Develop and deploy agents on Vertex AI Agent Engine with Agent Development Kit
This page demonstrates how to create and deploy an agent to Vertex AI Agent Engine Runtime using the Agent Development Kit (ADK). This quickstart guides you through the following steps:
Set up your Google Cloud project.
Install the Vertex AI SDK for Python and ADK.
Develop a currency exchange agent.
Deploy the agent to Vertex AI Agent Engine Runtime.
Test the deployed agent.
You can also use the following alternative quickstarts for ADK:
ADK quickstart: The ADK quickstart runs entirely on your machine and assumes you're using a local IDE and terminal access.
Agent Starter Pack: a collection of production-ready generative AI agent templates built for Vertex AI Agent Engine.
For the quickstart using supported frameworks other than Agent Development Kit, see Develop and deploy agents on Vertex AI Agent Engine.
Before you begin
- Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get 300ドル in free credits to run, test, and deploy workloads.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
-
Create a project: To create a project, you need the Project Creator role
(
roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.createpermission. Learn how to grant roles.
-
Verify that billing is enabled for your Google Cloud project.
-
Enable the Vertex AI and Cloud Storage APIs.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (
roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enablepermission. Learn how to grant roles. -
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
-
Create a project: To create a project, you need the Project Creator role
(
roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.createpermission. Learn how to grant roles.
-
Verify that billing is enabled for your Google Cloud project.
-
Enable the Vertex AI and Cloud Storage APIs.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (
roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enablepermission. Learn how to grant roles.
To get the permissions that you need to use Vertex AI Agent Engine, ask your administrator to grant you the following IAM roles on your project:
-
Vertex AI User (
roles/aiplatform.user) -
Storage Admin (
roles/storage.admin)
For more information about granting roles, see Manage access to projects, folders, and organizations.
You might also be able to get the required permissions through custom roles or other predefined roles.
Install and initialize the Vertex AI SDK for Python
Run the following command to install the Vertex AI SDK for Python and other required packages:
pipinstall--upgrade--quietgoogle-cloud-aiplatform[agent_engines,adk]>=1.112Authenticate as a user
Local Shell
Run the following command:
gcloudauthapplication-defaultloginColab
Run the following code:
fromgoogle.colabimport auth auth.authenticate_user(project_id="PROJECT_ID")Cloud Shell
No action required.
Express mode
If you're using Vertex AI in express mode, no action is required.
Run the following code to import Vertex AI Agent Engine and initialize the SDK:
Google Cloud project
importvertexai client = vertexai .Client( project="PROJECT_ID", # Your project ID. location="LOCATION", # Your cloud region. )Where:
PROJECT_IDis the Google Cloud project ID under which you develop and deploy agentsLOCATIONis one of the supported regions.
Express mode
If you're using Vertex AI in express mode, run the following code:
importvertexai fromvertexaiimport agent_engines vertexai .init( key="API_KEY" )where API_KEY is the API key you use to authenticate the agent.
Develop an agent
Develop a currency exchange tool for your agent:
defget_exchange_rate( currency_from: str = "USD", currency_to: str = "EUR", currency_date: str = "latest", ): """Retrieves the exchange rate between two currencies on a specified date.""" importrequests response = requests.get( f"https://api.frankfurter.app/{currency_date}", params={"from": currency_from, "to": currency_to}, ) return response.json()Instantiate an agent:
fromgoogle.adk.agentsimport Agent fromvertexaiimport agent_engines agent = Agent( model="gemini-2.0-flash", name='currency_exchange_agent', tools=[get_exchange_rate], ) app = agent_engines.AdkApp(agent=agent)Test the agent locally:
async for event in app.async_stream_query( user_id="USER_ID", message="What is the exchange rate from US dollars to SEK today?", ): print(event)where USER_ID is a user-defined ID with a character limit of 128.
Deploy an agent
Deploy the agent by creating a reasoningEngine resource in Vertex AI:
remote_agent = client.agent_engines.create(
agent=app,
config={
"requirements": ["google-cloud-aiplatform[agent_engines,adk]"],
"staging_bucket": "STAGING_BUCKET",
}
)
where STAGING_BUCKET is a Cloud Storage bucket prefixed by gs://.
Use an agent
Test the deployed agent by sending a query:
async for event in remote_agent.async_stream_query(
user_id="USER_ID",
message="What is the exchange rate from US dollars to SEK today?",
):
print(event)
Clean up
To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.
remote_agent.delete(force=True)