Adding reflection

from upsonic import Agent

# Enable reflection for financial analysis
agent = Agent(
    name="FinancialAnalyst",
    enable_thinking_tool=True,
    debug=True
)

# Enable reflection for all tasks
agent = Agent(
    name="RiskAnalyst",
    enable_thinking_tool=True
)

from upsonic import Task

# Override agent settings for specific task
task = Task(
    description="Analyze market volatility and provide investment recommendations",
    enable_thinking_tool=True  # Enable reflection for this specific task
)

result = agent.print_do(task)

# Agent with reflection enabled
agent = Agent(
    name="BankingAgent",
    enable_thinking_tool=True
)

# Simple task without reflection
simple_task = Task(
    description="What is the current interest rate?",
    enable_thinking_tool=False  # Disable reflection for simple queries
)

# Complex task with reflection
complex_task = Task(
    description="Analyze portfolio risk and recommend rebalancing strategy",
    enable_thinking_tool=True  # Keep reflection for complex analysis
)

from upsonic import Agent, Task

# Create agent with reflection capabilities
agent = Agent(
    name="RiskAssessmentAgent",
    enable_thinking_tool=True,
    debug=True
)

# Create a complex financial analysis task
task = Task(
    description="""
    Analyze the following investment scenario and provide comprehensive risk assessment:
    
    A client has $500K to invest across:
    1. 40% in technology stocks (high volatility)
    2. 30% in government bonds (low risk)
    3. 20% in real estate REITs (medium risk)
    4. 10% in cryptocurrency (very high volatility)
    
    Consider market conditions, diversification benefits, and provide specific recommendations
    for risk mitigation and portfolio optimization.
    """,
    enable_thinking_tool=True
)

# Execute with reflection
result = agent.print_do(task)

task = Task(
    description="""
    Review the following banking transaction for compliance:
    
    Transaction: $50,000 wire transfer from Business Account A to Personal Account B
    Customer: High-net-worth individual with $2M+ in assets
    Purpose: Real estate investment
    Timing: End of quarter
    
    Analyze for:
    1. AML (Anti-Money Laundering) compliance
    2. KYC (Know Your Customer) requirements
    3. Regulatory reporting obligations
    4. Risk assessment and documentation needs
    """,
    enable_thinking_tool=True
)

result = agent.print_do(task)

1. Agent analyzes the request and creates a plan
2. Orchestrator executes: get_market_data → analyze_risk → check_compliance
3. Agent synthesizes results into final recommendation

# Monitor performance with debug mode
agent = Agent(
    name="FinancialAnalyst",
    enable_thinking_tool=True,
    debug=True  # Shows detailed execution metrics
)