google__adk-docs/docs/deploy/gke.md

Deploy to Google Kubernetes Engine (GKE)

Supported in ADK Python Go

GKE is the Google Cloud 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.

The following example shows you how to deploy a simple agent to GKE. The Python agent is a FastAPI application that uses Gemini Flash as the LLM. The Go agent uses the ADK launcher and a statically-linked binary in a minimal container. You can use Agent Platform or AI Studio as the LLM provider with the environment variable GOOGLE_GENAI_USE_ENTERPRISE.

Set environment variables

Set the variables as described in the Setup and Installation guide. You will 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_ENTERPRISE=true # Set to true if using Agent Platform
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

Enable APIs and permissions

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

Grant necessary roles to the default compute engine service account required by the gcloud builds submit command.

ROLES_TO_ASSIGN=(
    "roles/artifactregistry.writer"
    "roles/storage.objectViewer"
    "roles/logging.viewer"
    "roles/logging.logWriter"
)

for ROLE in "${ROLES_TO_ASSIGN[@]}"; do
    gcloud projects add-iam-policy-binding "${GOOGLE_CLOUD_PROJECT}" \
        --member="serviceAccount:${GOOGLE_CLOUD_PROJECT_NUMBER}-compute@developer.gserviceaccount.com" \
        --role="${ROLE}"
done

Deployment payload

When you deploy your ADK agent workflow to the Google Cloud GKE, the following content is uploaded to the service:

• Your ADK agent code
• Any dependencies declared in your ADK agent code
• ADK API server code version used by your agent

The default deployment does not include the ADK web user interface libraries, unless you specify it as deployment setting, such as the --with_ui option for adk deploy gke command.

Deployment options

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.

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.

!!! note "If you're creating a GKE Standard cluster"

Make sure [Workload Identity](https://cloud.google.com/kubernetes-engine/docs/how-to/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

Create your agent

Use the capital_agent example defined on the LLM agents page as a reference.

=== "Python"

Organize your project files as follows:

```txt
your-project-directory/
├── capital_agent/
│   ├── __init__.py
│   └── agent.py       # Your agent code
├── main.py            # FastAPI application entry point
├── requirements.txt   # Python dependencies
└── Dockerfile         # Container build instructions
```

=== "Go"

Organize your project files as follows:

```txt
your-project-directory/
├── main.go       # Agent code and launcher entry point
├── go.mod        # Go module definition
├── go.sum        # Go module checksums
└── Dockerfile    # Container build instructions
```

Code files

=== "Python"

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

1. This is the Capital Agent example inside the `capital_agent` directory

    ```python title="capital_agent/agent.py"
    from google.adk.agents import LlmAgent 

    # Define a tool function
    def get_capital_city(country: str) -> str:
      """Retrieves the capital city for a given country."""
      # Replace with actual logic (e.g., API call, database lookup)
      capitals = {"france": "Paris", "japan": "Tokyo", "canada": "Ottawa"}
      return capitals.get(country.lower(), f"Sorry, I don't know the capital of {country}.")

    # Add the tool to the agent
    capital_agent = LlmAgent(
        model="gemini-flash-latest",
        name="capital_agent", #name of your agent
        description="Answers user questions about the capital city of a given country.",
        instruction="""You are an agent that provides the capital city of a country... (previous instruction text)""",
        tools=[get_capital_city] # Provide the function directly
    )

    # ADK will discover the root_agent instance
    root_agent = capital_agent
    ```

    Mark your directory as a python package

    ```python title="capital_agent/__init__.py"

    from . import agent
    ```

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

    ```python title="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 service URI (e.g., SQLite)
    # Note: Use 'sqlite+aiosqlite' instead of 'sqlite' because DatabaseSessionService requires an async driver
    SESSION_SERVICE_URI = "sqlite+aiosqlite:///./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,
    )

    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.*

3. List the necessary Python packages:

    ```txt title="requirements.txt"
    google-adk
    # Add any other dependencies your agent needs
    ```

4. Define the container image:

    ```dockerfile title="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"]
    ```

=== "Go"

Create the following files in the root of `your-project-directory/`.

1. Define the agent and embed the ADK launcher. The launcher handles the `web`,
   `api`, and `webui` subcommands that start the REST API server and web interface:

    ```go title="main.go"
    package main

    import (
        "context"
        "fmt"
        "log"
        "os"
        "strings"

        "google.golang.org/adk/v2/agent"
        "google.golang.org/adk/v2/agent/llmagent"
        "google.golang.org/adk/v2/cmd/launcher"
        "google.golang.org/adk/v2/cmd/launcher/full"
        "google.golang.org/adk/v2/model/gemini"
        "google.golang.org/adk/v2/tool"
        "google.golang.org/adk/v2/tool/functiontool"
        "google.golang.org/genai"
    )

    type getCapitalCityArgs struct {
        Country string `json:"country" jsonschema:"The country to look up."`
    }

    func getCapitalCity(_ tool.Context, args getCapitalCityArgs) (string, error) {
        capitals := map[string]string{
            "france":  "Paris",
            "japan":   "Tokyo",
            "canada":  "Ottawa",
        }
        capital, ok := capitals[strings.ToLower(args.Country)]
        if !ok {
            return "", fmt.Errorf("capital not found for %s", args.Country)
        }
        return capital, nil
    }

    func main() {
        ctx := context.Background()

        model, err := gemini.NewModel(ctx, "gemini-flash-latest", &genai.ClientConfig{
            APIKey: os.Getenv("GOOGLE_API_KEY"),
        })
        if err != nil {
            log.Fatalf("Failed to create model: %v", err)
        }

        capitalTool, err := functiontool.New(
            functiontool.Config{
                Name:        "get_capital_city",
                Description: "Retrieves the capital city for a given country.",
            },
            getCapitalCity,
        )
        if err != nil {
            log.Fatalf("Failed to create tool: %v", err)
        }

        capitalAgent, err := llmagent.New(llmagent.Config{
            Name:        "capital_agent",
            Model:       model,
            Description: "Answers questions about capital cities.",
            Instruction: "You are an agent that provides the capital city of a country.",
            Tools:       []tool.Tool{capitalTool},
        })
        if err != nil {
            log.Fatalf("Failed to create agent: %v", err)
        }

        config := &launcher.Config{
            AgentLoader: agent.NewSingleLoader(capitalAgent),
        }

        l := full.NewLauncher()
        if err = l.Execute(ctx, config, os.Args[1:]); err != nil {
            log.Fatalf("Run failed: %v\n\n%s", err, l.CommandLineSyntax())
        }
    }
    ```

    To use Agent Platform instead of AI Studio, set `genai.ClientConfig` to use
    the Agent Platform backend:

    ```go
    model, err := gemini.NewModel(ctx, "gemini-flash-latest", &genai.ClientConfig{
        Backend:  genai.BackendVertexAI,
        Project:  os.Getenv("GOOGLE_CLOUD_PROJECT"),
        Location: os.Getenv("GOOGLE_CLOUD_LOCATION"),
    })
    ```

2. Define the container image. Go compiles to a self-contained static binary,
   so the container uses a minimal distroless base image — no runtime
   dependencies or package manager required:

    ```dockerfile title="Dockerfile"
    # Stage 1: Build the Go binary
    FROM golang:1.25 AS builder
    WORKDIR /app

    COPY go.mod go.sum ./
    RUN go mod download

    COPY . .
    # Compile a statically linked Linux/amd64 binary
    RUN CGO_ENABLED=0 GOOS=linux GOARCH=amd64 \
        go build -ldflags="-s -w" -o capital_agent .

    # Stage 2: Copy the binary into a minimal runtime image
    FROM gcr.io/distroless/static-debian12
    COPY --from=builder /app/capital_agent /app/capital_agent
    EXPOSE 8080

    # Start the API server and web UI
    CMD ["/app/capital_agent", "web", "-port", "8080", "api", "webui"]
    ```

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 and push it to Artifact Registry:

=== "Python"

Use Cloud Build to build and push the image directly from your source directory:

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

=== "Go"

The multi-stage Dockerfile handles compilation inside the builder stage, so you
can use Cloud Build without needing a local Go toolchain:

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

Alternatively, compile the binary locally and build a smaller image without
the multi-stage Dockerfile — useful if you already have Go installed:

```bash
# Cross-compile for linux/amd64
CGO_ENABLED=0 GOOS=linux GOARCH=amd64 go build -ldflags="-s -w" -o capital_agent .

# Build and push the image
docker build -t $GOOGLE_CLOUD_LOCATION-docker.pkg.dev/$GOOGLE_CLOUD_PROJECT/adk-repo/adk-agent:latest .
docker push $GOOGLE_CLOUD_LOCATION-docker.pkg.dev/$GOOGLE_CLOUD_PROJECT/adk-repo/adk-agent:latest
```

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 Agent Platform

If your agent uses Agent Platform, 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 Agent Platform User role.

!!! note "Skip if using AI Studio"

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

PROJECT_ID=${GOOGLE_CLOUD_PROJECT}
PROJECT_NUM=${GOOGLE_CLOUD_PROJECT_NUMBER}
IAM_URL="principal://[iam.googleapis.com/projects/$](https://iam.googleapis.com/projects/$){PROJECT_NUM}"
WIP="locations/global/workloadIdentityPools/${PROJECT_ID}.svc.id.goog"
SA="subject/ns/default/sa/adk-agent-sa"

gcloud projects add-iam-policy-binding projects/${PROJECT_ID} \
    --role=roles/aiplatform.user \
    --member="${IAM_URL}/${WIP}/${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: $GOOGLE_CLOUD_LOCATION
          - name: GOOGLE_GENAI_USE_ENTERPRISE
            value: "$GOOGLE_GENAI_USE_ENTERPRISE"
          # If using AI Studio, set GOOGLE_GENAI_USE_ENTERPRISE 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

!!! note "Python only"

The `adk deploy gke` command is available for Python only. Go does not have
an equivalent CLI command. Go agents must be deployed using the manual
approach described in [Option 1](#option-1-manual-deployment-using-gcloud-and-kubectl).

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. Required CLIs:

   • 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].
   • kubectl: The Kubernetes CLI must be installed to deploy the application to your cluster.

3. Enabled Google Cloud APIs: Make sure the following APIs are enabled in your Google Cloud project:

   • Kubernetes Engine API (container.googleapis.com)
   • Cloud Build API (cloudbuild.googleapis.com)
   • Container Registry API (containerregistry.googleapis.com)

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

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

   • Storage Object Viewer (roles/storage.objectViewer): To allow Cloud Build to download the source code from the Cloud Storage bucket where gcloud builds submit uploads it.

   • Artifact Registry Create on Push Writer (roles/artifactregistry.createOnPushWriter): To allow Cloud Build to push the built container image to Artifact Registry. This role also permits the on-the-fly creation of the special gcr.io repository within Artifact Registry if needed on the first push.

   • Logs Writer (roles/logging.logWriter): To allow Cloud Build to write build logs to Cloud Logging.

Configure Workload Identity for Agent Platform

If your agent uses Agent Platform, the workload running in your cluster needs permission to call the Agent Platform API. Unlike the manual path, adk deploy gke generates a manifest that uses the default Kubernetes service account in the default namespace. Grant the Agent Platform User role to that service account through Workload Identity so the agent can access models such as Gemini.

!!! tip "Skip if using AI Studio"

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

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/default \
    --condition=None

If you are using a Google Cloud project and skip this step, the agent's pods start successfully, but requests to the model fail with a 403 PERMISSION_DENIED error when verifying your deployment.

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
--service_type	The type of Kubernetes service to create. Accepts ClusterIP (default) or LoadBalancer.	No
--with_ui	Deploys both the agent's back-end API and a companion front-end user interface.	No
--log_level	Sets the logging level for the deployment process. Options: debug, info, warning, error, critical.	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. It defaults to a ClusterIP service, which is only accessible within the cluster. To expose your agent to the internet with a public IP address, you must specify --service_type=LoadBalancer.

Example of use

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 \
    --log_level info \
    ~/agents/multi_tool_agent/

Verify your deployment

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

• 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.

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

kubectl get service

By default, the service type is ClusterIP, and EXTERNAL-IP is <none>.

NAME                       TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE
adk-default-service-name   ClusterIP   10.12.1.2       <none>        80/TCP    2m

To test your agent, you can use port-forwarding:

kubectl port-forward svc/adk-default-service-name 8080:80

You can then access your agent at http://localhost:8080.

If you deployed with --service_type=LoadBalancer, it may take a few minutes for an external IP to be assigned. Once the EXTERNAL-IP is available, you can navigate to it to interact with your agent.

Test 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

```bash
export APP_URL=$(kubectl get service adk-agent -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
```

!!! note "Go: API path prefix"
    The Go ADK server serves all REST endpoints under the `/api` path prefix
    by default. Prepend `/api` to every path in the examples below when
    testing a Go deployment. For example:

    | Python | Go |
    |---|---|
    | `$APP_URL/list-apps` | `$APP_URL/api/list-apps` |
    | `$APP_URL/apps/…` | `$APP_URL/api/apps/…` |
    | `$APP_URL/run_sse` | `$APP_URL/api/run_sse` |

    The prefix can be changed at startup with `-path_prefix` on the `api`
    subcommand, e.g. `CMD ["/app/capital_agent", "web", "-port", "8080", "api", "-path_prefix", ""]`
    removes the prefix entirely.

#### 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.

```bash
curl -X POST \
    $APP_URL/apps/capital_agent/users/user_123/sessions/session_abc \
    -H "Content-Type: application/json" \
    -d '{"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.

!!! note "Go: JSON field names are camelCase"
    The Python ADK REST API uses `snake_case` field names in the JSON request
    body (e.g. `app_name`, `user_id`, `new_message`). The Go ADK REST API
    uses `camelCase` (e.g. `appName`, `userId`, `newMessage`). Use the
    correct format for your deployment language.

=== "Python"

    ```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
        }'
    ```

=== "Go"

    ```bash
    curl -X POST $APP_URL/api/run_sse \
        -H "Content-Type: application/json" \
        -d '{
        "appName": "capital_agent",
        "userId": "user_123",
        "sessionId": "session_abc",
        "newMessage": {
            "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's models

This usually means that the Kubernetes service account does not have the necessary permission to access the Agent Platform API. Ensure that you have created the service account and bound it to the Agent Platform User role as described in the Configure Kubernetes Service Account for Agent Platform section. If you deployed with adk deploy gke, bind the default service account instead, as described in the Configure Workload Identity for Agent Platform 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.

404 or Not Found response

This usually means there is an error in your request. Check the application logs to diagnose the problem.

export POD_NAME=$(kubectl get pod -l app=adk-agent -o jsonpath='{.items[0].metadata.name}')
kubectl logs $POD_NAME

Attempt to write a readonly database

!!! note "Python only" This error applies to Python deployments that use SQLite for session storage. Go deployments use an in-memory session service by default and are not affected by this issue.

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 and deploy the application again.

Insufficient Permission to Stream Logs ERROR: (gcloud.builds.submit)

This error can occur when you don't have sufficient permissions to stream build logs, or your VPC-SC security policy restricts access to the default logs bucket. To check the progress of the build, follow the link provided in the error message or navigate to the Cloud Build page in the Google Cloud Console.

You can also verify the image was built and pushed to the Artifact Registry using the command under the Build the container image section.

Gemini models not supported in Live Api

When using the ADK Dev UI for your deployed agent, text-based chat works, but voice (e.g., clicking the microphone button) fail. You might see a websockets.exceptions.ConnectionClosedError in the pod logs indicating that your model is "not supported in the live api".

This error occurs because the agent is configured with a model (like gemini-flash-latest in the example) that does not support the Gemini Live API. The Live API is required for real-time, bidirectional streaming of audio and video.

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