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# my_simple_agent.py
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from typing import Optional, Iterator
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from hello_agents import SimpleAgent, HelloAgentsLLM, Config, Message
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import re
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class MySimpleAgent(SimpleAgent):
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"""
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重写的简单对话Agent
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展示如何基于框架基类构建自定义Agent
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"""
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def __init__(
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self,
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name: str,
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llm: HelloAgentsLLM,
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system_prompt: Optional[str] = None,
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config: Optional[Config] = None,
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tool_registry: Optional['ToolRegistry'] = None,
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enable_tool_calling: bool = True
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):
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super().__init__(name, llm, system_prompt, config)
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self.tool_registry = tool_registry
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self.enable_tool_calling = enable_tool_calling and tool_registry is not None
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print(f"✅ {name} 初始化完成，工具调用: {'启用' if self.enable_tool_calling else '禁用'}")
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def run(self, input_text: str, max_tool_iterations: int = 3, **kwargs) -> str:
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"""
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重写的运行方法 - 实现简单对话逻辑，支持可选工具调用
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"""
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print(f"🤖 {self.name} 正在处理: {input_text}")
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# 构建消息列表
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messages = []
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# 添加系统消息（可能包含工具信息）
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enhanced_system_prompt = self._get_enhanced_system_prompt()
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messages.append({"role": "system", "content": enhanced_system_prompt})
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# 添加历史消息
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for msg in self._history:
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messages.append({"role": msg.role, "content": msg.content})
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# 添加当前用户消息
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messages.append({"role": "user", "content": input_text})
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# 如果没有启用工具调用，使用简单对话逻辑
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if not self.enable_tool_calling:
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response = self.llm.invoke(messages, **kwargs)
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self.add_message(Message(input_text, "user"))
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self.add_message(Message(response, "assistant"))
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print(f"✅ {self.name} 响应完成")
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return response
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# 支持多轮工具调用的逻辑
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return self._run_with_tools(messages, input_text, max_tool_iterations, **kwargs)
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def _get_enhanced_system_prompt(self) -> str:
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"""构建增强的系统提示词，包含工具信息"""
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base_prompt = self.system_prompt or "你是一个有用的AI助手。"
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if not self.enable_tool_calling or not self.tool_registry:
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return base_prompt
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# 获取工具描述
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tools_description = self.tool_registry.get_tools_description()
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if not tools_description or tools_description == "暂无可用工具":
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return base_prompt
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tools_section = "\n\n## 可用工具\n"
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tools_section += "你可以使用以下工具来帮助回答问题：\n"
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tools_section += tools_description + "\n"
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tools_section += "\n## 工具调用格式\n"
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tools_section += "当需要使用工具时，请使用以下格式：\n"
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tools_section += "`[TOOL_CALL:{tool_name}:{parameters}]`\n"
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tools_section += "例如：`[TOOL_CALL:search:Python编程]` 或 `[TOOL_CALL:memory:recall=用户信息]`\n\n"
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tools_section += "工具调用结果会自动插入到对话中，然后你可以基于结果继续回答。\n"
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return base_prompt + tools_section
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def _run_with_tools(self, messages: list, input_text: str, max_tool_iterations: int, **kwargs) -> str:
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"""支持工具调用的运行逻辑"""
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current_iteration = 0
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final_response = ""
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while current_iteration < max_tool_iterations:
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# 调用LLM
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response = self.llm.invoke(messages, **kwargs)
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# 检查是否有工具调用
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tool_calls = self._parse_tool_calls(response)
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if tool_calls:
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print(f"🔧 检测到 {len(tool_calls)} 个工具调用")
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# 执行所有工具调用并收集结果
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tool_results = []
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clean_response = response
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for call in tool_calls:
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result = self._execute_tool_call(call['tool_name'], call['parameters'])
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tool_results.append(result)
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# 从响应中移除工具调用标记
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clean_response = clean_response.replace(call['original'], "")
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# 构建包含工具结果的消息
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messages.append({"role": "assistant", "content": clean_response})
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# 添加工具结果
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tool_results_text = "\n\n".join(tool_results)
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messages.append({"role": "user", "content": f"工具执行结果：\n{tool_results_text}\n\n请基于这些结果给出完整的回答。"})
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current_iteration += 1
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continue
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# 没有工具调用，这是最终回答
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final_response = response
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break
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# 如果超过最大迭代次数，获取最后一次回答
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if current_iteration >= max_tool_iterations and not final_response:
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final_response = self.llm.invoke(messages, **kwargs)
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# 保存到历史记录
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self.add_message(Message(input_text, "user"))
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self.add_message(Message(final_response, "assistant"))
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print(f"✅ {self.name} 响应完成")
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return final_response
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def _parse_tool_calls(self, text: str) -> list:
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"""解析文本中的工具调用"""
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pattern = r'\[TOOL_CALL:([^:]+):([^\]]+)\]'
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matches = re.findall(pattern, text)
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tool_calls = []
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for tool_name, parameters in matches:
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tool_calls.append({
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'tool_name': tool_name.strip(),
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'parameters': parameters.strip(),
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'original': f'[TOOL_CALL:{tool_name}:{parameters}]'
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})
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return tool_calls
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def _execute_tool_call(self, tool_name: str, parameters: str) -> str:
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"""执行工具调用"""
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if not self.tool_registry:
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return f"❌ 错误：未配置工具注册表"
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try:
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# 智能参数解析
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if tool_name == 'calculator':
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# 计算器工具直接传入表达式
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result = self.tool_registry.execute_tool(tool_name, parameters)
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else:
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# 其他工具使用智能参数解析
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param_dict = self._parse_tool_parameters(tool_name, parameters)
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tool = self.tool_registry.get_tool(tool_name)
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if not tool:
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return f"❌ 错误：未找到工具 '{tool_name}'"
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result = tool.run(param_dict)
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return f"🔧 工具 {tool_name} 执行结果：\n{result}"
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except Exception as e:
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return f"❌ 工具调用失败：{str(e)}"
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def _parse_tool_parameters(self, tool_name: str, parameters: str) -> dict:
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"""智能解析工具参数"""
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param_dict = {}
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if '=' in parameters:
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# 格式: key=value 或 action=search,query=Python
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if ',' in parameters:
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# 多个参数：action=search,query=Python,limit=3
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pairs = parameters.split(',')
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for pair in pairs:
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if '=' in pair:
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key, value = pair.split('=', 1)
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param_dict[key.strip()] = value.strip()
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else:
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# 单个参数：key=value
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key, value = parameters.split('=', 1)
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param_dict[key.strip()] = value.strip()
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else:
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# 直接传入参数，根据工具类型智能推断
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if tool_name == 'search':
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param_dict = {'query': parameters}
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elif tool_name == 'memory':
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param_dict = {'action': 'search', 'query': parameters}
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else:
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param_dict = {'input': parameters}
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return param_dict
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def stream_run(self, input_text: str, **kwargs) -> Iterator[str]:
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"""
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自定义的流式运行方法
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"""
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print(f"🌊 {self.name} 开始流式处理: {input_text}")
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messages = []
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if self.system_prompt:
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messages.append({"role": "system", "content": self.system_prompt})
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for msg in self._history:
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messages.append({"role": msg.role, "content": msg.content})
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messages.append({"role": "user", "content": input_text})
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# 流式调用LLM
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full_response = ""
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print("📝 实时响应: ", end="")
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for chunk in self.llm.stream_invoke(messages, **kwargs):
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full_response += chunk
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print(chunk, end="", flush=True)
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yield chunk
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print() # 换行
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# 保存完整对话到历史记录
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self.add_message(Message(input_text, "user"))
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self.add_message(Message(full_response, "assistant"))
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print(f"✅ {self.name} 流式响应完成")
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def add_tool(self, tool) -> None:
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"""添加工具到Agent（便利方法）"""
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if not self.tool_registry:
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from hello_agents import ToolRegistry
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self.tool_registry = ToolRegistry()
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self.enable_tool_calling = True
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self.tool_registry.register_tool(tool)
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print(f"🔧 工具 '{tool.name}' 已添加")
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def has_tools(self) -> bool:
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"""检查是否有可用工具"""
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return self.enable_tool_calling and self.tool_registry is not None
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def remove_tool(self, tool_name: str) -> bool:
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"""移除工具（便利方法）"""
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if self.tool_registry:
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self.tool_registry.unregister(tool_name)
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return True
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return False
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def list_tools(self) -> list:
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"""列出所有可用工具"""
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if self.tool_registry:
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return self.tool_registry.list_tools()
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return [] |