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Working with Google ADK

LangDB’s Google ADK integration provides end-to-end tracing for your ADK agent pipelines.

Checkout: https://app.langdb.ai/sharing/threads/8425e068-77de-4f41-8aa9-d1111fc7d2b7

Installation​

Enable end-to-end tracing for your Google ADK agents by installing the pylangdb client with the ADK feature flag:

pip install 'pylangdb[adk]'

Quick Start​

Set your environment variables before initializing running the script:

export LANGDB_API_KEY="<your_langdb_api_key>"
export LANGDB_PROJECT_ID="<your_langdb_project_id>"

Initialize LangDB before creating or running any ADK agents:

from pylangdb.adk import init
# Initialise LangDB
init()

# Then proceed with your normal ADK setup:
from google.adk.agents import Agent
# ...define and run agents...

Once initialized, LangDB automatically discovers all agents and sub-agents (including nested folders), wraps their key methods at runtime, and links sessions for full end-to-end tracing across your workflow as well.

Complete Google ADK Python Example​

Here's a full example of a Google ADK agent implementation that you can instrument with LangDB. This sample is based on the official Google ADK Quickstart.

Example code​

Check out the full sample on GitHub: https://github.com/langdb/langdb-samples/tree/main/examples/google-adk/multi-tool-agent

Setup Environment​

pip install google-adk litellm 'pylangdb[adk]'

Project Structure​

Create the following project structure:

parent_folder/
└── multi_tool_agent/
    ├── __init__.py
    ├── agent.py
    └── .env

init.py​

Create an __init__.py file in the multi_tool_agent folder:

from . import agent

.env​

Create .env file for your secrets

LANGDB_API_KEY="<your_langdb_api_key>"
LANGDB_PROJECT_ID="<your_langdb_project_id>"

agent.py​

Create an agent.py file with the following code:

# First initialize LangDB before defining any agents
from pylangdb.adk import init
init()

import datetime
from zoneinfo import ZoneInfo
from google.adk.agents import Agent

def get_weather(city: str) -> dict:
    if city.lower() != "new york":
        return {"status": "error", "error_message": f"Weather information for '{city}' is not available."}
    return {"status": "success", "report": "The weather in New York is sunny with a temperature of 25 degrees Celsius (77 degrees Fahrenheit)."}

def get_current_time(city: str) -> dict:
    if city.lower() != "new york":
        return {"status": "error", "error_message": f"Sorry, I don't have timezone information for {city}."}
    tz = ZoneInfo("America/New_York")
    now = datetime.datetime.now(tz)
    return {"status": "success", "report": f'The current time in {city} is {now.strftime("%Y-%m-%d %H:%M:%S %Z%z")}'}

root_agent = Agent(
    name="weather_time_agent",
    model="gemini-2.0-flash",
    description=("Agent to answer questions about the time and weather in a city." ),
    instruction=("You are a helpful agent who can answer user questions about the time and weather in a city."),
    tools=[get_weather, get_current_time],
)

Running Your Agent​

Navigate to the parent directory of your agent project and use the following commands:

adk web

Open the URL provided (usually http://localhost:8000) in your browser and select "multi_tool_agent" from the dropdown menu.

Once your agent is running, try these example queries to test its functionality:

Whats the weather in New York?

These queries will trigger the agent to use the functions we defined and provide responses based on the our agent workflow.

Traces on LangDB​

When you run queries against your ADK agent, LangDB automatically captures detailed traces of all agent interactions:

Next Steps: Advanced Google ADK Integration​

This guide covered the basics of integrating LangDB with Google ADK using a simple weather and time agent example. For more complex scenarios and advanced use cases, check out our comprehensive resources in Guides Section.