mop_system.py

# mop_system.py
import asyncio
import aiohttp
from typing import Dict, List, Any
from dataclasses import dataclass

@dataclass
class AgentConfig:
    endpoint: str
    model_name: str
    role: str
    temperature: float
    max_tokens: int

class BaseAgent:
    def __init__(self, config: AgentConfig):
        self.config = config
        self.conversation_history: List[Dict] = []

    async def generate_response(self, prompt: str, context: Dict = None) -> str:
        async with aiohttp.ClientSession() as session:
            payload = {
                "model": self.config.model_name,
                "messages": [
                    {"role": "system", "content": self.config.role},
                    {"role": "user", "content": prompt}
                ],
                "temperature": self.config.temperature,
                "max_tokens": self.config.max_tokens
            }
            
            if context:
                payload["messages"].insert(1, {
                    "role": "assistant",
                    "content": str(context)
                })

            async with session.post(self.config.endpoint, json=payload) as response:
                result = await response.json()
                return result["choices"][0]["message"]["content"]

class MemoryAgent(BaseAgent):
    def __init__(self, config: AgentConfig):
        super().__init__(config)
        self.memory_store = sqlite3.connect('memory.sqlite')

    async def process_memory(self, input_data: Dict) -> Dict:
        prompt = f"""
        Process this memory:
        Input: {input_data}
        
        Tasks:
        1. Analyze importance (0-1)
        2. Extract key concepts
        3. Identify relationships with existing memories
        4. Determine storage location (STM/LTM)
        
        Provide structured response.
        """
        
        response = await self.generate_response(prompt)
        return self.parse_memory_decision(response)

class EvolutionAgent(BaseAgent):
    async def evaluate_fitness(self, memory_item: Dict) -> float:
        prompt = f"""
        Evaluate this memory's fitness:
        Memory: {memory_item}
        
        Consider:
        1. Relevance to current knowledge
        2. Usage patterns
        3. Information density
        4. Connection strength
        
        Provide numerical score (0-1) with reasoning.
        """
        
        response = await self.generate_response(prompt)
        return self.parse_fitness_score(response)

class SupervisorAgent(BaseAgent):
    async def coordinate_agents(self, agent_states: Dict) -> Dict:
        prompt = f"""
        Current agent states:
        {agent_states}
        
        Tasks:
        1. Evaluate system health
        2. Identify optimization opportunities
        3. Suggest resource allocation
        4. Determine priority actions
        
        Provide coordination instructions.
        """
        
        response = await self.generate_response(prompt)
        return self.parse_supervision_decision(response)

class MOPOrchestrator:
    def __init__(self):
        self.agents = {
            "memory": MemoryAgent(AgentConfig(
                endpoint="http://localhost:1234/v1/chat/completions",
                model_name="phi-2",
                role="You are a memory management specialist...",
                temperature=0.3,
                max_tokens=512
            )),
            "evolution": EvolutionAgent(AgentConfig(
                endpoint="http://localhost:1235/v1/chat/completions",
                model_name="mistral-7b",
                role="You are an evolution and optimization specialist...",
                temperature=0.7,
                max_tokens=1024
            )),
            "supervisor": SupervisorAgent(AgentConfig(
                endpoint="http://localhost:1236/v1/chat/completions",
                model_name="llama2",
                role="You are a system supervisor and coordinator...",
                temperature=0.5,
                max_tokens=2048
            ))
        }
        self.message_queue = asyncio.Queue()

    async def process_input(self, input_data: Dict):
        # 1. Submit to Memory Agent
        memory_result = await self.agents["memory"].process_memory(input_data)
        
        # 2. Evolution Agent evaluates
        fitness_score = await self.agents["evolution"].evaluate_fitness(memory_result)
        
        # 3. Supervisor coordinates
        coordination = await self.agents["supervisor"].coordinate_agents({
            "memory_state": memory_result,
            "fitness_score": fitness_score
        })
        
        return self.compile_response(memory_result, fitness_score, coordination)