"""Middleware for memory mechanism."""

from typing import override

from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime

from src.agents.memory.queue import get_memory_queue
from src.config.memory_config import get_memory_config


class MemoryMiddlewareState(AgentState):
    """Compatible with the `ThreadState` schema."""

    pass


class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
    """Middleware that queues conversation for memory update after agent execution.

    This middleware:
    1. After each agent execution, queues the conversation for memory update
    2. The queue uses debouncing to batch multiple updates together
    3. Memory is updated asynchronously via LLM summarization
    """

    state_schema = MemoryMiddlewareState

    @override
    def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
        """Queue conversation for memory update after agent completes.

        Args:
            state: The current agent state.
            runtime: The runtime context.

        Returns:
            None (no state changes needed from this middleware).
        """
        config = get_memory_config()
        if not config.enabled:
            return None

        # Get thread ID from runtime context
        thread_id = runtime.context.get("thread_id")
        if not thread_id:
            print("MemoryMiddleware: No thread_id in context, skipping memory update")
            return None

        # Get messages from state
        messages = state.get("messages", [])
        if not messages:
            print("MemoryMiddleware: No messages in state, skipping memory update")
            return None

        # Only queue if there's meaningful conversation
        # At minimum need one user message and one assistant response
        user_messages = [m for m in messages if getattr(m, "type", None) == "human"]
        assistant_messages = [m for m in messages if getattr(m, "type", None) == "ai"]

        if not user_messages or not assistant_messages:
            return None

        # Queue the conversation for memory update
        queue = get_memory_queue()
        queue.add(thread_id=thread_id, messages=list(messages))

        return None