chore: import upstream snapshot with attribution
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This commit is contained in:
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# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Sample agent using Vertex AI Agent Engine Sandbox for computer use.
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This sample demonstrates how to use the AgentEngineSandboxComputer with ADK
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to create a computer use agent that operates in a remote sandbox environment.
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Prerequisites:
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1. A GCP project with Agent Engine setup (https://docs.cloud.google.com/agent-builder/agent-engine/set-up)
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2. A service account with roles/iam.serviceAccountTokenCreator permission
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3. Environment variables in contributing/samples/.env:
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- GOOGLE_CLOUD_PROJECT: Your GCP project ID
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- VMAAS_SERVICE_ACCOUNT: Your service account email
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- VMAAS_SANDBOX_NAME: (Optional) Existing sandbox resource name for BYOS mode
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- VMAAS_SANDBOX_TEMPLATE_NAME: (Optional) Sandbox template name to create a new sandbox (mutually exclusive with VMAAS_SANDBOX_NAME)
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- VMAAS_SANDBOX_SNAPSHOT_NAME: (Optional) Sandbox snapshot name to create a new sandbox (mutually exclusive with VMAAS_SANDBOX_NAME)
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Usage:
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# Run via ADK web UI
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adk web contributing/samples/sandbox_computer_use
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# Run via main.py
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cd contributing/samples
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python -m sandbox_computer_use.main
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"""
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import os
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from dotenv import load_dotenv
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from google.adk import Agent
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from google.adk.integrations.vmaas import AgentEngineSandboxComputer
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from google.adk.tools.computer_use.computer_use_toolset import ComputerUseToolset
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# Load environment variables from .env file
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load_dotenv(override=True)
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# Configuration from environment variables
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PROJECT_ID = os.environ.get("GOOGLE_CLOUD_PROJECT")
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SERVICE_ACCOUNT = os.environ.get("VMAAS_SERVICE_ACCOUNT")
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# Optional: Use existing sandbox (BYOS mode)
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# Format: projects/{project}/locations/{location}/reasoningEngines/{id}/sandboxEnvironments/{id}
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SANDBOX_NAME = os.environ.get("SANDBOX_NAME") or os.environ.get(
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"VMAAS_SANDBOX_NAME"
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)
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SANDBOX_TEMPLATE_NAME = os.environ.get("VMAAS_SANDBOX_TEMPLATE_NAME")
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SANDBOX_SNAPSHOT_NAME = os.environ.get("VMAAS_SANDBOX_SNAPSHOT_NAME")
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# Create the sandbox computer
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sandbox_computer = AgentEngineSandboxComputer(
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project_id=PROJECT_ID,
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service_account_email=SERVICE_ACCOUNT,
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sandbox_name=SANDBOX_NAME,
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sandbox_template_name=SANDBOX_TEMPLATE_NAME,
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sandbox_snapshot_name=SANDBOX_SNAPSHOT_NAME,
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search_engine_url="https://www.google.com",
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)
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# Create agent with the computer use toolset
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root_agent = Agent(
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model="gemini-2.5-computer-use-preview-10-2025",
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name="sandbox_computer_use_agent",
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description=(
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"A computer use agent that operates a browser in a remote Vertex AI"
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" sandbox environment to complete user tasks."
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),
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instruction="""You are a computer use agent that can operate a web browser
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to help users complete tasks. You have access to browser controls including:
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- Navigation (go to URLs, back, forward, search)
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- Mouse actions (click, hover, scroll, drag and drop)
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- Keyboard input (type text, key combinations)
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- Screenshots (to see the current state)
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When given a task:
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1. Think about what steps are needed to accomplish it
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2. Take actions one at a time, observing the results
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3. If something doesn't work, try alternative approaches
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4. Report back when the task is complete or if you encounter issues
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Be careful with sensitive information and always respect website terms of service.
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""",
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tools=[ComputerUseToolset(computer=sandbox_computer)],
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)
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