Learn by Example

from aakit import Agent

# Define tools for accessing customer data
def get_customer_info(customer_id: str) -> str:
    """Retrieve customer information from database."""
    # Your database logic here
    return f"Customer {customer_id} info..."

def search_knowledge_base(query: str) -> str:
    """Search company knowledge base."""
    # Your search logic here
    return f"Found solutions for: {query}"

# Create support agent with tools and memory
support_agent = Agent(
    name="customer_support",
    instruction="""You are a helpful customer support agent.
    Be empathetic, professional, and solution-oriented.
    Use the available tools to help customers effectively.""",
    model="gpt-4",
    tools=[get_customer_info, search_knowledge_base],
    memory="redis",  # Persistent conversation memory
    reasoning="react"
)

# Handle customer inquiry with conversation context
with support_agent.conversation(title="Customer #123 Support") as chat:
    # First inquiry
    response1 = chat.send("I'm having trouble with my subscription billing")
    print(f"Agent: {response1}")
    
    # Follow-up with context
    response2 = chat.send("It says my card was declined")
    print(f"Agent: {response2}")  # Agent remembers the billing context
    
    # Save conversation for review
    chat.save("customer_123_support.json")