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from hello_agents.protocols import ANPDiscovery, register_service
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from hello_agents import SimpleAgent, HelloAgentsLLM
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from hello_agents.tools.builtin import ANPTool
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import random
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from dotenv import load_dotenv
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load_dotenv()
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llm = HelloAgentsLLM()
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# 1. 创建服务发现中心
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discovery = ANPDiscovery()
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# 2. 注册多个计算节点
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for i in range(10):
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register_service(
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discovery=discovery,
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service_id=f"compute_node_{i}",
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service_name=f"计算节点{i}",
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service_type="compute",
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capabilities=["data_processing", "ml_training"],
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endpoint=f"http://node{i}:8000",
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metadata={
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"load": random.uniform(0.1, 0.9),
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"cpu_cores": random.choice([4, 8, 16]),
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"memory_gb": random.choice([16, 32, 64]),
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"gpu": random.choice([True, False])
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}
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)
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print(f"✅ 注册了 {len(discovery.list_all_services())} 个计算节点")
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# 3. 创建任务调度Agent
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scheduler = SimpleAgent(
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name="任务调度器",
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llm=llm,
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system_prompt="""你是一个智能任务调度器，负责：
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1. 分析任务需求
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2. 选择最合适的计算节点
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3. 分配任务
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选择节点时考虑：负载、CPU核心数、内存、GPU等因素。
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使用 service_discovery 工具时，必须提供 action 参数：
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- 查看所有节点：{"action": "discover_services", "service_type": "compute"}
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- 获取网络统计：{"action": "get_stats"}"""
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)
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# 添加ANP工具
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anp_tool = ANPTool(
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name="service_discovery",
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description="服务发现工具，可以查找和选择计算节点",
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discovery=discovery
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)
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scheduler.add_tool(anp_tool)
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# 4. 智能任务分配
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def assign_task(task_description):
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print(f"\n任务：{task_description}")
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print("=" * 50)
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# 让Agent智能选择节点
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response = scheduler.run(f"""
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请为以下任务选择最合适的计算节点：
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{task_description}
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步骤：
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1. 使用 service_discovery 工具查看所有可用的计算节点（service_type="compute"）
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2. 分析每个节点的特点（负载、CPU核心数、内存、GPU等）
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3. 根据任务需求选择最合适的节点
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4. 说明选择理由
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请直接给出最终选择的节点ID和理由。
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""")
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print(response)
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print("=" * 50)
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# 测试不同类型的任务
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assign_task("训练一个大型深度学习模型，需要GPU支持")
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assign_task("处理大量文本数据，需要高内存")
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assign_task("运行轻量级数据分析任务")
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