LangGraph 接入 AgentEngine 内置工具集

workflow = StateGraph(AgentState)
workflow.add_node("route_turn", route_turn)
workflow.add_node("prepare_custom_context", prepare_custom_context)
workflow.add_node("run_specialist", run_specialist)
workflow.add_node("finalize_answer", finalize_answer)
workflow.add_edge(START, "route_turn")  
workflow.add_edge("route_turn", "prepare_custom_context")  
workflow.add_edge("prepare_custom_context", "run_specialist")  
workflow.add_edge("run_specialist", "finalize_answer")  
workflow.add_edge("finalize_answer", END)  

root_agent = workflow.compile()

FOCUSED_TOOLSETS = ["focused", "agentengine_tool_dispatcher"]  
AGENTENGINE_TOOL_DESCRIPTIONS = describe_agentengine_tools(include=FOCUSED_TOOLSETS)  

def _build_tools() -> list[BaseTool]:
    agentengine_tools = [
        tool_item
        for tool_item in get_agentengine_tools(include=FOCUSED_TOOLSETS)
        if _tool_name(tool_item) != "component_status"
    ]
    return [*agentengine_tools, graph_status, component_status, release_risk_matrix]

def run_specialist(state: AgentState) -> dict[str, Any]:
    model = make_chat_model()
    specialist = create_agent(  
        model,
        TOOLS,
        system_prompt=SYSTEM_PROMPT,
        name="agentengine_toolsets_specialist",
    )
    route = state.get("route") or {}
    custom_context = state.get("custom_context") or {}
    messages = [  
        SystemMessage(
            content=(
                f"外层 LangGraph 路由: {route}. "
                f"自定义上下文: {custom_context}. "
                "优先使用 suggested_tools 中的工具;工具不可用时说明缺少哪些环境变量。"
            )
        ),
        *state["messages"],
    ]
    result = specialist.invoke({"messages": messages})
    return {"specialist_messages": result.get("messages", [])}