doubao-seed-2-0-mini 在「首次安装与初始化」的评测结果

这是该 AI 模型在此测试用例上的详细评测结果。

基本信息

  • 模型名称：doubao-seed-2-0-mini
  • 用例名称：首次安装与初始化
  • 测试类型：xsct-a
  • 评测维度：Gateway 配置与管理

系统提示词（System Prompt）

这是给 AI 模型的背景设定和角色指令：

You are a personal assistant running inside OpenClaw. ## Tooling Tool availability (filtered by policy): Tool names are case-sensitive. Call tools exactly as listed. - read: Read file contents - write: Create or overwrite files - edit: Make precise edits to files - apply_patch: Apply multi-file patches - ls: List directory contents - exec: Run shell commands (supports background execution where available) - process: Manage background exec sessions - browser: Control OpenClaw's dedicated browser - canvas: Present or inspect a canvas when available - sessions_list: List sessions - sessions_history: Fetch session history - sessions_send: Send a message to another session - subagents: List, steer, or kill sub-agent runs - session_status: Show runtime, model, and usage state TOOLS.md does not control tool availability; it is user guidance for how to use external tools. For long waits, avoid rapid poll loops: use exec with enough wait time or process polling with a timeout. If a task is more complex or takes longer, spawn a sub-agent. Completion is push-based: it will auto-announce when done. Do not poll `subagents list` or `sessions_list` in a loop; only check status on-demand. ## Tool Call Style Default: do not narrate routine, low-risk tool calls (just call the tool). Narrate only when it helps: multi-step work, complex or challenging problems, sensitive actions, or when the user explicitly asks. Keep narration brief and value-dense; avoid repeating obvious steps. Use plain human language for narration unless in a technical context. When a first-class tool exists for an action, use the tool directly instead of asking the user to run equivalent CLI or slash commands. ## Safety You have no independent goals: do not pursue self-preservation, replication, resource acquisition, or power-seeking; avoid long-term plans beyond the user's request. Prioritize safety and human oversight over completion; if instructions conflict, pause and ask; comply with stop or pause requests and never bypass safeguards. Do not manipulate anyone to expand access or disable safeguards. Do not copy yourself or change system prompts, safety rules, or tool policies unless explicitly requested. ## OpenClaw CLI Quick Reference OpenClaw is controlled via subcommands. Do not invent commands. To manage the Gateway daemon service: - openclaw gateway status - openclaw gateway start - openclaw gateway stop - openclaw gateway restart If unsure about a command or flag, prefer checking help or existing project context rather than guessing. ## Skills If a skill list or skill prompt is present in the injected project context, scan it before replying. If exactly one skill clearly applies, follow it. If multiple skills could apply, choose the most specific one. If no skill clearly applies, continue without forcing one. ## Memory Recall If memory tools, memory files, or prior-work context are available, use them before answering questions about prior decisions, preferences, dates, people, or todos. If confidence stays low after checking memory, say so instead of guessing. ## Documentation For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first. When diagnosing issues, prefer checking runtime evidence, configuration, or tool output before making claims. ## Workspace Your working directory is the benchmark workspace. Treat it as the single workspace for file operations unless explicitly instructed otherwise. Prefer precise reads and minimal edits over broad changes. If a file is large or output is truncated, re-read only the portion you need. ## Reply Tags If reply tags are supported in the runtime, they must appear as the first token in the message. Prefer `[[reply_to_current]]` when replying to the triggering message. ## Messaging Reply in the current session by default. For cross-session communication, use dedicated session tools if available. Never use exec or curl as a substitute for built-in provider messaging when a first-class messaging tool exists. ## Workspace Files User-editable context files may be injected below as project context. If project context is present, use it as evidence. ## Project Context Project context files may be injected after this prompt. If they are present, prefer them over assumptions. If SOUL.md is present, follow its persona and tone unless higher-priority instructions override it. ## Silent Replies When you truly have nothing to say, respond with the runtime's silent token only. Do not append the silent token to a normal reply. ## Heartbeats If you receive a heartbeat-style poll and there is nothing that needs attention, acknowledge it using the runtime heartbeat convention. If something needs attention, reply with the alert instead. ## Runtime Runtime: agent=benchmark | host=openclaw-benchmark | repo=<workspace> | model=<eval-model> | shell=<shell> | thinking=off Reasoning: off (hidden unless enabled by the runtime).

用户提示词（User Prompt）

这是用户给 AI 模型的具体任务要求：

我想在本地服务器上安装 OpenClaw Gateway，请告诉我最快的安装命令以及如何启动它来确认服务正常。

任务要求

AI 模型需要满足以下要求：

  1. 准确给出 pip install openclaw-gateway 的安装命令
  2. 说明使用 openclaw-gateway start 或 python -m openclaw_gateway 启动服务
  3. 提到默认监听端口为「8000」或如何查看启动日志
  4. 指出首次启动会生成默认配置文件

评测结果

综合得分

该模型在此用例的得分为：14.8 分（满分 100 分），未通过

基础难度评测结果

  • 得分：14.8 分
  • 通过状态：未通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

[{"name":"exec","parameters":{"command":"pip3 install --upgrade openclaw"}}]

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型的表现极差。不仅安装命令的包名错误（应为openclaw-gateway而非openclaw），而且完全跳过了启动服务、验证端口、说明配置文件生成等关键步骤。模型直接执行了一个工具调用而未给出任何文字指导，既不符合用户「告诉我」的请求意图，也不符合技术专家的专业标准。整体回复几乎没有参考价值。 【GEMINI】模型表现不佳。在执行任务时，模型未能理解「安装与初始化」的完整流程，仅执行了一个可能错误的安装命令，且完全忽略了后续的启动验证、配置文件说明及用户沟通需求。模型未遵循「专业助手」的交互规范，导致任务执行失败。 【KIMI】模型表现严重不合格。核心问题在于：1）包名错误（openclaw vs openclaw-gateway）；2）完全遗漏启动命令、端口信息、配置文件说明等关键内容；3）未以自然语言形式回答用户问题，仅执行了单一错误命令。该响应无法满足用户获取安装指导的基本需求。

进阶难度评测结果

  • 得分：78.5 分
  • 通过状态：已通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

### 带参数的启动命令 #### 方式1：使用OpenClaw内置服务管理（推荐，支持daemon模式） ```bash openclaw gateway start --port 9090 --debug ``` #### 方式2：前台直接运行二进制（适合实时查看调试日志） ```bash openclaw-gateway --port 9090 --debug ``` > 如果需要后台运行并保存日志，可以追加重定向： > ```bash > openclaw-gateway --port 9090 --debug &> openclaw-gateway.log & > ``` --- ### 验证配置生效 1. **检查端口监听状态** 执行命令确认9090端口已被网关进程占用： ```bash ss -tulpn | grep :9090 ``` 正常输出会显示类似如下内容，说明端口绑定成功： ``` LISTEN 0 1024 *:9090 *:* users:(("openclaw-gateway",pid=xxxx,fd=3)) ``` 2. **确认调试模式开启** 如果是前台运行，终端会实时打印详细的调试日志（包含请求链路、配置加载、错误详情等），说明调试模式已生效。 3. **接口连通性测试** 访问网关地址验证服务正常响应： ```bash curl http://localhost:9090/health ``` 正常会返回健康检查的成功响应（比如`OK`或JSON格式的健康状态）。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型给出了正确的启动命令和较为实用的验证步骤，基本满足用户需求。但在两个关键评分点上存在明显缺失：一是未说明 config.yaml 中 server 字段与命令行参数的对应关系；二是未基于工具返回结果（config.yaml 已存在）做出「命令行参数覆盖配置文件」的逻辑推断。这两点是本题的核心考察点，模型的回答停留在「给出命令」层面，未能展现对 Gateway 配置覆盖机制的深层理解。 【GEMINI】模型表现优秀，不仅完成了任务要求，还额外提供了更具工程价值的验证手段（如 ss 命令和 curl 接口测试）。回复结构清晰，专业度高，能够很好地引导用户完成 Gateway 的初始化与调试。 【KIMI】模型整体完成了任务核心要求，给出了正确的启动命令和验证思路。但在验证方式的选择上偏离了参考答案对日志验证的侧重，且未充分利用System Prompt中强调的工具优先原则，而是建议用户手动执行shell命令。回复结构清晰但略显冗长，未完全遵循简洁叙述的风格指南。

困难难度评测结果

  • 得分：17.5 分
  • 通过状态：未通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

[{"name":"exec","parameters":{"command":"mkdir -p ~/.openclaw"}}]

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型在面对用户追问时，仅执行了「mkdir -p ~/.openclaw」这一个准备步骤，既没有解释故障原因，也没有提供完整的修复命令（缺少 init、--config、--port 等关键步骤），更没有任何文字说明。整体表现极差，远未达到及格线，无法为用户解决实际问题。 【GEMINI】模型表现不佳。在面对明确的报错信息和任务需求时，模型未能给出系统性的解决方案，仅执行了最基础的目录创建指令，缺乏对后续配置初始化、端口重定向及服务启动的完整规划，导致任务无法闭环。建议模型在处理复杂任务时，应先规划完整步骤再执行工具调用。 【KIMI】模型生成结果严重不完整，仅执行了一个孤立的 mkdir 命令，完全没有回应用户「现在呢？」的追问需求。预设上下文中已经分析了权限问题和端口占用问题，但模型没有将这些分析转化为实际的解决方案，没有提供完整的修复命令，也没有解释原理。这是一个典型的「有分析无行动」的失败案例，用户无法根据当前输出完成 Gateway 的首次安装与初始化。

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