"""
Task Mode with Custom Tools
Demonstrates task mode where member agents use custom Python function tools.
Shows how agents with specialized tools can be orchestrated via tasks.
Run: .venvs/demo/bin/python cookbook/03_teams/task_mode/06_custom_tools.py
"""
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.team.mode import TeamMode
from agno.team.team import Team
from agno.tools import tool
# ---------------------------------------------------------------------------
# Tools
# ---------------------------------------------------------------------------
@tool
def calculate_compound_interest(
principal: float, annual_rate: float, years: int, compounds_per_year: int = 12
) -> str:
"""Calculate compound interest on an investment.
Args:
principal: Initial investment amount in dollars.
annual_rate: Annual interest rate as a percentage (e.g., 5.0 for 5%).
years: Number of years to compound.
compounds_per_year: How many times interest compounds per year. Defaults to 12 (monthly).
"""
rate = annual_rate / 100
amount = principal * (1 + rate / compounds_per_year) ** (compounds_per_year * years)
interest = amount - principal
return (
f"Investment: ${principal:,.2f}\n"
f"Rate: {annual_rate}% compounded {compounds_per_year}x/year\n"
f"Duration: {years} years\n"
f"Final value: ${amount:,.2f}\n"
f"Total interest earned: ${interest:,.2f}"
)
@tool
def calculate_monthly_payment(principal: float, annual_rate: float, years: int) -> str:
"""Calculate monthly loan payment using amortization formula.
Args:
principal: Loan amount in dollars.
annual_rate: Annual interest rate as a percentage (e.g., 5.0 for 5%).
years: Loan term in years.
"""
monthly_rate = (annual_rate / 100) / 12
num_payments = years * 12
if monthly_rate == 0:
payment = principal / num_payments
else:
payment = (
principal
* (monthly_rate * (1 + monthly_rate) ** num_payments)
/ ((1 + monthly_rate) ** num_payments - 1)
)
total_paid = payment * num_payments
total_interest = total_paid - principal
return (
f"Loan: ${principal:,.2f} at {annual_rate}% for {years} years\n"
f"Monthly payment: ${payment:,.2f}\n"
f"Total paid: ${total_paid:,.2f}\n"
f"Total interest: ${total_interest:,.2f}"
)
@tool
def assess_risk_score(
debt_to_income_ratio: float, credit_score: int, years_employed: int
) -> str:
"""Assess financial risk based on key metrics.
Args:
debt_to_income_ratio: Monthly debt payments divided by monthly income (e.g., 0.3 for 30%).
credit_score: Credit score (300-850).
years_employed: Years at current employer.
"""
score = 0
if credit_score >= 750:
score += 40
elif credit_score >= 700:
score += 30
elif credit_score >= 650:
score += 20
else:
score += 10
if debt_to_income_ratio <= 0.28:
score += 30
elif debt_to_income_ratio <= 0.36:
score += 20
else:
score += 10
if years_employed >= 5:
score += 30
elif years_employed >= 2:
score += 20
else:
score += 10
if score >= 80:
risk = "LOW"
elif score >= 60:
risk = "MODERATE"
else:
risk = "HIGH"
return (
f"Risk Assessment:\n"
f" Credit score: {credit_score} -> {'Excellent' if credit_score >= 750 else 'Good' if credit_score >= 700 else 'Fair' if credit_score >= 650 else 'Poor'}\n"
f" Debt-to-income: {debt_to_income_ratio:.0%} -> {'Good' if debt_to_income_ratio <= 0.28 else 'Acceptable' if debt_to_income_ratio <= 0.36 else 'High'}\n"
f" Employment: {years_employed} years -> {'Stable' if years_employed >= 5 else 'Moderate' if years_employed >= 2 else 'New'}\n"
f" Overall risk: {risk} (score: {score}/100)"
)
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
calculator = Agent(
name="Financial Calculator",
role="Performs financial calculations including interest, loans, and projections",
model=OpenAIResponses(id="gpt-5.2-mini"),
tools=[calculate_compound_interest, calculate_monthly_payment],
instructions=[
"You are a financial calculator.",
"Use the provided tools to perform precise calculations.",
"Always show the full calculation results.",
],
)
risk_assessor = Agent(
name="Risk Assessor",
role="Evaluates financial risk based on client metrics",
model=OpenAIResponses(id="gpt-5.2-mini"),
tools=[assess_risk_score],
instructions=[
"You are a financial risk assessor.",
"Use the risk assessment tool to evaluate client financial health.",
"Provide clear interpretation of the results.",
],
)
advisor = Agent(
name="Financial Advisor",
role="Provides financial advice and recommendations",
model=OpenAIResponses(id="gpt-5.2-mini"),
instructions=[
"You are a financial advisor.",
"Based on calculations and risk assessments, provide actionable advice.",
"Be specific with recommendations and explain your reasoning.",
],
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
finance_team = Team(
name="Financial Advisory Team",
mode=TeamMode.tasks,
model=OpenAIResponses(id="gpt-5.2"),
members=[calculator, risk_assessor, advisor],
instructions=[
"You are a financial advisory team leader.",
"For financial advice requests:",
"1. Use the Financial Calculator for any number crunching",
"2. Use the Risk Assessor to evaluate the client's risk profile",
"3. These two tasks are independent -- run them in parallel",
"4. Then have the Financial Advisor synthesize findings into recommendations",
"Always use the proper tools for calculations -- do not estimate.",
],
show_members_responses=True,
markdown=True,
max_iterations=10,
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
finance_team.print_response(
"I'm considering buying a house for $450,000 with a 20% down payment. "
"I can get a 30-year mortgage at 6.5%. My credit score is 720, "
"debt-to-income ratio is 0.25, and I've been at my job for 4 years. "
"I also want to know what $50,000 invested at 8% for 20 years would grow to. "
"Give me a complete financial picture and your recommendation."
)
