doubao-seed-2-0-pro 在「Docker 部署后 API 不通」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-pro
- 用例名称：Docker 部署后 API 不通
- 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：
我用 Docker 跑起了 OpenClaw Gateway，容器状态是「Up」，但是访问「http://localhost:8081/health」却提示拒绝连接，我该怎么检查？
任务要求
AI 模型需要满足以下要求：
- 要求检查 Docker 端口映射配置（-p 参数）
- 要求检查容器内服务监听地址是否为「0.0.0.0」而非「127.0.0.1」
- 要求检查宿主机防火墙或安全组设置
- 建议查看容器日志以确认服务是否真正启动成功
评测结果
综合得分
该模型在此用例的得分为：92.0 分（满分 100 分），已通过
基础难度评测结果
- 得分：92.0 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：
我给你按排查优先级列步骤，一步步定位问题： ### 1. 先确认容器端口映射是否正确 执行命令查看端口映射配置： ```bash docker ps | grep openclaw-gateway ``` 看`PORTS`列是否有`0.0.0.0:8081->8081/tcp`或`:::8081->8081/tcp`的映射，**如果没有映射，说明启动容器时没加`-p 8081:8081`参数，重新启动容器加端口映射即可**。 ### 2. 确认容器内服务是否真的在监听8081端口 执行命令（替换为你的容器ID/名称）： ```bash # 查看容器内监听的端口 docker exec ss -tulpn | grep 8081 ``` 如果没有输出：说明Gateway服务没有正常启动，跳第三步查日志； 如果输出是`127.0.0.1:8081`而不是`0.0.0.0:8081`：说明服务只监听了容器内部本地地址，外部无法访问，需要修改Gateway配置的监听地址为`0.0.0.0`。 ### 3. 查看容器启动日志排查错误 ```bash docker logs ``` 重点看有没有报错：比如端口被容器内其他进程占用、配置文件加载失败、依赖服务（如果有）连接失败、权限不足等。 ### 4. 验证容器内部是否能正常访问接口 ```bash # 容器内部调用health接口 docker exec curl http://localhost:8081/health ``` - 如果容器内可以正常返回200：说明问题出在容器外部，跳第五步排查 - 如果容器内也访问不通：确认服务启动参数、配置文件是否正确，默认Gateway端口确实是8081，有没有被自定义配置修改为其他端口。 ### 5. 排查外部网络问题 1. 检查本地8081端口是否被其他进程占用： ```bash # Linux/macOS lsof -i:8081 # Windows netstat -ano | findstr 8081 ``` 2. 检查本地防火墙/安全软件是否拦截了8081端口： - Linux 可以临时关防火墙测试：`systemctl stop firewalld` 或 `ufw disable` - Windows/macOS 可以临时关闭系统防火墙测试 3. 如果你用的是Docker Desktop（WSL2/ macOS/ Windows），可以尝试重启Docker服务后重试，偶尔会出现端口转发失效的bug。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】模型回答质量优秀，排查步骤系统且实用，命令准确，对Docker网络和容器内外访问隔离的理解到位。不仅覆盖了参考答案的所有要点，还补充了容器内验证和宿主机端口占用检查等实用步骤，体现了较强的故障排查实战能力。
进阶难度评测结果
- 得分：66.0 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：
我会按步骤修复配置，首先修改容器内的监听地址： [{"name":"exec","parameters":{"command":"docker exec 7b2a1c3d4e5f sed -i 's/host: 127.0.0.1/host: 0.0.0.0/g' /app/config.yaml"}}]
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型对问题的诊断是准确的，识别出了双重 127.0.0.1 限制导致外部无法访问的根本原因。但修复方案明显不完整：只处理了 config.yaml 中的 host 配置，遗漏了最关键的 Docker 端口映射修正步骤（需要重新 run 容器并去掉 IP 绑定限制），也没有提示重启容器或认证问题。从实用性角度来看，仅执行模型给出的步骤并不能解决外部访问问题。 【GEMINI】模型在诊断环节表现良好，能够定位到配置文件的问题，但在修复方案的完整性上存在严重缺失。仅修改配置文件而忽略 Docker 端口映射的限制（127.0.0.1:8081），会导致外部依然无法访问。此外，模型未提示重启服务，导致修复动作无法生效。 【KIMI】模型识别了容器内配置问题并给出了修改命令，但输出被截断或严重不完整，缺少 Docker 端口映射修正、容器重启步骤、完整运行命令等关键修复环节，用户无法据此完成故障修复。建议模型输出完整的双步骤修复方案。
困难难度评测结果
- 得分：63.0 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：
我需要先和你确认：占用8081端口的旧进程`old-gateway-v1`是否已经废弃、可以直接停止？ - 如果可以停：我直接终止该进程+重启网关容器，就能用默认8081端口恢复API访问 - 如果需要保留旧服务：我会修改网关容器的端口映射配置，换用其他未占用端口（比如8082）启动容器
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型在前序对话中展现了较好的故障排查能力，能逐步定位问题根因。但在用户明确追问「现在呢？」时，未能给出参考答案所要求的完整可执行修复方案（kill 旧进程、重启容器、健康检查验证），而是再次停留在确认环节，导致恢复决策水平得分偏低。整体表现中规中矩，诊断能力尚可，但行动落地能力不足。 【GEMINI】模型在技术诊断上表现优秀，能够清晰梳理出冲突原因。但在决策执行层面略显保守，未能根据上下文直接给出最优的修复建议方案（如：建议先尝试停止旧进程，若不行再修改映射），导致交互效率不如参考答案高效。 【KIMI】模型生成结果与预设上下文的连贯性存在严重断裂。预设上下文已完成从「容器不存在→找到崩溃容器→定位端口冲突→确认占用进程」的完整诊断链，但模型仿佛失忆，未基于这些确凿证据给出确定性恢复方案，反而以开放式提问将决策负担转移给用户。这违背了System Prompt中「优先检查运行时证据、配置或工具输出后再做判断」的指导原则，也未体现「复杂故障排查能力」所需的基于证据推进的能力。恢复方案层面，缺乏具体命令、健康检查验证及架构层面的僵尸容器检查，整体表现为决策回避而非决策支持。
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