# Traditional approach: Cross your fingers 🤞
system_prompt = "You are a helpful assistant. Please follow these 47 rules..."

# Parlant approach: Ensured compliance ✅
await agent.create_guideline(
    condition="Customer asks about refunds",
    action="Check order status first to see if eligible",
    tools=[check_order_status],
)

import parlant.sdk as p

@p.tool
async def get_weather(context: p.ToolContext, city: str) -> p.ToolResult:
    # Your weather API logic here
    return p.ToolResult(f"Sunny, 72°F in {city}")

@p.tool
async def get_datetime(context: p.ToolContext) -> p.ToolResult:
    from datetime import datetime
    return p.ToolResult(datetime.now())

async def main():
    async with p.Server() as server:
        agent = await server.create_agent(
            name="WeatherBot",
            description="Helpful weather assistant"
        )

        # Have the agent's context be updated on every response (though
        # update interval is customizable) using a context variable.
        await agent.create_variable(name="current-datetime", tool=get_datetime)

        # Control and guide agent behavior with natural language
        await agent.create_guideline(
            condition="User asks about weather",
            action="Get current weather and provide tips and suggestions",
            tools=[get_weather]
        )

        # Add other (reliably enforced) behavioral modeling elements
        # ...

        # 🎉 Test playground ready at http://localhost:8800
        # Integrate the official React widget into your app,
        # or follow the tutorial to build your own frontend!

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

from parlant.testing import Suite, InteractionBuilder
from parlant.testing.steps import AgentMessage, CustomerMessage

suite = Suite(server_url="http://localhost:8800/", agent_id="your_agent")

@suite.scenario
async def test_booking_flow():
    async with suite.session() as session:
        # Build conversation history
        history = (
            InteractionBuilder()
            .step(CustomerMessage("Man it's cold today"))
            .step(AgentMessage("Tell me about it, I'm freezing my nuts and bolts off."))
            .step(CustomerMessage("Where are you from? I'm from Boston"))
            .step(AgentMessage("What a dream! I'm stuck in a data center in San Fran..."))
            .build()
        )

        # Preload session with event history
        await session.add_events(history)

        # Send customer message
        response = await session.send("What's the temperature there today?")

        # Assert on agent response using LLM-as-a-Judge
        await response.should("provide weather details for San Francisco")