MCP Multi-Agent Orchestration Server

from abc import ABC, abstractmethod from typing import Dict, List, Any, Optional from enum import Enum class LLMProvider(Enum): """Supported LLM providers""" OPENAI = "openai" CLAUDE = "claude" GROK = "grok" class LLMInterface(ABC): """Abstract interface for Language Model providers""" @abstractmethod async def chat_completion( self, messages: List[Dict[str, Any]], model: Optional[str] = None, temperature: Optional[float] = None, max_tokens: Optional[int] = None, response_format: Optional[Dict[str, Any]] = None, **kwargs ) -> Dict[str, Any]: """ Generic chat completion interface Args: messages: List of message dicts with 'role' and 'content' model: Specific model to use (override default) temperature: Sampling temperature (0.0 to 1.0) max_tokens: Maximum tokens in response response_format: Response format constraints (e.g., {"type": "json_object"}) **kwargs: Provider-specific parameters Returns: Dict with 'choices' containing response data """ pass @abstractmethod async def simple_completion( self, prompt: str, model: Optional[str] = None, temperature: Optional[float] = None, max_tokens: Optional[int] = None, response_format: Optional[Dict[str, Any]] = None, **kwargs ) -> str: """ Simple text completion interface Args: prompt: Text prompt model: Specific model to use temperature: Sampling temperature max_tokens: Maximum tokens in response response_format: Response format constraints **kwargs: Provider-specific parameters Returns: str: Response content """ pass @abstractmethod def get_provider_name(self) -> str: """Get the name of this LLM provider""" pass @abstractmethod def get_available_models(self) -> List[str]: """Get list of available models for this provider""" pass @abstractmethod def get_default_model(self) -> str: """Get the default model for this provider""" pass @abstractmethod async def validate_connection(self) -> bool: """Test if the provider connection is working""" pass @abstractmethod async def create_embedding( self, text: str, model: Optional[str] = None, **kwargs ) -> List[float]: """ Create text embeddings Args: text: Text to create embeddings for model: Specific embedding model to use **kwargs: Provider-specific parameters Returns: List[float]: Embedding vector """ pass