Build and run the idun-assistant copilot

from langchain_core.messages import SystemMessage, ToolMessage
from langgraph.graph import START, MessagesState, StateGraph
from langgraph.prebuilt import tools_condition

from idun_agent_engine import get_prompt
from idun_agent_engine.mcp.helpers import get_langchain_tools

_model = _make_model()  # env-driven Gemini/Anthropic/OpenAI selector
_prompt = get_prompt("system_prompt")
SYSTEM_PROMPT = _prompt.content if _prompt else ""


async def call_model(state: MessagesState) -> dict:
    tools = await get_langchain_tools()
    bound = _model.bind_tools(tools) if tools else _model
    messages = [SystemMessage(content=SYSTEM_PROMPT), *state["messages"]]
    response = await bound.ainvoke(messages)
    return {"messages": [response]}


async def call_tools(state: MessagesState) -> dict:
    tools = list(await get_langchain_tools() or [])
    by_name = {t.name: t for t in tools}
    out: list[ToolMessage] = []
    for call in getattr(state["messages"][-1], "tool_calls", []) or []:
        tool = by_name.get(call["name"])
        content = (
            str(await tool.ainvoke(call["args"]))
            if tool else f"Tool {call['name']!r} not available."
        )
        out.append(ToolMessage(content=content, tool_call_id=call["id"], name=call["name"]))
    return {"messages": out}


workflow = StateGraph(MessagesState)
workflow.add_node("call_model", call_model)
workflow.add_node("tools", call_tools)
workflow.add_edge(START, "call_model")
workflow.add_conditional_edges("call_model", tools_condition)
workflow.add_edge("tools", "call_model")