Merge branch 'main' into feat/container

Author: rohityan

contributing/samples/live_bidi_streaming_multi_agent/agent.py

@@ -30,11 +30,10 @@ def roll_die(sides: int) -> int:
 roll_agent = Agent(
     name="roll_agent",
     model=Gemini(
-        # see https://docs.cloud.google.com/vertex-ai/generative-ai/docs/migrate
-        # for vertex model names
-        model="gemini-live-2.5-flash-native-audio",  # vertex
-        # see https://ai.google.dev/gemini-api/docs/models for AIS model names
-        # model='gemini-2.5-flash-native-audio-latest',  # for AI studio
+        # Find supported models in Vertex here: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/live-api
+        model="gemini-live-2.5-flash-native-audio",  # Vertex
+        # Find supported models in Gemini API here: https://ai.google.dev/gemini-api/docs/models
+        # model='gemini-2.5-flash-native-audio-preview-12-2025',  # Gemini API
         speech_config=types.SpeechConfig(
             voice_config=types.VoiceConfig(
                 prebuilt_voice_config=types.PrebuiltVoiceConfig(
@@ -85,11 +84,10 @@ def check_prime(nums: list[int]) -> str:
 prime_agent = Agent(
     name="prime_agent",
     model=Gemini(
-        # see https://docs.cloud.google.com/vertex-ai/generative-ai/docs/migrate
-        # for vertex model names
-        model="gemini-live-2.5-flash-native-audio",  # vertex
-        # see https://ai.google.dev/gemini-api/docs/models for AIS model names
-        # model='gemini-2.5-flash-native-audio-latest',  # for AI studio
+        # Find supported models in Vertex here: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/live-api
+        model="gemini-live-2.5-flash-native-audio",  # Vertex
+        # Find supported models in Gemini API here: https://ai.google.dev/gemini-api/docs/models
+        # model='gemini-2.5-flash-native-audio-preview-12-2025',  # Gemini API
         speech_config=types.SpeechConfig(
             voice_config=types.VoiceConfig(
                 prebuilt_voice_config=types.PrebuiltVoiceConfig(
@@ -128,13 +126,11 @@ def get_current_weather(location: str):
 
 
 root_agent = Agent(
-    # find supported models here: https://google.github.io/adk-docs/get-started/streaming/quickstart-streaming/
     model=Gemini(
-        # see https://docs.cloud.google.com/vertex-ai/generative-ai/docs/migrate
-        # for vertex model names
-        model="gemini-live-2.5-flash-native-audio",  # vertex
-        # see https://ai.google.dev/gemini-api/docs/models for AIS model names
-        # model='gemini-2.5-flash-native-audio-latest',  # for AI studio
+        # Find supported models in Vertex here: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/live-api
+        model="gemini-live-2.5-flash-native-audio",  # Vertex
+        # Find supported models in Gemini API here: https://ai.google.dev/gemini-api/docs/models
+        # model='gemini-2.5-flash-native-audio-preview-12-2025',  # Gemini API
         speech_config=types.SpeechConfig(
             voice_config=types.VoiceConfig(
                 prebuilt_voice_config=types.PrebuiltVoiceConfig(

contributing/samples/live_bidi_streaming_parallel_tools_agent/agent.py

@@ -29,7 +29,10 @@ def turn_on_tv():
 
 
 root_agent = Agent(
-    model="gemini-live-2.5-flash-native-audio",
+    # Find supported models in Vertex here: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/live-api
+    model="gemini-live-2.5-flash-native-audio",  # Vertex
+    # Find supported models in Gemini API here: https://ai.google.dev/gemini-api/docs/models
+    # model='gemini-2.5-flash-native-audio-preview-12-2025',  # Gemini API
     name="Home_helper",
     instruction="Be polite and answer all user's questions.",
     tools=[turn_on_lights, turn_on_tv],

contributing/samples/live_bidi_streaming_single_agent/agent.py

@@ -65,11 +65,10 @@ async def check_prime(nums: list[int]) -> str:
 
 
 root_agent = Agent(
-    # see https://docs.cloud.google.com/vertex-ai/generative-ai/docs/migrate
-    # for vertex model names
-    model='gemini-live-2.5-flash-native-audio',  # vertex
-    # see https://ai.google.dev/gemini-api/docs/models for AIS model names
-    # model='gemini-2.5-flash-native-audio-latest',  # for AI studio
+    # Find supported models in Vertex here: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/live-api
+    model='gemini-live-2.5-flash-native-audio',  # Vertex
+    # Find supported models in Gemini API here: https://ai.google.dev/gemini-api/docs/models
+    # model='gemini-2.5-flash-native-audio-preview-12-2025',  # Gemini API
     name='roll_dice_agent',
     description=(
         'hello world agent that can roll a dice of 6 sides and check prime'

contributing/samples/live_bidi_streaming_tools_agent/agent.py

@@ -124,11 +124,10 @@ def stop_streaming(function_name: str):
 
 
 root_agent = Agent(
-    # see https://docs.cloud.google.com/vertex-ai/generative-ai/docs/migrate
-    # for vertex model names
-    model="gemini-live-2.5-flash-native-audio",  # vertex
-    # see https://ai.google.dev/gemini-api/docs/models for AIS model names
-    # model='gemini-2.5-flash-native-audio-latest',  # for AI studio
+    # Find supported models in Vertex here: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/live-api
+    model="gemini-live-2.5-flash-native-audio",  # Vertex
+    # Find supported models in Gemini API here: https://ai.google.dev/gemini-api/docs/models
+    # model='gemini-2.5-flash-native-audio-preview-12-2025',  # Gemini API
     name="video_streaming_agent",
     instruction="""
       You are a monitoring agent. You can do video monitoring and stock price monitoring

contributing/samples/live_tool_callbacks_agent/agent.py

@@ -217,9 +217,10 @@ async def after_tool_async_callback(
 
 # Create the agent with tool callbacks
 root_agent = Agent(
-    # find supported models here: https://google.github.io/adk-docs/get-started/streaming/quickstart-streaming/
-    model="gemini-2.0-flash-live-preview-04-09",  # for Vertex project
-    # model="gemini-live-2.5-flash-preview",  # for AI studio key
+    # Find supported models in Vertex here: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/live-api
+    model="gemini-live-2.5-flash-native-audio",  # Vertex
+    # Find supported models in Gemini API here: https://ai.google.dev/gemini-api/docs/models
+    # model='gemini-2.5-flash-native-audio-preview-12-2025',  # Gemini API
     name="tool_callbacks_agent",
     description=(
         "Live streaming agent that demonstrates tool callbacks functionality. "

contributing/samples/sandbox_computer_use/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import agent

contributing/samples/sandbox_computer_use/agent.py

@@ -0,0 +1,89 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Sample agent using Vertex AI Agent Engine Sandbox for computer use.
+
+This sample demonstrates how to use the AgentEngineSandboxComputer with ADK
+to create a computer use agent that operates in a remote sandbox environment.
+
+Prerequisites:
+  1. A GCP project with Agent Engine setup (https://docs.cloud.google.com/agent-builder/agent-engine/set-up)
+  2. A service account with roles/iam.serviceAccountTokenCreator permission
+  3. Environment variables in contributing/samples/.env:
+     - GOOGLE_CLOUD_PROJECT: Your GCP project ID
+     - VMAAS_SERVICE_ACCOUNT: Your service account email
+     - VMAAS_SANDBOX_NAME: (Optional) Existing sandbox resource name for BYOS mode
+
+Usage:
+  # Run via ADK web UI
+  adk web contributing/samples/sandbox_computer_use
+
+  # Run via main.py
+  cd contributing/samples
+  python -m sandbox_computer_use.main
+"""
+
+import os
+
+from dotenv import load_dotenv
+from google.adk import Agent
+from google.adk.integrations.vmaas import AgentEngineSandboxComputer
+from google.adk.tools.computer_use.computer_use_toolset import ComputerUseToolset
+
+# Load environment variables from .env file
+load_dotenv(override=True)
+
+# Configuration from environment variables
+PROJECT_ID = os.environ.get("GOOGLE_CLOUD_PROJECT")
+SERVICE_ACCOUNT = os.environ.get("VMAAS_SERVICE_ACCOUNT")
+
+# Optional: Use existing sandbox (BYOS mode)
+# Format: projects/{project}/locations/{location}/reasoningEngines/{id}/sandboxEnvironments/{id}
+SANDBOX_NAME = os.environ.get("SANDBOX_NAME") or os.environ.get(
+    "VMAAS_SANDBOX_NAME"
+)
+
+# Create the sandbox computer
+sandbox_computer = AgentEngineSandboxComputer(
+    project_id=PROJECT_ID,
+    service_account_email=SERVICE_ACCOUNT,
+    sandbox_name=SANDBOX_NAME,
+    search_engine_url="https://www.google.com",
+)
+
+# Create agent with the computer use toolset
+root_agent = Agent(
+    model="gemini-2.5-computer-use-preview-10-2025",
+    name="sandbox_computer_use_agent",
+    description=(
+        "A computer use agent that operates a browser in a remote Vertex AI"
+        " sandbox environment to complete user tasks."
+    ),
+    instruction="""You are a computer use agent that can operate a web browser
+to help users complete tasks. You have access to browser controls including:
+- Navigation (go to URLs, back, forward, search)
+- Mouse actions (click, hover, scroll, drag and drop)
+- Keyboard input (type text, key combinations)
+- Screenshots (to see the current state)
+
+When given a task:
+1. Think about what steps are needed to accomplish it
+2. Take actions one at a time, observing the results
+3. If something doesn't work, try alternative approaches
+4. Report back when the task is complete or if you encounter issues
+
+Be careful with sensitive information and always respect website terms of service.
+""",
+    tools=[ComputerUseToolset(computer=sandbox_computer)],
+)

contributing/samples/sandbox_computer_use/main.py

@@ -0,0 +1,162 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Main script to run the sandbox computer use agent.
+
+This script demonstrates how to run the sandbox computer use agent
+programmatically using the InMemoryRunner.
+
+Prerequisites:
+  1. Set environment variables:
+     - GOOGLE_CLOUD_PROJECT: Your GCP project ID
+     - VMAAS_SERVICE_ACCOUNT: Your service account email with
+       roles/iam.serviceAccountTokenCreator permission
+
+Usage:
+  cd contributing/samples
+  python -m sandbox_computer_use.main
+"""
+
+import asyncio
+import os
+import time
+
+from dotenv import load_dotenv
+from google.adk.cli.utils import logs
+from google.adk.runners import InMemoryRunner
+from google.adk.sessions.session import Session
+from google.genai import types
+
+# Import the agent module
+from . import agent
+
+load_dotenv(override=True)
+logs.log_to_tmp_folder()
+
+
+async def run_prompt(
+    runner: InMemoryRunner,
+    session: Session,
+    user_id: str,
+    message: str,
+) -> None:
+  """Run a single prompt and print the response.
+
+  Args:
+    runner: The agent runner.
+    session: The session to use.
+    user_id: The user ID.
+    message: The user message.
+  """
+  content = types.Content(
+      role="user", parts=[types.Part.from_text(text=message)]
+  )
+  print(f"\n** User says: {message}")
+  print("-" * 40)
+
+  async for event in runner.run_async(
+      user_id=user_id,
+      session_id=session.id,
+      new_message=content,
+  ):
+    if event.content and event.content.parts:
+      for part in event.content.parts:
+        if part.text:
+          print(f"** {event.author}: {part.text}")
+        elif hasattr(part, "inline_data") and part.inline_data:
+          # Screenshot received
+          print(f"** {event.author}: [Screenshot received]")
+
+
+async def main():
+  """Main function to run the sandbox computer use agent."""
+  # Validate environment
+  project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
+  service_account = os.environ.get("VMAAS_SERVICE_ACCOUNT")
+
+  if not project_id:
+    print("ERROR: GOOGLE_CLOUD_PROJECT environment variable is not set.")
+    print("Please set it to your GCP project ID.")
+    return
+
+  if not service_account:
+    print("ERROR: VMAAS_SERVICE_ACCOUNT environment variable is not set.")
+    print(
+        "Please set it to your service account email with"
+        " roles/iam.serviceAccountTokenCreator permission."
+    )
+    return
+
+  print("=" * 60)
+  print("Sandbox Computer Use Agent Demo")
+  print("=" * 60)
+  print(f"Project: {project_id}")
+  print(f"Service Account: {service_account}")
+  print("=" * 60)
+
+  app_name = "sandbox_computer_use_demo"
+  user_id = "demo_user"
+
+  # Create runner and session
+  runner = InMemoryRunner(
+      agent=agent.root_agent,
+      app_name=app_name,
+  )
+  session = await runner.session_service.create_session(
+      app_name=app_name, user_id=user_id
+  )
+
+  print(f"\nSession created: {session.id}")
+  print("\nStarting agent interaction...")
+
+  start_time = time.time()
+
+  # Example interaction: Navigate and describe
+  await run_prompt(
+      runner,
+      session,
+      user_id,
+      "Navigate to https://www.google.com and tell me what you see.",
+  )
+
+  # Example interaction: Search for something
+  await run_prompt(
+      runner,
+      session,
+      user_id,
+      "Search for 'Vertex AI Agent Engine' and tell me the first result.",
+  )
+
+  end_time = time.time()
+
+  print("\n" + "=" * 60)
+  print(f"Demo completed in {end_time - start_time:.2f} seconds")
+  print("=" * 60)
+
+  # Print session state to show sandbox info
+  session = await runner.session_service.get_session(
+      app_name=app_name, user_id=user_id, session_id=session.id
+  )
+  print("\nSession state (sandbox info):")
+  for key, value in session.state.items():
+    if key.startswith("_vmaas_"):
+      # Mask token for security
+      if "token" in key.lower():
+        print(f"  {key}: [REDACTED]")
+      else:
+        print(f"  {key}: {value}")
+
+
+if __name__ == "__main__":
+  asyncio.run(main())

contributing/samples/toolbox_agent/README.md

@@ -15,11 +15,11 @@ Before starting, ensure you have Python installed on your system.
 Run the following command to download and install the MCP Toolbox binary.
 
 > [!NOTE]
-> You can find the latest version on the [Releases page](https://github.com/googleapis/genai-toolbox/releases) and update the version in the URL below.
+> You can find the latest version on the [Releases page](https://github.com/googleapis/mcp-toolbox/releases) and update the version in the URL below.
 
 ```bash
 export OS="linux/amd64" # one of linux/amd64, darwin/arm64, darwin/amd64, or windows/amd64
-curl -O https://storage.googleapis.com/genai-toolbox/v0.31.0/$OS/toolbox
+curl -O https://storage.googleapis.com/mcp-toolbox-for-databases/v1.1.0/$OS/toolbox
 chmod +x toolbox
 ```