fix: stabilize canvas agent mode for kimi

app/(admin)/settings/admin-settings-client.tsx

@@ -8310,6 +8310,12 @@ export function AdminSettingsClient() {
                         />
                       </div>
 
+                      <div className="rounded-lg border border-amber-200 bg-amber-50 px-3 py-2 text-xs leading-5 text-amber-900">
+                        无限画布里“对话框”的 Agent 对话/识图默认模型，不在本区域设置；
+                        它取自 AI 模型管理里被标记为“Agent模式”的多模态模型。
+                        这里的配置只影响 AI详情生成专用模型，以及 Agent模式自动出图时的默认生图模型。
+                      </div>
+
                       <div className="space-y-2 mt-4">
                         <Label htmlFor="canvasDetailAgentModelId">
                           AI详情生成 - Agent专用模型
@@ -8352,8 +8358,7 @@ export function AdminSettingsClient() {
                         </Select>
                         <div className="text-xs text-muted-foreground">
                           用于 AI详情生成 的分镜规划与排版复审。未设置时沿用
-                          Agent 对话默认模型；建议选择官方 Kimi K2.5
+                          Agent 对话默认模型；仅影响 AI详情生成，不影响普通无限画布对话框的 Agent 对话。建议选择官方 Kimi K2.5 多模态模型。
-                          多模态模型。
                         </div>
                       </div>
 
@@ -8465,7 +8470,7 @@ export function AdminSettingsClient() {
                           </SelectContent>
                         </Select>
                         <div className="text-xs text-muted-foreground">
-                          当用户选择"Agent模式"时，系统实际使用的生图模型。"系统智能选择"会根据用户意图（如抠图、换背景等）自动路由到最佳模型。
+                          当用户选择"Agent模式"并进入执行出图时，系统实际使用的默认生图模型。"系统智能选择"会根据用户意图（如抠图、换背景等）自动路由到最佳模型；这里不控制无限画布对话框里的 Agent 对话/识图模型。
                         </div>
                       </div>
                     </>

app/(chat)/api/chat/route.ts

@@ -45,6 +45,7 @@ import {
 } from "@/lib/ai/rmbg/service";
 import { getLanguageModelById } from "@/lib/ai/dynamic-provider";
 import { resolveDetailAgentLocalMoonshotModel } from "@/lib/ai/detail-agent-model-runtime";
+import { shouldForceInlineVisionInputs } from "@/lib/ai/model-compat";
 import { inlineVisionImages } from "@/lib/ai/vision-inline";
 import { createDocument } from "@/lib/ai/tools/create-document";
 import { getWeather } from "@/lib/ai/tools/get-weather";
@@ -140,6 +141,7 @@ import {
   resolveClarificationBypass,
   resolveClarificationMessage,
 } from "@/lib/ai/runtime/clarification-interruption";
+import { resolveCanvasAgentClarificationGate } from "@/lib/ai/runtime/middlewares/canvas-agent-clarification-gate";
 import {
   pruneTaskBoundaryContext,
   pruneTaskBoundaryHistoryMessages,
@@ -2089,18 +2091,188 @@ function ensureImageAnalysisSummarySection(message: string, summary: string): st
  * 注意：gemini-image 和 huanke-gemini 都需要 URL 格式（不是 Base64）
  * 因为这些 provider 内部会自行处理图片上传到 S3
  */
-async function convertMessagesWithImages(messages: ChatMessage[], providerType: string) {
+async function convertMessagesWithImages(
+  messages: ChatMessage[],
+  providerType:
+    | string
+    | {
+        providerType?: string;
+        providerBaseUrl?: string | null;
+        modelId?: string | null;
+        displayName?: string | null;
+        forceInlineImageUrls?: boolean;
+        loggerPrefix?: string;
+      },
+) {
+  type ImageConversionTarget = {
+    providerType?: string;
+    providerBaseUrl?: string | null;
+    modelId?: string | null;
+    displayName?: string | null;
+    forceInlineImageUrls?: boolean;
+    loggerPrefix?: string;
+  };
+
+  const normalizeTarget = (
+    target: string | ImageConversionTarget,
+  ): Required<Pick<ImageConversionTarget, "providerType" | "forceInlineImageUrls" | "loggerPrefix">> &
+    Omit<ImageConversionTarget, "providerType" | "forceInlineImageUrls" | "loggerPrefix"> => {
+    if (typeof target === "string") {
+      return {
+        providerType: target,
+        providerBaseUrl: null,
+        modelId: null,
+        displayName: null,
+        forceInlineImageUrls: false,
+        loggerPrefix: "[Chat API] Vision inline",
+      };
+    }
+
+    return {
+      providerType: String(target.providerType || ""),
+      providerBaseUrl: target.providerBaseUrl || null,
+      modelId: target.modelId || null,
+      displayName: target.displayName || null,
+      forceInlineImageUrls: target.forceInlineImageUrls === true,
+      loggerPrefix: target.loggerPrefix || "[Chat API] Vision inline",
+    };
+  };
+
+  const extractImagePartUrl = (part: unknown): string | null => {
+    const partAny = part as any;
+    const candidateUrl =
+      partAny?.type === "file"
+        ? partAny.url || partAny.data
+        : partAny?.type === "image"
+          ? partAny.image || partAny.url
+          : partAny?.type === "image_url"
+            ? partAny.image_url?.url
+            : undefined;
+
+    if (typeof candidateUrl !== "string") return null;
+    const normalized = candidateUrl.trim();
+    if (!/^https?:\/\//i.test(normalized)) return null;
+    return normalized;
+  };
+
+  const replaceImagePartUrl = (part: unknown, nextUrl: string): unknown => {
+    const partAny = part as any;
+    if (partAny?.type === "file") {
+      return {
+        ...partAny,
+        url: nextUrl,
+        data: nextUrl,
+        mediaType: String(partAny.mediaType || "").startsWith("image/")
+          ? partAny.mediaType
+          : "image/jpeg",
+        mimeType: String(partAny.mimeType || "").startsWith("image/")
+          ? partAny.mimeType
+          : "image/jpeg",
+      };
+    }
+    if (partAny?.type === "image") {
+      return {
+        ...partAny,
+        image: nextUrl,
+        url: nextUrl,
+      };
+    }
+    if (partAny?.type === "image_url") {
+      return {
+        ...partAny,
+        image_url: {
+          ...(partAny.image_url || {}),
+          url: nextUrl,
+        },
+      };
+    }
+    return part;
+  };
+
+  const inlineImageUrlsInMessages = async (
+    sourceMessages: ChatMessage[],
+    target: ReturnType<typeof normalizeTarget>,
+  ) => {
+    const uniqueUrls = Array.from(
+      new Set(
+        sourceMessages
+          .flatMap((message) => message.parts || [])
+          .map((part) => extractImagePartUrl(part))
+          .filter((url): url is string => Boolean(url)),
+      ),
+    );
+
+    if (uniqueUrls.length === 0) {
+      return sourceMessages;
+    }
+
+    const { inlineDataUrls } = await inlineVisionImages(uniqueUrls, {
+      maxImages: Math.min(8, uniqueUrls.length),
+      maxSide: 1200,
+      timeoutMs: 12000,
+      retries: 2,
+      loggerPrefix: target.loggerPrefix,
+    }).catch((error) => {
+      console.warn(`${target.loggerPrefix} failed to inline message images:`, error);
+      return { inlineDataUrls: [], skipped: [] };
+    });
+
+    if (inlineDataUrls.length === 0) {
+      return sourceMessages;
+    }
+
+    const inlineMap = new Map<string, string>();
+    for (let index = 0; index < inlineDataUrls.length; index += 1) {
+      const originalUrl = uniqueUrls[index];
+      const inlineUrl = inlineDataUrls[index];
+      if (originalUrl && inlineUrl) {
+        inlineMap.set(originalUrl, inlineUrl);
+      }
+    }
+
+    if (inlineMap.size === 0) {
+      return sourceMessages;
+    }
+
+    console.log(`${target.loggerPrefix} inlined chat message images:`, {
+      total: uniqueUrls.length,
+      inlined: inlineMap.size,
+      providerType: target.providerType,
+      modelId: target.modelId,
+      displayName: target.displayName,
+    });
+
+    return sourceMessages.map((message) => ({
+      ...message,
+      parts: (message.parts || []).map((part) => {
+        const originalUrl = extractImagePartUrl(part);
+        if (!originalUrl) return part;
+        const inlineUrl = inlineMap.get(originalUrl);
+        return inlineUrl ? replaceImagePartUrl(part, inlineUrl) : part;
+      }) as typeof message.parts,
+    })) as ChatMessage[];
+  };
+
+  const target = normalizeTarget(providerType);
+  let preparedMessages = messages;
+
+  if (target.forceInlineImageUrls) {
+    preparedMessages = await inlineImageUrlsInMessages(preparedMessages, target);
+  }
+
+  const normalizedProviderType = target.providerType;
+
   if (
-    providerType === "bfl-image" ||
+    normalizedProviderType === "bfl-image" ||
-    providerType === "ruxa-image" ||
+    normalizedProviderType === "ruxa-image" ||
-    providerType === "fal-image" ||
+    normalizedProviderType === "fal-image" ||
-    providerType === "openai-image" ||
+    normalizedProviderType === "openai-image" ||
-    providerType === "midjourney" ||
+    normalizedProviderType === "midjourney" ||
-    providerType === "runway"
+    normalizedProviderType === "runway"
   ) {
     const convertedMessages = [];
 
-    for (const message of messages) {
+    for (const message of preparedMessages) {
       const content: any[] = [];
 
       for (const part of message.parts) {
@@ -2149,8 +2321,8 @@ async function convertMessagesWithImages(messages: ChatMessage[], providerType:
   // 所有 provider 都使用 URL 格式传输图片
   // gemini-image 和 huanke-gemini 内部会将 Base64 转换为 S3 URL
   // 这里不再进行 Base64 转换，直接返回标准格式
-  if (providerType !== "gemini") {
+  if (normalizedProviderType !== "gemini") {
-    return convertToModelMessages(messages);
+    return convertToModelMessages(preparedMessages);
   }
   
   // 🆕 检测是否有标注图片
@@ -2162,7 +2334,7 @@ async function convertMessagesWithImages(messages: ChatMessage[], providerType:
   // 对于 Gemini，需要特殊处理图片
   const convertedMessages = [];
   
-  for (const message of messages) {
+  for (const message of preparedMessages) {
     const content: any[] = [];
     
     // 🆕 如果是用户消息且有标注图片，在文本前添加系统提示
@@ -6407,6 +6579,34 @@ export async function POST(request: Request) {
       }
     }
 
+    if (shouldRunAgentConversation) {
+      const canvasClarificationGate = resolveCanvasAgentClarificationGate({
+        text: requestTextContent,
+        composerMode,
+        contextPayload: effectiveContextPayload,
+        mediaInput: requestResolvedMediaInput,
+        targetResolution: requestTargetResolution,
+        taskBoundaryDecision,
+        executionMode: smartAutoExecution?.mode,
+        actionKind: resolvedConversationAction.kind,
+      });
+
+      if (canvasClarificationGate.shouldClarify) {
+        smartAutoNeedsClarification = true;
+        smartAutoClarificationQuestion =
+          canvasClarificationGate.clarificationMessage || smartAutoClarificationQuestion;
+        smartAutoFriendlyMessage =
+          canvasClarificationGate.clarificationMessage || smartAutoFriendlyMessage;
+        console.log("[Smart Auto Mode] 🧭 Canvas clarification gate triggered:", {
+          reason: canvasClarificationGate.reason,
+          executionMode: smartAutoExecution?.mode,
+          resolutionMode: requestTargetResolution?.resolutionMode,
+          selectedCount: requestResolvedMediaInput?.selectedImageRefs.length || 0,
+          candidateCount: requestTargetResolution?.candidateRefs.length || 0,
+        });
+      }
+    }
+
     if (shouldRunAgentConversation && smartAutoNeedsClarification) {
       const clarificationMessage = resolveClarificationMessage({
         clarificationQuestion: smartAutoClarificationQuestion,
@@ -9054,6 +9254,10 @@ export async function POST(request: Request) {
     const currentModelConfig = isUuidModelId
       ? await getAiModelById({ id: finalModelId }).catch(() => undefined)
       : undefined;
+    const currentProviderConfig =
+      currentModelConfig?.providerId
+        ? await getAiProviderById({ id: currentModelConfig.providerId }).catch(() => null)
+        : null;
     const runtimeProviderType = String(
       resolvedProviderType || (languageModel as any).provider || ""
     );
@@ -10878,7 +11082,19 @@ export async function POST(request: Request) {
         // 转换消息，对于 Gemini 和千问视频将图片 URL 转换为 base64
         const convertedMessages = await convertMessagesWithImages(
           injectHistoryImagesToLastUserMessage(uiMessagesForModel, smartAutoHistoryImages),
-          (languageModel as any).provider || ""
+          {
+            providerType: (languageModel as any).provider || "",
+            providerBaseUrl: currentProviderConfig?.baseUrl || null,
+            modelId: currentModelConfig?.modelId || actualModelId,
+            displayName: currentModelConfig?.displayName || actualModelId,
+            forceInlineImageUrls: shouldForceInlineVisionInputs({
+              providerType: resolvedProviderType || currentProviderConfig?.type || runtimeProviderType,
+              providerBaseUrl: currentProviderConfig?.baseUrl || null,
+              modelId: currentModelConfig?.modelId || actualModelId,
+              displayName: currentModelConfig?.displayName || actualModelId,
+            }),
+            loggerPrefix: "[Chat API] Stream text vision inline",
+          }
         );
         console.log("[Chat API] Messages converted, count:", convertedMessages.length); // 调试日志
 

lib/ai/auto-agent-graph.ts

@@ -1,6 +1,7 @@
 import { Annotation, StateGraph, START, END } from "@langchain/langgraph";
 import { generateText } from "ai";
 import sharp from "sharp";
+import { getCompatibleTextGenerationTemperature } from "@/lib/ai/model-compat";
 import type { AnalysisResult } from "@/lib/ai/smart-analyzer";
 import type { RouteResult } from "@/lib/ai/model-router";
 import type { AgentSkillRouteResult } from "@/lib/ai/skill-router";
@@ -1359,7 +1360,7 @@ async function buildExecutionPlanWithAgent(
       ]
         .filter(Boolean)
         .join("\n\n"),
-      temperature: 0.1,
+      temperature: getCompatibleTextGenerationTemperature(plannerModelConfig, 0.1),
       maxTokens: 420,
       abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
     } as any);
@@ -1482,7 +1483,7 @@ async function generateAgentConversation(
               content: [{ type: "text" as const, text: visionPrompt }, ...visionInputs],
             },
           ],
-          temperature: 0.2,
+          temperature: getCompatibleTextGenerationTemperature(visionModelConfig, 0.2),
           maxTokens: 360,
           abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
         } as any);
@@ -1544,7 +1545,7 @@ async function generateAgentConversation(
       model: reasoningModel,
       system: systemPrompt,
       prompt,
-      temperature: 0.35,
+      temperature: getCompatibleTextGenerationTemperature(reasoningModelConfig, 0.35),
       maxTokens: 520,
       abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
     } as any);
@@ -1626,7 +1627,7 @@ async function recommendImageParamsWithAgent(
 - 若用户未指定比例，优先 auto。
 - negativePrompt 仅在明显需要避坑时填写。`,
       prompt: requestText,
-      temperature: 0.15,
+      temperature: getCompatibleTextGenerationTemperature(plannerModelConfig, 0.15),
       maxTokens: 220,
       abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
     } as any);
@@ -1719,7 +1720,7 @@ async function recommendVideoPlanWithAgent(
 - 有参考图时优先 first-frame 或 first-last-frame。
 - 避免抽象空话，必须可执行。`,
       prompt,
-      temperature: 0.2,
+      temperature: getCompatibleTextGenerationTemperature(plannerModelConfig, 0.2),
       maxTokens: 720,
       abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
     } as any);
@@ -1799,7 +1800,7 @@ async function refineRouteWithAgent(
 意图：${state.analysis.intent}/${state.analysis.subIntent || "none"}
 候选模型：
 ${candidatesText}`,
-      temperature: 0.1,
+      temperature: getCompatibleTextGenerationTemperature(plannerModelConfig, 0.1),
       maxTokens: 120,
       abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
     } as any);

lib/ai/model-compat.ts

@@ -0,0 +1,81 @@
+type ModelCompatDescriptor = {
+  providerType?: string | null;
+  providerBaseUrl?: string | null;
+  modelId?: string | null;
+  displayName?: string | null;
+  provider?: {
+    type?: string | null;
+    baseUrl?: string | null;
+  } | null;
+  model?: {
+    modelId?: string | null;
+    displayName?: string | null;
+  } | null;
+};
+
+const KIMI_MODEL_PATTERN = /\bkimi(?:[-_.\s]?[a-z0-9.]+)?\b/i;
+
+function normalizeProviderType(input: ModelCompatDescriptor): string {
+  return String(input.providerType || input.provider?.type || "")
+    .trim()
+    .toLowerCase();
+}
+
+function normalizeProviderBaseUrl(input: ModelCompatDescriptor): string {
+  return String(input.providerBaseUrl || input.provider?.baseUrl || "")
+    .trim()
+    .toLowerCase();
+}
+
+function normalizeModelText(input: ModelCompatDescriptor): string {
+  return `${String(input.displayName || input.model?.displayName || "")} ${String(input.modelId || input.model?.modelId || "")}`
+    .trim()
+    .toLowerCase();
+}
+
+export function isMoonshotKimiModel(
+  input?: ModelCompatDescriptor | null,
+): boolean {
+  if (!input) {
+    return false;
+  }
+
+  const providerType = normalizeProviderType(input);
+  const providerBaseUrl = normalizeProviderBaseUrl(input);
+  const modelText = normalizeModelText(input);
+
+  if (providerType.includes("moonshot")) {
+    return true;
+  }
+
+  if (providerBaseUrl.includes("moonshot.cn")) {
+    return true;
+  }
+
+  if (modelText.includes("moonshot")) {
+    return true;
+  }
+
+  if (KIMI_MODEL_PATTERN.test(modelText)) {
+    return true;
+  }
+
+  return false;
+}
+
+export function getCompatibleTextGenerationTemperature(
+  input: ModelCompatDescriptor | null | undefined,
+  preferredTemperature?: number,
+): number | undefined {
+  if (isMoonshotKimiModel(input)) {
+    return 1;
+  }
+
+  return preferredTemperature;
+}
+
+export function shouldForceInlineVisionInputs(
+  input: ModelCompatDescriptor | null | undefined,
+): boolean {
+  return isMoonshotKimiModel(input);
+}

lib/ai/prompt-optimizer.ts

@@ -170,6 +170,7 @@ const EDIT_TEMPLATES: Record<
 };
 
 import { generateText } from "ai";
+import { getCompatibleTextGenerationTemperature } from "@/lib/ai/model-compat";
 
 function inferStyleTransferTarget(originalPrompt: string, extracted?: string): string {
   const text = (originalPrompt || "").trim();
@@ -674,7 +675,7 @@ export async function optimizePromptWithLLM(params: {
       model,
       system: optimizerSystemPrompt,
       prompt: finalUserMessage,
-      temperature: 0.2,
+      temperature: getCompatibleTextGenerationTemperature(selectedOptimizerModel, 0.2),
       maxTokens: 200,
     } as any);
 

lib/ai/smart-analyzer.ts

@@ -6,6 +6,7 @@
  */
 
 import { generateText } from "ai";
+import { getCompatibleTextGenerationTemperature } from "@/lib/ai/model-compat";
 import sharp from "sharp";
 import { isAiModelMultimodal } from "@/lib/db/ai-model-flags";
 // 使用动态导入避免循环依赖问题
@@ -1505,6 +1506,7 @@ export async function summarizeImageForExecutionReport(params: {
           if (fallbackVisionImages.length === 0) continue;
 
           try {
+            const compatibleTemperature = getCompatibleTextGenerationTemperature(candidate, 0.1);
             const { text: summaryText } = await generateText({
               model,
               messages: [
@@ -1519,7 +1521,9 @@ export async function summarizeImageForExecutionReport(params: {
                   ],
                 },
               ],
-              temperature: 0.1,
+              ...(compatibleTemperature !== undefined
+                ? { temperature: compatibleTemperature }
+                : {}),
               maxTokens: 120,
               abortSignal: AbortSignal.timeout(ANALYZER_TIMEOUT_MS),
             } as any);
@@ -2107,6 +2111,7 @@ ${historyText}
   );
 
   // 调用模型分析（有图时携带图片，确保真实视觉分析）
+  const analyzerTemperature = getCompatibleTextGenerationTemperature(selectedAnalyzerModel, 0.1);
   const requestPayload = hasImage && visionImages.length > 0
     && !cachedVisualSummary
     ? {
@@ -2121,7 +2126,7 @@ ${historyText}
             ],
           },
         ],
-        temperature: 0.1,
+        ...(analyzerTemperature !== undefined ? { temperature: analyzerTemperature } : {}),
         maxTokens: 360,
         abortSignal: AbortSignal.timeout(ANALYZER_TIMEOUT_MS),
       }
@@ -2129,7 +2134,7 @@ ${historyText}
         model,
         system: ANALYZER_SYSTEM_PROMPT,
         prompt: userMessage,
-        temperature: 0.1,
+        ...(analyzerTemperature !== undefined ? { temperature: analyzerTemperature } : {}),
         maxTokens: 300,
         abortSignal: AbortSignal.timeout(ANALYZER_TIMEOUT_MS),
       };