diff --git a/bun.lock b/bun.lock index eb621da..2c467af 100644 --- a/bun.lock +++ b/bun.lock @@ -27,7 +27,6 @@ "drizzle-seed": "0.3.1", "drizzle-zod": "0.8.3", "env": "workspace:*", - "json-schema-to-zod": "2.0.0", "lodash-es": "4.18.1", "log4js": "6.9.1", "logger": "workspace:*", @@ -43,7 +42,6 @@ "vue": "3.5.32", "vue-router": "5.0.4", "zod": "4.3.6", - "zod-to-json-schema": "3.24.5", }, "devDependencies": { "@iconify-json/ph": "^1.2.2", @@ -1230,8 +1228,6 @@ "json-schema": ["json-schema@0.4.0", "", {}, "sha512-es94M3nTIfsEPisRafak+HDLfHXnKBhV3vU5eqPcS3flIWqcxJWgXHXiey3YrpaNsanY5ei1VoYEbOzijuq9BA=="], - "json-schema-to-zod": ["json-schema-to-zod@2.0.0", "", { "bin": { "json-schema-to-zod": "dist/cjs/cli.js" } }, "sha512-H8ya7OWKOPEvOnD+/zdGryywMWdrEKP1K0a1OYXrIITZFQRr4DKjN/jCwYZ22XZcktvwrrCE8dxY3XgFkyZ9Yg=="], - "json5": ["json5@2.2.3", "https://registry.npmmirror.com/json5/-/json5-2.2.3.tgz", { "bin": { "json5": "lib/cli.js" } }, "sha512-XmOWe7eyHYH14cLdVPoyg+GOH3rYX++KpzrylJwSW98t3Nk+U8XOl8FWKOgwtzdb8lXGf6zYwDUzeHMWfxasyg=="], "jsondiffpatch": ["jsondiffpatch@0.6.0", "", { "dependencies": { "@types/diff-match-patch": "^1.0.36", "chalk": "^5.3.0", "diff-match-patch": "^1.0.5" }, "bin": { "jsondiffpatch": "bin/jsondiffpatch.js" } }, "sha512-3QItJOXp2AP1uv7waBkao5nCvhEv+QmJAd38Ybq7wNI74Q+BBmnLn4EDKz6yI9xGAIQoUF87qHt+kc1IVxB4zQ=="], @@ -2064,7 +2060,7 @@ "zod": ["zod@4.3.6", "https://registry.npmmirror.com/zod/-/zod-4.3.6.tgz", {}, "sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg=="], - "zod-to-json-schema": ["zod-to-json-schema@3.24.5", "", { "peerDependencies": { "zod": "^3.24.1" } }, "sha512-/AuWwMP+YqiPbsJx5D6TfgRTc4kTLjsh5SOcd4bLsfUg2RcEXrFMJl1DGgdHy2aCfsIA/cr/1JM0xcB2GZji8g=="], + "zod-to-json-schema": ["zod-to-json-schema@3.25.2", "", { "peerDependencies": { "zod": "^3.25.28 || ^4" } }, "sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA=="], "zwitch": ["zwitch@2.0.4", "https://registry.npmmirror.com/zwitch/-/zwitch-2.0.4.tgz", {}, "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A=="], @@ -2074,8 +2070,6 @@ "@ai-sdk/ui-utils/@ai-sdk/provider-utils": ["@ai-sdk/provider-utils@2.2.8", "", { "dependencies": { "@ai-sdk/provider": "1.1.3", "nanoid": "^3.3.8", "secure-json-parse": "^2.7.0" }, "peerDependencies": { "zod": "^3.23.8" } }, "sha512-fqhG+4sCVv8x7nFzYnFo19ryhAa3w096Kmc3hWxMQfW/TubPOmt3A6tYZhl4mUfQWWQMsuSkLrtjlWuXBVSGQA=="], - "@ai-sdk/ui-utils/zod-to-json-schema": ["zod-to-json-schema@3.25.2", "", { "peerDependencies": { "zod": "^3.25.28 || ^4" } }, "sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA=="], - "@babel/code-frame/js-tokens": ["js-tokens@4.0.0", "https://registry.npmmirror.com/js-tokens/-/js-tokens-4.0.0.tgz", {}, "sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ=="], "@babel/core/semver": ["semver@6.3.1", "https://registry.npmmirror.com/semver/-/semver-6.3.1.tgz", { "bin": { "semver": "bin/semver.js" } }, "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA=="], diff --git a/docs/agent-tool-session.md b/docs/agent-tool-session.md new file mode 100644 index 0000000..2b0df86 --- /dev/null +++ b/docs/agent-tool-session.md @@ -0,0 +1,4468 @@ +# 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:** +``` +/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue +file + +220: +283: +284: +285:
+286: +287: {{ errorMessage }} +288:
+289: + +(Showing lines 220-289 of 921. Use offset=290 to continue.) + +``` + +--- + +## 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": " ", + "oldString": " " +} +``` + +**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:** +``` +/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts +file + +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) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts +file + +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.) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts +file + +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.) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts +file + +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.) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/server/service/agent-tool/executors/fetch/fetch.ts +file + +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; +16: +17: export const fetchExecutor: ToolExecutor = { +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 { +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 = { +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) + +``` + +--- + +## 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` 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` 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`，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` 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:** +``` +/home/dash/coding/nuxt-app/server/api/llm/chat/index.post.ts +file + +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) + +``` + +--- + +## 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>> { + + 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:** +``` +/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts +file + +180: +181: return result; +182: } +183: +184: export async function getEnabledToolsForLlm(): Promise>> { +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 = {}; +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; +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) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts +file + +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.) + +``` + +--- + +## 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'. +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, right: string | SQLWrapper): SQL', 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'. + 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': sql, fieldAlias + Overload 3 of 3, '(left: never, right: unknown): SQL', 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/home/dash/coding/nuxt-app/server/service/agent-tool/registry.ts +file + +1: import type { JSONSchema7 } from "json-schema"; +2: +3: export interface ToolExecutor { +4: buildInputSchema(config: TConfig): JSONSchema7; +5: buildDescription(config: TConfig): string; +6: execute(input: unknown, config: TConfig, ctx: ToolContext): Promise; +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>(); +27: +28: export function registerToolType(type: string, executor: ToolExecutor): void { +29: registry.set(type, executor); +30: } +31: +32: export function getExecutor(type: string): ToolExecutor | 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) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts +file + +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.) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts +file + +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.) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts +file + +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.) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/server/service/agent-tool/index.ts +file + +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.) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/app/composables/useLlmChat.ts +file + +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.) + +``` + +--- + +## 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:
+ + 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:** +``` +/home/dash/coding/nuxt-app/app/pages/settings/llm-test/index.vue +file + +275:
+276: +277: +278:
+279: {{ part.text }} +280:
+281: +282: +283: +284: +285: +286: +287:
+288: +289: {{ errorMessage }} + +(Showing lines 275-289 of 982. Use offset=290 to continue.) + +``` + +--- + +## 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:** +``` +/home/dash/coding/nuxt-app/app/components/index/CardDetailModal.vue +file + +1: