원격 MCP 서버 사용

from typing import Annotated, TypedDict
from langchain_core.messages import BaseMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode
from agent_engine_sdk_langgraph import App
app = App(app_name="my-mcp-agent")
class AgentState(TypedDict):
    messages: Annotated[list[BaseMessage], add_messages]
@app.entrypoint
def build_agent():
    llm = app.llm(ChatOpenAI(model="gpt-4o-mini"))
    tools = app.get_tools()
    llm_with_tools = llm.bind_tools(app.get_tool_schemas())
    def call_model(state: AgentState):
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    def should_continue(state: AgentState):
        last = state["messages"][-1]
        return "tools" if getattr(last, "tool_calls", None) else "end"
    graph = StateGraph(AgentState)
    graph.add_node("agent", call_model)
    graph.add_node("tools", ToolNode(tools))
    graph.add_edge(START, "agent")
    graph.add_conditional_edges(
        "agent", should_continue, {"tools": "tools", "end": END}
    )
    graph.add_edge("tools", "agent")
    return graph.compile(checkpointer=app.checkpointer())
app.run()

import "dotenv/config";
import { BaseMessage } from "@langchain/core/messages";
import { Annotation, END, START, StateGraph } from "@langchain/langgraph";
import { ToolNode } from "@langchain/langgraph/prebuilt";
import { ChatOpenAI } from "@langchain/openai";
import { App } from "@mongodb-js/agent-engine-sdk-langgraph";
export const app = new App({ appName: "my-mcp-agent" });
const AgentStateAnnotation = Annotation.Root({
  messages: Annotation<BaseMessage[]>({
    reducer: (left, right) => left.concat(right),
    default: () => [],
  }),
});
type AgentState = typeof AgentStateAnnotation.State;
export const buildAgent = app.entrypoint(() => {
  const llm = app.llm(new ChatOpenAI({ model: "gpt-4o-mini" }));
  const tools = [...app.getTools()];
  const llmWithTools = llm.bindTools([...app.getToolSchemas()]);
  const callModel = async (state: AgentState) => {
    const response = await llmWithTools.invoke(state.messages);
    return { messages: [response] };
  };
  const shouldContinue = (state: AgentState) => {
    const last = state.messages[state.messages.length - 1];
    const toolCalls = (last as { tool_calls?: unknown[] }).tool_calls;
    return Array.isArray(toolCalls) && toolCalls.length > 0
      ? "tools"
      : END;
  };
  return new StateGraph(AgentStateAnnotation)
    .addNode("agent", callModel)
    .addNode("tools", new ToolNode(tools))
    .addEdge(START, "agent")
    .addConditionalEdges("agent", shouldContinue, {
      tools: "tools",
      [END]: END,
    })
    .addEdge("tools", "agent")
    .compile({ checkpointer: app.checkpointer() });
});
app.run();