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Deploy to GKE

GKE is Google Clouds managed Kubernetes service. It allows you to deploy and manage containerized applications using Kubernetes.

To deploy your agent you will need to have a Kubernetes cluster running on GKE. You can create a cluster using the Google Cloud Console or the gcloud command line tool.

In this example we will deploy a simple agent to GKE. The agent will be a FastAPI application that uses Gemini 2.0 Flash as the LLM. We can use Vertex AI or AI Studio as the LLM provider using a Environment variable.

Agent sample

For each of the commands, we will reference a capital_agent sample defined in on the LLM agent page. We will assume it's in a capital_agent directory.

To proceed, confirm that your agent code is configured as follows:

  1. Agent code is in a file called agent.py within your agent directory.
  2. Your agent variable is named root_agent.
  3. __init__.py is within your agent directory and contains from . import agent.

Environment variables

Set your environment variables as described in the Setup and Installation guide. You also need to install the kubectl command line tool. You can find instructions to do so in the Google Kubernetes Engine Documentation.

export GOOGLE_CLOUD_PROJECT=your-project-id # Your GCP project ID
export GOOGLE_CLOUD_LOCATION=us-central1 # Or your preferred location
export GOOGLE_GENAI_USE_VERTEXAI=true # Set to true if using Vertex AI
export GOOGLE_CLOUD_PROJECT_NUMBER=$(gcloud projects describe --format json $GOOGLE_CLOUD_PROJECT | jq -r ".projectNumber")

If you don't have jq installed, you can use the following command to get the project number:

gcloud projects describe $GOOGLE_CLOUD_PROJECT

And copy the project number from the output.

export GOOGLE_CLOUD_PROJECT_NUMBER=YOUR_PROJECT_NUMBER

Deployment commands

gcloud CLI

You can deploy your agent to GKE using the gcloud and kubectl cli and Kubernetes manifest files.

Ensure you have authenticated with Google Cloud (gcloud auth login and gcloud config set project <your-project-id>).

Enable APIs

Enable the necessary APIs for your project. You can do this using the gcloud command line tool.

gcloud services enable \
    container.googleapis.com \
    artifactregistry.googleapis.com \
    cloudbuild.googleapis.com \
    aiplatform.googleapis.com

Create a GKE cluster

You can create a GKE cluster using the gcloud command line tool. This example creates an Autopilot cluster named adk-cluster in the us-central1 region.

If creating a GKE Standard cluster, make sure Workload Identity is enabled. Workload Identity is enabled by default in an AutoPilot cluster.

gcloud container clusters create-auto adk-cluster \
    --location=$GOOGLE_CLOUD_LOCATION \
    --project=$GOOGLE_CLOUD_PROJECT

After creating the cluster, you need to connect to it using kubectl. This command configures kubectl to use the credentials for your new cluster.

gcloud container clusters get-credentials adk-cluster \
    --location=$GOOGLE_CLOUD_LOCATION \
    --project=$GOOGLE_CLOUD_PROJECT

Project Structure

Organize your project files as follows:

your-project-directory/
├── capital_agent/
│   ├── __init__.py
│   └── agent.py       # Your agent code (see "Agent sample" tab)
├── main.py            # FastAPI application entry point
├── requirements.txt   # Python dependencies
└── Dockerfile         # Container build instructions

Create the following files (main.py, requirements.txt, Dockerfile) in the root of your-project-directory/.

Code files

  1. This file sets up the FastAPI application using get_fast_api_app() from ADK:

    main.py
    import os

import uvicorn
from fastapi import FastAPI
from google.adk.cli.fast_api import get_fast_api_app

# Get the directory where main.py is located
AGENT_DIR = os.path.dirname(os.path.abspath(__file__))
# Example session DB URL (e.g., SQLite)
SESSION_DB_URL = "sqlite:///./sessions.db"
# Example allowed origins for CORS
ALLOWED_ORIGINS = ["http://localhost", "http://localhost:8080", "*"]
# Set web=True if you intend to serve a web interface, False otherwise
SERVE_WEB_INTERFACE = True

# Call the function to get the FastAPI app instance
# Ensure the agent directory name ('capital_agent') matches your agent folder
app: FastAPI = get_fast_api_app(
    agent_dir=AGENT_DIR,
    session_db_url=SESSION_DB_URL,
    allow_origins=ALLOWED_ORIGINS,
    web=SERVE_WEB_INTERFACE,
)

# You can add more FastAPI routes or configurations below if needed
# Example:
# @app.get("/hello")
# async def read_root():
#     return {"Hello": "World"}

if __name__ == "__main__":
    # Use the PORT environment variable provided by Cloud Run, defaulting to 8080
    uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 8080)))

    Note: We specify agent_dir to the directory main.py is in and use os.environ.get("PORT", 8080) for Cloud Run compatibility.

  2. List the necessary Python packages:

    requirements.txt
    google_adk
# Add any other dependencies your agent needs

  3. Define the container image:

    Dockerfile
    FROM python:3.13-slim
WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

RUN adduser --disabled-password --gecos "" myuser && \
    chown -R myuser:myuser /app

COPY . .

USER myuser

ENV PATH="/home/myuser/.local/bin:$PATH"

CMD ["sh", "-c", "uvicorn main:app --host 0.0.0.0 --port $PORT"]

Build the container image

You need to create a Google Artifact Registry repository to store your container images. You can do this using the gcloud command line tool.

gcloud artifacts repositories create adk-repo \
    --repository-format=docker \
    --location=$GOOGLE_CLOUD_LOCATION \
    --description="ADK repository"

Build the container image using the gcloud command line tool. This example builds the image and tags it as adk-repo/adk-agent:latest.

gcloud builds submit \
    --tag $GOOGLE_CLOUD_LOCATION-docker.pkg.dev/$GOOGLE_CLOUD_PROJECT/adk-repo/adk-agent:latest \
    --project=$GOOGLE_CLOUD_PROJECT \
    .

Verify the image is built and pushed to the Artifact Registry:

gcloud artifacts docker images list \
  $GOOGLE_CLOUD_LOCATION-docker.pkg.dev/$GOOGLE_CLOUD_PROJECT/adk-repo \
  --project=$GOOGLE_CLOUD_PROJECT

Configure Kubernetes Service Account for Vertex AI

If your agent uses Vertex AI, you need to create a Kubernetes service account with the necessary permissions. This example creates a service account named adk-agent-sa and binds it to the Vertex AI User role.

If you are using AI Studio and accessing the model with an API key you can skip this step.

kubectl create serviceaccount adk-agent-sa

gcloud projects add-iam-policy-binding projects/${GOOGLE_CLOUD_PROJECT} \
    --role=roles/aiplatform.user \
    --member=principal://iam.googleapis.com/projects/${GOOGLE_CLOUD_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GOOGLE_CLOUD_PROJECT}.svc.id.goog/subject/ns/default/sa/adk-agent-sa \
    --condition=None

Create the Kubernetes manifest files

Create a Kubernetes deployment manifest file named deployment.yaml in your project directory. This file defines how to deploy your application on GKE.

deployment.yaml
cat <<  EOF > deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: adk-agent
spec:
  replicas: 1
  selector:
    matchLabels:
      app: adk-agent
  template:
    metadata:
      labels:
        app: adk-agent
    spec:
      serviceAccount: adk-agent-sa
      containers:
      - name: adk-agent
        imagePullPolicy: Always
        image: $GOOGLE_CLOUD_LOCATION-docker.pkg.dev/$GOOGLE_CLOUD_PROJECT/adk-repo/adk-agent:latest
        resources:
          limits:
            memory: "128Mi"
            cpu: "500m"
            ephemeral-storage: "128Mi"
          requests:
            memory: "128Mi"
            cpu: "500m"
            ephemeral-storage: "128Mi"
        ports:
        - containerPort: 8080
        env:
          - name: PORT
            value: "8080"
          - name: GOOGLE_CLOUD_PROJECT
            value: GOOGLE_CLOUD_PROJECT
          - name: GOOGLE_CLOUD_LOCATION
            value: GOOGLE_CLOUD_LOCATION
          - name: GOOGLE_GENAI_USE_VERTEXAI
            value: GOOGLE_GENAI_USE_VERTEXAI
          # If using AI Studio, set GOOGLE_GENAI_USE_VERTEXAI to false and set the following:
          # - name: GOOGLE_API_KEY
          #   value: GOOGLE_API_KEY
          # Add any other necessary environment variables your agent might need
---
apiVersion: v1
kind: Service
metadata:
  name: adk-agent
spec:       
  type: LoadBalancer
  ports:
    - port: 80
      targetPort: 8080
  selector:
    app: adk-agent
EOF

Deploy the Application

Deploy the application using the kubectl command line tool. This command applies the deployment and service manifest files to your GKE cluster.

kubectl apply -f deployment.yaml

After a few moments, you can check the status of your deployment using:

kubectl get pods -l=app=adk-agent

This command lists the pods associated with your deployment. You should see a pod with a status of Running.

Once the pod is running, you can check the status of the service using:

kubectl get service adk-agent

If the output shows a External IP, it means your service is accessible from the internet. It may take a few minutes for the external IP to be assigned.

You can get the external IP address of your service using:

kubectl get svc adk-agent -o=jsonpath='{.status.loadBalancer.ingress[0].ip}'

Testing your agent

Once your agent is deployed to GKE, you can interact with it via the deployed UI (if enabled) or directly with its API endpoints using tools like curl. You'll need the service URL provided after deployment.

UI Testing

If you deployed your agent with the UI enabled:

You can test your agent by simply navigating to the kubernetes service URL in your web browser.

The ADK dev UI allows you to interact with your agent, manage sessions, and view execution details directly in the browser.

To verify your agent is working as intended, you can:

  1. Select your agent from the dropdown menu.
  2. Type a message and verify that you receive an expected response from your agent.

If you experience any unexpected behavior, check the pod logs for your agent using:

kubectl logs -l app=adk-agent

API Testing (curl)

You can interact with the agent's API endpoints using tools like curl. This is useful for programmatic interaction or if you deployed without the UI.

Set the application URL

Replace the example URL with the actual URL of your deployed Cloud Run service.

export APP_URL="KUBERNETES_SERVICE_URL"

List available apps

Verify the deployed application name.

curl -X GET $APP_URL/list-apps

(Adjust the app_name in the following commands based on this output if needed. The default is often the agent directory name, e.g., capital_agent).

Create or Update a Session

Initialize or update the state for a specific user and session. Replace capital_agent with your actual app name if different. The values user_123 and session_abc are example identifiers; you can replace them with your desired user and session IDs.

curl -X POST \
    $APP_URL/apps/capital_agent/users/user_123/sessions/session_abc \
    -H "Content-Type: application/json" \
    -d '{"state": {"preferred_language": "English", "visit_count": 5}}'

Run the Agent

Send a prompt to your agent. Replace capital_agent with your app name and adjust the user/session IDs and prompt as needed.

curl -X POST $APP_URL/run_sse \
    -H "Content-Type: application/json" \
    -d '{
    "app_name": "capital_agent",
    "user_id": "user_123",
    "session_id": "session_abc",
    "new_message": {
        "role": "user",
        "parts": [{
        "text": "What is the capital of Canada?"
        }]
    },
    "streaming": false
    }'
  • Set "streaming": true if you want to receive Server-Sent Events (SSE).
  • The response will contain the agent's execution events, including the final answer.

Troubleshooting

These are some common issues you might encounter when deploying your agent to GKE:

403 Permission Denied for Gemini 2.0 Flash

This usually means that the Kubernetes service account does not have the necessary permission to access the Vertex AI API. Ensure that you have created the service account and bound it to the Vertex AI User role as described in the Configure Kubernetes Service Account for Vertex AI section. If you are using AI Studio, ensure that you have set the GOOGLE_API_KEY environment variable in the deployment manifest and it is valid.

Attempt to write a readonly database

You might see there is no session id created in the UI and the agent does not respond to any messages. This is usually caused by the SQLite database being read-only. This can happen if you run the agent locally and then create the container image which copies the SQLite database into the container. The database is then read-only in the container.

sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) attempt to write a readonly database
[SQL: UPDATE app_states SET state=?, update_time=CURRENT_TIMESTAMP WHERE app_states.app_name = ?]

To fix this issue, you can either:

Delete the SQLite database file from your local machine before building the container image. This will create a new SQLite database when the container is started.

rm -f sessions.db

or (recommended) you can add a .dockerignore file to your project directory to exclude the SQLite database from being copied into the container image.

.dockerignore
sessions.db

Build the container image abd deploy the application again.

Cleanup

To delete the GKE cluster and all associated resources, run:

gcloud container clusters delete adk-cluster \
    --location=$GOOGLE_CLOUD_LOCATION \
    --project=$GOOGLE_CLOUD_PROJECT

To delete the Artifact Registry repository, run:

gcloud artifacts repositories delete adk-repo \
    --location=$GOOGLE_CLOUD_LOCATION \
    --project=$GOOGLE_CLOUD_PROJECT

You can also delete the project if you no longer need it. This will delete all resources associated with the project, including the GKE cluster, Artifact Registry repository, and any other resources you created.

gcloud projects delete $GOOGLE_CLOUD_PROJECT