Merge branch 'main' of github.com:CherryHQ/cherry-studio into feat/gemini-3-flash

src/main/services/MCPService.ts

@@ -249,6 +249,26 @@ class McpService {
           StdioClientTransport | SSEClientTransport | InMemoryTransport | StreamableHTTPClientTransport
         > => {
           // Create appropriate transport based on configuration
+
+          // Special case for nowledgeMem - uses HTTP transport instead of in-memory
+          if (isBuiltinMCPServer(server) && server.name === BuiltinMCPServerNames.nowledgeMem) {
+            const nowledgeMemUrl = 'http://127.0.0.1:14242/mcp'
+            const options: StreamableHTTPClientTransportOptions = {
+              fetch: async (url, init) => {
+                return net.fetch(typeof url === 'string' ? url : url.toString(), init)
+              },
+              requestInit: {
+                headers: {
+                  ...defaultAppHeaders(),
+                  APP: 'Cherry Studio'
+                }
+              },
+              authProvider
+            }
+            getServerLogger(server).debug(`Using StreamableHTTPClientTransport for ${server.name}`)
+            return new StreamableHTTPClientTransport(new URL(nowledgeMemUrl), options)
+          }
+
           if (isBuiltinMCPServer(server) && server.name !== BuiltinMCPServerNames.mcpAutoInstall) {
             getServerLogger(server).debug(`Using in-memory transport`)
             const [clientTransport, serverTransport] = InMemoryTransport.createLinkedPair()

src/renderer/src/aiCore/utils/__tests__/reasoning.test.ts

@@ -11,6 +11,7 @@ import { beforeEach, describe, expect, it, vi } from 'vitest'
 
 import {
   getAnthropicReasoningParams,
+  getAnthropicThinkingBudget,
   getBedrockReasoningParams,
   getCustomParameters,
   getGeminiReasoningParams,
@@ -89,7 +90,8 @@ vi.mock('@renderer/config/models', async (importOriginal) => {
     isQwenAlwaysThinkModel: vi.fn(() => false),
     isSupportedThinkingTokenHunyuanModel: vi.fn(() => false),
     isSupportedThinkingTokenModel: vi.fn(() => false),
-    isGPT51SeriesModel: vi.fn(() => false)
+    isGPT51SeriesModel: vi.fn(() => false),
+    findTokenLimit: vi.fn(actual.findTokenLimit)
   }
 })
 
@@ -649,7 +651,7 @@ describe('reasoning utils', () => {
       expect(result).toEqual({
         thinking: {
           type: 'enabled',
-          budgetTokens: 2048
+          budgetTokens: 4096
         }
       })
     })
@@ -729,7 +731,7 @@ describe('reasoning utils', () => {
       const result = getGeminiReasoningParams(assistant, model)
       expect(result).toEqual({
         thinkingConfig: {
-          thinkingBudget: 16448,
+          thinkingBudget: expect.any(Number),
           includeThoughts: true
         }
       })
@@ -893,7 +895,7 @@ describe('reasoning utils', () => {
       expect(result).toEqual({
         reasoningConfig: {
           type: 'enabled',
-          budgetTokens: 2048
+          budgetTokens: 4096
         }
       })
     })
@@ -994,4 +996,89 @@ describe('reasoning utils', () => {
       })
     })
   })
+
+  describe('getAnthropicThinkingBudget', () => {
+    it('should return undefined when reasoningEffort is undefined', async () => {
+      const result = getAnthropicThinkingBudget(4096, undefined, 'claude-3-7-sonnet')
+      expect(result).toBeUndefined()
+    })
+
+    it('should return undefined when reasoningEffort is none', async () => {
+      const result = getAnthropicThinkingBudget(4096, 'none', 'claude-3-7-sonnet')
+      expect(result).toBeUndefined()
+    })
+
+    it('should return undefined when tokenLimit is not found', async () => {
+      const { findTokenLimit } = await import('@renderer/config/models')
+      vi.mocked(findTokenLimit).mockReturnValue(undefined)
+
+      const result = getAnthropicThinkingBudget(4096, 'medium', 'unknown-model')
+      expect(result).toBeUndefined()
+    })
+
+    it('should calculate budget correctly when maxTokens is provided', async () => {
+      const { findTokenLimit } = await import('@renderer/config/models')
+      vi.mocked(findTokenLimit).mockReturnValue({ min: 1024, max: 32768 })
+
+      const result = getAnthropicThinkingBudget(4096, 'medium', 'claude-3-7-sonnet')
+      // EFFORT_RATIO['medium'] = 0.5
+      // budget = Math.floor((32768 - 1024) * 0.5 + 1024)
+      // = Math.floor(31744 * 0.5 + 1024) = Math.floor(15872 + 1024) = 16896
+      // budgetTokens = Math.min(16896, 4096) = 4096
+      // result = Math.max(1024, 4096) = 4096
+      expect(result).toBe(4096)
+    })
+
+    it('should use tokenLimit.max when maxTokens is undefined', async () => {
+      const { findTokenLimit } = await import('@renderer/config/models')
+      vi.mocked(findTokenLimit).mockReturnValue({ min: 1024, max: 32768 })
+
+      const result = getAnthropicThinkingBudget(undefined, 'medium', 'claude-3-7-sonnet')
+      // When maxTokens is undefined, budget is not constrained by maxTokens
+      // EFFORT_RATIO['medium'] = 0.5
+      // budget = Math.floor((32768 - 1024) * 0.5 + 1024)
+      // = Math.floor(31744 * 0.5 + 1024) = Math.floor(15872 + 1024) = 16896
+      // result = Math.max(1024, 16896) = 16896
+      expect(result).toBe(16896)
+    })
+
+    it('should enforce minimum budget of 1024', async () => {
+      const { findTokenLimit } = await import('@renderer/config/models')
+      vi.mocked(findTokenLimit).mockReturnValue({ min: 100, max: 1000 })
+
+      const result = getAnthropicThinkingBudget(500, 'low', 'claude-3-7-sonnet')
+      // EFFORT_RATIO['low'] = 0.05
+      // budget = Math.floor((1000 - 100) * 0.05 + 100)
+      // = Math.floor(900 * 0.05 + 100) = Math.floor(45 + 100) = 145
+      // budgetTokens = Math.min(145, 500) = 145
+      // result = Math.max(1024, 145) = 1024
+      expect(result).toBe(1024)
+    })
+
+    it('should respect effort ratio for high reasoning effort', async () => {
+      const { findTokenLimit } = await import('@renderer/config/models')
+      vi.mocked(findTokenLimit).mockReturnValue({ min: 1024, max: 32768 })
+
+      const result = getAnthropicThinkingBudget(8192, 'high', 'claude-3-7-sonnet')
+      // EFFORT_RATIO['high'] = 0.8
+      // budget = Math.floor((32768 - 1024) * 0.8 + 1024)
+      // = Math.floor(31744 * 0.8 + 1024) = Math.floor(25395.2 + 1024) = 26419
+      // budgetTokens = Math.min(26419, 8192) = 8192
+      // result = Math.max(1024, 8192) = 8192
+      expect(result).toBe(8192)
+    })
+
+    it('should use full token limit when maxTokens is undefined and reasoning effort is high', async () => {
+      const { findTokenLimit } = await import('@renderer/config/models')
+      vi.mocked(findTokenLimit).mockReturnValue({ min: 1024, max: 32768 })
+
+      const result = getAnthropicThinkingBudget(undefined, 'high', 'claude-3-7-sonnet')
+      // When maxTokens is undefined, budget is not constrained by maxTokens
+      // EFFORT_RATIO['high'] = 0.8
+      // budget = Math.floor((32768 - 1024) * 0.8 + 1024)
+      // = Math.floor(31744 * 0.8 + 1024) = Math.floor(25395.2 + 1024) = 26419
+      // result = Math.max(1024, 26419) = 26419
+      expect(result).toBe(26419)
+    })
+  })
 })

src/renderer/src/aiCore/utils/reasoning.ts

@@ -10,6 +10,7 @@ import {
   GEMINI_FLASH_MODEL_REGEX,
   getModelSupportedReasoningEffortOptions,
   isDeepSeekHybridInferenceModel,
+  isDoubaoSeed18Model,
   isDoubaoSeedAfter251015,
   isDoubaoThinkingAutoModel,
   isGemini3ThinkingTokenModel,
@@ -28,6 +29,7 @@ import {
   isSupportedThinkingTokenDoubaoModel,
   isSupportedThinkingTokenGeminiModel,
   isSupportedThinkingTokenHunyuanModel,
+  isSupportedThinkingTokenMiMoModel,
   isSupportedThinkingTokenModel,
   isSupportedThinkingTokenQwenModel,
   isSupportedThinkingTokenZhipuModel
@@ -389,7 +391,7 @@ export function getReasoningEffort(assistant: Assistant, model: Model): Reasonin
 
   // Use thinking, doubao, zhipu, etc.
   if (isSupportedThinkingTokenDoubaoModel(model)) {
-    if (isDoubaoSeedAfter251015(model)) {
+    if (isDoubaoSeedAfter251015(model) || isDoubaoSeed18Model(model)) {
       return { reasoningEffort }
     }
     if (reasoningEffort === 'high') {
@@ -408,6 +410,12 @@ export function getReasoningEffort(assistant: Assistant, model: Model): Reasonin
     return { thinking: { type: 'enabled' } }
   }
 
+  if (isSupportedThinkingTokenMiMoModel(model)) {
+    return {
+      thinking: { type: 'enabled' }
+    }
+  }
+
   // Default case: no special thinking settings
   return {}
 }
@@ -479,16 +487,14 @@ export function getAnthropicThinkingBudget(
     return undefined
   }
 
-  const budgetTokens = Math.max(
+  const budget = Math.floor((tokenLimit.max - tokenLimit.min) * effortRatio + tokenLimit.min)
-    1024,
+
-    Math.floor(
+  let budgetTokens = budget
-      Math.min(
+  if (maxTokens !== undefined) {
-        (tokenLimit.max - tokenLimit.min) * effortRatio + tokenLimit.min,
+    budgetTokens = Math.min(budget, maxTokens)
-        (maxTokens || DEFAULT_MAX_TOKENS) * effortRatio
+  }
-      )
+
-    )
+  return Math.max(1024, budgetTokens)
-  )
-  return budgetTokens
 }
 
 /**

src/renderer/src/assets/images/models/mimo.svg

@@ -0,0 +1,17 @@
+<svg width="100" height="100" viewBox="0 0 100 100" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <g transform="translate(10, 42) scale(1.35)">
+    <!-- m -->
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+  </g>
+</svg>

src/renderer/src/assets/images/providers/mimo.svg

@@ -0,0 +1,17 @@
+<svg width="100" height="100" viewBox="0 0 100 100" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <g transform="translate(10, 42) scale(1.35)">
+    <!-- m -->
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+    <!-- m -->
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+    <path d="M39.4394 16.0004C39.1023 16.0004 38.779 15.8664 38.5406 15.6281C38.3023 15.3897 38.1684 15.0665 38.1684 14.7294V4.67982L33.5467 9.45382C33.3117 9.69584 32.9901 9.83457 32.6523 9.83949C32.3156 9.84442 31.9894 9.71513 31.7474 9.48008C31.5054 9.24503 31.3674 8.92346 31.3623 8.58613C31.3573 8.24879 31.4863 7.92331 31.7214 7.6813L38.5284 0.662093C38.7044 0.483575 38.9304 0.361405 39.1767 0.311007C39.4233 0.260609 39.6787 0.284243 39.9114 0.378925C40.1437 0.473608 40.3427 0.635093 40.4837 0.842994C40.6247 1.05089 40.7007 1.29589 40.7027 1.54704V14.7294C40.7017 15.0649 40.5687 15.3866 40.3327 15.6246C40.0957 15.8625 39.7747 15.9976 39.4394 16.0004Z" fill="currentColor"/>
+    <path d="M32.6324 9.82618C32.4634 9.82746 32.2964 9.79516 32.1394 9.73115C31.9834 9.66713 31.8414 9.57269 31.7214 9.45329L24.9151 2.43147C24.7921 2.31326 24.6942 2.1715 24.6271 2.01463C24.5601 1.85777 24.5253 1.68901 24.5249 1.51842C24.5244 1.34783 24.5583 1.1789 24.6246 1.02169C24.6908 0.864476 24.788 0.722207 24.9104 0.603357C25.0327 0.484507 25.1778 0.391509 25.3369 0.329905C25.4959 0.268302 25.6658 0.239353 25.8363 0.244785C26.0068 0.250217 26.1745 0.289918 26.3293 0.361522C26.4841 0.433126 26.623 0.535168 26.7375 0.661566L33.5467 7.66764C33.7204 7.84732 33.8374 8.0741 33.8824 8.31974C33.9284 8.56538 33.9014 8.81903 33.8034 9.04911C33.7064 9.27919 33.5434 9.47554 33.3354 9.61371C33.1267 9.75189 32.8824 9.82577 32.6324 9.82618Z" fill="currentColor"/>
+    <!-- o -->
+    <path d="M50.9434 15.9814C49.5534 15.9865 48.1864 15.6287 46.9774 14.9433C45.7674 14.2579 44.7584 13.2687 44.0484 12.0735C43.3384 10.8783 42.9534 9.5185 42.9304 8.12863C42.9074 6.73875 43.2474 5.36692 43.9164 4.1488C44.0844 3.86356 44.3564 3.65487 44.6754 3.56707C44.9944 3.47927 45.3344 3.51928 45.6244 3.67859C45.9144 3.8379 46.1314 4.10397 46.2274 4.42026C46.3244 4.73656 46.2944 5.07816 46.1434 5.3725C45.5764 6.40664 45.3594 7.59693 45.5264 8.76468C45.6924 9.93243 46.2334 11.0147 47.0674 11.8489C47.9014 12.6831 48.9834 13.2244 50.1514 13.3914C51.3184 13.5584 52.5094 13.3421 53.5434 12.7751C53.8384 12.6125 54.1864 12.5738 54.5104 12.6676C54.8344 12.7614 55.1074 12.98 55.2704 13.2753C55.4324 13.5706 55.4714 13.9184 55.3774 14.2422C55.2834 14.566 55.0654 14.8393 54.7694 15.0019C53.5974 15.6455 52.2814 15.9824 50.9434 15.9814Z" fill="currentColor"/>
+    <path d="M56.8104 12.5052C56.5944 12.5044 56.3834 12.4484 56.1954 12.3424C55.9014 12.1795 55.6824 11.9066 55.5894 11.5833C55.4954 11.26 55.5324 10.9126 55.6944 10.6171C56.2614 9.58297 56.4784 8.39268 56.3114 7.22493C56.1454 6.05718 55.6044 4.97496 54.7704 4.14073C53.9364 3.30649 52.8544 2.76525 51.6864 2.59825C50.5194 2.43125 49.3284 2.64749 48.2944 3.21452C48.1474 3.30059 47.9854 3.3564 47.8164 3.37863C47.6484 3.40087 47.4774 3.38908 47.3134 3.34397C47.1494 3.29886 46.9964 3.22134 46.8624 3.116C46.7294 3.01066 46.6184 2.87964 46.5364 2.73069C46.4544 2.58174 46.4034 2.41788 46.3864 2.24882C46.3684 2.07975 46.3854 1.90891 46.4354 1.7464C46.4854 1.58389 46.5674 1.43301 46.6764 1.3027C46.7854 1.17238 46.9194 1.06527 47.0704 0.987704C48.5874 0.155491 50.3324 -0.162266 52.0454 0.0821474C53.7574 0.326561 55.3454 1.11995 56.5684 2.34319C57.7914 3.56642 58.5844 5.15347 58.8294 6.86604C59.0734 8.5786 58.7554 10.3242 57.9234 11.8408C57.8144 12.0411 57.6534 12.2084 57.4574 12.3253C57.2624 12.4422 57.0384 12.5043 56.8104 12.5052Z" fill="currentColor"/>
+  </g>
+</svg>

src/renderer/src/config/models/__tests__/reasoning.test.ts

@@ -733,6 +733,11 @@ describe('getThinkModelType - Comprehensive Coverage', () => {
       expect(getThinkModelType(createModel({ id: 'doubao-seed-1-6-lite-251015' }))).toBe('doubao_after_251015')
     })
 
+    it('should return doubao_after_251015 for Doubao-Seed-1.8 models', () => {
+      expect(getThinkModelType(createModel({ id: 'doubao-seed-1-8-251215' }))).toBe('doubao_after_251015')
+      expect(getThinkModelType(createModel({ id: 'doubao-seed-1.8' }))).toBe('doubao_after_251015')
+    })
+
     it('should return doubao_no_auto for other Doubao thinking models', () => {
       expect(getThinkModelType(createModel({ id: 'doubao-1.5-thinking-vision-pro' }))).toBe('doubao_no_auto')
     })
@@ -863,6 +868,7 @@ describe('getThinkModelType - Comprehensive Coverage', () => {
       // auto > after_251015 > no_auto
       expect(getThinkModelType(createModel({ id: 'doubao-seed-1.6' }))).toBe('doubao')
       expect(getThinkModelType(createModel({ id: 'doubao-seed-1-6-251015' }))).toBe('doubao_after_251015')
+      expect(getThinkModelType(createModel({ id: 'doubao-seed-1-8-251215' }))).toBe('doubao_after_251015')
       expect(getThinkModelType(createModel({ id: 'doubao-1.5-thinking-vision-pro' }))).toBe('doubao_no_auto')
     })
 

src/renderer/src/config/models/default.ts

@@ -746,6 +746,12 @@ export const SYSTEM_MODELS: Record<SystemProviderId | 'defaultModel', Model[]> =
     }
   ],
   doubao: [
+    {
+      id: 'doubao-seed-1-8-251215',
+      provider: 'doubao',
+      name: 'Doubao-Seed-1.8',
+      group: 'Doubao-Seed-1.8'
+    },
     {
       id: 'doubao-1-5-vision-pro-32k-250115',
       provider: 'doubao',
@@ -1785,5 +1791,13 @@ export const SYSTEM_MODELS: Record<SystemProviderId | 'defaultModel', Model[]> =
       provider: 'cerebras',
       group: 'qwen'
     }
+  ],
+  mimo: [
+    {
+      id: 'mimo-v2-flash',
+      name: 'Mimo V2 Flash',
+      provider: 'mimo',
+      group: 'Mimo'
+    }
   ]
 }

src/renderer/src/config/models/logo.ts

@@ -103,6 +103,7 @@ import MicrosoftModelLogo from '@renderer/assets/images/models/microsoft.png'
 import MicrosoftModelLogoDark from '@renderer/assets/images/models/microsoft_dark.png'
 import MidjourneyModelLogo from '@renderer/assets/images/models/midjourney.png'
 import MidjourneyModelLogoDark from '@renderer/assets/images/models/midjourney_dark.png'
+import MiMoModelLogo from '@renderer/assets/images/models/mimo.svg'
 import {
   default as MinicpmModelLogo,
   default as MinicpmModelLogoDark
@@ -301,7 +302,8 @@ export function getModelLogoById(modelId: string): string | undefined {
     bytedance: BytedanceModelLogo,
     ling: LingModelLogo,
     ring: LingModelLogo,
-    '(V_1|V_1_TURBO|V_2|V_2A|V_2_TURBO|DESCRIBE|UPSCALE)': IdeogramModelLogo
+    '(V_1|V_1_TURBO|V_2|V_2A|V_2_TURBO|DESCRIBE|UPSCALE)': IdeogramModelLogo,
+    mimo: MiMoModelLogo
   } as const satisfies Record<string, string>
 
   for (const key in logoMap) {

src/renderer/src/config/models/reasoning.ts

@@ -53,6 +53,7 @@ export const MODEL_SUPPORTED_REASONING_EFFORT = {
   doubao_no_auto: ['high'] as const,
   doubao_after_251015: ['minimal', 'low', 'medium', 'high'] as const,
   hunyuan: ['auto'] as const,
+  mimo: ['auto'] as const,
   zhipu: ['auto'] as const,
   perplexity: ['low', 'medium', 'high'] as const,
   deepseek_hybrid: ['auto'] as const
@@ -82,6 +83,7 @@ export const MODEL_SUPPORTED_OPTIONS: ThinkingOptionConfig = {
   doubao: ['default', 'none', ...MODEL_SUPPORTED_REASONING_EFFORT.doubao] as const,
   doubao_no_auto: ['default', 'none', ...MODEL_SUPPORTED_REASONING_EFFORT.doubao_no_auto] as const,
   doubao_after_251015: ['default', ...MODEL_SUPPORTED_REASONING_EFFORT.doubao_after_251015] as const,
+  mimo: ['default', 'none', ...MODEL_SUPPORTED_REASONING_EFFORT.mimo] as const,
   hunyuan: ['default', 'none', ...MODEL_SUPPORTED_REASONING_EFFORT.hunyuan] as const,
   zhipu: ['default', 'none', ...MODEL_SUPPORTED_REASONING_EFFORT.zhipu] as const,
   perplexity: ['default', ...MODEL_SUPPORTED_REASONING_EFFORT.perplexity] as const,
@@ -149,7 +151,7 @@ const _getThinkModelType = (model: Model): ThinkingModelType => {
   } else if (isSupportedThinkingTokenDoubaoModel(model)) {
     if (isDoubaoThinkingAutoModel(model)) {
       thinkingModelType = 'doubao'
-    } else if (isDoubaoSeedAfter251015(model)) {
+    } else if (isDoubaoSeedAfter251015(model) || isDoubaoSeed18Model(model)) {
       thinkingModelType = 'doubao_after_251015'
     } else {
       thinkingModelType = 'doubao_no_auto'
@@ -162,6 +164,8 @@ const _getThinkModelType = (model: Model): ThinkingModelType => {
     thinkingModelType = 'zhipu'
   } else if (isDeepSeekHybridInferenceModel(model)) {
     thinkingModelType = 'deepseek_hybrid'
+  } else if (isSupportedThinkingTokenMiMoModel(model)) {
+    thinkingModelType = 'mimo'
   }
   return thinkingModelType
 }
@@ -271,7 +275,8 @@ function _isSupportedThinkingTokenModel(model: Model): boolean {
     isSupportedThinkingTokenClaudeModel(model) ||
     isSupportedThinkingTokenDoubaoModel(model) ||
     isSupportedThinkingTokenHunyuanModel(model) ||
-    isSupportedThinkingTokenZhipuModel(model)
+    isSupportedThinkingTokenZhipuModel(model) ||
+    isSupportedThinkingTokenMiMoModel(model)
   )
 }
 
@@ -465,7 +470,7 @@ export function isQwenAlwaysThinkModel(model?: Model): boolean {
 
 // Doubao 支持思考模式的模型正则
 export const DOUBAO_THINKING_MODEL_REGEX =
-  /doubao-(?:1[.-]5-thinking-vision-pro|1[.-]5-thinking-pro-m|seed-1[.-]6(?:-flash)?(?!-(?:thinking)(?:-|$))|seed-code(?:-preview)?(?:-\d+)?)(?:-[\w-]+)*/i
+  /doubao-(?:1[.-]5-thinking-vision-pro|1[.-]5-thinking-pro-m|seed-1[.-][68](?:-flash)?(?!-(?:thinking)(?:-|$))|seed-code(?:-preview)?(?:-\d+)?)(?:-[\w-]+)*/i
 
 // 支持 auto 的 Doubao 模型 doubao-seed-1.6-xxx doubao-seed-1-6-xxx  doubao-1-5-thinking-pro-m-xxx
 // Auto thinking is no longer supported after version 251015, see https://console.volcengine.com/ark/region:ark+cn-beijing/model/detail?Id=doubao-seed-1-6
@@ -483,6 +488,11 @@ export function isDoubaoSeedAfter251015(model: Model): boolean {
   return result
 }
 
+export function isDoubaoSeed18Model(model: Model): boolean {
+  const pattern = /doubao-seed-1[.-]8(?:-[\w-]+)?/i
+  return pattern.test(model.id) || pattern.test(model.name)
+}
+
 export function isSupportedThinkingTokenDoubaoModel(model?: Model): boolean {
   if (!model) {
     return false
@@ -564,6 +574,11 @@ export const isSupportedThinkingTokenZhipuModel = (model: Model): boolean => {
   return ['glm-4.5', 'glm-4.6'].some((id) => modelId.includes(id))
 }
 
+export const isSupportedThinkingTokenMiMoModel = (model: Model): boolean => {
+  const modelId = getLowerBaseModelName(model.id, '/')
+  return ['mimo-v2-flash'].some((id) => modelId.includes(id))
+}
+
 export const isDeepSeekHybridInferenceModel = (model: Model) => {
   const { idResult, nameResult } = withModelIdAndNameAsId(model, (model) => {
     const modelId = getLowerBaseModelName(model.id)
@@ -602,6 +617,8 @@ export const isZhipuReasoningModel = (model?: Model): boolean => {
   return isSupportedThinkingTokenZhipuModel(model) || modelId.includes('glm-z1')
 }
 
+export const isMiMoReasoningModel = isSupportedThinkingTokenMiMoModel
+
 export const isStepReasoningModel = (model?: Model): boolean => {
   if (!model) {
     return false
@@ -652,6 +669,7 @@ export function isReasoningModel(model?: Model): boolean {
     isDeepSeekHybridInferenceModel(model) ||
     isLingReasoningModel(model) ||
     isMiniMaxReasoningModel(model) ||
+    isMiMoReasoningModel(model) ||
     modelId.includes('magistral') ||
     modelId.includes('pangu-pro-moe') ||
     modelId.includes('seed-oss') ||

src/renderer/src/config/models/tooluse.ts

@@ -25,12 +25,13 @@ export const FUNCTION_CALLING_MODELS = [
   'learnlm(?:-[\\w-]+)?',
   'gemini(?:-[\\w-]+)?', // 提前排除了gemini的嵌入模型
   'grok-3(?:-[\\w-]+)?',
-  'doubao-seed-1[.-]6(?:-[\\w-]+)?',
+  'doubao-seed-1[.-][68](?:-[\\w-]+)?',
   'doubao-seed-code(?:-[\\w-]+)?',
   'kimi-k2(?:-[\\w-]+)?',
   'ling-\\w+(?:-[\\w-]+)?',
   'ring-\\w+(?:-[\\w-]+)?',
-  'minimax-m2'
+  'minimax-m2',
+  'mimo-v2-flash'
 ] as const
 
 const FUNCTION_CALLING_EXCLUDED_MODELS = [

src/renderer/src/config/models/vision.ts

@@ -45,7 +45,7 @@ const visionAllowedModels = [
   'deepseek-vl(?:[\\w-]+)?',
   'kimi-latest',
   'gemma-3(?:-[\\w-]+)',
-  'doubao-seed-1[.-]6(?:-[\\w-]+)?',
+  'doubao-seed-1[.-][68](?:-[\\w-]+)?',
   'doubao-seed-code(?:-[\\w-]+)?',
   'kimi-thinking-preview',
   `gemma3(?:[-:\\w]+)?`,

src/renderer/src/config/providers.ts

@@ -31,6 +31,7 @@ import JinaProviderLogo from '@renderer/assets/images/providers/jina.png'
 import LanyunProviderLogo from '@renderer/assets/images/providers/lanyun.png'
 import LMStudioProviderLogo from '@renderer/assets/images/providers/lmstudio.png'
 import LongCatProviderLogo from '@renderer/assets/images/providers/longcat.png'
+import MiMoProviderLogo from '@renderer/assets/images/providers/mimo.svg'
 import MinimaxProviderLogo from '@renderer/assets/images/providers/minimax.png'
 import MistralProviderLogo from '@renderer/assets/images/providers/mistral.png'
 import ModelScopeProviderLogo from '@renderer/assets/images/providers/modelscope.png'
@@ -695,6 +696,17 @@ export const SYSTEM_PROVIDERS_CONFIG: Record<SystemProviderId, SystemProvider> =
     models: SYSTEM_MODELS.cerebras,
     isSystem: true,
     enabled: false
+  },
+  mimo: {
+    id: 'mimo',
+    name: 'Xiaomi MiMo',
+    type: 'openai',
+    apiKey: '',
+    apiHost: 'https://api.xiaomimimo.com',
+    anthropicApiHost: 'https://api.xiaomimimo.com/anthropic',
+    models: SYSTEM_MODELS.mimo,
+    isSystem: true,
+    enabled: false
   }
 } as const
 
@@ -763,7 +775,8 @@ export const PROVIDER_LOGO_MAP: AtLeast<SystemProviderId, string> = {
   huggingface: HuggingfaceProviderLogo,
   sophnet: SophnetProviderLogo,
   gateway: AIGatewayProviderLogo,
-  cerebras: CerebrasProviderLogo
+  cerebras: CerebrasProviderLogo,
+  mimo: MiMoProviderLogo
 } as const
 
 export function getProviderLogo(providerId: string) {
@@ -1434,5 +1447,16 @@ export const PROVIDER_URLS: Record<SystemProviderId, ProviderUrls> = {
       docs: 'https://inference-docs.cerebras.ai/introduction',
       models: 'https://inference-docs.cerebras.ai/models/overview'
     }
+  },
+  mimo: {
+    api: {
+      url: 'https://api.xiaomimimo.com'
+    },
+    websites: {
+      official: 'https://platform.xiaomimimo.com/',
+      apiKey: 'https://platform.xiaomimimo.com/#/console/usage',
+      docs: 'https://platform.xiaomimimo.com/#/docs/welcome',
+      models: 'https://platform.xiaomimimo.com/'
+    }
   }
 }

src/renderer/src/i18n/label.ts

@@ -88,7 +88,8 @@ const providerKeyMap = {
   huggingface: 'provider.huggingface',
   sophnet: 'provider.sophnet',
   gateway: 'provider.ai-gateway',
-  cerebras: 'provider.cerebras'
+  cerebras: 'provider.cerebras',
+  mimo: 'provider.mimo'
 } as const
 
 /**
@@ -330,7 +331,8 @@ const builtInMcpDescriptionKeyMap: Record<BuiltinMCPServerName, string> = {
   [BuiltinMCPServerNames.difyKnowledge]: 'settings.mcp.builtinServersDescriptions.dify_knowledge',
   [BuiltinMCPServerNames.python]: 'settings.mcp.builtinServersDescriptions.python',
   [BuiltinMCPServerNames.didiMCP]: 'settings.mcp.builtinServersDescriptions.didi_mcp',
-  [BuiltinMCPServerNames.browser]: 'settings.mcp.builtinServersDescriptions.browser'
+  [BuiltinMCPServerNames.browser]: 'settings.mcp.builtinServersDescriptions.browser',
+  [BuiltinMCPServerNames.nowledgeMem]: 'settings.mcp.builtinServersDescriptions.nowledge_mem'
 } as const
 
 export const getBuiltInMcpServerDescriptionLabel = (key: string): string => {

src/renderer/src/i18n/locales/en-us.json

@@ -2643,6 +2643,7 @@
     "lanyun": "LANYUN",
     "lmstudio": "LM Studio",
     "longcat": "LongCat AI",
+    "mimo": "Xiaomi MiMo",
     "minimax": "MiniMax",
     "mistral": "Mistral",
     "modelscope": "ModelScope",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "Automatically install MCP service (beta)",
         "memory": "Persistent memory implementation based on a local knowledge graph. This enables the model to remember user-related information across different conversations. Requires configuring the MEMORY_FILE_PATH environment variable.",
         "no": "No description",
+        "nowledge_mem": "Requires Nowledge Mem app running locally. Keeps AI chats, tools, notes, agents, and files in private memory on your computer. Download from https://mem.nowledge.co/",
         "python": "Execute Python code in a secure sandbox environment. Run Python with Pyodide, supporting most standard libraries and scientific computing packages",
         "sequentialthinking": "A MCP server implementation that provides tools for dynamic and reflective problem solving through structured thinking processes"
       },

src/renderer/src/i18n/locales/zh-cn.json

@@ -2643,6 +2643,7 @@
     "lanyun": "蓝耘科技",
     "lmstudio": "LM Studio",
     "longcat": "龙猫",
+    "mimo": "Xiaomi MiMo",
     "minimax": "MiniMax",
     "mistral": "Mistral",
     "modelscope": "ModelScope 魔搭",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "自动安装 MCP 服务（测试版）",
         "memory": "基于本地知识图谱的持久性记忆基础实现。这使得模型能够在不同对话间记住用户的相关信息。需要配置 MEMORY_FILE_PATH 环境变量。",
         "no": "无描述",
+        "nowledge_mem": "需要本地运行 Nowledge Mem 应用。将 AI 对话、工具、笔记、智能体和文件保存在本地计算机的私有记忆中。请从 https://mem.nowledge.co/ 下载",
         "python": "在安全的沙盒环境中执行 Python 代码。使用 Pyodide 运行 Python，支持大多数标准库和科学计算包",
         "sequentialthinking": "一个 MCP 服务器实现，提供了通过结构化思维过程进行动态和反思性问题解决的工具"
       },

src/renderer/src/i18n/locales/zh-tw.json

@@ -2643,6 +2643,7 @@
     "lanyun": "藍耘",
     "lmstudio": "LM Studio",
     "longcat": "龍貓",
+    "mimo": "[to be translated]:Xiaomi MiMo",
     "minimax": "MiniMax",
     "mistral": "Mistral",
     "modelscope": "ModelScope 魔搭",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "自動安裝 MCP 服務（測試版）",
         "memory": "基於本機知識圖譜的持久性記憶基礎實做。這使得模型能夠在不同對話間記住使用者的相關資訊。需要設定 MEMORY_FILE_PATH 環境變數。",
         "no": "無描述",
+        "nowledge_mem": "需要本機執行 Nowledge Mem 應用程式。將 AI 對話、工具、筆記、代理和檔案保存在電腦上的私人記憶體中。請從 https://mem.nowledge.co/ 下載",
         "python": "在安全的沙盒環境中執行 Python 程式碼。使用 Pyodide 執行 Python，支援大多數標準函式庫和科學計算套件",
         "sequentialthinking": "一個 MCP 伺服器實做，提供了透過結構化思維過程進行動態和反思性問題解決的工具"
       },

src/renderer/src/i18n/translate/de-de.json

@@ -2643,6 +2643,7 @@
     "lanyun": "Lanyun Technologie",
     "lmstudio": "LM Studio",
     "longcat": "Meißner Riesenhamster",
+    "mimo": "[to be translated]:Xiaomi MiMo",
     "minimax": "MiniMax",
     "mistral": "Mistral",
     "modelscope": "ModelScope",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "MCP-Service automatisch installieren (Beta-Version)",
         "memory": "MCP-Server mit persistenter Erinnerungsbasis auf lokalem Wissensgraphen, der Informationen über verschiedene Dialoge hinweg speichert. MEMORY_FILE_PATH-Umgebungsvariable muss konfiguriert werden",
         "no": "Keine Beschreibung",
+        "nowledge_mem": "Erfordert lokal laufende Nowledge Mem App. Speichert KI-Chats, Tools, Notizen, Agenten und Dateien in einem privaten Speicher auf Ihrem Computer. Download unter https://mem.nowledge.co/",
         "python": "Python-Code in einem sicheren Sandbox-Umgebung ausführen. Verwendung von Pyodide für Python, Unterstützung für die meisten Standardbibliotheken und wissenschaftliche Pakete",
         "sequentialthinking": "MCP-Server-Implementierung mit strukturiertem Denkprozess, der dynamische und reflektierende Problemlösungen ermöglicht"
       },

src/renderer/src/i18n/translate/el-gr.json

@@ -2643,6 +2643,7 @@
     "lanyun": "Λανιούν Τεχνολογία",
     "lmstudio": "LM Studio",
     "longcat": "Τσίρο",
+    "mimo": "[to be translated]:Xiaomi MiMo",
     "minimax": "MiniMax",
     "mistral": "Mistral",
     "modelscope": "ModelScope Magpie",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "Αυτόματη εγκατάσταση υπηρεσίας MCP (προβολή)",
         "memory": "Βασική υλοποίηση μόνιμης μνήμης με βάση τοπικό γράφημα γνώσης. Αυτό επιτρέπει στο μοντέλο να θυμάται πληροφορίες σχετικές με τον χρήστη ανάμεσα σε διαφορετικές συνομιλίες. Απαιτείται η ρύθμιση της μεταβλητής περιβάλλοντος MEMORY_FILE_PATH.",
         "no": "Χωρίς περιγραφή",
+        "nowledge_mem": "[to be translated]:Requires Nowledge Mem app running locally. Keeps AI chats, tools, notes, agents, and files in private memory on your computer. Download from https://mem.nowledge.co/",
         "python": "Εκτελέστε κώδικα Python σε ένα ασφαλές περιβάλλον sandbox. Χρησιμοποιήστε το Pyodide για να εκτελέσετε Python, υποστηρίζοντας την πλειονότητα των βιβλιοθηκών της τυπικής βιβλιοθήκης και των πακέτων επιστημονικού υπολογισμού",
         "sequentialthinking": "ένας εξυπηρετητής MCP που υλοποιείται, παρέχοντας εργαλεία για δυναμική και αναστοχαστική επίλυση προβλημάτων μέσω δομημένων διαδικασιών σκέψης"
       },

src/renderer/src/i18n/translate/es-es.json

@@ -2643,6 +2643,7 @@
     "lanyun": "Tecnología Lanyun",
     "lmstudio": "Estudio LM",
     "longcat": "Totoro",
+    "mimo": "[to be translated]:Xiaomi MiMo",
     "minimax": "Minimax",
     "mistral": "Mistral",
     "modelscope": "ModelScope Módulo",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "Instalación automática del servicio MCP (versión beta)",
         "memory": "Implementación básica de memoria persistente basada en un grafo de conocimiento local. Esto permite que el modelo recuerde información relevante del usuario entre diferentes conversaciones. Es necesario configurar la variable de entorno MEMORY_FILE_PATH.",
         "no": "sin descripción",
+        "nowledge_mem": "[to be translated]:Requires Nowledge Mem app running locally. Keeps AI chats, tools, notes, agents, and files in private memory on your computer. Download from https://mem.nowledge.co/",
         "python": "Ejecuta código Python en un entorno sandbox seguro. Usa Pyodide para ejecutar Python, compatible con la mayoría de las bibliotecas estándar y paquetes de cálculo científico.",
         "sequentialthinking": "Una implementación de servidor MCP que proporciona herramientas para la resolución dinámica y reflexiva de problemas mediante un proceso de pensamiento estructurado"
       },

src/renderer/src/i18n/translate/fr-fr.json

@@ -2643,6 +2643,7 @@
     "lanyun": "Technologie Lan Yun",
     "lmstudio": "Studio LM",
     "longcat": "Mon voisin Totoro",
+    "mimo": "[to be translated]:Xiaomi MiMo",
     "minimax": "MiniMax",
     "mistral": "Mistral",
     "modelscope": "ModelScope MoDa",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "Installation automatique du service MCP (version bêta)",
         "memory": "Implémentation de base de mémoire persistante basée sur un graphe de connaissances local. Cela permet au modèle de se souvenir des informations relatives à l'utilisateur entre différentes conversations. Nécessite la configuration de la variable d'environnement MEMORY_FILE_PATH.",
         "no": "sans description",
+        "nowledge_mem": "[to be translated]:Requires Nowledge Mem app running locally. Keeps AI chats, tools, notes, agents, and files in private memory on your computer. Download from https://mem.nowledge.co/",
         "python": "Exécutez du code Python dans un environnement bac à sable sécurisé. Utilisez Pyodide pour exécuter Python, prenant en charge la plupart des bibliothèques standard et des packages de calcul scientifique.",
         "sequentialthinking": "Un serveur MCP qui fournit des outils permettant une résolution dynamique et réflexive des problèmes à travers un processus de pensée structuré"
       },

src/renderer/src/i18n/translate/ja-jp.json

@@ -2643,6 +2643,7 @@
     "lanyun": "LANYUN",
     "lmstudio": "LM Studio",
     "longcat": "トトロ",
+    "mimo": "[to be translated]:Xiaomi MiMo",
     "minimax": "MiniMax",
     "mistral": "Mistral",
     "modelscope": "ModelScope",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "MCPサービスの自動インストール（ベータ版）",
         "memory": "ローカルのナレッジグラフに基づく永続的なメモリの基本的な実装です。これにより、モデルは異なる会話間でユーザーの関連情報を記憶できるようになります。MEMORY_FILE_PATH 環境変数の設定が必要です。",
         "no": "説明なし",
+        "nowledge_mem": "Nowledge Mem アプリをローカルで実行する必要があります。AI チャット、ツール、ノート、エージェント、ファイルをコンピューター上のプライベートメモリに保存します。https://mem.nowledge.co/ からダウンロードしてください",
         "python": "安全なサンドボックス環境でPythonコードを実行します。Pyodideを使用してPythonを実行し、ほとんどの標準ライブラリと科学計算パッケージをサポートしています。",
         "sequentialthinking": "構造化された思考プロセスを通じて動的かつ反省的な問題解決を行うためのツールを提供するMCPサーバーの実装"
       },

src/renderer/src/i18n/translate/pt-pt.json

@@ -2643,6 +2643,7 @@
     "lanyun": "Lanyun Tecnologia",
     "lmstudio": "Estúdio LM",
     "longcat": "Totoro",
+    "mimo": "[to be translated]:Xiaomi MiMo",
     "minimax": "Minimax",
     "mistral": "Mistral",
     "modelscope": "ModelScope MôDá",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "Instalação automática do serviço MCP (beta)",
         "memory": "Implementação base de memória persistente baseada em grafos de conhecimento locais. Isso permite que o modelo lembre informações relevantes do utilizador entre diferentes conversas. É necessário configurar a variável de ambiente MEMORY_FILE_PATH.",
         "no": "sem descrição",
+        "nowledge_mem": "Requer a aplicação Nowledge Mem em execução localmente. Mantém conversas de IA, ferramentas, notas, agentes e ficheiros numa memória privada no seu computador. Transfira de https://mem.nowledge.co/",
         "python": "Executar código Python num ambiente sandbox seguro. Utilizar Pyodide para executar Python, suportando a maioria das bibliotecas padrão e pacotes de computação científica",
         "sequentialthinking": "Uma implementação de servidor MCP que fornece ferramentas para resolução dinâmica e reflexiva de problemas através de um processo de pensamento estruturado"
       },

src/renderer/src/i18n/translate/ru-ru.json

@@ -2643,6 +2643,7 @@
     "lanyun": "LANYUN",
     "lmstudio": "LM Studio",
     "longcat": "Тоторо",
+    "mimo": "[to be translated]:Xiaomi MiMo",
     "minimax": "MiniMax",
     "mistral": "Mistral",
     "modelscope": "ModelScope",
@@ -3939,6 +3940,7 @@
         "mcp_auto_install": "Автоматическая установка службы MCP (бета-версия)",
         "memory": "реализация постоянной памяти на основе локального графа знаний. Это позволяет модели запоминать информацию о пользователе между различными диалогами. Требуется настроить переменную среды MEMORY_FILE_PATH.",
         "no": "без описания",
+        "nowledge_mem": "Требуется запущенное локально приложение Nowledge Mem. Хранит чаты ИИ, инструменты, заметки, агентов и файлы в приватной памяти на вашем компьютере. Скачать можно на https://mem.nowledge.co/",
         "python": "Выполняйте код Python в безопасной песочнице. Запускайте Python с помощью Pyodide, поддерживается большинство стандартных библиотек и пакетов для научных вычислений",
         "sequentialthinking": "MCP серверная реализация, предоставляющая инструменты для динамического и рефлексивного решения проблем посредством структурированного мыслительного процесса"
       },

src/renderer/src/pages/settings/ProviderSettings/ProviderSetting.tsx

@@ -80,7 +80,8 @@ const ANTHROPIC_COMPATIBLE_PROVIDER_IDS = [
   SystemProviderIds.minimax,
   SystemProviderIds.silicon,
   SystemProviderIds.qiniu,
-  SystemProviderIds.dmxapi
+  SystemProviderIds.dmxapi,
+  SystemProviderIds.mimo
 ] as const
 type AnthropicCompatibleProviderId = (typeof ANTHROPIC_COMPATIBLE_PROVIDER_IDS)[number]
 

src/renderer/src/store/mcp.ts

@@ -183,6 +183,16 @@ export const builtinMCPServers: BuiltinMCPServer[] = [
     provider: 'CherryAI',
     installSource: 'builtin',
     isTrusted: true
+  },
+  {
+    id: nanoid(),
+    name: BuiltinMCPServerNames.nowledgeMem,
+    reference: 'https://mem.nowledge.co/',
+    type: 'inMemory',
+    isActive: false,
+    provider: 'Nowledge',
+    installSource: 'builtin',
+    isTrusted: true
   }
 ] as const
 

src/renderer/src/store/migrate.ts

@@ -3046,6 +3046,7 @@ const migrateConfig = {
           assistant.settings.reasoning_effort = 'default'
         }
       })
+      addProvider(state, 'mimo')
       logger.info('migrate 187 success')
       return state
     } catch (error) {

src/renderer/src/types/index.ts

@@ -103,6 +103,7 @@ const ThinkModelTypes = [
   'doubao',
   'doubao_no_auto',
   'doubao_after_251015',
+  'mimo',
   'hunyuan',
   'zhipu',
   'perplexity',
@@ -752,7 +753,8 @@ export const BuiltinMCPServerNames = {
   difyKnowledge: '@cherry/dify-knowledge',
   python: '@cherry/python',
   didiMCP: '@cherry/didi-mcp',
-  browser: '@cherry/browser'
+  browser: '@cherry/browser',
+  nowledgeMem: '@cherry/nowledge-mem'
 } as const
 
 export type BuiltinMCPServerName = (typeof BuiltinMCPServerNames)[keyof typeof BuiltinMCPServerNames]

src/renderer/src/types/provider.ts

@@ -189,7 +189,8 @@ export const SystemProviderIdSchema = z.enum([
   'huggingface',
   'sophnet',
   'gateway',
-  'cerebras'
+  'cerebras',
+  'mimo'
 ])
 
 export type SystemProviderId = z.infer<typeof SystemProviderIdSchema>
@@ -258,7 +259,8 @@ export const SystemProviderIds = {
   longcat: 'longcat',
   huggingface: 'huggingface',
   gateway: 'gateway',
-  cerebras: 'cerebras'
+  cerebras: 'cerebras',
+  mimo: 'mimo'
 } as const satisfies Record<SystemProviderId, SystemProviderId>
 
 type SystemProviderIdTypeMap = typeof SystemProviderIds

yarn.lock

@@ -11246,7 +11246,7 @@ __metadata:
   languageName: node
   linkType: hard
 
-"buffer-equal-constant-time@npm:1.0.1":
+"buffer-equal-constant-time@npm:^1.0.1":
   version: 1.0.1
   resolution: "buffer-equal-constant-time@npm:1.0.1"
   checksum: 10c0/fb2294e64d23c573d0dd1f1e7a466c3e978fe94a4e0f8183937912ca374619773bef8e2aceb854129d2efecbbc515bbd0cc78d2734a3e3031edb0888531bbc8e
@@ -17233,24 +17233,24 @@ __metadata:
   languageName: node
   linkType: hard
 
-"jwa@npm:^2.0.0":
+"jwa@npm:^2.0.1":
-  version: 2.0.0
+  version: 2.0.1
-  resolution: "jwa@npm:2.0.0"
+  resolution: "jwa@npm:2.0.1"
   dependencies:
-    buffer-equal-constant-time: "npm:1.0.1"
+    buffer-equal-constant-time: "npm:^1.0.1"
     ecdsa-sig-formatter: "npm:1.0.11"
     safe-buffer: "npm:^5.0.1"
-  checksum: 10c0/6baab823b93c038ba1d2a9e531984dcadbc04e9eb98d171f4901b7a40d2be15961a359335de1671d78cb6d987f07cbe5d350d8143255977a889160c4d90fcc3c
+  checksum: 10c0/ab3ebc6598e10dc11419d4ed675c9ca714a387481466b10e8a6f3f65d8d9c9237e2826f2505280a739cf4cbcf511cb288eeec22b5c9c63286fc5a2e4f97e78cf
   languageName: node
   linkType: hard
 
 "jws@npm:^4.0.0":
-  version: 4.0.0
+  version: 4.0.1
-  resolution: "jws@npm:4.0.0"
+  resolution: "jws@npm:4.0.1"
   dependencies:
-    jwa: "npm:^2.0.0"
+    jwa: "npm:^2.0.1"
     safe-buffer: "npm:^5.0.1"
-  checksum: 10c0/f1ca77ea5451e8dc5ee219cb7053b8a4f1254a79cb22417a2e1043c1eb8a569ae118c68f24d72a589e8a3dd1824697f47d6bd4fb4bebb93a3bdf53545e721661
+  checksum: 10c0/6be1ed93023aef570ccc5ea8d162b065840f3ef12f0d1bb3114cade844de7a357d5dc558201d9a65101e70885a6fa56b17462f520e6b0d426195510618a154d0
   languageName: node
   linkType: hard