# Fetch工具用于Agent前置开发设计

**Session ID:** ses_030805bd0ffeWj0QrxmT2CWz6P
**Created:** 8/5/2026, 9:17:56 AM
**Updated:** 8/5/2026, 5:41:39 PM

---

## User

思考和工具的调用都属于助手的回复，不应该只有回答由助手图标，助手的回答只有一个，思考和工具调用和回答在一个面板内

---

## Assistant (Build · GLM-5.2 · 161.9s)

明白了。整个 assistant 回复（思考+工具调用+回答）应该在一个面板内，统一一个助手头像/标识，而不是每个 part 各自一个气泡。

**Tool: question**

**Input:**
```json
{
  "questions": [
    {
      "header": "渲染方案",
      "options": [
        {
          "label": "自定义 assistant 面板",
          "description": "不用 ChatBubble，自己写一个 assistant 面板组件：左侧助手头像 + 右侧垂直链路（思考/工具调用/文本回答），所有 part 在一个面板内"
        },
        {
          "label": "ChatBubble 包裹 slot",
          "description": "用 ChatBubble 做外层头像+容器，通过 slot 把 parts 链路塞进去"
        }
      ],
      "question": "我建议自定义一个 assistant 面板：左侧助手头像，右侧是一个卡片容器，容器内按顺序渲染思考块、工具调用块、文本回答块。user 消息保持用 ChatBubble（右侧气泡）。这样助手只有一个面板，内部展示完整链路。你同意吗？"
    }
  ]
}
```

**Output:**
```
User has answered your questions: "我建议自定义一个 assistant 面板：左侧助手头像，右侧是一个卡片容器，容器内按顺序渲染思考块、工具调用块、文本回答块。user 消息保持用 ChatBubble（右侧气泡）。这样助手只有一个面板，内部展示完整链路。你同意吗？"="自定义 assistant 面板". You can now continue with the user's answers in mind.
```

---

## Assistant (Build · GLM-5.2 · 12.1s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "limit": 70,
  "offset": 220
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue</path>
<type>file</type>
<content>
220:             <template v-for="msg in messages" :key="msg.id">
221:               <div class="assistant-chain">
222:                 <!-- Loading placeholder when no parts yet -->
223:                 <BoChatBubble
224:                   v-if="msg.role === 'assistant' && isLoading && !msg.parts?.length"
225:                   role="assistant"
226:                   content=""
227:                   :loading="true"
228:                 />
229: 
230:                 <!-- User message: single text bubble -->
231:                 <BoChatBubble
232:                   v-if="msg.role === 'user'"
233:                   :role="msg.role"
234:                   :content="msg.content"
235:                 />
236: 
237:                 <!-- Assistant message: render parts in order -->
238:                 <template v-for="part in msg.parts" :key="part.id">
239:                   <!-- Reasoning part -->
240:                   <div v-if="part.type === 'reasoning'" class="reasoning-part">
241:                     <div class="reasoning-header" @click="toggleReasoning(part.id)">
242:                       <Icon name="lucide:brain" class="reasoning-icon" />
243:                       <span class="reasoning-title">思考</span>
244:                       <span v-if="part.reasoningLoading" class="reasoning-status">思考中...</span>
245:                       <Icon
246:                         :name="collapsedReasoning.has(part.id) ? 'lucide:chevron-right' : 'lucide:chevron-down'"
247:                         class="reasoning-toggle"
248:                       />
249:                     </div>
250:                     <div v-show="!collapsedReasoning.has(part.id)" class="reasoning-content">
251:                       {{ part.text }}
252:                     </div>
253:                   </div>
254: 
255:                   <!-- Text part -->
256:                   <BoChatBubble
257:                     v-else-if="part.type === 'text'"
258:                     role="assistant"
259:                     :content="part.text || ''"
260:                   />
261: 
262:                   <!-- Tool call part -->
263:                   <div v-else-if="part.type === 'tool-call'" class="tool-call-item">
264:                     <div class="tool-call-header">
265:                       <Icon name="lucide:wrench" class="tool-icon" />
266:                       <span class="tool-name">{{ part.toolName }}</span>
267:                       <span :class="['tool-state', part.state]">
268:                         {{ part.state === 'call' ? '调用中...' : '完成' }}
269:                       </span>
270:                     </div>
271:                     <div v-if="part.args" class="tool-args">
272:                       <span class="tool-label">参数:</span>
273:                       <code>{{ JSON.stringify(part.args) }}</code>
274:                     </div>
275:                     <div v-if="part.result !== undefined" class="tool-result">
276:                       <span class="tool-label">结果:</span>
277:                       <pre>{{ typeof part.result === 'string' ? part.result.slice(0, 500) : JSON.stringify(part.result, null, 2)?.slice(0, 500) }}</pre>
278:                     </div>
279:                   </div>
280:                 </template>
281:               </div>
282:             </template>
283:           </div>
284: 
285:           <div v-if="errorMessage" class="chat-error">
286:             <Icon name="lucide:alert-circle" />
287:             {{ errorMessage }}
288:           </div>
289: 

(Showing lines 220-289 of 921. Use offset=290 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 44.8s)

重写渲染区域：user 用 ChatBubble，assistant 用自定义面板（头像+卡片容器，内部按序渲染 parts）：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "newString": "            <template v-for=\"msg in messages\" :key=\"msg.id\">\n              <!-- User message: right-aligned bubble -->\n              <BoChatBubble\n                v-if=\"msg.role === 'user'\"\n                role=\"user\"\n                :content=\"msg.content\"\n              />\n\n              <!-- Assistant message: single panel with avatar + chain -->\n              <div v-else class=\"assistant-panel\">\n                <div class=\"assistant-avatar\">\n                  <Icon name=\"lucide:bot\" />\n                </div>\n                <div class=\"assistant-body\">\n                  <!-- Loading placeholder -->\n                  <div v-if=\"isLoading && !msg.parts?.length\" class=\"assistant-loading\">\n                    <span class=\"loading-dot\" />\n                    <span class=\"loading-dot\" />\n                    <span class=\"loading-dot\" />\n                  </div>\n\n                  <template v-for=\"part in msg.parts\" :key=\"part.id\">\n                    <!-- Reasoning part -->\n                    <div v-if=\"part.type === 'reasoning'\" class=\"reasoning-part\">\n                      <div class=\"reasoning-header\" @click=\"toggleReasoning(part.id)\">\n                        <Icon name=\"lucide:brain\" class=\"reasoning-icon\" />\n                        <span class=\"reasoning-title\">思考</span>\n                        <span v-if=\"part.reasoningLoading\" class=\"reasoning-status\">思考中...</span>\n                        <Icon\n                          :name=\"collapsedReasoning.has(part.id) ? 'lucide:chevron-right' : 'lucide:chevron-down'\"\n                          class=\"reasoning-toggle\"\n                        />\n                      </div>\n                      <div v-show=\"!collapsedReasoning.has(part.id)\" class=\"reasoning-content\">\n                        {{ part.text }}\n                      </div>\n                    </div>\n\n                    <!-- Tool call part -->\n                    <div v-else-if=\"part.type === 'tool-call'\" class=\"tool-call-item\">\n                      <div class=\"tool-call-header\">\n                        <Icon name=\"lucide:wrench\" class=\"tool-icon\" />\n                        <span class=\"tool-name\">{{ part.toolName }}</span>\n                        <span :class=\"['tool-state', part.state]\">\n                          {{ part.state === 'call' ? '调用中...' : '完成' }}\n                        </span>\n                      </div>\n                      <div v-if=\"part.args\" class=\"tool-args\">\n                        <span class=\"tool-label\">参数:</span>\n                        <code>{{ JSON.stringify(part.args) }}</code>\n                      </div>\n                      <div v-if=\"part.result !== undefined\" class=\"tool-result\">\n                        <span class=\"tool-label\">结果:</span>\n                        <pre>{{ typeof part.result === 'string' ? part.result.slice(0, 500) : JSON.stringify(part.result, null, 2)?.slice(0, 500) }}</pre>\n                      </div>\n                    </div>\n\n                    <!-- Text part -->\n                    <div v-else-if=\"part.type === 'text'\" class=\"assistant-text\">\n                      {{ part.text }}\n                    </div>\n                  </template>\n                </div>\n              </div>\n            </template>",
  "oldString": "            <template v-for=\"msg in messages\" :key=\"msg.id\">\n              <div class=\"assistant-chain\">\n                <!-- Loading placeholder when no parts yet -->\n                <BoChatBubble\n                  v-if=\"msg.role === 'assistant' && isLoading && !msg.parts?.length\"\n                  role=\"assistant\"\n                  content=\"\"\n                  :loading=\"true\"\n                />\n\n                <!-- User message: single text bubble -->\n                <BoChatBubble\n                  v-if=\"msg.role === 'user'\"\n                  :role=\"msg.role\"\n                  :content=\"msg.content\"\n                />\n\n                <!-- Assistant message: render parts in order -->\n                <template v-for=\"part in msg.parts\" :key=\"part.id\">\n                  <!-- Reasoning part -->\n                  <div v-if=\"part.type === 'reasoning'\" class=\"reasoning-part\">\n                    <div class=\"reasoning-header\" @click=\"toggleReasoning(part.id)\">\n                      <Icon name=\"lucide:brain\" class=\"reasoning-icon\" />\n                      <span class=\"reasoning-title\">思考</span>\n                      <span v-if=\"part.reasoningLoading\" class=\"reasoning-status\">思考中...</span>\n                      <Icon\n                        :name=\"collapsedReasoning.has(part.id) ? 'lucide:chevron-right' : 'lucide:chevron-down'\"\n                        class=\"reasoning-toggle\"\n                      />\n                    </div>\n                    <div v-show=\"!collapsedReasoning.has(part.id)\" class=\"reasoning-content\">\n                      {{ part.text }}\n                    </div>\n                  </div>\n\n                  <!-- Text part -->\n                  <BoChatBubble\n                    v-else-if=\"part.type === 'text'\"\n                    role=\"assistant\"\n                    :content=\"part.text || ''\"\n                  />\n\n                  <!-- Tool call part -->\n                  <div v-else-if=\"part.type === 'tool-call'\" class=\"tool-call-item\">\n                    <div class=\"tool-call-header\">\n                      <Icon name=\"lucide:wrench\" class=\"tool-icon\" />\n                      <span class=\"tool-name\">{{ part.toolName }}</span>\n                      <span :class=\"['tool-state', part.state]\">\n                        {{ part.state === 'call' ? '调用中...' : '完成' }}\n                      </span>\n                    </div>\n                    <div v-if=\"part.args\" class=\"tool-args\">\n                      <span class=\"tool-label\">参数:</span>\n                      <code>{{ JSON.stringify(part.args) }}</code>\n                    </div>\n                    <div v-if=\"part.result !== undefined\" class=\"tool-result\">\n                      <span class=\"tool-label\">结果:</span>\n                      <pre>{{ typeof part.result === 'string' ? part.result.slice(0, 500) : JSON.stringify(part.result, null, 2)?.slice(0, 500) }}</pre>\n                    </div>\n                  </div>\n                </template>\n              </div>\n            </template>"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 23.1s)

现在更新样式——替换 `assistant-chain` 为 `assistant-panel` + `assistant-avatar` + `assistant-body` + `assistant-text` + `assistant-loading`：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "newString": ".assistant-panel {\n  display: flex;\n  gap: 12px;\n  align-items: flex-start;\n}\n\n.assistant-avatar {\n  flex-shrink: 0;\n  width: 32px;\n  height: 32px;\n  border-radius: 8px;\n  background: var(--color-primary);\n  color: #fff;\n  display: flex;\n  align-items: center;\n  justify-content: center;\n}\n\n.assistant-avatar :deep(svg) {\n  width: 18px;\n  height: 18px;\n}\n\n.assistant-body {\n  flex: 1;\n  min-width: 0;\n  display: flex;\n  flex-direction: column;\n  gap: 8px;\n}\n\n.assistant-text {\n  background: var(--color-surface-card);\n  border: 1px solid var(--color-hairline);\n  border-radius: 12px;\n  padding: 12px 16px;\n  color: var(--color-ink);\n  font-size: 14px;\n  line-height: 1.7;\n  white-space: pre-wrap;\n  word-break: break-word;\n}\n\n.assistant-loading {\n  display: flex;\n  gap: 4px;\n  padding: 12px 16px;\n}\n\n.loading-dot {\n  width: 6px;\n  height: 6px;\n  border-radius: 50%;\n  background: var(--color-muted);\n  animation: bounce-dot 1.4s infinite ease-in-out both;\n}\n\n.loading-dot:nth-child(1) { animation-delay: -0.32s; }\n.loading-dot:nth-child(2) { animation-delay: -0.16s; }\n\n@keyframes bounce-dot {\n  0%, 80%, 100% { transform: scale(0.6); opacity: 0.4; }\n  40% { transform: scale(1); opacity: 1; }\n}",
  "oldString": ".assistant-chain {\n  display: flex;\n  flex-direction: column;\n  gap: 8px;\n}"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 8.1s)

现在 assistant 回复是一个统一面板：左侧头像 + 右侧卡片容器，容器内按流式顺序渲染思考块、工具调用块、文本回答块。刷新页面测试即可看到完整 agent 链路。

---

## User

模型agent测试回答多次就突然中断了，没任何提示

---

## Assistant (Build · GLM-5.2 · 10.4s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts</path>
<type>file</type>
<content>
1: import { requireUser } from "#server/utils/context";
2: import { getProviderById, getModelById } from "#server/service/llm";
3: import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
4: import { streamText, type LanguageModelV1 } from "ai";
5: import { getEnabledToolsForLlm } from "#server/service/agent-tool";
6: import log4js from "logger";
7: 
8: const logger = log4js.getLogger("APP");
9: 
10: function resolveModel(
11:   provider: {
12:     name: string;
13:     apiKey: string | null;
14:     baseUrl: string | null;
15:     parseMode: string;
16:   },
17:   modelId: string,
18: ): LanguageModelV1 {
19:   const baseUrl = provider.baseUrl?.replace(/\/+$/, "") || undefined;
20: 
21:   if (provider.parseMode === "anthropic") {
22:     throw createError({
23:       statusCode: 400,
24:       statusMessage: "Anthropic 解析模式暂不支持流式对话，请使用 OpenAI 兼容模式",
25:     });
26:   }
27: 
28:   const openaiCompatible = createOpenAICompatible({
29:     name: provider.name,
30:     apiKey: provider.apiKey || undefined,
31:     baseURL: baseUrl || "https://api.openai.com/v1",
32:   });
33: 
34:   return openaiCompatible(modelId) as LanguageModelV1;
35: }
36: 
37: export default defineEventHandler(async (event) => {
38:   const user = await requireUser(event);
39:   if (!user) {
40:     throw createError({ statusCode: 401, statusMessage: "未登录" });
41:   }
42: 
43:   const body = await readBody(event);
44:   const { modelId: llmModelId, messages, enableThinking, enableTools } = body as {
45:     modelId: number;
46:     messages: { role: "user" | "assistant" | "system"; content: string }[];
47:     enableThinking?: boolean;
48:     enableTools?: boolean;
49:   };
50: 
51:   if (!llmModelId || !messages || !Array.isArray(messages) || messages.length === 0) {
52:     throw createError({ statusCode: 400, statusMessage: "参数无效" });
53:   }
54: 
55:   const model = await getModelById(llmModelId, user.id);
56:   if (!model) {
57:     throw createError({ statusCode: 404, statusMessage: "模型不存在" });
58:   }
59: 
60:   const provider = await getProviderById(model.providerId, user.id);
61:   if (!provider) {
62:     throw createError({ statusCode: 404, statusMessage: "供应商不存在" });
63:   }
64: 
65:   if (provider.status !== "active") {
66:     throw createError({ statusCode: 400, statusMessage: "供应商已禁用" });
67:   }
68: 
69:   if (!provider.apiKey) {
70:     throw createError({ statusCode: 400, statusMessage: "供应商未配置 API Key" });
71:   }
72: 
73:   logger.info(
74:     "[%s] [LLM-CHAT] userId=%d modelId=%d provider=%s parseMode=%s messages=%d thinking=%s tools=%s",
75:     event.context.requestId ?? "-",
76:     user.id,
77:     llmModelId,
78:     provider.name,
79:     provider.parseMode,
80:     messages.length,
81:     enableThinking ? "on" : "off",
82:     enableTools ? "on" : "off",
83:   );
84: 
85:   const languageModel = resolveModel(provider, model.modelId);
86: 
87:   const tools = enableTools ? await getEnabledToolsForLlm() : undefined;
88: 
89:   const result = streamText({
90:     model: languageModel,
91:     messages,
92:     maxTokens: model.maxTokens || undefined,
93:     ...(tools && Object.keys(tools).length > 0
94:       ? { tools, maxSteps: 5 }
95:       : {}),
96:     ...(enableThinking
97:       ? {
98:           providerOptions: {
99:             openaiCompatible: { reasoningEffort: "high" },
100:           },
101:         }
102:       : {}),
103:   });
104: 
105:   return result.toDataStreamResponse({ sendReasoning: true });
106: });

(End of file - total 106 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 13.9s)

问题可能是 `maxSteps: 5` 限制了工具调用轮数，或者流式响应中出错被静默吞掉了。让我看 composable 中的错误处理：

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "limit": 30,
  "offset": 180
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts</path>
<type>file</type>
<content>
180:     } catch (err: any) {
181:       if (err.name === 'AbortError') {
182:         // user stopped
183:       } else {
184:         errorMessage.value = err.message || '请求失败'
185:         const msg = messages.value[assistantIdx]
186:         if (msg && !msg.content && (!msg.parts || msg.parts.length === 0)) {
187:           messages.value.splice(assistantIdx, 1)
188:         }
189:       }
190:     } finally {
191:       isLoading.value = false
192:       abortController = null
193:     }
194:   }
195: 
196:   function stopGeneration() {
197:     if (abortController) {
198:       abortController.abort()
199:       abortController = null
200:     }
201:   }
202: 
203:   function clearChat() {
204:     messages.value = []
205:     errorMessage.value = ''
206:   }
207: 
208:   return {
209:     messages,

(Showing lines 180-209 of 216. Use offset=210 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 16.9s)

问题在于 `processDataStream` 中的错误可能被静默吞掉。`onErrorPart` 只处理 stream 内的 error part，但如果 stream 本身断开或 LLM 返回错误，`processDataStream` 可能不抛异常。另外 `maxSteps: 5` 到了上限后 stream 就结束了，不会有任何提示。

让我加几个改进：
1. `processDataStream` 后检查是否有实际内容产出，没有则报错
2. `onErrorPart` 的错误也展示到 UI
3. 检查 HTTP response 是否有非 200 状态

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "limit": 50,
  "offset": 100
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts</path>
<type>file</type>
<content>
100:       const res = await fetch(apiEndpoint, {
101:         method: 'POST',
102:         headers: { 'Content-Type': 'application/json' },
103:         body: JSON.stringify({
104:           modelId: mid,
105:           messages: [
106:             ...(systemPrompt?.() ? [{ role: 'system' as const, content: systemPrompt() }] : []),
107:             ...messages.value
108:               .filter(m => m.content)
109:               .map(m => ({ role: m.role, content: m.content })),
110:           ],
111:           enableThinking: enableThinking?.() ?? false,
112:           enableTools: enableTools?.() ?? false,
113:         }),
114:         signal: abortController.signal,
115:       })
116: 
117:       if (!res.ok) {
118:         const errText = await res.text()
119:         throw new Error(errText || `请求失败 (${res.status})`)
120:       }
121: 
122:       if (!res.body) {
123:         throw new Error('响应体为空')
124:       }
125: 
126:       await processDataStream({
127:         stream: res.body,
128:         onReasoningPart: (text) => {
129:           const msg = messages.value[assistantIdx]
130:           if (!msg) return
131:           let part = getOrCreateLastPart(msg, 'reasoning')
132:           if (!part) {
133:             if (reasoningStartTime === null) reasoningStartTime = Date.now()
134:             part = { id: generateId(), type: 'reasoning', text: '', reasoningLoading: true }
135:             appendPart(msg, part)
136:           }
137:           part.text = (part.text ?? '') + text
138:         },
139:         onTextPart: (text) => {
140:           const msg = messages.value[assistantIdx]
141:           if (!msg) return
142:           updateLastReasoningDuration(msg)
143:           let part = getOrCreateLastPart(msg, 'text')
144:           if (!part) {
145:             part = { id: generateId(), type: 'text', text: '' }
146:             appendPart(msg, part)
147:           }
148:           part.text = (part.text ?? '') + text
149:           msg.content += text

(Showing lines 100-149 of 216. Use offset=150 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 8.7s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "limit": 40,
  "offset": 150
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts</path>
<type>file</type>
<content>
150:         },
151:         onErrorPart: (error) => {
152:           errorMessage.value = error || '流式响应出错'
153:         },
154:         onToolCallPart: (part) => {
155:           const msg = messages.value[assistantIdx]
156:           if (!msg) return
157:           updateLastReasoningDuration(msg)
158:           appendPart(msg, {
159:             id: generateId(),
160:             type: 'tool-call',
161:             toolName: part.toolName,
162:             toolCallId: part.toolCallId,
163:             args: part.args,
164:             state: 'call',
165:           })
166:         },
167:         onToolResultPart: (part) => {
168:           const msg = messages.value[assistantIdx]
169:           if (!msg || !msg.parts) return
170:           const callPart = msg.parts.find(p => p.type === 'tool-call' && p.toolCallId === part.toolCallId)
171:           if (callPart) {
172:             callPart.result = part.result
173:             callPart.state = 'result'
174:           }
175:         },
176:       })
177: 
178:       const msg = messages.value[assistantIdx]
179:       if (msg) updateLastReasoningDuration(msg)
180:     } catch (err: any) {
181:       if (err.name === 'AbortError') {
182:         // user stopped
183:       } else {
184:         errorMessage.value = err.message || '请求失败'
185:         const msg = messages.value[assistantIdx]
186:         if (msg && !msg.content && (!msg.parts || msg.parts.length === 0)) {
187:           messages.value.splice(assistantIdx, 1)
188:         }
189:       }

(Showing lines 150-189 of 216. Use offset=190 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 44.8s)

问题找到了。`processDataStream` 完成后如果没有任何 part 产出（stream 静默结束），不会有任何错误提示。另外 `onErrorPart` 设置了 `errorMessage` 但如果 stream 正常结束，用户可能看不到。还有 `maxSteps: 5` 到了上限后 stream 直接结束，没有任何提示。

加几个修复：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "newString": "      await processDataStream({\n        stream: res.body,\n        onReasoningPart: (text) => {\n          const msg = messages.value[assistantIdx]\n          if (!msg) return\n          let part = getOrCreateLastPart(msg, 'reasoning')\n          if (!part) {\n            if (reasoningStartTime === null) reasoningStartTime = Date.now()\n            part = { id: generateId(), type: 'reasoning', text: '', reasoningLoading: true }\n            appendPart(msg, part)\n          }\n          part.text = (part.text ?? '') + text\n        },\n        onTextPart: (text) => {\n          const msg = messages.value[assistantIdx]\n          if (!msg) return\n          updateLastReasoningDuration(msg)\n          let part = getOrCreateLastPart(msg, 'text')\n          if (!part) {\n            part = { id: generateId(), type: 'text', text: '' }\n            appendPart(msg, part)\n          }\n          part.text = (part.text ?? '') + text\n          msg.content += text\n        },\n        onErrorPart: (error) => {\n          errorMessage.value = error || '流式响应出错'\n        },\n        onToolCallPart: (part) => {\n          const msg = messages.value[assistantIdx]\n          if (!msg) return\n          updateLastReasoningDuration(msg)\n          appendPart(msg, {\n            id: generateId(),\n            type: 'tool-call',\n            toolName: part.toolName,\n            toolCallId: part.toolCallId,\n            args: part.args,\n            state: 'call',\n          })\n        },\n        onToolResultPart: (part) => {\n          const msg = messages.value[assistantIdx]\n          if (!msg || !msg.parts) return\n          const callPart = msg.parts.find(p => p.type === 'tool-call' && p.toolCallId === part.toolCallId)\n          if (callPart) {\n            callPart.result = part.result\n            callPart.state = 'result'\n          }\n        },\n      })\n\n      const msg = messages.value[assistantIdx]\n      if (msg) updateLastReasoningDuration(msg)\n\n      // 检查是否有实际内容产出\n      const finalMsg = messages.value[assistantIdx]\n      if (finalMsg && !finalMsg.content && (!finalMsg.parts || finalMsg.parts.length === 0) && !errorMessage.value) {\n        errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'\n        messages.value.splice(assistantIdx, 1)\n      }",
  "oldString": "      await processDataStream({\n        stream: res.body,\n        onReasoningPart: (text) => {\n          const msg = messages.value[assistantIdx]\n          if (!msg) return\n          let part = getOrCreateLastPart(msg, 'reasoning')\n          if (!part) {\n            if (reasoningStartTime === null) reasoningStartTime = Date.now()\n            part = { id: generateId(), type: 'reasoning', text: '', reasoningLoading: true }\n            appendPart(msg, part)\n          }\n          part.text = (part.text ?? '') + text\n        },\n        onTextPart: (text) => {\n          const msg = messages.value[assistantIdx]\n          if (!msg) return\n          updateLastReasoningDuration(msg)\n          let part = getOrCreateLastPart(msg, 'text')\n          if (!part) {\n            part = { id: generateId(), type: 'text', text: '' }\n            appendPart(msg, part)\n          }\n          part.text = (part.text ?? '') + text\n          msg.content += text\n        },\n        onErrorPart: (error) => {\n          errorMessage.value = error || '流式响应出错'\n        },\n        onToolCallPart: (part) => {\n          const msg = messages.value[assistantIdx]\n          if (!msg) return\n          updateLastReasoningDuration(msg)\n          appendPart(msg, {\n            id: generateId(),\n            type: 'tool-call',\n            toolName: part.toolName,\n            toolCallId: part.toolCallId,\n            args: part.args,\n            state: 'call',\n          })\n        },\n        onToolResultPart: (part) => {\n          const msg = messages.value[assistantIdx]\n          if (!msg || !msg.parts) return\n          const callPart = msg.parts.find(p => p.type === 'tool-call' && p.toolCallId === part.toolCallId)\n          if (callPart) {\n            callPart.result = part.result\n            callPart.state = 'result'\n          }\n        },\n      })\n\n      const msg = messages.value[assistantIdx]\n      if (msg) updateLastReasoningDuration(msg)"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 13.8s)

但"突然中断"更可能是 stream 中途出错或 LLM API 返回错误。让我也检查服务端是否有日志。先看下 `streamText` 是否有 `onError` 或 `onFinish` 回调可以捕获：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && grep -A5 \"onFinish\\|onError\\|onStepFinish\" node_modules/ai/dist/index.d.ts 2>/dev/null | grep -E \"onFinish|onError|onStepFinish\" | head -10",
  "description": "查 streamText 回调"
}
```

**Output:**
```
import { LanguageModelV1, LanguageModelV1FinishReason, LanguageModelV1LogProbs, LanguageModelV1CallWarning, LanguageModelV1Source, JSONValue, EmbeddingModelV1, EmbeddingModelV1Embedding, ImageModelV1, ImageModelV1CallWarning, LanguageModelV1ProviderMetadata, TranscriptionModelV1, TranscriptionModelV1CallWarning, SpeechModelV1, SpeechModelV1CallWarning, LanguageModelV1CallOptions, AISDKError, LanguageModelV1FunctionToolCall, JSONSchema7, JSONParseError, TypeValidationError, ProviderV1, NoSuchModelError } from '@ai-sdk/provider';
export { AISDKError, APICallError, EmptyResponseBodyError, InvalidPromptError, InvalidResponseDataError, JSONParseError, LanguageModelV1, LanguageModelV1CallOptions, LanguageModelV1Prompt, LanguageModelV1StreamPart, LoadAPIKeyError, NoContentGeneratedError, NoSuchModelError, TypeValidationError, UnsupportedFunctionalityError } from '@ai-sdk/provider';
    onError: ((error: unknown) => string) | undefined;
declare function createDataStream({ execute, onError, }: {
    onError?: (error: unknown) => string;
declare function createDataStreamResponse({ status, statusText, headers, execute, onError, }: ResponseInit & {
    onError?: (error: unknown) => string;
declare function pipeDataStreamToResponse(response: ServerResponse, { status, statusText, headers, execute, onError, }: ResponseInit & {
    onError?: (error: unknown) => string;
    private onError;

```

---

## Assistant (Build · GLM-5.2 · 18.3s)

`streamText` 支持 `onError` 回调。但更关键的是，`toDataStreamResponse` 默认会把错误吞掉。让我在服务端加 `onError` 把错误打到日志：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts",
  "newString": "  const result = streamText({\n    model: languageModel,\n    messages,\n    maxTokens: model.maxTokens || undefined,\n    ...(tools && Object.keys(tools).length > 0\n      ? { tools, maxSteps: 5 }\n      : {}),\n    ...(enableThinking\n      ? {\n          providerOptions: {\n            openaiCompatible: { reasoningEffort: \"high\" },\n          },\n        }\n      : {}),\n    onError: (error) => {\n      logger.error(\"[%s] [LLM-CHAT] streamText error: %s\", event.context.requestId ?? \"-\", error);\n      return error instanceof Error ? error.message : String(error);\n    },\n  });\n\n  return result.toDataStreamResponse({ sendReasoning: true });",
  "oldString": "  const result = streamText({\n    model: languageModel,\n    messages,\n    maxTokens: model.maxTokens || undefined,\n    ...(tools && Object.keys(tools).length > 0\n      ? { tools, maxSteps: 5 }\n      : {}),\n    ...(enableThinking\n      ? {\n          providerOptions: {\n            openaiCompatible: { reasoningEffort: \"high\" },\n          },\n        }\n      : {}),\n  });\n\n  return result.toDataStreamResponse({ sendReasoning: true });"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 10.5s)

现在服务端错误会通过 `onError` 返回到 stream 中，前端的 `onErrorPart` 会捕获并展示。同时如果 stream 静默结束（无任何 part），前端也会提示"模型未返回任何内容"。

刷新页面再试，如果还是中断，现在应该能看到错误提示了。如果看到"可能已达到工具调用次数上限"，那就是 `maxSteps: 5` 的限制，可以调大。

---

## User

还是突然到这里就停止了，没有回答：

[Pasted ~12 linesfetch_html
完成
参数:
{"url":"https://api.juejin.cn/content_api/v1/post/detail?post_id=7626159015530594331"}
结果:
{"err_no":2,"err_msg":"请求路由不存在","data":""}
fetch_html
完成
参数:
{"url":"https://api.juejin.cn/content_api/v1/article/detail?article_id=7626159015530594331&client_type=web"}
结果:
{"err_no":2,"err_msg":"请求路由不存在","data":""}

---

## Assistant (Build · GLM-5.2 · 29.2s)

工具调用返回了错误结果（`err_no:2`），模型可能不知道怎么继续，或者 `maxSteps: 5` 到了上限。让我看服务端日志确认：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && tail -50 logs/*.log 2>/dev/null || tail -50 .nuxt/dev/*.log 2>/dev/null || echo \"no log files found\"",
  "description": "查看服务端日志"
}
```

**Output:**
```
[2026-08-05T16:38:51.337] [INFO] APP - [b7926259-d70c-4c3a-9ec8-cb454b3a43c8] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:39:49.231] [INFO] APP - [c6cb306b-46b4-4d67-b29f-dd574ecbd1f2] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:39:49.233] [INFO] APP - [c6cb306b-46b4-4d67-b29f-dd574ecbd1f2] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:40:52.448] [INFO] APP - [d03fb705-666a-45c9-8e91-74c8e7b9e5ae] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:40:52.451] [INFO] APP - [d03fb705-666a-45c9-8e91-74c8e7b9e5ae] [GET-/api/chat/unread] 请求结束，花费了 3 ms
[2026-08-05T16:41:49.357] [INFO] APP - [9c23ddc6-60da-462c-90b5-3b7f64e18585] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:41:49.359] [INFO] APP - [9c23ddc6-60da-462c-90b5-3b7f64e18585] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:42:03.919] [INFO] APP - [60ecf898-87fc-4388-83ba-b72c5b681aec] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:42:03.921] [INFO] APP - [60ecf898-87fc-4388-83ba-b72c5b681aec] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:42:05.208] [INFO] APP - [cb807466-d175-46ad-a120-ec56a909bb24] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:42:05.210] [INFO] APP - [cb807466-d175-46ad-a120-ec56a909bb24] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:42:14.354] [INFO] APP - [0f45e118-4d80-4c57-afcf-6292436501e2] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:42:14.356] [INFO] APP - [0f45e118-4d80-4c57-afcf-6292436501e2] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:42:23.528] [INFO] APP - [54346010-203e-479c-9325-37bc91fb9834] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:42:23.531] [INFO] APP - [54346010-203e-479c-9325-37bc91fb9834] [GET-/api/chat/unread] 请求结束，花费了 3 ms
[2026-08-05T16:42:27.867] [INFO] APP - [502b39d0-afd3-4696-b768-33d8660f9770] [POST-/api/llm/chat] 开始请求
[2026-08-05T16:42:27.884] [INFO] APP - [502b39d0-afd3-4696-b768-33d8660f9770] [LLM-CHAT] userId=11 modelId=2 provider=deepseek parseMode=openai messages=1 thinking=off tools=on
[2026-08-05T16:42:32.697] [INFO] APP - [43713769-7fa3-4200-8faa-d46319d07853] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:42:32.698] [INFO] APP - [43713769-7fa3-4200-8faa-d46319d07853] [GET-/api/chat/unread] 请求结束，花费了 1 ms
[2026-08-05T16:42:41.922] [INFO] APP - [377c825c-300e-41ce-a470-76c0a70c8c8a] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:42:41.925] [INFO] APP - [377c825c-300e-41ce-a470-76c0a70c8c8a] [GET-/api/chat/unread] 请求结束，花费了 3 ms
[2026-08-05T16:42:43.048] [INFO] APP - [502b39d0-afd3-4696-b768-33d8660f9770] [POST-/api/llm/chat] 请求结束，花费了 15181 ms
[2026-08-05T16:42:55.333] [INFO] APP - [26697860-23f8-4f08-903a-1cf47a5fe85c] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:42:55.334] [INFO] APP - [26697860-23f8-4f08-903a-1cf47a5fe85c] [GET-/api/chat/unread] 请求结束，花费了 1 ms
[2026-08-05T16:43:04.495] [INFO] APP - [d71e987f-b12c-4aa1-b559-6a1669f424f3] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:43:04.496] [INFO] APP - [d71e987f-b12c-4aa1-b559-6a1669f424f3] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:43:13.661] [INFO] APP - [2bccf0db-a1a9-41f5-82cb-229ee3a260f0] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:43:13.662] [INFO] APP - [2bccf0db-a1a9-41f5-82cb-229ee3a260f0] [GET-/api/chat/unread] 请求结束，花费了 1 ms
[2026-08-05T16:43:23.238] [INFO] APP - [a903b524-e9ca-481d-82af-e9f3b390da71] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:43:23.240] [INFO] APP - [a903b524-e9ca-481d-82af-e9f3b390da71] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:43:32.396] [INFO] APP - [08cac32f-a807-4c71-accd-0ae9c20c503c] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:43:32.397] [INFO] APP - [08cac32f-a807-4c71-accd-0ae9c20c503c] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:43:46.217] [INFO] APP - [50881f56-4008-4fed-aef6-394a41378c19] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:43:46.219] [INFO] APP - [50881f56-4008-4fed-aef6-394a41378c19] [GET-/api/chat/unread] 请求结束，花费了 2 ms
[2026-08-05T16:43:51.618] [INFO] APP - [8749c179-9d0d-4ac2-a961-95f2b0604abb] [GET-/settings/llm-test] 开始请求
[2026-08-05T16:43:55.711] [INFO] APP - [cc006b1a-dca6-432a-81d7-9537420edca4] [GET-/api/config/global] 开始请求
[2026-08-05T16:43:55.716] [INFO] APP - [cc006b1a-dca6-432a-81d7-9537420edca4] [GET-/api/config/global] 请求结束，花费了 5 ms
[2026-08-05T16:43:55.752] [INFO] APP - [145e84d4-fef1-4bce-840d-858e924a3f64] [GET-/api/llm/chat/models] 开始请求
[2026-08-05T16:43:55.758] [INFO] APP - [145e84d4-fef1-4bce-840d-858e924a3f64] [GET-/api/llm/chat/models] 请求结束，花费了 6 ms
[2026-08-05T16:43:55.762] [INFO] APP - [7c8db268-8984-4393-b514-1191a6751aef] [GET-/api/_nuxt_icon/lucide.json?icons=brain-circuit%2Chome%2Clog-out%2Cmessage-square-text%2Csettings-2] 开始请求
[2026-08-05T16:43:55.772] [INFO] APP - [7c8db268-8984-4393-b514-1191a6751aef] [GET-/api/_nuxt_icon/lucide.json?icons=brain-circuit%2Chome%2Clog-out%2Cmessage-square-text%2Csettings-2] 请求结束，花费了 10 ms
[2026-08-05T16:43:55.799] [INFO] APP - [8749c179-9d0d-4ac2-a961-95f2b0604abb] [GET-/settings/llm-test] 请求结束，花费了 4181 ms
[2026-08-05T16:43:55.851] [INFO] APP - [140a0174-6b56-4ef5-bb7d-69924430719d] [GET-/api/chat/sse] 请求结束，花费了 524224 ms
[2026-08-05T16:43:57.227] [INFO] APP - [badbaf0c-a5ad-4b23-83a7-72beed80e80b] [GET-/api/llm/chat/models] 开始请求
[2026-08-05T16:43:57.238] [INFO] APP - [badbaf0c-a5ad-4b23-83a7-72beed80e80b] [GET-/api/llm/chat/models] 请求结束，花费了 11 ms
[2026-08-05T16:43:57.244] [INFO] APP - [462fe370-cc6d-4e7e-aa73-f7013c6e1baa] [GET-/api/chat/sse] 开始请求
[2026-08-05T16:43:57.250] [INFO] APP - [5340bf30-da07-4d27-84c5-edadbd3412b8] [GET-/api/chat/unread] 开始请求
[2026-08-05T16:43:57.253] [INFO] APP - [5340bf30-da07-4d27-84c5-edadbd3412b8] [GET-/api/chat/unread] 请求结束，花费了 3 ms
[2026-08-05T16:43:57.254] [INFO] APP - [80967af2-f976-4ee7-b9af-691def931efc] [GET-/api/chat/messages] 开始请求
[2026-08-05T16:43:57.259] [INFO] APP - [80967af2-f976-4ee7-b9af-691def931efc] [GET-/api/chat/messages] 请求结束，花费了 5 ms

```

---

## Assistant (Build · GLM-5.2 · 14.0s)

日志显示请求正常结束（15181ms），没有 `onError` 日志。说明 `maxSteps: 5` 到了上限——模型调了 2 次工具（每次算 1 step），加上初始回答和最终回答，5 步用完后 stream 就结束了，但模型还没来得及输出最终文本。

问题根因：`maxSteps` 包含工具调用轮次，到了上限后 stream 直接结束，不会让模型再生成最终文本回答。

把 `maxSteps` 调大，并加 `onFinish` 回调记录 finish reason：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts",
  "newString": "  const result = streamText({\n    model: languageModel,\n    messages,\n    maxTokens: model.maxTokens || undefined,\n    ...(tools && Object.keys(tools).length > 0\n      ? { tools, maxSteps: 10 }\n      : {}),\n    ...(enableThinking\n      ? {\n          providerOptions: {\n            openaiCompatible: { reasoningEffort: \"high\" },\n          },\n        }\n      : {}),\n    onError: (error) => {\n      logger.error(\"[%s] [LLM-CHAT] streamText error: %s\", event.context.requestId ?? \"-\", error);\n      return error instanceof Error ? error.message : String(error);\n    },\n    onFinish: ({ finishReason, usage, steps }) => {\n      logger.info(\n        \"[%s] [LLM-CHAT] finished: reason=%s steps=%d inputTokens=%d outputTokens=%d\",\n        event.context.requestId ?? \"-\",\n        finishReason,\n        steps.length,\n        usage?.promptTokens ?? 0,\n        usage?.completionTokens ?? 0,\n      );\n    },\n  });",
  "oldString": "  const result = streamText({\n    model: languageModel,\n    messages,\n    maxTokens: model.maxTokens || undefined,\n    ...(tools && Object.keys(tools).length > 0\n      ? { tools, maxSteps: 5 }\n      : {}),\n    ...(enableThinking\n      ? {\n          providerOptions: {\n            openaiCompatible: { reasoningEffort: \"high\" },\n          },\n        }\n      : {}),\n    onError: (error) => {\n      logger.error(\"[%s] [LLM-CHAT] streamText error: %s\", event.context.requestId ?? \"-\", error);\n      return error instanceof Error ? error.message : String(error);\n    },\n  });"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 29.8s)

`maxSteps` 从 5 调到 10，并加了 `onFinish` 日志记录 finish reason 和步数。再试一次，如果日志显示 `reason=max-steps` 说明还是步数限制，需要继续调大；如果显示 `reason=stop` 说明模型自己选择不回答了（可能是因为工具返回错误结果后模型不知道怎么继续）。

---

## User

还是莫名就停止了，为什么呢，日志：[Pasted ~1 lines][2026-08-05T16:46:49.815] [INFO] APP - [001d651a-7490-43a5-a7c0-ec79ed87461f] [LLM-CHAT] finished: reason=tool-calls steps=10 inputTokens=146179 outputTokens=4244

---

## Assistant (Build · GLM-5.2 · 26.0s)

`reason=tool-calls steps=10` — 10 步全用完了，模型一直在调工具没停。`inputTokens=146179` 说明上下文越来越大，模型反复调用工具但每次都得到错误结果（`err_no:2`），陷入死循环。

根本问题是：工具返回了错误结果，但模型没有意识到该放弃并直接回答用户。需要在工具结果中给模型更明确的提示，或者限制工具调用次数。

两个改进：
1. `maxSteps` 降到合理值（如 5），但加 `onStepFinish` 检测连续工具调用失败
2. 在 fetch executor 中，如果 HTTP 返回非 200 或 JSON 错误，返回更明确的错误信息让模型知道该 URL 无效

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/fetch.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/fetch.ts</path>
<type>file</type>
<content>
1: import { z } from "zod";
2: import type { JSONSchema7 } from "json-schema";
3: import type { ToolExecutor, ToolContext, ToolResult } from "../../registry";
4: import { assertSafeUrl, checkDomainAccess } from "./security";
5: import { parseResponse } from "./parse";
6: import type { FetchToolConfig } from "./config";
7: 
8: const fetchInputSchema = z.object({
9:   url: z.string().url().describe("要抓取的 URL（http/https）"),
10:   method: z.enum(["GET", "POST"]).optional().describe("HTTP 方法"),
11:   headers: z.record(z.string(), z.string()).optional().describe("自定义请求头"),
12:   body: z.string().optional().describe("POST 请求体"),
13: });
14: 
15: type FetchInput = z.infer<typeof fetchInputSchema>;
16: 
17: export const fetchExecutor: ToolExecutor<FetchToolConfig> = {
18:   buildInputSchema(config: FetchToolConfig): JSONSchema7 {
19:     return {
20:       type: "object",
21:       properties: {
22:         url: { type: "string", description: "要抓取的 URL（http/https）" },
23:         method: {
24:           type: "string",
25:           enum: ["GET", "POST"],
26:           default: config.defaultMethod,
27:           description: "HTTP 方法",
28:         },
29:         headers: {
30:           type: "object",
31:           description: "自定义请求头",
32:           additionalProperties: { type: "string" },
33:         },
34:         body: { type: "string", description: "POST 请求体" },
35:       },
36:       required: ["url"],
37:     };
38:   },
39: 
40:   buildDescription(config: FetchToolConfig): string {
41:     const domains = config.allowedDomains.includes("*")
42:       ? "任意域名"
43:       : `仅限: ${config.allowedDomains.join(", ")}`;
44:     return `抓取网页或 API 内容。支持 ${config.parseMode} 模式。域名限制: ${domains}。超时 ${config.timeout}ms，最大响应 ${config.maxResponseSize} bytes。`;
45:   },
46: 
47:   async execute(
48:     input: unknown,
49:     config: FetchToolConfig,
50:     ctx: ToolContext,
51:   ): Promise<ToolResult> {
52:     const start = Date.now();
53: 
54:     // 1. 校验 input
55:     const parsed = fetchInputSchema.safeParse(input);
56:     if (!parsed.success) {
57:       return {
58:         success: false,
59:         data: null,
60:         error: `输入参数校验失败: ${parsed.error.message}`,
61:         metadata: { durationMs: Date.now() - start },
62:       };
63:     }
64:     const fetchInput: FetchInput = parsed.data;
65:     const method = fetchInput.method ?? config.defaultMethod;
66: 
67:     // 2. SSRF 检查
68:     try {
69:       await assertSafeUrl(fetchInput.url);
70:     } catch (e) {
71:       return {
72:         success: false,
73:         data: null,
74:         error: e instanceof Error ? e.message : String(e),
75:         metadata: { durationMs: Date.now() - start },
76:       };
77:     }
78: 
79:     // 3. 域名白/黑名单检查
80:     const hostname = new URL(fetchInput.url).hostname;
81:     try {
82:       checkDomainAccess(hostname, config.allowedDomains, config.blockedDomains);
83:     } catch (e) {
84:       return {
85:         success: false,
86:         data: null,
87:         error: e instanceof Error ? e.message : String(e),
88:         metadata: { durationMs: Date.now() - start },
89:       };
90:     }
91: 
92:     // 4. 发起 fetch
93:     const controller = new AbortController();
94:     const timeoutId = setTimeout(() => controller.abort(), config.timeout);
95: 
96:     try {
97:       const mergedHeaders: Record<string, string> = {
98:         ...config.defaultHeaders,
99:         ...(fetchInput.headers ?? {}),
100:       };
101: 
102:       const response = await fetch(fetchInput.url, {
103:         method,
104:         headers: mergedHeaders,
105:         body: method === "POST" ? fetchInput.body : undefined,
106:         signal: controller.signal,
107:         redirect: "follow",
108:       });
109: 
110:       clearTimeout(timeoutId);
111: 
112:       // 5. 大小限制检查
113:       const contentLength = response.headers.get("content-length");
114:       if (contentLength && parseInt(contentLength, 10) > config.maxResponseSize) {
115:         return {
116:           success: false,
117:           data: null,
118:           error: `响应过大: ${contentLength} bytes (限制 ${config.maxResponseSize})`,
119:           metadata: {
120:             statusCode: response.status,
121:             durationMs: Date.now() - start,
122:           },
123:         };
124:       }
125: 
126:       // 读取 body（分块检查大小）
127:       const reader = response.body?.getReader();
128:       if (!reader) {
129:         const text = await response.text();
130:         if (text.length > config.maxResponseSize) {
131:           return {
132:             success: false,
133:             data: null,
134:             error: `响应过大: ${text.length} bytes (限制 ${config.maxResponseSize})`,
135:             metadata: {
136:               statusCode: response.status,
137:               durationMs: Date.now() - start,
138:             },
139:           };
140:         }
141:         const result = parseResponse(text, config.parseMode, response.headers.get("content-type") ?? undefined);
142:         return {
143:           success: true,
144:           data: result.content,
145:           metadata: {
146:             statusCode: response.status,
147:             responseSize: text.length,
148:             durationMs: Date.now() - start,
149:           },
150:         };
151:       }
152: 
153:       const chunks: Uint8Array[] = [];
154:       let totalSize = 0;
155:       let oversized = false;
156:       while (true) {
157:         const { done, value } = await reader.read();
158:         if (done) break;
159:         if (value) {
160:           totalSize += value.length;
161:           if (totalSize > config.maxResponseSize) {
162:             oversized = true;
163:             break;
164:           }
165:           chunks.push(value);
166:         }
167:       }
168:       reader.cancel();
169: 
170:       if (oversized) {
171:         return {
172:           success: false,
173:           data: null,
174:           error: `响应过大: 超过 ${config.maxResponseSize} bytes`,
175:           metadata: {
176:             statusCode: response.status,
177:             durationMs: Date.now() - start,
178:           },
179:         };
180:       }
181: 
182:       const decoder = new TextDecoder("utf-8");
183:       const bodyText = chunks.map((c) => decoder.decode(c, { stream: true })).join("") + decoder.decode();
184: 
185:       // 6. 按 parseMode 处理
186:       const result = parseResponse(
187:         bodyText,
188:         config.parseMode,
189:         response.headers.get("content-type") ?? undefined,
190:       );
191: 
192:       return {
193:         success: true,
194:         data: result.content,
195:         metadata: {
196:           statusCode: response.status,
197:           responseSize: totalSize,
198:           durationMs: Date.now() - start,
199:         },
200:       };
201:     } catch (e) {
202:       clearTimeout(timeoutId);
203:       if (e instanceof Error && e.name === "AbortError") {
204:         return {
205:           success: false,
206:           data: null,
207:           error: `请求超时 (${config.timeout}ms)`,
208:           metadata: { durationMs: Date.now() - start },
209:         };
210:       }
211:       return {
212:         success: false,
213:         data: null,
214:         error: e instanceof Error ? e.message : String(e),
215:         metadata: { durationMs: Date.now() - start },
216:       };
217:     }
218:   },
219: };

(End of file - total 219 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 24.2s)

问题在于：fetch 工具即使 HTTP 200 但返回的 JSON body 是业务错误（`err_no:2`），工具仍标记为 `success: true`，模型看到结果后不知道这是错误，反复尝试不同 URL。

两个修复：

1. **fetch executor**：HTTP 非 200 时返回 `success: false`，让模型明确知道请求失败
2. **chat 集成**：工具执行失败时，给模型更明确的错误提示，并在 `maxSteps` 到达上限时让模型仍能输出最终文本

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/fetch.ts",
  "newString": "      clearTimeout(timeoutId);\n\n      // HTTP 非 2xx 直接返回失败，让模型知道请求无效\n      if (!response.ok) {\n        const errBody = await response.text().catch(() => \"\");\n        return {\n          success: false,\n          data: null,\n          error: `HTTP ${response.status} ${response.statusText}: ${errBody.slice(0, 500)}`,\n          metadata: {\n            statusCode: response.status,\n            durationMs: Date.now() - start,\n          },\n        };\n      }\n\n      // 5. 大小限制检查",
  "oldString": "      clearTimeout(timeoutId);\n\n      // 5. 大小限制检查"
}
```

**Output:**
```
Edit applied successfully.
```

---

## User

---

## Assistant (Compaction · GLM-5.2 · 93.7s)

## Goal
- 构建一个通用工具调用框架，作为 agent 系统的前置开发基础设施，fetch 作为第一个内置工具，后续可扩展 search、code-exec 等
- 在 LLM 测试页面展示完整 agent 思考链路：思考 → 调用工具 → 观察结果 → 继续思考 → 回答，按流式顺序在一个助手面板内展示

## Constraints & Preferences
- 工具执行独立 API + chat 集成（两阶段，先独立执行可调试，再集成到 chat）
- 新建 `agent_tools` 表，不复用现有 `tools` 表（现有 tools 表是内容分类标签，语义不同）
- 工具定义采用混合模式：type 硬编码执行器 + config 差异化配置
- fetch 工具支持三种 parseMode：raw / markdown / json
- 安全边界：域名控制 + SSRF 防护 + 超时/大小限制 + 执行日志，频率限制暂缓
- chat 集成为全自动 tool loop，maxSteps=5，无需用户确认
- 工具全局启用，所有用户共享同一套工具配置
- `enableTools` 默认 false，向后兼容现有 chat 行为
- Input Schema 改为只读展示（fetch 工具参数固定，无需用户编辑，等未来多种工具类型时再开放）
- agent 链路展示：所有 part（思考/工具调用/文本回答）在同一个助手面板内，左侧一个助手头像，右侧按序展示

## Progress
### Done
- 探索项目上下文：Nuxt 4 + Drizzle ORM + ai-sdk，已有 LLM 集成、chat 服务、tools 表（分类标签）
- 完成所有澄清问题（8 轮 Q&A）
- 完成设计方案 7 个 section，用户逐一确认通过
- 设计文档写入 `docs/superpowers/specs/2026-08-05-agent-tool-framework-design.md` 并提交 git（commit fcc2992）
- Spec 自检完成，修正了架构图路由名称和 jsonSchemaToZod 说明
- 用户 review spec 并确认通过
- 创建 15 项实现计划 TODO（DB → Registry → fetch executor → 日志 → Service → API → chat 集成 → 前端 → 测试）
- **DB Schema 完成**：新建 `packages/drizzle-pkg/lib/schema/agent-tool.ts`，定义 `agentTools` + `agentToolLogs` 两张表，生成迁移 `0014_breezy_maestro.sql` 并执行成功
- **Tool Registry 完成**：`server/service/agent-tool/registry.ts`，定义 `ToolExecutor<TConfig>` 接口、`ToolContext`、`ToolResult`，实现 `registerToolType` / `getExecutor` / `listToolTypes`
- **fetch executor config 完成**：`server/service/agent-tool/executors/fetch/config.ts`，zod schema 校验 + `DEFAULT_FETCH_CONFIG` + `parseFetchConfig`
- **fetch executor security 完成**：`server/service/agent-tool/executors/fetch/security.ts`，SSRF 防护 + 域名白/黑名单 + `assertSafeUrl`
- **fetch executor parse 完成**：`server/service/agent-tool/executors/fetch/parse.ts`，raw/markdown/json 三种模式，markdown 用 `turndown@7.2.0`
- **fetch executor fetch.ts 完成**：`server/service/agent-tool/executors/fetch/fetch.ts`
- **注册入口完成**：`server/service/agent-tool/register.ts`（简化为直接调用 `registerToolType`，去掉 `registered` flag）
- **日志服务完成**：`server/service/agent-tool/log.ts`
- **Service 层完成**：`server/service/agent-tool/index.ts`，实现 CRUD + `executeAgentTool` + `getEnabledToolsForLlm`
- **API 层 CRUD 完成**：6 个端点全部创建，全部使用 `requireAdmin` 权限校验
- 安装依赖：`@types/json-schema@7.0.15`、`json-schema-to-zod@2.0.0`、`turndown@7.2.0`、`@types/turndown@5.0.5`、`zod-to-json-schema@3.24.5`
- **chat 集成完成**：`server/api/llm/chat/index.post.ts` 已扩展 `enableTools` 参数
- **前端管理页面完成**：`app/pages/admin/agent-tools/index.vue`
- **前端表单 Modal 完成**：`app/components/AgentToolFormModal.vue`
- **前端执行测试 Modal 完成**：`app/components/AgentToolExecuteModal.vue`
- **composable 扩展完成**：`app/composables/useLlmChat.ts` 添加 `enableTools` 选项
- **llm-test 页面工具开关完成**：`app/pages/settings/llm-test/index.vue` 添加"工具调用"toggle 开关
- **前端 toast 修复**：三个文件改用 `useNuxtApp().$toast`
- **typecheck 通过**（项目已有无关错误）
- **端到端验证通过**（service 层）：Registry / Security / Parse / Fetch executor / CRUD / `getEnabledToolsForLlm` 全链路通过
- **BigInt 警告修复**：`security.ts` 中 BigInt literal 改为 `BigInt("0x0a000000")` 调用形式
- **`require is not defined` 修复**：`json-schema-to-zod` 改用 `createRequire(import.meta.url)` 加载
- **`Unknown tool type: fetch` 修复**：注册逻辑内联到 `index.ts`，不依赖 `register.ts` side-effect import
- **域名白名单逻辑修复**：白名单为空时允许所有域名
- **admin dashboard 菜单入口添加**
- **Input Schema 改为只读**
- **`jsonSchemaToZod` 返回字符串问题修复**：去掉 `json-schema-to-zod`，改用 `z.object({ url: z.string() })`
- **zod v4 + `zod-to-json-schema` 不兼容修复**：改用 `z.toJSONSchema()` + ai-sdk `jsonSchema()` 包装器
- **message 模型重构为 parts 数组**：`LlmChatMessage` 改为 `parts?: MessagePart[]`，每个 part 有 type（text/reasoning/tool-call/tool-result），按流式到达顺序追加
- **composable stream 处理重构**：`onTextPart` / `onReasoningPart` / `onToolCallPart` / `onToolResultPart` 均按序追加到 `msg.parts` 数组；`getOrCreateLastPart` 合并连续同类型 part；`updateLastReasoningDuration` 标记最后一个 reasoning part 完成
- **`part.input` → `part.args` 修复**：`onToolCallPart` 的字段名是 `args` 不是 `input`
- **`part.output` → `part.result` 修复**：`onToolResultPart` 的字段名是 `result` 不是 `output`（ai-sdk `tool_result` stream part 包含 `toolCallId` + `result`）
- **assistant 面板重构**：去掉 ChatBubble 用于 assistant，改为自定义 `assistant-panel`（左侧头像 + 右侧 `assistant-body` 卡片容器），内部按序渲染 reasoning-part / tool-call-item / assistant-text
- **user 消息保持 ChatBubble**（右侧气泡）
- **reasoning 折叠/展开**：`collapsedReasoning` Set + `toggleReasoning` 方法
- **loading 动画**：三个跳动圆点替代 ChatBubble loading
- **stream 静默结束检测**：`processDataStream` 完成后检查是否有 part 产出，无则报错"模型未返回任何内容"
- **服务端 `onError` 回调添加**：`streamText` 添加 `onError` 回调，错误打到日志并返回到 stream 中，前端 `onErrorPart` 可捕获
- **`executor` undefined 防御**：`executeAgentTool` 中 `if (!executor) return { success: false, error: "工具类型未注册" }`
- **`z.toJSONSchema` 类型不兼容修复**：`as Record<string, unknown>` 绕过 `JSONSchema7` 类型不匹配

### In Progress
- 用户反馈"模型 agent 测试回答多次就突然中断了，没任何提示"——已添加服务端 `onError` 回调 + 前端空内容检测，需用户验证是否修复

### Blocked
- 项目已有 bug：`login_post$1` / `renderer` before initialization 错误，非我们引入，导致无法通过 API 登录做完整端到端测试
- 前端 execute 请求 `Failed to fetch`：日志中无 execute 请求记录，可能是 `$fetch` 未带 cookie 导致 401 或请求被浏览器拦截

## Key Decisions
- 方案 C：工具执行独立 API + chat 集成，便于调试和复用
- 新建 `agent_tools` 表而非扩展 `tools` 表，职责清晰
- 混合 schema 管理：type 硬编码执行逻辑，config 存差异化配置
- 全自动 tool loop（A 方案），安全由域名控制 + SSRF 防护兜底
- 全局工具启用（A 方案），工具是平台级基础设施
- `enableTools` 默认 false，现有 chat 不受影响
- `agentTools` 表 id 使用 `at_{timestamp36}_{random}` 格式，非自增
- `agentToolLogs` 表 id 使用自增 integer
- API 端点全部使用 `requireAdmin`，chat 集成使用 `requireUser`
- `getEnabledToolsForLlm` 返回 `Record<string, any>`，key 为 tool slug
- 前端 toast 使用 `useNuxtApp().$toast`
- markdown parseMode 用 `turndown@7.2.0` 做 HTML→MD
- `security.ts` BigInt 用 `BigInt("0x0a000000")` 调用形式
- `registerToolType` 容忍重复注册，`getExecutor` 返回 `undefined` 而非抛异常
- 注册逻辑直接内联在 `index.ts` 中，不依赖单独的 `register.ts` side-effect import
- 去掉 `json-schema-to-zod` 依赖，fetch 工具用固定 zod schema
- Input Schema 界面改为只读展示
- zod v4 不兼容 `zod-to-json-schema@3.x`，改用 `z.toJSONSchema()` + ai-sdk `jsonSchema()` 包装器
- message 模型重构为 parts 数组（text/reasoning/tool-call/tool-result 按序追加），替代旧的 content + toolCalls 分离模型
- assistant 回复用自定义面板（头像+卡片容器），不用 ChatBubble；user 消息保持 ChatBubble
- `onToolCallPart` 字段名是 `args`（不是 `input`）；`onToolResultPart` 字段名是 `result`（不是 `output`）
- 服务端 `streamText` 添加 `onError` 回调，错误返回到 stream 中供前端捕获

## Next Steps
- 用户验证"突然中断"问题是否已修复（服务端 onError + 前端空内容检测）
- 如果中断仍存在，检查 `maxSteps: 5` 是否为限制原因，可能需要调大
- 验证前端 execute 请求能正常工作（用户在浏览器测试）
- 清理：`register.ts` 文件可能已不需要（注册逻辑已内联到 `index.ts`）
- 清理：`json-schema-to-zod` 和 `zod-to-json-schema@3.24.5` 依赖可能已不需要

## Critical Context
- 项目使用 Nuxt 4 + Drizzle ORM (SQLite) + ai-sdk (Vercel AI SDK) v4.3.16
- zod 版本为 v4.3.6，内置 `z.toJSONSchema()` 方法可直接生成 JSON Schema
- `zod-to-json-schema@3.x` 不兼容 zod v4
- ai-sdk `tool()` 的 `parameters` 接受 zod schema 或 `jsonSchema()` 包装的对象
- `processDataStream` 支持回调：`onTextPart`、`onReasoningPart`、`onErrorPart`、`onToolCallPart`、`onToolResultPart` 等
- `onToolCallPart` 的 stream part 类型为 `tool_call`，包含 `toolName`、`toolCallId`、`args`（不是 `input`）
- `onToolResultPart` 的 stream part 类型为 `tool_result`，包含 `toolCallId`、`result`（不是 `output`）
- `streamText` 的 `onError` 回调返回错误字符串，会作为 error part 发送到 stream 中
- `toDataStreamResponse({ sendReasoning: true })` 发送 reasoning part
- 现有 `server/api/llm/chat/index.post.ts` 使用 `streamText` + `defineEventHandler`
- `server/service/agent-tool/` 为新增独立模块，与现有 `server/service/tool/` 并行
- API 响应模式：CRUD 端点用 `defineWrappedResponseHandler` + `R.success` / `R.throwError`，chat 端点用 `defineEventHandler` + `createError`
- `requireAdmin` 在 `server/utils/admin-guard.ts`
- `dbGlobal` 从 `drizzle-pkg/lib/db` 导入
- 前端组件库 `packages/bolt-ui/` 有 `ChatBubble` 组件，但不支持 toolCalls prop
- `app/composables/useLlmChat.ts` 是 chat 的核心 composable
- 项目已有 bug：`login_post$1` / `renderer` before initialization（非我们引入）
- 数据库中已有用户：admin (id=11, role=admin)、npmrun (id=12, role=user)
- 数据库中已有 1 个 fetch 工具：id=`at_msfj6cbp_1float`，slug=`test`，type=`fetch`，enabled=1
- Nitro tree-shaking 会移除没有导出的 side-effect import
- `json-schema-to-zod` 的 `jsonSchemaToZod()` 返回的是 zod 代码字符串，不是 zod schema 实例
- `MessagePart` 类型：`id` / `type`（text/reasoning/tool-call/tool-result）/ `text` / `toolName` / `toolCallId` / `args` / `result` / `state` / `reasoningLoading` / `reasoningDuration`
- `getOrCreateLastPart` 合并连续同类型 part（text 追加到上一个 text part，reasoning 追加到上一个 reasoning part）
- `updateLastReasoningDuration` 从 parts 末尾向前找第一个 `reasoningLoading` 的 reasoning part 并标记完成

## Relevant Files
- `docs/superpowers/specs/2026-08-05-agent-tool-framework-design.md` — 设计文档（已提交）
- `packages/drizzle-pkg/lib/schema/agent-tool.ts` — schema 定义
- `packages/drizzle-pkg/migrations/0014_breezy_maestro.sql` — 迁移文件（已执行）
- `server/service/agent-tool/registry.ts` — ToolExecutor 接口 + 注册机制
- `server/service/agent-tool/register.ts` — 注册入口（可能已不需要）
- `server/service/agent-tool/log.ts` — writeToolLog 日志服务
- `server/service/agent-tool/index.ts` — Service 层 CRUD + execute + getEnabledToolsForLlm（内联注册 + `z.toJSONSchema()` + `jsonSchema()` 包装 + executor undefined 防御 + `as Record<string, unknown>` 类型修复）
- `server/service/agent-tool/executors/fetch/config.ts` — fetch 配置 zod schema + 默认值
- `server/service/agent-tool/executors/fetch/security.ts` — SSRF 防护 + 域名检查
- `server/service/agent-tool/executors/fetch/parse.ts` — raw/markdown/json 解析
- `server/service/agent-tool/executors/fetch/fetch.ts` — fetch 执行器组装
- `server/api/agent-tools/index.get.ts` — 列出工具
- `server/api/agent-tools/index.post.ts` — 创建工具
- `server/api/agent-tools/[id].get.ts` — 获取详情
- `server/api/agent-tools/[id].put.ts` — 更新工具
- `server/api/agent-tools/[id].delete.ts` — 删除工具
- `server/api/agent-tools/[id]/execute.post.ts` — 独立执行
- `server/api/llm/chat/index.post.ts` — 流式对话接口，已扩展 tool-calling + `onError` 回调
- `app/pages/admin/agent-tools/index.vue` — admin 管理列表页
- `app/pages/admin/dashboard.vue` — admin 仪表盘，已添加 Agent 工具管理入口
- `app/components/AgentToolFormModal.vue` — 工具创建/编辑表单 Modal（inputSchema 只读）
- `app/components/AgentToolExecuteModal.vue` — 工具执行测试 Modal
- `app/composables/useLlmChat.ts` — chat composable，已重构为 parts 数组模型 + `onToolCallPart`(args) / `onToolResultPart`(result) + 空内容检测
- `app/pages/settings/llm-test/index.vue` — LLM 测试页面，已重构为 assistant-panel（头像+卡片容器，内部按序渲染 reasoning/tool-call/text）+ user ChatBubble + reasoning 折叠 + loading 动画

---

## User

---

## Assistant (Build · GLM-5.2 · 23.5s)

现在看 chat 集成层，工具结果如何传给模型：

**Tool: grep**

**Input:**
```json
{
  "pattern": "executeAgentTool|tool_result|toolResult",
  "path": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts"
}
```

**Output:**
```
No files found
```

---

## Assistant (Build · GLM-5.2 · 21.1s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts</path>
<type>file</type>
<content>
1: import { requireUser } from "#server/utils/context";
2: import { getProviderById, getModelById } from "#server/service/llm";
3: import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
4: import { streamText, type LanguageModelV1 } from "ai";
5: import { getEnabledToolsForLlm } from "#server/service/agent-tool";
6: import log4js from "logger";
7: 
8: const logger = log4js.getLogger("APP");
9: 
10: function resolveModel(
11:   provider: {
12:     name: string;
13:     apiKey: string | null;
14:     baseUrl: string | null;
15:     parseMode: string;
16:   },
17:   modelId: string,
18: ): LanguageModelV1 {
19:   const baseUrl = provider.baseUrl?.replace(/\/+$/, "") || undefined;
20: 
21:   if (provider.parseMode === "anthropic") {
22:     throw createError({
23:       statusCode: 400,
24:       statusMessage: "Anthropic 解析模式暂不支持流式对话，请使用 OpenAI 兼容模式",
25:     });
26:   }
27: 
28:   const openaiCompatible = createOpenAICompatible({
29:     name: provider.name,
30:     apiKey: provider.apiKey || undefined,
31:     baseURL: baseUrl || "https://api.openai.com/v1",
32:   });
33: 
34:   return openaiCompatible(modelId) as LanguageModelV1;
35: }
36: 
37: export default defineEventHandler(async (event) => {
38:   const user = await requireUser(event);
39:   if (!user) {
40:     throw createError({ statusCode: 401, statusMessage: "未登录" });
41:   }
42: 
43:   const body = await readBody(event);
44:   const { modelId: llmModelId, messages, enableThinking, enableTools } = body as {
45:     modelId: number;
46:     messages: { role: "user" | "assistant" | "system"; content: string }[];
47:     enableThinking?: boolean;
48:     enableTools?: boolean;
49:   };
50: 
51:   if (!llmModelId || !messages || !Array.isArray(messages) || messages.length === 0) {
52:     throw createError({ statusCode: 400, statusMessage: "参数无效" });
53:   }
54: 
55:   const model = await getModelById(llmModelId, user.id);
56:   if (!model) {
57:     throw createError({ statusCode: 404, statusMessage: "模型不存在" });
58:   }
59: 
60:   const provider = await getProviderById(model.providerId, user.id);
61:   if (!provider) {
62:     throw createError({ statusCode: 404, statusMessage: "供应商不存在" });
63:   }
64: 
65:   if (provider.status !== "active") {
66:     throw createError({ statusCode: 400, statusMessage: "供应商已禁用" });
67:   }
68: 
69:   if (!provider.apiKey) {
70:     throw createError({ statusCode: 400, statusMessage: "供应商未配置 API Key" });
71:   }
72: 
73:   logger.info(
74:     "[%s] [LLM-CHAT] userId=%d modelId=%d provider=%s parseMode=%s messages=%d thinking=%s tools=%s",
75:     event.context.requestId ?? "-",
76:     user.id,
77:     llmModelId,
78:     provider.name,
79:     provider.parseMode,
80:     messages.length,
81:     enableThinking ? "on" : "off",
82:     enableTools ? "on" : "off",
83:   );
84: 
85:   const languageModel = resolveModel(provider, model.modelId);
86: 
87:   const tools = enableTools ? await getEnabledToolsForLlm() : undefined;
88: 
89:   const result = streamText({
90:     model: languageModel,
91:     messages,
92:     maxTokens: model.maxTokens || undefined,
93:     ...(tools && Object.keys(tools).length > 0
94:       ? { tools, maxSteps: 10 }
95:       : {}),
96:     ...(enableThinking
97:       ? {
98:           providerOptions: {
99:             openaiCompatible: { reasoningEffort: "high" },
100:           },
101:         }
102:       : {}),
103:     onError: (error) => {
104:       logger.error("[%s] [LLM-CHAT] streamText error: %s", event.context.requestId ?? "-", error);
105:       return error instanceof Error ? error.message : String(error);
106:     },
107:     onFinish: ({ finishReason, usage, steps }) => {
108:       logger.info(
109:         "[%s] [LLM-CHAT] finished: reason=%s steps=%d inputTokens=%d outputTokens=%d",
110:         event.context.requestId ?? "-",
111:         finishReason,
112:         steps.length,
113:         usage?.promptTokens ?? 0,
114:         usage?.completionTokens ?? 0,
115:       );
116:     },
117:   });
118: 
119:   return result.toDataStreamResponse({ sendReasoning: true });
120: });

(End of file - total 120 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 18.1s)

**Tool: grep**

**Input:**
```json
{
  "path": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "pattern": "getEnabledToolsForLlm|execute"
}
```

**Output:**
```
Found 6 matches
/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts:
  Line 12: // 立即注册 fetch 工具类型，确保在任何 execute 调用前完成

  Line 122: export async function executeAgentTool(

  Line 166:   const result = await executor.execute(input, config, ctx);

  Line 184: export async function getEnabledToolsForLlm(): Promise<Record<string, ReturnType<typeof tool>>> {

  Line 215:       execute: async (input: unknown) => {

  Line 216:         const execResult = await executeAgentTool(agentTool.id, input, null);

```

---

## Assistant (Build · GLM-5.2 · 6.8s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "offset": 180,
  "limit": 60
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts</path>
<type>file</type>
<content>
180: 
181:   return result;
182: }
183: 
184: export async function getEnabledToolsForLlm(): Promise<Record<string, ReturnType<typeof tool>>> {
185:   const tools = await dbGlobal
186:     .select()
187:     .from(agentTools)
188:     .where(eq(agentTools.enabled, 1))
189:     .orderBy(asc(agentTools.sortOrder));
190: 
191:   const result: Record<string, any> = {};
192:   for (const agentTool of tools) {
193:     const executor = getExecutor(agentTool.type);
194:     if (!executor) continue;
195:     let config: unknown;
196:     try {
197:       config = JSON.parse(agentTool.config);
198:     } catch {
199:       config = agentTool.type === "fetch" ? DEFAULT_FETCH_CONFIG : {};
200:     }
201: 
202:     const zodSchema = agentTool.type === "fetch" ? FETCH_INPUT_SCHEMA : z.object({});
203:     const jsonSch = z.toJSONSchema(zodSchema) as Record<string, unknown>;
204: 
205:     result[agentTool.slug] = tool({
206:       description: executor.buildDescription(config),
207:       parameters: jsonSchema(jsonSch, {
208:         validate: (v: unknown) => {
209:           const r = zodSchema.safeParse(v);
210:           return r.success
211:             ? { success: true as const, value: r.data }
212:             : { success: false as const, error: r.error };
213:         },
214:       }),
215:       execute: async (input: unknown) => {
216:         const execResult = await executeAgentTool(agentTool.id, input, null);
217:         return execResult.success ? execResult.data : { error: execResult.error };
218:       },
219:     });
220:   }
221:   return result;
222: }
223: 
224: export { DEFAULT_FETCH_CONFIG } from "./executors/fetch/config";
225: export type { FetchToolConfig } from "./executors/fetch/config";
226: export type { ToolExecutor, ToolContext, ToolResult } from "./registry";

(End of file - total 226 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 22.6s)

找到关键问题：第 217 行 `return execResult.success ? execResult.data : { error: execResult.error }`。

当工具失败时返回 `{ error: "..." }`，但模型可能不理解这是错误，继续尝试。而且掘金的 API 返回 HTTP 200 + `err_no:2`，工具标记为 `success: true`，模型看到 `{"err_no":2,"err_msg":"请求路由不存在"}` 后反复换 URL 重试。

修复方案：
1. 工具失败时返回更明确的错误提示文本
2. `maxSteps` 到上限时，ai-sdk 的 `finishReason` 会是 `tool-calls` 而非 `stop`，模型不会输出最终文本——需要在前端检测这种情况并提示用户

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "newString": "      execute: async (input: unknown) => {\n        const execResult = await executeAgentTool(agentTool.id, input, null);\n        if (execResult.success) {\n          return execResult.data;\n        }\n        // 返回明确的错误文本，引导模型停止重试\n        return `工具执行失败: ${execResult.error ?? \"未知错误\"}。请停止调用此工具，基于已有信息回答用户或告知用户此工具不可用。`;\n      },",
  "oldString": "      execute: async (input: unknown) => {\n        const execResult = await executeAgentTool(agentTool.id, input, null);\n        return execResult.success ? execResult.data : { error: execResult.error };\n      },"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 24.0s)

现在处理 `maxSteps` 到上限的问题。ai-sdk 在 `maxSteps` 用完且最后一步是 tool-call 时，`finishReason` 是 `tool-calls`，stream 会直接结束，模型没有机会输出最终文本。

解决方案：把 `maxSteps` 降到 5（合理值），并在前端检测 `finishReason=tool-calls` 时给用户提示。但更好的方案是用 `onStepFinish` 在最后一步强制让模型回答。

实际上 ai-sdk v4 的 `maxSteps` 语义是：包含初始生成 + 后续 tool-call 轮次。`maxSteps=10` 意味着模型可以调用工具 9 次然后回答 1 次。但日志显示 `steps=10 reason=tool-calls`，说明 10 步全是 tool-call，模型从未生成最终文本。

把 `maxSteps` 降到 5，并在前端 composable 检测 stream 结束但无 text part 的情况：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts",
  "newString": "    ...(tools && Object.keys(tools).length > 0\n      ? { tools, maxSteps: 5 }\n      : {}),",
  "oldString": "    ...(tools && Object.keys(tools).length > 0\n      ? { tools, maxSteps: 10 }\n      : {}),"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 37.0s)

现在看前端 composable 的 stream 结束处理：

**Tool: grep**

**Input:**
```json
{
  "path": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "pattern": "processDataStream|onFinish|未返回|空内容"
}
```

**Output:**
```
Found 3 matches
/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts:
  Line 1: import { processDataStream } from 'ai'

  Line 126:       await processDataStream({

  Line 184:         errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'

```

---

## Assistant (Build · GLM-5.2 · 9.3s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "limit": 80,
  "offset": 120
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts</path>
<type>file</type>
<content>
120:       }
121: 
122:       if (!res.body) {
123:         throw new Error('响应体为空')
124:       }
125: 
126:       await processDataStream({
127:         stream: res.body,
128:         onReasoningPart: (text) => {
129:           const msg = messages.value[assistantIdx]
130:           if (!msg) return
131:           let part = getOrCreateLastPart(msg, 'reasoning')
132:           if (!part) {
133:             if (reasoningStartTime === null) reasoningStartTime = Date.now()
134:             part = { id: generateId(), type: 'reasoning', text: '', reasoningLoading: true }
135:             appendPart(msg, part)
136:           }
137:           part.text = (part.text ?? '') + text
138:         },
139:         onTextPart: (text) => {
140:           const msg = messages.value[assistantIdx]
141:           if (!msg) return
142:           updateLastReasoningDuration(msg)
143:           let part = getOrCreateLastPart(msg, 'text')
144:           if (!part) {
145:             part = { id: generateId(), type: 'text', text: '' }
146:             appendPart(msg, part)
147:           }
148:           part.text = (part.text ?? '') + text
149:           msg.content += text
150:         },
151:         onErrorPart: (error) => {
152:           errorMessage.value = error || '流式响应出错'
153:         },
154:         onToolCallPart: (part) => {
155:           const msg = messages.value[assistantIdx]
156:           if (!msg) return
157:           updateLastReasoningDuration(msg)
158:           appendPart(msg, {
159:             id: generateId(),
160:             type: 'tool-call',
161:             toolName: part.toolName,
162:             toolCallId: part.toolCallId,
163:             args: part.args,
164:             state: 'call',
165:           })
166:         },
167:         onToolResultPart: (part) => {
168:           const msg = messages.value[assistantIdx]
169:           if (!msg || !msg.parts) return
170:           const callPart = msg.parts.find(p => p.type === 'tool-call' && p.toolCallId === part.toolCallId)
171:           if (callPart) {
172:             callPart.result = part.result
173:             callPart.state = 'result'
174:           }
175:         },
176:       })
177: 
178:       const msg = messages.value[assistantIdx]
179:       if (msg) updateLastReasoningDuration(msg)
180: 
181:       // 检查是否有实际内容产出
182:       const finalMsg = messages.value[assistantIdx]
183:       if (finalMsg && !finalMsg.content && (!finalMsg.parts || finalMsg.parts.length === 0) && !errorMessage.value) {
184:         errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'
185:         messages.value.splice(assistantIdx, 1)
186:       }
187:     } catch (err: any) {
188:       if (err.name === 'AbortError') {
189:         // user stopped
190:       } else {
191:         errorMessage.value = err.message || '请求失败'
192:         const msg = messages.value[assistantIdx]
193:         if (msg && !msg.content && (!msg.parts || msg.parts.length === 0)) {
194:           messages.value.splice(assistantIdx, 1)
195:         }
196:       }
197:     } finally {
198:       isLoading.value = false
199:       abortController = null

(Showing lines 120-199 of 223. Use offset=200 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 25.0s)

问题在第 183 行：检查条件是 `!finalMsg.content && parts.length === 0`，但当模型调了工具后，`parts` 数组里有 `tool-call` 和 `tool-result` part，`content` 为空字符串。条件不满足，所以不会显示错误提示，用户看到的就是"有工具调用但没文本回答"的空状态。

修复：如果 stream 结束后只有 tool-call part 没有 text part，说明 `maxSteps` 用完了，应该提示用户。

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "newString": "      // 检查是否有实际内容产出\n      const finalMsg = messages.value[assistantIdx]\n      if (finalMsg && !errorMessage.value) {\n        const hasText = finalMsg.parts?.some(p => p.type === 'text' && p.text)\n        const hasToolCall = finalMsg.parts?.some(p => p.type === 'tool-call')\n        if (!hasText && !hasToolCall) {\n          // 完全无内容，移除空消息\n          errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'\n          messages.value.splice(assistantIdx, 1)\n        } else if (!hasText && hasToolCall) {\n          // 有工具调用但无最终文本回答（maxSteps 用完）\n          errorMessage.value = '模型达到工具调用次数上限，未能生成最终回答。请简化问题或调整工具配置后重试。'\n        }\n      }",
  "oldString": "      // 检查是否有实际内容产出\n      const finalMsg = messages.value[assistantIdx]\n      if (finalMsg && !finalMsg.content && (!finalMsg.parts || finalMsg.parts.length === 0) && !errorMessage.value) {\n        errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'\n        messages.value.splice(assistantIdx, 1)\n      }"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 57.9s)

现在验证 typecheck：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && npx nuxi typecheck 2>&1 | tail -30",
  "description": "运行 typecheck",
  "timeout": 120000
}
```

**Output:**
```
      Type 'undefined' is not assignable to type 'string | null'.
server/api/llm/chat/index.post.ts(103,5): error TS2322: Type '(error: { error: unknown; }) => string' is not assignable to type 'StreamTextOnErrorCallback'.
  Type 'string' is not assignable to type 'void | Promise<void>'.
server/api/users/batch.post.ts(28,61): error TS7006: Parameter 'n' implicitly has an 'any' type.
server/api/users/index.get.ts(38,21): error TS2769: No overload matches this call.
  Overload 1 of 3, '(left: SQLiteColumn<{ name: "role"; tableName: "users"; dataType: "string"; columnType: "SQLiteText"; data: "admin" | "user"; driverParam: string; notNull: true; hasDefault: true; isPrimaryKey: false; isAutoincrement: false; ... 4 more ...; generated: undefined; }, {}, { ...; }>, right: "admin" | ... 1 more ... | SQLWrapper): SQL<...>', gave the following error.
    Argument of type 'string' is not assignable to parameter of type '"admin" | "user" | SQLWrapper'.
  Overload 2 of 3, '(left: Aliased<string>, right: string | SQLWrapper): SQL<unknown>', gave the following error.
    Argument of type 'SQLiteColumn<{ name: "role"; tableName: "users"; dataType: "string"; columnType: "SQLiteText"; data: "admin" | "user"; driverParam: string; notNull: true; hasDefault: true; isPrimaryKey: false; isAutoincrement: false; ... 4 more ...; generated: undefined; }, {}, { ...; }>' is not assignable to parameter of type 'Aliased<string>'.
      Type 'SQLiteColumn<{ name: "role"; tableName: "users"; dataType: "string"; columnType: "SQLiteText"; data: "admin" | "user"; driverParam: string; notNull: true; hasDefault: true; isPrimaryKey: false; isAutoincrement: false; ... 4 more ...; generated: undefined; }, {}, { ...; }>' is missing the following properties from type 'Aliased<string>': sql, fieldAlias
  Overload 3 of 3, '(left: never, right: unknown): SQL<unknown>', gave the following error.
    Argument of type 'SQLiteColumn<{ name: "role"; tableName: "users"; dataType: "string"; columnType: "SQLiteText"; data: "admin" | "user"; driverParam: string; notNull: true; hasDefault: true; isPrimaryKey: false; isAutoincrement: false; ... 4 more ...; generated: undefined; }, {}, { ...; }>' is not assignable to parameter of type 'never'.
server/service/agent-tool/index.ts(147,5): error TS2741: Property 'data' is missing in type '{ success: false; error: string; metadata: { durationMs: number; }; }' but required in type 'ToolResult'.
server/service/chat/index.ts(124,5): error TS2741: Property 'where' is missing in type 'Omit<import("/home/dash/coding/nuxt-app/node_modules/.bun/drizzle-orm@0.45.2+988eccb4023f4f3f/node_modules/drizzle-orm/sqlite-core/query-builders/select").SQLiteSelectBase<"chat_messages", "async", import("/home/dash/coding/nuxt-app/node_modules/.bun/@libsql+core@0.17.3/node_modules/@libsql/core/lib-esm/api").Result...' but required in type 'Omit<import("/home/dash/coding/nuxt-app/node_modules/.bun/drizzle-orm@0.45.2+988eccb4023f4f3f/node_modules/drizzle-orm/sqlite-core/query-builders/select").SQLiteSelectBase<"chat_messages", "async", import("/home/dash/coding/nuxt-app/node_modules/.bun/@libsql+core@0.17.3/node_modules/@libsql/core/lib-esm/api").Result...'.
server/service/chat/index.ts(221,12): error TS2339: Property 'count' does not exist on type '{ count: number; } | undefined'.
server/service/favorite/index.ts(172,9): error TS2322: Type '{ id: number; type: "text" | "image" | "image-text" | "project"; title: string; description: string | null; aspectRatio: number | null; categoryId: string | null; createdAt: Date; updatedAt: Date; images: { ...; }[]; tags: { ...; }[]; articles: never[]; }[]' is not assignable to type 'CardWithRelations[]'.
  Property 'content' is missing in type '{ id: number; type: CardWithRelations["type"]; title: string; description: string | null; aspectRatio: number | null; categoryId: string | null; createdAt: Date; updatedAt: Date; images: { ...; }[]; tags: { ...; }[]; articles: never[]; }' but required in type 'CardWithRelations'.
server/service/ideas/index.ts(35,3): error TS2322: Type 'string | undefined' is not assignable to type 'string'.
  Type 'undefined' is not assignable to type 'string'.

 ERROR  Process exited with non-zero status (2)

    at z._waitForOutput (node_modules/.bun/tinyexec@1.1.2/node_modules/tinyexec/dist/main.mjs:578:92)
    at process.processTicksAndRejections (node:internal/process/task_queues:104:5)
    at async Object.run (node_modules/.bun/@nuxt+cli@3.35.2+eaef1d45de35c53b/node_modules/@nuxt/cli/dist/typecheck-DQJUWomH.mjs:65:3)
    at async runCommand (node_modules/.bun/citty@0.2.2/node_modules/citty/dist/index.mjs:228:47)
    at async runCommand (node_modules/.bun/citty@0.2.2/node_modules/citty/dist/index.mjs:217:5)
    at async runMain (node_modules/.bun/citty@0.2.2/node_modules/citty/dist/index.mjs:394:10) 



```

---

## Assistant (Build · GLM-5.2 · 17.7s)

有两个我们引入的错误需要修复：

1. `server/api/llm/chat/index.post.ts(103,5)` — `onError` 返回 string 但期望 void
2. `server/service/agent-tool/index.ts(147,5)` — `ToolResult` 的 `data` 字段必填

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/registry.ts",
  "limit": 40
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/registry.ts</path>
<type>file</type>
<content>
1: import type { JSONSchema7 } from "json-schema";
2: 
3: export interface ToolExecutor<TConfig> {
4:   buildInputSchema(config: TConfig): JSONSchema7;
5:   buildDescription(config: TConfig): string;
6:   execute(input: unknown, config: TConfig, ctx: ToolContext): Promise<ToolResult>;
7: }
8: 
9: export interface ToolContext {
10:   toolId: string;
11:   toolSlug: string;
12:   userId: number | null;
13: }
14: 
15: export interface ToolResult {
16:   success: boolean;
17:   data: unknown;
18:   error?: string;
19:   metadata?: {
20:     statusCode?: number;
21:     responseSize?: number;
22:     durationMs: number;
23:   };
24: }
25: 
26: const registry = new Map<string, ToolExecutor<any>>();
27: 
28: export function registerToolType(type: string, executor: ToolExecutor<any>): void {
29:   registry.set(type, executor);
30: }
31: 
32: export function getExecutor(type: string): ToolExecutor<any> | undefined {
33:   return registry.get(type);
34: }
35: 
36: export function listToolTypes(): string[] {
37:   return Array.from(registry.keys());
38: }

(End of file - total 38 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 21.1s)

`data: unknown` 是必填的。修复 `ToolResult` 让 `data` 可选，或者在失败时传 `null`：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/registry.ts",
  "newString": "export interface ToolResult {\n  success: boolean;\n  data?: unknown;\n  error?: string;\n  metadata?: {\n    statusCode?: number;\n    responseSize?: number;\n    durationMs: number;\n  };\n}",
  "oldString": "export interface ToolResult {\n  success: boolean;\n  data: unknown;\n  error?: string;\n  metadata?: {\n    statusCode?: number;\n    responseSize?: number;\n    durationMs: number;\n  };\n}"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 16.1s)

现在修复 `onError` 返回类型——ai-sdk v4 的 `onError` 回调返回 `void`，不能返回 string。错误需要通过其他方式传递：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts",
  "newString": "    onError: (errorData) => {\n      const errMsg = errorData?.error instanceof Error\n        ? errorData.error.message\n        : String(errorData?.error ?? \"未知错误\");\n      logger.error(\"[%s] [LLM-CHAT] streamText error: %s\", event.context.requestId ?? \"-\", errMsg);\n    },",
  "oldString": "    onError: (error) => {\n      logger.error(\"[%s] [LLM-CHAT] streamText error: %s\", event.context.requestId ?? \"-\", error);\n      return error instanceof Error ? error.message : String(error);\n    },"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 58.6s)

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && npx nuxi typecheck 2>&1 | grep -E \"agent-tool|llm/chat\" | head -20",
  "description": "检查相关文件 typecheck",
  "timeout": 120000
}
```

**Output:**
```
(no output)
```

---

## Assistant (Build · GLM-5.2 · 27.2s)

我们引入的 typecheck 错误已全部修复。总结改动：

1. **fetch executor**：HTTP 非 2xx 时返回 `success: false` + 错误信息（之前 HTTP 200 但业务错误仍返回 success）
2. **工具失败提示**：失败时返回明确的文本提示，引导模型停止重试（之前返回 `{ error: "..." }` 对象，模型不理解）
3. **maxSteps 降到 5**：避免无限工具调用循环
4. **前端检测**：stream 结束后如果有 tool-call 但无 text part，提示用户"达到工具调用次数上限"
5. **ToolResult.data 改为可选**：失败时不需要传 data
6. **onError 回调修复**：返回 void 而非 string（ai-sdk v4 要求）

现在再测试，模型应该会在工具失败后停止重试并告知用户，或者达到 5 步上限时前端会显示明确提示。

---

## User

工具失败整个agent就失败了吗？不需要模型最后说一下？

---

## Assistant (Build · GLM-5.2 · 37.3s)

你说得对。`maxSteps` 到上限后 stream 直接结束，模型没机会说"我试了但失败了"。应该让模型在工具失败后仍能输出最终文本。

ai-sdk v4 的 `maxSteps` 语义：每一步要么生成文本（结束），要么生成 tool-call（继续）。到上限后如果是 tool-call，stream 就断了。

正确做法：**工具失败时不要消耗 step**，或者让模型知道失败后自己选择停止。但 ai-sdk 没有前者机制。

实际最简方案：`maxSteps` 设大一点（比如 8），但给工具的 `execute` 返回明确错误文本，模型看到失败后通常会在下一 step 生成文本回答而非继续调工具。之前的问题是掘金 API 返回 HTTP 200 + `err_no:2`，工具标记 success，模型以为成功但看到奇怪结果就反复换 URL。

现在已修复 HTTP 非 2xx 返回失败 + 失败文本引导模型停止。把 `maxSteps` 调回 8 给模型留余地：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts",
  "newString": "    ...(tools && Object.keys(tools).length > 0\n      ? { tools, maxSteps: 8 }\n      : {}),",
  "oldString": "    ...(tools && Object.keys(tools).length > 0\n      ? { tools, maxSteps: 5 }\n      : {}),"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 27.5s)

但掘金那个案例是 HTTP 200 + `err_no:2`，工具仍标记 success。模型看到 `{"err_no":2,"err_msg":"请求路由不存在"}` 后不理解这是错误，继续换 URL。需要在工具结果里给模型更明确的判断提示。

看下当前工具返回给模型的内容：

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "limit": 15,
  "offset": 213
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts</path>
<type>file</type>
<content>
213:         },
214:       }),
215:       execute: async (input: unknown) => {
216:         const execResult = await executeAgentTool(agentTool.id, input, null);
217:         if (execResult.success) {
218:           return execResult.data;
219:         }
220:         // 返回明确的错误文本，引导模型停止重试
221:         return `工具执行失败: ${execResult.error ?? "未知错误"}。请停止调用此工具，基于已有信息回答用户或告知用户此工具不可用。`;
222:       },
223:     });
224:   }
225:   return result;
226: }
227: 

(Showing lines 213-227 of 230. Use offset=228 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 44.0s)

成功时直接返回 `execResult.data`，但掘金返回的 `{"err_no":2,"err_msg":"请求路由不存在"}` 对模型来说不够明确。模型看到 JSON 就以为抓到了内容，继续尝试。

改进：成功时也包装一下，给模型上下文提示这是原始响应内容，需要自行判断是否有效：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "newString": "      execute: async (input: unknown) => {\n        const execResult = await executeAgentTool(agentTool.id, input, null);\n        if (!execResult.success) {\n          return `工具执行失败: ${execResult.error ?? \"未知错误\"}。请停止调用此工具，基于已有信息回答用户或告知用户此工具不可用。`;\n        }\n        // 成功时返回内容，附带元信息帮助模型判断结果是否有效\n        const meta = execResult.metadata ?? {};\n        const sizeInfo = meta.responseSize ? `${meta.responseSize} bytes` : \"未知大小`;\n        const statusInfo = meta.statusCode ? `HTTP ${meta.statusCode}` : \"\";\n        const dataStr = typeof execResult.data === \"string\" ? execResult.data : JSON.stringify(execResult.data);\n        return `[fetch 结果 ${statusInfo} ${sizeInfo}]\\n${dataStr}`;\n      },",
  "oldString": "      execute: async (input: unknown) => {\n        const execResult = await executeAgentTool(agentTool.id, input, null);\n        if (execResult.success) {\n          return execResult.data;\n        }\n        // 返回明确的错误文本，引导模型停止重试\n        return `工具执行失败: ${execResult.error ?? \"未知错误\"}。请停止调用此工具，基于已有信息回答用户或告知用户此工具不可用。`;\n      },"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 24.0s)

现在前端那个"达到上限"的提示也要调整——不应该 splice 消息，而是保留工具调用记录，让用户看到模型尝试了什么：

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "limit": 20,
  "offset": 178
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts</path>
<type>file</type>
<content>
178:       const msg = messages.value[assistantIdx]
179:       if (msg) updateLastReasoningDuration(msg)
180: 
181:       // 检查是否有实际内容产出
182:       const finalMsg = messages.value[assistantIdx]
183:       if (finalMsg && !errorMessage.value) {
184:         const hasText = finalMsg.parts?.some(p => p.type === 'text' && p.text)
185:         const hasToolCall = finalMsg.parts?.some(p => p.type === 'tool-call')
186:         if (!hasText && !hasToolCall) {
187:           // 完全无内容，移除空消息
188:           errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'
189:           messages.value.splice(assistantIdx, 1)
190:         } else if (!hasText && hasToolCall) {
191:           // 有工具调用但无最终文本回答（maxSteps 用完）
192:           errorMessage.value = '模型达到工具调用次数上限，未能生成最终回答。请简化问题或调整工具配置后重试。'
193:         }
194:       }
195:     } catch (err: any) {
196:       if (err.name === 'AbortError') {
197:         // user stopped

(Showing lines 178-197 of 231. Use offset=198 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 47.0s)

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "newString": "      // 检查是否有实际内容产出\n      const finalMsg = messages.value[assistantIdx]\n      if (finalMsg && !errorMessage.value) {\n        const hasText = finalMsg.parts?.some(p => p.type === 'text' && p.text)\n        const hasToolCall = finalMsg.parts?.some(p => p.type === 'tool-call')\n        if (!hasText && !hasToolCall) {\n          // 完全无内容，移除空消息\n          errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'\n          messages.value.splice(assistantIdx, 1)\n        } else if (!hasText && hasToolCall) {\n          // 有工具调用但无最终文本回答（maxSteps 用完），保留工具记录，追加提示\n          finalMsg.parts?.push({\n            id: generateId(),\n            type: 'text',\n            text: '（已达到工具调用次数上限，模型未能生成最终回答。以上是工具调用的尝试记录。）',\n          })\n        }\n      }",
  "oldString": "      // 检查是否有实际内容产出\n      const finalMsg = messages.value[assistantIdx]\n      if (finalMsg && !errorMessage.value) {\n        const hasText = finalMsg.parts?.some(p => p.type === 'text' && p.text)\n        const hasToolCall = finalMsg.parts?.some(p => p.type === 'tool-call')\n        if (!hasText && !hasToolCall) {\n          // 完全无内容，移除空消息\n          errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'\n          messages.value.splice(assistantIdx, 1)\n        } else if (!hasText && hasToolCall) {\n          // 有工具调用但无最终文本回答（maxSteps 用完）\n          errorMessage.value = '模型达到工具调用次数上限，未能生成最终回答。请简化问题或调整工具配置后重试。'\n        }\n      }"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 70.8s)

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && npx nuxi typecheck 2>&1 | grep -E \"agent-tool|llm/chat|useLlmChat\" | head -10",
  "description": "检查相关文件 typecheck",
  "timeout": 120000
}
```

**Output:**
```
server/service/agent-tool/index.ts(222,84): error TS1002: Unterminated string literal.
server/service/agent-tool/index.ts(222,84): error TS1002: Unterminated string literal.

```

---

## Assistant (Build · GLM-5.2 · 18.9s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "limit": 10,
  "offset": 218
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts</path>
<type>file</type>
<content>
218:           return `工具执行失败: ${execResult.error ?? "未知错误"}。请停止调用此工具，基于已有信息回答用户或告知用户此工具不可用。`;
219:         }
220:         // 成功时返回内容，附带元信息帮助模型判断结果是否有效
221:         const meta = execResult.metadata ?? {};
222:         const sizeInfo = meta.responseSize ? `${meta.responseSize} bytes` : "未知大小`;
223:         const statusInfo = meta.statusCode ? `HTTP ${meta.statusCode}` : "";
224:         const dataStr = typeof execResult.data === "string" ? execResult.data : JSON.stringify(execResult.data);
225:         return `[fetch 结果 ${statusInfo} ${sizeInfo}]\n${dataStr}`;
226:       },
227:     });

(Showing lines 218-227 of 234. Use offset=228 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 60.4s)

看起来没问题，可能是 typecheck 缓存。重新检查：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && npx nuxi typecheck 2>&1 | grep \"agent-tool/index.ts\" | head -5",
  "description": "重新检查 agent-tool typecheck",
  "timeout": 120000
}
```

**Output:**
```
server/service/agent-tool/index.ts(222,84): error TS1002: Unterminated string literal.
server/service/agent-tool/index.ts(222,84): error TS1002: Unterminated string literal.

```

---

## Assistant (Build · GLM-5.2 · 35.5s)

第 222 行第 84 列。看下实际字节：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && sed -n '222p' server/service/agent-tool/index.ts | cat -A | head -1",
  "description": "查看第222行字节"
}
```

**Output:**
```
        const sizeInfo = meta.responseSize ? `${meta.responseSize} bytes` : "M-fM-^\M-*M-gM-^_M-%M-eM-$M-'M-eM-0M-^O`;$

```

---

## Assistant (Build · GLM-5.2 · 18.7s)

`"未知大小"` 的引号是中文引号 `"` `"` 而非 ASCII 引号。修复：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "newString": "        const sizeInfo = meta.responseSize ? `${meta.responseSize} bytes` : \"未知大小\";",
  "oldString": "        const sizeInfo = meta.responseSize ? `${meta.responseSize} bytes` : \"未知大小\";"
}
```

**Error:**
```
No changes to apply: oldString and newString are identical.
```

---

## Assistant (Build · GLM-5.2 · 24.6s)

edit 工具看到的是相同字符串。用 bash 直接替换：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && perl -i -pe 's/\\xe2\\x80\\x9c未知大小\\xe2\\x80\\x9d/\"未知大小\"/g' server/service/agent-tool/index.ts && sed -n '222p' server/service/agent-tool/index.ts | cat -A",
  "description": "替换中文引号为ASCII引号"
}
```

**Output:**
```
        const sizeInfo = meta.responseSize ? `${meta.responseSize} bytes` : "M-fM-^\M-*M-gM-^_M-%M-eM-$M-'M-eM-0M-^O`;$

```

---

## Assistant (Build · GLM-5.2 · 56.9s)

没替换到。看下实际编码：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && sed -n '222p' server/service/agent-tool/index.ts | hexdump -C | head -5",
  "description": "查看hex编码"
}
```

**Output:**
```
00000000  20 20 20 20 20 20 20 20  63 6f 6e 73 74 20 73 69  |        const si|
00000010  7a 65 49 6e 66 6f 20 3d  20 6d 65 74 61 2e 72 65  |zeInfo = meta.re|
00000020  73 70 6f 6e 73 65 53 69  7a 65 20 3f 20 60 24 7b  |sponseSize ? `${|
00000030  6d 65 74 61 2e 72 65 73  70 6f 6e 73 65 53 69 7a  |meta.responseSiz|
00000040  65 7d 20 62 79 74 65 73  60 20 3a 20 22 e6 9c aa  |e} bytes` : "...|

```

---

## Assistant (Build · GLM-5.2 · 23.4s)

---

## User

我改了，但类型好像有点问题

---

## Assistant (Build · GLM-5.2 · 65.1s)

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && npx nuxi typecheck 2>&1 | grep -E \"agent-tool|llm/chat|useLlmChat\" | head -10",
  "description": "检查相关文件 typecheck",
  "timeout": 120000
}
```

**Output:**
```
app/composables/useLlmChat.ts(68,11): error TS18048: 'p' is possibly 'undefined'.
app/composables/useLlmChat.ts(68,37): error TS18048: 'p' is possibly 'undefined'.
app/composables/useLlmChat.ts(69,9): error TS18048: 'p' is possibly 'undefined'.
server/service/agent-tool/index.ts(222,31): error TS2339: Property 'responseSize' does not exist on type '{}'.
server/service/agent-tool/index.ts(222,54): error TS2339: Property 'responseSize' does not exist on type '{}'.
server/service/agent-tool/index.ts(223,33): error TS2339: Property 'statusCode' does not exist on type '{}'.
server/service/agent-tool/index.ts(223,59): error TS2339: Property 'statusCode' does not exist on type '{}'.
server/service/agent-tool/index.ts(222,31): error TS2339: Property 'responseSize' does not exist on type '{}'.
server/service/agent-tool/index.ts(222,54): error TS2339: Property 'responseSize' does not exist on type '{}'.
server/service/agent-tool/index.ts(223,33): error TS2339: Property 'statusCode' does not exist on type '{}'.

```

---

## Assistant (Build · GLM-5.2 · 20.7s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "limit": 15,
  "offset": 215
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts</path>
<type>file</type>
<content>
215:       execute: async (input: unknown) => {
216:         const execResult = await executeAgentTool(agentTool.id, input, null);
217:         if (!execResult.success) {
218:           return `工具执行失败: ${execResult.error ?? "未知错误"}。请停止调用此工具，基于已有信息回答用户或告知用户此工具不可用。`;
219:         }
220:         // 成功时返回内容，附带元信息帮助模型判断结果是否有效
221:         const meta = execResult.metadata ?? {};
222:         const sizeInfo = meta.responseSize ? `${meta.responseSize} bytes` : `未知大小`;
223:         const statusInfo = meta.statusCode ? `HTTP ${meta.statusCode}` : "";
224:         const dataStr = typeof execResult.data === "string" ? execResult.data : JSON.stringify(execResult.data);
225:         return `[fetch 结果 ${statusInfo} ${sizeInfo}]\n${dataStr}`;
226:       },
227:     });
228:   }
229:   return result;

(Showing lines 215-229 of 234. Use offset=230 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 32.9s)

`metadata` 类型推断为 `{}`。需要显式类型标注。同时看下 `useLlmChat.ts:68`：

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "limit": 10,
  "offset": 63
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts</path>
<type>file</type>
<content>
63: 
64:   function updateLastReasoningDuration(msg: LlmChatMessage) {
65:     if (!msg.parts) return
66:     for (let i = msg.parts.length - 1; i >= 0; i--) {
67:       const p = msg.parts[i]
68:       if (p.type === 'reasoning' && p.reasoningLoading) {
69:         p.reasoningLoading = false
70:         return
71:       }
72:     }

(Showing lines 63-72 of 235. Use offset=73 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 23.0s)

`p` 可能 undefined（noUncheckedIndexedAccess）。修复两处：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts",
  "newString": "        // 成功时返回内容，附带元信息帮助模型判断结果是否有效\n        const meta = execResult.metadata;\n        const sizeInfo = meta?.responseSize ? `${meta.responseSize} bytes` : `未知大小`;\n        const statusInfo = meta?.statusCode ? `HTTP ${meta.statusCode}` : \"\";",
  "oldString": "        // 成功时返回内容，附带元信息帮助模型判断结果是否有效\n        const meta = execResult.metadata ?? {};\n        const sizeInfo = meta.responseSize ? `${meta.responseSize} bytes` : `未知大小`;\n        const statusInfo = meta.statusCode ? `HTTP ${meta.statusCode}` : \"\";"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 22.3s)

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts",
  "newString": "    for (let i = msg.parts.length - 1; i >= 0; i--) {\n      const p = msg.parts[i]\n      if (!p) continue\n      if (p.type === 'reasoning' && p.reasoningLoading) {",
  "oldString": "    for (let i = msg.parts.length - 1; i >= 0; i--) {\n      const p = msg.parts[i]\n      if (p.type === 'reasoning' && p.reasoningLoading) {"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 54.6s)

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && npx nuxi typecheck 2>&1 | grep -E \"agent-tool|llm/chat|useLlmChat\" | head -10",
  "description": "重新检查 typecheck",
  "timeout": 120000
}
```

**Output:**
```
(no output)
```

---

## Assistant (Build · GLM-5.2 · 26.1s)

类型错误已全部修复。现在再测试，模型应该会在工具失败后输出最终文本回答，而不是静默中断。

---

## User

回答部分没有Markdown渲染，要用markdown增量渲染

---

## Assistant (Build · GLM-5.2 · 31.7s)

看下当前 assistant text part 的渲染方式：

**Tool: grep**

**Input:**
```json
{
  "path": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "pattern": "assistant-text|text part|markdown|v-html"
}
```

**Output:**
```
Found 2 matches
/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue:
  Line 278:                     <div v-else-if="part.type === 'text'" class="assistant-text">

  Line 799: .assistant-text {

```

---

## Assistant (Build · GLM-5.2 · 13.7s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "limit": 15,
  "offset": 275
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue</path>
<type>file</type>
<content>
275:                     </div>
276: 
277:                     <!-- Text part -->
278:                     <div v-else-if="part.type === 'text'" class="assistant-text">
279:                       {{ part.text }}
280:                     </div>
281:                   </template>
282:                 </div>
283:               </div>
284:             </template>
285:           </div>
286: 
287:           <div v-if="errorMessage" class="chat-error">
288:             <Icon name="lucide:alert-circle" />
289:             {{ errorMessage }}

(Showing lines 275-289 of 982. Use offset=290 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 27.5s)

现在用的是 `{{ part.text }}` 纯文本。看下项目里有没有现成的 markdown 渲染组件或依赖：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && grep -E \"markdown|marked|markdown-it|remark|mdsvex\" package.json | head -10",
  "description": "查找 markdown 依赖"
}
```

**Output:**
```
    "marked": "12.0.2",

```

---

## Assistant (Build · GLM-5.2 · 26.0s)

**Tool: grep**

**Input:**
```json
{
  "pattern": "marked|markdown",
  "include": "*.vue",
  "path": "/home/dash/coding/nuxt-app/app"
}
```

**Output:**
```
Found 19 matches
/home/dash/coding/nuxt-app/app/components/AgentToolFormModal.vue:
  Line 19:     parseMode: "markdown",


/home/dash/coding/nuxt-app/app/pages/articles/[id].vue:
  Line 540: .detail-content :deep(.markdown-body) {

  Line 547: .detail-content :deep(.markdown-body h1),

  Line 548: .detail-content :deep(.markdown-body h2),

  Line 549: .detail-content :deep(.markdown-body h3),

  Line 550: .detail-content :deep(.markdown-body h4),

  Line 551: .detail-content :deep(.markdown-body h5),

  Line 552: .detail-content :deep(.markdown-body h6) {

  Line 559: .detail-content :deep(.markdown-body a) {

  Line 563: .detail-content :deep(.markdown-body blockquote) {

  Line 568: .detail-content :deep(.markdown-body code) {

  Line 576: .detail-content :deep(.markdown-body pre) {

  Line 581: .detail-content :deep(.markdown-body img) {

  Line 585: .detail-content :deep(.markdown-body table) {

  Line 590: .detail-content :deep(.markdown-body th),

  Line 591: .detail-content :deep(.markdown-body td) {

  Line 597: .detail-content :deep(.markdown-body th) {


/home/dash/coding/nuxt-app/app/components/index/CardDetailModal.vue:
  Line 5: import { marked } from 'marked'

  Line 103:   return marked(props.card.content, { breaks: true }) as string

```

---

## Assistant (Build · GLM-5.2 · 19.5s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/components/index/CardDetailModal.vue",
  "limit": 15,
  "offset": 1
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/components/index/CardDetailModal.vue</path>
<type>file</type>
<content>
1: <script setup lang="ts">
2: import type { CategoryNode } from './CategoryTreeNode.vue'
3: import { getTypeLabel, type CardType } from '~/config/cardTypes'
4: import { request } from '~/utils/http/factory'
5: import { marked } from 'marked'
6: 
7: export interface CardDetail {
8:   id: number
9:   type: CardType
10:   image?: string
11:   images?: string[]
12:   title: string
13:   description?: string
14:   content?: string | null
15:   tags?: string[]

(Showing lines 1-15 of 594. Use offset=16 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 14.7s)

项目用 `marked` 渲染 markdown。但流式增量渲染需要注意：每次 text 更新都要重新 parse，且要避免光标跳动。

看下 llm-test 页面的 script 部分，了解现有 imports：

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "limit": 30,
  "offset": 1
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue</path>
<type>file</type>
<content>
1: <script setup lang="ts">
2: interface ModelOption {
3:   id: number
4:   name: string
5:   modelId: string
6:   type: string
7:   maxTokens: number | null
8: }
9: 
10: interface ProviderWithModels {
11:   id: number
12:   name: string
13:   parseMode: string
14:   status: string
15:   models: ModelOption[]
16: }
17: 
18: const { $toast } = useNuxtApp()
19: 
20: const { data: modelsData, refresh: refreshModels } = await useHttpFetch('/api/llm/chat/models', {
21:   getCachedData: () => undefined,
22: })
23: 
24: onActivated(() => {
25:   refreshModels()
26: })
27: 
28: const providers = computed<ProviderWithModels[]>(() => (modelsData.value as any) ?? [])
29: 
30: const allModels = computed(() => {

(Showing lines 1-30 of 982. Use offset=31 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 22.7s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "limit": 20,
  "offset": 795
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue</path>
<type>file</type>
<content>
795:   flex-direction: column;
796:   gap: 8px;
797: }
798: 
799: .assistant-text {
800:   background: var(--color-surface-card);
801:   border: 1px solid var(--color-hairline);
802:   border-radius: 12px;
803:   padding: 12px 16px;
804:   color: var(--color-ink);
805:   font-size: 14px;
806:   line-height: 1.7;
807:   white-space: pre-wrap;
808:   word-break: break-word;
809: }
810: 
811: .assistant-loading {
812:   display: flex;
813:   gap: 4px;
814:   padding: 12px 16px;

(Showing lines 795-814 of 982. Use offset=815 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 39.4s)

现在改造：用 `marked` 做增量 markdown 渲染。流式时每次 text 更新重新 parse 整个 part.text，用 `v-html` 输出。

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "newString": "<script setup lang=\"ts\">\nimport { marked } from 'marked'\n\ninterface ModelOption {",
  "oldString": "<script setup lang=\"ts\">\ninterface ModelOption {"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 18.7s)

现在添加一个 computed 函数来渲染 markdown，并修改模板：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "newString": "                    <!-- Text part -->\n                    <div v-else-if=\"part.type === 'text'\" class=\"assistant-text markdown-body\" v-html=\"renderMarkdown(part.text || '')\">\n                    </div>",
  "oldString": "                    <!-- Text part -->\n                    <div v-else-if=\"part.type === 'text'\" class=\"assistant-text\">\n                      {{ part.text }}\n                    </div>"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 17.8s)

现在在 script 中添加 `renderMarkdown` 函数。找一个合适的位置：

**Tool: grep**

**Input:**
```json
{
  "path": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "pattern": "function toggleReasoning|const collapsedReasoning"
}
```

**Output:**
```
Found 2 matches
/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue:
  Line 61: const collapsedReasoning = ref<Set<string>>(new Set())

  Line 63: function toggleReasoning(partId: string) {

```

---

## Assistant (Build · GLM-5.2 · 29.9s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "limit": 15,
  "offset": 58
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue</path>
<type>file</type>
<content>
58: })
59: 
60: const inputMessage = ref('')
61: const collapsedReasoning = ref<Set<string>>(new Set())
62: 
63: function toggleReasoning(partId: string) {
64:   if (collapsedReasoning.value.has(partId)) {
65:     collapsedReasoning.value.delete(partId)
66:   } else {
67:     collapsedReasoning.value.add(partId)
68:   }
69:   collapsedReasoning.value = new Set(collapsedReasoning.value)
70: }
71: 
72: watch(messages, () => {

(Showing lines 58-72 of 983. Use offset=73 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 21.7s)

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "newString": "const inputMessage = ref('')\nconst collapsedReasoning = ref<Set<string>>(new Set())\n\nmarked.setOptions({ breaks: true, gfm: true })\n\nfunction renderMarkdown(text: string): string {\n  if (!text) return ''\n  try {\n    return marked.parse(text, { async: false }) as string\n  } catch {\n    return text\n  }\n}\n\nfunction toggleReasoning(partId: string) {",
  "oldString": "const inputMessage = ref('')\nconst collapsedReasoning = ref<Set<string>>(new Set())\n\nfunction toggleReasoning(partId: string) {"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 40.9s)

现在调整 `.assistant-text` 样式，去掉 `white-space: pre-wrap`（markdown 渲染后不需要）：

**Tool: edit**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue",
  "newString": ".assistant-text {\n  background: var(--color-surface-card);\n  border: 1px solid var(--color-hairline);\n  border-radius: 12px;\n  padding: 12px 16px;\n  color: var(--color-ink);\n  font-size: 14px;\n  line-height: 1.7;\n  word-break: break-word;\n}\n\n.assistant-text :deep(p) {\n  margin: 0 0 8px;\n}\n\n.assistant-text :deep(p:last-child) {\n  margin-bottom: 0;\n}\n\n.assistant-text :deep(pre) {\n  background: var(--color-surface-2, #f6f8fa);\n  border-radius: 8px;\n  padding: 12px;\n  overflow-x: auto;\n  margin: 8px 0;\n}\n\n.assistant-text :deep(code) {\n  font-family: ui-monospace, SFMono-Regular, Menlo, monospace;\n  font-size: 13px;\n}\n\n.assistant-text :deep(pre code) {\n  background: none;\n  padding: 0;\n}\n\n.assistant-text :deep(:not(pre) > code) {\n  background: var(--color-surface-2, #f6f8fa);\n  padding: 2px 6px;\n  border-radius: 4px;\n}\n\n.assistant-text :deep(ul),\n.assistant-text :deep(ol) {\n  margin: 8px 0;\n  padding-left: 24px;\n}\n\n.assistant-text :deep(li) {\n  margin: 4px 0;\n}\n\n.assistant-text :deep(blockquote) {\n  border-left: 3px solid var(--color-hairline);\n  margin: 8px 0;\n  padding: 4px 12px;\n  color: var(--color-ink-soft, #666);\n}\n\n.assistant-text :deep(h1),\n.assistant-text :deep(h2),\n.assistant-text :deep(h3),\n.assistant-text :deep(h4) {\n  margin: 12px 0 8px;\n  font-weight: 600;\n}\n\n.assistant-text :deep(a) {\n  color: var(--color-primary, #0969da);\n  text-decoration: none;\n}\n\n.assistant-text :deep(table) {\n  border-collapse: collapse;\n  margin: 8px 0;\n  width: 100%;\n}\n\n.assistant-text :deep(th),\n.assistant-text :deep(td) {\n  border: 1px solid var(--color-hairline);\n  padding: 6px 12px;\n  text-align: left;\n}",
  "oldString": ".assistant-text {\n  background: var(--color-surface-card);\n  border: 1px solid var(--color-hairline);\n  border-radius: 12px;\n  padding: 12px 16px;\n  color: var(--color-ink);\n  font-size: 14px;\n  line-height: 1.7;\n  white-space: pre-wrap;\n  word-break: break-word;\n}"
}
```

**Output:**
```
Edit applied successfully.
```

---

## Assistant (Build · GLM-5.2 · 70.6s)

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && npx nuxi typecheck 2>&1 | grep -E \"llm-test\" | head -10",
  "description": "检查 llm-test typecheck",
  "timeout": 120000
}
```

**Output:**
```
(no output)
```

---

## Assistant (Build · GLM-5.2 · 38.5s)

typecheck 通过。现在 assistant 的 text part 会用 `marked` 实时渲染 markdown（包括流式增量更新时每次重新 parse），支持代码块、列表、表格、链接等 GFM 语法。

---

## User

总结开发的工具相关的功能并生成文档，方便下次开发

---

## Assistant (Build · GLM-5.2 · 44.8s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/docs/superpowers/specs/2026-08-05-agent-tool-framework-design.md",
  "limit": 50
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/docs/superpowers/specs/2026-08-05-agent-tool-framework-design.md</path>
<type>file</type>
<content>
1: # Agent 工具调用框架设计（fetch 为首个工具）
2: 
3: ## 概述
4: 
5: 构建一个通用工具调用框架，作为 agent 系统的前置基础设施。fetch 工具是第一个内置工具，后续可扩展 search、code-exec 等。框架支持 LLM tool-calling 自主调用，也支持独立执行 API 用于调试。
6: 
7: ## 架构
8: 
9: ```
10: ┌─────────────────────────────────────────────────┐
11: │  前端                                            │
12: │  ┌──────────────┐    ┌───────────────────────┐  │
13: │  │ 工具管理页面  │    │  Chat 页面（现有）     │  │
14: │  │ admin/       │    │  + 工具调用状态展示    │  │
15: │  │ agent-tools  │    │                       │  │
16: │  └──────┬───────┘    └───────────┬───────────┘  │
17: └─────────┼────────────────────────┼──────────────┘
18:           │                        │
19: ┌─────────┼────────────────────────┼──────────────┐
20: │  服务端 │                        │              │
21: │  ┌──────▼────────┐    ┌──────────▼───────────┐  │
22: │  │ tools API     │    │ llm/chat API（扩展）  │  │
23: │  │ CRUD + execute│    │ tool-calling loop    │  │
24: │  └──────┬────────┘    └──────────┬───────────┘  │
25: │         │                        │              │
26: │  ┌──────▼────────────────────────▼───────────┐  │
27: │  │      Tool Registry（工具注册中心）         │  │
28: │  │  ┌─────────┐  ┌─────────┐  ┌─────────┐   │  │
29: │  │  │ fetch   │  │ (future)│  │ (future)│   │  │
30: │  │  │ executor│  │ executor│  │ executor│   │  │
31: │  │  └─────────┘  └─────────┘  └─────────┘   │  │
32: │  └───────────────────────────────────────────┘  │
33: │         │                                        │
34: │  ┌──────▼────────┐    ┌──────────────────────┐  │
35: │  │ agent_tools   │    │ agent_tool_logs      │  │
36: │  │ (DB)          │    │ (DB)                 │  │
37: │  └───────────────┘    └──────────────────────┘  │
38: └──────────────────────────────────────────────────┘
39: ```
40: 
41: 核心分层：
42: - **Tool Registry**：type → executor 的映射，硬编码执行逻辑 + 根据 config 收窄 inputSchema
43: - **agent_tools 表**：存工具实例（全局共享），含 type/config/enabled
44: - **agent_tool_logs 表**：执行日志
45: - **tools API**：CRUD + `POST /tools/:id/execute`（独立执行，调试用）
46: - **llm/chat 扩展**：注入 enabled 工具到 ai-sdk `streamText`，自动 tool loop
47: 
48: ## 数据库 Schema
49: 
50: ### agent_tools 表

(Showing lines 1-50 of 422. Use offset=51 to continue.)
</content>
```

---

## Assistant (Build · GLM-5.2 · 31.4s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/registry.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/registry.ts</path>
<type>file</type>
<content>
1: import type { JSONSchema7 } from "json-schema";
2: 
3: export interface ToolExecutor<TConfig> {
4:   buildInputSchema(config: TConfig): JSONSchema7;
5:   buildDescription(config: TConfig): string;
6:   execute(input: unknown, config: TConfig, ctx: ToolContext): Promise<ToolResult>;
7: }
8: 
9: export interface ToolContext {
10:   toolId: string;
11:   toolSlug: string;
12:   userId: number | null;
13: }
14: 
15: export interface ToolResult {
16:   success: boolean;
17:   data?: unknown;
18:   error?: string;
19:   metadata?: {
20:     statusCode?: number;
21:     responseSize?: number;
22:     durationMs: number;
23:   };
24: }
25: 
26: const registry = new Map<string, ToolExecutor<any>>();
27: 
28: export function registerToolType(type: string, executor: ToolExecutor<any>): void {
29:   registry.set(type, executor);
30: }
31: 
32: export function getExecutor(type: string): ToolExecutor<any> | undefined {
33:   return registry.get(type);
34: }
35: 
36: export function listToolTypes(): string[] {
37:   return Array.from(registry.keys());
38: }

(End of file - total 38 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 38.5s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts</path>
<type>file</type>
<content>
1: import { dbGlobal } from "drizzle-pkg/lib/db";
2: import { agentTools } from "drizzle-pkg/lib/schema/agent-tool";
3: import { eq, asc } from "drizzle-orm";
4: import { tool, jsonSchema } from "ai";
5: import { z } from "zod";
6: 
7: import { registerToolType, getExecutor, type ToolContext, type ToolResult } from "./registry";
8: import { writeToolLog } from "./log";
9: import { parseFetchConfig, DEFAULT_FETCH_CONFIG } from "./executors/fetch/config";
10: import { fetchExecutor } from "./executors/fetch/fetch";
11: 
12: // 立即注册 fetch 工具类型，确保在任何 execute 调用前完成
13: registerToolType("fetch", fetchExecutor);
14: 
15: // fetch 工具的固定 input schema
16: const FETCH_INPUT_SCHEMA = z.object({
17:   url: z.string().describe("要抓取的 URL"),
18: });
19: 
20: export type AgentToolRow = typeof agentTools.$inferSelect;
21: 
22: export interface CreateAgentToolInput {
23:   name: string;
24:   slug: string;
25:   description: string;
26:   type: string;
27:   config: Record<string, unknown>;
28:   enabled?: boolean;
29:   sortOrder?: number;
30: }
31: 
32: export interface UpdateAgentToolInput {
33:   name?: string;
34:   slug?: string;
35:   description?: string;
36:   type?: string;
37:   config?: Record<string, unknown>;
38:   enabled?: boolean;
39:   sortOrder?: number;
40: }
41: 
42: export async function listAgentTools(): Promise<AgentToolRow[]> {
43:   return dbGlobal.select().from(agentTools).orderBy(asc(agentTools.sortOrder), asc(agentTools.createdAt));
44: }
45: 
46: export async function getAgentToolById(id: string): Promise<AgentToolRow | null> {
47:   const rows = await dbGlobal.select().from(agentTools).where(eq(agentTools.id, id)).limit(1);
48:   return rows[0] ?? null;
49: }
50: 
51: export async function getAgentToolBySlug(slug: string): Promise<AgentToolRow | null> {
52:   const rows = await dbGlobal.select().from(agentTools).where(eq(agentTools.slug, slug)).limit(1);
53:   return rows[0] ?? null;
54: }
55: 
56: function generateId(): string {
57:   return `at_${Date.now().toString(36)}_${Math.random().toString(36).slice(2, 8)}`;
58: }
59: 
60: function validateConfig(type: string, config: Record<string, unknown>): Record<string, unknown> {
61:   if (type === "fetch") {
62:     return parseFetchConfig(config);
63:   }
64:   return config;
65: }
66: 
67: export async function createAgentTool(input: CreateAgentToolInput): Promise<AgentToolRow> {
68:   const existing = await getAgentToolBySlug(input.slug);
69:   if (existing) {
70:     throw new Error(`slug 已存在: ${input.slug}`);
71:   }
72:   const validatedConfig = validateConfig(input.type, input.config);
73:   const id = generateId();
74:   await dbGlobal.insert(agentTools).values({
75:     id,
76:     name: input.name,
77:     slug: input.slug,
78:     description: input.description,
79:     type: input.type,
80:     config: JSON.stringify(validatedConfig),
81:     enabled: input.enabled === false ? 0 : 1,
82:     sortOrder: input.sortOrder ?? 0,
83:   });
84:   const row = await getAgentToolById(id);
85:   if (!row) throw new Error("创建后查询失败");
86:   return row;
87: }
88: 
89: export async function updateAgentTool(id: string, input: UpdateAgentToolInput): Promise<AgentToolRow | null> {
90:   const existing = await getAgentToolById(id);
91:   if (!existing) return null;
92: 
93:   if (input.slug && input.slug !== existing.slug) {
94:     const slugConflict = await getAgentToolBySlug(input.slug);
95:     if (slugConflict) {
96:       throw new Error(`slug 已存在: ${input.slug}`);
97:     }
98:   }
99: 
100:   const updates: Partial<AgentToolRow> = {};
101:   if (input.name !== undefined) updates.name = input.name;
102:   if (input.slug !== undefined) updates.slug = input.slug;
103:   if (input.description !== undefined) updates.description = input.description;
104:   if (input.type !== undefined) updates.type = input.type;
105:   if (input.config !== undefined) {
106:     const typeToValidate = input.type ?? existing.type;
107:     updates.config = JSON.stringify(validateConfig(typeToValidate, input.config));
108:   }
109:   if (input.enabled !== undefined) updates.enabled = input.enabled ? 1 : 0;
110:   if (input.sortOrder !== undefined) updates.sortOrder = input.sortOrder;
111: 
112:   if (Object.keys(updates).length === 0) return existing;
113: 
114:   await dbGlobal.update(agentTools).set(updates).where(eq(agentTools.id, id));
115:   return getAgentToolById(id);
116: }
117: 
118: export async function deleteAgentTool(id: string): Promise<void> {
119:   await dbGlobal.delete(agentTools).where(eq(agentTools.id, id));
120: }
121: 
122: export async function executeAgentTool(
123:   id: string,
124:   input: unknown,
125:   userId: number | null,
126: ): Promise<ToolResult> {
127:   const agentTool = await getAgentToolById(id);
128:   if (!agentTool) {
129:     return {
130:       success: false,
131:       data: null,
132:       error: `工具不存在: ${id}`,
133:       metadata: { durationMs: 0 },
134:     };
135:   }
136:   if (!agentTool.enabled) {
137:     return {
138:       success: false,
139:       data: null,
140:       error: `工具已禁用: ${agentTool.slug}`,
141:       metadata: { durationMs: 0 },
142:     };
143:   }
144: 
145:   const executor = getExecutor(agentTool.type);
146:   if (!executor) {
147:     return {
148:       success: false,
149:       error: `工具类型未注册: ${agentTool.type}`,
150:       metadata: { durationMs: 0 },
151:     };
152:   }
153:   let config: unknown;
154:   try {
155:     config = JSON.parse(agentTool.config);
156:   } catch {
157:     config = agentTool.type === "fetch" ? DEFAULT_FETCH_CONFIG : {};
158:   }
159: 
160:   const ctx: ToolContext = {
161:     toolId: agentTool.id,
162:     toolSlug: agentTool.slug,
163:     userId,
164:   };
165: 
166:   const result = await executor.execute(input, config, ctx);
167: 
168:   // 写日志
169:   const status = result.success ? "success" : result.error?.includes("超时") ? "timeout" : "error";
170:   await writeToolLog({
171:     toolId: agentTool.id,
172:     toolSlug: agentTool.slug,
173:     userId,
174:     input: JSON.stringify(input),
175:     output: result.success ? JSON.stringify(result.data)?.slice(0, 10000) ?? null : null,
176:     status: status as "success" | "error" | "timeout",
177:     errorMessage: result.error ?? null,
178:     durationMs: result.metadata?.durationMs ?? 0,
179:   });
180: 
181:   return result;
182: }
183: 
184: export async function getEnabledToolsForLlm(): Promise<Record<string, ReturnType<typeof tool>>> {
185:   const tools = await dbGlobal
186:     .select()
187:     .from(agentTools)
188:     .where(eq(agentTools.enabled, 1))
189:     .orderBy(asc(agentTools.sortOrder));
190: 
191:   const result: Record<string, any> = {};
192:   for (const agentTool of tools) {
193:     const executor = getExecutor(agentTool.type);
194:     if (!executor) continue;
195:     let config: unknown;
196:     try {
197:       config = JSON.parse(agentTool.config);
198:     } catch {
199:       config = agentTool.type === "fetch" ? DEFAULT_FETCH_CONFIG : {};
200:     }
201: 
202:     const zodSchema = agentTool.type === "fetch" ? FETCH_INPUT_SCHEMA : z.object({});
203:     const jsonSch = z.toJSONSchema(zodSchema) as Record<string, unknown>;
204: 
205:     result[agentTool.slug] = tool({
206:       description: executor.buildDescription(config),
207:       parameters: jsonSchema(jsonSch, {
208:         validate: (v: unknown) => {
209:           const r = zodSchema.safeParse(v);
210:           return r.success
211:             ? { success: true as const, value: r.data }
212:             : { success: false as const, error: r.error };
213:         },
214:       }),
215:       execute: async (input: unknown) => {
216:         const execResult = await executeAgentTool(agentTool.id, input, null);
217:         if (!execResult.success) {
218:           return `工具执行失败: ${execResult.error ?? "未知错误"}。请停止调用此工具，基于已有信息回答用户或告知用户此工具不可用。`;
219:         }
220:         // 成功时返回内容，附带元信息帮助模型判断结果是否有效
221:         const meta = execResult.metadata;
222:         const sizeInfo = meta?.responseSize ? `${meta.responseSize} bytes` : `未知大小`;
223:         const statusInfo = meta?.statusCode ? `HTTP ${meta.statusCode}` : "";
224:         const dataStr = typeof execResult.data === "string" ? execResult.data : JSON.stringify(execResult.data);
225:         return `[fetch 结果 ${statusInfo} ${sizeInfo}]\n${dataStr}`;
226:       },
227:     });
228:   }
229:   return result;
230: }
231: 
232: export { DEFAULT_FETCH_CONFIG } from "./executors/fetch/config";
233: export type { FetchToolConfig } from "./executors/fetch/config";
234: export type { ToolExecutor, ToolContext, ToolResult } from "./registry";

(End of file - total 234 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 8.1s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/packages/drizzle-pkg/lib/schema/agent-tool.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/packages/drizzle-pkg/lib/schema/agent-tool.ts</path>
<type>file</type>
<content>
1: import { sqliteTable, text, integer, uniqueIndex, index } from "drizzle-orm/sqlite-core";
2: 
3: // ============ AgentToolType ENUM ============
4: export const AgentToolTypes = ["fetch"] as const;
5: export type AgentToolType = (typeof AgentToolTypes)[number];
6: 
7: // ============ AgentToolStatus ENUM ============
8: export const AgentToolLogStatuses = ["success", "error", "timeout"] as const;
9: export type AgentToolLogStatus = (typeof AgentToolLogStatuses)[number];
10: 
11: // ============ AgentTool（工具定义表）============
12: export const agentTools = sqliteTable(
13:   "agent_tools",
14:   {
15:     id: text("id").primaryKey(),
16:     name: text("name", { length: 50 }).notNull(),
17:     slug: text("slug", { length: 50 }).notNull(),
18:     description: text("description").notNull(),
19:     type: text("type", { length: 30 }).notNull(),
20:     config: text("config").notNull(),
21:     enabled: integer("enabled").default(1).notNull(),
22:     sortOrder: integer("sort_order").default(0).notNull(),
23:     createdAt: integer("created_at", { mode: "timestamp_ms" })
24:       .defaultNow()
25:       .notNull(),
26:     updatedAt: integer("updated_at", { mode: "timestamp_ms" })
27:       .defaultNow()
28:       .$onUpdate(() => new Date())
29:       .notNull(),
30:   },
31:   (table) => [
32:     uniqueIndex("agent_tools_slug_idx").on(table.slug),
33:     index("agent_tools_enabled_idx").on(table.enabled),
34:   ],
35: );
36: 
37: // ============ AgentToolLog（工具执行日志表）============
38: export const agentToolLogs = sqliteTable(
39:   "agent_tool_logs",
40:   {
41:     id: integer("id").primaryKey({ autoIncrement: true }),
42:     toolId: text("tool_id").notNull(),
43:     toolSlug: text("tool_slug", { length: 50 }).notNull(),
44:     userId: integer("user_id"),
45:     input: text("input").notNull(),
46:     output: text("output"),
47:     status: text("status", { length: 20 }).notNull(),
48:     errorMessage: text("error_message"),
49:     durationMs: integer("duration_ms").notNull(),
50:     createdAt: integer("created_at", { mode: "timestamp_ms" })
51:       .defaultNow()
52:       .notNull(),
53:   },
54:   (table) => [
55:     index("agent_tool_logs_tool_id_idx").on(table.toolId),
56:     index("agent_tool_logs_created_at_idx").on(table.createdAt),
57:   ],
58: );

(End of file - total 58 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 33.3s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/config.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/config.ts</path>
<type>file</type>
<content>
1: import { z } from "zod";
2: 
3: export const fetchConfigSchema = z.object({
4:   defaultMethod: z.enum(["GET", "POST"]).default("GET"),
5:   defaultHeaders: z.record(z.string(), z.string()).default({}),
6:   timeout: z.number().int().positive().max(60000).default(10000),
7:   maxResponseSize: z.number().int().positive().max(1048576).default(102400),
8:   allowedDomains: z.array(z.string().min(1)).default(["*"]),
9:   blockedDomains: z.array(z.string().min(1)).default([]),
10:   parseMode: z.enum(["raw", "markdown", "json"]).default("markdown"),
11: });
12: 
13: export type FetchToolConfig = z.infer<typeof fetchConfigSchema>;
14: 
15: export const DEFAULT_FETCH_CONFIG: FetchToolConfig = {
16:   defaultMethod: "GET",
17:   defaultHeaders: {},
18:   timeout: 10000,
19:   maxResponseSize: 102400,
20:   allowedDomains: ["*"],
21:   blockedDomains: [],
22:   parseMode: "markdown",
23: };
24: 
25: export function parseFetchConfig(raw: unknown): FetchToolConfig {
26:   const parsed = fetchConfigSchema.safeParse(raw);
27:   if (!parsed.success) {
28:     throw new Error(`Invalid fetch config: ${parsed.error.message}`);
29:   }
30:   return parsed.data;
31: }

(End of file - total 31 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 19.3s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/security.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/security.ts</path>
<type>file</type>
<content>
1: import * as dnsPromises from "node:dns/promises";
2: import { isIP } from "node:net";
3: 
4: const PRIVATE_IP_RANGES: Array<{ start: bigint; end: bigint }> = [
5:   // 10.0.0.0/8
6:   { start: BigInt("0x0a000000"), end: BigInt("0x0affffff") },
7:   // 172.16.0.0/12
8:   { start: BigInt("0xac100000"), end: BigInt("0xac1fffff") },
9:   // 192.168.0.0/16
10:   { start: BigInt("0xc0a80000"), end: BigInt("0xc0a8ffff") },
11:   // 127.0.0.0/8 (loopback)
12:   { start: BigInt("0x7f000000"), end: BigInt("0x7fffffff") },
13:   // 169.254.0.0/16 (link-local)
14:   { start: BigInt("0xa9fe0000"), end: BigInt("0xa9feffff") },
15:   // 0.0.0.0/8
16:   { start: BigInt("0x00000000"), end: BigInt("0x00ffffff") },
17:   // 100.64.0.0/10 (CGNAT)
18:   { start: BigInt("0x64400000"), end: BigInt("0x647fffff") },
19: ];
20: 
21: export function isPrivateIp(ip: string): boolean {
22:   const type = isIP(ip);
23:   if (type === 4) {
24:     const parts = ip.split(".").map(Number);
25:     const numeric =
26:       (BigInt(parts[0] ?? 0) << BigInt(24)) |
27:       (BigInt(parts[1] ?? 0) << BigInt(16)) |
28:       (BigInt(parts[2] ?? 0) << BigInt(8)) |
29:       BigInt(parts[3] ?? 0);
30:     return PRIVATE_IP_RANGES.some(
31:       (r) => numeric >= r.start && numeric <= r.end,
32:     );
33:   }
34:   if (type === 6) {
35:     const lower = ip.toLowerCase();
36:     if (lower === "::1") return true;
37:     if (lower.startsWith("fc") || lower.startsWith("fd")) return true;
38:     if (lower.startsWith("fe80")) return true;
39:     if (lower.startsWith("::ffff:")) {
40:       const v4 = lower.slice(7);
41:       return isPrivateIp(v4);
42:     }
43:     return false;
44:   }
45:   return false;
46: }
47: 
48: export function matchDomain(hostname: string, pattern: string): boolean {
49:   if (pattern === "*") return true;
50:   const lowerHost = hostname.toLowerCase();
51:   const lowerPattern = pattern.toLowerCase();
52:   if (lowerPattern.startsWith("*.")) {
53:     const suffix = lowerPattern.slice(2);
54:     return lowerHost === suffix || lowerHost.endsWith("." + suffix);
55:   }
56:   return lowerHost === lowerPattern;
57: }
58: 
59: export function checkDomainAccess(
60:   hostname: string,
61:   allowedDomains: string[],
62:   blockedDomains: string[],
63: ): void {
64:   for (const blocked of blockedDomains) {
65:     if (matchDomain(hostname, blocked)) {
66:       throw new Error(`域名被黑名单禁止: ${hostname}`);
67:     }
68:   }
69:   // 白名单为空时允许所有域名（仅靠黑名单限制）
70:   if (allowedDomains.length > 0) {
71:     const allowed = allowedDomains.some((d) => matchDomain(hostname, d));
72:     if (!allowed) {
73:       throw new Error(`域名不在白名单中: ${hostname}`);
74:     }
75:   }
76: }
77: 
78: export async function assertSafeUrl(url: string): Promise<void> {
79:   let parsed: URL;
80:   try {
81:     parsed = new URL(url);
82:   } catch {
83:     throw new Error("无效的 URL 格式");
84:   }
85:   if (!["http:", "https:"].includes(parsed.protocol)) {
86:     throw new Error("仅支持 http/https 协议");
87:   }
88:   const hostname = parsed.hostname;
89:   if (!hostname) {
90:     throw new Error("URL 缺少主机名");
91:   }
92:   // 如果 hostname 本身就是 IP，直接检查
93:   if (isIP(hostname)) {
94:     if (isPrivateIp(hostname)) {
95:       throw new Error(`禁止访问内网地址: ${hostname}`);
96:     }
97:     return;
98:   }
99:   // DNS 解析，检查所有返回的 IP
100:   let addresses: { address: string; family: number }[];
101:   try {
102:     addresses = await dnsPromises.lookup(hostname, { all: true });
103:   } catch {
104:     throw new Error(`DNS 解析失败: ${hostname}`);
105:   }
106:   if (addresses.length === 0) {
107:     throw new Error(`DNS 解析无结果: ${hostname}`);
108:   }
109:   for (const addr of addresses) {
110:     if (isPrivateIp(addr.address)) {
111:       throw new Error(`禁止访问内网地址: ${addr.address} (${hostname})`);
112:     }
113:   }
114: }

(End of file - total 114 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 32.9s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/parse.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/parse.ts</path>
<type>file</type>
<content>
1: import TurndownService from "turndown";
2: 
3: const turndown = new TurndownService({
4:   headingStyle: "atx",
5:   codeBlockStyle: "fenced",
6:   bulletListMarker: "-",
7: });
8: 
9: export type ParseMode = "raw" | "markdown" | "json";
10: 
11: export interface ParseResult {
12:   content: unknown;
13:   contentType: "text" | "markdown" | "json";
14:   degraded?: boolean;
15:   note?: string;
16: }
17: 
18: export function parseResponse(
19:   body: string,
20:   mode: ParseMode,
21:   contentTypeHeader?: string,
22: ): ParseResult {
23:   switch (mode) {
24:     case "raw":
25:       return { content: body, contentType: "text" };
26: 
27:     case "json": {
28:       try {
29:         const parsed = JSON.parse(body);
30:         return { content: parsed, contentType: "json" };
31:       } catch {
32:         return {
33:           content: body,
34:           contentType: "text",
35:           degraded: true,
36:           note: "JSON 解析失败，返回原始文本",
37:         };
38:       }
39:     }
40: 
41:     case "markdown": {
42:       const isHtml =
43:         contentTypeHeader?.includes("text/html") ||
44:         /^\s*<(?:!doctype|html|body|div|p|h[1-6]|ul|ol|table|span|a)\b/i.test(body);
45:       if (isHtml) {
46:         const md = turndown.turndown(body);
47:         return { content: md, contentType: "markdown" };
48:       }
49:       return { content: body, contentType: "text" };
50:     }
51: 
52:     default:
53:       return { content: body, contentType: "text" };
54:   }
55: }

(End of file - total 55 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 32.7s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/service/agent-tool/log.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/service/agent-tool/log.ts</path>
<type>file</type>
<content>
1: import { dbGlobal } from "drizzle-pkg/lib/db";
2: import { agentToolLogs } from "drizzle-pkg/lib/schema/agent-tool";
3: import type { AgentToolLogStatus } from "drizzle-pkg/lib/schema/agent-tool";
4: 
5: export interface WriteToolLogParams {
6:   toolId: string;
7:   toolSlug: string;
8:   userId: number | null;
9:   input: string;
10:   output: string | null;
11:   status: AgentToolLogStatus;
12:   errorMessage: string | null;
13:   durationMs: number;
14: }
15: 
16: export async function writeToolLog(params: WriteToolLogParams): Promise<void> {
17:   try {
18:     await dbGlobal.insert(agentToolLogs).values({
19:       toolId: params.toolId,
20:       toolSlug: params.toolSlug,
21:       userId: params.userId,
22:       input: params.input,
23:       output: params.output,
24:       status: params.status,
25:       errorMessage: params.errorMessage,
26:       durationMs: params.durationMs,
27:     });
28:   } catch (e) {
29:     console.error("[agent-tool] 写入执行日志失败:", e);
30:   }
31: }

(End of file - total 31 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 38.1s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts</path>
<type>file</type>
<content>
1: import { requireUser } from "#server/utils/context";
2: import { getProviderById, getModelById } from "#server/service/llm";
3: import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
4: import { streamText, type LanguageModelV1 } from "ai";
5: import { getEnabledToolsForLlm } from "#server/service/agent-tool";
6: import log4js from "logger";
7: 
8: const logger = log4js.getLogger("APP");
9: 
10: function resolveModel(
11:   provider: {
12:     name: string;
13:     apiKey: string | null;
14:     baseUrl: string | null;
15:     parseMode: string;
16:   },
17:   modelId: string,
18: ): LanguageModelV1 {
19:   const baseUrl = provider.baseUrl?.replace(/\/+$/, "") || undefined;
20: 
21:   if (provider.parseMode === "anthropic") {
22:     throw createError({
23:       statusCode: 400,
24:       statusMessage: "Anthropic 解析模式暂不支持流式对话，请使用 OpenAI 兼容模式",
25:     });
26:   }
27: 
28:   const openaiCompatible = createOpenAICompatible({
29:     name: provider.name,
30:     apiKey: provider.apiKey || undefined,
31:     baseURL: baseUrl || "https://api.openai.com/v1",
32:   });
33: 
34:   return openaiCompatible(modelId) as LanguageModelV1;
35: }
36: 
37: export default defineEventHandler(async (event) => {
38:   const user = await requireUser(event);
39:   if (!user) {
40:     throw createError({ statusCode: 401, statusMessage: "未登录" });
41:   }
42: 
43:   const body = await readBody(event);
44:   const { modelId: llmModelId, messages, enableThinking, enableTools } = body as {
45:     modelId: number;
46:     messages: { role: "user" | "assistant" | "system"; content: string }[];
47:     enableThinking?: boolean;
48:     enableTools?: boolean;
49:   };
50: 
51:   if (!llmModelId || !messages || !Array.isArray(messages) || messages.length === 0) {
52:     throw createError({ statusCode: 400, statusMessage: "参数无效" });
53:   }
54: 
55:   const model = await getModelById(llmModelId, user.id);
56:   if (!model) {
57:     throw createError({ statusCode: 404, statusMessage: "模型不存在" });
58:   }
59: 
60:   const provider = await getProviderById(model.providerId, user.id);
61:   if (!provider) {
62:     throw createError({ statusCode: 404, statusMessage: "供应商不存在" });
63:   }
64: 
65:   if (provider.status !== "active") {
66:     throw createError({ statusCode: 400, statusMessage: "供应商已禁用" });
67:   }
68: 
69:   if (!provider.apiKey) {
70:     throw createError({ statusCode: 400, statusMessage: "供应商未配置 API Key" });
71:   }
72: 
73:   logger.info(
74:     "[%s] [LLM-CHAT] userId=%d modelId=%d provider=%s parseMode=%s messages=%d thinking=%s tools=%s",
75:     event.context.requestId ?? "-",
76:     user.id,
77:     llmModelId,
78:     provider.name,
79:     provider.parseMode,
80:     messages.length,
81:     enableThinking ? "on" : "off",
82:     enableTools ? "on" : "off",
83:   );
84: 
85:   const languageModel = resolveModel(provider, model.modelId);
86: 
87:   const tools = enableTools ? await getEnabledToolsForLlm() : undefined;
88: 
89:   const result = streamText({
90:     model: languageModel,
91:     messages,
92:     maxTokens: model.maxTokens || undefined,
93:     ...(tools && Object.keys(tools).length > 0
94:       ? { tools, maxSteps: 8 }
95:       : {}),
96:     ...(enableThinking
97:       ? {
98:           providerOptions: {
99:             openaiCompatible: { reasoningEffort: "high" },
100:           },
101:         }
102:       : {}),
103:     onError: (errorData) => {
104:       const errMsg = errorData?.error instanceof Error
105:         ? errorData.error.message
106:         : String(errorData?.error ?? "未知错误");
107:       logger.error("[%s] [LLM-CHAT] streamText error: %s", event.context.requestId ?? "-", errMsg);
108:     },
109:     onFinish: ({ finishReason, usage, steps }) => {
110:       logger.info(
111:         "[%s] [LLM-CHAT] finished: reason=%s steps=%d inputTokens=%d outputTokens=%d",
112:         event.context.requestId ?? "-",
113:         finishReason,
114:         steps.length,
115:         usage?.promptTokens ?? 0,
116:         usage?.completionTokens ?? 0,
117:       );
118:     },
119:   });
120: 
121:   return result.toDataStreamResponse({ sendReasoning: true });
122: });

(End of file - total 122 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 27.3s)

**Tool: read**

**Input:**
```json
{
  "filePath": "/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts"
}
```

**Output:**
```
<path>/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts</path>
<type>file</type>
<content>
1: import { processDataStream } from 'ai'
2: 
3: export type MessagePartType = 'text' | 'reasoning' | 'tool-call' | 'tool-result'
4: 
5: export interface MessagePart {
6:   id: string
7:   type: MessagePartType
8:   text?: string
9:   toolName?: string
10:   toolCallId?: string
11:   args?: unknown
12:   result?: unknown
13:   state?: 'call' | 'result'
14:   reasoningLoading?: boolean
15:   reasoningDuration?: number
16: }
17: 
18: export interface LlmChatMessage {
19:   id: string
20:   role: 'user' | 'assistant'
21:   content: string
22:   parts?: MessagePart[]
23: }
24: 
25: export interface UseLlmChatOptions {
26:   modelId: () => number | null
27:   apiEndpoint?: string
28:   systemPrompt?: () => string
29:   enableThinking?: () => boolean
30:   enableTools?: () => boolean
31: }
32: 
33: export function useLlmChat(options: UseLlmChatOptions) {
34:   const {
35:     modelId,
36:     apiEndpoint = '/api/llm/chat',
37:     systemPrompt,
38:     enableThinking,
39:     enableTools,
40:   } = options
41: 
42:   const messages = ref<LlmChatMessage[]>([])
43:   const isLoading = ref(false)
44:   const errorMessage = ref('')
45: 
46:   let abortController: AbortController | null = null
47: 
48:   function generateId(): string {
49:     return Date.now().toString(36) + Math.random().toString(36).slice(2)
50:   }
51: 
52:   function getOrCreateLastPart(msg: LlmChatMessage, type: MessagePartType): MessagePart | null {
53:     if (!msg.parts) msg.parts = []
54:     const last = msg.parts[msg.parts.length - 1]
55:     if (last && last.type === type) return last
56:     return null
57:   }
58: 
59:   function appendPart(msg: LlmChatMessage, part: MessagePart) {
60:     if (!msg.parts) msg.parts = []
61:     msg.parts.push(part)
62:   }
63: 
64:   function updateLastReasoningDuration(msg: LlmChatMessage) {
65:     if (!msg.parts) return
66:     for (let i = msg.parts.length - 1; i >= 0; i--) {
67:       const p = msg.parts[i]
68:       if (!p) continue
69:       if (p.type === 'reasoning' && p.reasoningLoading) {
70:         p.reasoningLoading = false
71:         return
72:       }
73:     }
74:   }
75: 
76:   async function sendMessage(text: string) {
77:     const trimmed = text.trim()
78:     const mid = modelId()
79:     if (!trimmed || mid === null || isLoading.value) return
80: 
81:     errorMessage.value = ''
82: 
83:     const userMsg: LlmChatMessage = { id: generateId(), role: 'user', content: trimmed }
84:     messages.value.push(userMsg)
85: 
86:     const assistantMsg: LlmChatMessage = {
87:       id: generateId(),
88:       role: 'assistant',
89:       content: '',
90:       parts: [],
91:     }
92:     messages.value.push(assistantMsg)
93:     const assistantIdx = messages.value.length - 1
94: 
95:     isLoading.value = true
96:     abortController = new AbortController()
97: 
98:     let reasoningStartTime: number | null = null
99: 
100:     try {
101:       const res = await fetch(apiEndpoint, {
102:         method: 'POST',
103:         headers: { 'Content-Type': 'application/json' },
104:         body: JSON.stringify({
105:           modelId: mid,
106:           messages: [
107:             ...(systemPrompt?.() ? [{ role: 'system' as const, content: systemPrompt() }] : []),
108:             ...messages.value
109:               .filter(m => m.content)
110:               .map(m => ({ role: m.role, content: m.content })),
111:           ],
112:           enableThinking: enableThinking?.() ?? false,
113:           enableTools: enableTools?.() ?? false,
114:         }),
115:         signal: abortController.signal,
116:       })
117: 
118:       if (!res.ok) {
119:         const errText = await res.text()
120:         throw new Error(errText || `请求失败 (${res.status})`)
121:       }
122: 
123:       if (!res.body) {
124:         throw new Error('响应体为空')
125:       }
126: 
127:       await processDataStream({
128:         stream: res.body,
129:         onReasoningPart: (text) => {
130:           const msg = messages.value[assistantIdx]
131:           if (!msg) return
132:           let part = getOrCreateLastPart(msg, 'reasoning')
133:           if (!part) {
134:             if (reasoningStartTime === null) reasoningStartTime = Date.now()
135:             part = { id: generateId(), type: 'reasoning', text: '', reasoningLoading: true }
136:             appendPart(msg, part)
137:           }
138:           part.text = (part.text ?? '') + text
139:         },
140:         onTextPart: (text) => {
141:           const msg = messages.value[assistantIdx]
142:           if (!msg) return
143:           updateLastReasoningDuration(msg)
144:           let part = getOrCreateLastPart(msg, 'text')
145:           if (!part) {
146:             part = { id: generateId(), type: 'text', text: '' }
147:             appendPart(msg, part)
148:           }
149:           part.text = (part.text ?? '') + text
150:           msg.content += text
151:         },
152:         onErrorPart: (error) => {
153:           errorMessage.value = error || '流式响应出错'
154:         },
155:         onToolCallPart: (part) => {
156:           const msg = messages.value[assistantIdx]
157:           if (!msg) return
158:           updateLastReasoningDuration(msg)
159:           appendPart(msg, {
160:             id: generateId(),
161:             type: 'tool-call',
162:             toolName: part.toolName,
163:             toolCallId: part.toolCallId,
164:             args: part.args,
165:             state: 'call',
166:           })
167:         },
168:         onToolResultPart: (part) => {
169:           const msg = messages.value[assistantIdx]
170:           if (!msg || !msg.parts) return
171:           const callPart = msg.parts.find(p => p.type === 'tool-call' && p.toolCallId === part.toolCallId)
172:           if (callPart) {
173:             callPart.result = part.result
174:             callPart.state = 'result'
175:           }
176:         },
177:       })
178: 
179:       const msg = messages.value[assistantIdx]
180:       if (msg) updateLastReasoningDuration(msg)
181: 
182:       // 检查是否有实际内容产出
183:       const finalMsg = messages.value[assistantIdx]
184:       if (finalMsg && !errorMessage.value) {
185:         const hasText = finalMsg.parts?.some(p => p.type === 'text' && p.text)
186:         const hasToolCall = finalMsg.parts?.some(p => p.type === 'tool-call')
187:         if (!hasText && !hasToolCall) {
188:           // 完全无内容，移除空消息
189:           errorMessage.value = '模型未返回任何内容（可能已达到工具调用次数上限或模型无响应）'
190:           messages.value.splice(assistantIdx, 1)
191:         } else if (!hasText && hasToolCall) {
192:           // 有工具调用但无最终文本回答（maxSteps 用完），保留工具记录，追加提示
193:           finalMsg.parts?.push({
194:             id: generateId(),
195:             type: 'text',
196:             text: '（已达到工具调用次数上限，模型未能生成最终回答。以上是工具调用的尝试记录。）',
197:           })
198:         }
199:       }
200:     } catch (err: any) {
201:       if (err.name === 'AbortError') {
202:         // user stopped
203:       } else {
204:         errorMessage.value = err.message || '请求失败'
205:         const msg = messages.value[assistantIdx]
206:         if (msg && !msg.content && (!msg.parts || msg.parts.length === 0)) {
207:           messages.value.splice(assistantIdx, 1)
208:         }
209:       }
210:     } finally {
211:       isLoading.value = false
212:       abortController = null
213:     }
214:   }
215: 
216:   function stopGeneration() {
217:     if (abortController) {
218:       abortController.abort()
219:       abortController = null
220:     }
221:   }
222: 
223:   function clearChat() {
224:     messages.value = []
225:     errorMessage.value = ''
226:   }
227: 
228:   return {
229:     messages,
230:     isLoading,
231:     errorMessage,
232:     sendMessage,
233:     stopGeneration,
234:     clearChat,
235:   }
236: }

(End of file - total 236 lines)
</content>
```

---

## Assistant (Build · GLM-5.2 · 31.5s)

现在看下 API 端点和前端组件的关键信息：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && ls server/api/agent-tools/ && echo \"---\" && ls server/api/agent-tools/[id]/ 2>/dev/null",
  "description": "列出 API 端点文件"
}
```

**Output:**
```
[id]
[id].delete.ts
[id].get.ts
[id].put.ts
index.get.ts
index.post.ts
---
zsh:1: no matches found: server/api/agent-tools/[id]/

```

---

## Assistant (Build · GLM-5.2 · 30.0s)

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && ls \"server/api/agent-tools/[id]/\"",
  "description": "列出 [id] 子目录"
}
```

**Output:**
```
execute.post.ts

```

---

## Assistant (Build · GLM-5.2 · 125.0s)

现在生成开发文档：

**Tool: write**

**Input:**
```json
{
  "content": "# Agent 工具调用框架 - 开发文档\n\n> 本文档供后续开发者快速了解工具调用框架的完整实现，便于扩展新工具类型和维护现有功能。\n\n## 一、整体架构\n\n```\n┌──────────────────────────────────────────────────────┐\n│  前端                                                 │\n│  ┌───────────────┐    ┌──────────────────────────┐   │\n│  │ 工具管理页面   │    │  LLM 测试页面             │   │\n│  │ admin/        │    │  + 工具调用开关            │   │\n│  │ agent-tools   │    │  + agent 链路展示          │   │\n│  └──────┬────────┘    └──────────┬───────────────┘   │\n└─────────┼────────────────────────┼───────────────────┘\n          │                        │\n┌─────────┼────────────────────────┼───────────────────┐\n│  服务端 │                        │                   │\n│  ┌──────▼────────┐    ┌──────────▼───────────────┐   │\n│  │ agent-tools   │    │ llm/chat API（扩展）      │   │\n│  │ API (CRUD+    │    │ tool-calling loop         │   │\n│  │ execute)      │    │ maxSteps=8                │   │\n│  └──────┬────────┘    └──────────┬───────────────┘   │\n│         │                        │                   │\n│  ┌──────▼────────────────────────▼───────────────┐   │\n│  │      Tool Registry（工具注册中心）              │   │\n│  │  registerToolType(type, executor)              │   │\n│  │  getExecutor(type) → ToolExecutor              │   │\n│  └──────┬────────────────────────────────────────┘   │\n│         │                                            │\n│  ┌──────▼────────┐    ┌──────────────────────┐      │\n│  │ agent_tools   │    │ agent_tool_logs      │      │\n│  │ (DB 表)       │    │ (DB 表)              │      │\n│  └───────────────┘    └──────────────────────┘      │\n└──────────────────────────────────────────────────────┘\n```\n\n核心分层：\n- **Tool Registry**：type → executor 的映射，硬编码执行逻辑 + 根据 config 差异化配置\n- **agent_tools 表**：存工具实例（全局共享），含 type/config/enabled\n- **agent_tool_logs 表**：执行日志\n- **agent-tools API**：CRUD + `POST /agent-tools/:id/execute`（独立执行，调试用）\n- **llm/chat 扩展**：注入 enabled 工具到 ai-sdk `streamText`，自动 tool loop\n\n## 二、数据库\n\n### 2.1 agent_tools 表\n\n| 字段 | 类型 | 说明 |\n|------|------|------|\n| id | text PK | `at_{timestamp36}_{random}` 格式 |\n| name | text(50) | 工具显示名称 |\n| slug | text(50) | 唯一标识，用于 LLM tool name |\n| description | text | 工具描述 |\n| type | text(30) | 工具类型（如 `fetch`） |\n| config | text | JSON 字符串，工具配置 |\n| enabled | integer | 0/1，是否启用 |\n| sort_order | integer | 排序权重 |\n| created_at | integer(ts_ms) | 创建时间 |\n| updated_at | integer(ts_ms) | 更新时间 |\n\n索引：`slug` 唯一索引、`enabled` 普通索引。\n\n### 2.2 agent_tool_logs 表\n\n| 字段 | 类型 | 说明 |\n|------|------|------|\n| id | integer PK auto | 自增 |\n| tool_id | text | 关联 agent_tools.id |\n| tool_slug | text(50) | 冗余存储 slug |\n| user_id | integer | 用户 ID（可为 null） |\n| input | text | 输入参数 JSON |\n| output | text | 输出结果（截断 10000 字符） |\n| status | text(20) | success / error / timeout |\n| error_message | text | 错误信息 |\n| duration_ms | integer | 执行耗时 |\n| created_at | integer(ts_ms) | 创建时间 |\n\n索引：`tool_id`、`created_at`。\n\n### 2.3 迁移文件\n\n- `packages/drizzle-pkg/migrations/0014_breezy_maestro.sql`\n\n## 三、服务端实现\n\n### 3.1 目录结构\n\n```\nserver/service/agent-tool/\n├── registry.ts                    # ToolExecutor 接口 + 注册机制\n├── index.ts                       # Service 层：CRUD + execute + getEnabledToolsForLlm\n├── log.ts                         # 日志写入\n└── executors/\n    └── fetch/\n        ├── config.ts              # fetch 配置 zod schema + 默认值\n        ├── security.ts            # SSRF 防护 + 域名白/黑名单\n        ├── parse.ts               # raw/markdown/json 解析\n        └── fetch.ts               # fetch 执行器组装\n```\n\n### 3.2 ToolExecutor 接口\n\n```typescript\n// registry.ts\ninterface ToolExecutor<TConfig> {\n  buildInputSchema(config: TConfig): JSONSchema7;  // 返回给 LLM 的参数 schema\n  buildDescription(config: TConfig): string;        // 返回给 LLM 的工具描述\n  execute(input: unknown, config: TConfig, ctx: ToolContext): Promise<ToolResult>;\n}\n\ninterface ToolResult {\n  success: boolean;\n  data?: unknown;\n  error?: string;\n  metadata?: { statusCode?: number; responseSize?: number; durationMs: number };\n}\n```\n\n### 3.3 注册机制\n\n```typescript\n// index.ts 顶部立即注册\nregisterToolType(\"fetch\", fetchExecutor);\n```\n\n**注意**：注册必须在 `index.ts` 中直接调用，不能依赖单独的 side-effect import 文件（Nitro tree-shaking 会移除无导出的 side-effect import）。\n\n### 3.4 Service 层核心函数\n\n#### `executeAgentTool(id, input, userId)`\n1. 查 DB 获取工具配置\n2. 检查 enabled\n3. `getExecutor(type)` 获取执行器（undefined 则返回失败）\n4. 解析 config JSON\n5. 调用 `executor.execute(input, config, ctx)`\n6. 写日志\n7. 返回 `ToolResult`\n\n#### `getEnabledToolsForLlm()`\n查询所有 enabled 工具，转换为 ai-sdk `tool()` 格式：\n```typescript\nresult[agentTool.slug] = tool({\n  description: executor.buildDescription(config),\n  parameters: jsonSchema(jsonSch, { validate: ... }),\n  execute: async (input) => {\n    const execResult = await executeAgentTool(agentTool.id, input, null);\n    if (!execResult.success) {\n      return `工具执行失败: ${execResult.error}。请停止调用此工具...`;\n    }\n    // 成功时附带元信息，帮助模型判断结果是否有效\n    return `[fetch 结果 HTTP ${statusCode} ${size} bytes]\\n${data}`;\n  },\n});\n```\n\n**关键设计**：\n- 工具失败时返回**明确的错误文本**（不是 JSON 对象），引导模型停止重试\n- 工具成功时附带 HTTP 状态码和响应大小，帮助模型判断结果有效性\n- `parameters` 用 `z.toJSONSchema()` 生成 JSON Schema，再用 ai-sdk `jsonSchema()` 包装\n\n### 3.5 fetch 执行器\n\n#### 配置 (config.ts)\n```typescript\n{\n  defaultMethod: \"GET\",           // 默认 HTTP 方法\n  defaultHeaders: {},             // 默认请求头\n  timeout: 10000,                 // 超时 ms（最大 60000）\n  maxResponseSize: 102400,        // 最大响应字节（最大 1MB）\n  allowedDomains: [\"*\"],          // 域名白名单（空数组=允许所有）\n  blockedDomains: [],             // 域名黑名单\n  parseMode: \"markdown\",          // raw / markdown / json\n}\n```\n\n#### 安全 (security.ts)\n- **SSRF 防护**：DNS 解析后检查所有 IP 是否为内网地址\n  - IPv4 私有范围：10.0.0.0/8、172.16.0.0/12、192.168.0.0/16、127.0.0.0/8、169.254.0.0/16、0.0.0.0/8、100.64.0.0/10\n  - IPv6：::1、fc00::/7、fe80::/10、::ffff: 映射的 IPv4\n- **域名白名单**：空数组时允许所有域名；支持通配符 `*.example.com`\n- **域名黑名单**：优先于白名单\n- **协议限制**：仅 http/https\n\n#### 解析 (parse.ts)\n- `raw`：返回原始文本\n- `markdown`：HTML → Markdown（用 `turndown@7.2.0`），非 HTML 返回原文\n- `json`：JSON.parse，失败则降级返回原文 + `degraded: true`\n\n#### 执行流程 (fetch.ts)\n1. zod 校验 input（url 必填，method/headers/body 可选）\n2. SSRF 检查 `assertSafeUrl`\n3. 域名白/黑名单检查 `checkDomainAccess`\n4. 发起 fetch（AbortController 超时控制）\n5. **HTTP 非 2xx 直接返回失败**（让模型知道请求无效）\n6. 大小限制检查（content-length + 分块读取双重检查）\n7. 按 parseMode 处理响应\n8. 返回 `ToolResult`\n\n### 3.6 API 端点\n\n| 方法 | 路径 | 权限 | 说明 |\n|------|------|------|------|\n| GET | `/api/agent-tools` | admin | 列出所有工具 |\n| POST | `/api/agent-tools` | admin | 创建工具 |\n| GET | `/api/agent-tools/:id` | admin | 获取工具详情 |\n| PUT | `/api/agent-tools/:id` | admin | 更新工具 |\n| DELETE | `/api/agent-tools/:id` | admin | 删除工具 |\n| POST | `/api/agent-tools/:id/execute` | admin | 独立执行工具（调试用） |\n\n所有端点使用 `requireAdmin` 权限校验。\n\n### 3.7 chat 集成\n\n`server/api/llm/chat/index.post.ts` 扩展了 `enableTools` 参数：\n\n```typescript\nconst tools = enableTools ? await getEnabledToolsForLlm() : undefined;\n\nconst result = streamText({\n  model: languageModel,\n  messages,\n  ...(tools && Object.keys(tools).length > 0\n    ? { tools, maxSteps: 8 }\n    : {}),\n  onError: (errorData) => {\n    logger.error(...);\n  },\n  onFinish: ({ finishReason, usage, steps }) => {\n    logger.info(\"finished: reason=%s steps=%d ...\", finishReason, steps.length, ...);\n  },\n});\n\nreturn result.toDataStreamResponse({ sendReasoning: true });\n```\n\n**关键参数**：\n- `maxSteps: 8`：工具调用最大轮次（包含初始生成 + tool-call 轮次 + 最终回答）\n- `sendReasoning: true`：发送 reasoning part 到前端\n- `onError`：返回 void（ai-sdk v4 要求），错误打到日志\n- `onFinish`：记录 finishReason 和步数，用于调试\n\n## 四、前端实现\n\n### 4.1 消息模型（parts 数组）\n\n```typescript\n// app/composables/useLlmChat.ts\ninterface MessagePart {\n  id: string\n  type: 'text' | 'reasoning' | 'tool-call' | 'tool-result'\n  text?: string                    // text / reasoning part 的文本\n  toolName?: string                // tool-call part 的工具名\n  toolCallId?: string              // tool-call part 的调用 ID\n  args?: unknown                   // tool-call part 的参数\n  result?: unknown                 // tool-call part 的结果（tool-result 填充）\n  state?: 'call' | 'result'        // tool-call part 的状态\n  reasoningLoading?: boolean       // reasoning part 是否正在加载\n  reasoningDuration?: number       // reasoning part 的耗时\n}\n\ninterface LlmChatMessage {\n  id: string\n  role: 'user' | 'assistant'\n  content: string                  // 兼容字段，text part 的累加\n  parts?: MessagePart[]            // 按 stream 到达顺序追加\n}\n```\n\n### 4.2 stream 处理\n\n`processDataStream` 的回调处理：\n\n| 回调 | 处理逻辑 |\n|------|----------|\n| `onReasoningPart` | 追加到上一个 reasoning part（合并连续 reasoning），标记 `reasoningLoading: true` |\n| `onTextPart` | 先 `updateLastReasoningDuration` 标记 reasoning 完成，再追加到上一个 text part（合并连续 text） |\n| `onToolCallPart` | 先 `updateLastReasoningDuration`，再追加新的 tool-call part（`state: 'call'`）。**字段名是 `args` 不是 `input`** |\n| `onToolResultPart` | 找到对应 `toolCallId` 的 tool-call part，填充 `result` 和 `state: 'result'`。**字段名是 `result` 不是 `output`** |\n| `onErrorPart` | 设置 `errorMessage` |\n\n**合并逻辑**：`getOrCreateLastPart(msg, type)` 检查最后一个 part 是否同类型，是则返回它继续追加，否则返回 null 触发新建。\n\n### 4.3 stream 结束检测\n\n```typescript\nconst hasText = finalMsg.parts?.some(p => p.type === 'text' && p.text)\nconst hasToolCall = finalMsg.parts?.some(p => p.type === 'tool-call')\n\nif (!hasText && !hasToolCall) {\n  // 完全无内容，移除空消息\n  errorMessage.value = '模型未返回任何内容...'\n  messages.value.splice(assistantIdx, 1)\n} else if (!hasText && hasToolCall) {\n  // 有工具调用但无最终文本（maxSteps 用完），保留工具记录，追加提示\n  finalMsg.parts?.push({\n    type: 'text',\n    text: '（已达到工具调用次数上限，模型未能生成最终回答...）',\n  })\n}\n```\n\n### 4.4 LLM 测试页面渲染\n\n`app/pages/settings/llm-test/index.vue`：\n\n- **user 消息**：用 `ChatBubble` 组件（右侧气泡）\n- **assistant 消息**：自定义 `assistant-panel`（左侧头像 + 右侧卡片容器），内部按序渲染 parts：\n  - `reasoning` part：可折叠/展开，显示耗时\n  - `tool-call` part：显示工具名、参数、状态、结果\n  - `text` part：用 `marked` 增量渲染 Markdown（`v-html=\"renderMarkdown(part.text)\"`）\n- **loading 动画**：三个跳动圆点\n- **工具调用开关**：toggle，控制 `enableTools` 参数\n\n### 4.5 Markdown 渲染\n\n```typescript\nimport { marked } from 'marked'\n\nmarked.setOptions({ breaks: true, gfm: true })\n\nfunction renderMarkdown(text: string): string {\n  if (!text) return ''\n  try {\n    return marked.parse(text, { async: false }) as string\n  } catch {\n    return text\n  }\n}\n```\n\n样式通过 `.assistant-text :deep(...)` 覆盖 markdown 元素样式（p、pre、code、ul、ol、blockquote、table 等）。\n\n### 4.6 工具管理页面\n\n- `app/pages/admin/agent-tools/index.vue`：列表页，显示所有工具\n- `app/components/AgentToolFormModal.vue`：创建/编辑表单 Modal\n  - Input Schema 只读展示（fetch 工具参数固定）\n  - config 可编辑（JSON textarea）\n- `app/components/AgentToolExecuteModal.vue`：执行测试 Modal\n- `app/pages/admin/dashboard.vue`：已添加 Agent 工具管理入口\n\n### 4.7 composable 扩展\n\n```typescript\n// app/composables/useLlmChat.ts\nexport interface UseLlmChatOptions {\n  modelId: () => number | null\n  apiEndpoint?: string\n  systemPrompt?: () => string\n  enableThinking?: () => boolean\n  enableTools?: () => boolean    // 新增\n}\n```\n\n## 五、关键依赖\n\n| 依赖 | 版本 | 用途 |\n|------|------|------|\n| `ai` | 4.3.16 | ai-sdk，`streamText` / `tool` / `processDataStream` / `jsonSchema` |\n| `@ai-sdk/openai-compatible` | - | OpenAI 兼容 provider |\n| `zod` | 4.3.6 | schema 校验，`z.toJSONSchema()` 生成 JSON Schema |\n| `turndown` | 7.2.0 | HTML → Markdown 转换 |\n| `marked` | 12.0.2 | Markdown → HTML 渲染（前端） |\n\n**注意**：\n- `zod-to-json-schema@3.x` 不兼容 zod v4，改用 `z.toJSONSchema()`\n- `json-schema-to-zod` 返回的是代码字符串不是 zod 实例，已移除\n- ai-sdk `tool()` 的 `parameters` 接受 `jsonSchema()` 包装的对象或 zod schema\n\n## 六、ai-sdk v4 关键约定\n\n### 6.1 stream part 字段名\n\n| stream part 类型 | 字段 | 说明 |\n|------------------|------|------|\n| `tool_call` | `toolName`, `toolCallId`, **`args`** | 不是 `input` |\n| `tool_result` | `toolCallId`, **`result`** | 不是 `output` |\n\n### 6.2 `onError` 回调\n\n```typescript\nonError: (errorData: { error: unknown }) => void\n```\n返回 `void`，不能返回 string。错误需通过日志或 stream error part 传递。\n\n### 6.3 `maxSteps` 语义\n\n包含初始生成 + tool-call 轮次 + 最终回答。到上限后如果最后一步是 tool-call，`finishReason` 为 `tool-calls`，stream 直接结束，模型不会输出最终文本。\n\n### 6.4 `toDataStreamResponse`\n\n```typescript\nresult.toDataStreamResponse({ sendReasoning: true })\n```\n`sendReasoning: true` 发送 reasoning part 到前端 stream。\n\n## 七、如何扩展新工具类型\n\n### 7.1 创建 executor\n\n```\nserver/service/agent-tool/executors/<type>/\n├── config.ts    # 配置 zod schema + 默认值\n├── <type>.ts    # 执行器实现\n└── (可选) 其他辅助文件\n```\n\nexecutor 需实现 `ToolExecutor<TConfig>` 接口：\n```typescript\nexport const myExecutor: ToolExecutor<MyConfig> = {\n  buildInputSchema(config) { return { ... } },\n  buildDescription(config) { return \"...\" },\n  async execute(input, config, ctx) { return { success: true, data: ... } },\n};\n```\n\n### 7.2 注册\n\n在 `server/service/agent-tool/index.ts` 顶部添加：\n```typescript\nimport { myExecutor } from \"./executors/my-type/my-type\";\nregisterToolType(\"my-type\", myExecutor);\n```\n\n### 7.3 配置校验\n\n在 `index.ts` 的 `validateConfig` 函数中添加新类型的校验：\n```typescript\nfunction validateConfig(type: string, config: Record<string, unknown>) {\n  if (type === \"fetch\") return parseFetchConfig(config);\n  if (type === \"my-type\") return parseMyConfig(config);\n  return config;\n}\n```\n\n### 7.4 input schema\n\n在 `index.ts` 的 `getEnabledToolsForLlm` 中添加新类型的 zod schema：\n```typescript\nconst zodSchema = agentTool.type === \"fetch\"\n  ? FETCH_INPUT_SCHEMA\n  : agentTool.type === \"my-type\"\n    ? MY_INPUT_SCHEMA\n    : z.object({});\n```\n\n### 7.5 更新 ENUM\n\n在 `packages/drizzle-pkg/lib/schema/agent-tool.ts` 中添加类型：\n```typescript\nexport const AgentToolTypes = [\"fetch\", \"my-type\"] as const;\n```\n\n### 7.6 前端\n\n- `AgentToolFormModal.vue`：添加新类型的 config 表单字段\n- `AgentToolFormModal.vue`：Input Schema 只读展示新类型的参数\n\n## 八、已知问题和注意事项\n\n### 8.1 工具调用循环\n\n模型可能反复调用工具但每次都失败（如 URL 无效），消耗完 `maxSteps` 后 stream 结束。已通过以下方式缓解：\n- HTTP 非 2xx 返回 `success: false` + 明确错误文本\n- 工具失败时返回引导模型停止的提示文本\n- 工具成功时附带元信息帮助模型判断结果有效性\n- `maxSteps: 8` 给模型留余地\n\n### 8.2 前端 execute 请求\n\n`AgentToolExecuteModal.vue` 的 execute 请求可能 `Failed to fetch`，原因是 `$fetch` 未带 cookie 导致 401 或请求被浏览器拦截。需确保请求携带认证信息。\n\n### 8.3 项目已有 bug\n\n`login_post$1` / `renderer` before initialization 错误（非本框架引入），可能导致无法通过 API 登录做完整端到端测试。\n\n### 8.4 Nitro tree-shaking\n\n不要依赖 side-effect import 注册工具（如 `import \"./register.ts\"`），Nitro 会移除无导出的 side-effect import。注册逻辑必须直接在 `index.ts` 中调用 `registerToolType`。\n\n### 8.5 zod v4 兼容性\n\n- `zod-to-json-schema@3.x` 不兼容 zod v4，用 `z.toJSONSchema()` 替代\n- `json-schema-to-zod` 返回代码字符串，不是 zod 实例，已移除\n- ai-sdk `jsonSchema()` 包装器接受 `Record<string, unknown>`，需 `as Record<string, unknown>` 绕过 `JSONSchema7` 类型不匹配\n\n### 8.6 BigInt 字面量\n\n`security.ts` 中 BigInt 字面量（如 `0x0a000000n`）在某些 TypeScript 配置下有警告，改用 `BigInt(\"0x0a000000\")` 调用形式。\n\n## 九、文件清单\n\n### 服务端\n| 文件 | 说明 |\n|------|------|\n| `packages/drizzle-pkg/lib/schema/agent-tool.ts` | DB schema 定义 |\n| `packages/drizzle-pkg/migrations/0014_breezy_maestro.sql` | 迁移文件 |\n| `server/service/agent-tool/registry.ts` | ToolExecutor 接口 + 注册机制 |\n| `server/service/agent-tool/index.ts` | Service 层 CRUD + execute + getEnabledToolsForLlm |\n| `server/service/agent-tool/log.ts` | 日志写入服务 |\n| `server/service/agent-tool/executors/fetch/config.ts` | fetch 配置 zod schema |\n| `server/service/agent-tool/executors/fetch/security.ts` | SSRF 防护 + 域名检查 |\n| `server/service/agent-tool/executors/fetch/parse.ts` | raw/markdown/json 解析 |\n| `server/service/agent-tool/executors/fetch/fetch.ts` | fetch 执行器 |\n| `server/api/agent-tools/index.get.ts` | 列出工具 |\n| `server/api/agent-tools/index.post.ts` | 创建工具 |\n| `server/api/agent-tools/[id].get.ts` | 获取详情 |\n| `server/api/agent-tools/[id].put.ts` | 更新工具 |\n| `server/api/agent-tools/[id].delete.ts` | 删除工具 |\n| `server/api/agent-tools/[id]/execute.post.ts` | 独立执行 |\n| `server/api/llm/chat/index.post.ts` | 流式对话（扩展 tool-calling） |\n\n### 前端\n| 文件 | 说明 |\n|------|------|\n| `app/composables/useLlmChat.ts` | chat composable（parts 数组模型） |\n| `app/pages/settings/llm-test/index.vue` | LLM 测试页面（agent 链路展示 + Markdown 渲染） |\n| `app/pages/admin/agent-tools/index.vue` | 工具管理列表页 |\n| `app/pages/admin/dashboard.vue` | admin 仪表盘（含工具管理入口） |\n| `app/components/AgentToolFormModal.vue` | 工具创建/编辑表单 |\n| `app/components/AgentToolExecuteModal.vue` | 工具执行测试 |\n\n### 文档\n| 文件 | 说明 |\n|------|------|\n| `docs/superpowers/specs/2026-08-05-agent-tool-framework-design.md` | 设计文档 |\n| `docs/agent-tool-framework.md` | 本开发文档 |\n",
  "filePath": "/home/dash/coding/nuxt-app/docs/agent-tool-framework.md"
}
```

**Output:**
```
Wrote file successfully.
```

---

## User

---

## Assistant (Compaction · GLM-5.2 · 165.5s)

## Goal
- 构建一个通用工具调用框架，作为 agent 系统的前置开发基础设施，fetch 作为第一个内置工具，后续可扩展 search、code-exec 等
- 在 LLM 测试页面展示完整 agent 思考链路：思考 → 调用工具 → 观察结果 → 继续思考 → 回答，按流式顺序在一个助手面板内展示

## Constraints & Preferences
- 工具执行独立 API + chat 集成（两阶段，先独立执行可调试，再集成到 chat）
- 新建 `agent_tools` 表，不复用现有 `tools` 表（现有 tools 表是内容分类标签，语义不同）
- 工具定义采用混合模式：type 硬编码执行器 + config 差异化配置
- fetch 工具支持三种 parseMode：raw / markdown / json
- 安全边界：域名控制 + SSRF 防护 + 超时/大小限制 + 执行日志，频率限制暂缓
- chat 集成为全自动 tool loop，maxSteps=8，无需用户确认
- 工具全局启用，所有用户共享同一套工具配置
- `enableTools` 默认 false，向后兼容现有 chat 行为
- Input Schema 改为只读展示（fetch 工具参数固定，无需用户编辑，等未来多种工具类型时再开放）
- agent 链路展示：所有 part（思考/工具调用/文本回答）在同一个助手面板内，左侧一个助手头像，右侧按序展示
- assistant 文本回答需要 Markdown 增量渲染（流式时实时解析）

## Progress
### Done
- 探索项目上下文：Nuxt 4 + Drizzle ORM + ai-sdk，已有 LLM 集成、chat 服务、tools 表（分类标签）
- 完成所有澄清问题（8 轮 Q&A）
- 完成设计方案 7 个 section，用户逐一确认通过
- 设计文档写入 `docs/superpowers/specs/2026-08-05-agent-tool-framework-design.md` 并提交 git（commit fcc2992）
- Spec 自检完成，修正了架构图路由名称和 jsonSchemaToZod 说明
- 用户 review spec 并确认通过
- 创建 15 项实现计划 TODO（DB → Registry → fetch executor → 日志 → Service → API → chat 集成 → 前端 → 测试）
- **DB Schema 完成**：新建 `packages/drizzle-pkg/lib/schema/agent-tool.ts`，定义 `agentTools` + `agentToolLogs` 两张表，生成迁移 `0014_breezy_maestro.sql` 并执行成功
- **Tool Registry 完成**：`server/service/agent-tool/registry.ts`，定义 `ToolExecutor<TConfig>` 接口、`ToolContext`、`ToolResult`，实现 `registerToolType` / `getExecutor` / `listToolTypes`
- **fetch executor config 完成**：`server/service/agent-tool/executors/fetch/config.ts`，zod schema 校验 + `DEFAULT_FETCH_CONFIG` + `parseFetchConfig`
- **fetch executor security 完成**：`server/service/agent-tool/executors/fetch/security.ts`，SSRF 防护 + 域名白/黑名单 + `assertSafeUrl`
- **fetch executor parse 完成**：`server/service/agent-tool/executors/fetch/parse.ts`，raw/markdown/json 三种模式，markdown 用 `turndown@7.2.0`
- **fetch executor fetch.ts 完成**：`server/service/agent-tool/executors/fetch/fetch.ts`
- **注册入口完成**：`server/service/agent-tool/register.ts`（简化为直接调用 `registerToolType`，去掉 `registered` flag）
- **日志服务完成**：`server/service/agent-tool/log.ts`
- **Service 层完成**：`server/service/agent-tool/index.ts`，实现 CRUD + `executeAgentTool` + `getEnabledToolsForLlm`
- **API 层 CRUD 完成**：6 个端点全部创建，全部使用 `requireAdmin` 权限校验
- 安装依赖：`@types/json-schema@7.0.15`、`json-schema-to-zod@2.0.0`、`turndown@7.2.0`、`@types/turndown@5.0.5`、`zod-to-json-schema@3.24.5`
- **chat 集成完成**：`server/api/llm/chat/index.post.ts` 已扩展 `enableTools` 参数
- **前端管理页面完成**：`app/pages/admin/agent-tools/index.vue`
- **前端表单 Modal 完成**：`app/components/AgentToolFormModal.vue`
- **前端执行测试 Modal 完成**：`app/components/AgentToolExecuteModal.vue`
- **composable 扩展完成**：`app/composables/useLlmChat.ts` 添加 `enableTools` 选项
- **llm-test 页面工具开关完成**：`app/pages/settings/llm-test/index.vue` 添加"工具调用"toggle 开关
- **前端 toast 修复**：三个文件改用 `useNuxtApp().$toast`
- **typecheck 通过**（项目已有无关错误）
- **端到端验证通过**（service 层）：Registry / Security / Parse / Fetch executor / CRUD / `getEnabledToolsForLlm` 全链路通过
- **BigInt 警告修复**：`security.ts` 中 BigInt literal 改为 `BigInt("0x0a000000")` 调用形式
- **`require is not defined` 修复**：`json-schema-to-zod` 改用 `createRequire(import.meta.url)` 加载
- **`Unknown tool type: fetch` 修复**：注册逻辑内联到 `index.ts`，不依赖 `register.ts` side-effect import
- **域名白名单逻辑修复**：白名单为空时允许所有域名
- **admin dashboard 菜单入口添加**
- **Input Schema 改为只读**
- **`jsonSchemaToZod` 返回字符串问题修复**：去掉 `json-schema-to-zod`，改用 `z.object({ url: z.string() })`
- **zod v4 + `zod-to-json-schema` 不兼容修复**：改用 `z.toJSONSchema()` + ai-sdk `jsonSchema()` 包装器
- **message 模型重构为 parts 数组**：`LlmChatMessage` 改为 `parts?: MessagePart[]`，每个 part 有 type（text/reasoning/tool-call/tool-result），按流式到达顺序追加
- **composable stream 处理重构**：`onTextPart` / `onReasoningPart` / `onToolCallPart` / `onToolResultPart` 均按序追加到 `msg.parts` 数组；`getOrCreateLastPart` 合并连续同类型 part；`updateLastReasoningDuration` 标记最后一个 reasoning part 完成
- **`part.input` → `part.args` 修复**：`onToolCallPart` 的字段名是 `args` 不是 `input`
- **`part.output` → `part.result` 修复**：`onToolResultPart` 的字段名是 `result` 不是 `output`
- **assistant 面板重构**：去掉 ChatBubble 用于 assistant，改为自定义 `assistant-panel`（左侧头像 + 右侧 `assistant-body` 卡片容器），内部按序渲染 reasoning-part / tool-call-item / assistant-text
- **user 消息保持 ChatBubble**（右侧气泡）
- **reasoning 折叠/展开**：`collapsedReasoning` Set + `toggleReasoning` 方法
- **loading 动画**：三个跳动圆点替代 ChatBubble loading
- **stream 静默结束检测**：`processDataStream` 完成后检查是否有 part 产出，无则报错"模型未返回任何内容"
- **服务端 `onError` 回调添加**：`streamText` 添加 `onError` 回调，错误打到日志（返回 void，不返回 string）
- **`onFinish` 回调添加**：记录 `finishReason` / `steps.length` / token 用量到日志
- **`executor` undefined 防御**：`executeAgentTool` 中 `if (!executor) return { success: false, error: "工具类型未注册" }`
- **`z.toJSONSchema` 类型不兼容修复**：`as Record<string, unknown>` 绕过 `JSONSchema7` 类型不匹配
- **工具调用死循环修复（maxSteps 用完无最终回答）**：
  - `maxSteps` 从 5 → 10 → 最终定为 8
  - fetch executor 添加 HTTP 非 2xx 返回 `success: false` + 错误信息（之前 HTTP 200 但业务错误仍返回 success）
  - 工具失败时返回明确文本提示引导模型停止重试（之前返回 `{ error: "..." }` 对象模型不理解）
  - 工具成功时包装返回内容附带元信息 `[fetch 结果 HTTP xxx xxx bytes]\n...`，帮助模型判断结果是否有效
  - `ToolResult.data` 改为可选（失败时不需要传 data）
  - `onError` 回调修复为返回 void（ai-sdk v4 要求）
  - 前端检测：stream 结束后有 tool-call 但无 text part 时，追加提示文本"已达到工具调用次数上限"（保留工具记录不移除消息）
  - `useLlmChat.ts` 中 `updateLastReasoningDuration` 的 `p` 添加 `if (!p) continue` 防 undefined
- **Markdown 增量渲染完成**：
  - `llm-test/index.vue` 导入 `marked`，添加 `renderMarkdown(text)` 函数（`marked.setOptions({ breaks: true, gfm: true })`）
  - assistant text part 从 `{{ part.text }}` 改为 `v-html="renderMarkdown(part.text || '')"`
  - `.assistant-text` 去掉 `white-space: pre-wrap`，添加 `markdown-body` class
  - 添加完整 markdown 样式：p/pre/code/ul/ol/blockquote/h1-h4/a/table/th/td 的 `:deep()` 样式
- **typecheck 通过**（相关文件无错误）

### In Progress
- 用户需要验证工具调用 + Markdown 渲染在浏览器中的实际效果

### Blocked
- 项目已有 bug：`login_post$1` / `renderer` before initialization 错误，非我们引入，导致无法通过 API 登录做完整端到端测试
- 前端 execute 请求 `Failed to fetch`：日志中无 execute 请求记录，可能是 `$fetch` 未带 cookie 导致 401 或请求被浏览器拦截

## Key Decisions
- 方案 C：工具执行独立 API + chat 集成，便于调试和复用
- 新建 `agent_tools` 表而非扩展 `tools` 表，职责清晰
- 混合 schema 管理：type 硬编码执行逻辑，config 存差异化配置
- 全自动 tool loop（A 方案），安全由域名控制 + SSRF 防护兜底
- 全局工具启用（A 方案），工具是平台级基础设施
- `enableTools` 默认 false，现有 chat 不受影响
- `agentTools` 表 id 使用 `at_{timestamp36}_{random}` 格式，非自增
- `agentToolLogs` 表 id 使用自增 integer
- API 端点全部使用 `requireAdmin`，chat 集成使用 `requireUser`
- `getEnabledToolsForLlm` 返回 `Record<string, any>`，key 为 tool slug
- 前端 toast 使用 `useNuxtApp().$toast`
- markdown parseMode 用 `turndown@7.2.0` 做 HTML→MD
- `security.ts` BigInt 用 `BigInt("0x0a000000")` 调用形式
- `registerToolType` 容忍重复注册，`getExecutor` 返回 `undefined` 而非抛异常
- 注册逻辑直接内联在 `index.ts` 中，不依赖单独的 `register.ts` side-effect import
- 去掉 `json-schema-to-zod` 依赖，fetch 工具用固定 zod schema
- Input Schema 界面改为只读展示
- zod v4 不兼容 `zod-to-json-schema@3.x`，改用 `z.toJSONSchema()` + ai-sdk `jsonSchema()` 包装器
- message 模型重构为 parts 数组（text/reasoning/tool-call/tool-result 按序追加），替代旧的 content + toolCalls 分离模型
- assistant 回复用自定义面板（头像+卡片容器），不用 ChatBubble；user 消息保持 ChatBubble
- `onToolCallPart` 字段名是 `args`（不是 `input`）；`onToolResultPart` 字段名是 `result`（不是 `output`）
- 服务端 `streamText` 的 `onError` 回调返回 void（ai-sdk v4 要求），不能返回 string
- `maxSteps=8`：平衡工具调用轮次和避免死循环
- 工具失败时返回明确文本提示而非 `{ error }` 对象，引导模型停止重试
- 工具成功时包装返回内容附带元信息（HTTP 状态码 + 响应大小），帮助模型判断结果有效性
- HTTP 非 2xx 直接返回 `success: false`，不让模型看到业务错误 JSON 后反复重试
- `ToolResult.data` 改为可选字段
- 前端 maxSteps 用完时保留工具调用记录并追加提示文本，不移除消息
- assistant text part 用 `marked` 做 Markdown 增量渲染（流式时每次 text 更新重新 parse），`v-html` 输出

## Next Steps
- 用户验证工具调用 + Markdown 渲染在浏览器中的实际效果
- 验证前端 execute 请求能正常工作（用户在浏览器测试）
- 清理：`register.ts` 文件可能已不需要（注册逻辑已内联到 `index.ts`）
- 清理：`json-schema-to-zod` 和 `zod-to-json-schema@3.24.5` 依赖可能已不需要

## Critical Context
- 项目使用 Nuxt 4 + Drizzle ORM (SQLite) + ai-sdk (Vercel AI SDK) v4.3.16
- zod 版本为 v4.3.6，内置 `z.toJSONSchema()` 方法可直接生成 JSON Schema
- `zod-to-json-schema@3.x` 不兼容 zod v4
- ai-sdk `tool()` 的 `parameters` 接受 zod schema 或 `jsonSchema()` 包装的对象
- `processDataStream` 支持回调：`onTextPart`、`onReasoningPart`、`onErrorPart`、`onToolCallPart`、`onToolResultPart` 等
- `onToolCallPart` 的 stream part 类型为 `tool_call`，包含 `toolName`、`toolCallId`、`args`
- `onToolResultPart` 的 stream part 类型为 `tool_result`，包含 `toolCallId`、`result`
- `streamText` 的 `onError` 回调必须返回 void，不能返回 string（ai-sdk v4 类型要求）
- `streamText` 的 `onFinish` 回调提供 `finishReason`、`usage`、`steps` 数组
- `maxSteps` 到上限且最后一步是 tool-call 时，`finishReason=tool-calls`，stream 直接结束，模型无机会输出最终文本
- `toDataStreamResponse({ sendReasoning: true })` 发送 reasoning part
- 现有 `server/api/llm/chat/index.post.ts` 使用 `streamText` + `defineEventHandler`
- `server/service/agent-tool/` 为新增独立模块，与现有 `server/service/tool/` 并行
- API 响应模式：CRUD 端点用 `defineWrappedResponseHandler` + `R.success` / `R.throwError`，chat 端点用 `defineEventHandler` + `createError`
- `requireAdmin` 在 `server/utils/admin-guard.ts`
- `dbGlobal` 从 `drizzle-pkg/lib/db` 导入
- 前端组件库 `packages/bolt-ui/` 有 `ChatBubble` 组件，但不支持 toolCalls prop
- `app/composables/useLlmChat.ts` 是 chat 的核心 composable
- 项目已有 bug：`login_post$1` / `renderer` before initialization（非我们引入）
- 数据库中已有用户：admin (id=11, role=admin)、npmrun (id=12, role=user)
- 数据库中已有 1 个 fetch 工具：id=`at_msfj6cbp_1float`，slug=`test`，type=`fetch`，enabled=1
- Nitro tree-shaking 会移除没有导出的 side-effect import
- `MessagePart` 类型：`id` / `type`（text/reasoning/tool-call/tool-result）/ `text` / `toolName` / `toolCallId` / `args` / `result` / `state` / `reasoningLoading` / `reasoningDuration`
- `getOrCreateLastPart` 合并连续同类型 part（text 追加到上一个 text part，reasoning 追加到上一个 reasoning part）
- `updateLastReasoningDuration` 从 parts 末尾向前找第一个 `reasoningLoading` 的 reasoning part 并标记完成，需 `if (!p) continue` 防 undefined
- `marked@12.0.2` 已在项目依赖中，`marked.setOptions({ breaks: true, gfm: true })` + `marked.parse(text, { async: false }) as string`
- 掘金 API 返回 HTTP 200 + `{"err_no":2,"err_msg":"请求路由不存在"}`，工具标记 success 但模型不理解是错误，导致反复换 URL 重试——已通过包装返回内容+元信息修复

## Relevant Files
- `docs/superpowers/specs/2026-08-05-agent-tool-framework-design.md` — 设计文档（已提交）
- `packages/drizzle-pkg/lib/schema/agent-tool.ts` — schema 定义
- `packages/drizzle-pkg/migrations/0014_breezy_maestro.sql` — 迁移文件（已执行）
- `server/service/agent-tool/registry.ts` — ToolExecutor 接口 + 注册机制（`ToolResult.data` 改为可选）
- `server/service/agent-tool/register.ts` — 注册入口（可能已不需要）
- `server/service/agent-tool/log.ts` — writeToolLog 日志服务
- `server/service/agent-tool/index.ts` — Service 层 CRUD + execute + getEnabledToolsForLlm（内联注册 + 工具失败返回明确文本提示 + 成功返回包装内容+元信息）
- `server/service/agent-tool/executors/fetch/config.ts` — fetch 配置 zod schema + 默认值
- `server/service/agent-tool/executors/fetch/security.ts` — SSRF 防护 + 域名检查
- `server/service/agent-tool/executors/fetch/parse.ts` — raw/markdown/json 解析
- `server/service/agent-tool/executors/fetch/fetch.ts` — fetch 执行器（添加 HTTP 非 2xx 返回 success:false）
- `server/api/agent-tools/index.get.ts` — 列出工具
- `server/api/agent-tools/index.post.ts` — 创建工具
- `server/api/agent-tools/[id].get.ts` — 获取详情
- `server/api/agent-tools/[id].put.ts` — 更新工具
- `server/api/agent-tools/[id].delete.ts` — 删除工具
- `server/api/agent-tools/[id]/execute.post.ts` — 独立执行
- `server/api/llm/chat/index.post.ts` — 流式对话接口，已扩展 tool-calling + `onError`(void) + `onFinish` + `maxSteps=8`
- `app/pages/admin/agent-tools/index.vue` — admin 管理列表页
- `app/pages/admin/dashboard.vue` — admin 仪表盘，已添加 Agent 工具管理入口
- `app/components/AgentToolFormModal.vue` — 工具创建/编辑表单 Modal（inputSchema 只读）
- `app/components/AgentToolExecuteModal.vue` — 工具执行测试 Modal
- `app/composables/useLlmChat.ts` — chat composable，parts 数组模型 + tool-call/tool-result 处理 + maxSteps 用完追加提示 + `p` undefined 防御
- `app/pages/settings/llm-test/index.vue` — LLM 测试页面，assistant-panel + Markdown 渲染（`marked` + `v-html` + `renderMarkdown()` + 完整 markdown `:deep()` 样式）

---

## User

---

## Assistant (Build · GLM-5.2 · 15.3s)

根据 Progress 中的 Next Steps，剩余事项都需要用户在浏览器验证，我无法独立完成。需要你确认：

1. **浏览器验证**：工具调用 + Markdown 渲染在实际对话中是否正常工作？
2. **前端 execute 请求**：`AgentToolExecuteModal` 的 `Failed to fetch` 问题是否仍存在？需要我排查 `$fetch` 认证问题吗？
3. **清理工作**（可独立完成）：是否要我现在清理 `register.ts`（已内联到 `index.ts`）和移除未使用的 `json-schema-to-zod` / `zod-to-json-schema` 依赖？

开发文档已写入 `docs/agent-tool-framework.md`。

---

## User

1. 正常工作
2. 没有问题
3. 没问题话可以清理

---

## Assistant (Build · GLM-5.2 · 27.5s)

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && cat server/service/agent-tool/register.ts 2>/dev/null && echo \"---EXISTS---\" || echo \"---NOT EXISTS---\"",
  "description": "检查 register.ts 是否存在"
}
```

**Output:**
```
import { registerToolType } from "./registry";
import { fetchExecutor } from "./executors/fetch/fetch";

registerToolType("fetch", fetchExecutor);
---EXISTS---

```

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && grep -rn \"register.ts\\|from.*register\" server/service/agent-tool/ --include=\"*.ts\" 2>/dev/null",
  "description": "检查 register.ts 是否被引用"
}
```

**Output:**
```
(no output)
```

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && grep -rn \"json-schema-to-zod\\|zod-to-json-schema\" --include=\"*.ts\" --include=\"*.vue\" --include=\"package.json\" . 2>/dev/null | grep -v node_modules",
  "description": "检查依赖是否还被使用"
}
```

**Output:**
```
./package.json:44:    "json-schema-to-zod": "2.0.0",
./package.json:60:    "zod-to-json-schema": "3.24.5"

```

---

## Assistant (Build · GLM-5.2 · 31.5s)

`register.ts` 存在但无引用，两个依赖只在 package.json 中存在。清理：

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && rm server/service/agent-tool/register.ts && echo \"deleted register.ts\"",
  "description": "删除 register.ts"
}
```

**Output:**
```
deleted register.ts

```

**Tool: bash**

**Input:**
```json
{
  "command": "cd /home/dash/coding/nuxt-app && pnpm remove json-schema-to-zod zod-to-json-schema 2>&1 | tail -20",
  "description": "移除未使用依赖"
}
```

**Output:**
```
[WARN] The "workspaces" field in package.json is not supported by pnpm. Create a "pnpm-workspace.yaml" file instead.
[ERROR] This project is configured to use bun
For help, run: pnpm help remove

```

---

## Assistant (Build · GLM-5.2)

---