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长期记忆允许你的智能体跨不同对话和会话存储及检索信息。与仅限于单个线程的短期记忆不同，长期记忆可以跨线程持久化，并可随时检索。 长期记忆基于 LangGraph 存储构建，将数据以 JSON 文档的形式按命名空间和键进行组织存储。

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使用方法

要为智能体添加长期记忆，请创建一个存储并将其传递给 create_agent： 
import { createAgent } from "langchain";
import { InMemoryStore } from "@langchain/langgraph";

// InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store 在生产环境中 use.
const store = new InMemoryStore();

const agent = createAgent({
  model: "google-genai:gemini-3.1-pro-preview",
  tools: [],
  store,
});
工具随后可以使用 runtime.store 参数从存储中读取和写入数据。请参阅在工具中读取长期记忆和从工具写入长期记忆了解示例。
要深入了解记忆类型（语义记忆、情景记忆、程序记忆）及写入记忆的策略，请参阅记忆概念指南。

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记忆存储

LangGraph 将长期记忆以 JSON 文档的形式存储在存储中。 每条记忆都按自定义的 namespace（类似文件夹）和唯一的 key（类似文件名）进行组织。命名空间通常包含用户或组织 ID 或其他标签，以便更方便地组织信息。 这种结构支持记忆的层级化组织。随后可以通过内容过滤器支持跨命名空间搜索。 
import { InMemoryStore } from "@langchain/langgraph";

const embed = (texts: string[]): number[][] => {
  // Replace with an actual embedding function or LangChain embeddings object
  return texts.map(() => [1.0, 2.0]);
};

// InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store 在生产环境中 use.
const store = new InMemoryStore({ index: { embed, dims: 2 } });
const userId = "my-user";
const applicationContext = "chitchat";
const namespace = [userId, applicationContext];

await store.put(namespace, "a-memory", {
  rules: [
    "User likes short, direct language",
    "User only speaks English & TypeScript",
  ],
  "my-key": "my-value",
});

// get the "memory" by ID
const item = await store.get(namespace, "a-memory");

// search for "memories" within this namespace, filtering on content equivalence, sorted by vector similarity
const items = await store.search(namespace, {
  filter: { "my-key": "my-value" },
  query: "language preferences",
});
有关记忆存储的更多信息，请参阅持久化指南。

​
在工具中读取长期记忆

import * as z from "zod";
import { createAgent, tool, type ToolRuntime } from "langchain";
import { InMemoryStore } from "@langchain/langgraph";

// InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store 在生产环境中.
const store = new InMemoryStore();
const contextSchema = z.object({
  userId: z.string(),
});

// Write sample data to the store using the put method
await store.put(
  ["users"], // Namespace to group related data together (users namespace for user data)
  "user_123", // Key within the namespace (user ID as key)
  {
    name: "John Smith",
    language: "English",
  }, // Data to store for the given user
);

const getUserInfo = tool(
  // Look up user info.
  async (_, runtime: ToolRuntime<unknown, z.infer<typeof contextSchema>>) => {
    // Access the store - same as that provided to `createAgent`
    const userId = runtime.context.userId;
    if (!userId) {
      throw new Error("userId is required");
    }
    // Retrieve data from store - returns StoreValue object with value and metadata
    const userInfo = await runtime.store.get(["users"], userId);
    return userInfo?.value ? JSON.stringify(userInfo.value) : "Unknown user";
  },
  {
    name: "getUserInfo",
    description: "Look up user info by userId from the store.",
    schema: z.object({}),
  },
);

const agent = createAgent({
  model: "google-genai:gemini-3.1-pro-preview",
  tools: [getUserInfo],
  contextSchema,
  // Pass store to agent - enables agent to access store when running tools
  store,
});

// Run the agent
const result = await agent.invoke(
  { messages: [{ role: "user", content: "look up user information" }] },
  { context: { userId: "user_123" } },
);

console.log(result.messages.at(-1)?.content);

/**
 * Outputs:
 * User Information:
 * - **Name:** John Smith
 * - **Language:** English
 */

​
从工具写入长期记忆

import * as z from "zod";
import { tool, createAgent, type ToolRuntime } from "langchain";
import { InMemoryStore } from "@langchain/langgraph";

// InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store 在生产环境中.
const store = new InMemoryStore();

const contextSchema = z.object({
  userId: z.string(),
});

// Schema defines the structure of user information for the LLM
const UserInfo = z.object({
  name: z.string(),
});

// Tool that allows agent to update user information (useful for chat applications)
const saveUserInfo = tool(
  async (
    userInfo: z.infer<typeof UserInfo>,
    runtime: ToolRuntime<unknown, z.infer<typeof contextSchema>>,
  ) => {
    const userId = runtime.context.userId;
    if (!userId) {
      throw new Error("userId is required");
    }
    // Store data in the store (namespace, key, data)
    await runtime.store.put(["users"], userId, userInfo);
    return "Successfully saved user info.";
  },
  {
    name: "save_user_info",
    description: "Save user info",
    schema: UserInfo,
  },
);

const agent = createAgent({
  model: "google-genai:gemini-3.1-pro-preview",
  tools: [saveUserInfo],
  contextSchema,
  store,
});

// Run the agent
await agent.invoke(
  { messages: [{ role: "user", content: "My name is John Smith" }] },
  // userId passed in context to identify whose information is being updated
  { context: { userId: "user_123" } },
);

// You can access the store directly to get the value
const result = await store.get(["users"], "user_123");
console.log(result?.value); // 输出: { name: "John Smith" }
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