Agent Development Kit (ADK) is an open-source Python framework that helps developers build, manage, and deploy multi-agent systems. It’s designed to be modular and flexible, making it easy to use for both simple and complex agent-based applications.

In this tutorial, we’ll create a simple AI agent using ADK. The agent will have access to two tools:

get_company_overview
get_earnings

Step 1: Setting up the dependencies

Google API Key

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

https://aistudio.google.com/apikey

AlphaVantage API Key

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

https://www.alphavantage.co/

“Get your free API key”

this direct link

Python Libraries

We only need one package:

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pip install google-adk

Step 2: Creating the Folder structure

Set up your project folder with the following structure:

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

init.py

Paste the following code into multi_agent/__init__.py:

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from . import agent

.env

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

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GOOGLE_GENAI_USE_VERTEXAI=FALSEGOOGLE_API_KEY="<YOUR_GOOGLE_API_KEY>"ALPHA_VANTAGE_API_KEY="<YOUR_ALPHA_VANTAGE_KEY"

Replace the placeholders with your actual API keys

agent.py

Paste the following code in the agent.py file:

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from google.adk.agents import Agentimport requestsimport osfrom typing import OptionalALPHA_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],)

In this script, we define a financial analysis agent using the Google Agent Development Kit (ADK). The agent is designed to answer user queries by accessing real-time financial data through the Alpha Vantage API. Specifically, it exposes two tools: get_company_overview and get_earnings. The get_company_overview function retrieves key company details such as sector, market capitalization, P/E ratio, and 52-week high/low values. The get_earnings function provides both annual and quarterly earnings data, including reported EPS and surprise percentages.To create the agent, we use the Agent class from the google.adk.agents module, giving it a name, a model (e.g., Gemini 2.0 Flash), a description, and an instruction prompt. The agent is then equipped with the two tools mentioned above, allowing it to respond to questions related to company financials.

Step 3: Running the Agent

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

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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 code:

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adk web

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