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feat: add Google ADK tracing notebook example for Openlayer integration
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‎examples/tracing/google-adk/google_adk_tracing.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Google ADK Tracing with Openlayer\n",
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"\n",
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"This notebook demonstrates how to trace Google Agent Development Kit (ADK) agents with Openlayer.\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"Install the required packages:\n",
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"```bash\n",
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"pip install openlayer google-adk wrapt\n",
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"```\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"First, configure your Openlayer credentials and Google Cloud credentials:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# Openlayer configuration\n",
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"os.environ[\"OPENLAYER_API_KEY\"] = \"your-api-key-here\"\n",
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"os.environ[\"OPENLAYER_INFERENCE_PIPELINE_ID\"] = \"your-pipeline-id-here\"\n",
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"\n",
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"# Google AI API configuration (Option 1: Using Google AI Studio)\n",
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"# Get your API key from: https://aistudio.google.com/apikey\n",
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"os.environ[\"GOOGLE_API_KEY\"] = \"your-google-ai-api-key-here\"\n",
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"\n",
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"# Google Cloud Vertex AI configuration (Option 2: Using Google Cloud)\n",
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"# Uncomment these if you're using Vertex AI instead of Google AI\n",
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"# os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"path/to/your/service-account-key.json\"\n",
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"# os.environ[\"GOOGLE_CLOUD_PROJECT\"] = \"your-project-id\"\n",
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"# os.environ[\"GOOGLE_CLOUD_LOCATION\"] = \"us-central1\"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Enable Google ADK Tracing\n",
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"\n",
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"Enable tracing before creating any agents. This patches Google ADK globally to send traces to Openlayer:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from openlayer.lib.integrations import trace_google_adk\n",
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"\n",
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"# Enable tracing (must be called before creating agents)\n",
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"trace_google_adk()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example 1: Basic Agent with LLM Calls\n",
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"\n",
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"Create a simple agent that responds to user queries:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from google.genai import types\n",
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"from google.adk.agents import LlmAgent\n",
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"from google.adk.runners import Runner\n",
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"from google.adk.sessions import InMemorySessionService\n",
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"\n",
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"# Setup constants\n",
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"APP_NAME = \"openlayer_demo\"\n",
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"USER_ID = \"user_123\"\n",
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"SESSION_ID = \"session_123\"\n",
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"\n",
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"# Create session service (shared across examples)\n",
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"session_service = InMemorySessionService()\n",
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"\n",
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"# Create a basic agent\n",
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"agent = LlmAgent(\n",
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" model=\"gemini-2.0-flash-exp\",\n",
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" name=\"Assistant\",\n",
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" instruction=\"You are a helpful assistant. Provide concise and accurate responses.\"\n",
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")\n",
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"\n",
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"# Create runner\n",
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"runner = Runner(\n",
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" agent=agent,\n",
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" app_name=APP_NAME,\n",
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" session_service=session_service\n",
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")\n",
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"\n",
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"# Define async function to run the agent\n",
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"async def run_basic_agent():\n",
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" # Create session\n",
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" await session_service.create_session(\n",
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" app_name=APP_NAME,\n",
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" user_id=USER_ID,\n",
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" session_id=SESSION_ID\n",
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" )\n",
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" \n",
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" # Run the agent\n",
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" query = \"What is the capital of France?\"\n",
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" content = types.Content(role='user', parts=[types.Part(text=query)])\n",
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" \n",
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" # Process events and get response\n",
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" async for event in runner.run_async(user_id=USER_ID, session_id=SESSION_ID, new_message=content):\n",
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" if event.is_final_response() and event.content:\n",
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" final_answer = event.content.parts[0].text.strip()\n",
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"\n",
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"# Run the async function\n",
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"await run_basic_agent()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example 2: Agent with Tools/Functions\n",
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"\n",
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"Create an agent with custom tools that can be called during execution:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"\n",
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"# Define custom tools as regular Python functions\n",
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"def get_weather(city: str) -> str:\n",
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" \"\"\"Retrieves the current weather report for a specified city.\n",
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" \n",
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" Args:\n",
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" city: The name of the city for which to retrieve the weather report.\n",
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" \n",
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" Returns:\n",
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" str: Weather report or error message.\n",
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" \"\"\"\n",
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" if city.lower() == \"san francisco\":\n",
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" return \"The weather in San Francisco is sunny with a temperature of 72°F (22°C).\"\n",
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" else:\n",
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" return f\"Sorry, weather information for '{city}' is not available.\"\n",
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"\n",
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"def calculate(expression: str) -> str:\n",
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" \"\"\"Evaluates a mathematical expression.\n",
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" \n",
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" Args:\n",
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" expression: A mathematical expression to evaluate.\n",
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" \n",
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" Returns:\n",
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" str: Calculation result or error message.\n",
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" \"\"\"\n",
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" try:\n",
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" result = eval(expression)\n",
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" return f\"The result is {result}\"\n",
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" except Exception as e:\n",
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" return f\"Error: {str(e)}\"\n",
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"\n",
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"# Use different session IDs for tool agent\n",
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"TOOL_USER_ID = \"user_456\"\n",
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"TOOL_SESSION_ID = \"session_456\"\n",
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"\n",
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"# Create agent with tools (pass functions directly)\n",
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"tool_agent = LlmAgent(\n",
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" model=\"gemini-2.0-flash-exp\",\n",
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" name=\"ToolAgent\",\n",
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" instruction=\"You are a helpful assistant with access to weather and calculation tools. Use them when appropriate.\",\n",
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" tools=[get_weather, calculate]\n",
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")\n",
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"\n",
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"# Create runner for tool agent (reuse the session_service)\n",
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"tool_runner = Runner(\n",
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" agent=tool_agent,\n",
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" app_name=APP_NAME,\n",
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" session_service=session_service\n",
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")\n",
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"\n",
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"# Define async function to run the tool agent\n",
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"async def run_tool_agent():\n",
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" # Create session\n",
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" await session_service.create_session(\n",
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" app_name=APP_NAME,\n",
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" user_id=TOOL_USER_ID,\n",
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" session_id=TOOL_SESSION_ID\n",
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" )\n",
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" \n",
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" # Run the agent with a query that requires tool use\n",
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" query = \"What's the weather in San Francisco? Also, what is 15 * 24?\"\n",
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" content = types.Content(role='user', parts=[types.Part(text=query)])\n",
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" \n",
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" # Process events and get response\n",
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" async for event in tool_runner.run_async(\n",
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" user_id=TOOL_USER_ID,\n",
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" session_id=TOOL_SESSION_ID,\n",
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" new_message=content\n",
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" ):\n",
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" if event.is_final_response() and event.content:\n",
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" final_answer = event.content.parts[0].text.strip()\n",
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"\n",
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"# Run the async function\n",
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"await run_tool_agent()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## View Traces in Openlayer\n",
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"\n",
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"After running these examples, you can view the traces in your Openlayer dashboard:\n",
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"\n",
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"1. Go to https://app.openlayer.com\n",
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"2. Navigate to your inference pipeline\n",
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"3. View the traces tab to see:\n",
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" - Agent execution steps\n",
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" - LLM calls with token counts\n",
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" - Tool executions with inputs and outputs\n",
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" - Latency for each operation\n",
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" - Complete execution hierarchy\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Disable Tracing\n",
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"\n",
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"When you're done, you can disable tracing to restore ADK's default behavior:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from openlayer.lib.integrations import unpatch_google_adk\n",
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"\n",
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"# Disable tracing\n",
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"unpatch_google_adk()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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