google/agent-platform-migrate-from-ai-studio
>- 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).
npx skills add https://github.com/google/skills --skill agent-platform-migrate-from-ai-studio
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).
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(formerly Vertex AI).
you want to apply toward Gemini API inferencing costs.
and billing with existing Google Cloud infrastructure (Compute Engine, Cloud
SQL, BigQuery).
agents) on Google Cloud VMs, and want the entire system to run under a
unified Google Cloud billing structure.
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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
to learn more about the differences between the two offerings.
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Google Cloud Free Trial credits
To use your credits for Gemini models, you must route calls through the Agent
Platform.
method during setup to verify identity.
Billing Console.
form of payment when credits are exhausted, you should establish a budget
alert:
spend.
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}"
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:[email protected]" \
--role="roles/aiplatform.user"
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.
gcloud projects add-iam-policy-binding "{project_id}" \
--member="serviceAccount:[email protected]" \
--role="roles/aiplatform.user"
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.
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You can continue to use the unified
(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)
To call Gemini models in Agent Platform from an Agent Development Kit agent,
follow these steps.
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
running in Google Cloud or locally:
export GOOGLE_CLOUD_PROJECT="{project_id}"
export GOOGLE_CLOUD_LOCATION="global"
export GOOGLE_GENAI_USE_ENTERPRISE=TRUE
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,
Google Cloud users can now access
Antigravity 2.0, including the Antigravity CLI, with Gemini Enterprise Agent
Platform.
to your local environment.
agy
authenticated Google Cloud Workspace or user identity.
global)./model command to select a different Gemini model inAgent Platform.
To configure an OpenClaw agent to use Gemini models in Agent Platform, Follow
these steps.
⚠️ Important: OpenClaw 2026.5.28 currently has a google-vertex bug. Please use
2026.5.20 for now.
Account) has the aiplatform.user IAM role - see Authentication and
Authorization section above.
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: $PROJECT_NUMBER"
GOOGLE_APPLICATION_CREDENTIALSusing the Google Cloud project number. Then, scp these credentials to the
GCE VM.
mkdir -p ~/.config/gcloud
gcloud iam service-accounts keys create ~/.config/gcloud/application_default_credentials.json --iam-account="${PROJECT_NUMBER}[email protected]"
> ⚠️ 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
~/.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
> 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"
}
}
openclaw gateway restart
openclaw models status
openclaw agent --agent main --message "Hello world!"
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