# Simple thing (2 lines) - Just works!
from connectonion import Agent
agent = Agent("assistant").input("Hello!")

# Complicated thing (still possible) - Production ready!
agent = Agent("production",
              model="gpt-5",                    # Latest models
              tools=[search, analyze, execute], # Your functions as tools
              system_prompt=company_prompt,     # Custom behavior
              max_iterations=10,                # Safety controls
              trust="prompt")                    # Multi-agent ready

import os  
from connectonion import Agent

# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "your-api-key-here"

# 1. Define tools as simple functions
def search(query: str) -> str:
    """Search for information."""
    return f"Found information about {query}"

def calculate(expression: str) -> float:
    """Perform mathematical calculations."""
    return eval(expression)  # Use safely in production

# 2. Create an agent with tools and personality
agent = Agent(
    name="my_assistant",
    system_prompt="You are a helpful and friendly assistant.",
    tools=[search, calculate]
    # max_iterations=10 is the default - agent will try up to 10 tool calls per task
)

# 3. Use the agent
result = agent.input("What is 25 * 4?")
print(result)  # Agent will use the calculate function

result = agent.input("Search for Python tutorials") 
print(result)  # Agent will use the search function

# 4. View behavior history (automatic!)
print(agent.history.summary())

def my_tool(param: str, optional_param: int = 10) -> str:
    """This docstring becomes the tool description."""
    return f"Processed {param} with value {optional_param}"

# Use it directly - no wrapping needed!
agent = Agent("assistant", tools=[my_tool])

# 1. Direct string prompt
agent = Agent(
    name="helpful_tutor",
    system_prompt="You are an enthusiastic teacher who loves to educate.",
    tools=[my_tools]
)

# 2. Load from file (any text file, no extension restrictions)
agent = Agent(
    name="support_agent",
    system_prompt="prompts/customer_support.md"  # Automatically loads file content
)

# 3. Using Path object
from pathlib import Path
agent = Agent(
    name="coder",
    system_prompt=Path("prompts") / "senior_developer.txt"
)

# 4. None for default prompt
agent = Agent("basic_agent")  # Uses default: "You are a helpful assistant..."

from connectonion.tools import Calculator, CurrentTime, ReadFile

agent = Agent("assistant", tools=[Calculator(), CurrentTime(), ReadFile()])

def calculate(expression: str) -> float:
    """Perform mathematical calculations."""
    return eval(expression)  # Use safely in production

def get_time(format: str = "%Y-%m-%d %H:%M:%S") -> str:
    """Get current date and time."""
    from datetime import datetime
    return datetime.now().strftime(format)

def read_file(filepath: str) -> str:
    """Read contents of a text file."""
    with open(filepath, 'r') as f:
        return f.read()

# Use them directly!
agent = Agent("assistant", tools=[calculate, get_time, read_file])

def weather(city: str) -> str:
    """Get current weather for a city."""
    # Your weather API logic here
    return f"Weather in {city}: Sunny, 22°C"

# That's it! Use it directly
agent = Agent(name="weather_agent", tools=[weather])

from connectonion.tools import Tool

class WeatherTool(Tool):
    def __init__(self):
        super().__init__(
            name="weather",
            description="Get current weather for a city"
        )
    
    def run(self, city: str) -> str:
        return f"Weather in {city}: Sunny, 22°C"
    
    def get_parameters_schema(self):
        return {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
        }

agent = Agent(name="weather_agent", tools=[WeatherTool()])

print(agent.history.summary())
# Agent: my_assistant
# Total tasks completed: 5
# Total tool calls: 8
# Total execution time: 12.34 seconds
# History file: ~/.connectonion/agents/my_assistant/behavior.json
# 
# Tool usage:
#   calculator: 5 calls
#   current_time: 3 calls

agent = Agent(name="test", api_key="your-api-key-here")

agent = Agent(name="test", model="gpt-5")  # Default: gpt-5-mini

# Default: 10 iterations (good for most tasks)
agent = Agent(name="assistant", tools=[...])

# Complex tasks may need more iterations
research_agent = Agent(
    name="researcher", 
    tools=[search, analyze, summarize, write_file],
    max_iterations=25  # Allow more steps for complex workflows
)

# Simple agents can use fewer iterations for safety
calculator = Agent(
    name="calc", 
    tools=[calculate],
    max_iterations=5  # Prevent runaway calculations
)

# Per-request override for specific complex tasks
result = agent.input(
    "Analyze all project files and generate comprehensive report",
    max_iterations=50  # Override for this specific task
)

result = agent.input(
    "Calculate 15 * 8, then tell me what time you did this calculation"
)
# Agent will use calculator first, then current_time tool

from connectonion.llm import LLM

class CustomLLM(LLM):
    def complete(self, messages, tools=None):
        # Your custom LLM implementation
        pass

agent = Agent(name="test", llm=CustomLLM())