MongoDB Atlasで LingGraph.js エージェントに長期メモリを追加

// mongodb-client.tsimport { MongoClient } from "mongodb";import "dotenv/config";
export const client = new MongoClient(process.env.MONGODB_URI as string);
export async function connectClient() {  await client.connect();  await client.db("admin").command({ ping: 1 });  console.log("Pinged your deployment. You successfully connected to MongoDB!");}

// long-term-store.tsimport type { MongoClient } from "mongodb";import { MongoDBStore } from "@langchain/langgraph-checkpoint-mongodb";
const DB_NAME = "hr_database";const COLLECTION_NAME = "long_term_memory";
export function createMongoDBStore(client: MongoClient) {  const store = new MongoDBStore({    client,    dbName: DB_NAME,    collectionName: COLLECTION_NAME,  });
  return store;}

// agent-with-long-term-memory.tsimport { ChatOpenAI } from "@langchain/openai";import {  AIMessage,  BaseMessage,  HumanMessage,} from "@langchain/core/messages";import {  ChatPromptTemplate,  MessagesPlaceholder,} from "@langchain/core/prompts";import { StateGraph, Annotation } from "@langchain/langgraph";import { tool } from "@langchain/core/tools";import { ToolNode } from "@langchain/langgraph/prebuilt";import { MongoDBSaver } from "@langchain/langgraph-checkpoint-mongodb";import type { MongoClient } from "mongodb";import type { MongoDBStore } from "@langchain/langgraph-checkpoint-mongodb";import { putUserMemory, getUserMemories } from "./memory-api";
export async function callAgent(  client: MongoClient,  store: MongoDBStore,  query: string,  threadId: string,  userId: string,) {  const dbName = "hr_database";  const db = client.db(dbName);  const collection = db.collection("employees");
  // 1. Retrieve long-term memories for the user  const existingMemories = await getUserMemories(store, userId, "last_response");
  // 2. Define graph state  const GraphState = Annotation.Root({    messages: Annotation<BaseMessage[]>({      reducer: (x, y) => x.concat(y),    }),    userId: Annotation<string>(),    memories: Annotation<unknown | null>(),  });
  // 3. Define tools (for example, MongoDB Vector Search retriever)  const employeeLookupTool = tool(/* ...reuse from Build an AI Agent tutorial... */);  const tools = [employeeLookupTool];  const toolNode = new ToolNode<typeof GraphState.State>(tools);
  // 4. Configure the chat model  const model = new ChatOpenAI({    model: "gpt-5.4-mini",  }).bindTools(tools);
  // 5. Define the model node  async function callModel(state: typeof GraphState.State) {    const prompt = ChatPromptTemplate.fromMessages([      "system",      `You are a helpful HR chatbot agent.Use the provided tools and long-term memories to answer questions.Long-term memories (if any) are in the "memories" field.`,      new MessagesPlaceholder("messages"),    ]);
    const formatted = await prompt.formatMessages({      messages: state.messages,    });
    const result = await model.invoke(formatted);    return { messages: [result] };  }
  // 6. Define routing logic  function shouldContinue(state: typeof GraphState.State) {    const messages = state.messages;    const lastMessage = messages[messages.length - 1] as AIMessage;
    if (lastMessage.tool_calls?.length) {      return "tools";    }
    return "__end__";  }
  // 7. Build the workflow  const workflow = new StateGraph(GraphState)    .addNode("agent", callModel)    .addNode("tools", toolNode)    .addEdge("__start__", "agent")    .addConditionalEdges("agent", shouldContinue)    .addEdge("tools", "agent");
  // 8. Configure short-term memory (checkpointer)  const checkpointer = new MongoDBSaver({ client, dbName });
  const app = workflow.compile({    checkpointer,    store,  });
  // 9. Invoke the graph with both short-term and long-term memory  const finalState = await app.invoke(    {      messages: [new HumanMessage(query)],      userId,      memories: existingMemories,    },    {      recursionLimit: 15,      configurable: { thread_id: threadId },    },  );
  const last = finalState.messages[finalState.messages.length - 1];  const content = last.content;
  // 10. Optionally, update long-term memory with new facts  await putUserMemory(store, userId, "last_response", {    type: "fact",    data: { content },    updatedAt: new Date().toISOString(),  });
  return content;}