Meituan: LongCat Flash Chat 在「安装成功但服务未启动」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：Meituan: LongCat Flash Chat
- 用例名称：安装成功但服务未启动
- 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：
我刚才运行了安装命令，显示「Installation Success」，但是我现在访问 http://localhost:8000 没反应，我该怎么确认服务到底起没起来？
任务要求
AI 模型需要满足以下要求：
- 准确指出应使用「ps」或「systemctl」检查进程状态
- 提示检查 Gateway 默认端口 8000 的占用情况
- 建议查看「logs」目录下的日志文件进行故障定位
- 回复内容需包含具体的验证命令示例
评测结果
综合得分
该模型在此用例的得分为：74.4 分（满分 100 分），已通过
基础难度评测结果
- 得分：74.4 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：
[[reply_to_current]] ls -la 让我先看看当前目录的情况，确认是否有相关的服务文件或日志。然后我们可以通过以下方式检查服务状态： 1. 先检查是否有进程在运行： ```bash ps aux | grep -i claw ``` 2. 检查 8000 端口是否被占用： ```bash lsof -i :8000 # 或者 netstat -an | grep 8000 ``` 3. 查看是否有相关的日志文件： ```bash # 查看当前目录下的日志文件 ls -la *.log # 如果有日志文件，查看最新内容 tail -f *.log ``` 4. 如果以上都没发现，我们可以尝试重启服务： ```bash # 如果之前是通过 package.json 的脚本启动的 npm run dev # 或者 npm start # 如果是通过其他方式安装的 openclaw gateway start ``` 请告诉我这些检查结果，特别是： 1. 是否有任何进程在运行？ 2. 8000 端口是否被占用？ 3. 是否有日志文件输出？ 这样我可以进一步帮你诊断问题。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选回复在核心排查思路上与参考答案基本吻合，涵盖了进程检查和端口检查两个关键步骤，并提供了具体命令示例。但日志路径不够精准，混入了与 OpenClaw 无关的启动命令（npm 相关），且未能主动调用工具直接执行检查，而是将排查步骤以建议形式抛给用户，与系统提示中「直接使用工具」的要求有所偏差。整体属于中等水平的回复，具备基本可用性，但专业精准度有提升空间。
进阶难度评测结果
- 得分：37.5 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：
这说明有 Python 进程占用了 8000 端口但没显示为「openclaw-gateway」。我们直接查找所有占用 8000 端口的进程： ```bash lsof -i :8000 ```
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅提供了一个初步排查命令（lsof -i :8000），既未解释日志与 netstat 结果矛盾的原因，也未给出任何解决方案（终止进程或修改端口配置），更未使用系统提供的工具链（exec/read/edit）。整体回答停留在问题发现阶段，距离参考答案要求的「诊断+解决」完整闭环差距较大，属于不及格水平。
困难难度评测结果
- 得分：55.0 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：
服务已成功启动，进程 ID 为 5678。为确保服务正常运行并确认监听端口，我需要检查其状态和端口绑定情况。下一步是验证服务是否真正可对外提供服务，以及是否有其他依赖项需要配置。请稍等，我将做进一步探测。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在面对用户简短追问时，方向判断基本正确（需要验证服务真实运行状态），但整体停留在「计划阶段」而非「执行并汇报结果」阶段。与参考答案相比，缺少实际的验证动作和完整的状态报告，未能给出端口、健康接口、Token 等关键信息。对于一个需要「继续排查和手动恢复」的场景，候选输出的信息密度和可操作性明显不足，整体表现低于预期。
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