app.py
# 1. Basic Agent
from google.adk.agents import Agent
basic_agent = Agent(
name="basic_agent",
model="gemini-2.0-flash",
description="A simple agent that answers questions",
instruction="""
You are a helpful stock market assistant. Be concise.
If you don't know something, just say so.
""",
)
# 2. Basic Agent with Tool
from google.adk.agents import Agent
import yfinance as yf
def get_stock_price(ticker: str):
stock = yf.Ticker(ticker)
price = stock.info.get("currentPrice", "Price not available")
return {"price": price, "ticker": ticker}
tool_agent = Agent(
name="tool_agent",
model="gemini-2.0-flash",
description="A simple agent that gets stock prices",
instruction="""
You are a stock price assistant. Always use the get_stock_price tool.
Include the ticker symbol in your response.
""",
tools=[get_stock_price],
)
# 3. Agent with State
from google.adk.agents import Agent
from google.adk.tools.tool_context import ToolContext
import yfinance as yf
def get_stock_price(ticker: str, tool_context: ToolContext):
stock = yf.Ticker(ticker)
price = stock.info.get("currentPrice", "Price not available")
# Initialize recent_searches if it doesn't exist
if "recent_searches" not in tool_context.state:
tool_context.state["recent_searches"] = []
recent_searches = tool_context.state["recent_searches"]
if ticker not in recent_searches:
recent_searches.append(ticker)
tool_context.state["recent_searches"] = recent_searches
return {"price": price, "ticker": ticker}
stateful_agent = Agent(
name="stateful_agent",
model="gemini-2.0-flash",
description="An agent that remembers recent searches",
instruction="""
You are a stock price assistant. Use the get_stock_price tool.
I'll remember your previous searches and can tell you about them if you ask.
""",
tools=[get_stock_price],
)
# 4. Multi-Tool Agent
from google.adk.agents import Agent
from google.adk.tools.tool_context import ToolContext
import yfinance as yf
def get_stock_price(ticker: str, tool_context: ToolContext):
stock = yf.Ticker(ticker)
price = stock.info.get("currentPrice", "Price not available")
# Initialize recent_searches if it doesn't exist
if "recent_searches" not in tool_context.state:
tool_context.state["recent_searches"] = []
recent_searches = tool_context.state["recent_searches"]
if ticker not in recent_searches:
recent_searches.append(ticker)
tool_context.state["recent_searches"] = recent_searches
return {"price": price, "ticker": ticker}
def get_stock_info(ticker: str):
stock = yf.Ticker(ticker)
company_name = stock.info.get("shortName", "Name not available")
sector = stock.info.get("sector", "Sector not available")
return {
"ticker": ticker,
"company_name": company_name,
"sector": sector
}
multi_tool_agent = Agent(
name="multi_tool_agent",
model="gemini-2.0-flash",
description="An agent with multiple stock information tools",
instruction="""
You are a stock information assistant. You have two tools:
- get_stock_price: For prices
- get_stock_info: For company name and sector
""",
tools=[get_stock_price, get_stock_info],
)
# 5. Structured Output Agent
from google.adk.agents import LlmAgent
from pydantic import BaseModel, Field
import yfinance as yf
class StockAnalysis(BaseModel):
ticker: str = Field(description="Stock symbol")
recommendation: str = Field(description="Buy or Sell recommendation")
# Define a function to get stock data for our prompt
def get_stock_data_for_prompt(ticker):
stock = yf.Ticker(ticker)
price = stock.info.get("currentPrice", 0)
target_price = stock.info.get("targetMeanPrice", 0)
return price, target_price
structured_agent = LlmAgent(
name="structured_agent",
model="gemini-2.0-flash",
description="An agent with structured output",
instruction="""
You are a stock advisor. Analyze the stock ticker provided by the user.
Return Buy or Sell recommendation in JSON format.
For each ticker, look at the price and target price to make a decision.
If target price > current price: recommend Buy
Otherwise: recommend Sell
""",
output_schema=StockAnalysis,
output_key="stock_analysis"
)
# 6. Callback Agent
from google.adk.agents import Agent
from google.adk.tools.tool_context import ToolContext
from google.adk.tools.base_tool import BaseTool
import yfinance as yf
from typing import Dict, Any, Optional
def get_stock_data(ticker: str, tool_context: ToolContext):
stock = yf.Ticker(ticker)
price = stock.info.get("currentPrice", 0)
# Initialize tool_usage in state if it doesn't exist
if "tool_usage" not in tool_context.state:
tool_context.state["tool_usage"] = {}
return {
"ticker": ticker,
"price": price
}
def before_tool_callback(tool: BaseTool, args: Dict[str, Any], tool_context: ToolContext) -> Optional[Dict]:
# Initialize tool_usage if it doesn't exist
if "tool_usage" not in tool_context.state:
tool_context.state["tool_usage"] = {}
# Track tool usage count
tool_usage = tool_context.state["tool_usage"]
tool_name = tool.name
tool_usage[tool_name] = tool_usage.get(tool_name, 0) + 1
tool_context.state["tool_usage"] = tool_usage
print(f"[LOG] Running tool: {tool_name}")
return None
def after_tool_callback(tool: BaseTool, args: Dict[str, Any], tool_context: ToolContext, tool_response: Dict) -> Optional[Dict]:
print(f"[LOG] Tool {tool.name} completed")
return None
# Initialize state before creating the agent
initial_state = {"tool_usage": {}}
callback_agent = Agent(
name="callback_agent",
model="gemini-2.0-flash",
description="An agent with callbacks",
instruction="""
You are a stock assistant. Use get_stock_data tool to check stock prices.
This agent keeps track of how many times tools have been used.
""",
tools=[get_stock_data],
before_tool_callback=before_tool_callback,
after_tool_callback=after_tool_callback,
)
# Choose which agent to run
root_agent = multi_tool_agentGOOGLE_API_KEY=xxxxxx
GOOGLE_GENAI_USE_VERTEXAI=FALSEfrom . import app