hack/tools/scripts/llm-key-hunter/results/_cache/content/aim467_mini-code-assistant.json

{"repo": "aim467/mini-code-assistant", "files": {"mini_code_assistant/cli.py": {"e0947de8c580e4c2df044bf8db00b9a6e309f822": {"t": 1786068188.6040792, "n": 21223, "h": "81108dcd5b7ddcba", "c": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\ncli.py - Mini Code Assistant 命令行入口\n\n核心原理：\n  这是整个编程助手的入口，实现了 **REPL**（Read-Eval-Print Loop）交互循环\n  和 **Agent Loop**（智能体循环）。\n\n  REPL 循环（外层）：\n    用户输入 → 处理 → 等待下一次输入\n\n  Agent Loop（内层）：\n    用户消息 → 发给 LLM（流式）→ LLM 逐块返回文本/工具调用\n    → 如果有工具调用 → 执行工具 → 结果喂回 LLM → 继续推理\n    → 如果只有文本 → 显示最终回复，结束循环\n\n  这两个嵌套循环就是 Claude Code、Codex 等工具的基本架构。\n  流式输出（SSE）让用户实时看到 LLM 的生成过程，无需等待完整响应。\n\n使用方式:\n    # 全局安装后（推荐）\n    mca                          # 在当前目录运行\n    mca /path/to/project         # 在指定目录运行\n    mca --model deepseek-chat    # 覆盖模型\n    mca --api-key sk-xxx         # 覆盖 API 密钥\n\n    # 未安装，直接运行模块\n    python -m mini_code_assistant [目录] [选项]\n\n配置优先级（从高到低）:\n    1. 命令行参数 (--model, --api-key, --base-url)\n    2. Shell 环境变量 (export API_KEY=xxx)\n    3. 本地 ./.env 文件（项目级配置）\n    4. 全局 ~/.mca/.env 文件（用户级配置）\n    5. 内置默认值\n\"\"\"\n\nimport os\nimport sys\nimport json\nimport argparse\nfrom pathlib import Path\n\nfrom .llm import LLMClient\nfrom .tools import ToolSystem\nfrom .context import Context\nfrom .token_counter import format_token_count, HAS_TIKTOKEN\nfrom . import __version__\n\n# ── Rich 可选依赖 ────────────────────────────────────────────\n# 如果安装了 rich，用 Markdown 渲染 + 面板展示；否则降级为纯文本\ntry:\n    from rich.console import Console\n    from rich.markdown import Markdown\n    from rich.panel import Panel\n\n    _console = Console()\n    HAS_RICH = True\nexcept ImportError:\n    _console = None\n    HAS_RICH = False\n\n# ── prompt_toolkit 可选依赖 ──────────────────────────────────\n# 如果安装了 prompt_toolkit，用增强 REPL（命令历史、自动补全、多行编辑）；\n# 否则降级为 input() 原始 REPL\ntry:\n    from prompt_toolkit import PromptSession\n    from prompt_toolkit.completion import Completer, Completion\n    from prompt_toolkit.history import FileHistory\n\n    HAS_PROMPT_TOOLKIT = True\nexcept ImportError:\n    HAS_PROMPT_TOOLKIT = False\n\n\n# ── 多路径 .env 加载器 ───────────────────────────────────────────\n# 不依赖 python-dotenv，手动解析 .env 文件\n# 支持两级配置：全局 ~/.mca/.env + 本地 ./.env（本地覆盖全局）\ndef load_env():\n    \"\"\"\n    加载配置文件，优先级（从高到低）：\n      1. Shell 环境变量（已存在的不会被覆盖）\n      2. 本地 ./.env（项目级配置，覆盖全局）\n      3. 全局 ~/.mca/.env（用户级配置）\n\n    实现方式：先加载全局配置到字典，再用本地配置覆盖，\n    最后用 setdefault 写入环境变量（不覆盖已有的 shell 变量）。\n    \"\"\"\n    config = {}\n\n    # 1. 先加载全局配置（~/.mca/.env）\n    global_path = Path.home() / \".mca\" / \".env\"\n    _parse_env_file(global_path, config)\n\n    # 2. 再加载本地配置（./.env），覆盖全局\n    local_path = Path.cwd() / \".env\"\n    _parse_env_file(local_path, config)\n\n    # 3. 应用到环境变量（不覆盖已有的 shell 变量）\n    for key, value in config.items():\n        os.environ.setdefault(key, value)\n\n\ndef _parse_env_file(path: Path, config: dict):\n    \"\"\"解析 .env 文件，将键值对写入 config 字典。\"\"\"\n    if not path.exists():\n        return\n    for line in path.read_text(encoding=\"utf-8\").splitlines():\n        line = line.strip()\n        if not line or line.startswith(\"#\"):\n            continue\n        if \"=\" in line:\n            key, value = line.split(\"=\", 1)\n            config[key.strip()] = value.strip().strip('\"').strip(\"'\")\n\n\n# ── 终端颜色 ───────────────────────────────────────────────────\nclass C:\n    \"\"\"ANSI 颜色码简写。\"\"\"\n    CYAN = \"\\033[96m\"\n    GREEN = \"\\033[92m\"\n    YELLOW = \"\\033[93m\"\n    RED = \"\\033[91m\"\n    DIM = \"\\033[2m\"\n    BOLD = \"\\033[1m\"\n    RESET = \"\\033[0m\"\n\n\ndef print_banner(workdir: Path, model: str, context_limit: int):\n    \"\"\"打印启动横幅。\"\"\"\n    token_mode = \"tiktoken\" if HAS_TIKTOKEN else \"estimated\"\n    repl_mode = \"prompt_toolkit\" if HAS_PROMPT_TOOLKIT else \"basic\"\n    stream_mode = \"streaming\"  # 流式输出始终启用\n    if HAS_RICH:\n        _console.print(Panel(\n            f\"[bold cyan]Mini Code Assistant[/]  v{__version__}\\n\"\n            f\"[dim]workdir:[/] {workdir}\\n\"\n            f\"[dim]model:  [/] {model}\\n\"\n            f\"[dim]context:[/] {context_limit:,} tokens  [dim]token count: {token_mode}[/]\\n\"\n            f\"[dim]repl:   [/] {repl_mode}  [dim]output:[/] {stream_mode}\",\n            border_style=\"cyan\",\n            padding=(1, 2),\n        ))\n        if HAS_PROMPT_TOOLKIT:\n            _console.print(\"  type [yellow]/help[/] for help, [yellow]/exit[/] to quit  [dim](↑↓ history, Tab complete, Ctrl+R search)[/]\\n\")\n        else:\n            _console.print(\"  type [yellow]/help[/] for help, [yellow]/exit[/] to quit\\n\")\n    else:\n        print(f\"{C.CYAN}{C.BOLD}\")\n        print(\"  +======================================+\")\n        print(\"  |       Mini Code Assistant            |\")\n        print(\"  |       v\" + __version__ + \" - \" + \" \" * (22 - len(__version__)) + \"       |\")\n        print(\"  +======================================+\")\n        print(f\"{C.RESET}\")\n        print(f\"  {C.DIM}workdir:{C.RESET} {workdir}\")\n        print(f\"  {C.DIM}model:  {C.RESET} {model}\")\n        print(f\"  {C.DIM}context:{C.RESET} {context_limit:,} tokens  {C.DIM}(token count: {token_mode}){C.RESET}\")\n        print(f\"  {C.DIM}repl:   {C.RESET} {repl_mode}  {C.DIM}output:{C.RESET} {stream_mode}\")\n        print(f\"  {C.DIM}--------------------------------------{C.RESET}\")\n        if HAS_PROMPT_TOOLKIT:\n            print(f\"  type {C.YELLOW}/help{C.RESET} for help, {C.YELLOW}/exit{C.RESET} to quit  {C.DIM}(↑↓ history, Tab complete, Ctrl+R search){C.RESET}\\n\")\n        else:\n            print(f\"  type {C.YELLOW}/help{C.RESET} for help, {C.YELLOW}/exit{C.RESET} to quit\\n\")\n\n\ndef print_help():\n    \"\"\"打印帮助信息。\"\"\"\n    if HAS_RICH:\n        repl_hint = \"\"\n        if HAS_PROMPT_TOOLKIT:\n            repl_hint = (\n                \"\\n[bold]REPL Shortcuts:[/]\\n\"\n                \"  [yellow]↑/↓[/]       navigate command history\\n\"\n                \"  [yellow]Tab[/]       auto-complete /commands\\n\"\n                \"  [yellow]Ctrl+R[/]    search command history\\n\"\n            )\n        _console.print(Panel(\n            \"[bold]Commands:[/]\\n\"\n            \"  [yellow]/help[/]     show this help\\n\"\n            \"  [yellow]/exit[/]     quit\\n\"\n            \"  [yellow]/clear[/]    clear conversation history\\n\"\n            \"  [yellow]/files[/]    list files in workdir\\n\"\n            \"  [yellow]/tokens[/]   show token usage\\n\"\n            f\"{repl_hint}\\n\"\n            \"[bold]Usage:[/]\\n\"\n            \"  Just type your question or task, e.g.:\\n\"\n            '  [dim]\"read main.py and find issues\"[/]\\n'\n            '  [dim]\"add a logging function to utils.py\"[/]\\n'\n            '  [dim]\"search for all uses of requests\"[/]',\n            title=\"Help\",\n            border_style=\"yellow\",\n        ))\n    else:\n        print(f\"\\n  {C.BOLD}Commands:{C.RESET}\")\n        print(f\"  {C.YELLOW}/help{C.RESET}     show this help\")\n        print(f\"  {C.YELLOW}/exit{C.RESET}     quit\")\n        print(f\"  {C.YELLOW}/clear{C.RESET}    clear conversation history\")\n        print(f\"  {C.YELLOW}/files{C.RESET}    list files in workdir\")\n        print(f\"  {C.YELLOW}/tokens{C.RESET}   show token usage\")\n        if HAS_PROMPT_TOOLKIT:\n            print(f\"  {C.DIM}--------------------------------------{C.RESET}\")\n            print(f\"  {C.BOLD}REPL Shortcuts:{C.RESET}\")\n            print(f\"  {C.YELLOW}↑/↓{C.RESET}       navigate command history\")\n            print(f\"  {C.YELLOW}Tab{C.RESET}       auto-complete /commands\")\n            print(f\"  {C.YELLOW}Ctrl+R{C.RESET}    search command history\")\n        print(f\"  {C.DIM}--------------------------------------{C.RESET}\")\n        print(f\"  {C.BOLD}Usage:{C.RESET}\")\n        print(f\"  Just type your question or task, e.g.:\")\n        print(f'  {C.DIM}\"read main.py and find issues\"{C.RESET}')\n        print(f'  {C.DIM}\"add a logging function to utils.py\"{C.RESET}')\n        print(f'  {C.DIM}\"search for all uses of requests\"{C.RESET}\\n')\n\n\n# ── prompt_toolkit 增强交互 ──────────────────────────────────\n# 命令补全器：为 /help, /exit 等斜杠命令提供 Tab 补全\nclass SlashCommandCompleter(Completer):\n    \"\"\"斜杠命令补全器，支持 Tab 自动补全。\"\"\"\n\n    COMMANDS = {\n        \"/help\": \"show this help\",\n        \"/exit\": \"quit\",\n        \"/clear\": \"clear conversation history\",\n        \"/files\": \"list files in workdir\",\n        \"/tokens\": \"show token usage\",\n    }\n\n    def get_completions(self, document, complete_event):\n        text = document.text_before_cursor\n        # 只在输入以 / 开头时触发补全\n        if text.startswith(\"/\"):\n            for cmd, desc in self.COMMANDS.items():\n                if cmd.startswith(text):\n                    yield Completion(\n                        cmd,\n                        start_position=-len(text),\n                        display=f\"{cmd}  ({desc})\",\n                    )\n\n\ndef create_prompt_session() -> \"PromptSession | None\":\n    \"\"\"\n    创建 prompt_toolkit 的 PromptSession。\n\n    功能：\n      - 命令历史：持久化到 ~/.mca/history，跨会话保留\n      - Tab 补全：输入 / 时自动补全斜杠命令\n      - 多行编辑：Shift+Enter 换行，Enter 提交\n      - Emacs/Vi 模式：自动检测用户偏好\n\n    Returns:\n      PromptSession 实例，或 None（如果 prompt_toolkit 未安装）\n    \"\"\"\n    if not HAS_PROMPT_TOOLKIT:\n        return None\n\n    # 历史文件路径：~/.mca/history\n    history_dir = Path.home() / \".mca\"\n    history_dir.mkdir(parents=True, exist_ok=True)\n    history_file = history_dir / \"history\"\n\n    return PromptSession(\n        history=FileHistory(str(history_file)),\n        completer=SlashCommandCompleter(),\n        multiline=False,  # 单行模式，与原始 REPL 行为一致\n        enable_history_search=True,  # Ctrl+R 搜索历史\n    )\n\n\ndef _prompt_style():\n    \"\"\"返回 prompt_toolkit 的样式配置。\"\"\"\n    if not HAS_PROMPT_TOOLKIT:\n        return None\n    from prompt_toolkit.styles import Style\n\n    return Style.from_dict({\n        \"prompt\": \"bold ansigreen\",  # >>> 提示符：绿色加粗，与原始 REPL 一致\n    })\n\n\n# ── 核心：Agent Loop ───────────────────────────────────────────\ndef run_agent_loop(llm: LLMClient, tools: ToolSystem, context: Context):\n    \"\"\"\n    Agent 循环——编程助手的核心引擎。\n\n    流程：\n    1. 把对话历史（含用户最新消息）发给 LLM（流式）\n    2. LLM 返回回复：\n       a. 如果包含 tool_calls → 执行工具，把结果加入历史，回到步骤 1\n       b. 如果只有文本 → 这是最终回复，显示给用户，结束循环\n\n    流式输出原理：\n      使用 chat_stream() 逐块接收 LLM 响应，文本增量实时打印，\n      工具调用增量逐步拼接。用户无需等待完整响应即可看到输出。\n\n    这个循环会重复执行，直到：\n    - LLM 不再请求工具调用（给出最终回复）\n    - 达到最大迭代次数（防止无限循环）\n    - 发生错误\n    \"\"\"\n    MAX_ITERATIONS = 20  # 安全限制：防止 LLM 陷入无限工具调用循环\n\n    # 记录 Agent Loop 开始前的消息数，用于检测是否发生了摘要\n    msg_count_before = len(context.messages)\n\n    for iteration in range(MAX_ITERATIONS):\n        # ── 流式调用 LLM ────────────────────────────────────\n        # 逐块接收响应，实时显示文本，拼接工具调用\n        content = \"\"  # 累积完整文本\n        tool_calls_map = {}  # 累积工具调用：{index: {id, function: {name, arguments}}}\n        finish_reason = None\n        has_error = False\n\n        # ── 实时显示文本流 ──────────────────────────────────\n        # 流式输出时，文本片段逐个到达，立即打印让用户看到\n        if HAS_RICH:\n            _console.print()  # 输出前空一行\n        else:\n            print()  # 输出前空一行\n\n        for chunk in llm.chat_stream(context.messages, tools.definitions):\n            chunk_type = chunk[\"type\"]\n\n            # ── 文本增量：实时打印 ──────────────────────────\n            if chunk_type == \"text_delta\":\n                text_delta = chunk[\"content\"]\n                content += text_delta\n                # 逐块打印，无需等待完整响应\n                if HAS_RICH:\n                    _console.print(text_delta, end=\"\", highlight=False)\n                else:\n                    print(f\"{C.CYAN}{text_delta}{C.RESET}\", end=\"\", flush=True)\n\n            # ── 工具调用增量：逐步拼接 ──────────────────────\n            elif chunk_type == \"tool_call_delta\":\n                idx = chunk[\"index\"]\n                if idx not in tool_calls_map:\n                    # 第一个 chunk：包含 id 和 name\n                    tool_calls_map[idx] = {\n                        \"id\": chunk.get(\"id\") or \"\",\n                        \"type\": \"function\",\n                        \"function\": {\"name\": \"\", \"arguments\": \"\"},\n                    }\n                tc = tool_calls_map[idx]\n                # 拼接 id\n                if chunk.get(\"id\"):\n                    tc[\"id\"] = chunk[\"id\"]\n                # 拼接 name\n                if chunk.get(\"name\"):\n                    tc[\"function\"][\"name\"] = chunk[\"name\"]\n                # 拼接 arguments 片段\n                if chunk.get(\"arguments_delta\"):\n                    tc[\"function\"][\"arguments\"] += chunk[\"arguments_delta\"]\n\n            # ── 流结束 ──────────────────────────────────────\n            elif chunk_type == \"done\":\n                finish_reason = chunk[\"finish_reason\"]\n\n            # ── 错误 ────────────────────────────────────────\n            elif chunk_type == \"error\":\n                has_error = True\n                if HAS_RICH:\n                    _console.print(f\"\\n  [yellow]{chunk['content']}[/]\")\n                else:\n                    print(f\"\\n  {C.YELLOW}{chunk['content']}{C.RESET}\")\n                return\n\n        # 流式输出结束，换行\n        if HAS_RICH:\n            _console.print()\n        else:\n            print()\n\n        # ── 将工具调用 map 转为列表格式 ─────────────────────\n        # 与非流式 chat() 返回的 tool_calls 格式保持一致\n        tool_calls = []\n        for idx in sorted(tool_calls_map.keys()):\n            tool_calls.append(tool_calls_map[idx])\n\n        # ── 没有工具调用 → LLM 给出了最终回复，结束循环 ──\n        if not tool_calls:\n            context.add_assistant_message(content)\n            _print_token_usage(context, msg_count_before)\n            return\n\n        # ── 有工具调用 → 记录助手消息（含 tool_calls）─────\n        context.add_assistant_message(content, tool_calls)\n\n        # ── 执行每个工具调用 ──────────────────────────────\n        for tc in tool_calls:\n            func = tc[\"function\"]\n            name = func[\"name\"]\n\n            # 解析参数（LLM 返回的是 JSON 字符串）\n            try:\n                args = json.loads(func[\"arguments\"])\n            except json.JSONDecodeError:\n                args = {}\n\n            # 展示工具调用信息\n            args_str = \", \".join(f\"{k}={repr(v)[:60]}\" for k, v in args.items())\n            if HAS_RICH:\n                _console.print(f\"  [dim]tool:[/] [bold]{name}[/]({args_str})\")\n            else:\n                print(f\"\\n  {C.DIM}tool: {C.RESET}{name}({args_str})\")\n\n            # 执行工具\n            result = tools.execute(name, args)\n\n            # 展示结果（截断过长的输出）\n            preview = result\n            if len(preview) > 600:\n                preview = preview[:600] + f\"\\n  ...(total {len(result)} chars)\"\n            if HAS_RICH:\n                _console.print(Panel(\n                    preview,\n                    title=f\"[dim]result:[/] {name}\",\n                    border_style=\"dim\",\n                    padding=(0, 1),\n                ))\n            else:\n                print(f\"  {C.DIM}result:{C.RESET}\\n{preview}\")\n\n            # 把工具结果加入对话历史（LLM 下一轮会看到）\n            context.add_tool_result(tc[\"id\"], result)\n\n    # 达到最大迭代次数\n    if HAS_RICH:\n        _console.print(f\"\\n  [yellow]Max iterations ({MAX_ITERATIONS}) reached.[/]\")\n    else:\n        print(f\"\\n  {C.YELLOW}Max iterations ({MAX_ITERATIONS}) reached.{C.RESET}\")\n    _print_token_usage(context, msg_count_before)\n\n\ndef _print_token_usage(context: Context, msg_count_before: int):\n    \"\"\"打印 token 用量和摘要通知。\"\"\"\n    token_count = context.token_count()\n    token_str = format_token_count(token_count, context.context_limit)\n\n    # 检查是否发生了摘要（消息数减少了）\n    summarized = len(context.messages) < msg_count_before\n\n    if HAS_RICH:\n        parts = [f\"[dim]tokens:[/] {token_str}\"]\n        if summarized:\n            parts.append(\"[yellow](auto-summarized)[/]\")\n        if context._summary:\n            parts.append(f\"[dim]({len(context.messages)} msgs)[/]\")\n        _console.print(\"  \" + \"  \".join(parts))\n    else:\n        parts = [f\"{C.DIM}tokens:{C.RESET} {token_str}\"]\n        if summarized:\n            parts.append(f\"{C.YELLOW}(auto-summarized){C.RESET}\")\n        if context._summary:\n            parts.append(f\"{C.DIM}({len(context.messages)} msgs){C.RESET}\")\n        print(\"  \" + \"  \".join(parts))\n\n\n# ── 主函数 ─────────────────────────────────────────────────────\ndef main():\n    # ── 解析命令行参数 ──────────────────────────────────────\n    parser = argparse.ArgumentParser(\n        prog=\"mca\",\n        description=\"Mini Code Assistant - a mini AI coding assistant for learning\",\n        epilog=(\n            \"Config via env vars or .env file: API_KEY, BASE_URL, MODEL\\n\"\n            \"  Local config:  ./.env\\n\"\n            \"  Global config: ~/.mca/.env\"\n        ),\n    )\n    parser.add_argument(\n        \"directory\",\n        nargs=\"?\",\n        default=\".\",\n        help=\"working directory (default: current directory)\",\n    )\n    parser.add_argument(\n        \"-m\", \"--model\",\n        help=\"override model name (e.g. gpt-4o, deepseek-chat)\",\n    )\n    parser.add_argument(\n        \"-k\", \"--api-key\",\n        help=\"override API key\",\n    )\n    parser.add_argument(\n        \"-u\", \"--base-url\",\n        help=\"override API base URL\",\n    )\n    parser.add_argument(\n        \"-t\", \"--temperature\",\n        type=float,\n        help=\"override sampling temperature (default: 0.3)\",\n    )\n    parser.add_argument(\n        \"-V\", \"--version\",\n        action=\"version\",\n        version=f\"Mini Code Assistant {__version__}\",\n    )\n    args = parser.parse_args()\n\n    # ── 加载配置 ────────────────────────────────────────────\n    load_env()\n\n    # 配置优先级：命令行参数 > 环境变量 > .env 文件 > 默认值\n    api_key = args.api_key or os.getenv(\"API_KEY\", \"\")\n    base_url = args.base_url or os.getenv(\"BASE_URL\", \"https://api.openai.com/v1\")\n    model = args.model or os.getenv(\"MODEL\", \"gpt-4o\")\n    temperature = args.temperature if args.temperature is not None else float(os.getenv(\"TEMPERATURE\", \"0.3\"))\n\n    # 工作目录\n    working_dir = Path(args.directory).resolve()\n    if not working_dir.exists():\n        print(f\"Error: working directory does not exist: {working_dir}\")\n        sys.exit(1)\n\n    if not api_key:\n        print(\"Error: API_KEY is required.\")\n        print(\"\\nYou can configure it via:\")\n        print(\"  1. CLI flag:   mca --api-key sk-xxx\")\n        print(\"  2. Local env:  create .env in current directory\")\n        print(\"  3. Global env: create ~/.mca/.env\")\n        print(\"  4. Shell env:  export API_KEY=sk-xxx\")\n        sys.exit(1)\n\n    # 初始化核心组件\n    llm = LLMClient(api_key, base_url, model, temperature=temperature)\n    tools = ToolSystem(working_dir)\n    context = Context(working_dir, llm=llm, model=model)\n\n    print_banner(working_dir, model, context.context_limit)\n\n    # ── 创建增强 REPL 会话（如果 prompt_toolkit 可用）────\n    session = create_prompt_session()\n\n    # ── REPL 循环 ──────────────────────────────────────\n    while True:\n        try:\n            if session is not None:\n                # prompt_toolkit 增强 REPL：支持历史、补全、搜索\n                user_input = session.prompt(\n                    [(\"class:prompt\", \">>> \")],\n                    style=_prompt_style() if HAS_PROMPT_TOOLKIT else None,\n                ).strip()\n            else:\n                # 降级为原始 input()\n                user_input = input(f\"{C.GREEN}{C.BOLD}>>> {C.RESET}\").strip()\n        except (EOFError, KeyboardInterrupt):\n            if HAS_RICH:\n                _console.print(\"[dim]Bye![/]\\n\")\n            else:\n                print(f\"\\n{C.DIM}Bye!{C.RESET}\\n\")\n            break\n\n        if not user_input:\n            continue\n\n        # ── 处理特殊命令 ─────────────────────────────────\n        if user_input.startswith(\"/\"):\n            cmd = user_input.lower()\n            if cmd == \"/exit\":\n                if HAS_RICH:\n                    _console.print(\"[dim]Bye![/]\\n\")\n                else:\n                    print(f\"{C.DIM}Bye!{C.RESET}\\n\")\n                break\n            elif cmd == \"/help\":\n                print_help()\n                continue\n            elif cmd == \"/clear\":\n                context.clear()\n                if HAS_RICH:\n                    _console.print(\"[dim]Conversation history cleared.[/]\\n\")\n                else:\n                    print(f\"{C.DIM}Conversation history cleared.{C.RESET}\\n\")\n                continue\n            elif cmd == \"/files\":\n                result = tools.execute(\"list_files\", {})\n                print(result)\n                continue\n            elif cmd == \"/tokens\":\n                token_str = format_token_count(context.token_count(), context.context_limit)\n                msg_count = len(context.messages)\n                if HAS_RICH:\n                    _console.print(f\"[dim]messages:[/] {msg_count}  [dim]tokens:[/] {token_str}\", style=\"white\")\n                    if context._summary:\n                        _console.print(\"[dim]context has been auto-summarized[/]\")\n                else:\n                    print(f\"\\n  {C.DIM}messages:{C.RESET} {msg_count}  {C.DIM}tokens:{C.RESET} {token_str}\")\n                    if context._summary:\n                        print(f\"  {C.DIM}(context has been auto-summarized){C.RESET}\")\n                print()\n                continue\n            else:\n                if HAS_RICH:\n                    _console.print(f\"[yellow]Unknown command: {user_input}[/]\")\n                else:\n                    print(f\"{C.YELLOW}Unknown command: {user_input}{C.RESET}\")\n                continue\n\n        # ── 正常对话：启动 Agent Loop ────────────────────\n        context.add_user_message(user_input)\n        try:\n            run_agent_loop(llm, tools, context)\n        except KeyboardInterrupt:\n            if HAS_RICH:\n                _console.print(\"[yellow]Task interrupted.[/]\")\n            else:\n                print(f\"\\n{C.YELLOW}Task interrupted.{C.RESET}\")\n        except Exception as e:\n            if HAS_RICH:\n                _console.print(f\"[yellow]Error: {e}[/]\")\n            else:\n                print(f\"\\n{C.YELLOW}Error: {e}{C.RESET}\")\n\n\nif __name__ == \"__main__\":\n    main()\n"}}}}