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

// mongodb-client.ts
import { 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.ts
import 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.ts
import { 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;
}