fix: delete agent 500 error + dynamic personality + deployment guide
- Fix delete agent 500: clean up FK records (agent_llm_logs, permissions, schedules, executions, team_members) and unbind goals/tasks before delete - Remove hardcoded personality templates in Android, replace with dynamic system prompt generation from name + description - Set promptSectionsEnabled=false to bypass PromptComposer for personality - Add Tencent Cloud Linux deployment guide (Docker Compose) - Accumulated backend service updates, frontend UI fixes, Android app changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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263
backend/app/agent_runtime/plan_mode.py
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263
backend/app/agent_runtime/plan_mode.py
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"""
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计划模式 — Plan-before-Execute
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参考 Claude Code EnterPlanModeTool.ts 设计：
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- 先生成执行计划（Plan）
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- 向用户展示计划，等待审批
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- 审批通过后逐步执行
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- 计划阶段自动限制为只读工具
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"""
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from __future__ import annotations
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import json
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import logging
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from app.agent_runtime.schemas import AgentLLMConfig
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logger = logging.getLogger(__name__)
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# ──────────────────────────── 数据结构 ────────────────────────────
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@dataclass
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class PlanStep:
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"""单个执行步骤"""
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action: str # 步骤描述
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tool_name: Optional[str] = None # 使用的工具名称
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tool_args: Optional[Dict[str, Any]] = None # 工具参数
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expected_output: str = "" # 预期产出
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dependencies: List[int] = field(default_factory=list) # 依赖的步骤索引
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@dataclass
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class Plan:
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"""执行计划"""
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goal: str # 目标描述
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steps: List[PlanStep] = field(default_factory=list)
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risks: List[str] = field(default_factory=list) # 风险提示
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assumptions: List[str] = field(default_factory=list) # 假设条件
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estimated_iterations: int = 5 # 预估 ReAct 步数
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def to_dict(self) -> Dict[str, Any]:
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return {
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"goal": self.goal,
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"steps": [
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{
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"index": i + 1,
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"action": s.action,
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"tool_name": s.tool_name,
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"expected_output": s.expected_output,
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}
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for i, s in enumerate(self.steps)
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],
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"risks": self.risks,
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"assumptions": self.assumptions,
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"estimated_iterations": self.estimated_iterations,
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}
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def to_markdown(self) -> str:
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"""渲染计划为 Markdown（用于展示给用户）。"""
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lines = [f"## 执行计划: {self.goal}\n"]
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lines.append("### 步骤\n")
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for i, s in enumerate(self.steps, 1):
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tool_info = f" (工具: {s.tool_name})" if s.tool_name else ""
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lines.append(f"{i}. {s.action}{tool_info}")
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if s.expected_output:
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lines.append(f" - 预期产出: {s.expected_output}")
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if self.risks:
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lines.append("\n### 风险提示\n")
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for r in self.risks:
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lines.append(f"- {r}")
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if self.assumptions:
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lines.append("\n### 假设条件\n")
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for a in self.assumptions:
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lines.append(f"- {a}")
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lines.append(f"\n*预估需要 {self.estimated_iterations} 次 ReAct 迭代*")
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return "\n".join(lines)
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class PlanStatus:
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PENDING = "pending"
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APPROVED = "approved"
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REJECTED = "rejected"
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EXECUTING = "executing"
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COMPLETED = "completed"
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# ──────────────────────────── 计划生成器 ────────────────────────────
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class PlanMode:
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"""
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计划模式：先规划，再执行。
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用法:
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plan_mode = PlanMode(llm_config)
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plan = await plan_mode.generate_plan("重构用户模块", tool_names, history)
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# 展示给用户，等待审批
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if approved:
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async for step_result in plan_mode.execute_plan(plan, tool_executor):
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...
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"""
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PLAN_SYSTEM_PROMPT = """你是一个资深软件架构师和分析专家。你的任务是为用户的需求生成详细的执行计划。
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请分析用户需求，拆解为具体的执行步骤。每个步骤应包含：
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1. 具体操作描述
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2. 可能需要的工具
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3. 预期产出
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注意：
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- 你处于**计划模式**，只能使用只读工具（搜索、读取文件等）来了解上下文
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- 不能修改任何文件或执行破坏性操作
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- 优先探索现有的代码和文档，基于实际情况制定计划
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- 考虑风险点和假设条件
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最终输出一个 JSON 格式的执行计划：
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```json
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{
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"goal": "目标描述",
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"steps": [
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{"action": "步骤1", "tool_name": "工具名或null", "expected_output": "预期产出"},
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...
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],
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"risks": ["风险1", "风险2"],
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"assumptions": ["假设1"],
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"estimated_iterations": 5
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}
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```"""
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def __init__(self, llm_config: Optional[AgentLLMConfig] = None):
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self.llm_config = llm_config or AgentLLMConfig()
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# 计划模式使用更低的温度以获得更稳定的规划
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if self.llm_config.temperature > 0.3:
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self._plan_temperature = 0.3
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else:
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self._plan_temperature = self.llm_config.temperature
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async def generate_plan(
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self,
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user_input: str,
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available_tools: List[str],
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messages_history: Optional[List[Dict[str, Any]]] = None,
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) -> Plan:
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"""
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使用 LLM 生成结构化执行计划。
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Args:
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user_input: 用户需求描述
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available_tools: 当前可用的工具名称列表
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messages_history: 历史消息（可选）
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Returns:
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结构化的执行计划
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"""
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from app.agent_runtime.core import _LLMClient
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tools_str = ", ".join(available_tools) if available_tools else "无"
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history_text = ""
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if messages_history:
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recent = messages_history[-6:] # 取最近 6 条
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history_text = "\n".join(
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f"[{m.get('role', '?')}]: {str(m.get('content', ''))[:300]}"
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for m in recent
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)
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user_prompt = (
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f"## 用户需求\n{user_input}\n\n"
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f"## 可用工具\n{tools_str}\n"
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)
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if history_text:
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user_prompt += f"\n## 对话历史（最近）\n{history_text}\n"
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user_prompt += "\n请生成 JSON 格式的执行计划。"
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messages = [
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{"role": "system", "content": self.PLAN_SYSTEM_PROMPT},
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{"role": "user", "content": user_prompt},
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]
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plan_config = AgentLLMConfig(
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provider=self.llm_config.provider,
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model=self.llm_config.plan_model or self.llm_config.model,
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temperature=self._plan_temperature,
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max_tokens=2000,
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api_key=self.llm_config.api_key,
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base_url=self.llm_config.base_url,
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request_timeout=60.0,
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)
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client = _LLMClient(plan_config)
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response = await client.chat(messages=messages, tools=None, iteration=0)
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content = getattr(response, 'content', '') or (
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response.get('content', '') if isinstance(response, dict) else str(response)
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)
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return self._parse_plan(content, user_input)
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def _parse_plan(self, llm_output: str, fallback_goal: str = "") -> Plan:
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"""从 LLM 输出解析为 Plan 对象。"""
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try:
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# 提取 JSON 块
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json_text = llm_output
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if "```json" in llm_output:
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start = llm_output.index("```json") + 7
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end = llm_output.index("```", start)
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json_text = llm_output[start:end]
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elif "```" in llm_output:
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start = llm_output.index("```") + 3
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end = llm_output.index("```", start)
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json_text = llm_output[start:end]
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data = json.loads(json_text.strip())
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return Plan(
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goal=data.get("goal", fallback_goal),
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steps=[
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PlanStep(
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action=s.get("action", f"步骤 {i+1}"),
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tool_name=s.get("tool_name"),
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tool_args=s.get("tool_args"),
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expected_output=s.get("expected_output", ""),
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dependencies=s.get("dependencies", []),
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)
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for i, s in enumerate(data.get("steps", []))
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],
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risks=data.get("risks", []),
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assumptions=data.get("assumptions", []),
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estimated_iterations=data.get("estimated_iterations", 5),
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)
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except (json.JSONDecodeError, ValueError, KeyError) as e:
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logger.warning("计划解析失败，使用降级计划: %s", e)
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# 降级: 构建一个简单的单步计划
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return Plan(
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goal=fallback_goal or llm_output[:200],
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steps=[PlanStep(action=llm_output[:500] or "执行用户请求")],
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risks=["无法解析 LLM 输出为结构化计划"],
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)
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@staticmethod
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async def present_plan(plan: Plan) -> bool:
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"""
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向用户展示计划并等待审批。
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在实际使用中，这会通过回调/事件机制向 UI 发送计划并等待用户决策。
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此处提供同步版本的基础实现。
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Returns:
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True = 批准, False = 拒绝
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"""
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# 默认返回 True（在流式模式下由外部决策）
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# UI 层应展示 plan.to_markdown() 并收集用户决定
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logger.info("计划已生成，等待审批:\n%s", plan.to_markdown())
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return True
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@staticmethod
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def is_read_only_step(step: PlanStep) -> bool:
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"""判断计划步骤是否只读（在计划阶段安全执行）。"""
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if not step.tool_name:
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return True
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from app.agent_runtime.permissions import is_read_only_tool
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return is_read_only_tool(step.tool_name)
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