from praisonaiagents import Agent

agent = Agent(instructions="You are a helpful assistant")
response = agent.start("Hello!")

# Bool - enable with defaults
agent = Agent(instructions="...", memory=True)

# String preset
agent = Agent(instructions="...", output="verbose")

# URL scheme
agent = Agent(instructions="...", memory="redis://localhost:6379")

# Dict
agent = Agent(instructions="...", memory={"backend": "postgres", "user_id": "u1"})

# Array with overrides
agent = Agent(instructions="...", output=["verbose", {"stream": False}])

# Config instance
from praisonaiagents import MemoryConfig
agent = Agent(instructions="...", memory=MemoryConfig(backend="redis"))

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful AI assistant that provides concise, accurate answers."
)
response = agent.start("What is the capital of France?")
print(response)

from praisonaiagents import Agent, tool

@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"Weather in {city}: Sunny, 22°C"

agent = Agent(
    name="Weather Assistant",
    instructions="Help users check the weather",
    tools=[get_weather]
)
response = agent.start("What's the weather in Paris?")

from praisonaiagents import Agent

billing_agent = Agent(name="Billing", instructions="Handle billing inquiries")
tech_agent = Agent(name="Tech Support", instructions="Solve technical issues")

main_agent = Agent(
    name="Customer Service",
    instructions="Route customers to the right department",
    handoffs=[billing_agent, tech_agent]
)
response = main_agent.start("I have a billing question")

from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    instructions="Answer questions using the knowledge base",
    knowledge=["research_papers/", "data.pdf"]
)
response = agent.start("Summarize the key findings")

from praisonaiagents import Agent

agent = Agent(
    name="Story Writer",
    instructions="Write creative stories"
)

# Method 1: start() - streams automatically in terminal (TTY)
# Best for interactive/beginner use
for chunk in agent.start("Write a short story"):
    print(chunk, end="", flush=True)

# Method 2: iter_stream() - always streams, no display
# Best for app integration
full_response = ""
for chunk in agent.iter_stream("Write a short story"):
    full_response += chunk
    # Custom processing here

# Method 3: run() - silent, returns result directly
# Best for production/scripted use
result = agent.run("Write a short story")
print(result)

# Method 2: Use any preset + stream=True in start() (per-call control)
agent = Agent(
    name="Story Writer",
    instructions="Write creative stories",
    output="verbose"  # Any preset
)

for chunk in agent.start("Write a short story", stream=True):
    print(chunk, end="", flush=True)

# Method 3: Silent streaming (no verbose output, clean stream)
agent = Agent(
    name="Writer",
    instructions="You are concise",
    output="silent"
)

for chunk in agent.start("Write a haiku", stream=True):
    print(chunk, end="", flush=True)

# Method 4: Collect full response while streaming
chunks = []
for chunk in agent.start("List 3 tips"):
    chunks.append(chunk)
    print(chunk, end="", flush=True)
full_response = "".join(chunks)

from praisonaiagents import Agent

# Using Ollama
agent = Agent(
    name="Local Assistant",
    instructions="You are a helpful assistant",
    llm="ollama/llama3",
    base_url="http://localhost:11434"
)

# Using Anthropic
agent = Agent(
    name="Claude Assistant",
    instructions="You are a helpful assistant",
    llm="anthropic/claude-3-sonnet-20240229"
)

from praisonaiagents import Agent

agent = Agent(
    name="Advanced Agent",
    instructions="You are an advanced assistant",
    output="verbose",           # Output preset
    execution="thorough",       # Execution preset
    memory=True,                # Enable memory with defaults
    planning=True,              # Enable planning mode
    reflection=True,            # Enable self-reflection
)

import asyncio
from praisonaiagents import Agent

async def main():
    agent = Agent(
        name="AsyncAgent",
        instructions="Handle async operations efficiently"
    )
    
    # Async chat
    result = await agent.achat("Process this request")
    print(result)
    
    # Async start
    result = await agent.astart("Another request")
    print(result)

asyncio.run(main())