google__adk-docs/docs/deploy/gke.md

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 options

Option 1: Manual Deployment using gcloud and kubectl

You can deploy your agent to GKE either manually using Kubernetes manifests or automatically using the adk deploy gke command. Choose the approach that best suits your workflow.

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

Option 1: Manual Deployment using gcloud and kubectl

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:

   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 service URI (e.g., SQLite)
   SESSION_SERVICE_URI = "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(
       agents_dir=AGENT_DIR,
       session_service_uri=SESSION_SERVICE_URI,
       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:

   google_adk
   # Add any other dependencies your agent needs
   

3. Define the container image:

   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.

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}'

Option 2: Automated Deployment using adk deploy gke (Coming Soon)

ADK provides a CLI command to streamline GKE deployment. This avoids the need to manually build images, write Kubernetes manifests, or push to Artifact Registry.

Prerequisites

Before you begin, ensure you have the following set up:

1. A running GKE cluster: You need an active Kubernetes cluster on Google Cloud.

2. gcloud CLI: The Google Cloud CLI must be installed, authenticated, and configured to use your target project. Run gcloud auth login and gcloud config set project [YOUR_PROJECT_ID].

3. Required IAM Permissions: The user or service account running the command needs, at a minimum, the following roles:

   • Kubernetes Engine Developer (roles/container.developer): To interact with the GKE cluster.

   • Artifact Registry Writer (roles/artifactregistry.writer): To push the agent's container image.

4. Docker: The Docker daemon must be running on your local machine to build the container image.

The deploy gke Command

The command takes the path to your agent and parameters specifying the target GKE cluster.

Syntax

adk deploy gke [OPTIONS] AGENT_PATH

Arguments & Options

Argument	Description	Required
AGENT_PATH	The local file path to your agent's root directory.	Yes
--project	The Google Cloud Project ID where your GKE cluster is located.	Yes
--cluster_name	The name of your GKE cluster.	Yes
--region	The Google Cloud region of your cluster (e.g., us-central1).	Yes
--with_ui	Deploys both the agent's back-end API and a companion front-end user interface.	No
--verbosity	Sets the logging level for the deployment process. Options: debug, info, warning, error.	No

How It Works

When you run the adk deploy gke command, the ADK performs the following steps automatically:

• Containerization: It builds a Docker container image from your agent's source code.

• Image Push: It tags the container image and pushes it to your project's Artifact Registry.

• Manifest Generation: It dynamically generates the necessary Kubernetes manifest files (a Deployment and a Service).

• Cluster Deployment: It applies these manifests to your specified GKE cluster, which triggers the following:

The Deployment instructs GKE to pull the container image from Artifact Registry and run it in one or more Pods.

The Service creates a stable network endpoint for your agent. By default, this is a LoadBalancer service, which provisions a public IP address to expose your agent to the internet.

Example Usage

Here is a practical example of deploying an agent located at ~/agents/multi_tool_agent/ to a GKE cluster named test.

adk deploy gke \
    --project myproject \
    --cluster_name test \
    --region us-central1 \
    --with_ui \
    --verbosity info \
    ~/agents/multi_tool_agent/

Verifying Your Deployment

If you used adk deploy gke, verify the deployment using kubectl:

1. Check the Pods: Ensure your agent's pods are in the Running state.

kubectl get pods

You should see output like adk-default-service-name-xxxx-xxxx ... 1/1 Running in the default namespace.

2. Find the External IP: Get the public IP address for your agent's service.

kubectl get service
NAME                       TYPE           CLUSTER-IP      EXTERNAL-IP     PORT(S)        AGE
adk-default-service-name   LoadBalancer   34.118.228.70   34.63.153.253   80:32581/TCP   5d20h

We can navigate to the external IP and interact with the agent via UI

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"

### 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:

```bash
kubectl logs -l app=adk-agent
```

=== "API Testing (curl)"

### 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.

```bash
export APP_URL="KUBERNETES_SERVICE_URL"
```

#### List available apps

Verify the deployed application name.

```bash
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.

```bash
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.

```bash
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.

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