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Documentation Index

Fetch the complete documentation index at: https://docs.pandaprobe.com/llms.txt

Use this file to discover all available pages before exploring further.

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Installation

pip install "pandaprobe[google-adk]"

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Setup

from pandaprobe.integrations.google_adk import GoogleADKAdapter

adapter = GoogleADKAdapter(
    session_id="conversation-123",
    user_id="user-abc",
    tags=["production"],
)
adapter.instrument()
We recommend using UUIDs for session_id and user_id so traces can be grouped reliably across runs.
Call instrument() once at application startup, before creating any ADK agents or runners.

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Usage

from google.adk.agents import LlmAgent
from google.adk.runners import Runner

agent = LlmAgent(
    name="my-agent",
    model="gemini-2.5-flash",
    instruction="You are a helpful assistant.",
)

runner = Runner(agent=agent, app_name="my-app", session_service=session_service)

async for event in runner.run_async(session_id=session.id, user_id="user-1", new_message=content):
    if event.content and event.content.parts:
        print(event.content.parts[0].text, end="")

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What gets traced

ADK ComponentSpan KindDescription
Runner.run_asyncCHAINRoot trace boundary
BaseAgent.run_asyncAGENTAgent execution with session messages as input
BaseLlmFlow._call_llm_asyncLLMLLM calls with model, params, thinking, token usage, TTFT
BaseTool.run_asyncTOOLBase tool execution
FunctionTool.run_asyncTOOLFunction tool execution
McpTool.run_asyncTOOLMCP tool execution

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Token usage mapping

ADK FieldPandaProbe Field
prompt_token_countprompt_tokens
candidates_token_countcompletion_tokens
total_token_counttotal_tokens
thoughts_token_countreasoning_tokens
cached_content_token_countcache_read_tokens

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Thinking mode

When using Gemini models with thinking enabled, thought parts are automatically separated from answer parts. Thinking content is stored in metadata as reasoning_summary.

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Example with tools

This example defines two function tools and runs an LlmAgent asynchronously using GoogleADKAdapter. We trace the agent via GoogleADKAdapter: 
import asyncio
import uuid

from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai.types import Content, Part

import pandaprobe
from pandaprobe.integrations.google_adk import GoogleADKAdapter


def get_weather(city: str) -> dict:
    """Get the current weather for a city."""
    weather_data = {
        "london": {"condition": "Cloudy", "temp": "15°C", "humidity": "70%"},
        "tokyo": {"condition": "Sunny", "temp": "28°C", "humidity": "45%"},
        "new york": {"condition": "Partly cloudy", "temp": "22°C", "humidity": "55%"},
        "paris": {"condition": "Rainy", "temp": "12°C", "humidity": "85%"},
    }
    return weather_data.get(city.lower(), {"error": f"No data for {city}"})


def get_population(city: str) -> dict:
    """Get the approximate population of a city."""
    populations = {
        "london": {"population": "8.8 million"},
        "tokyo": {"population": "13.9 million"},
        "new york": {"population": "8.3 million"},
        "paris": {"population": "2.2 million"},
    }
    return populations.get(city.lower(), {"error": f"No data for {city}"})


agent = LlmAgent(
    name="city_info_agent",
    model="gemini-3.1-flash-lite-preview",
    instruction=(
        "You are a helpful assistant with access to weather and population tools. "
        "Use the tools to answer questions about cities."
    ),
    tools=[get_weather, get_population],
)

APP_NAME = "tool_agent"
USER_ID = "user_1"
SESSION_ID = str(uuid.uuid4())


async def main():
    adapter = GoogleADKAdapter(
        session_id=SESSION_ID,
        user_id=USER_ID,
        tags=["tool-agent", "example"],
    )
    adapter.instrument()

    session_service = InMemorySessionService()
    await session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID)

    runner = Runner(agent=agent, app_name=APP_NAME, session_service=session_service)

    user_message = Content(
        role="user",
        parts=[Part(text="What's the weather like in London and what's its population?")],
    )

    async for event in runner.run_async(user_id=USER_ID, session_id=SESSION_ID, new_message=user_message):
        if event.is_final_response():
            text = " ".join(p.text for p in event.content.parts if p.text)
            print(f"Agent: {text}")

    pandaprobe.flush()
    pandaprobe.shutdown()
    print("\nTrace sent to PandaProbe backend.")


if __name__ == "__main__":
    asyncio.run(main())
This produces a trace with: CHAIN (runner) → AGENT (city_info_agent) → LLM (model call) → TOOL (get_weather) → TOOL (get_population) → LLM (final response).