Migrating from Gemini API in AI Studio to Agent Platform

When to Invoke This Skill

• You want to migrate an application from Google AI Studio to Agent Platform (formerly Vertex AI).
• You have Google Cloud credits (e.g., the $300 Welcome Free Trial) that you want to apply toward Gemini API inferencing costs.
• You need to unify your inferencing pipelines, IAM permissions, telemetry, and billing with existing Google Cloud infrastructure (Compute Engine, Cloud SQL, BigQuery).
• You are deploying open-source orchestration engines (like OpenClaw or ADK agents) on Google Cloud VMs, and want the entire system to run under a unified Google Cloud billing structure.

Gemini API Comparison

Migration Guide

Billing and Credits

1. Create a Google Cloud billing account. You must provide a valid payment method during setup to verify identity.
2. If you are a new customer, ensure your $300 Welcome credit is active in the Billing Console.
3. Avoid Billing Surprises: To prevent automatic fallback to your standard form of payment when credits are exhausted, you should establish a budget alert:
   • Go to Billing -> Budgets & Alerts -> Create Budget.
   • Set the threshold to map to your credit limit or maximum comfortable spend.

Enable the Agent Platform API

gcloud services enable aiplatform.googleapis.com --project="{project_id}"

Authentication & Authorization (IAM)

User Auth

bash <(curl -sSL https://storage.googleapis.com/cloud-samples-data/adc/setup_adc.sh)

gcloud auth login
gcloud auth application-default login

gcloud projects add-iam-policy-binding "{project_id}" \
    --member="user:YOUR_EMAIL@domain.com" \
    --role="roles/aiplatform.user"

Service Auth

1. Grant the virtual machine's underlying Service Account the user role:

gcloud projects add-iam-policy-binding "{project_id}" \
    --member="serviceAccount:PROJECT_NUMBER-compute@developer.gserviceaccount.com" \
    --role="roles/aiplatform.user"

1. Compute Engine Access Scopes: Legacy access scopes can override IAM bindings. When provisioning or modifying your Compute Engine instance, you must verify that the VM access scope is configured to either Allow full access to all Cloud APIs (https://www.googleapis.com/auth/cloud-platform) or explicitly includes the standard cloud-platform scope.

Use the Gemini API in Agent Platform

SDKs (Client Libraries)

export GOOGLE_CLOUD_PROJECT="{project_id}"
export GOOGLE_CLOUD_LOCATION="global"  # Or your chosen regional endpoint
export GOOGLE_GENAI_USE_ENTERPRISE=TRUE

from google import genai

# The client automatically picks up the GOOGLE_GENAI_USE_ENTERPRISE=TRUE environment flag
client = genai.Client()

response = client.models.generate_content(
    model='gemini-3-flash-preview',
    contents='Hello world!',
)
print(response.text)

Agent Development Kit (ADK)

1. Authenticate to Google Cloud.

gcloud auth application-default login

1. Set env variables. Ensure these are set no matter if your ADK agent is running in Google Cloud or locally:

export GOOGLE_CLOUD_PROJECT="{project_id}"
export GOOGLE_CLOUD_LOCATION="global"
export GOOGLE_GENAI_USE_ENTERPRISE=TRUE

1. Initialize the ADK agent. You can use the same model string you used with AI Studio (e.g. gemini-3-flash-preview).

from google.adk.agents.llm_agent import Agent

def get_current_time(city: str) -> dict:
    """Returns the current time in a specified city."""
    return {"status": "success", "city": city, "time": "10:30 AM"}

root_agent = Agent(
    model='gemini-3-flash-preview',
    name='root_agent',
    description="Tells the current time in a specified city.",
    instruction="You are a helpful assistant that tells the current time in cities. Use the 'get_current_time' tool for this purpose.",
    tools=[get_current_time],
)

Antigravity CLI

1. Install the Antigravity CLI to your local environment.
2. Start the Antigravity CLI.
   bashCopy

   agy

3. Follow the CLI setup prompts - select Use a Google Cloud Project.
4. Complete the OAuth flow in the opened browser window using your authenticated Google Cloud Workspace or user identity.
5. Copy the confirmation token, and paste it directly back into your terminal.
6. Follow the prompts to enter your Google Cloud Project ID.
7. Select your Google Cloud location (e.g. global).
8. Optionally, run the /model command to select a different Gemini model in Agent Platform.

OpenClaw

1. Install OpenClaw using OpenClaw's official instructions.

1. Ensure that the runtime where OpenClaw is running (e.g. GCE VM with Service Account) has the aiplatform.user IAM role - see Authentication and Authorization section above.
2. Get the project number from the user's project ID. Outside of GCE, run:

export PROJECT_NUMBER=$(gcloud projects describe "$PROJECT_ID" --format="value(projectNumber)")
echo "Project number: $PROJECT_NUMBER"

export PROJECT_NUMBER=$(curl "http://metadata.google.internal/computeMetadata/v1/project/project-id" -H "Metadata-Flavor: Google")
echo "Project number: $PROJECT_NUMBER"

1. From outside the GCE instance, generate GOOGLE_APPLICATION_CREDENTIALS using the Google Cloud project number. Then, scp these credentials to the GCE VM.

mkdir -p ~/.config/gcloud
gcloud iam service-accounts keys create ~/.config/gcloud/application_default_credentials.json --iam-account="${PROJECT_NUMBER}-compute@developer.gserviceaccount.com"

⚠️ Troubleshooting Policy Constraints on New Orgs: New Google Cloud organizations (created on or after May 4th, 2024) enforce a legacy Organization Policy, restricting Service Account Key Creation. To complete this step, this Org Policy must be temporarily disabled. An Organization Policy Admin must set this policy to Inactive. To the agent: Check if this policy is enforced, and if it is: pause, and prompt the user to disable it at this Console link: https://console.cloud.google.com/iam-admin/orgpolicies

1. Edit the configuration file that's usually located at: ~/.openclaw/openclaw.json. Ensure you prefix the Gemini model with google-vertex/.

⚠️ Important: Do not use Gemini 3.5 models, since OpenClaw's google-vertex provider does not support it yet. Older models work. When using the Gemini 3 Flash Preview model in Agent Platform, always set the location to global, NOT a regional endpoint.

{
  "env": {
    "vars": {
      "GOOGLE_CLOUD_PROJECT": "PROJECT_ID",
      "GOOGLE_CLOUD_LOCATION": "global",
      "GOOGLE_APPLICATION_CREDENTIALS": "~/.config/gcloud/application_default_credentials.json"
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "google-vertex/gemini-3-flash-preview"
      },
      "workspace": "~/.openclaw/workspace",
      "compaction": {
        "mode": "safeguard"
      },
      "heartbeat": {
        "model": "google-vertex/gemini-3-flash-preview"
      }
    },
    "list": [
      {
        "id": "main",
        "workspace": "~/.openclaw/workspace",
        "model": "google-vertex/gemini-3-flash-preview"
      }
    ]
  },
  "session": {
    "dmScope": "per-channel-peer"
  },
  "tools": {
    "profile": "coding"
  }
}

1. Restart OpenClaw.

openclaw gateway restart

1. Verify the OpenClaw connection to Agent Platform:

openclaw models status
openclaw agent --agent main --message "Hello world!"

Additional Resources

• Google Cloud Free Trial Features & Limits
• Migrate from Google AI Studio to Gemini Enterprise Agent Platform
• Gemini Enterprise Agent Platform - Models
• Agent Development Kit Documentation - Connect to Models in Agent Platform
• OpenClaw Documentation - Connect to Google models
• Google Cloud Budget Alerts - Setup Guide