Step-by-Step Guide to Create an AI Agent with Google ADK

The Agent Development Kit (ADK) is an open-source Python framework that enables developers to build, manage, and deploy multi-agent systems. Its modular and flexible design makes it suitable for both simple and complex agent-based applications.

Creating a Simple AI Agent

In this tutorial, we will create a simple AI agent using ADK that has access to two tools: get_company_overview and get_earnings.

Step 1: Setting Up the Dependencies

Google API Key

To use Google’s AI services, you’ll need an API key:

• Visit Google AI Studio.
• Sign in and generate your API key.
• Copy and store it securely for later use.

AlphaVantage API Key

For accessing financial data, we will use the Alpha Vantage API:

• Go to Alpha Vantage.
• Click “Get your free API key” or visit the direct link.
• Enter your email and follow the instructions.
• Once you receive your API key, copy and save it securely for authentication.

Python Libraries

We need to install the following package:

pip install google-adk

Step 2: Creating the Folder Structure

Set up your project folder with the following structure:

parent_folder/
│
└───multi_agent/
    ├── __init__.py
    ├── agent.py
    └── .env

__init__.py

Paste the following code into multi_agent/__init__.py:

from . import agent

.env

Create a .env file inside the multi_agent folder and paste the following:

GOOGLE_GENAI_USE_VERTEXAI=FALSE
GOOGLE_API_KEY=""
ALPHA_VANTAGE_API_KEY=""

Replace the placeholders with your actual API keys.

agent.py

Paste the following code in the agent.py file:

from google.adk.agents import Agent
import requests
import os
from typing import Optional

ALPHA_VANTAGE_API_KEY = os.getenv("ALPHA_VANTAGE_API_KEY")

def get_company_overview(symbol: str) -> dict:
    """
    Get comprehensive company information and financial metrics
   
    Args:
        symbol: Stock ticker symbol (e.g., IBM)
   
    Returns:
        dict: Company overview data or error
    """
    if not ALPHA_VANTAGE_API_KEY:
        return {"status": "error", "error": "Missing API key"}
   
    base_url = "https://www.alphavantage.co/query"
    params = {
        "function": "OVERVIEW",
        "symbol": symbol,
        "apikey": ALPHA_VANTAGE_API_KEY
    }
   
    try:
        response = requests.get(base_url, params=params)
        response.raise_for_status()
        data = response.json()
       
        if "Error Message" in data:
            return {"status": "error", "error": data["Error Message"]}
           
        # Filter key metrics
        key_metrics = {
            "Description": data.get("Description"),
            "Sector": data.get("Sector"),
            "MarketCap": data.get("MarketCapitalization"),
            "PERatio": data.get("PERatio"),
            "ProfitMargin": data.get("ProfitMargin"),
            "52WeekHigh": data.get("52WeekHigh"),
            "52WeekLow": data.get("52WeekLow")
        }
       
        return {
            "status": "success",
            "symbol": symbol,
            "overview": key_metrics
        }
       
    except Exception as e:
        return {"status": "error", "error": str(e)}

def get_earnings(symbol: str) -> dict:
    """
    Get annual and quarterly earnings (EPS) data with analyst estimates and surprises
   
    Args:
        symbol: Stock ticker symbol (e.g., IBM)
   
    Returns:
        dict: Earnings data with estimates or error message
    """
    if not ALPHA_VANTAGE_API_KEY:
        return {"status": "error", "error": "Missing API key"}
   
    base_url = "https://www.alphavantage.co/query"
    params = {
        "function": "EARNINGS",
        "symbol": symbol,
        "apikey": ALPHA_VANTAGE_API_KEY
    }
   
    try:
        response = requests.get(base_url, params=params)
        response.raise_for_status()
        data = response.json()
       
        if "Error Message" in data:
            return {"status": "error", "error": data["Error Message"]}
           
        # Process annual and quarterly earnings
        annual_earnings = data.get("annualEarnings", [])[:5]  # Last 5 years
        quarterly_earnings = data.get("quarterlyEarnings", [])[:4]  # Last 4 quarters
       
        # Format surprise percentages
        for q in quarterly_earnings:
            if "surprisePercentage" in q:
                q["surprise"] = f"{q['surprisePercentage']}%"
       
        return {
            "status": "success",
            "symbol": symbol,
            "annual_earnings": annual_earnings,
            "quarterly_earnings": quarterly_earnings,
            "metrics": {
                "latest_eps": quarterly_earnings[0]["reportedEPS"] if quarterly_earnings else None
            }
        }
       
    except Exception as e:
        return {"status": "error", "error": str(e)}

root_agent = Agent(
    name="Financial_analyst_agent",
    model="gemini-2.0-flash",
    description=(
        "Agent to give company overviews with key financial metrics."
    ),
    instruction=(
        "You are a helpful AI agent that provides company overviews and earnings information."
    ),
    tools=[get_company_overview, get_earnings],
)

Step 3: Running the Agent

To run the agent, navigate to the parent directory of your agent project (e.g., using cd ..):

parent_folder/      ← Navigate to this directory in your terminal
│
└───multi_agent/
    ├── __init__.py     # Initializes the module
    ├── agent.py        # Contains the agent logic and tools
    └── .env            # Stores your API keys securely

After navigating, run the following command:

adk web

Open the URL provided (usually http://localhost:8000 or http://127.0.0.1:8000) directly in your browser. You will see a simple chat interface where you can interact with your agent using the input textbox.

Additionally, you can inspect each step of the agent’s reasoning by clicking on Actions. This allows you to view:

• The tools being called
• The inputs and outputs of each function
• The responses generated by the language model

GitHub Repository

You can find the entire code along with the folder structure at this GitHub link.