重构 API 路由并新增工作流编排功能
后端： - 重构 agents, heartbeats, locks, meetings, resources, roles, workflows 路由 - 新增 orchestrator 和 providers 路由 - 新增 CLI 调用器和流程编排服务 - 添加日志配置和依赖项 前端： - 更新 AgentsPage、SettingsPage、WorkflowPage 页面 - 扩展 api.ts 新增 API 接口 其他： - 清理测试 agent 数据文件 - 新增示例工作流和项目审计报告 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Claude Code
279
backend/app/services/cli_invoker.py
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279
backend/app/services/cli_invoker.py
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"""
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CLI 调用器
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通过子进程调用真实的 AI CLI 工具（Claude Code / Kimi CLI / OpenCode），
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将 prompt 发送给 CLI 并捕获输出。
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支持的 CLI：
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- claude: Claude Code CLI（使用 -p 参数发送单轮 prompt）
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- kimi: Kimi CLI（使用 -p 参数发送单轮 prompt）
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- opencode: OpenCode CLI
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"""
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import asyncio
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import logging
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import os
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import re
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import time
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import shutil
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from typing import Optional, Tuple
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from dataclasses import dataclass
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logger = logging.getLogger(__name__)
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# CLI 命令映射：model 前缀 → (二进制名, 构造参数的函数)
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CLI_REGISTRY = {
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"claude": "claude",
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"kimi": "kimi",
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"opencode": "opencode",
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}
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@dataclass
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class CLIResult:
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"""CLI 调用结果"""
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content: str
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cli_name: str
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exit_code: int
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latency: float
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success: bool
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error: str = ""
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def detect_available_clis() -> dict:
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"""检测系统中可用的 CLI 工具"""
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available = {}
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for name, binary in CLI_REGISTRY.items():
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path = shutil.which(binary)
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if path:
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available[name] = path
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return available
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def resolve_cli(model: str) -> Optional[str]:
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"""
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根据 agent 的 model 字段判断应使用哪个 CLI
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规则：
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- 以 "claude" 开头 → claude CLI
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- 以 "kimi" 开头 → kimi CLI
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- 以 "opencode" 开头 → opencode CLI
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- 完全匹配 CLI 名 → 直接使用
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"""
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model_lower = model.lower().strip()
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for prefix in CLI_REGISTRY:
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if model_lower.startswith(prefix):
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return prefix
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if model_lower in CLI_REGISTRY:
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return model_lower
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return None
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async def invoke_cli(
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cli_name: str,
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prompt: str,
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timeout: int = 120,
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max_tokens: int = 1024,
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system_prompt: str = "",
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) -> CLIResult:
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"""
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调用指定的 CLI 工具并返回结果
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参数:
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cli_name: CLI 名称（claude / kimi / opencode）
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prompt: 要发送的 prompt
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timeout: 超时秒数
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max_tokens: 最大 token 数
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"""
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binary = CLI_REGISTRY.get(cli_name)
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if not binary:
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return CLIResult(
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content="", cli_name=cli_name, exit_code=-1,
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latency=0, success=False, error=f"未知 CLI: {cli_name}"
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)
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# 必须获取完整路径，否则 subprocess 在不同环境下可能找不到
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full_path = shutil.which(binary)
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if not full_path:
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return CLIResult(
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content="", cli_name=cli_name, exit_code=-1,
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latency=0, success=False, error=f"CLI 未安装: {binary}"
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)
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cmd = _build_command(cli_name, prompt, max_tokens, full_path, system_prompt)
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logger.info(f"调用 CLI [{cli_name}]: {full_path} (prompt 长度={len(prompt)})")
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# Windows 下需要设置 PYTHONIOENCODING 解决 GBK 编码问题
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env = dict(os.environ)
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env["PYTHONIOENCODING"] = "utf-8"
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start = time.time()
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try:
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proc = await asyncio.create_subprocess_exec(
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*cmd,
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stdin=asyncio.subprocess.PIPE,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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env=env,
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)
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try:
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# 立即关闭 stdin，防止 CLI 阻塞等待输入
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stdout, stderr = await asyncio.wait_for(
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proc.communicate(input=b""), timeout=timeout
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)
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except asyncio.TimeoutError:
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proc.kill()
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await proc.communicate()
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return CLIResult(
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content="", cli_name=cli_name, exit_code=-1,
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latency=time.time() - start, success=False,
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error=f"CLI 超时 ({timeout}s)"
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)
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latency = time.time() - start
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stdout_text = stdout.decode("utf-8", errors="replace").strip()
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stderr_text = stderr.decode("utf-8", errors="replace").strip()
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# 过滤掉 OpenCode 的 INFO 日志行和 kimi 的框线
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stdout_text = _clean_output(cli_name, stdout_text)
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if proc.returncode == 0 and stdout_text:
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logger.info(f"CLI [{cli_name}] 完成: {latency:.1f}s, {len(stdout_text)} chars")
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return CLIResult(
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content=stdout_text,
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cli_name=cli_name,
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exit_code=0,
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latency=round(latency, 2),
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success=True,
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)
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else:
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error_msg = stderr_text or f"退出码 {proc.returncode}"
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logger.warning(f"CLI [{cli_name}] 失败: {error_msg}")
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return CLIResult(
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content=stdout_text or "",
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cli_name=cli_name,
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exit_code=proc.returncode or -1,
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latency=round(latency, 2),
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success=False,
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error=error_msg,
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)
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except FileNotFoundError:
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return CLIResult(
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content="", cli_name=cli_name, exit_code=-1,
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latency=0, success=False, error=f"找不到命令: {binary}"
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)
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except Exception as e:
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return CLIResult(
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content="", cli_name=cli_name, exit_code=-1,
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latency=time.time() - start, success=False, error=str(e)
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)
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def _build_command(
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cli_name: str, prompt: str, max_tokens: int, full_path: str, system_prompt: str = ""
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) -> list:
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"""
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为不同 CLI 构造命令行参数
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使用完整二进制路径确保跨环境兼容
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"""
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default_sys = (
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"这是一个角色扮演讨论场景，不是编程任务。"
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"请直接用中文回答，不要使用任何工具、不要读取文件、不要执行代码。"
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"直接给出你作为角色的观点和建议，2-3句话即可。"
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)
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sys_prompt = system_prompt or default_sys
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if cli_name == "claude":
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return [
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full_path,
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"-p", prompt,
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"--output-format", "text",
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"--system-prompt", sys_prompt,
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]
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elif cli_name == "kimi":
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return [
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full_path,
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"-p", f"{sys_prompt}\n\n{prompt}",
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]
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elif cli_name == "opencode":
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return [
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full_path,
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"run", f"{sys_prompt}\n\n{prompt}",
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"--model", "opencode/minimax-m2.5-free",
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]
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else:
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return [full_path, "-p", prompt]
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def _clean_output(cli_name: str, text: str) -> str:
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"""清理 CLI 输出中的框线、日志、prompt 回显等噪音"""
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if cli_name == "kimi":
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return _clean_kimi_output(text)
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lines = text.splitlines()
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cleaned = []
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for line in lines:
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if line.strip().startswith("INFO "):
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continue
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cleaned.append(line)
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result = "\n".join(cleaned).strip()
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return result if result else text.strip()
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def _clean_kimi_output(text: str) -> str:
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"""
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Kimi CLI 输出格式：
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┌─────────────────────┐
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│ (prompt 回显) │
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└─────────────────────┘
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• 思考过程...
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• 实际回复内容
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需要：1) 移除框线和框内的 prompt 回显
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2) 只保留最后一个 bullet 作为实际回复
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"""
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lines = text.splitlines()
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# 找到框线结束位置（最后一个 └ 或 ╰ 行）
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box_end = -1
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for i, line in enumerate(lines):
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stripped = line.strip()
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if stripped and stripped[0] in "└╰" and all(
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c in "└┘─╰╯ " for c in stripped
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):
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box_end = i
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# 跳过框线区域
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content_lines = lines[box_end + 1:] if box_end >= 0 else lines
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# Kimi 用 • 输出思考过程和最终回复，最后一个 • 块通常是实际回复
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bullets = []
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current_bullet = []
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for line in content_lines:
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stripped = line.strip()
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if not stripped:
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if current_bullet:
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current_bullet.append(line)
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continue
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if stripped.startswith("• ") or stripped.startswith("? "):
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if current_bullet:
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bullets.append("\n".join(current_bullet))
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current_bullet = [stripped.lstrip("•? ").strip()]
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elif current_bullet:
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current_bullet.append(stripped)
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if current_bullet:
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bullets.append("\n".join(current_bullet))
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if not bullets:
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return text.strip()
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# 最后一个 bullet 是实际回复
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return bullets[-1].strip()
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