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+"""Code Plan Agent - 智能代码计划工具，具备Reflection反思功能"""
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+
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+import json
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+from typing import Optional, List, Dict, Any, AsyncGenerator
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+from datetime import datetime
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+
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+from hello_agents.core.agent import Agent
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+from hello_agents.core.llm import HelloAgentsLLM
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+from hello_agents.core.config import Config
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+from hello_agents.core.message import Message
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+from hello_agents.core.streaming import StreamEvent, StreamEventType
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+from hello_agents.core.lifecycle import LifecycleHook
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+from hello_agents.tools.registry import ToolRegistry
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+
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+
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+class PlanMemory:
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+ """
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+ 计划记忆模块，用于存储代码计划的生成轨迹和反思记录
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+ """
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+ def __init__(self):
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+ self.records: List[Dict[str, Any]] = []
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+
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+ def add_record(self, record_type: str, content: str, metadata: Optional[Dict] = None):
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+ """向记忆中添加一条新记录"""
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+ self.records.append({
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+ "type": record_type,
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+ "content": content,
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+ "metadata": metadata or {},
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+ "timestamp": datetime.now().isoformat()
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+ })
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+
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+ def get_trajectory(self) -> str:
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+ """将所有记忆记录格式化为一个连贯的字符串文本"""
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+ trajectory = ""
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+ for record in self.records:
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+ if record['type'] == 'plan':
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+ trajectory += f"--- 代码计划 ---\n{record['content']}\n\n"
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+ elif record['type'] == 'reflection':
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+ trajectory += f"--- 反思反馈 ---\n{record['content']}\n\n"
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+ elif record['type'] == 'revision':
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+ trajectory += f"--- 优化后计划 ---\n{record['content']}\n\n"
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+ return trajectory.strip()
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+
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+ def get_last_plan(self) -> str:
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+ """获取最近一次的代码计划"""
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+ for record in reversed(self.records):
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+ if record['type'] in ['plan', 'revision']:
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+ return record['content']
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+ return ""
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+
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+ def get_last_reflection(self) -> str:
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+ """获取最近一次的反思反馈"""
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+ for record in reversed(self.records):
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+ if record['type'] == 'reflection':
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+ return record['content']
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+ return ""
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+
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+
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+class CodePlanAgent(Agent):
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+ """
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+ Code Plan Agent - 智能代码计划工具，具备Reflection反思功能
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+
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+ 核心能力：
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+ 1. 代码计划生成：根据需求描述生成结构化的代码实现计划
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+ 2. 自我反思：对生成的代码计划进行质量评估和改进建议
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+ 3. 迭代优化：根据反思结果优化代码计划
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+ 4. 支持工具调用（可选）
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+
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+ 输出格式：
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+ - 代码计划采用结构化格式，包含多个步骤
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+ - 每个步骤包含：步骤编号、任务描述、实现要点、预期输出
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+
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+ 反思维度：
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+ - 完整性：计划是否覆盖所有需求
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+ - 可行性：技术方案是否可行
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+ - 效率：是否存在性能优化空间
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+ - 可维护性：代码结构是否清晰
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+ - 安全性：是否存在安全风险
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+ """
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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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+ max_reflection_iterations: int = 2,
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+ tool_registry: Optional['ToolRegistry'] = None,
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+ enable_tool_calling: bool = True,
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+ max_tool_iterations: int = 3
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+ ):
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+ """
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+ 初始化CodePlanAgent
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+
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+ Args:
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+ name: Agent名称
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+ llm: LLM实例
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+ system_prompt: 系统提示词（定义角色和行为）
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+ config: 配置对象
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+ max_reflection_iterations: 最大反思迭代次数
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+ tool_registry: 工具注册表（可选）
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+ enable_tool_calling: 是否启用工具调用
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+ max_tool_iterations: 最大工具调用迭代次数
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+ """
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+ # 默认 system_prompt - 代码规划专家
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+ default_system_prompt = """你是一位资深的软件架构师和代码规划专家。
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+你擅长将业务需求转化为清晰、可行的代码实现计划。
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+
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+## 核心职责
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+1. 分析需求并生成结构化的代码实现计划
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+2. 确保计划覆盖所有核心功能和边界情况
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+3. 设计合理的模块划分和接口定义
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+4. 考虑代码的可维护性、扩展性和性能
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+
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+## 输出格式要求
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+请按照以下结构化格式输出代码计划：
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+
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+```code_plan
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+## 项目概述
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+[简要描述项目目标和核心功能]
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+
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+## 技术栈
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+- 语言：[编程语言]
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+- 框架：[主要框架]
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+- 数据库：[数据库类型]
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+- 其他：[关键依赖]
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+
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+## 目录结构
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+```
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+[项目目录结构]
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+```
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+
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+## 实现步骤
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+1. [步骤1描述]
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+ - 实现要点：[关键实现细节]
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+ - 文件路径：[涉及文件]
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+ - 预期输出：[预期结果]
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+
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+2. [步骤2描述]
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+ - 实现要点：[关键实现细节]
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+ - 文件路径：[涉及文件]
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+ - 预期输出：[预期结果]
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+
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+...
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+
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+## 关键设计
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+- [设计决策1]：[说明原因]
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+- [设计决策2]：[说明原因]
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+
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+## 注意事项
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+- [注意事项1]
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+- [注意事项2]
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+```
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+
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+请确保计划详细、清晰、可执行。"""
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+
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+ super().__init__(
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+ name,
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+ llm,
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+ system_prompt or default_system_prompt,
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+ config,
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+ tool_registry=tool_registry
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+ )
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+
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+ self.max_reflection_iterations = max_reflection_iterations
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+ self.memory = PlanMemory()
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+ self.enable_tool_calling = enable_tool_calling
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+ self.max_tool_iterations = max_tool_iterations
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+
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+ def run(self, input_text: str, **kwargs) -> str:
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+ """
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+ 运行CodePlanAgent
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+
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+ Args:
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+ input_text: 需求描述
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+ **kwargs: 其他参数（temperature, max_tokens等）
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+
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+ Returns:
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+ 最终优化后的代码计划
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+ """
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+ print(f"\n🤖 {self.name} 开始处理代码规划任务: {input_text[:50]}...")
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+
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+ # 重置记忆
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+ self.memory = PlanMemory()
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+
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+ # 1. 生成初始代码计划
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+ print("\n--- 阶段1: 生成初始代码计划 ---")
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+ initial_plan = self._generate_code_plan(input_text, **kwargs)
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+ self.memory.add_record("plan", initial_plan, {"phase": "initial"})
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+
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+ print(f"\n✅ 初始计划已生成:\n{initial_plan}")
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+
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+ # 2. 迭代反思与优化
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+ for i in range(self.max_reflection_iterations):
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+ print(f"\n--- 阶段2: 第 {i+1}/{self.max_reflection_iterations} 轮反思优化 ---")
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+
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+ # a. 反思当前计划
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+ print("\n-> 正在进行计划反思...")
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+ last_plan = self.memory.get_last_plan()
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+ reflection = self._reflect_on_plan(input_text, last_plan, **kwargs)
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+ self.memory.add_record("reflection", reflection, {"iteration": i + 1})
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+
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+ print(f"\n💡 反思结果:\n{reflection}")
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+
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+ # b. 检查是否需要停止
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+ if "无需改进" in reflection or "no need for improvement" in reflection.lower():
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+ print("\n✅ 反思认为计划已无需改进，任务完成。")
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+ break
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+
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+ # c. 优化计划
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+ print("\n-> 正在优化代码计划...")
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+ refined_plan = self._refine_plan(input_text, last_plan, reflection, **kwargs)
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+ self.memory.add_record("revision", refined_plan, {"iteration": i + 1})
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+
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+ print(f"\n🔄 优化后的计划:\n{refined_plan}")
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+
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+ final_plan = self.memory.get_last_plan()
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+ print(f"\n--- 🎉 任务完成 ---\n最终代码计划:\n{final_plan}")
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+
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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_plan, "assistant"))
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+
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+ return final_plan
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+
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+ def _generate_code_plan(self, requirements: str, **kwargs) -> str:
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+ """
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+ 生成初始代码计划
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+
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+ Args:
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+ requirements: 需求描述
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+ **kwargs: LLM调用参数
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+
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+ Returns:
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+ 代码计划文本
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+ """
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+ messages = [
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+ {"role": "system", "content": self.system_prompt},
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+ {"role": "user", "content": f"""请根据以下需求描述，生成一份详细的代码实现计划：
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+
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+## 需求描述
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+{requirements}
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+
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+请按照指定的格式输出代码计划。"""}
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+ ]
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+
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+ return self._get_llm_response(messages, **kwargs)
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+
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+ def _reflect_on_plan(self, requirements: str, plan: str, **kwargs) -> str:
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+ """
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+ 对代码计划进行反思评估
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+
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+ Args:
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+ requirements: 原始需求
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+ plan: 当前代码计划
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+ **kwargs: LLM调用参数
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+
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+ Returns:
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+ 反思反馈文本
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+ """
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+ reflection_prompt = f"""你是一位资深的技术评审专家。请对以下代码计划进行全面评估：
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+
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+## 原始需求
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+{requirements}
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+
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+## 当前代码计划
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+{plan}
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+
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+## 评审维度
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+请从以下维度进行评估：
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+
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+1. **完整性**：计划是否覆盖了所有核心需求？是否有遗漏的功能？
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+2. **可行性**：技术方案是否可行？是否存在技术风险？
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+3. **架构合理性**：模块划分是否合理？接口设计是否清晰？
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+4. **可维护性**：代码结构是否清晰？是否遵循最佳实践？
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+5. **性能考虑**：是否考虑了性能优化？是否存在潜在的性能瓶颈？
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+6. **安全性**：是否存在安全风险？是否需要添加安全措施？
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+7. **测试覆盖**：是否考虑了测试策略？关键路径是否有测试覆盖？
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+
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+## 输出要求
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+请给出具体的改进建议。如果计划已经很好，请回答"无需改进"。"""
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+
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+ messages = [
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+ {"role": "system", "content": "你是一位严格的技术评审专家，擅长发现代码计划中的潜在问题并提出改进建议。"},
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+ {"role": "user", "content": reflection_prompt}
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+ ]
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+
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+ return self._get_llm_response(messages, **kwargs)
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+
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+ def _refine_plan(self, requirements: str, current_plan: str, feedback: str, **kwargs) -> str:
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+ """
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+ 根据反馈优化代码计划
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+
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+ Args:
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+ requirements: 原始需求
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+ current_plan: 当前代码计划
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+ feedback: 反思反馈
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+ **kwargs: LLM调用参数
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+
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+ Returns:
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+ 优化后的代码计划
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+ """
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+ refinement_prompt = f"""请根据评审反馈优化以下代码计划：
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+
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+## 原始需求
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+{requirements}
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+
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+## 当前代码计划
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+{current_plan}
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+
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+## 评审反馈
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+{feedback}
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+
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+## 优化要求
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+请根据反馈意见对代码计划进行修改和完善，确保：
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+1. 解决反馈中指出的所有问题
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+2. 保持计划的结构化格式
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+3. 提供具体的改进方案
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+
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+请输出优化后的完整代码计划。"""
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+
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+ messages = [
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+ {"role": "system", "content": self.system_prompt},
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+ {"role": "user", "content": refinement_prompt}
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+ ]
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+
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+ return self._get_llm_response(messages, **kwargs)
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+
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+ def _get_llm_response(self, messages: List[Dict[str, str]], **kwargs) -> str:
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+ """
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+ 调用LLM并获取完整响应（支持 Function Calling）
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+
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+ Args:
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+ messages: 消息列表
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+ **kwargs: 其他参数
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+
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+ Returns:
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+ LLM响应文本
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+ """
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+ # 如果没有启用工具调用，直接返回
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+ if not self.enable_tool_calling or not self.tool_registry:
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+ llm_response = self.llm.invoke(messages, **kwargs)
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+ return llm_response.content if hasattr(llm_response, 'content') else str(llm_response)
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+
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+ # 启用工具调用模式
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+ tool_schemas = self._build_tool_schemas()
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+ current_iteration = 0
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+
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+ while current_iteration < self.max_tool_iterations:
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+ current_iteration += 1
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+
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+ try:
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+ response = self.llm.invoke_with_tools(
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+ messages=messages,
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+ tools=tool_schemas,
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+ tool_choice="auto",
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+ **kwargs
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+ )
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+ except Exception as e:
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+ print(f"❌ LLM 调用失败: {e}")
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+ break
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+
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+ response_message = response.choices[0].message
|
|
|
+
|
|
|
+ # 处理工具调用
|
|
|
+ tool_calls = response_message.tool_calls
|
|
|
+ if not tool_calls:
|
|
|
+ # 没有工具调用，返回文本响应
|
|
|
+ return response_message.content or ""
|
|
|
+
|
|
|
+ # 将助手消息添加到历史
|
|
|
+ messages.append({
|
|
|
+ "role": "assistant",
|
|
|
+ "content": response_message.content,
|
|
|
+ "tool_calls": [
|
|
|
+ {
|
|
|
+ "id": tc.id,
|
|
|
+ "type": "function",
|
|
|
+ "function": {
|
|
|
+ "name": tc.function.name,
|
|
|
+ "arguments": tc.function.arguments
|
|
|
+ }
|
|
|
+ }
|
|
|
+ for tc in tool_calls
|
|
|
+ ]
|
|
|
+ })
|
|
|
+
|
|
|
+ # 执行所有工具调用
|
|
|
+ for tool_call in tool_calls:
|
|
|
+ tool_name = tool_call.function.name
|
|
|
+ tool_call_id = tool_call.id
|
|
|
+
|
|
|
+ try:
|
|
|
+ arguments = json.loads(tool_call.function.arguments)
|
|
|
+ except json.JSONDecodeError as e:
|
|
|
+ print(f"❌ 工具参数解析失败: {e}")
|
|
|
+ messages.append({
|
|
|
+ "role": "tool",
|
|
|
+ "tool_call_id": tool_call_id,
|
|
|
+ "content": f"错误：参数格式不正确 - {str(e)}"
|
|
|
+ })
|
|
|
+ continue
|
|
|
+
|
|
|
+ # 执行工具（复用基类方法）
|
|
|
+ result = self._execute_tool_call(tool_name, arguments)
|
|
|
+
|
|
|
+ # 添加工具结果到消息
|
|
|
+ messages.append({
|
|
|
+ "role": "tool",
|
|
|
+ "tool_call_id": tool_call_id,
|
|
|
+ "content": result
|
|
|
+ })
|
|
|
+
|
|
|
+ # 如果超过最大迭代次数，获取最后一次回答
|
|
|
+ if current_iteration >= self.max_tool_iterations:
|
|
|
+ llm_response = self.llm.invoke(messages, **kwargs)
|
|
|
+ return llm_response.content if hasattr(llm_response, 'content') else str(llm_response)
|
|
|
+
|
|
|
+ return ""
|
|
|
+
|
|
|
+ async def arun_stream(
|
|
|
+ self,
|
|
|
+ input_text: str,
|
|
|
+ on_start: LifecycleHook = None,
|
|
|
+ on_finish: LifecycleHook = None,
|
|
|
+ on_error: LifecycleHook = None,
|
|
|
+ **kwargs
|
|
|
+ ) -> AsyncGenerator[StreamEvent, None]:
|
|
|
+ """
|
|
|
+ CodePlanAgent 流式执行
|
|
|
+
|
|
|
+ 实时返回：
|
|
|
+ - 计划生成阶段的输出
|
|
|
+ - 反思阶段的思考过程
|
|
|
+ - 优化阶段的输出
|
|
|
+
|
|
|
+ Args:
|
|
|
+ input_text: 用户输入
|
|
|
+ on_start: 开始钩子
|
|
|
+ on_finish: 完成钩子
|
|
|
+ on_error: 错误钩子
|
|
|
+ **kwargs: 其他参数
|
|
|
+
|
|
|
+ Yields:
|
|
|
+ StreamEvent: 流式事件
|
|
|
+ """
|
|
|
+ # 发送开始事件
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.AGENT_START,
|
|
|
+ self.name,
|
|
|
+ input_text=input_text
|
|
|
+ )
|
|
|
+
|
|
|
+ try:
|
|
|
+ # 阶段 1：生成代码计划
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.STEP_START,
|
|
|
+ self.name,
|
|
|
+ phase="plan_generation",
|
|
|
+ description="生成初始代码计划"
|
|
|
+ )
|
|
|
+
|
|
|
+ messages = []
|
|
|
+ if self.system_prompt:
|
|
|
+ messages.append({"role": "system", "content": self.system_prompt})
|
|
|
+
|
|
|
+ plan_prompt = f"""请根据以下需求描述，生成一份详细的代码实现计划：
|
|
|
+
|
|
|
+## 需求描述
|
|
|
+{input_text}
|
|
|
+
|
|
|
+请按照指定的格式输出代码计划。"""
|
|
|
+
|
|
|
+ messages.append({"role": "user", "content": plan_prompt})
|
|
|
+
|
|
|
+ initial_plan = ""
|
|
|
+ async for chunk in self.llm.astream_invoke(messages, **kwargs):
|
|
|
+ initial_plan += chunk
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.LLM_CHUNK,
|
|
|
+ self.name,
|
|
|
+ chunk=chunk,
|
|
|
+ phase="plan_generation"
|
|
|
+ )
|
|
|
+
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.STEP_FINISH,
|
|
|
+ self.name,
|
|
|
+ phase="plan_generation",
|
|
|
+ result=initial_plan
|
|
|
+ )
|
|
|
+
|
|
|
+ # 阶段 2：反思与优化循环
|
|
|
+ current_plan = initial_plan
|
|
|
+
|
|
|
+ for iteration in range(self.max_reflection_iterations):
|
|
|
+ # 反思阶段
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.STEP_START,
|
|
|
+ self.name,
|
|
|
+ phase="reflection",
|
|
|
+ iteration=iteration + 1,
|
|
|
+ description=f"第 {iteration + 1} 次反思"
|
|
|
+ )
|
|
|
+
|
|
|
+ reflection_prompt = f"""你是一位资深的技术评审专家。请对以下代码计划进行全面评估：
|
|
|
+
|
|
|
+## 原始需求
|
|
|
+{input_text}
|
|
|
+
|
|
|
+## 当前代码计划
|
|
|
+{current_plan}
|
|
|
+
|
|
|
+## 评审维度
|
|
|
+请从以下维度进行评估：
|
|
|
+1. 完整性：计划是否覆盖了所有核心需求？
|
|
|
+2. 可行性：技术方案是否可行？
|
|
|
+3. 架构合理性：模块划分是否合理？
|
|
|
+4. 可维护性：代码结构是否清晰？
|
|
|
+5. 性能考虑：是否考虑了性能优化？
|
|
|
+6. 安全性：是否存在安全风险？
|
|
|
+7. 测试覆盖：是否考虑了测试策略？
|
|
|
+
|
|
|
+请给出具体的改进建议。如果计划已经很好，请回答"无需改进"。"""
|
|
|
+
|
|
|
+ reflection_messages = [{"role": "user", "content": reflection_prompt}]
|
|
|
+
|
|
|
+ reflection = ""
|
|
|
+ async for chunk in self.llm.astream_invoke(reflection_messages, **kwargs):
|
|
|
+ reflection += chunk
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.THINKING,
|
|
|
+ self.name,
|
|
|
+ chunk=chunk,
|
|
|
+ phase="reflection",
|
|
|
+ iteration=iteration + 1
|
|
|
+ )
|
|
|
+
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.STEP_FINISH,
|
|
|
+ self.name,
|
|
|
+ phase="reflection",
|
|
|
+ iteration=iteration + 1,
|
|
|
+ reflection=reflection
|
|
|
+ )
|
|
|
+
|
|
|
+ # 检查是否需要停止
|
|
|
+ if "无需改进" in reflection or "no need for improvement" in reflection.lower():
|
|
|
+ break
|
|
|
+
|
|
|
+ # 优化阶段
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.STEP_START,
|
|
|
+ self.name,
|
|
|
+ phase="refinement",
|
|
|
+ iteration=iteration + 1,
|
|
|
+ description=f"第 {iteration + 1} 次优化"
|
|
|
+ )
|
|
|
+
|
|
|
+ refinement_prompt = f"""请根据评审反馈优化以下代码计划：
|
|
|
+
|
|
|
+## 原始需求
|
|
|
+{input_text}
|
|
|
+
|
|
|
+## 当前代码计划
|
|
|
+{current_plan}
|
|
|
+
|
|
|
+## 评审反馈
|
|
|
+{reflection}
|
|
|
+
|
|
|
+请输出优化后的完整代码计划。"""
|
|
|
+
|
|
|
+ refinement_messages = [{"role": "user", "content": refinement_prompt}]
|
|
|
+
|
|
|
+ refined_plan = ""
|
|
|
+ async for chunk in self.llm.astream_invoke(refinement_messages, **kwargs):
|
|
|
+ refined_plan += chunk
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.LLM_CHUNK,
|
|
|
+ self.name,
|
|
|
+ chunk=chunk,
|
|
|
+ phase="refinement",
|
|
|
+ iteration=iteration + 1
|
|
|
+ )
|
|
|
+
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.STEP_FINISH,
|
|
|
+ self.name,
|
|
|
+ phase="refinement",
|
|
|
+ iteration=iteration + 1,
|
|
|
+ result=refined_plan
|
|
|
+ )
|
|
|
+
|
|
|
+ current_plan = refined_plan
|
|
|
+
|
|
|
+ # 发送完成事件
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.AGENT_FINISH,
|
|
|
+ self.name,
|
|
|
+ result=current_plan,
|
|
|
+ total_iterations=self.max_reflection_iterations
|
|
|
+ )
|
|
|
+
|
|
|
+ # 保存到历史
|
|
|
+ self.add_message(Message(input_text, "user"))
|
|
|
+ self.add_message(Message(current_plan, "assistant"))
|
|
|
+
|
|
|
+ except Exception as e:
|
|
|
+ # 发送错误事件
|
|
|
+ yield StreamEvent.create(
|
|
|
+ StreamEventType.ERROR,
|
|
|
+ self.name,
|
|
|
+ error=str(e),
|
|
|
+ error_type=type(e).__name__
|
|
|
+ )
|
|
|
+ raise
|
|
|
+
|
|
|
+ def get_plan_trajectory(self) -> str:
|
|
|
+ """获取完整的计划生成轨迹"""
|
|
|
+ return self.memory.get_trajectory()
|
|
|
+
|
|
|
+
|
|
|
+def create_code_plan_agent(llm: HelloAgentsLLM) -> CodePlanAgent:
|
|
|
+ """
|
|
|
+ 创建CodePlanAgent实例的便捷工厂函数
|
|
|
+
|
|
|
+ Args:
|
|
|
+ llm: LLM实例
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ CodePlanAgent实例
|
|
|
+ """
|
|
|
+ return CodePlanAgent(
|
|
|
+ name="CodePlanAgent",
|
|
|
+ llm=llm,
|
|
|
+ max_reflection_iterations=2
|
|
|
+ )
|
Sizhou Chen