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# test_simple_agent.py
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from dotenv import load_dotenv
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from hello_agents import HelloAgentsLLM, ToolRegistry
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from hello_agents.tools import CalculatorTool
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from my_simple_agent import MySimpleAgent
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# 加载环境变量
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load_dotenv()
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# 创建LLM实例
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llm = HelloAgentsLLM()
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# 测试1：基础对话Agent（无工具）
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print("=== 测试1：基础对话 ===")
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basic_agent = MySimpleAgent(
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name="基础助手",
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llm=llm,
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system_prompt="你是一个友好的AI助手，请用简洁明了的方式回答问题。"
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)
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response1 = basic_agent.run("你好，请介绍一下自己")
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print(f"基础对话响应: {response1}\n")
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# 测试2：带工具的Agent
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print("=== 测试2：工具增强对话 ===")
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tool_registry = ToolRegistry()
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calculator = CalculatorTool()
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tool_registry.register_tool(calculator)
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enhanced_agent = MySimpleAgent(
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name="增强助手",
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llm=llm,
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system_prompt="你是一个智能助手，可以使用工具来帮助用户。",
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tool_registry=tool_registry,
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enable_tool_calling=True
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)
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response2 = enhanced_agent.run("请帮我计算 15 * 8 + 32")
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print(f"工具增强响应: {response2}\n")
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# 测试3：流式响应
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print("=== 测试3：流式响应 ===")
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print("流式响应: ", end="")
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for chunk in basic_agent.stream_run("请解释什么是人工智能"):
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pass # 内容已在stream_run中实时打印
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# 测试4：动态添加工具
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print("\n=== 测试4：动态工具管理 ===")
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print(f"添加工具前: {basic_agent.has_tools()}")
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basic_agent.add_tool(calculator)
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print(f"添加工具后: {basic_agent.has_tools()}")
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print(f"可用工具: {basic_agent.list_tools()}")
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# 查看对话历史
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print(f"\n对话历史: {len(basic_agent.get_history())} 条消息")
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