长期记忆

import * as z from "zod";
import { createAgent, tool } from "langchain";
import { InMemoryStore, type Runtime } 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: Runtime<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-4o-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
 */

import * as z from "zod";
import { tool, createAgent, type AgentRuntime } from "langchain";
import { InMemoryStore, type Runtime } 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: Runtime<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-4o-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" }