"""LangGraph Supervisor construction -- connects registry, agents, LLM, and persistence."""

from __future__ import annotations

from typing import TYPE_CHECKING

from langgraph.prebuilt import create_react_agent
from langgraph_supervisor import create_supervisor

from app.agents import get_tools_by_names

if TYPE_CHECKING:
    from langchain_core.language_models import BaseChatModel
    from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
    from langgraph.graph.state import CompiledStateGraph

    from app.registry import AgentRegistry

SUPERVISOR_PROMPT = (
    "You are a customer support supervisor. "
    "Route customer requests to the appropriate agent based on their description. "
    "For order status and tracking queries, use the order_lookup agent. "
    "For order modifications like cancellations, use the order_actions agent. "
    "For anything else, use the fallback agent."
)


def build_agent_nodes(
    registry: AgentRegistry,
    llm: BaseChatModel,
) -> list:
    """Create LangGraph react agent nodes from registry configurations."""
    agent_nodes = []
    for agent_config in registry.list_agents():
        tools = get_tools_by_names(agent_config.tools)

        system_prompt = (
            f"You are the {agent_config.name} agent. "
            f"Personality: {agent_config.personality.tone}. "
            f"{agent_config.description} "
            f"Permission level: {agent_config.permission}."
        )

        agent_node = create_react_agent(
            model=llm,
            tools=tools,
            name=agent_config.name,
            prompt=system_prompt,
        )
        agent_nodes.append(agent_node)

    return agent_nodes


def build_graph(
    registry: AgentRegistry,
    llm: BaseChatModel,
    checkpointer: AsyncPostgresSaver,
) -> CompiledStateGraph:
    """Build and compile the LangGraph supervisor graph."""
    agent_nodes = build_agent_nodes(registry, llm)

    workflow = create_supervisor(
        agent_nodes,
        model=llm,
        prompt=SUPERVISOR_PROMPT,
        output_mode="full_history",
    )

    return workflow.compile(checkpointer=checkpointer)