长期记忆

import { createAgent } from "langchain";
import { InMemoryStore } from "@langchain/langgraph";

// InMemoryStore 将数据保存到内存字典中。在生产环境中使用时，请使用数据库支持的存储。
const store = new InMemoryStore();

const agent = createAgent({
  model: "claude-sonnet-4-6",
  tools: [],
  store,
});

import { InMemoryStore } from "@langchain/langgraph";

const embed = (texts: string[]): number[][] => {
  // 替换为实际的嵌入函数或 LangChain 嵌入对象
  return texts.map(() => [1.0, 2.0]);
};

// InMemoryStore 将数据保存到内存字典中。在生产环境中使用时，请使用数据库支持的存储。
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",
});

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

// 在此命名空间内搜索 "memories"，根据内容等效性进行过滤，并按向量相似度排序
const items = await store.search(namespace, {
  filter: { "my-key": "my-value" },
  query: "language preferences",
});

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

// InMemoryStore 将数据保存到内存字典中。在生产环境中使用数据库支持的存储。
const store = new InMemoryStore();
const contextSchema = z.object({
  userId: z.string(),
});

// 使用 put 方法将示例数据写入存储
await store.put(
  ["users"], // 命名空间，用于将相关数据分组在一起（用户数据使用 users 命名空间）
  "user_123", // 命名空间内的键（用户 ID 作为键）
  {
    name: "John Smith",
    language: "English",
  }, // 为给定用户存储的数据
);

const getUserInfo = tool(
  // 查找用户信息。
  async (_, runtime: ToolRuntime<unknown, z.infer<typeof contextSchema>>) => {
    // 访问存储 - 与提供给 `createAgent` 的存储相同
    const userId = runtime.context.userId;
    if (!userId) {
      throw new Error("userId is required");
    }
    // 从存储中检索数据 - 返回包含值和元数据的 StoreValue 对象
    const userInfo = await runtime.store.get(["users"], userId);
    return userInfo?.value ? JSON.stringify(userInfo.value) : "Unknown user";
  },
  {
    name: "getUserInfo",
    description: "通过 userId 从存储中查找用户信息。",
    schema: z.object({}),
  },
);

const agent = createAgent({
  model: "claude-sonnet-4-6",
  tools: [getUserInfo],
  contextSchema,
  // 将存储传递给代理 - 使代理在运行工具时能够访问存储
  store,
});

// 运行代理
const result = await agent.invoke(
  { messages: [{ role: "user", content: "查找用户信息" }] },
  { context: { userId: "user_123" } },
);

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

/**
 * 输出：
 * 用户信息：
 * - **姓名：** John Smith
 * - **语言：** English
 */

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: "claude-sonnet-4-6",
  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" }