// 上一代做法，1.x 仍能用但不推荐
const memory = new BufferMemory({ returnMessages: true });
const chain = new ConversationChain({ llm: model, memory });

npm install langchain @langchain/anthropic @langchain/langgraph @langchain/core

// buffer-demo.ts
import { createAgent } from "langchain";
import { MemorySaver } from "@langchain/langgraph";
 
const agent = createAgent({
  model: "anthropic:claude-sonnet-4-6",
  tools: [],
  systemPrompt: "你是一个简洁的助手，会记住用户提过的信息。",
  checkpointer: new MemorySaver(),
});
 
const config = { configurable: { thread_id: "demo-thread-1" } };
 
await agent.invoke(
  { messages: [{ role: "user", content: "我叫张三，今年 28" }] },
  config
);
 
const r = await agent.invoke(
  { messages: [{ role: "user", content: "我多大了？" }] },
  config
);
 
console.log(r.messages.at(-1)?.content);
// → "你 28 岁。"

ANTHROPIC_API_KEY=sk-ant-xxx npx tsx buffer-demo.ts

thread "demo-thread-1"
  ├── checkpoint-1: [HumanMsg("我叫张三")]
  ├── checkpoint-2: [Human, AIMsg("你好张三")]
  ├── checkpoint-3: [..., HumanMsg("我多大了")]
  └── checkpoint-4: [..., AIMsg("你 28 岁")]

const state = await agent.getState(config);
console.log("当前消息条数:", state.values.messages.length);
console.log("下一步要执行的节点:", state.next);

for await (const snapshot of agent.getStateHistory(config)) {
  console.log(snapshot.config, snapshot.values.messages.length);
}

import { MemorySaver } from "@langchain/langgraph";
const checkpointer = new MemorySaver();

import { SqliteSaver } from "@langchain/langgraph-checkpoint-sqlite";
 
const checkpointer = SqliteSaver.fromConnString("./data/checkpoints.db");
 
const agent = createAgent({
  model: "anthropic:claude-sonnet-4-6",
  tools: [],
  checkpointer,
});

npm install @langchain/langgraph-checkpoint-sqlite

import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";
 
const checkpointer = PostgresSaver.fromConnString(
  process.env.DATABASE_URL!  // postgres://user:pass@host:5432/db
);
 
// 首次需要建表（幂等，重复跑没事）
await checkpointer.setup();
 
const agent = createAgent({
  model: "anthropic:claude-sonnet-4-6",
  tools: [],
  checkpointer,
});

npm install @langchain/langgraph-checkpoint-postgres pg

import { createAgent, createMiddleware } from "langchain";
import { MemorySaver } from "@langchain/langgraph";
import { trimMessages } from "@langchain/core/messages";
 
const trimmer = trimMessages({
  maxTokens: 4000,
  strategy: "last",
  tokenCounter: (msgs) => msgs.reduce((s, m) => s + (m.text?.length ?? 0) / 3, 0),
  includeSystem: true,
  startOn: "human",
});
 
const windowMiddleware = createMiddleware({
  name: "window",
  beforeModel: async (state) => {
    const trimmed = await trimmer.invoke(state.messages);
    return { messages: trimmed };
  },
});
 
const agent = createAgent({
  model: "anthropic:claude-sonnet-4-6",
  tools: [],
  middleware: [windowMiddleware],
  checkpointer: new MemorySaver(),
});

// 用户开了个新对话
const newConfig = { configurable: { thread_id: crypto.randomUUID() } };
await agent.invoke({ messages: [...] }, newConfig);

const snapshots = [];
for await (const s of agent.getStateHistory(config)) {
  snapshots.push(s);
}
const earlier = snapshots[5]; // 比如回到第 5 个 checkpoint
 
// 从这里岔出去，往一个新 thread 写后续状态
const branchConfig = {
  configurable: {
    thread_id: "branch-1",
    checkpoint_id: earlier.config.configurable!.checkpoint_id,
  },
};

// chat-bot.ts
import { createAgent, createMiddleware } from "langchain";
import { MemorySaver } from "@langchain/langgraph";
import { trimMessages } from "@langchain/core/messages";
import readline from "node:readline";
 
const trimmer = trimMessages({
  maxTokens: 3000,
  strategy: "last",
  tokenCounter: (msgs) =>
    msgs.reduce((s, m) => s + (typeof m.text === "string" ? m.text.length / 3 : 0), 0),
  includeSystem: true,
  startOn: "human",
});
 
const windowMw = createMiddleware({
  name: "window",
  beforeModel: async (state) => ({ messages: await trimmer.invoke(state.messages) }),
});
 
const agent = createAgent({
  model: "anthropic:claude-sonnet-4-6",
  tools: [],
  systemPrompt: "你是一个友好的中文助手。回答尽量简短。",
  middleware: [windowMw],
  checkpointer: new MemorySaver(),
});
 
// CLI 对话循环，所有对话都在同一个 thread
const config = { configurable: { thread_id: "cli-session" } };
 
const rl = readline.createInterface({ input: process.stdin, output: process.stdout });
const ask = (q: string) => new Promise<string>((res) => rl.question(q, res));
 
console.log("输入 quit 退出\n");
while (true) {
  const input = (await ask("你: ")).trim();
  if (input === "quit") break;
 
  const res = await agent.invoke(
    { messages: [{ role: "user", content: input }] },
    config
  );
 
  const last = res.messages.at(-1);
  console.log(`AI: ${last?.text ?? ""}\n`);
}
rl.close();

你: 我叫张三，在做 LangChain.js 项目
AI: 你好张三，做的什么类型的项目？
你: 一个客服 bot
AI: 客服场景挺合适，要不要先聊聊数据源？
你: 我刚才叫什么？
AI: 你叫张三。
你: quit

await agent.invoke({ messages: [...] }); // 错：没有 config