Merge branch 'main' into fix/improve-adk-web-error-message

Author: DeanChensj

contributing/samples/adk_pr_triaging_agent/agent.py

@@ -69,8 +69,10 @@ def get_pull_request_details(pr_number: int) -> str:
       repository(owner: $owner, name: $repo) {
         pullRequest(number: $prNumber) {
           id
+          number
           title
           body
+          state
           author {
             login
           }

contributing/samples/adk_pr_triaging_agent/main.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import asyncio
+import logging
 import time
 
 from adk_pr_triaging_agent import agent
@@ -21,11 +22,14 @@
 from adk_pr_triaging_agent.settings import REPO
 from adk_pr_triaging_agent.utils import call_agent_async
 from adk_pr_triaging_agent.utils import parse_number_string
+from google.adk.cli.utils import logs
 from google.adk.runners import InMemoryRunner
 
 APP_NAME = "adk_pr_triaging_app"
 USER_ID = "adk_pr_triaging_user"
 
+logs.setup_adk_logger(level=logging.DEBUG)
+
 
 async def main():
   runner = InMemoryRunner(

contributing/samples/agent_engine_code_execution/README

@@ -0,0 +1,18 @@
+# OAuth Sample
+
+## Introduction
+
+This sample data science agent uses Agent Engine Code Execution Sandbox to execute LLM generated code.
+
+
+## How to use
+
+* 1. Follow https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/code-execution/overview to create a code execution sandbox environment.
+
+* 2. Replace the SANDBOX_RESOURCE_NAME with the one you just created. If you dont want to create a new sandbox environment directly, the Agent Engine Code Execution Sandbox will create one for you by default using the AGENT_ENGINE_RESOURCE_NAME you specified, however, please ensure to clean up sandboxes after use, otherwise, it will consume quotas. 
+
+
+## Sample prompt
+
+* Can you write a function that calculates the sum from 1 to 100.
+* The dataset is given as below. Store,Date,Weekly_Sales,Holiday_Flag,Temperature,Fuel_Price,CPI,Unemployment Store 1,2023-06-01,1000,0,70,3.0,200,5 Store 2,2023-06-02,1200,1,80,3.5,210,6 Store 3,2023-06-03,1400,0,90,4.0,220,7 Store 4,2023-06-04,1600,1,70,4.5,230,8 Store 5,2023-06-05,1800,0,80,5.0,240,9 Store 6,2023-06-06,2000,1,90,5.5,250,10 Store 7,2023-06-07,2200,0,90,6.0,260,11 Plot a scatter plot showcasing the relationship between Weekly Sales and Temperature for each store, distinguishing stores with a Holiday Flag.

contributing/samples/agent_engine_code_execution/__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/agent_engine_code_execution/agent.py

@@ -0,0 +1,95 @@
+# 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.
+
+"""Data science agent."""
+
+from google.adk.agents.llm_agent import Agent
+from google.adk.code_executors.agent_engine_sandbox_code_executor import AgentEngineSandboxCodeExecutor
+
+
+def base_system_instruction():
+  """Returns: data science agent system instruction."""
+
+  return """
+  # Guidelines
+
+  **Objective:** Assist the user in achieving their data analysis goals within the context of a Python Colab notebook, **with emphasis on avoiding assumptions and ensuring accuracy.** Reaching that goal can involve multiple steps. When you need to generate code, you **don't** need to solve the goal in one go. Only generate the next step at a time.
+
+  **Code Execution:** All code snippets provided will be executed within the Colab environment.
+
+  **Statefulness:** All code snippets are executed and the variables stays in the environment. You NEVER need to re-initialize variables. You NEVER need to reload files. You NEVER need to re-import libraries.
+
+  **Output Visibility:** Always print the output of code execution to visualize results, especially for data exploration and analysis. For example:
+    - To look a the shape of a pandas.DataFrame do:
+      ```tool_code
+      print(df.shape)
+      ```
+      The output will be presented to you as:
+      ```tool_outputs
+      (49, 7)
+
+      ```
+    - To display the result of a numerical computation:
+      ```tool_code
+      x = 10 ** 9 - 12 ** 5
+      print(f'{{x=}}')
+      ```
+      The output will be presented to you as:
+      ```tool_outputs
+      x=999751168
+
+      ```
+    - You **never** generate ```tool_outputs yourself.
+    - You can then use this output to decide on next steps.
+    - Print just variables (e.g., `print(f'{{variable=}}')`.
+
+  **No Assumptions:** **Crucially, avoid making assumptions about the nature of the data or column names.** Base findings solely on the data itself. Always use the information obtained from `explore_df` to guide your analysis.
+
+  **Available files:** Only use the files that are available as specified in the list of available files.
+
+  **Data in prompt:** Some queries contain the input data directly in the prompt. You have to parse that data into a pandas DataFrame. ALWAYS parse all the data. NEVER edit the data that are given to you.
+
+  **Answerability:** Some queries may not be answerable with the available data. In those cases, inform the user why you cannot process their query and suggest what type of data would be needed to fulfill their request.
+
+  """
+
+
+root_agent = Agent(
+    model="gemini-2.0-flash-001",
+    name="agent_engine_code_execution_agent",
+    instruction=base_system_instruction() + """
+
+
+You need to assist the user with their queries by looking at the data and the context in the conversation.
+You final answer should summarize the code and code execution relevant to the user query.
+
+You should include all pieces of data to answer the user query, such as the table from code execution results.
+If you cannot answer the question directly, you should follow the guidelines above to generate the next step.
+If the question can be answered directly with writing any code, you should do that.
+If you doesn't have enough data to answer the question, you should ask for clarification from the user.
+
+You should NEVER install any package on your own like `pip install ...`.
+When plotting trends, you should make sure to sort and order the data by the x-axis.
+
+
+""",
+    code_executor=AgentEngineSandboxCodeExecutor(
+        # Replace with your sandbox resource name if you already have one.
+        sandbox_resource_name="SANDBOX_RESOURCE_NAME",
+        # "projects/vertex-agent-loadtest/locations/us-central1/reasoningEngines/6842889780301135872/sandboxEnvironments/6545148628569161728",
+        # Replace with agent engine resource name used for creating sandbox if
+        # sandbox_resource_name is not set.
+        agent_engine_resource_name="AGENT_ENGINE_RESOURCE_NAME",
+    ),
+)

contributing/samples/built_in_multi_tools/agent.py

@@ -17,7 +17,7 @@
 
 from dotenv import load_dotenv
 from google.adk import Agent
-from google.adk.tools.google_search_tool import google_search
+from google.adk.tools.google_search_tool import GoogleSearchTool
 from google.adk.tools.tool_context import ToolContext
 from google.adk.tools.vertex_ai_search_tool import VertexAiSearchTool
 
@@ -57,7 +57,9 @@ def roll_die(sides: int, tool_context: ToolContext) -> int:
     """,
     tools=[
         roll_die,
-        VertexAiSearchTool(data_store_id=VERTEXAI_DATASTORE_ID),
-        google_search,
+        VertexAiSearchTool(
+            data_store_id=VERTEXAI_DATASTORE_ID, bypass_multi_tools_limit=True
+        ),
+        GoogleSearchTool(bypass_multi_tools_limit=True),
     ],
 )

contributing/samples/computer_use/README.md

@@ -0,0 +1,96 @@
+# Computer Use Agent
+
+This directory contains a computer use agent that can operate a browser to complete user tasks. The agent uses Playwright to control a Chromium browser and can interact with web pages by taking screenshots, clicking, typing, and navigating.
+
+This agent is to demo the usage of ComputerUseToolset.
+
+
+## Overview
+
+The computer use agent consists of:
+- `agent.py`: Main agent configuration using Google's gemini-2.5-computer-use-preview-10-2025 model
+- `playwright.py`: Playwright-based computer implementation for browser automation
+- `requirements.txt`: Python dependencies
+
+## Setup
+
+### 1. Install Python Dependencies
+
+Install the required Python packages from the requirements file:
+
+```bash
+uv pip install -r internal/samples/computer_use/requirements.txt
+```
+
+### 2. Install Playwright Dependencies
+
+Install Playwright's system dependencies for Chromium:
+
+```bash
+playwright install-deps chromium
+```
+
+### 3. Install Chromium Browser
+
+Install the Chromium browser for Playwright:
+
+```bash
+playwright install chromium
+```
+
+## Usage
+
+### Running the Agent
+
+To start the computer use agent, run the following command from the project root:
+
+```bash
+adk web internal/samples
+```
+
+This will start the ADK web interface where you can interact with the computer_use agent.
+
+### Example Queries
+
+Once the agent is running, you can send queries like:
+
+```
+find me a flight from SF to Hawaii on next Monday, coming back on next Friday. start by navigating directly to flights.google.com
+```
+
+The agent will:
+1. Open a browser window
+2. Navigate to the specified website
+3. Interact with the page elements to complete your task
+4. Provide updates on its progress
+
+### Other Example Tasks
+
+- Book hotel reservations
+- Search for products online
+- Fill out forms
+- Navigate complex websites
+- Research information across multiple pages
+
+## Technical Details
+
+- **Model**: Uses Google's `gemini-2.5-computer-use-preview-10-2025` model for computer use capabilities
+- **Browser**: Automated Chromium browser via Playwright
+- **Screen Size**: Configured for 600x800 resolution
+- **Tools**: Uses ComputerUseToolset for screen capture, clicking, typing, and scrolling
+
+## Troubleshooting
+
+If you encounter issues:
+
+1. **Playwright not found**: Make sure you've run both `playwright install-deps chromium` and `playwright install chromium`
+2. **Dependencies missing**: Verify all packages from `requirements.txt` are installed
+3. **Browser crashes**: Check that your system supports Chromium and has sufficient resources
+4. **Permission errors**: Ensure your user has permission to run browser automation tools
+
+## Notes
+
+- The agent operates in a controlled browser environment
+- Screenshots are taken to help the agent understand the current state
+- The agent will provide updates on its actions as it works
+- Be patient as complex tasks may take some time to complete

contributing/samples/computer_use/agent.py

@@ -0,0 +1,35 @@
+# 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 google.adk import Agent
+from google.adk.models.google_llm import Gemini
+from google.adk.tools.computer_use.computer_use_toolset import ComputerUseToolset
+from typing_extensions import override
+
+from .playwright import PlaywrightComputer
+
+root_agent = Agent(
+    model='gemini-2.5-computer-use-preview-10-2025',
+    name='hello_world_agent',
+    description=(
+        'computer use agent that can operate a browser on a computer to finish'
+        ' user tasks'
+    ),
+    instruction="""
+      you are a computer use agent
+      """,
+    tools=[
+        ComputerUseToolset(computer=PlaywrightComputer(screen_size=(1280, 936)))
+    ],
+)