agent-platform-migrate-from-ai-studio

name agent-platform-migrate-from-ai-studio metadata {"category":"AiAndMachineLearning"} description Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry). Migrating from Gemini API in AI Studio to Agent Platform Use this skill when you need to transition an application from the developer-centric Google AI Studio ecosystem ( generativelanguage.googleapis.com ) to the enterprise-grade Google Cloud Agent Platform ( aiplatform.googleapis.com ). 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 Feature / Control Google AI Studio (Gemini Developer API) Agent Platform (Enterprise Gemini API) API Endpoint generativelanguage.googleapis.com aiplatform.googleapis.com Target Audience Developers, startups, students, researchers building production apps. Enterprise production, MLOps engineers GCP Credit Support No (GCP credits/Free Trial cannot be applied) Yes (Fully covered by Welcome or custom credits) Data Privacy Data may be reviewed to improve Google products Prompts/responses are never used for training Security & IAM API key, OAuth Google Cloud IAM (Service Accounts, OAuth 2.0, VPC-SC) Compliance & SLAs None (Best-effort availability) 24/7 Enterprise Support, SLAs, HIPAA, SOC2 Throughput Options Shared / Rate-limited Pay-as-you-go OR Provisioned Throughput MLOps Ecosystem Basic prompt management Model Registry, Model Monitoring, Pipeline Evaluation Inferencing Scope Global endpoints only Both Global and strict Regional endpoints See Google Cloud Documentation to learn more about the differences between the two offerings. Migration Guide Billing and Credits Google Cloud Free Trial credits do not apply to AI Studio . To use your credits for Gemini models, you must route calls through the Agent Platform. Create a Google Cloud billing account. You must provide a valid payment method during setup to verify identity. If you are a new customer, ensure your $300 Welcome credit is active in the Billing Console. 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 You must explicitly enable the Agent Platform API on your target Google Cloud Project. Run the following command via your local shell: gcloud services enable aiplatform.googleapis.com --project= "{project_id}" Authentication & Authorization (IAM) User Auth For local debugging or script execution, authenticate using Application Default Credentials (ADC). Option 1 - Automated Script : bash <(curl -sSL https://storage.googleapis.com/cloud-samples-data/adc/setup_adc.sh) Option 2 - Manual Setup : gcloud auth login gcloud auth application-default login Grant your user identity the required IAM role to perform inferencing calls: gcloud projects add-iam-policy-binding "{project_id}" \ --member= "user:YOUR_EMAIL@domain.com" \ --role= "roles/aiplatform.user" Service Auth When running your application on Google Cloud infrastructure such as a Compute Engine VM, authenticate using the machine's attached Service Account. For example, the Compute Engine Default Service Account . 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" 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) You can continue to use the unified Google GenAI SDK ( google-genai ). This SDK works with both AI Studio and Agent Platform. You only need to switch the routing flags via your runtime environment variables to target the Agent Platform backend. Set your target environment details: export GOOGLE_CLOUD_PROJECT= "{project_id}" export GOOGLE_CLOUD_LOCATION= "global" # Or your chosen regional endpoint export GOOGLE_GENAI_USE_ENTERPRISE=TRUE Now, your standard python code shifts from using AI Studio to Agent Platform without altering the core initialization blocks: 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) To call Gemini models in Agent Platform from an Agent Development Kit agent, follow these steps. Authenticate to Google Cloud. If running an ADK agent in Google Cloud (e.g. Agent Platform Runtime), use the agent's assigned service account. Alternatively, if running ADK locally, run: gcloud auth application-default login 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 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], ) To learn more about integrating ADK agents with Agent Platform, see the ADK documentation . Antigravity CLI Google Cloud users can now access Antigravity 2.0, including the Antigravity CLI, with Gemini Enterprise Agent Platform. Install the Antigravity CLI to your local environment. Start the Antigravity CLI. agy Follow the CLI setup prompts - select Use a Google Cloud Project . Complete the OAuth flow in the opened browser window using your authenticated Google Cloud Workspace or user identity. Copy the confirmation token, and paste it directly back into your terminal. Follow the prompts to enter your Google Cloud Project ID. Select your Google Cloud location (e.g. global ). Optionally, run the /model command to select a different Gemini model in Agent Platform. OpenClaw To configure an OpenClaw agent to use Gemini models in Agent Platform, Follow these steps. Install OpenClaw using OpenClaw's official instructions . ⚠️ Important: OpenClaw 2026.5.28 currently has a google-vertex bug. Please use 2026.5.20 for now. 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. 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: $PROJECT_NUMBER " Within a Compute Engine VM, run: export PROJECT_NUMBER=$(curl "http://metadata.google.internal/computeMetadata/v1/project/project-id" -H "Metadata-Flavor: Google" ) echo "Project number: $PROJECT_NUMBER " 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 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" : {