并发代理#

在本节中，我们将探讨多个代理并发工作的使用。我们涵盖了三种主要模式：

单一消息与多个处理器
演示单个消息如何被订阅同一主题的多个代理同时处理。
多个消息与多个处理器
阐释如何根据主题将特定消息类型路由到专用代理。
直接消息传递
重点介绍代理之间以及从运行时到代理发送消息。

import asyncio
from dataclasses import dataclass

from autogen_core import (
    AgentId,
    ClosureAgent,
    ClosureContext,
    DefaultTopicId,
    MessageContext,
    RoutedAgent,
    SingleThreadedAgentRuntime,
    TopicId,
    TypeSubscription,
    default_subscription,
    message_handler,
    type_subscription,
)

@dataclass
class Task:
    task_id: str


@dataclass
class TaskResponse:
    task_id: str
    result: str

单一消息与多个处理器#

第一种模式展示了单个消息如何被多个代理同时处理。

每个 Processor 代理使用 default_subscription() 装饰器订阅默认主题。
当向默认主题发布消息时，所有注册的代理都将独立处理该消息。

注意

下面，我们使用 default_subscription() 装饰器订阅 Processor；还有一种不使用装饰器订阅代理的替代方法，如订阅和发布主题所示，这种方式使得同一个代理类可以订阅到不同的主题。

@default_subscription
class Processor(RoutedAgent):
    @message_handler
    async def on_task(self, message: Task, ctx: MessageContext) -> None:
        print(f"{self._description} starting task {message.task_id}")
        await asyncio.sleep(2)  # Simulate work
        print(f"{self._description} finished task {message.task_id}")

runtime = SingleThreadedAgentRuntime()

await Processor.register(runtime, "agent_1", lambda: Processor("Agent 1"))
await Processor.register(runtime, "agent_2", lambda: Processor("Agent 2"))

runtime.start()

await runtime.publish_message(Task(task_id="task-1"), topic_id=DefaultTopicId())

await runtime.stop_when_idle()

Agent 1 starting task task-1
Agent 2 starting task task-1
Agent 1 finished task task-1
Agent 2 finished task task-1

多个消息与多个处理器#

其次，这种模式演示了将不同类型的消息路由到特定处理器。

UrgentProcessor 订阅“紧急”主题
NormalProcessor 订阅“普通”主题

我们使用 type_subscription() 装饰器让代理订阅特定的主题类型。

TASK_RESULTS_TOPIC_TYPE = "task-results"
task_results_topic_id = TopicId(type=TASK_RESULTS_TOPIC_TYPE, source="default")


@type_subscription(topic_type="urgent")
class UrgentProcessor(RoutedAgent):
    @message_handler
    async def on_task(self, message: Task, ctx: MessageContext) -> None:
        print(f"Urgent processor starting task {message.task_id}")
        await asyncio.sleep(1)  # Simulate work
        print(f"Urgent processor finished task {message.task_id}")

        task_response = TaskResponse(task_id=message.task_id, result="Results by Urgent Processor")
        await self.publish_message(task_response, topic_id=task_results_topic_id)


@type_subscription(topic_type="normal")
class NormalProcessor(RoutedAgent):
    @message_handler
    async def on_task(self, message: Task, ctx: MessageContext) -> None:
        print(f"Normal processor starting task {message.task_id}")
        await asyncio.sleep(3)  # Simulate work
        print(f"Normal processor finished task {message.task_id}")

        task_response = TaskResponse(task_id=message.task_id, result="Results by Normal Processor")
        await self.publish_message(task_response, topic_id=task_results_topic_id)

注册代理后，我们可以向“紧急”和“普通”主题发布消息。

runtime = SingleThreadedAgentRuntime()

await UrgentProcessor.register(runtime, "urgent_processor", lambda: UrgentProcessor("Urgent Processor"))
await NormalProcessor.register(runtime, "normal_processor", lambda: NormalProcessor("Normal Processor"))

runtime.start()

await runtime.publish_message(Task(task_id="normal-1"), topic_id=TopicId(type="normal", source="default"))
await runtime.publish_message(Task(task_id="urgent-1"), topic_id=TopicId(type="urgent", source="default"))

await runtime.stop_when_idle()

Normal processor starting task normal-1
Urgent processor starting task urgent-1
Urgent processor finished task urgent-1
Normal processor finished task normal-1

收集结果#

在前面的示例中，我们依靠控制台打印来验证任务完成情况。然而，在实际应用中，我们通常希望以编程方式收集和处理结果。

为了收集这些消息，我们将使用 ClosureAgent。我们定义了一个专用主题 TASK_RESULTS_TOPIC_TYPE，UrgentProcessor 和 NormalProcessor 都将结果发布到此主题。然后 ClosureAgent 将处理来自此主题的消息。

queue = asyncio.Queue[TaskResponse]()


async def collect_result(_agent: ClosureContext, message: TaskResponse, ctx: MessageContext) -> None:
    await queue.put(message)


runtime.start()

CLOSURE_AGENT_TYPE = "collect_result_agent"
await ClosureAgent.register_closure(
    runtime,
    CLOSURE_AGENT_TYPE,
    collect_result,
    subscriptions=lambda: [TypeSubscription(topic_type=TASK_RESULTS_TOPIC_TYPE, agent_type=CLOSURE_AGENT_TYPE)],
)

await runtime.publish_message(Task(task_id="normal-1"), topic_id=TopicId(type="normal", source="default"))
await runtime.publish_message(Task(task_id="urgent-1"), topic_id=TopicId(type="urgent", source="default"))

await runtime.stop_when_idle()

Normal processor starting task normal-1
Urgent processor starting task urgent-1
Urgent processor finished task urgent-1
Normal processor finished task normal-1

while not queue.empty():
    print(await queue.get())

TaskResponse(task_id='urgent-1', result='Results by Urgent Processor')
TaskResponse(task_id='normal-1', result='Results by Normal Processor')

直接消息#

与前面的模式相反，此模式侧重于直接消息。这里我们演示了两种发送方式：

代理之间的直接消息传递
从运行时向特定代理发送消息

以下示例中需要考虑的事项：

消息使用 AgentId 进行寻址。
发送方可以预期接收来自目标代理的响应。
我们只注册 WorkerAgent 类一次；但是，我们向两个不同的 worker 发送任务。
- 如何实现？如代理生命周期所述，当使用 AgentId 传递消息时，运行时将获取实例，如果不存在则创建一个。在这种情况下，运行时在发送这两个消息时创建了两个 worker 实例。

class WorkerAgent(RoutedAgent):
    @message_handler
    async def on_task(self, message: Task, ctx: MessageContext) -> TaskResponse:
        print(f"{self.id} starting task {message.task_id}")
        await asyncio.sleep(2)  # Simulate work
        print(f"{self.id} finished task {message.task_id}")
        return TaskResponse(task_id=message.task_id, result=f"Results by {self.id}")


class DelegatorAgent(RoutedAgent):
    def __init__(self, description: str, worker_type: str):
        super().__init__(description)
        self.worker_instances = [AgentId(worker_type, f"{worker_type}-1"), AgentId(worker_type, f"{worker_type}-2")]

    @message_handler
    async def on_task(self, message: Task, ctx: MessageContext) -> TaskResponse:
        print(f"Delegator received task {message.task_id}.")

        subtask1 = Task(task_id="task-part-1")
        subtask2 = Task(task_id="task-part-2")

        worker1_result, worker2_result = await asyncio.gather(
            self.send_message(subtask1, self.worker_instances[0]), self.send_message(subtask2, self.worker_instances[1])
        )

        combined_result = f"Part 1: {worker1_result.result}, " f"Part 2: {worker2_result.result}"
        task_response = TaskResponse(task_id=message.task_id, result=combined_result)
        return task_response

runtime = SingleThreadedAgentRuntime()

await WorkerAgent.register(runtime, "worker", lambda: WorkerAgent("Worker Agent"))
await DelegatorAgent.register(runtime, "delegator", lambda: DelegatorAgent("Delegator Agent", "worker"))

runtime.start()

delegator = AgentId("delegator", "default")
response = await runtime.send_message(Task(task_id="main-task"), recipient=delegator)

print(f"Final result: {response.result}")
await runtime.stop_when_idle()

Delegator received task main-task.
worker/worker-1 starting task task-part-1
worker/worker-2 starting task task-part-2
worker/worker-1 finished task task-part-1
worker/worker-2 finished task task-part-2
Final result: Part 1: Results by worker/worker-1, Part 2: Results by worker/worker-2

额外资源#

如果您对并发处理的更多信息感兴趣，请查看代理混合模式，该模式严重依赖于并发代理。