const agent = actor({
  // 内存状态，自动持久化
  state: {
    messages: [] as Message[],
  },
  // 长期运行的 Actor 进程
  run: async (c) => {
    // 从队列处理传入消息
    for await (const msg of c.queue.iter()) {
      c.state.messages.push({
        role: "user",
        content: msg.body.text,
      });

      // 调用 LLM
      const response = streamText({
        model: openai("gpt-5"),
        messages: c.state.messages,
      });

      // 实时事件广播给所有连接客户端
      for await (const delta of response.textStream) {
        c.broadcast("token", delta);
      }

      c.state.messages.push({
        role: "assistant",
        content: await response.text,
      });
    }
  },
});

import { actor, client } from "rivetkit";

// 定义 Actor
const myActor = actor({
  state: {
    count: 0,
  },
  run: async (c) => {
    for await (const msg of c.queue.iter()) {
      c.state.count += 1;
      console.log(`Count: ${c.state.count}`);
    }
  },
});

// 创建客户端
const cli = client({
  token: process.env.RIVET_TOKEN!,
});

// 获取或创建 Actor
const actor = cli.actor.getOrCreate("my-actor").connect();

// 发送消息
await actor.queue.send("increment");

const workflowActor = actor({
  state: { steps: [], result: null },
  run: async (c) => {
    for await (const step of c.workflow.steps()) {
      // 执行步骤
      const result = await processStep(step);
      // 检查点持久化
      c.state.steps.push({ ...step, result });
    }
  },
});

const agentActor = actor({
  state: {
    context: [] as Message[],
    tools: [] as Tool[],
  },
  run: async (c) => {
    for await (const task of c.queue.iter()) {
      // 调用 LLM
      const response = await llm.call(c.state.context);
      // 执行工具
      for (const toolCall of response.toolCalls) {
        const result = await executeTool(toolCall);
        c.state.context.push(result);
      }
    }
  },
});

const workspaceActor = actor({
  state: {
    sandboxes: new Map(),
    queue: [] as Task[],
  },
  run: async (c) => {
    // 协调多个沙箱
    for await (const task of c.queue.iter()) {
      const sandbox = await createSandbox();
      c.state.sandboxes.set(task.id, sandbox);
      // 处理完成后清理
      c.scheduler.delay(30 * 60_000, () => {
        sandbox.terminate();
        c.state.sandboxes.delete(task.id);
      });
    }
  },
});

const docActor = actor({
  state: {
    content: "",
    users: new Set<string>(),
  },
  run: async (c) => {
    for await (const change of c.queue.iter()) {
      c.state.content = applyChange(c.state.content, change);
      // 广播给所有用户
      c.broadcast("change", {
        content: c.state.content,
        user: change.user,
      });
    }
  },
});