agent to begin the comic creation pipeline. Otherwise, I answer general questions directly.




  
  
  Running the ADK Web Interface




First- start the ADK interface:






xbill@penguin:~/gemini-cli-azure/adkui-appservice$ adk web --host 0.0.0.0
2026-03-30 10:21:46,051 - INFO - service_factory.py:266 - Using in-memory memory service
2026-03-30 10:21:46,051 - INFO - local_storage.py:84 - Using per-agent session storage rooted at /home/xbill/gemini-cli-azure/adkui-appservice
2026-03-30 10:21:46,051 - INFO - local_storage.py:110 - Using file artifact service at /home/xbill/gemini-cli-azure/adkui-appservice/.adk/artifacts
/home/xbill/.pyenv/versions/3.13.12/lib/python3.13/site-packages/google/adk/cli/fast_api.py:192: 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__ ()
INFO: Started server process [11701]
INFO: Waiting for application startup.






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  Connecting to the ADK




The ADK web interface is started with the place holder 0.0.0.0 address — which tells the service to listen on all interfaces. Depending on your local environment setup- you may need to use the actual local interface. There are 2 potential options:







http://0.0.0.0:8000 

http://127.0.0.1:8000






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This will bring up the ADK UI:







  
  
  Run a Sub Agent




Connect to the ADK and select “Agent3”:






The first interaction just verifies the connection to the agent:







what do you do?

I manage a linear pipeline of four ADK Sequential Agents to transform a user's seed idea into a fully rendered, responsive HTML5 comic book. If your request starts with "Create me a comic of...", I'll delegate the task to my sub-agent to begin the comic creation pipeline. Otherwise, I'll answer your question directly.






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Give the Agent this prompt:







Create a comic of Bob and Clippy in a Battle Royal with Tux.






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The sub agent will then generate the comic book images:







  
  
  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:







  
  
  What did I just do?




The initial steps validate that the Python code and the ADK environment is functional and that the Agent code will run correctly. The next step is to deploy this environment to the Azure App Service.




  
  
  Deploying to Azure App Service




To start the deployment:







xbill@penguin:~/gemini-cli-azure/adkui-appservice$ make deploy
Building the Docker image...
docker build -t adk-image .






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You can validate the final result by checking the messages:







Deploying to Azure App Service...
Updating Web App adk-app-penguin with image adkacrpenguinv2.azurecr.io/adk-image:latest...

Deployment complete. Visit: [http://adk-app-penguin.azurewebsites.net](http://adk-app-penguin.azurewebsites.net)
xbill@penguin:~/gemini-cli-azure/adkui-appservice$






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You can then get the endpoint:







xbill@penguin:~/gemini-cli-azure/adkui-appservice$ make endpoint
--- Azure Endpoint ---
adk-app-penguin.azurewebsites.net

xbill@penguin:~/gemini-cli-azure/adkui-appservice$ make status
Checking Azure App Service status for adk-app-penguin...
Name State Host
--------------- ------- ---------------------------------
adk-app-penguin Running adk-app-penguin.azurewebsites.net






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The service will be visible in the Azure console:







  
  
  Running the ADK Web Interface




Start a connection to the Azure Deployed ADK:







http://adk-app-penguin.azurewebsites.net






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This will bring up the ADK UI.



Select the sub-agent “Agent3” and ask the Agent to create a comic about Bit:







Bit the Raccoon is a popular, unofficial community mascot for Microsoft Azure,
specifically representing the Cloud Advocate team and open-source initiatives. 
Created by Ashley McNamara, Bit is often depicted in tech-related, helpful 
scenarios to make complex cloud concepts more accessible and approachable 
to developers. X X +2 Role: Known as the "Developer Advocate Mascot," 
Bit is often used in presentations and content to represent the "open" 
aspect of Azure. Context: While Azure has an official anime-style mascot 
in Japan (Claudia Madobe), Bit is beloved within the broader developer 
community as a fun, tech-savvy character. 

Appearance: Bit is usually styled as an adorable, helpful raccoon, 
sometimes accompanied by other community mascots like "Brady the Ninja Sloth."
GitHub GitHub Bit is a favorite for stickers and frequently appears 
on social media to highlight new Azure features.  

Create a story about bit






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The Agent will get to work:







  
  
  Run the Viewer Agent




Connect to the ADK and select “Agent4”:







I've exported the comic as ADK artifacts for you!

You can now view the comic directly within the ADK environment. Please check the 'Artifacts' pane or tab in your ADK Visual Builder / Studio UI.

You'll find the following:

view_full_comic.html: This is a complete, self-contained HTML file with embedded images for the full comic experience.
comic_preview.md: A markdown summary of the comic.
Individual panel images (e.g., panel_panel_1.png).
If you are working in a local terminal, you can still view the original local files by opening output/comic.html in your browser or by running ./comic.sh. However, the ADK artifacts are the best way to view it while hosted!






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The Agent will generate the artifacts:






The Comic can then be viewed:







  
  
  Summary




The Agent Development Kit was used to visually define a basic agent and added a tool that calls the Image Gemini Model. This Agent was tested locally with the CLI and then with the ADK web tool. Then, the Agent was packaged and deployed to the Azure App Service. Several sample ADK agents were run directly from the App Service deployment in Azure. This approach validates that cross cloud tools can be used — even with more complex agents.
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Marinko Mijatovic •

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