"""LangGraph single-node graph template.

Returns a predefined response. Replace logic and configuration as needed.
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Dict, TypedDict

from langgraph.graph import StateGraph
from langgraph.runtime import Runtime


class Context(TypedDict):
    """Context parameters for the agent.

    Set these when creating assistants OR when invoking the graph.
    See: https://langchain-ai.github.io/langgraph/cloud/how-tos/configuration_cloud/
    """

    my_configurable_param: str


@dataclass
class State:
    """Input state for the agent.

    Defines the initial structure of incoming data.
    See: https://langchain-ai.github.io/langgraph/concepts/low_level/#state
    """

    changeme: str = "example"


async def call_model(state: State, runtime: Runtime[Context]) -> Dict[str, Any]:
    """Process input and returns output.

    Can use runtime context to alter behavior.
    """
    return {
        "changeme": "output from call_model. "
        f"Configured with {runtime.context.get('my_configurable_param')}"
    }


# Define the graph
graph = (
    StateGraph(State, context_schema=Context)
    .add_node(call_model)
    .add_edge("__start__", "call_model")
    .compile(name="New Graph")
)