# your_agent.py
import spade
from spade_llm import LLMAgent, LLMProvider

async def main():
    provider = LLMProvider.create_openai(
        api_key="your-api-key",
        model="gpt-4o-mini"
    )
    
    agent = LLMAgent(
        jid="assistant@localhost",  # Connects to built-in server
        password="password",
        provider=provider,
        system_prompt="You are a helpful assistant"
    )
    
    await agent.start()

if __name__ == "__main__":
    spade.run(main())

# OpenAI
provider = LLMProvider.create_openai(api_key="key", model="gpt-4o-mini")

# Ollama (local)
provider = LLMProvider.create_ollama(model="llama3.1:8b")

# LM Studio (local)
provider = LLMProvider.create_lm_studio(model="local-model")

from spade_llm import LLMTool

async def get_weather(city: str) -> str:
    return f"Weather in {city}: 22°C, sunny"

weather_tool = LLMTool(
    name="get_weather",
    description="Get weather for a city",
    parameters={
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"]
    },
    func=get_weather
)

agent = LLMAgent(
    jid="assistant@localhost",  # Uses built-in server
    password="password",
    provider=provider,
    tools=[weather_tool]
)

from spade_llm.guardrails import KeywordGuardrail, GuardrailAction

# Block harmful content
safety_filter = KeywordGuardrail(
    name="safety_filter",
    blocked_keywords=["hack", "exploit", "malware"],
    action=GuardrailAction.BLOCK,
    blocked_message="I cannot help with potentially harmful activities."
)

agent = LLMAgent(
    jid="assistant@localhost",  # Uses built-in server
    password="password",
    provider=provider,
    input_guardrails=[safety_filter]  # Filter incoming messages
)

def router(msg, response, context):
    if "technical" in response.lower():
        return "tech-support@example.com"
    return str(msg.sender)

agent = LLMAgent(
    jid="router@localhost",  # Uses built-in server
    password="password",
    provider=provider,
    routing_function=router
)

from spade_llm import ChatAgent

# Create chat interface
chat_agent = ChatAgent(
    jid="human@localhost",  # Uses built-in server
    password="password",
    target_agent_jid="assistant@localhost"
)

await chat_agent.start()
await chat_agent.run_interactive()  # Start interactive chat

# Agent-based memory: Single shared memory per agent
agent = LLMAgent(
    jid="assistant@localhost",  # Uses built-in server
    password="password",
    provider=provider,
    agent_base_memory=(True, "./memory.db")  # Enabled with custom path
)

# Agent-thread memory: Separate memory per conversation
agent = LLMAgent(
    jid="assistant@localhost",  # Uses built-in server
    password="password",
    provider=provider,
    agent_thread_memory=(True, "./thread_memory.db")  # Enabled with custom path
)

# Default memory paths (if path not specified)
agent = LLMAgent(
    jid="assistant@localhost",  # Uses built-in server
    password="password",
    provider=provider,
    agent_base_memory=(True, None)  # Uses default path
)

from spade_llm.context import SmartWindowSizeContext, FixedWindowSizeContext

# Smart context: Dynamic window sizing based on content
smart_context = SmartWindowSizeContext(
    max_tokens=4000,
    include_system_prompt=True,
    preserve_last_k_messages=5
)

# Fixed context: Traditional sliding window
fixed_context = FixedWindowSizeContext(
    max_messages=20,
    include_system_prompt=True
)

agent = LLMAgent(
    jid="assistant@localhost",  # Uses built-in server
    password="password",
    provider=provider,
    context_manager=smart_context
)

from spade_llm import HumanInTheLoopTool

# Create tool for human consultation
human_tool = HumanInTheLoopTool(
    human_expert_jid="expert@localhost",  # Uses built-in server
    timeout=300.0  # 5 minutes
)

agent = LLMAgent(
    jid="assistant@localhost",  # Uses built-in server
    password="password",
    provider=provider,
    tools=[human_tool]  # Pass tools in constructor
)

# Start web interface for human expert
# python -m spade_llm.human_interface.web_server
# Open http://localhost:8080 and connect as expert