Skip to main content

​
概览

LangChain agents 使用 LangGraph 持久化 来启用长期记忆。这是一个更高级的主题，需要了解 LangGraph 才能使用。

​
记忆存储

LangGraph 将长期记忆作为 JSON 文档存储在 store 中。 每个记忆都在一个自定义的 namespace（类似于文件夹）和一个独特的 key（类似于文件名）下组织。命名空间通常包含用户或组织 ID 或其他便于组织信息的标签。 这种结构支持记忆的分层组织。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 in production 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

A tool the agent can use to look up user information
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 in production.
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: "gpt-4.1-mini",
  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

Example of a tool that updates user information
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 in production.
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: "gpt-4.1-mini",
  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); // Output: { name: "John Smith" }
在 GitHub 上编辑此页面 或 提交问题。
将这些文档连接 到 Claude、VSCode 等，通过 MCP 获得实时解答。