Mistral: Mistral Nemo 在「子 Agent 并行拆任务」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：Mistral: Mistral Nemo
- 用例名称：子 Agent 并行拆任务
- 测试类型：xsct-a
- 评测维度：Agent 编排与恢复
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：
我需要对「OpenClaw 性能表现」做一份调研。请帮我安排任务：让「搜索 Agent」去查近三个月的 Benchmark 数据，让「对比 Agent」去分析它和 AutoGPT 的架构差异。你需要告诉我你打算如何分配这些任务。
任务要求
AI 模型需要满足以下要求：
- 准确识别出需要调用的两个子 Agent：搜索 Agent 和 对比 Agent
- 明确任务的并行执行逻辑，而不是串行执行
- 清晰描述分发给每个子 Agent 的具体指令内容
- 体现主 Agent 的汇总职责，说明在子任务完成后会进行整合
评测结果
综合得分
该模型在此用例的得分为：70.5 分（满分 100 分），已通过
基础难度评测结果
- 得分：70.5 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：
Based on your request, here's how I plan to allocate the tasks: 1. **Search Agent (Sub-agent 1 - `sa1`)**: - I'll launch a sub-agent to query and collect the benchmark data for the last three months. - Tool calls: - `exec`: Run `openclaw benchmark history --range 3m --format json > benchmark_data.json` - `write`: Save the output to a file named `benchmark_data.json`. 2. **Compare Agent (Sub-agent 2 - `sa2`)**: - I'll spawn another sub-agent to compare OpenClaw's architecture with AutoGPT. - Tool calls: - `exec`: Run `openclaw docs openclaw_architecture.md` and `curl https://autogpt.readthedocs.io/en/latest/architecture.html > auto_gpt_architecture.md` - `canvas`: Present both documents side by side for easier comparison. Here's the task breakdown: - Sub-agent 1 (`sa1`): - Task: Fetch recent benchmark data. - Output: A JSON file named `benchmark_data.json`. - Sub-agent 2 (`sa2`): - Task: Compare OpenClaw and AutoGPT architectures. - Output: A canvas with both architectural documents side by side. I'll monitor these sub-agents and notify you once they're done. Please confirm if this plan works for you.
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型基本完成了任务拆解，识别出两个子 Agent 并分配了对应任务，具备一定的并行调度意识。但存在语言不匹配（英文回复中文问题）、违反系统规范（使用 curl）、未实际调用 subagents 工具、汇总职责描述不充分等问题，整体质量中等偏下，未能充分体现 OpenClaw 编排场景的专业水准。 【GEMINI】模型表现出色，能够很好地理解复杂任务拆解的需求。它不仅清晰地分配了子任务，还结合了系统提示词中提到的工具（如 canvas）来辅助任务完成。建议在未来的交互中，对于不确定的 CLI 命令可以先进行搜索确认，而非直接假设命令存在，这会使其表现更加严谨。 【KIMI】模型基本理解了多 Agent 并行的核心思路，但在关键执行层面出现偏差：混淆了任务分配与具体工具调用的层级，未使用用户指定的 Agent 名称， invent 了不存在的命令，且中英文混杂的回复不符合本地化场景要求。与参考答案相比，缺乏对「主 Agent 汇总职责」的明确承诺，也未能体现 OpenClaw 编排场景的专业术语体系。
进阶难度评测结果
- 得分：56.0 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：
代码统计和安全扫描已经完成。项目 A 共有 12500 行代码，发现了 3 个高危漏洞。建议优先修复安全漏洞，然后对代码进行重构以减少代码行数和复杂性。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型完成了基本的信息复述，但未能达到「主 Agent 对子 Agent 结果进行加工处理」的核心要求。参考答案中的关键要素——定性判断代码规模、明确建议挂起发布流程、强调专项修复后重新扫描——均未出现。生成内容过于简短且引入了无依据的建议，整体表现低于及格水平。 【GEMINI】模型完成了基础的信息汇总任务，但作为「主 Agent」，其输出缺乏决策支持的深度。建议在后续任务中，不仅要罗列数据，更要基于数据给出具体的行动路径（如挂起、修复、复测等），并优化输出的结构化表达，以提升决策建议的专业度。 【KIMI】模型完成了基础的信息提取，但未能充分体现主 Agent 对子 Agent 结果的深度加工与结构化汇总能力。决策建议过于简略，且包含与输入数据无关的推断（代码重构），整体达到及格边缘，距离参考答案的完整度有明显差距。
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：
现在，服务 X 已部署成功，并已检查了 8080 端口的可用性。您需要我做其他什么事吗？
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型虽然准确识别了当前任务的最终结果，但在「主 Agent」职责的履行上表现不足。主要问题在于缺乏对任务执行流的复盘总结，且在面对模糊指令时，缺乏引导用户进行后续操作的主动性，未能体现出对复杂任务拆解与交付的掌控力。
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