Vertex AI

Use Google Vertex AI with RadarOS. Enterprise-grade Gemini models with Google Cloud authentication.

Vertex AI

Use Google’s Gemini models through Vertex AI — Google Cloud’s enterprise ML platform. Same models as the Gemini API, but with Google Cloud IAM authentication, VPC support, and enterprise compliance.

Use vertex() when you need Google Cloud authentication (service accounts, ADC). Use google() when you have a simple API key.

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Setup

Install

Vertex AI uses the same SDK as Gemini:

npm install @google/genai

Authentication

Vertex AI uses Application Default Credentials (ADC). Authenticate via one of:

# Option 1: gcloud CLI (development)
gcloud auth application-default login

# Option 2: Service account key (production)
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"

Set your project and region:

export GOOGLE_CLOUD_PROJECT="my-project-id"
export GOOGLE_CLOUD_LOCATION="us-central1"  # optional, defaults to us-central1

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Factory

import { vertex } from "@radaros/core";

const model = vertex("gemini-2.0-flash", {
  project: "my-gcp-project",
  location: "us-central1",
});

modelId · string · required

The Gemini model identifier (same model IDs as the Gemini API).

config · object

Optional configuration. See Config below.

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Supported Models

Model ID	Description
gemini-2.5-flash	Fast, efficient. Great for high-throughput and latency-sensitive apps.
gemini-2.5-pro	Highest capability. Best for complex reasoning and long-context tasks.
gemini-2.0-flash	Previous generation flash model.

All Gemini models available in your Vertex AI region can be used.

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Config

project · string · required

Google Cloud project ID. Falls back to GOOGLE_CLOUD_PROJECT env var.

location · string · default: "us-central1"

Google Cloud region. Falls back to GOOGLE_CLOUD_LOCATION env var.

credentials · string

Service account key JSON string or file path. If omitted, uses Application Default Credentials.

Example

const model = vertex("gemini-2.5-flash", {
  project: "my-project-id",
  location: "europe-west4",
});

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Vertex AI vs Gemini API

Feature	google() (Gemini API)	vertex() (Vertex AI)
Auth	API key	Google Cloud IAM / ADC
Pricing	Pay-per-use	GCP billing
VPC / Private	No	Yes
Enterprise compliance	Limited	SOC2, HIPAA, etc.
Same models	Yes	Yes
Multi-modal	Yes	Yes

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Full Example

import { Agent, vertex, defineTool } from "@radaros/core";
import { z } from "zod";

const agent = new Agent({
  name: "enterprise-assistant",
  model: vertex("gemini-2.5-flash", {
    project: "my-project",
    location: "us-central1",
  }),
  instructions: "You are an enterprise assistant with access to internal tools.",
  tools: [
    defineTool({
      name: "lookup_employee",
      description: "Look up an employee by name",
      parameters: z.object({
        name: z.string().describe("Employee name"),
      }),
      execute: async (args) => `Employee ${args.name} found in Engineering.`,
    }),
  ],
  logLevel: "info",
});

const output = await agent.run("Look up John Smith");
console.log(output.text);

---

Environment Variables

Variable	Description
GOOGLE_CLOUD_PROJECT	Default GCP project ID
GOOGLE_CLOUD_LOCATION	Default GCP region (default: us-central1)
GOOGLE_APPLICATION_CREDENTIALS	Path to service account key JSON