[autofix.ci] apply automated fixes

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

.claude/settings.json

@@ -0,0 +1,9 @@
+{
+  "enabledPlugins": {
+    "feature-dev@claude-plugins-official": true,
+    "context7@claude-plugins-official": true,
+    "typescript-lsp@claude-plugins-official": true,
+    "pyright-lsp@claude-plugins-official": true,
+    "ralph-loop@claude-plugins-official": true
+  }
+}

.claude/settings.json.example

@@ -1,19 +0,0 @@
-{
-    "permissions": {
-      "allow": [],
-      "deny": []
-    },
-    "env": {
-      "__comment": "Environment variables for MCP servers. Override in .claude/settings.local.json with actual values.",
-      "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
-    },
-    "enabledMcpjsonServers": [
-      "context7",
-      "sequential-thinking",
-      "github",
-      "fetch",
-      "playwright",
-      "ide"
-    ],
-    "enableAllProjectMcpServers": true
-  }

.claude/skills/component-refactoring/SKILL.md

@@ -0,0 +1,483 @@
+---
+name: component-refactoring
+description: Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component --json` shows complexity > 50 or lineCount > 300, when the user asks for code splitting, hook extraction, or complexity reduction, or when `pnpm analyze-component` warns to refactor before testing; avoid for simple/well-structured components, third-party wrappers, or when the user explicitly wants testing without refactoring.
+---
+
+# Dify Component Refactoring Skill
+
+Refactor high-complexity React components in the Dify frontend codebase with the patterns and workflow below.
+
+> **Complexity Threshold**: Components with complexity > 50 (measured by `pnpm analyze-component`) should be refactored before testing.
+
+## Quick Reference
+
+### Commands (run from `web/`)
+
+Use paths relative to `web/` (e.g., `app/components/...`).
+Use `refactor-component` for refactoring prompts and `analyze-component` for testing prompts and metrics.
+
+```bash
+cd web
+
+# Generate refactoring prompt
+pnpm refactor-component <path>
+
+# Output refactoring analysis as JSON
+pnpm refactor-component <path> --json
+
+# Generate testing prompt (after refactoring)
+pnpm analyze-component <path>
+
+# Output testing analysis as JSON
+pnpm analyze-component <path> --json
+```
+
+### Complexity Analysis
+
+```bash
+# Analyze component complexity
+pnpm analyze-component <path> --json
+
+# Key metrics to check:
+# - complexity: normalized score 0-100 (target < 50)
+# - maxComplexity: highest single function complexity
+# - lineCount: total lines (target < 300)
+```
+
+### Complexity Score Interpretation
+
+| Score | Level | Action |
+|-------|-------|--------|
+| 0-25 | 🟢 Simple | Ready for testing |
+| 26-50 | 🟡 Medium | Consider minor refactoring |
+| 51-75 | 🟠 Complex | **Refactor before testing** |
+| 76-100 | 🔴 Very Complex | **Must refactor** |
+
+## Core Refactoring Patterns
+
+### Pattern 1: Extract Custom Hooks
+
+**When**: Component has complex state management, multiple `useState`/`useEffect`, or business logic mixed with UI.
+
+**Dify Convention**: Place hooks in a `hooks/` subdirectory or alongside the component as `use-<feature>.ts`.
+
+```typescript
+// ❌ Before: Complex state logic in component
+const Configuration: FC = () => {
+  const [modelConfig, setModelConfig] = useState<ModelConfig>(...)
+  const [datasetConfigs, setDatasetConfigs] = useState<DatasetConfigs>(...)
+  const [completionParams, setCompletionParams] = useState<FormValue>({})
+  
+  // 50+ lines of state management logic...
+  
+  return <div>...</div>
+}
+
+// ✅ After: Extract to custom hook
+// hooks/use-model-config.ts
+export const useModelConfig = (appId: string) => {
+  const [modelConfig, setModelConfig] = useState<ModelConfig>(...)
+  const [completionParams, setCompletionParams] = useState<FormValue>({})
+  
+  // Related state management logic here
+  
+  return { modelConfig, setModelConfig, completionParams, setCompletionParams }
+}
+
+// Component becomes cleaner
+const Configuration: FC = () => {
+  const { modelConfig, setModelConfig } = useModelConfig(appId)
+  return <div>...</div>
+}
+```
+
+**Dify Examples**:
+- `web/app/components/app/configuration/hooks/use-advanced-prompt-config.ts`
+- `web/app/components/app/configuration/debug/hooks.tsx`
+- `web/app/components/workflow/hooks/use-workflow.ts`
+
+### Pattern 2: Extract Sub-Components
+
+**When**: Single component has multiple UI sections, conditional rendering blocks, or repeated patterns.
+
+**Dify Convention**: Place sub-components in subdirectories or as separate files in the same directory.
+
+```typescript
+// ❌ Before: Monolithic JSX with multiple sections
+const AppInfo = () => {
+  return (
+    <div>
+      {/* 100 lines of header UI */}
+      {/* 100 lines of operations UI */}
+      {/* 100 lines of modals */}
+    </div>
+  )
+}
+
+// ✅ After: Split into focused components
+// app-info/
+//   ├── index.tsx           (orchestration only)
+//   ├── app-header.tsx      (header UI)
+//   ├── app-operations.tsx  (operations UI)
+//   └── app-modals.tsx      (modal management)
+
+const AppInfo = () => {
+  const { showModal, setShowModal } = useAppInfoModals()
+  
+  return (
+    <div>
+      <AppHeader appDetail={appDetail} />
+      <AppOperations onAction={handleAction} />
+      <AppModals show={showModal} onClose={() => setShowModal(null)} />
+    </div>
+  )
+}
+```
+
+**Dify Examples**:
+- `web/app/components/app/configuration/` directory structure
+- `web/app/components/workflow/nodes/` per-node organization
+
+### Pattern 3: Simplify Conditional Logic
+
+**When**: Deep nesting (> 3 levels), complex ternaries, or multiple `if/else` chains.
+
+```typescript
+// ❌ Before: Deeply nested conditionals
+const Template = useMemo(() => {
+  if (appDetail?.mode === AppModeEnum.CHAT) {
+    switch (locale) {
+      case LanguagesSupported[1]:
+        return <TemplateChatZh />
+      case LanguagesSupported[7]:
+        return <TemplateChatJa />
+      default:
+        return <TemplateChatEn />
+    }
+  }
+  if (appDetail?.mode === AppModeEnum.ADVANCED_CHAT) {
+    // Another 15 lines...
+  }
+  // More conditions...
+}, [appDetail, locale])
+
+// ✅ After: Use lookup tables + early returns
+const TEMPLATE_MAP = {
+  [AppModeEnum.CHAT]: {
+    [LanguagesSupported[1]]: TemplateChatZh,
+    [LanguagesSupported[7]]: TemplateChatJa,
+    default: TemplateChatEn,
+  },
+  [AppModeEnum.ADVANCED_CHAT]: {
+    [LanguagesSupported[1]]: TemplateAdvancedChatZh,
+    // ...
+  },
+}
+
+const Template = useMemo(() => {
+  const modeTemplates = TEMPLATE_MAP[appDetail?.mode]
+  if (!modeTemplates) return null
+  
+  const TemplateComponent = modeTemplates[locale] || modeTemplates.default
+  return <TemplateComponent appDetail={appDetail} />
+}, [appDetail, locale])
+```
+
+### Pattern 4: Extract API/Data Logic
+
+**When**: Component directly handles API calls, data transformation, or complex async operations.
+
+**Dify Convention**: Use `@tanstack/react-query` hooks from `web/service/use-*.ts` or create custom data hooks.
+
+```typescript
+// ❌ Before: API logic in component
+const MCPServiceCard = () => {
+  const [basicAppConfig, setBasicAppConfig] = useState({})
+  
+  useEffect(() => {
+    if (isBasicApp && appId) {
+      (async () => {
+        const res = await fetchAppDetail({ url: '/apps', id: appId })
+        setBasicAppConfig(res?.model_config || {})
+      })()
+    }
+  }, [appId, isBasicApp])
+  
+  // More API-related logic...
+}
+
+// ✅ After: Extract to data hook using React Query
+// use-app-config.ts
+import { useQuery } from '@tanstack/react-query'
+import { get } from '@/service/base'
+
+const NAME_SPACE = 'appConfig'
+
+export const useAppConfig = (appId: string, isBasicApp: boolean) => {
+  return useQuery({
+    enabled: isBasicApp && !!appId,
+    queryKey: [NAME_SPACE, 'detail', appId],
+    queryFn: () => get<AppDetailResponse>(`/apps/${appId}`),
+    select: data => data?.model_config || {},
+  })
+}
+
+// Component becomes cleaner
+const MCPServiceCard = () => {
+  const { data: config, isLoading } = useAppConfig(appId, isBasicApp)
+  // UI only
+}
+```
+
+**React Query Best Practices in Dify**:
+- Define `NAME_SPACE` for query key organization
+- Use `enabled` option for conditional fetching
+- Use `select` for data transformation
+- Export invalidation hooks: `useInvalidXxx`
+
+**Dify Examples**:
+- `web/service/use-workflow.ts`
+- `web/service/use-common.ts`
+- `web/service/knowledge/use-dataset.ts`
+- `web/service/knowledge/use-document.ts`
+
+### Pattern 5: Extract Modal/Dialog Management
+
+**When**: Component manages multiple modals with complex open/close states.
+
+**Dify Convention**: Modals should be extracted with their state management.
+
+```typescript
+// ❌ Before: Multiple modal states in component
+const AppInfo = () => {
+  const [showEditModal, setShowEditModal] = useState(false)
+  const [showDuplicateModal, setShowDuplicateModal] = useState(false)
+  const [showConfirmDelete, setShowConfirmDelete] = useState(false)
+  const [showSwitchModal, setShowSwitchModal] = useState(false)
+  const [showImportDSLModal, setShowImportDSLModal] = useState(false)
+  // 5+ more modal states...
+}
+
+// ✅ After: Extract to modal management hook
+type ModalType = 'edit' | 'duplicate' | 'delete' | 'switch' | 'import' | null
+
+const useAppInfoModals = () => {
+  const [activeModal, setActiveModal] = useState<ModalType>(null)
+  
+  const openModal = useCallback((type: ModalType) => setActiveModal(type), [])
+  const closeModal = useCallback(() => setActiveModal(null), [])
+  
+  return {
+    activeModal,
+    openModal,
+    closeModal,
+    isOpen: (type: ModalType) => activeModal === type,
+  }
+}
+```
+
+### Pattern 6: Extract Form Logic
+
+**When**: Complex form validation, submission handling, or field transformation.
+
+**Dify Convention**: Use `@tanstack/react-form` patterns from `web/app/components/base/form/`.
+
+```typescript
+// ✅ Use existing form infrastructure
+import { useAppForm } from '@/app/components/base/form'
+
+const ConfigForm = () => {
+  const form = useAppForm({
+    defaultValues: { name: '', description: '' },
+    onSubmit: handleSubmit,
+  })
+  
+  return <form.Provider>...</form.Provider>
+}
+```
+
+## Dify-Specific Refactoring Guidelines
+
+### 1. Context Provider Extraction
+
+**When**: Component provides complex context values with multiple states.
+
+```typescript
+// ❌ Before: Large context value object
+const value = {
+  appId, isAPIKeySet, isTrailFinished, mode, modelModeType,
+  promptMode, isAdvancedMode, isAgent, isOpenAI, isFunctionCall,
+  // 50+ more properties...
+}
+return <ConfigContext.Provider value={value}>...</ConfigContext.Provider>
+
+// ✅ After: Split into domain-specific contexts
+<ModelConfigProvider value={modelConfigValue}>
+  <DatasetConfigProvider value={datasetConfigValue}>
+    <UIConfigProvider value={uiConfigValue}>
+      {children}
+    </UIConfigProvider>
+  </DatasetConfigProvider>
+</ModelConfigProvider>
+```
+
+**Dify Reference**: `web/context/` directory structure
+
+### 2. Workflow Node Components
+
+**When**: Refactoring workflow node components (`web/app/components/workflow/nodes/`).
+
+**Conventions**:
+- Keep node logic in `use-interactions.ts`
+- Extract panel UI to separate files
+- Use `_base` components for common patterns
+
+```
+nodes/<node-type>/
+  ├── index.tsx              # Node registration
+  ├── node.tsx               # Node visual component
+  ├── panel.tsx              # Configuration panel
+  ├── use-interactions.ts    # Node-specific hooks
+  └── types.ts               # Type definitions
+```
+
+### 3. Configuration Components
+
+**When**: Refactoring app configuration components.
+
+**Conventions**:
+- Separate config sections into subdirectories
+- Use existing patterns from `web/app/components/app/configuration/`
+- Keep feature toggles in dedicated components
+
+### 4. Tool/Plugin Components
+
+**When**: Refactoring tool-related components (`web/app/components/tools/`).
+
+**Conventions**:
+- Follow existing modal patterns
+- Use service hooks from `web/service/use-tools.ts`
+- Keep provider-specific logic isolated
+
+## Refactoring Workflow
+
+### Step 1: Generate Refactoring Prompt
+
+```bash
+pnpm refactor-component <path>
+```
+
+This command will:
+- Analyze component complexity and features
+- Identify specific refactoring actions needed
+- Generate a prompt for AI assistant (auto-copied to clipboard on macOS)
+- Provide detailed requirements based on detected patterns
+
+### Step 2: Analyze Details
+
+```bash
+pnpm analyze-component <path> --json
+```
+
+Identify:
+- Total complexity score
+- Max function complexity
+- Line count
+- Features detected (state, effects, API, etc.)
+
+### Step 3: Plan
+
+Create a refactoring plan based on detected features:
+
+| Detected Feature | Refactoring Action |
+|------------------|-------------------|
+| `hasState: true` + `hasEffects: true` | Extract custom hook |
+| `hasAPI: true` | Extract data/service hook |
+| `hasEvents: true` (many) | Extract event handlers |
+| `lineCount > 300` | Split into sub-components |
+| `maxComplexity > 50` | Simplify conditional logic |
+
+### Step 4: Execute Incrementally
+
+1. **Extract one piece at a time**
+2. **Run lint, type-check, and tests after each extraction**
+3. **Verify functionality before next step**
+
+```
+For each extraction:
+  ┌────────────────────────────────────────┐
+  │ 1. Extract code                        │
+  │ 2. Run: pnpm lint:fix                  │
+  │ 3. Run: pnpm type-check:tsgo           │
+  │ 4. Run: pnpm test                      │
+  │ 5. Test functionality manually         │
+  │ 6. PASS? → Next extraction             │
+  │    FAIL? → Fix before continuing       │
+  └────────────────────────────────────────┘
+```
+
+### Step 5: Verify
+
+After refactoring:
+
+```bash
+# Re-run refactor command to verify improvements
+pnpm refactor-component <path>
+
+# If complexity < 25 and lines < 200, you'll see:
+# ✅ COMPONENT IS WELL-STRUCTURED
+
+# For detailed metrics:
+pnpm analyze-component <path> --json
+
+# Target metrics:
+# - complexity < 50
+# - lineCount < 300
+# - maxComplexity < 30
+```
+
+## Common Mistakes to Avoid
+
+### ❌ Over-Engineering
+
+```typescript
+// ❌ Too many tiny hooks
+const useButtonText = () => useState('Click')
+const useButtonDisabled = () => useState(false)
+const useButtonLoading = () => useState(false)
+
+// ✅ Cohesive hook with related state
+const useButtonState = () => {
+  const [text, setText] = useState('Click')
+  const [disabled, setDisabled] = useState(false)
+  const [loading, setLoading] = useState(false)
+  return { text, setText, disabled, setDisabled, loading, setLoading }
+}
+```
+
+### ❌ Breaking Existing Patterns
+
+- Follow existing directory structures
+- Maintain naming conventions
+- Preserve export patterns for compatibility
+
+### ❌ Premature Abstraction
+
+- Only extract when there's clear complexity benefit
+- Don't create abstractions for single-use code
+- Keep refactored code in the same domain area
+
+## References
+
+### Dify Codebase Examples
+
+- **Hook extraction**: `web/app/components/app/configuration/hooks/`
+- **Component splitting**: `web/app/components/app/configuration/`
+- **Service hooks**: `web/service/use-*.ts`
+- **Workflow patterns**: `web/app/components/workflow/hooks/`
+- **Form patterns**: `web/app/components/base/form/`
+
+### Related Skills
+
+- `frontend-testing` - For testing refactored components
+- `web/testing/testing.md` - Testing specification

.claude/skills/component-refactoring/references/complexity-patterns.md

@@ -0,0 +1,493 @@
+# Complexity Reduction Patterns
+
+This document provides patterns for reducing cognitive complexity in Dify React components.
+
+## Understanding Complexity
+
+### SonarJS Cognitive Complexity
+
+The `pnpm analyze-component` tool uses SonarJS cognitive complexity metrics:
+
+- **Total Complexity**: Sum of all functions' complexity in the file
+- **Max Complexity**: Highest single function complexity
+
+### What Increases Complexity
+
+| Pattern | Complexity Impact |
+|---------|-------------------|
+| `if/else` | +1 per branch |
+| Nested conditions | +1 per nesting level |
+| `switch/case` | +1 per case |
+| `for/while/do` | +1 per loop |
+| `&&`/`||` chains | +1 per operator |
+| Nested callbacks | +1 per nesting level |
+| `try/catch` | +1 per catch |
+| Ternary expressions | +1 per nesting |
+
+## Pattern 1: Replace Conditionals with Lookup Tables
+
+**Before** (complexity: ~15):
+
+```typescript
+const Template = useMemo(() => {
+  if (appDetail?.mode === AppModeEnum.CHAT) {
+    switch (locale) {
+      case LanguagesSupported[1]:
+        return <TemplateChatZh appDetail={appDetail} />
+      case LanguagesSupported[7]:
+        return <TemplateChatJa appDetail={appDetail} />
+      default:
+        return <TemplateChatEn appDetail={appDetail} />
+    }
+  }
+  if (appDetail?.mode === AppModeEnum.ADVANCED_CHAT) {
+    switch (locale) {
+      case LanguagesSupported[1]:
+        return <TemplateAdvancedChatZh appDetail={appDetail} />
+      case LanguagesSupported[7]:
+        return <TemplateAdvancedChatJa appDetail={appDetail} />
+      default:
+        return <TemplateAdvancedChatEn appDetail={appDetail} />
+    }
+  }
+  if (appDetail?.mode === AppModeEnum.WORKFLOW) {
+    // Similar pattern...
+  }
+  return null
+}, [appDetail, locale])
+```
+
+**After** (complexity: ~3):
+
+```typescript
+// Define lookup table outside component
+const TEMPLATE_MAP: Record<AppModeEnum, Record<string, FC<TemplateProps>>> = {
+  [AppModeEnum.CHAT]: {
+    [LanguagesSupported[1]]: TemplateChatZh,
+    [LanguagesSupported[7]]: TemplateChatJa,
+    default: TemplateChatEn,
+  },
+  [AppModeEnum.ADVANCED_CHAT]: {
+    [LanguagesSupported[1]]: TemplateAdvancedChatZh,
+    [LanguagesSupported[7]]: TemplateAdvancedChatJa,
+    default: TemplateAdvancedChatEn,
+  },
+  [AppModeEnum.WORKFLOW]: {
+    [LanguagesSupported[1]]: TemplateWorkflowZh,
+    [LanguagesSupported[7]]: TemplateWorkflowJa,
+    default: TemplateWorkflowEn,
+  },
+  // ...
+}
+
+// Clean component logic
+const Template = useMemo(() => {
+  if (!appDetail?.mode) return null
+  
+  const templates = TEMPLATE_MAP[appDetail.mode]
+  if (!templates) return null
+  
+  const TemplateComponent = templates[locale] ?? templates.default
+  return <TemplateComponent appDetail={appDetail} />
+}, [appDetail, locale])
+```
+
+## Pattern 2: Use Early Returns
+
+**Before** (complexity: ~10):
+
+```typescript
+const handleSubmit = () => {
+  if (isValid) {
+    if (hasChanges) {
+      if (isConnected) {
+        submitData()
+      } else {
+        showConnectionError()
+      }
+    } else {
+      showNoChangesMessage()
+    }
+  } else {
+    showValidationError()
+  }
+}
+```
+
+**After** (complexity: ~4):
+
+```typescript
+const handleSubmit = () => {
+  if (!isValid) {
+    showValidationError()
+    return
+  }
+  
+  if (!hasChanges) {
+    showNoChangesMessage()
+    return
+  }
+  
+  if (!isConnected) {
+    showConnectionError()
+    return
+  }
+  
+  submitData()
+}
+```
+
+## Pattern 3: Extract Complex Conditions
+
+**Before** (complexity: high):
+
+```typescript
+const canPublish = (() => {
+  if (mode !== AppModeEnum.COMPLETION) {
+    if (!isAdvancedMode)
+      return true
+
+    if (modelModeType === ModelModeType.completion) {
+      if (!hasSetBlockStatus.history || !hasSetBlockStatus.query)
+        return false
+      return true
+    }
+    return true
+  }
+  return !promptEmpty
+})()
+```
+
+**After** (complexity: lower):
+
+```typescript
+// Extract to named functions
+const canPublishInCompletionMode = () => !promptEmpty
+
+const canPublishInChatMode = () => {
+  if (!isAdvancedMode) return true
+  if (modelModeType !== ModelModeType.completion) return true
+  return hasSetBlockStatus.history && hasSetBlockStatus.query
+}
+
+// Clean main logic
+const canPublish = mode === AppModeEnum.COMPLETION
+  ? canPublishInCompletionMode()
+  : canPublishInChatMode()
+```
+
+## Pattern 4: Replace Chained Ternaries
+
+**Before** (complexity: ~5):
+
+```typescript
+const statusText = serverActivated
+  ? t('status.running')
+  : serverPublished
+    ? t('status.inactive')
+    : appUnpublished
+      ? t('status.unpublished')
+      : t('status.notConfigured')
+```
+
+**After** (complexity: ~2):
+
+```typescript
+const getStatusText = () => {
+  if (serverActivated) return t('status.running')
+  if (serverPublished) return t('status.inactive')
+  if (appUnpublished) return t('status.unpublished')
+  return t('status.notConfigured')
+}
+
+const statusText = getStatusText()
+```
+
+Or use lookup:
+
+```typescript
+const STATUS_TEXT_MAP = {
+  running: 'status.running',
+  inactive: 'status.inactive',
+  unpublished: 'status.unpublished',
+  notConfigured: 'status.notConfigured',
+} as const
+
+const getStatusKey = (): keyof typeof STATUS_TEXT_MAP => {
+  if (serverActivated) return 'running'
+  if (serverPublished) return 'inactive'
+  if (appUnpublished) return 'unpublished'
+  return 'notConfigured'
+}
+
+const statusText = t(STATUS_TEXT_MAP[getStatusKey()])
+```
+
+## Pattern 5: Flatten Nested Loops
+
+**Before** (complexity: high):
+
+```typescript
+const processData = (items: Item[]) => {
+  const results: ProcessedItem[] = []
+  
+  for (const item of items) {
+    if (item.isValid) {
+      for (const child of item.children) {
+        if (child.isActive) {
+          for (const prop of child.properties) {
+            if (prop.value !== null) {
+              results.push({
+                itemId: item.id,
+                childId: child.id,
+                propValue: prop.value,
+              })
+            }
+          }
+        }
+      }
+    }
+  }
+  
+  return results
+}
+```
+
+**After** (complexity: lower):
+
+```typescript
+// Use functional approach
+const processData = (items: Item[]) => {
+  return items
+    .filter(item => item.isValid)
+    .flatMap(item =>
+      item.children
+        .filter(child => child.isActive)
+        .flatMap(child =>
+          child.properties
+            .filter(prop => prop.value !== null)
+            .map(prop => ({
+              itemId: item.id,
+              childId: child.id,
+              propValue: prop.value,
+            }))
+        )
+    )
+}
+```
+
+## Pattern 6: Extract Event Handler Logic
+
+**Before** (complexity: high in component):
+
+```typescript
+const Component = () => {
+  const handleSelect = (data: DataSet[]) => {
+    if (isEqual(data.map(item => item.id), dataSets.map(item => item.id))) {
+      hideSelectDataSet()
+      return
+    }
+
+    formattingChangedDispatcher()
+    let newDatasets = data
+    if (data.find(item => !item.name)) {
+      const newSelected = produce(data, (draft) => {
+        data.forEach((item, index) => {
+          if (!item.name) {
+            const newItem = dataSets.find(i => i.id === item.id)
+            if (newItem)
+              draft[index] = newItem
+          }
+        })
+      })
+      setDataSets(newSelected)
+      newDatasets = newSelected
+    }
+    else {
+      setDataSets(data)
+    }
+    hideSelectDataSet()
+    
+    // 40 more lines of logic...
+  }
+  
+  return <div>...</div>
+}
+```
+
+**After** (complexity: lower):
+
+```typescript
+// Extract to hook or utility
+const useDatasetSelection = (dataSets: DataSet[], setDataSets: SetState<DataSet[]>) => {
+  const normalizeSelection = (data: DataSet[]) => {
+    const hasUnloadedItem = data.some(item => !item.name)
+    if (!hasUnloadedItem) return data
+    
+    return produce(data, (draft) => {
+      data.forEach((item, index) => {
+        if (!item.name) {
+          const existing = dataSets.find(i => i.id === item.id)
+          if (existing) draft[index] = existing
+        }
+      })
+    })
+  }
+  
+  const hasSelectionChanged = (newData: DataSet[]) => {
+    return !isEqual(
+      newData.map(item => item.id),
+      dataSets.map(item => item.id)
+    )
+  }
+  
+  return { normalizeSelection, hasSelectionChanged }
+}
+
+// Component becomes cleaner
+const Component = () => {
+  const { normalizeSelection, hasSelectionChanged } = useDatasetSelection(dataSets, setDataSets)
+  
+  const handleSelect = (data: DataSet[]) => {
+    if (!hasSelectionChanged(data)) {
+      hideSelectDataSet()
+      return
+    }
+    
+    formattingChangedDispatcher()
+    const normalized = normalizeSelection(data)
+    setDataSets(normalized)
+    hideSelectDataSet()
+  }
+  
+  return <div>...</div>
+}
+```
+
+## Pattern 7: Reduce Boolean Logic Complexity
+
+**Before** (complexity: ~8):
+
+```typescript
+const toggleDisabled = hasInsufficientPermissions
+  || appUnpublished
+  || missingStartNode
+  || triggerModeDisabled
+  || (isAdvancedApp && !currentWorkflow?.graph)
+  || (isBasicApp && !basicAppConfig.updated_at)
+```
+
+**After** (complexity: ~3):
+
+```typescript
+// Extract meaningful boolean functions
+const isAppReady = () => {
+  if (isAdvancedApp) return !!currentWorkflow?.graph
+  return !!basicAppConfig.updated_at
+}
+
+const hasRequiredPermissions = () => {
+  return isCurrentWorkspaceEditor && !hasInsufficientPermissions
+}
+
+const canToggle = () => {
+  if (!hasRequiredPermissions()) return false
+  if (!isAppReady()) return false
+  if (missingStartNode) return false
+  if (triggerModeDisabled) return false
+  return true
+}
+
+const toggleDisabled = !canToggle()
+```
+
+## Pattern 8: Simplify useMemo/useCallback Dependencies
+
+**Before** (complexity: multiple recalculations):
+
+```typescript
+const payload = useMemo(() => {
+  let parameters: Parameter[] = []
+  let outputParameters: OutputParameter[] = []
+
+  if (!published) {
+    parameters = (inputs || []).map((item) => ({
+      name: item.variable,
+      description: '',
+      form: 'llm',
+      required: item.required,
+      type: item.type,
+    }))
+    outputParameters = (outputs || []).map((item) => ({
+      name: item.variable,
+      description: '',
+      type: item.value_type,
+    }))
+  }
+  else if (detail && detail.tool) {
+    parameters = (inputs || []).map((item) => ({
+      // Complex transformation...
+    }))
+    outputParameters = (outputs || []).map((item) => ({
+      // Complex transformation...
+    }))
+  }
+  
+  return {
+    icon: detail?.icon || icon,
+    label: detail?.label || name,
+    // ...more fields
+  }
+}, [detail, published, workflowAppId, icon, name, description, inputs, outputs])
+```
+
+**After** (complexity: separated concerns):
+
+```typescript
+// Separate transformations
+const useParameterTransform = (inputs: InputVar[], detail?: ToolDetail, published?: boolean) => {
+  return useMemo(() => {
+    if (!published) {
+      return inputs.map(item => ({
+        name: item.variable,
+        description: '',
+        form: 'llm',
+        required: item.required,
+        type: item.type,
+      }))
+    }
+    
+    if (!detail?.tool) return []
+    
+    return inputs.map(item => ({
+      name: item.variable,
+      required: item.required,
+      type: item.type === 'paragraph' ? 'string' : item.type,
+      description: detail.tool.parameters.find(p => p.name === item.variable)?.llm_description || '',
+      form: detail.tool.parameters.find(p => p.name === item.variable)?.form || 'llm',
+    }))
+  }, [inputs, detail, published])
+}
+
+// Component uses hook
+const parameters = useParameterTransform(inputs, detail, published)
+const outputParameters = useOutputTransform(outputs, detail, published)
+
+const payload = useMemo(() => ({
+  icon: detail?.icon || icon,
+  label: detail?.label || name,
+  parameters,
+  outputParameters,
+  // ...
+}), [detail, icon, name, parameters, outputParameters])
+```
+
+## Target Metrics After Refactoring
+
+| Metric | Target |
+|--------|--------|
+| Total Complexity | < 50 |
+| Max Function Complexity | < 30 |
+| Function Length | < 30 lines |
+| Nesting Depth | ≤ 3 levels |
+| Conditional Chains | ≤ 3 conditions |

.claude/skills/component-refactoring/references/component-splitting.md

@@ -0,0 +1,477 @@
+# Component Splitting Patterns
+
+This document provides detailed guidance on splitting large components into smaller, focused components in Dify.
+
+## When to Split Components
+
+Split a component when you identify:
+
+1. **Multiple UI sections** - Distinct visual areas with minimal coupling that can be composed independently
+1. **Conditional rendering blocks** - Large `{condition && <JSX />}` blocks
+1. **Repeated patterns** - Similar UI structures used multiple times
+1. **300+ lines** - Component exceeds manageable size
+1. **Modal clusters** - Multiple modals rendered in one component
+
+## Splitting Strategies
+
+### Strategy 1: Section-Based Splitting
+
+Identify visual sections and extract each as a component.
+
+```typescript
+// ❌ Before: Monolithic component (500+ lines)
+const ConfigurationPage = () => {
+  return (
+    <div>
+      {/* Header Section - 50 lines */}
+      <div className="header">
+        <h1>{t('configuration.title')}</h1>
+        <div className="actions">
+          {isAdvancedMode && <Badge>Advanced</Badge>}
+          <ModelParameterModal ... />
+          <AppPublisher ... />
+        </div>
+      </div>
+      
+      {/* Config Section - 200 lines */}
+      <div className="config">
+        <Config />
+      </div>
+      
+      {/* Debug Section - 150 lines */}
+      <div className="debug">
+        <Debug ... />
+      </div>
+      
+      {/* Modals Section - 100 lines */}
+      {showSelectDataSet && <SelectDataSet ... />}
+      {showHistoryModal && <EditHistoryModal ... />}
+      {showUseGPT4Confirm && <Confirm ... />}
+    </div>
+  )
+}
+
+// ✅ After: Split into focused components
+// configuration/
+//   ├── index.tsx              (orchestration)
+//   ├── configuration-header.tsx
+//   ├── configuration-content.tsx
+//   ├── configuration-debug.tsx
+//   └── configuration-modals.tsx
+
+// configuration-header.tsx
+interface ConfigurationHeaderProps {
+  isAdvancedMode: boolean
+  onPublish: () => void
+}
+
+const ConfigurationHeader: FC<ConfigurationHeaderProps> = ({
+  isAdvancedMode,
+  onPublish,
+}) => {
+  const { t } = useTranslation()
+  
+  return (
+    <div className="header">
+      <h1>{t('configuration.title')}</h1>
+      <div className="actions">
+        {isAdvancedMode && <Badge>Advanced</Badge>}
+        <ModelParameterModal ... />
+        <AppPublisher onPublish={onPublish} />
+      </div>
+    </div>
+  )
+}
+
+// index.tsx (orchestration only)
+const ConfigurationPage = () => {
+  const { modelConfig, setModelConfig } = useModelConfig()
+  const { activeModal, openModal, closeModal } = useModalState()
+  
+  return (
+    <div>
+      <ConfigurationHeader
+        isAdvancedMode={isAdvancedMode}
+        onPublish={handlePublish}
+      />
+      <ConfigurationContent
+        modelConfig={modelConfig}
+        onConfigChange={setModelConfig}
+      />
+      {!isMobile && (
+        <ConfigurationDebug
+          inputs={inputs}
+          onSetting={handleSetting}
+        />
+      )}
+      <ConfigurationModals
+        activeModal={activeModal}
+        onClose={closeModal}
+      />
+    </div>
+  )
+}
+```
+
+### Strategy 2: Conditional Block Extraction
+
+Extract large conditional rendering blocks.
+
+```typescript
+// ❌ Before: Large conditional blocks
+const AppInfo = () => {
+  return (
+    <div>
+      {expand ? (
+        <div className="expanded">
+          {/* 100 lines of expanded view */}
+        </div>
+      ) : (
+        <div className="collapsed">
+          {/* 50 lines of collapsed view */}
+        </div>
+      )}
+    </div>
+  )
+}
+
+// ✅ After: Separate view components
+const AppInfoExpanded: FC<AppInfoViewProps> = ({ appDetail, onAction }) => {
+  return (
+    <div className="expanded">
+      {/* Clean, focused expanded view */}
+    </div>
+  )
+}
+
+const AppInfoCollapsed: FC<AppInfoViewProps> = ({ appDetail, onAction }) => {
+  return (
+    <div className="collapsed">
+      {/* Clean, focused collapsed view */}
+    </div>
+  )
+}
+
+const AppInfo = () => {
+  return (
+    <div>
+      {expand
+        ? <AppInfoExpanded appDetail={appDetail} onAction={handleAction} />
+        : <AppInfoCollapsed appDetail={appDetail} onAction={handleAction} />
+      }
+    </div>
+  )
+}
+```
+
+### Strategy 3: Modal Extraction
+
+Extract modals with their trigger logic.
+
+```typescript
+// ❌ Before: Multiple modals in one component
+const AppInfo = () => {
+  const [showEdit, setShowEdit] = useState(false)
+  const [showDuplicate, setShowDuplicate] = useState(false)
+  const [showDelete, setShowDelete] = useState(false)
+  const [showSwitch, setShowSwitch] = useState(false)
+  
+  const onEdit = async (data) => { /* 20 lines */ }
+  const onDuplicate = async (data) => { /* 20 lines */ }
+  const onDelete = async () => { /* 15 lines */ }
+  
+  return (
+    <div>
+      {/* Main content */}
+      
+      {showEdit && <EditModal onConfirm={onEdit} onClose={() => setShowEdit(false)} />}
+      {showDuplicate && <DuplicateModal onConfirm={onDuplicate} onClose={() => setShowDuplicate(false)} />}
+      {showDelete && <DeleteConfirm onConfirm={onDelete} onClose={() => setShowDelete(false)} />}
+      {showSwitch && <SwitchModal ... />}
+    </div>
+  )
+}
+
+// ✅ After: Modal manager component
+// app-info-modals.tsx
+type ModalType = 'edit' | 'duplicate' | 'delete' | 'switch' | null
+
+interface AppInfoModalsProps {
+  appDetail: AppDetail
+  activeModal: ModalType
+  onClose: () => void
+  onSuccess: () => void
+}
+
+const AppInfoModals: FC<AppInfoModalsProps> = ({
+  appDetail,
+  activeModal,
+  onClose,
+  onSuccess,
+}) => {
+  const handleEdit = async (data) => { /* logic */ }
+  const handleDuplicate = async (data) => { /* logic */ }
+  const handleDelete = async () => { /* logic */ }
+
+  return (
+    <>
+      {activeModal === 'edit' && (
+        <EditModal
+          appDetail={appDetail}
+          onConfirm={handleEdit}
+          onClose={onClose}
+        />
+      )}
+      {activeModal === 'duplicate' && (
+        <DuplicateModal
+          appDetail={appDetail}
+          onConfirm={handleDuplicate}
+          onClose={onClose}
+        />
+      )}
+      {activeModal === 'delete' && (
+        <DeleteConfirm
+          onConfirm={handleDelete}
+          onClose={onClose}
+        />
+      )}
+      {activeModal === 'switch' && (
+        <SwitchModal
+          appDetail={appDetail}
+          onClose={onClose}
+        />
+      )}
+    </>
+  )
+}
+
+// Parent component
+const AppInfo = () => {
+  const { activeModal, openModal, closeModal } = useModalState()
+  
+  return (
+    <div>
+      {/* Main content with openModal triggers */}
+      <Button onClick={() => openModal('edit')}>Edit</Button>
+      
+      <AppInfoModals
+        appDetail={appDetail}
+        activeModal={activeModal}
+        onClose={closeModal}
+        onSuccess={handleSuccess}
+      />
+    </div>
+  )
+}
+```
+
+### Strategy 4: List Item Extraction
+
+Extract repeated item rendering.
+
+```typescript
+// ❌ Before: Inline item rendering
+const OperationsList = () => {
+  return (
+    <div>
+      {operations.map(op => (
+        <div key={op.id} className="operation-item">
+          <span className="icon">{op.icon}</span>
+          <span className="title">{op.title}</span>
+          <span className="description">{op.description}</span>
+          <button onClick={() => op.onClick()}>
+            {op.actionLabel}
+          </button>
+          {op.badge && <Badge>{op.badge}</Badge>}
+          {/* More complex rendering... */}
+        </div>
+      ))}
+    </div>
+  )
+}
+
+// ✅ After: Extracted item component
+interface OperationItemProps {
+  operation: Operation
+  onAction: (id: string) => void
+}
+
+const OperationItem: FC<OperationItemProps> = ({ operation, onAction }) => {
+  return (
+    <div className="operation-item">
+      <span className="icon">{operation.icon}</span>
+      <span className="title">{operation.title}</span>
+      <span className="description">{operation.description}</span>
+      <button onClick={() => onAction(operation.id)}>
+        {operation.actionLabel}
+      </button>
+      {operation.badge && <Badge>{operation.badge}</Badge>}
+    </div>
+  )
+}
+
+const OperationsList = () => {
+  const handleAction = useCallback((id: string) => {
+    const op = operations.find(o => o.id === id)
+    op?.onClick()
+  }, [operations])
+
+  return (
+    <div>
+      {operations.map(op => (
+        <OperationItem
+          key={op.id}
+          operation={op}
+          onAction={handleAction}
+        />
+      ))}
+    </div>
+  )
+}
+```
+
+## Directory Structure Patterns
+
+### Pattern A: Flat Structure (Simple Components)
+
+For components with 2-3 sub-components:
+
+```
+component-name/
+  ├── index.tsx           # Main component
+  ├── sub-component-a.tsx
+  ├── sub-component-b.tsx
+  └── types.ts            # Shared types
+```
+
+### Pattern B: Nested Structure (Complex Components)
+
+For components with many sub-components:
+
+```
+component-name/
+  ├── index.tsx           # Main orchestration
+  ├── types.ts            # Shared types
+  ├── hooks/
+  │   ├── use-feature-a.ts
+  │   └── use-feature-b.ts
+  ├── components/
+  │   ├── header/
+  │   │   └── index.tsx
+  │   ├── content/
+  │   │   └── index.tsx
+  │   └── modals/
+  │       └── index.tsx
+  └── utils/
+      └── helpers.ts
+```
+
+### Pattern C: Feature-Based Structure (Dify Standard)
+
+Following Dify's existing patterns:
+
+```
+configuration/
+  ├── index.tsx           # Main page component
+  ├── base/               # Base/shared components
+  │   ├── feature-panel/
+  │   ├── group-name/
+  │   └── operation-btn/
+  ├── config/             # Config section
+  │   ├── index.tsx
+  │   ├── agent/
+  │   └── automatic/
+  ├── dataset-config/     # Dataset section
+  │   ├── index.tsx
+  │   ├── card-item/
+  │   └── params-config/
+  ├── debug/              # Debug section
+  │   ├── index.tsx
+  │   └── hooks.tsx
+  └── hooks/              # Shared hooks
+      └── use-advanced-prompt-config.ts
+```
+
+## Props Design
+
+### Minimal Props Principle
+
+Pass only what's needed:
+
+```typescript
+// ❌ Bad: Passing entire objects when only some fields needed
+<ConfigHeader appDetail={appDetail} modelConfig={modelConfig} />
+
+// ✅ Good: Destructure to minimum required
+<ConfigHeader
+  appName={appDetail.name}
+  isAdvancedMode={modelConfig.isAdvanced}
+  onPublish={handlePublish}
+/>
+```
+
+### Callback Props Pattern
+
+Use callbacks for child-to-parent communication:
+
+```typescript
+// Parent
+const Parent = () => {
+  const [value, setValue] = useState('')
+  
+  return (
+    <Child
+      value={value}
+      onChange={setValue}
+      onSubmit={handleSubmit}
+    />
+  )
+}
+
+// Child
+interface ChildProps {
+  value: string
+  onChange: (value: string) => void
+  onSubmit: () => void
+}
+
+const Child: FC<ChildProps> = ({ value, onChange, onSubmit }) => {
+  return (
+    <div>
+      <input value={value} onChange={e => onChange(e.target.value)} />
+      <button onClick={onSubmit}>Submit</button>
+    </div>
+  )
+}
+```
+
+### Render Props for Flexibility
+
+When sub-components need parent context:
+
+```typescript
+interface ListProps<T> {
+  items: T[]
+  renderItem: (item: T, index: number) => React.ReactNode
+  renderEmpty?: () => React.ReactNode
+}
+
+function List<T>({ items, renderItem, renderEmpty }: ListProps<T>) {
+  if (items.length === 0 && renderEmpty) {
+    return <>{renderEmpty()}</>
+  }
+  
+  return (
+    <div>
+      {items.map((item, index) => renderItem(item, index))}
+    </div>
+  )
+}
+
+// Usage
+<List
+  items={operations}
+  renderItem={(op, i) => <OperationItem key={i} operation={op} />}
+  renderEmpty={() => <EmptyState message="No operations" />}
+/>
+```

.claude/skills/component-refactoring/references/hook-extraction.md

@@ -0,0 +1,317 @@
+# Hook Extraction Patterns
+
+This document provides detailed guidance on extracting custom hooks from complex components in Dify.
+
+## When to Extract Hooks
+
+Extract a custom hook when you identify:
+
+1. **Coupled state groups** - Multiple `useState` hooks that are always used together
+1. **Complex effects** - `useEffect` with multiple dependencies or cleanup logic
+1. **Business logic** - Data transformations, validations, or calculations
+1. **Reusable patterns** - Logic that appears in multiple components
+
+## Extraction Process
+
+### Step 1: Identify State Groups
+
+Look for state variables that are logically related:
+
+```typescript
+// ❌ These belong together - extract to hook
+const [modelConfig, setModelConfig] = useState<ModelConfig>(...)
+const [completionParams, setCompletionParams] = useState<FormValue>({})
+const [modelModeType, setModelModeType] = useState<ModelModeType>(...)
+
+// These are model-related state that should be in useModelConfig()
+```
+
+### Step 2: Identify Related Effects
+
+Find effects that modify the grouped state:
+
+```typescript
+// ❌ These effects belong with the state above
+useEffect(() => {
+  if (hasFetchedDetail && !modelModeType) {
+    const mode = currModel?.model_properties.mode
+    if (mode) {
+      const newModelConfig = produce(modelConfig, (draft) => {
+        draft.mode = mode
+      })
+      setModelConfig(newModelConfig)
+    }
+  }
+}, [textGenerationModelList, hasFetchedDetail, modelModeType, currModel])
+```
+
+### Step 3: Create the Hook
+
+```typescript
+// hooks/use-model-config.ts
+import type { FormValue } from '@/app/components/header/account-setting/model-provider-page/declarations'
+import type { ModelConfig } from '@/models/debug'
+import { produce } from 'immer'
+import { useEffect, useState } from 'react'
+import { ModelModeType } from '@/types/app'
+
+interface UseModelConfigParams {
+  initialConfig?: Partial<ModelConfig>
+  currModel?: { model_properties?: { mode?: ModelModeType } }
+  hasFetchedDetail: boolean
+}
+
+interface UseModelConfigReturn {
+  modelConfig: ModelConfig
+  setModelConfig: (config: ModelConfig) => void
+  completionParams: FormValue
+  setCompletionParams: (params: FormValue) => void
+  modelModeType: ModelModeType
+}
+
+export const useModelConfig = ({
+  initialConfig,
+  currModel,
+  hasFetchedDetail,
+}: UseModelConfigParams): UseModelConfigReturn => {
+  const [modelConfig, setModelConfig] = useState<ModelConfig>({
+    provider: 'langgenius/openai/openai',
+    model_id: 'gpt-3.5-turbo',
+    mode: ModelModeType.unset,
+    // ... default values
+    ...initialConfig,
+  })
+  
+  const [completionParams, setCompletionParams] = useState<FormValue>({})
+  
+  const modelModeType = modelConfig.mode
+
+  // Fill old app data missing model mode
+  useEffect(() => {
+    if (hasFetchedDetail && !modelModeType) {
+      const mode = currModel?.model_properties?.mode
+      if (mode) {
+        setModelConfig(produce(modelConfig, (draft) => {
+          draft.mode = mode
+        }))
+      }
+    }
+  }, [hasFetchedDetail, modelModeType, currModel])
+
+  return {
+    modelConfig,
+    setModelConfig,
+    completionParams,
+    setCompletionParams,
+    modelModeType,
+  }
+}
+```
+
+### Step 4: Update Component
+
+```typescript
+// Before: 50+ lines of state management
+const Configuration: FC = () => {
+  const [modelConfig, setModelConfig] = useState<ModelConfig>(...)
+  // ... lots of related state and effects
+}
+
+// After: Clean component
+const Configuration: FC = () => {
+  const {
+    modelConfig,
+    setModelConfig,
+    completionParams,
+    setCompletionParams,
+    modelModeType,
+  } = useModelConfig({
+    currModel,
+    hasFetchedDetail,
+  })
+  
+  // Component now focuses on UI
+}
+```
+
+## Naming Conventions
+
+### Hook Names
+
+- Use `use` prefix: `useModelConfig`, `useDatasetConfig`
+- Be specific: `useAdvancedPromptConfig` not `usePrompt`
+- Include domain: `useWorkflowVariables`, `useMCPServer`
+
+### File Names
+
+- Kebab-case: `use-model-config.ts`
+- Place in `hooks/` subdirectory when multiple hooks exist
+- Place alongside component for single-use hooks
+
+### Return Type Names
+
+- Suffix with `Return`: `UseModelConfigReturn`
+- Suffix params with `Params`: `UseModelConfigParams`
+
+## Common Hook Patterns in Dify
+
+### 1. Data Fetching Hook (React Query)
+
+```typescript
+// Pattern: Use @tanstack/react-query for data fetching
+import { useQuery, useQueryClient } from '@tanstack/react-query'
+import { get } from '@/service/base'
+import { useInvalid } from '@/service/use-base'
+
+const NAME_SPACE = 'appConfig'
+
+// Query keys for cache management
+export const appConfigQueryKeys = {
+  detail: (appId: string) => [NAME_SPACE, 'detail', appId] as const,
+}
+
+// Main data hook
+export const useAppConfig = (appId: string) => {
+  return useQuery({
+    enabled: !!appId,
+    queryKey: appConfigQueryKeys.detail(appId),
+    queryFn: () => get<AppDetailResponse>(`/apps/${appId}`),
+    select: data => data?.model_config || null,
+  })
+}
+
+// Invalidation hook for refreshing data
+export const useInvalidAppConfig = () => {
+  return useInvalid([NAME_SPACE])
+}
+
+// Usage in component
+const Component = () => {
+  const { data: config, isLoading, error, refetch } = useAppConfig(appId)
+  const invalidAppConfig = useInvalidAppConfig()
+  
+  const handleRefresh = () => {
+    invalidAppConfig() // Invalidates cache and triggers refetch
+  }
+  
+  return <div>...</div>
+}
+```
+
+### 2. Form State Hook
+
+```typescript
+// Pattern: Form state + validation + submission
+export const useConfigForm = (initialValues: ConfigFormValues) => {
+  const [values, setValues] = useState(initialValues)
+  const [errors, setErrors] = useState<Record<string, string>>({})
+  const [isSubmitting, setIsSubmitting] = useState(false)
+
+  const validate = useCallback(() => {
+    const newErrors: Record<string, string> = {}
+    if (!values.name) newErrors.name = 'Name is required'
+    setErrors(newErrors)
+    return Object.keys(newErrors).length === 0
+  }, [values])
+
+  const handleChange = useCallback((field: string, value: any) => {
+    setValues(prev => ({ ...prev, [field]: value }))
+  }, [])
+
+  const handleSubmit = useCallback(async (onSubmit: (values: ConfigFormValues) => Promise<void>) => {
+    if (!validate()) return
+    setIsSubmitting(true)
+    try {
+      await onSubmit(values)
+    } finally {
+      setIsSubmitting(false)
+    }
+  }, [values, validate])
+
+  return { values, errors, isSubmitting, handleChange, handleSubmit }
+}
+```
+
+### 3. Modal State Hook
+
+```typescript
+// Pattern: Multiple modal management
+type ModalType = 'edit' | 'delete' | 'duplicate' | null
+
+export const useModalState = () => {
+  const [activeModal, setActiveModal] = useState<ModalType>(null)
+  const [modalData, setModalData] = useState<any>(null)
+
+  const openModal = useCallback((type: ModalType, data?: any) => {
+    setActiveModal(type)
+    setModalData(data)
+  }, [])
+
+  const closeModal = useCallback(() => {
+    setActiveModal(null)
+    setModalData(null)
+  }, [])
+
+  return {
+    activeModal,
+    modalData,
+    openModal,
+    closeModal,
+    isOpen: useCallback((type: ModalType) => activeModal === type, [activeModal]),
+  }
+}
+```
+
+### 4. Toggle/Boolean Hook
+
+```typescript
+// Pattern: Boolean state with convenience methods
+export const useToggle = (initialValue = false) => {
+  const [value, setValue] = useState(initialValue)
+
+  const toggle = useCallback(() => setValue(v => !v), [])
+  const setTrue = useCallback(() => setValue(true), [])
+  const setFalse = useCallback(() => setValue(false), [])
+
+  return [value, { toggle, setTrue, setFalse, set: setValue }] as const
+}
+
+// Usage
+const [isExpanded, { toggle, setTrue: expand, setFalse: collapse }] = useToggle()
+```
+
+## Testing Extracted Hooks
+
+After extraction, test hooks in isolation:
+
+```typescript
+// use-model-config.spec.ts
+import { renderHook, act } from '@testing-library/react'
+import { useModelConfig } from './use-model-config'
+
+describe('useModelConfig', () => {
+  it('should initialize with default values', () => {
+    const { result } = renderHook(() => useModelConfig({
+      hasFetchedDetail: false,
+    }))
+
+    expect(result.current.modelConfig.provider).toBe('langgenius/openai/openai')
+    expect(result.current.modelModeType).toBe(ModelModeType.unset)
+  })
+
+  it('should update model config', () => {
+    const { result } = renderHook(() => useModelConfig({
+      hasFetchedDetail: true,
+    }))
+
+    act(() => {
+      result.current.setModelConfig({
+        ...result.current.modelConfig,
+        model_id: 'gpt-4',
+      })
+    })
+
+    expect(result.current.modelConfig.model_id).toBe('gpt-4')
+  })
+})
+```

.claude/skills/frontend-code-review/SKILL.md

@@ -0,0 +1,73 @@
+---
+name: frontend-code-review
+description: "Trigger when the user requests a review of frontend files (e.g., `.tsx`, `.ts`, `.js`). Support both pending-change reviews and focused file reviews while applying the checklist rules."
+---
+
+# Frontend Code Review
+
+## Intent
+Use this skill whenever the user asks to review frontend code (especially `.tsx`, `.ts`, or `.js` files). Support two review modes:
+
+1. **Pending-change review** – inspect staged/working-tree files slated for commit and flag checklist violations before submission.
+2. **File-targeted review** – review the specific file(s) the user names and report the relevant checklist findings.
+
+Stick to the checklist below for every applicable file and mode.
+
+## Checklist
+See [references/code-quality.md](references/code-quality.md), [references/performance.md](references/performance.md), [references/business-logic.md](references/business-logic.md) for the living checklist split by category—treat it as the canonical set of rules to follow.
+
+Flag each rule violation with urgency metadata so future reviewers can prioritize fixes.
+
+## Review Process
+1. Open the relevant component/module. Gather lines that relate to class names, React Flow hooks, prop memoization, and styling.
+2. For each rule in the review point, note where the code deviates and capture a representative snippet.
+3. Compose the review section per the template below. Group violations first by **Urgent** flag, then by category order (Code Quality, Performance, Business Logic).
+
+## Required output
+When invoked, the response must exactly follow one of the two templates:
+
+### Template A (any findings)
+```
+# Code review
+Found <N> urgent issues need to be fixed:
+
+## 1 <brief description of bug>
+FilePath: <path> line <line>
+<relevant code snippet or pointer>
+
+
+### Suggested fix
+<brief description of suggested fix>
+
+---
+... (repeat for each urgent issue) ...
+
+Found <M> suggestions for improvement:
+
+## 1 <brief description of suggestion>
+FilePath: <path> line <line>
+<relevant code snippet or pointer>
+
+
+### Suggested fix
+<brief description of suggested fix>
+
+---
+
+... (repeat for each suggestion) ...
+```
+
+If there are no urgent issues, omit that section. If there are no suggestions, omit that section.
+
+If the issue number is more than 10, summarize as "10+ urgent issues" or "10+ suggestions" and just output the first 10 issues.
+
+Don't compress the blank lines between sections; keep them as-is for readability.
+
+If you use Template A (i.e., there are issues to fix) and at least one issue requires code changes, append a brief follow-up question after the structured output asking whether the user wants you to apply the suggested fix(es). For example: "Would you like me to use the Suggested fix section to address these issues?"
+
+### Template B (no issues)
+```
+## Code review
+No issues found.
+```
+

.claude/skills/frontend-code-review/references/business-logic.md

@@ -0,0 +1,15 @@
+# Rule Catalog — Business Logic
+
+## Can't use workflowStore in Node components
+
+IsUrgent: True
+
+### Description
+
+File path pattern of node components: `web/app/components/workflow/nodes/[nodeName]/node.tsx`
+
+Node components are also used when creating a RAG Pipe from a template, but in that context there is no workflowStore Provider, which results in a blank screen. [This Issue](https://github.com/langgenius/dify/issues/29168) was caused by exactly this reason.
+
+### Suggested Fix
+
+Use `import { useNodes } from 'reactflow'` instead of `import useNodes from '@/app/components/workflow/store/workflow/use-nodes'`.

.claude/skills/frontend-code-review/references/code-quality.md

@@ -0,0 +1,44 @@
+# Rule Catalog — Code Quality
+
+## Conditional class names use utility function
+
+IsUrgent: True
+Category: Code Quality
+
+### Description
+
+Ensure conditional CSS is handled via the shared `classNames` instead of custom ternaries, string concatenation, or template strings. Centralizing class logic keeps components consistent and easier to maintain.
+
+### Suggested Fix
+
+```ts
+import { cn } from '@/utils/classnames'
+const classNames = cn(isActive ? 'text-primary-600' : 'text-gray-500')
+```
+
+## Tailwind-first styling
+
+IsUrgent: True
+Category: Code Quality
+
+### Description
+
+Favor Tailwind CSS utility classes instead of adding new `.module.css` files unless a Tailwind combination cannot achieve the required styling. Keeping styles in Tailwind improves consistency and reduces maintenance overhead.
+
+Update this file when adding, editing, or removing Code Quality rules so the catalog remains accurate.
+
+## Classname ordering for easy overrides
+
+### Description
+
+When writing components, always place the incoming `className` prop after the component’s own class values so that downstream consumers can override or extend the styling. This keeps your component’s defaults but still lets external callers change or remove specific styles.
+
+Example:
+
+```tsx
+import { cn } from '@/utils/classnames'
+
+const Button = ({ className }) => {
+  return <div className={cn('bg-primary-600', className)}></div>
+}
+```

.claude/skills/frontend-code-review/references/performance.md

@@ -0,0 +1,45 @@
+# Rule Catalog — Performance
+
+## React Flow data usage
+
+IsUrgent: True
+Category: Performance
+
+### Description
+
+When rendering React Flow, prefer `useNodes`/`useEdges` for UI consumption and rely on `useStoreApi` inside callbacks that mutate or read node/edge state. Avoid manually pulling Flow data outside of these hooks.
+
+## Complex prop memoization
+
+IsUrgent: True
+Category: Performance
+
+### Description
+
+Wrap complex prop values (objects, arrays, maps) in `useMemo` prior to passing them into child components to guarantee stable references and prevent unnecessary renders.
+
+Update this file when adding, editing, or removing Performance rules so the catalog remains accurate.
+
+Wrong:
+
+```tsx
+<HeavyComp
+    config={{
+        provider: ...,
+        detail: ...
+    }}
+/>
+```
+
+Right:
+
+```tsx
+const config = useMemo(() => ({
+    provider: ...,
+    detail: ...
+}), [provider, detail]);
+
+<HeavyComp
+    config={config}
+/>
+```

.claude/skills/frontend-testing/SKILL.md

@@ -318,5 +318,5 @@ For more detailed information, refer to:
 
 - `web/vitest.config.ts` - Vitest configuration
 - `web/vitest.setup.ts` - Test environment setup
-- `web/testing/analyze-component.js` - Component analysis tool
+- `web/scripts/analyze-component.js` - Component analysis tool
 - Modules are not mocked automatically. Global mocks live in `web/vitest.setup.ts` (for example `react-i18next`, `next/image`); mock other modules like `ky` or `mime` locally in test files.

.claude/skills/frontend-testing/assets/component-test.template.tsx

@@ -28,17 +28,14 @@ import userEvent from '@testing-library/user-event'
 
 // i18n (automatically mocked)
 // WHY: Global mock in web/vitest.setup.ts is auto-loaded by Vitest setup
-// No explicit mock needed - it returns translation keys as-is
+// The global mock provides: useTranslation, Trans, useMixedTranslation, useGetLanguage
+// No explicit mock needed for most tests
+//
 // Override only if custom translations are required:
-// vi.mock('react-i18next', () => ({
-//   useTranslation: () => ({
-//     t: (key: string) => {
-//       const customTranslations: Record<string, string> = {
-//         'my.custom.key': 'Custom Translation',
-//       }
-//       return customTranslations[key] || key
-//     },
-//   }),
+// import { createReactI18nextMock } from '@/test/i18n-mock'
+// vi.mock('react-i18next', () => createReactI18nextMock({
+//   'my.custom.key': 'Custom Translation',
+//   'button.save': 'Save',
 // }))
 
 // Router (if component uses useRouter, usePathname, useSearchParams)

.claude/skills/frontend-testing/references/mocking.md

@@ -52,23 +52,29 @@ Modules are not mocked automatically. Use `vi.mock` in test files, or add global
 ### 1. i18n (Auto-loaded via Global Mock)
 
 A global mock is defined in `web/vitest.setup.ts` and is auto-loaded by Vitest setup.
-**No explicit mock needed** for most tests - it returns translation keys as-is.
 
-For tests requiring custom translations, override the mock:
+The global mock provides:
+
+- `useTranslation` - returns translation keys with namespace prefix
+- `Trans` component - renders i18nKey and components
+- `useMixedTranslation` (from `@/app/components/plugins/marketplace/hooks`)
+- `useGetLanguage` (from `@/context/i18n`) - returns `'en-US'`
+
+**Default behavior**: Most tests should use the global mock (no local override needed).
+
+**For custom translations**: Use the helper function from `@/test/i18n-mock`:
 
 ```typescript
-vi.mock('react-i18next', () => ({
-  useTranslation: () => ({
-    t: (key: string) => {
-      const translations: Record<string, string> = {
-        'my.custom.key': 'Custom translation',
-      }
-      return translations[key] || key
-    },
-  }),
+import { createReactI18nextMock } from '@/test/i18n-mock'
+
+vi.mock('react-i18next', () => createReactI18nextMock({
+  'my.custom.key': 'Custom translation',
+  'button.save': 'Save',
 }))
 ```
 
+**Avoid**: Manually defining `useTranslation` mocks that just return the key - the global mock already does this.
+
 ### 2. Next.js Router
 
 ```typescript

.claude/skills/skill-creator/SKILL.md

@@ -0,0 +1,355 @@
+---
+name: skill-creator
+description: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
+---
+
+# Skill Creator
+
+This skill provides guidance for creating effective skills.
+
+## About Skills
+
+Skills are modular, self-contained packages that extend Claude's capabilities by providing
+specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific
+domains or tasks—they transform Claude from a general-purpose agent into a specialized agent
+equipped with procedural knowledge that no model can fully possess.
+
+### What Skills Provide
+
+1. Specialized workflows - Multi-step procedures for specific domains
+2. Tool integrations - Instructions for working with specific file formats or APIs
+3. Domain expertise - Company-specific knowledge, schemas, business logic
+4. Bundled resources - Scripts, references, and assets for complex and repetitive tasks
+
+## Core Principles
+
+### Concise is Key
+
+The context window is a public good. Skills share the context window with everything else Claude needs: system prompt, conversation history, other Skills' metadata, and the actual user request.
+
+**Default assumption: Claude is already very smart.** Only add context Claude doesn't already have. Challenge each piece of information: "Does Claude really need this explanation?" and "Does this paragraph justify its token cost?"
+
+Prefer concise examples over verbose explanations.
+
+### Set Appropriate Degrees of Freedom
+
+Match the level of specificity to the task's fragility and variability:
+
+**High freedom (text-based instructions)**: Use when multiple approaches are valid, decisions depend on context, or heuristics guide the approach.
+
+**Medium freedom (pseudocode or scripts with parameters)**: Use when a preferred pattern exists, some variation is acceptable, or configuration affects behavior.
+
+**Low freedom (specific scripts, few parameters)**: Use when operations are fragile and error-prone, consistency is critical, or a specific sequence must be followed.
+
+Think of Claude as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).
+
+### Anatomy of a Skill
+
+Every skill consists of a required SKILL.md file and optional bundled resources:
+
+```
+skill-name/
+├── SKILL.md (required)
+│   ├── YAML frontmatter metadata (required)
+│   │   ├── name: (required)
+│   │   └── description: (required)
+│   └── Markdown instructions (required)
+└── Bundled Resources (optional)
+    ├── scripts/          - Executable code (Python/Bash/etc.)
+    ├── references/       - Documentation intended to be loaded into context as needed
+    └── assets/           - Files used in output (templates, icons, fonts, etc.)
+```
+
+#### SKILL.md (required)
+
+Every SKILL.md consists of:
+
+- **Frontmatter** (YAML): Contains `name` and `description` fields. These are the only fields that Claude reads to determine when the skill gets used, thus it is very important to be clear and comprehensive in describing what the skill is, and when it should be used.
+- **Body** (Markdown): Instructions and guidance for using the skill. Only loaded AFTER the skill triggers (if at all).
+
+#### Bundled Resources (optional)
+
+##### Scripts (`scripts/`)
+
+Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
+
+- **When to include**: When the same code is being rewritten repeatedly or deterministic reliability is needed
+- **Example**: `scripts/rotate_pdf.py` for PDF rotation tasks
+- **Benefits**: Token efficient, deterministic, may be executed without loading into context
+- **Note**: Scripts may still need to be read by Claude for patching or environment-specific adjustments
+
+##### References (`references/`)
+
+Documentation and reference material intended to be loaded as needed into context to inform Claude's process and thinking.
+
+- **When to include**: For documentation that Claude should reference while working
+- **Examples**: `references/finance.md` for financial schemas, `references/mnda.md` for company NDA template, `references/policies.md` for company policies, `references/api_docs.md` for API specifications
+- **Use cases**: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
+- **Benefits**: Keeps SKILL.md lean, loaded only when Claude determines it's needed
+- **Best practice**: If files are large (>10k words), include grep search patterns in SKILL.md
+- **Avoid duplication**: Information should live in either SKILL.md or references files, not both. Prefer references files for detailed information unless it's truly core to the skill—this keeps SKILL.md lean while making information discoverable without hogging the context window. Keep only essential procedural instructions and workflow guidance in SKILL.md; move detailed reference material, schemas, and examples to references files.
+
+##### Assets (`assets/`)
+
+Files not intended to be loaded into context, but rather used within the output Claude produces.
+
+- **When to include**: When the skill needs files that will be used in the final output
+- **Examples**: `assets/logo.png` for brand assets, `assets/slides.pptx` for PowerPoint templates, `assets/frontend-template/` for HTML/React boilerplate, `assets/font.ttf` for typography
+- **Use cases**: Templates, images, icons, boilerplate code, fonts, sample documents that get copied or modified
+- **Benefits**: Separates output resources from documentation, enables Claude to use files without loading them into context
+
+#### What to Not Include in a Skill
+
+A skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:
+
+- README.md
+- INSTALLATION_GUIDE.md
+- QUICK_REFERENCE.md
+- CHANGELOG.md
+- etc.
+
+The skill should only contain the information needed for an AI agent to do the job at hand. It should not contain auxilary context about the process that went into creating it, setup and testing procedures, user-facing documentation, etc. Creating additional documentation files just adds clutter and confusion.
+
+### Progressive Disclosure Design Principle
+
+Skills use a three-level loading system to manage context efficiently:
+
+1. **Metadata (name + description)** - Always in context (~100 words)
+2. **SKILL.md body** - When skill triggers (<5k words)
+3. **Bundled resources** - As needed by Claude (Unlimited because scripts can be executed without reading into context window)
+
+#### Progressive Disclosure Patterns
+
+Keep SKILL.md body to the essentials and under 500 lines to minimize context bloat. Split content into separate files when approaching this limit. When splitting out content into other files, it is very important to reference them from SKILL.md and describe clearly when to read them, to ensure the reader of the skill knows they exist and when to use them.
+
+**Key principle:** When a skill supports multiple variations, frameworks, or options, keep only the core workflow and selection guidance in SKILL.md. Move variant-specific details (patterns, examples, configuration) into separate reference files.
+
+**Pattern 1: High-level guide with references**
+
+```markdown
+# PDF Processing
+
+## Quick start
+
+Extract text with pdfplumber:
+[code example]
+
+## Advanced features
+
+- **Form filling**: See [FORMS.md](FORMS.md) for complete guide
+- **API reference**: See [REFERENCE.md](REFERENCE.md) for all methods
+- **Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns
+```
+
+Claude loads FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed.
+
+**Pattern 2: Domain-specific organization**
+
+For Skills with multiple domains, organize content by domain to avoid loading irrelevant context:
+
+```
+bigquery-skill/
+├── SKILL.md (overview and navigation)
+└── reference/
+    ├── finance.md (revenue, billing metrics)
+    ├── sales.md (opportunities, pipeline)
+    ├── product.md (API usage, features)
+    └── marketing.md (campaigns, attribution)
+```
+
+When a user asks about sales metrics, Claude only reads sales.md.
+
+Similarly, for skills supporting multiple frameworks or variants, organize by variant:
+
+```
+cloud-deploy/
+├── SKILL.md (workflow + provider selection)
+└── references/
+    ├── aws.md (AWS deployment patterns)
+    ├── gcp.md (GCP deployment patterns)
+    └── azure.md (Azure deployment patterns)
+```
+
+When the user chooses AWS, Claude only reads aws.md.
+
+**Pattern 3: Conditional details**
+
+Show basic content, link to advanced content:
+
+```markdown
+# DOCX Processing
+
+## Creating documents
+
+Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md).
+
+## Editing documents
+
+For simple edits, modify the XML directly.
+
+**For tracked changes**: See [REDLINING.md](REDLINING.md)
+**For OOXML details**: See [OOXML.md](OOXML.md)
+```
+
+Claude reads REDLINING.md or OOXML.md only when the user needs those features.
+
+**Important guidelines:**
+
+- **Avoid deeply nested references** - Keep references one level deep from SKILL.md. All reference files should link directly from SKILL.md.
+- **Structure longer reference files** - For files longer than 100 lines, include a table of contents at the top so Claude can see the full scope when previewing.
+
+## Skill Creation Process
+
+Skill creation involves these steps:
+
+1. Understand the skill with concrete examples
+2. Plan reusable skill contents (scripts, references, assets)
+3. Initialize the skill (run init_skill.py)
+4. Edit the skill (implement resources and write SKILL.md)
+5. Package the skill (run package_skill.py)
+6. Iterate based on real usage
+
+Follow these steps in order, skipping only if there is a clear reason why they are not applicable.
+
+### Step 1: Understanding the Skill with Concrete Examples
+
+Skip this step only when the skill's usage patterns are already clearly understood. It remains valuable even when working with an existing skill.
+
+To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.
+
+For example, when building an image-editor skill, relevant questions include:
+
+- "What functionality should the image-editor skill support? Editing, rotating, anything else?"
+- "Can you give some examples of how this skill would be used?"
+- "I can imagine users asking for things like 'Remove the red-eye from this image' or 'Rotate this image'. Are there other ways you imagine this skill being used?"
+- "What would a user say that should trigger this skill?"
+
+To avoid overwhelming users, avoid asking too many questions in a single message. Start with the most important questions and follow up as needed for better effectiveness.
+
+Conclude this step when there is a clear sense of the functionality the skill should support.
+
+### Step 2: Planning the Reusable Skill Contents
+
+To turn concrete examples into an effective skill, analyze each example by:
+
+1. Considering how to execute on the example from scratch
+2. Identifying what scripts, references, and assets would be helpful when executing these workflows repeatedly
+
+Example: When building a `pdf-editor` skill to handle queries like "Help me rotate this PDF," the analysis shows:
+
+1. Rotating a PDF requires re-writing the same code each time
+2. A `scripts/rotate_pdf.py` script would be helpful to store in the skill
+
+Example: When designing a `frontend-webapp-builder` skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:
+
+1. Writing a frontend webapp requires the same boilerplate HTML/React each time
+2. An `assets/hello-world/` template containing the boilerplate HTML/React project files would be helpful to store in the skill
+
+Example: When building a `big-query` skill to handle queries like "How many users have logged in today?" the analysis shows:
+
+1. Querying BigQuery requires re-discovering the table schemas and relationships each time
+2. A `references/schema.md` file documenting the table schemas would be helpful to store in the skill
+
+To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
+
+### Step 3: Initializing the Skill
+
+At this point, it is time to actually create the skill.
+
+Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
+
+When creating a new skill from scratch, always run the `init_skill.py` script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
+
+Usage:
+
+```bash
+scripts/init_skill.py <skill-name> --path <output-directory>
+```
+
+The script:
+
+- Creates the skill directory at the specified path
+- Generates a SKILL.md template with proper frontmatter and TODO placeholders
+- Creates example resource directories: `scripts/`, `references/`, and `assets/`
+- Adds example files in each directory that can be customized or deleted
+
+After initialization, customize or remove the generated SKILL.md and example files as needed.
+
+### Step 4: Edit the Skill
+
+When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of Claude to use. Include information that would be beneficial and non-obvious to Claude. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Claude instance execute these tasks more effectively.
+
+#### Learn Proven Design Patterns
+
+Consult these helpful guides based on your skill's needs:
+
+- **Multi-step processes**: See references/workflows.md for sequential workflows and conditional logic
+- **Specific output formats or quality standards**: See references/output-patterns.md for template and example patterns
+
+These files contain established best practices for effective skill design.
+
+#### Start with Reusable Skill Contents
+
+To begin implementation, start with the reusable resources identified above: `scripts/`, `references/`, and `assets/` files. Note that this step may require user input. For example, when implementing a `brand-guidelines` skill, the user may need to provide brand assets or templates to store in `assets/`, or documentation to store in `references/`.
+
+Added scripts must be tested by actually running them to ensure there are no bugs and that the output matches what is expected. If there are many similar scripts, only a representative sample needs to be tested to ensure confidence that they all work while balancing time to completion.
+
+Any example files and directories not needed for the skill should be deleted. The initialization script creates example files in `scripts/`, `references/`, and `assets/` to demonstrate structure, but most skills won't need all of them.
+
+#### Update SKILL.md
+
+**Writing Guidelines:** Always use imperative/infinitive form.
+
+##### Frontmatter
+
+Write the YAML frontmatter with `name` and `description`:
+
+- `name`: The skill name
+- `description`: This is the primary triggering mechanism for your skill, and helps Claude understand when to use the skill.
+  - Include both what the Skill does and specific triggers/contexts for when to use it.
+  - Include all "when to use" information here - Not in the body. The body is only loaded after triggering, so "When to Use This Skill" sections in the body are not helpful to Claude.
+  - Example description for a `docx` skill: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"
+
+Do not include any other fields in YAML frontmatter.
+
+##### Body
+
+Write instructions for using the skill and its bundled resources.
+
+### Step 5: Packaging a Skill
+
+Once development of the skill is complete, it must be packaged into a distributable .skill file that gets shared with the user. The packaging process automatically validates the skill first to ensure it meets all requirements:
+
+```bash
+scripts/package_skill.py <path/to/skill-folder>
+```
+
+Optional output directory specification:
+
+```bash
+scripts/package_skill.py <path/to/skill-folder> ./dist
+```
+
+The packaging script will:
+
+1. **Validate** the skill automatically, checking:
+
+   - YAML frontmatter format and required fields
+   - Skill naming conventions and directory structure
+   - Description completeness and quality
+   - File organization and resource references
+
+2. **Package** the skill if validation passes, creating a .skill file named after the skill (e.g., `my-skill.skill`) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.
+
+If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
+
+### Step 6: Iterate
+
+After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.
+
+**Iteration workflow:**
+
+1. Use the skill on real tasks
+2. Notice struggles or inefficiencies
+3. Identify how SKILL.md or bundled resources should be updated
+4. Implement changes and test again

.claude/skills/skill-creator/references/output-patterns.md

@@ -0,0 +1,86 @@
+# Output Patterns
+
+Use these patterns when skills need to produce consistent, high-quality output.
+
+## Template Pattern
+
+Provide templates for output format. Match the level of strictness to your needs.
+
+**For strict requirements (like API responses or data formats):**
+
+```markdown
+## Report structure
+
+ALWAYS use this exact template structure:
+
+# [Analysis Title]
+
+## Executive summary
+[One-paragraph overview of key findings]
+
+## Key findings
+- Finding 1 with supporting data
+- Finding 2 with supporting data
+- Finding 3 with supporting data
+
+## Recommendations
+1. Specific actionable recommendation
+2. Specific actionable recommendation
+```
+
+**For flexible guidance (when adaptation is useful):**
+
+```markdown
+## Report structure
+
+Here is a sensible default format, but use your best judgment:
+
+# [Analysis Title]
+
+## Executive summary
+[Overview]
+
+## Key findings
+[Adapt sections based on what you discover]
+
+## Recommendations
+[Tailor to the specific context]
+
+Adjust sections as needed for the specific analysis type.
+```
+
+## Examples Pattern
+
+For skills where output quality depends on seeing examples, provide input/output pairs:
+
+```markdown
+## Commit message format
+
+Generate commit messages following these examples:
+
+**Example 1:**
+Input: Added user authentication with JWT tokens
+Output:
+```
+
+feat(auth): implement JWT-based authentication
+
+Add login endpoint and token validation middleware
+
+```
+
+**Example 2:**
+Input: Fixed bug where dates displayed incorrectly in reports
+Output:
+```
+
+fix(reports): correct date formatting in timezone conversion
+
+Use UTC timestamps consistently across report generation
+
+```
+
+Follow this style: type(scope): brief description, then detailed explanation.
+```
+
+Examples help Claude understand the desired style and level of detail more clearly than descriptions alone.

.claude/skills/skill-creator/references/workflows.md

@@ -0,0 +1,28 @@
+# Workflow Patterns
+
+## Sequential Workflows
+
+For complex tasks, break operations into clear, sequential steps. It is often helpful to give Claude an overview of the process towards the beginning of SKILL.md:
+
+```markdown
+Filling a PDF form involves these steps:
+
+1. Analyze the form (run analyze_form.py)
+2. Create field mapping (edit fields.json)
+3. Validate mapping (run validate_fields.py)
+4. Fill the form (run fill_form.py)
+5. Verify output (run verify_output.py)
+```
+
+## Conditional Workflows
+
+For tasks with branching logic, guide Claude through decision points:
+
+```markdown
+1. Determine the modification type:
+   **Creating new content?** → Follow "Creation workflow" below
+   **Editing existing content?** → Follow "Editing workflow" below
+
+2. Creation workflow: [steps]
+3. Editing workflow: [steps]
+```

.claude/skills/skill-creator/scripts/init_skill.py

@@ -0,0 +1,300 @@
+#!/usr/bin/env python3
+"""
+Skill Initializer - Creates a new skill from template
+
+Usage:
+    init_skill.py <skill-name> --path <path>
+
+Examples:
+    init_skill.py my-new-skill --path skills/public
+    init_skill.py my-api-helper --path skills/private
+    init_skill.py custom-skill --path /custom/location
+"""
+
+import sys
+from pathlib import Path
+
+
+SKILL_TEMPLATE = """---
+name: {skill_name}
+description: [TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]
+---
+
+# {skill_title}
+
+## Overview
+
+[TODO: 1-2 sentences explaining what this skill enables]
+
+## Structuring This Skill
+
+[TODO: Choose the structure that best fits this skill's purpose. Common patterns:
+
+**1. Workflow-Based** (best for sequential processes)
+- Works well when there are clear step-by-step procedures
+- Example: DOCX skill with "Workflow Decision Tree" → "Reading" → "Creating" → "Editing"
+- Structure: ## Overview → ## Workflow Decision Tree → ## Step 1 → ## Step 2...
+
+**2. Task-Based** (best for tool collections)
+- Works well when the skill offers different operations/capabilities
+- Example: PDF skill with "Quick Start" → "Merge PDFs" → "Split PDFs" → "Extract Text"
+- Structure: ## Overview → ## Quick Start → ## Task Category 1 → ## Task Category 2...
+
+**3. Reference/Guidelines** (best for standards or specifications)
+- Works well for brand guidelines, coding standards, or requirements
+- Example: Brand styling with "Brand Guidelines" → "Colors" → "Typography" → "Features"
+- Structure: ## Overview → ## Guidelines → ## Specifications → ## Usage...
+
+**4. Capabilities-Based** (best for integrated systems)
+- Works well when the skill provides multiple interrelated features
+- Example: Product Management with "Core Capabilities" → numbered capability list
+- Structure: ## Overview → ## Core Capabilities → ### 1. Feature → ### 2. Feature...
+
+Patterns can be mixed and matched as needed. Most skills combine patterns (e.g., start with task-based, add workflow for complex operations).
+
+Delete this entire "Structuring This Skill" section when done - it's just guidance.]
+
+## [TODO: Replace with the first main section based on chosen structure]
+
+[TODO: Add content here. See examples in existing skills:
+- Code samples for technical skills
+- Decision trees for complex workflows
+- Concrete examples with realistic user requests
+- References to scripts/templates/references as needed]
+
+## Resources
+
+This skill includes example resource directories that demonstrate how to organize different types of bundled resources:
+
+### scripts/
+Executable code (Python/Bash/etc.) that can be run directly to perform specific operations.
+
+**Examples from other skills:**
+- PDF skill: `fill_fillable_fields.py`, `extract_form_field_info.py` - utilities for PDF manipulation
+- DOCX skill: `document.py`, `utilities.py` - Python modules for document processing
+
+**Appropriate for:** Python scripts, shell scripts, or any executable code that performs automation, data processing, or specific operations.
+
+**Note:** Scripts may be executed without loading into context, but can still be read by Claude for patching or environment adjustments.
+
+### references/
+Documentation and reference material intended to be loaded into context to inform Claude's process and thinking.
+
+**Examples from other skills:**
+- Product management: `communication.md`, `context_building.md` - detailed workflow guides
+- BigQuery: API reference documentation and query examples
+- Finance: Schema documentation, company policies
+
+**Appropriate for:** In-depth documentation, API references, database schemas, comprehensive guides, or any detailed information that Claude should reference while working.
+
+### assets/
+Files not intended to be loaded into context, but rather used within the output Claude produces.
+
+**Examples from other skills:**
+- Brand styling: PowerPoint template files (.pptx), logo files
+- Frontend builder: HTML/React boilerplate project directories
+- Typography: Font files (.ttf, .woff2)
+
+**Appropriate for:** Templates, boilerplate code, document templates, images, icons, fonts, or any files meant to be copied or used in the final output.
+
+---
+
+**Any unneeded directories can be deleted.** Not every skill requires all three types of resources.
+"""
+
+EXAMPLE_SCRIPT = '''#!/usr/bin/env python3
+"""
+Example helper script for {skill_name}
+
+This is a placeholder script that can be executed directly.
+Replace with actual implementation or delete if not needed.
+
+Example real scripts from other skills:
+- pdf/scripts/fill_fillable_fields.py - Fills PDF form fields
+- pdf/scripts/convert_pdf_to_images.py - Converts PDF pages to images
+"""
+
+def main():
+    print("This is an example script for {skill_name}")
+    # TODO: Add actual script logic here
+    # This could be data processing, file conversion, API calls, etc.
+
+if __name__ == "__main__":
+    main()
+'''
+
+EXAMPLE_REFERENCE = """# Reference Documentation for {skill_title}
+
+This is a placeholder for detailed reference documentation.
+Replace with actual reference content or delete if not needed.
+
+Example real reference docs from other skills:
+- product-management/references/communication.md - Comprehensive guide for status updates
+- product-management/references/context_building.md - Deep-dive on gathering context
+- bigquery/references/ - API references and query examples
+
+## When Reference Docs Are Useful
+
+Reference docs are ideal for:
+- Comprehensive API documentation
+- Detailed workflow guides
+- Complex multi-step processes
+- Information too lengthy for main SKILL.md
+- Content that's only needed for specific use cases
+
+## Structure Suggestions
+
+### API Reference Example
+- Overview
+- Authentication
+- Endpoints with examples
+- Error codes
+- Rate limits
+
+### Workflow Guide Example
+- Prerequisites
+- Step-by-step instructions
+- Common patterns
+- Troubleshooting
+- Best practices
+"""
+
+EXAMPLE_ASSET = """# Example Asset File
+
+This placeholder represents where asset files would be stored.
+Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
+
+Asset files are NOT intended to be loaded into context, but rather used within
+the output Claude produces.
+
+Example asset files from other skills:
+- Brand guidelines: logo.png, slides_template.pptx
+- Frontend builder: hello-world/ directory with HTML/React boilerplate
+- Typography: custom-font.ttf, font-family.woff2
+- Data: sample_data.csv, test_dataset.json
+
+## Common Asset Types
+
+- Templates: .pptx, .docx, boilerplate directories
+- Images: .png, .jpg, .svg, .gif
+- Fonts: .ttf, .otf, .woff, .woff2
+- Boilerplate code: Project directories, starter files
+- Icons: .ico, .svg
+- Data files: .csv, .json, .xml, .yaml
+
+Note: This is a text placeholder. Actual assets can be any file type.
+"""
+
+
+def title_case_skill_name(skill_name):
+    """Convert hyphenated skill name to Title Case for display."""
+    return " ".join(word.capitalize() for word in skill_name.split("-"))
+
+
+def init_skill(skill_name, path):
+    """
+    Initialize a new skill directory with template SKILL.md.
+
+    Args:
+        skill_name: Name of the skill
+        path: Path where the skill directory should be created
+
+    Returns:
+        Path to created skill directory, or None if error
+    """
+    # Determine skill directory path
+    skill_dir = Path(path).resolve() / skill_name
+
+    # Check if directory already exists
+    if skill_dir.exists():
+        print(f"❌ Error: Skill directory already exists: {skill_dir}")
+        return None
+
+    # Create skill directory
+    try:
+        skill_dir.mkdir(parents=True, exist_ok=False)
+        print(f"✅ Created skill directory: {skill_dir}")
+    except Exception as e:
+        print(f"❌ Error creating directory: {e}")
+        return None
+
+    # Create SKILL.md from template
+    skill_title = title_case_skill_name(skill_name)
+    skill_content = SKILL_TEMPLATE.format(skill_name=skill_name, skill_title=skill_title)
+
+    skill_md_path = skill_dir / "SKILL.md"
+    try:
+        skill_md_path.write_text(skill_content)
+        print("✅ Created SKILL.md")
+    except Exception as e:
+        print(f"❌ Error creating SKILL.md: {e}")
+        return None
+
+    # Create resource directories with example files
+    try:
+        # Create scripts/ directory with example script
+        scripts_dir = skill_dir / "scripts"
+        scripts_dir.mkdir(exist_ok=True)
+        example_script = scripts_dir / "example.py"
+        example_script.write_text(EXAMPLE_SCRIPT.format(skill_name=skill_name))
+        example_script.chmod(0o755)
+        print("✅ Created scripts/example.py")
+
+        # Create references/ directory with example reference doc
+        references_dir = skill_dir / "references"
+        references_dir.mkdir(exist_ok=True)
+        example_reference = references_dir / "api_reference.md"
+        example_reference.write_text(EXAMPLE_REFERENCE.format(skill_title=skill_title))
+        print("✅ Created references/api_reference.md")
+
+        # Create assets/ directory with example asset placeholder
+        assets_dir = skill_dir / "assets"
+        assets_dir.mkdir(exist_ok=True)
+        example_asset = assets_dir / "example_asset.txt"
+        example_asset.write_text(EXAMPLE_ASSET)
+        print("✅ Created assets/example_asset.txt")
+    except Exception as e:
+        print(f"❌ Error creating resource directories: {e}")
+        return None
+
+    # Print next steps
+    print(f"\n✅ Skill '{skill_name}' initialized successfully at {skill_dir}")
+    print("\nNext steps:")
+    print("1. Edit SKILL.md to complete the TODO items and update the description")
+    print("2. Customize or delete the example files in scripts/, references/, and assets/")
+    print("3. Run the validator when ready to check the skill structure")
+
+    return skill_dir
+
+
+def main():
+    if len(sys.argv) < 4 or sys.argv[2] != "--path":
+        print("Usage: init_skill.py <skill-name> --path <path>")
+        print("\nSkill name requirements:")
+        print("  - Hyphen-case identifier (e.g., 'data-analyzer')")
+        print("  - Lowercase letters, digits, and hyphens only")
+        print("  - Max 40 characters")
+        print("  - Must match directory name exactly")
+        print("\nExamples:")
+        print("  init_skill.py my-new-skill --path skills/public")
+        print("  init_skill.py my-api-helper --path skills/private")
+        print("  init_skill.py custom-skill --path /custom/location")
+        sys.exit(1)
+
+    skill_name = sys.argv[1]
+    path = sys.argv[3]
+
+    print(f"🚀 Initializing skill: {skill_name}")
+    print(f"   Location: {path}")
+    print()
+
+    result = init_skill(skill_name, path)
+
+    if result:
+        sys.exit(0)
+    else:
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

.claude/skills/skill-creator/scripts/package_skill.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+"""
+Skill Packager - Creates a distributable .skill file of a skill folder
+
+Usage:
+    python utils/package_skill.py <path/to/skill-folder> [output-directory]
+
+Example:
+    python utils/package_skill.py skills/public/my-skill
+    python utils/package_skill.py skills/public/my-skill ./dist
+"""
+
+import sys
+import zipfile
+from pathlib import Path
+from quick_validate import validate_skill
+
+
+def package_skill(skill_path, output_dir=None):
+    """
+    Package a skill folder into a .skill file.
+
+    Args:
+        skill_path: Path to the skill folder
+        output_dir: Optional output directory for the .skill file (defaults to current directory)
+
+    Returns:
+        Path to the created .skill file, or None if error
+    """
+    skill_path = Path(skill_path).resolve()
+
+    # Validate skill folder exists
+    if not skill_path.exists():
+        print(f"❌ Error: Skill folder not found: {skill_path}")
+        return None
+
+    if not skill_path.is_dir():
+        print(f"❌ Error: Path is not a directory: {skill_path}")
+        return None
+
+    # Validate SKILL.md exists
+    skill_md = skill_path / "SKILL.md"
+    if not skill_md.exists():
+        print(f"❌ Error: SKILL.md not found in {skill_path}")
+        return None
+
+    # Run validation before packaging
+    print("🔍 Validating skill...")
+    valid, message = validate_skill(skill_path)
+    if not valid:
+        print(f"❌ Validation failed: {message}")
+        print("   Please fix the validation errors before packaging.")
+        return None
+    print(f"✅ {message}\n")
+
+    # Determine output location
+    skill_name = skill_path.name
+    if output_dir:
+        output_path = Path(output_dir).resolve()
+        output_path.mkdir(parents=True, exist_ok=True)
+    else:
+        output_path = Path.cwd()
+
+    skill_filename = output_path / f"{skill_name}.skill"
+
+    # Create the .skill file (zip format)
+    try:
+        with zipfile.ZipFile(skill_filename, "w", zipfile.ZIP_DEFLATED) as zipf:
+            # Walk through the skill directory
+            for file_path in skill_path.rglob("*"):
+                if file_path.is_file():
+                    # Calculate the relative path within the zip
+                    arcname = file_path.relative_to(skill_path.parent)
+                    zipf.write(file_path, arcname)
+                    print(f"  Added: {arcname}")
+
+        print(f"\n✅ Successfully packaged skill to: {skill_filename}")
+        return skill_filename
+
+    except Exception as e:
+        print(f"❌ Error creating .skill file: {e}")
+        return None
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python utils/package_skill.py <path/to/skill-folder> [output-directory]")
+        print("\nExample:")
+        print("  python utils/package_skill.py skills/public/my-skill")
+        print("  python utils/package_skill.py skills/public/my-skill ./dist")
+        sys.exit(1)
+
+    skill_path = sys.argv[1]
+    output_dir = sys.argv[2] if len(sys.argv) > 2 else None
+
+    print(f"📦 Packaging skill: {skill_path}")
+    if output_dir:
+        print(f"   Output directory: {output_dir}")
+    print()
+
+    result = package_skill(skill_path, output_dir)
+
+    if result:
+        sys.exit(0)
+    else:
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

.claude/skills/skill-creator/scripts/quick_validate.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""
+Quick validation script for skills - minimal version
+"""
+
+import sys
+import os
+import re
+import yaml
+from pathlib import Path
+
+
+def validate_skill(skill_path):
+    """Basic validation of a skill"""
+    skill_path = Path(skill_path)
+
+    # Check SKILL.md exists
+    skill_md = skill_path / "SKILL.md"
+    if not skill_md.exists():
+        return False, "SKILL.md not found"
+
+    # Read and validate frontmatter
+    content = skill_md.read_text()
+    if not content.startswith("---"):
+        return False, "No YAML frontmatter found"
+
+    # Extract frontmatter
+    match = re.match(r"^---\n(.*?)\n---", content, re.DOTALL)
+    if not match:
+        return False, "Invalid frontmatter format"
+
+    frontmatter_text = match.group(1)
+
+    # Parse YAML frontmatter
+    try:
+        frontmatter = yaml.safe_load(frontmatter_text)
+        if not isinstance(frontmatter, dict):
+            return False, "Frontmatter must be a YAML dictionary"
+    except yaml.YAMLError as e:
+        return False, f"Invalid YAML in frontmatter: {e}"
+
+    # Define allowed properties
+    ALLOWED_PROPERTIES = {"name", "description", "license", "allowed-tools", "metadata"}
+
+    # Check for unexpected properties (excluding nested keys under metadata)
+    unexpected_keys = set(frontmatter.keys()) - ALLOWED_PROPERTIES
+    if unexpected_keys:
+        return False, (
+            f"Unexpected key(s) in SKILL.md frontmatter: {', '.join(sorted(unexpected_keys))}. "
+            f"Allowed properties are: {', '.join(sorted(ALLOWED_PROPERTIES))}"
+        )
+
+    # Check required fields
+    if "name" not in frontmatter:
+        return False, "Missing 'name' in frontmatter"
+    if "description" not in frontmatter:
+        return False, "Missing 'description' in frontmatter"
+
+    # Extract name for validation
+    name = frontmatter.get("name", "")
+    if not isinstance(name, str):
+        return False, f"Name must be a string, got {type(name).__name__}"
+    name = name.strip()
+    if name:
+        # Check naming convention (hyphen-case: lowercase with hyphens)
+        if not re.match(r"^[a-z0-9-]+$", name):
+            return False, f"Name '{name}' should be hyphen-case (lowercase letters, digits, and hyphens only)"
+        if name.startswith("-") or name.endswith("-") or "--" in name:
+            return False, f"Name '{name}' cannot start/end with hyphen or contain consecutive hyphens"
+        # Check name length (max 64 characters per spec)
+        if len(name) > 64:
+            return False, f"Name is too long ({len(name)} characters). Maximum is 64 characters."
+
+    # Extract and validate description
+    description = frontmatter.get("description", "")
+    if not isinstance(description, str):
+        return False, f"Description must be a string, got {type(description).__name__}"
+    description = description.strip()
+    if description:
+        # Check for angle brackets
+        if "<" in description or ">" in description:
+            return False, "Description cannot contain angle brackets (< or >)"
+        # Check description length (max 1024 characters per spec)
+        if len(description) > 1024:
+            return False, f"Description is too long ({len(description)} characters). Maximum is 1024 characters."
+
+    return True, "Skill is valid!"
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python quick_validate.py <skill_directory>")
+        sys.exit(1)
+
+    valid, message = validate_skill(sys.argv[1])
+    print(message)
+    sys.exit(0 if valid else 1)

.github/pull_request_template.md

@@ -20,4 +20,4 @@
 - [x] I understand that this PR may be closed in case there was no previous discussion or issues. (This doesn't apply to typos!)
 - [x] I've added a test for each change that was introduced, and I tried as much as possible to make a single atomic change.
 - [x] I've updated the documentation accordingly.
-- [x] I ran `dev/reformat`(backend) and `cd web && npx lint-staged`(frontend) to appease the lint gods
+- [x] I ran `make lint` and `make type-check` (backend) and `cd web && npx lint-staged` (frontend) to appease the lint gods

.github/workflows/api-tests.yml

@@ -22,12 +22,12 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Setup UV and Python
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           enable-cache: true
           python-version: ${{ matrix.python-version }}
@@ -57,7 +57,7 @@ jobs:
         run: sh .github/workflows/expose_service_ports.sh
 
       - name: Set up Sandbox
-        uses: hoverkraft-tech/compose-action@v2.0.2
+        uses: hoverkraft-tech/compose-action@v2
         with:
           compose-file: |
             docker/docker-compose.middleware.yaml

.github/workflows/autofix.yml

@@ -12,7 +12,7 @@ jobs:
     if: github.repository == 'langgenius/dify'
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Check Docker Compose inputs
         id: docker-compose-changes
@@ -27,7 +27,7 @@ jobs:
         with:
           python-version: "3.11"
 
-      - uses: astral-sh/setup-uv@v6
+      - uses: astral-sh/setup-uv@v7
 
       - name: Generate Docker Compose
         if: steps.docker-compose-changes.outputs.any_changed == 'true'

.github/workflows/build-push.yml

@@ -90,7 +90,7 @@ jobs:
           touch "/tmp/digests/${sanitized_digest}"
 
       - name: Upload digest
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: digests-${{ matrix.context }}-${{ env.PLATFORM_PAIR }}
           path: /tmp/digests/*

.github/workflows/db-migration-test.yml

@@ -13,13 +13,13 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
           persist-credentials: false
 
       - name: Setup UV and Python
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           enable-cache: true
           python-version: "3.12"
@@ -63,13 +63,13 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
           persist-credentials: false
 
       - name: Setup UV and Python
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           enable-cache: true
           python-version: "3.12"

.github/workflows/main-ci.yml

@@ -27,7 +27,7 @@ jobs:
       vdb-changed: ${{ steps.changes.outputs.vdb }}
       migration-changed: ${{ steps.changes.outputs.migration }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
       - uses: dorny/paths-filter@v3
         id: changes
         with:
@@ -38,6 +38,7 @@ jobs:
               - '.github/workflows/api-tests.yml'
             web:
               - 'web/**'
+              - '.github/workflows/web-tests.yml'
             vdb:
               - 'api/core/rag/datasource/**'
               - 'docker/**'

.github/workflows/style.yml

@@ -19,13 +19,13 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Check changed files
         id: changed-files
-        uses: tj-actions/changed-files@v46
+        uses: tj-actions/changed-files@v47
         with:
           files: |
             api/**
@@ -33,7 +33,7 @@ jobs:
 
       - name: Setup UV and Python
         if: steps.changed-files.outputs.any_changed == 'true'
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           enable-cache: false
           python-version: "3.12"
@@ -68,15 +68,17 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Check changed files
         id: changed-files
-        uses: tj-actions/changed-files@v46
+        uses: tj-actions/changed-files@v47
         with:
-          files: web/**
+          files: |
+            web/**
+            .github/workflows/style.yml
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -85,7 +87,7 @@ jobs:
           run_install: false
 
       - name: Setup NodeJS
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         if: steps.changed-files.outputs.any_changed == 'true'
         with:
           node-version: 22
@@ -108,20 +110,30 @@ jobs:
         working-directory: ./web
         run: pnpm run type-check:tsgo
 
+      - name: Web dead code check
+        if: steps.changed-files.outputs.any_changed == 'true'
+        working-directory: ./web
+        run: pnpm run knip
+
+      - name: Web build check
+        if: steps.changed-files.outputs.any_changed == 'true'
+        working-directory: ./web
+        run: pnpm run build
+
   superlinter:
     name: SuperLinter
     runs-on: ubuntu-latest
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
           persist-credentials: false
 
       - name: Check changed files
         id: changed-files
-        uses: tj-actions/changed-files@v46
+        uses: tj-actions/changed-files@v47
         with:
           files: |
             **.sh

.github/workflows/tool-test-sdks.yaml

@@ -25,12 +25,12 @@ jobs:
         working-directory: sdks/nodejs-client
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Use Node.js ${{ matrix.node-version }}
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: ${{ matrix.node-version }}
           cache: ''

.github/workflows/translate-i18n-base-on-english.yml

@@ -4,7 +4,8 @@ on:
   push:
     branches: [main]
     paths:
-      - 'web/i18n/en-US/*.ts'
+      - 'web/i18n/en-US/*.json'
+  workflow_dispatch:
 
 permissions:
   contents: write
@@ -18,6 +19,7 @@ jobs:
       run:
         working-directory: web
     steps:
+      # Keep use old checkout action version for https://github.com/peter-evans/create-pull-request/issues/4272
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
@@ -26,21 +28,28 @@ jobs:
       - name: Check for file changes in i18n/en-US
         id: check_files
         run: |
-          git fetch origin "${{ github.event.before }}" || true
-          git fetch origin "${{ github.sha }}" || true
-          changed_files=$(git diff --name-only "${{ github.event.before }}" "${{ github.sha }}" -- 'i18n/en-US/*.ts')
-          echo "Changed files: $changed_files"
-          if [ -n "$changed_files" ]; then
+          # Skip check for manual trigger, translate all files
+          if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
             echo "FILES_CHANGED=true" >> $GITHUB_ENV
-            file_args=""
-            for file in $changed_files; do
-              filename=$(basename "$file" .ts)
-              file_args="$file_args --file $filename"
-            done
-            echo "FILE_ARGS=$file_args" >> $GITHUB_ENV
-            echo "File arguments: $file_args"
+            echo "FILE_ARGS=" >> $GITHUB_ENV
+            echo "Manual trigger: translating all files"
           else
-            echo "FILES_CHANGED=false" >> $GITHUB_ENV
+            git fetch origin "${{ github.event.before }}" || true
+            git fetch origin "${{ github.sha }}" || true
+            changed_files=$(git diff --name-only "${{ github.event.before }}" "${{ github.sha }}" -- 'i18n/en-US/*.json')
+            echo "Changed files: $changed_files"
+            if [ -n "$changed_files" ]; then
+              echo "FILES_CHANGED=true" >> $GITHUB_ENV
+              file_args=""
+              for file in $changed_files; do
+                filename=$(basename "$file" .json)
+                file_args="$file_args --file $filename"
+              done
+              echo "FILE_ARGS=$file_args" >> $GITHUB_ENV
+              echo "File arguments: $file_args"
+            else
+              echo "FILES_CHANGED=false" >> $GITHUB_ENV
+            fi
           fi
 
       - name: Install pnpm
@@ -51,7 +60,7 @@ jobs:
 
       - name: Set up Node.js
         if: env.FILES_CHANGED == 'true'
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: 'lts/*'
           cache: pnpm
@@ -65,7 +74,7 @@ jobs:
       - name: Generate i18n translations
         if: env.FILES_CHANGED == 'true'
         working-directory: ./web
-        run: pnpm run auto-gen-i18n ${{ env.FILE_ARGS }}
+        run: pnpm run i18n:gen ${{ env.FILE_ARGS }}
 
       - name: Create Pull Request
         if: env.FILES_CHANGED == 'true'

.github/workflows/vdb-tests.yml

@@ -19,19 +19,19 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Free Disk Space
-        uses: endersonmenezes/free-disk-space@v2
+        uses: endersonmenezes/free-disk-space@v3
         with:
           remove_dotnet: true
           remove_haskell: true
           remove_tool_cache: true
 
       - name: Setup UV and Python
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           enable-cache: true
           python-version: ${{ matrix.python-version }}

.github/workflows/web-tests.yml

@@ -18,7 +18,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -29,7 +29,7 @@ jobs:
           run_install: false
 
       - name: Setup Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: 22
           cache: pnpm
@@ -360,7 +360,7 @@ jobs:
 
       - name: Upload Coverage Artifact
         if: steps.coverage-summary.outputs.has_coverage == 'true'
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: web-coverage-report
           path: web/coverage

.gitignore

@@ -235,3 +235,4 @@ scripts/stress-test/reports/
 
 # settings
 *.local.json
+*.local.md

.mcp.json

@@ -1,34 +0,0 @@
-{
-    "mcpServers": {
-      "context7": {
-        "type": "http",
-        "url": "https://mcp.context7.com/mcp"
-      },
-      "sequential-thinking": {
-        "type": "stdio",
-        "command": "npx",
-        "args": ["-y", "@modelcontextprotocol/server-sequential-thinking"],
-        "env": {}
-      },
-      "github": {
-        "type": "stdio",
-        "command": "npx",
-        "args": ["-y", "@modelcontextprotocol/server-github"],
-        "env": {
-          "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_PERSONAL_ACCESS_TOKEN}"
-        }
-      },
-      "fetch": {
-        "type": "stdio",
-        "command": "uvx",
-        "args": ["mcp-server-fetch"],
-        "env": {}
-      },
-      "playwright": {
-        "type": "stdio",
-        "command": "npx",
-        "args": ["-y", "@playwright/mcp@latest"],
-        "env": {}
-      }
-    }
-  }

Makefile

@@ -60,9 +60,10 @@ check:
 	@echo "✅ Code check complete"
 
 lint:
-	@echo "🔧 Running ruff format, check with fixes, and import linter..."
+	@echo "🔧 Running ruff format, check with fixes, import linter, and dotenv-linter..."
 	@uv run --project api --dev sh -c 'ruff format ./api && ruff check --fix ./api'
 	@uv run --directory api --dev lint-imports
+	@uv run --project api --dev dotenv-linter ./api/.env.example ./web/.env.example
 	@echo "✅ Linting complete"
 
 type-check:
@@ -122,7 +123,7 @@ help:
 	@echo "Backend Code Quality:"
 	@echo "  make format         - Format code with ruff"
 	@echo "  make check          - Check code with ruff"
-	@echo "  make lint           - Format and fix code with ruff"
+	@echo "  make lint           - Format, fix, and lint code (ruff, imports, dotenv)"
 	@echo "  make type-check     - Run type checking with basedpyright"
 	@echo "  make test           - Run backend unit tests"
 	@echo ""

api/.env.example

@@ -101,6 +101,15 @@ S3_ACCESS_KEY=your-access-key
 S3_SECRET_KEY=your-secret-key
 S3_REGION=your-region
 
+# Workflow run and Conversation archive storage (S3-compatible)
+ARCHIVE_STORAGE_ENABLED=false
+ARCHIVE_STORAGE_ENDPOINT=
+ARCHIVE_STORAGE_ARCHIVE_BUCKET=
+ARCHIVE_STORAGE_EXPORT_BUCKET=
+ARCHIVE_STORAGE_ACCESS_KEY=
+ARCHIVE_STORAGE_SECRET_KEY=
+ARCHIVE_STORAGE_REGION=auto
+
 # Azure Blob Storage configuration
 AZURE_BLOB_ACCOUNT_NAME=your-account-name
 AZURE_BLOB_ACCOUNT_KEY=your-account-key
@@ -128,6 +137,7 @@ TENCENT_COS_SECRET_KEY=your-secret-key
 TENCENT_COS_SECRET_ID=your-secret-id
 TENCENT_COS_REGION=your-region
 TENCENT_COS_SCHEME=your-scheme
+TENCENT_COS_CUSTOM_DOMAIN=your-custom-domain
 
 # Huawei OBS Storage Configuration
 HUAWEI_OBS_BUCKET_NAME=your-bucket-name
@@ -492,6 +502,8 @@ LOG_FILE_BACKUP_COUNT=5
 LOG_DATEFORMAT=%Y-%m-%d %H:%M:%S
 # Log Timezone
 LOG_TZ=UTC
+# Log output format: text or json
+LOG_OUTPUT_FORMAT=text
 # Log format
 LOG_FORMAT=%(asctime)s,%(msecs)d %(levelname)-2s [%(filename)s:%(lineno)d] %(req_id)s %(message)s
 
@@ -563,6 +575,10 @@ LOGSTORE_DUAL_WRITE_ENABLED=false
 # Enable dual-read fallback to SQL database when LogStore returns no results (default: true)
 # Useful for migration scenarios where historical data exists only in SQL database
 LOGSTORE_DUAL_READ_ENABLED=true
+# Control flag for whether to write the `graph` field to LogStore.
+# If LOGSTORE_ENABLE_PUT_GRAPH_FIELD is "true", write the full `graph` field;
+# otherwise write an empty {} instead. Defaults to writing the `graph` field.
+LOGSTORE_ENABLE_PUT_GRAPH_FIELD=true
 
 # Celery beat configuration
 CELERY_BEAT_SCHEDULER_TIME=1

api/.importlinter

@@ -3,9 +3,11 @@ root_packages =
     core
     configs
     controllers
+    extensions
     models
     tasks
     services
+include_external_packages = True
 
 [importlinter:contract:workflow]
 name = Workflow
@@ -33,6 +35,28 @@ ignore_imports =
     core.workflow.nodes.loop.loop_node -> core.workflow.graph
     core.workflow.nodes.loop.loop_node -> core.workflow.graph_engine.command_channels
 
+[importlinter:contract:workflow-infrastructure-dependencies]
+name = Workflow Infrastructure Dependencies
+type = forbidden
+source_modules =
+    core.workflow
+forbidden_modules =
+    extensions.ext_database
+    extensions.ext_redis
+allow_indirect_imports = True
+ignore_imports =
+    core.workflow.nodes.agent.agent_node -> extensions.ext_database
+    core.workflow.nodes.datasource.datasource_node -> extensions.ext_database
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> extensions.ext_database
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_database
+    core.workflow.nodes.llm.file_saver -> extensions.ext_database
+    core.workflow.nodes.llm.llm_utils -> extensions.ext_database
+    core.workflow.nodes.llm.node -> extensions.ext_database
+    core.workflow.nodes.tool.tool_node -> extensions.ext_database
+    core.workflow.graph_engine.command_channels.redis_channel -> extensions.ext_redis
+    core.workflow.graph_engine.manager -> extensions.ext_redis
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_redis
+
 [importlinter:contract:rsc]
 name = RSC
 type = layers

api/.ruff.toml

@@ -1,4 +1,8 @@
-exclude = ["migrations/*"]
+exclude = [
+    "migrations/*",
+    ".git",
+    ".git/**",
+]
 line-length = 120
 
 [format]

api/Dockerfile

@@ -50,16 +50,33 @@ WORKDIR /app/api
 
 # Create non-root user
 ARG dify_uid=1001
+ARG NODE_MAJOR=22
+ARG NODE_PACKAGE_VERSION=22.21.0-1nodesource1
+ARG NODESOURCE_KEY_FPR=6F71F525282841EEDAF851B42F59B5F99B1BE0B4
 RUN groupadd -r -g ${dify_uid} dify && \
     useradd -r -u ${dify_uid} -g ${dify_uid} -s /bin/bash dify && \
     chown -R dify:dify /app
 
 RUN \
     apt-get update \
+    && apt-get install -y --no-install-recommends \
+        ca-certificates \
+        curl \
+        gnupg \
+    && mkdir -p /etc/apt/keyrings \
+    && curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource.gpg \
+    && gpg --show-keys --with-colons /tmp/nodesource.gpg \
+        | awk -F: '/^fpr:/ {print $10}' \
+        | grep -Fx "${NODESOURCE_KEY_FPR}" \
+    && gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource.gpg \
+    && rm -f /tmp/nodesource.gpg \
+    && echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_${NODE_MAJOR}.x nodistro main" \
+        > /etc/apt/sources.list.d/nodesource.list \
+    && apt-get update \
     # Install dependencies
     && apt-get install -y --no-install-recommends \
         # basic environment
-        curl nodejs \
+        nodejs=${NODE_PACKAGE_VERSION} \
         # for gmpy2 \
         libgmp-dev libmpfr-dev libmpc-dev \
         # For Security
@@ -79,7 +96,8 @@ COPY --from=packages --chown=dify:dify ${VIRTUAL_ENV} ${VIRTUAL_ENV}
 ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"
 
 # Download nltk data
-RUN mkdir -p /usr/local/share/nltk_data && NLTK_DATA=/usr/local/share/nltk_data python -c "import nltk; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger'); nltk.download('stopwords')" \
+RUN mkdir -p /usr/local/share/nltk_data \
+    && NLTK_DATA=/usr/local/share/nltk_data python -c "import nltk; from unstructured.nlp.tokenize import download_nltk_packages; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger'); nltk.download('stopwords'); download_nltk_packages()" \
     && chmod -R 755 /usr/local/share/nltk_data
 
 ENV TIKTOKEN_CACHE_DIR=/app/api/.tiktoken_cache

api/app_factory.py

@@ -2,9 +2,11 @@ import logging
 import time
 
 from opentelemetry.trace import get_current_span
+from opentelemetry.trace.span import INVALID_SPAN_ID, INVALID_TRACE_ID
 
 from configs import dify_config
 from contexts.wrapper import RecyclableContextVar
+from core.logging.context import init_request_context
 from dify_app import DifyApp
 
 logger = logging.getLogger(__name__)
@@ -25,28 +27,35 @@ def create_flask_app_with_configs() -> DifyApp:
     # add before request hook
     @dify_app.before_request
     def before_request():
-        # add an unique identifier to each request
+        # Initialize logging context for this request
+        init_request_context()
         RecyclableContextVar.increment_thread_recycles()
 
-    # add after request hook for injecting X-Trace-Id header from OpenTelemetry span context
+    # add after request hook for injecting trace headers from OpenTelemetry span context
+    # Only adds headers when OTEL is enabled and has valid context
     @dify_app.after_request
-    def add_trace_id_header(response):
+    def add_trace_headers(response):
         try:
             span = get_current_span()
             ctx = span.get_span_context() if span else None
-            if ctx and ctx.is_valid:
-                trace_id_hex = format(ctx.trace_id, "032x")
-                # Avoid duplicates if some middleware added it
-                if "X-Trace-Id" not in response.headers:
-                    response.headers["X-Trace-Id"] = trace_id_hex
+
+            if not ctx or not ctx.is_valid:
+                return response
+
+            # Inject trace headers from OTEL context
+            if ctx.trace_id != INVALID_TRACE_ID and "X-Trace-Id" not in response.headers:
+                response.headers["X-Trace-Id"] = format(ctx.trace_id, "032x")
+            if ctx.span_id != INVALID_SPAN_ID and "X-Span-Id" not in response.headers:
+                response.headers["X-Span-Id"] = format(ctx.span_id, "016x")
+
         except Exception:
             # Never break the response due to tracing header injection
-            logger.warning("Failed to add trace ID to response header", exc_info=True)
+            logger.warning("Failed to add trace headers to response", exc_info=True)
         return response
 
     # Capture the decorator's return value to avoid pyright reportUnusedFunction
     _ = before_request
-    _ = add_trace_id_header
+    _ = add_trace_headers
 
     return dify_app
 

api/commands.py

@@ -235,7 +235,7 @@ def migrate_annotation_vector_database():
                 if annotations:
                     for annotation in annotations:
                         document = Document(
-                            page_content=annotation.question,
+                            page_content=annotation.question_text,
                             metadata={"annotation_id": annotation.id, "app_id": app.id, "doc_id": annotation.id},
                         )
                         documents.append(document)
@@ -1184,6 +1184,217 @@ def remove_orphaned_files_on_storage(force: bool):
         click.echo(click.style(f"Removed {removed_files} orphaned files, with {error_files} errors.", fg="yellow"))
 
 
+@click.command("file-usage", help="Query file usages and show where files are referenced.")
+@click.option("--file-id", type=str, default=None, help="Filter by file UUID.")
+@click.option("--key", type=str, default=None, help="Filter by storage key.")
+@click.option("--src", type=str, default=None, help="Filter by table.column pattern (e.g., 'documents.%' or '%.icon').")
+@click.option("--limit", type=int, default=100, help="Limit number of results (default: 100).")
+@click.option("--offset", type=int, default=0, help="Offset for pagination (default: 0).")
+@click.option("--json", "output_json", is_flag=True, help="Output results in JSON format.")
+def file_usage(
+    file_id: str | None,
+    key: str | None,
+    src: str | None,
+    limit: int,
+    offset: int,
+    output_json: bool,
+):
+    """
+    Query file usages and show where files are referenced in the database.
+
+    This command reuses the same reference checking logic as clear-orphaned-file-records
+    and displays detailed information about where each file is referenced.
+    """
+    # define tables and columns to process
+    files_tables = [
+        {"table": "upload_files", "id_column": "id", "key_column": "key"},
+        {"table": "tool_files", "id_column": "id", "key_column": "file_key"},
+    ]
+    ids_tables = [
+        {"type": "uuid", "table": "message_files", "column": "upload_file_id", "pk_column": "id"},
+        {"type": "text", "table": "documents", "column": "data_source_info", "pk_column": "id"},
+        {"type": "text", "table": "document_segments", "column": "content", "pk_column": "id"},
+        {"type": "text", "table": "messages", "column": "answer", "pk_column": "id"},
+        {"type": "text", "table": "workflow_node_executions", "column": "inputs", "pk_column": "id"},
+        {"type": "text", "table": "workflow_node_executions", "column": "process_data", "pk_column": "id"},
+        {"type": "text", "table": "workflow_node_executions", "column": "outputs", "pk_column": "id"},
+        {"type": "text", "table": "conversations", "column": "introduction", "pk_column": "id"},
+        {"type": "text", "table": "conversations", "column": "system_instruction", "pk_column": "id"},
+        {"type": "text", "table": "accounts", "column": "avatar", "pk_column": "id"},
+        {"type": "text", "table": "apps", "column": "icon", "pk_column": "id"},
+        {"type": "text", "table": "sites", "column": "icon", "pk_column": "id"},
+        {"type": "json", "table": "messages", "column": "inputs", "pk_column": "id"},
+        {"type": "json", "table": "messages", "column": "message", "pk_column": "id"},
+    ]
+
+    # Stream file usages with pagination to avoid holding all results in memory
+    paginated_usages = []
+    total_count = 0
+
+    # First, build a mapping of file_id -> storage_key from the base tables
+    file_key_map = {}
+    for files_table in files_tables:
+        query = f"SELECT {files_table['id_column']}, {files_table['key_column']} FROM {files_table['table']}"
+        with db.engine.begin() as conn:
+            rs = conn.execute(sa.text(query))
+            for row in rs:
+                file_key_map[str(row[0])] = f"{files_table['table']}:{row[1]}"
+
+    # If filtering by key or file_id, verify it exists
+    if file_id and file_id not in file_key_map:
+        if output_json:
+            click.echo(json.dumps({"error": f"File ID {file_id} not found in base tables"}))
+        else:
+            click.echo(click.style(f"File ID {file_id} not found in base tables.", fg="red"))
+        return
+
+    if key:
+        valid_prefixes = {f"upload_files:{key}", f"tool_files:{key}"}
+        matching_file_ids = [fid for fid, fkey in file_key_map.items() if fkey in valid_prefixes]
+        if not matching_file_ids:
+            if output_json:
+                click.echo(json.dumps({"error": f"Key {key} not found in base tables"}))
+            else:
+                click.echo(click.style(f"Key {key} not found in base tables.", fg="red"))
+            return
+
+    guid_regexp = "[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}"
+
+    # For each reference table/column, find matching file IDs and record the references
+    for ids_table in ids_tables:
+        src_filter = f"{ids_table['table']}.{ids_table['column']}"
+
+        # Skip if src filter doesn't match (use fnmatch for wildcard patterns)
+        if src:
+            if "%" in src or "_" in src:
+                import fnmatch
+
+                # Convert SQL LIKE wildcards to fnmatch wildcards (% -> *, _ -> ?)
+                pattern = src.replace("%", "*").replace("_", "?")
+                if not fnmatch.fnmatch(src_filter, pattern):
+                    continue
+            else:
+                if src_filter != src:
+                    continue
+
+        if ids_table["type"] == "uuid":
+            # Direct UUID match
+            query = (
+                f"SELECT {ids_table['pk_column']}, {ids_table['column']} "
+                f"FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
+            )
+            with db.engine.begin() as conn:
+                rs = conn.execute(sa.text(query))
+                for row in rs:
+                    record_id = str(row[0])
+                    ref_file_id = str(row[1])
+                    if ref_file_id not in file_key_map:
+                        continue
+                    storage_key = file_key_map[ref_file_id]
+
+                    # Apply filters
+                    if file_id and ref_file_id != file_id:
+                        continue
+                    if key and not storage_key.endswith(key):
+                        continue
+
+                    # Only collect items within the requested page range
+                    if offset <= total_count < offset + limit:
+                        paginated_usages.append(
+                            {
+                                "src": f"{ids_table['table']}.{ids_table['column']}",
+                                "record_id": record_id,
+                                "file_id": ref_file_id,
+                                "key": storage_key,
+                            }
+                        )
+                    total_count += 1
+
+        elif ids_table["type"] in ("text", "json"):
+            # Extract UUIDs from text/json content
+            column_cast = f"{ids_table['column']}::text" if ids_table["type"] == "json" else ids_table["column"]
+            query = (
+                f"SELECT {ids_table['pk_column']}, {column_cast} "
+                f"FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
+            )
+            with db.engine.begin() as conn:
+                rs = conn.execute(sa.text(query))
+                for row in rs:
+                    record_id = str(row[0])
+                    content = str(row[1])
+
+                    # Find all UUIDs in the content
+                    import re
+
+                    uuid_pattern = re.compile(guid_regexp, re.IGNORECASE)
+                    matches = uuid_pattern.findall(content)
+
+                    for ref_file_id in matches:
+                        if ref_file_id not in file_key_map:
+                            continue
+                        storage_key = file_key_map[ref_file_id]
+
+                        # Apply filters
+                        if file_id and ref_file_id != file_id:
+                            continue
+                        if key and not storage_key.endswith(key):
+                            continue
+
+                        # Only collect items within the requested page range
+                        if offset <= total_count < offset + limit:
+                            paginated_usages.append(
+                                {
+                                    "src": f"{ids_table['table']}.{ids_table['column']}",
+                                    "record_id": record_id,
+                                    "file_id": ref_file_id,
+                                    "key": storage_key,
+                                }
+                            )
+                        total_count += 1
+
+    # Output results
+    if output_json:
+        result = {
+            "total": total_count,
+            "offset": offset,
+            "limit": limit,
+            "usages": paginated_usages,
+        }
+        click.echo(json.dumps(result, indent=2))
+    else:
+        click.echo(
+            click.style(f"Found {total_count} file usages (showing {len(paginated_usages)} results)", fg="white")
+        )
+        click.echo("")
+
+        if not paginated_usages:
+            click.echo(click.style("No file usages found matching the specified criteria.", fg="yellow"))
+            return
+
+        # Print table header
+        click.echo(
+            click.style(
+                f"{'Src (Table.Column)':<50} {'Record ID':<40} {'File ID':<40} {'Storage Key':<60}",
+                fg="cyan",
+            )
+        )
+        click.echo(click.style("-" * 190, fg="white"))
+
+        # Print each usage
+        for usage in paginated_usages:
+            click.echo(f"{usage['src']:<50} {usage['record_id']:<40} {usage['file_id']:<40} {usage['key']:<60}")
+
+        # Show pagination info
+        if offset + limit < total_count:
+            click.echo("")
+            click.echo(
+                click.style(
+                    f"Showing {offset + 1}-{offset + len(paginated_usages)} of {total_count} results", fg="white"
+                )
+            )
+            click.echo(click.style(f"Use --offset {offset + limit} to see next page", fg="white"))
+
+
 @click.command("setup-system-tool-oauth-client", help="Setup system tool oauth client.")
 @click.option("--provider", prompt=True, help="Provider name")
 @click.option("--client-params", prompt=True, help="Client Params")

api/configs/extra/__init__.py

@@ -1,9 +1,11 @@
+from configs.extra.archive_config import ArchiveStorageConfig
 from configs.extra.notion_config import NotionConfig
 from configs.extra.sentry_config import SentryConfig
 
 
 class ExtraServiceConfig(
     # place the configs in alphabet order
+    ArchiveStorageConfig,
     NotionConfig,
     SentryConfig,
 ):

api/configs/extra/archive_config.py

@@ -0,0 +1,43 @@
+from pydantic import Field
+from pydantic_settings import BaseSettings
+
+
+class ArchiveStorageConfig(BaseSettings):
+    """
+    Configuration settings for workflow run logs archiving storage.
+    """
+
+    ARCHIVE_STORAGE_ENABLED: bool = Field(
+        description="Enable workflow run logs archiving to S3-compatible storage",
+        default=False,
+    )
+
+    ARCHIVE_STORAGE_ENDPOINT: str | None = Field(
+        description="URL of the S3-compatible storage endpoint (e.g., 'https://storage.example.com')",
+        default=None,
+    )
+
+    ARCHIVE_STORAGE_ARCHIVE_BUCKET: str | None = Field(
+        description="Name of the bucket to store archived workflow logs",
+        default=None,
+    )
+
+    ARCHIVE_STORAGE_EXPORT_BUCKET: str | None = Field(
+        description="Name of the bucket to store exported workflow runs",
+        default=None,
+    )
+
+    ARCHIVE_STORAGE_ACCESS_KEY: str | None = Field(
+        description="Access key ID for authenticating with storage",
+        default=None,
+    )
+
+    ARCHIVE_STORAGE_SECRET_KEY: str | None = Field(
+        description="Secret access key for authenticating with storage",
+        default=None,
+    )
+
+    ARCHIVE_STORAGE_REGION: str = Field(
+        description="Region for storage (use 'auto' if the provider supports it)",
+        default="auto",
+    )

api/configs/feature/__init__.py

@@ -587,6 +587,11 @@ class LoggingConfig(BaseSettings):
         default="INFO",
     )
 
+    LOG_OUTPUT_FORMAT: Literal["text", "json"] = Field(
+        description="Log output format: 'text' for human-readable, 'json' for structured JSON logs.",
+        default="text",
+    )
+
     LOG_FILE: str | None = Field(
         description="File path for log output.",
         default=None,

api/configs/middleware/storage/tencent_cos_storage_config.py

@@ -31,3 +31,8 @@ class TencentCloudCOSStorageConfig(BaseSettings):
         description="Protocol scheme for COS requests: 'https' (recommended) or 'http'",
         default=None,
     )
+
+    TENCENT_COS_CUSTOM_DOMAIN: str | None = Field(
+        description="Tencent Cloud COS custom domain setting",
+        default=None,
+    )

api/configs/middleware/vdb/milvus_config.py

@@ -16,7 +16,6 @@ class MilvusConfig(BaseSettings):
         description="Authentication token for Milvus, if token-based authentication is enabled",
         default=None,
     )
-
     MILVUS_USER: str | None = Field(
         description="Username for authenticating with Milvus, if username/password authentication is enabled",
         default=None,

api/controllers/common/fields.py

@@ -1,62 +1,59 @@
-from flask_restx import Api, Namespace, fields
+from __future__ import annotations
 
-from libs.helper import AppIconUrlField
+from typing import Any, TypeAlias
 
-parameters__system_parameters = {
-    "image_file_size_limit": fields.Integer,
-    "video_file_size_limit": fields.Integer,
-    "audio_file_size_limit": fields.Integer,
-    "file_size_limit": fields.Integer,
-    "workflow_file_upload_limit": fields.Integer,
-}
+from pydantic import BaseModel, ConfigDict, computed_field
+
+from core.file import helpers as file_helpers
+from models.model import IconType
+
+JSONValue: TypeAlias = str | int | float | bool | None | dict[str, Any] | list[Any]
+JSONObject: TypeAlias = dict[str, Any]
 
 
-def build_system_parameters_model(api_or_ns: Api | Namespace):
-    """Build the system parameters model for the API or Namespace."""
-    return api_or_ns.model("SystemParameters", parameters__system_parameters)
+class SystemParameters(BaseModel):
+    image_file_size_limit: int
+    video_file_size_limit: int
+    audio_file_size_limit: int
+    file_size_limit: int
+    workflow_file_upload_limit: int
 
 
-parameters_fields = {
-    "opening_statement": fields.String,
-    "suggested_questions": fields.Raw,
-    "suggested_questions_after_answer": fields.Raw,
-    "speech_to_text": fields.Raw,
-    "text_to_speech": fields.Raw,
-    "retriever_resource": fields.Raw,
-    "annotation_reply": fields.Raw,
-    "more_like_this": fields.Raw,
-    "user_input_form": fields.Raw,
-    "sensitive_word_avoidance": fields.Raw,
-    "file_upload": fields.Raw,
-    "system_parameters": fields.Nested(parameters__system_parameters),
-}
+class Parameters(BaseModel):
+    opening_statement: str | None = None
+    suggested_questions: list[str]
+    suggested_questions_after_answer: JSONObject
+    speech_to_text: JSONObject
+    text_to_speech: JSONObject
+    retriever_resource: JSONObject
+    annotation_reply: JSONObject
+    more_like_this: JSONObject
+    user_input_form: list[JSONObject]
+    sensitive_word_avoidance: JSONObject
+    file_upload: JSONObject
+    system_parameters: SystemParameters
 
 
-def build_parameters_model(api_or_ns: Api | Namespace):
-    """Build the parameters model for the API or Namespace."""
-    copied_fields = parameters_fields.copy()
-    copied_fields["system_parameters"] = fields.Nested(build_system_parameters_model(api_or_ns))
-    return api_or_ns.model("Parameters", copied_fields)
+class Site(BaseModel):
+    model_config = ConfigDict(from_attributes=True)
 
+    title: str
+    chat_color_theme: str | None = None
+    chat_color_theme_inverted: bool
+    icon_type: str | None = None
+    icon: str | None = None
+    icon_background: str | None = None
+    description: str | None = None
+    copyright: str | None = None
+    privacy_policy: str | None = None
+    custom_disclaimer: str | None = None
+    default_language: str
+    show_workflow_steps: bool
+    use_icon_as_answer_icon: bool
 
-site_fields = {
-    "title": fields.String,
-    "chat_color_theme": fields.String,
-    "chat_color_theme_inverted": fields.Boolean,
-    "icon_type": fields.String,
-    "icon": fields.String,
-    "icon_background": fields.String,
-    "icon_url": AppIconUrlField,
-    "description": fields.String,
-    "copyright": fields.String,
-    "privacy_policy": fields.String,
-    "custom_disclaimer": fields.String,
-    "default_language": fields.String,
-    "show_workflow_steps": fields.Boolean,
-    "use_icon_as_answer_icon": fields.Boolean,
-}
-
-
-def build_site_model(api_or_ns: Api | Namespace):
-    """Build the site model for the API or Namespace."""
-    return api_or_ns.model("Site", site_fields)
+    @computed_field(return_type=str | None)  # type: ignore
+    @property
+    def icon_url(self) -> str | None:
+        if self.icon and self.icon_type == IconType.IMAGE:
+            return file_helpers.get_signed_file_url(self.icon)
+        return None

api/controllers/console/app/app.py

@@ -1,13 +1,16 @@
+import re
 import uuid
-from typing import Literal
+from datetime import datetime
+from typing import Any, Literal, TypeAlias
 
 from flask import request
-from flask_restx import Resource, fields, marshal, marshal_with
-from pydantic import BaseModel, Field, field_validator
+from flask_restx import Resource
+from pydantic import AliasChoices, BaseModel, ConfigDict, Field, computed_field, field_validator
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 from werkzeug.exceptions import BadRequest
 
+from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import (
@@ -18,27 +21,19 @@ from controllers.console.wraps import (
     is_admin_or_owner_required,
     setup_required,
 )
+from core.file import helpers as file_helpers
 from core.ops.ops_trace_manager import OpsTraceManager
 from core.workflow.enums import NodeType
 from extensions.ext_database import db
-from fields.app_fields import (
-    deleted_tool_fields,
-    model_config_fields,
-    model_config_partial_fields,
-    site_fields,
-    tag_fields,
-)
-from fields.workflow_fields import workflow_partial_fields as _workflow_partial_fields_dict
-from libs.helper import AppIconUrlField, TimestampField
 from libs.login import current_account_with_tenant, login_required
 from models import App, Workflow
+from models.model import IconType
 from services.app_dsl_service import AppDslService, ImportMode
 from services.app_service import AppService
 from services.enterprise.enterprise_service import EnterpriseService
 from services.feature_service import FeatureService
 
 ALLOW_CREATE_APP_MODES = ["chat", "agent-chat", "advanced-chat", "workflow", "completion"]
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 
 
 class AppListQuery(BaseModel):
@@ -73,6 +68,48 @@ class AppListQuery(BaseModel):
             raise ValueError("Invalid UUID format in tag_ids.") from exc
 
 
+# XSS prevention: patterns that could lead to XSS attacks
+# Includes: script tags, iframe tags, javascript: protocol, SVG with onload, etc.
+_XSS_PATTERNS = [
+    r"<script[^>]*>.*?</script>",  # Script tags
+    r"<iframe\b[^>]*?(?:/>|>.*?</iframe>)",  # Iframe tags (including self-closing)
+    r"javascript:",  # JavaScript protocol
+    r"<svg[^>]*?\s+onload\s*=[^>]*>",  # SVG with onload handler (attribute-aware, flexible whitespace)
+    r"<.*?on\s*\w+\s*=",  # Event handlers like onclick, onerror, etc.
+    r"<object\b[^>]*(?:\s*/>|>.*?</object\s*>)",  # Object tags (opening tag)
+    r"<embed[^>]*>",  # Embed tags (self-closing)
+    r"<link[^>]*>",  # Link tags with javascript
+]
+
+
+def _validate_xss_safe(value: str | None, field_name: str = "Field") -> str | None:
+    """
+    Validate that a string value doesn't contain potential XSS payloads.
+
+    Args:
+        value: The string value to validate
+        field_name: Name of the field for error messages
+
+    Returns:
+        The original value if safe
+
+    Raises:
+        ValueError: If the value contains XSS patterns
+    """
+    if value is None:
+        return None
+
+    value_lower = value.lower()
+    for pattern in _XSS_PATTERNS:
+        if re.search(pattern, value_lower, re.DOTALL | re.IGNORECASE):
+            raise ValueError(
+                f"{field_name} contains invalid characters or patterns. "
+                "HTML tags, JavaScript, and other potentially dangerous content are not allowed."
+            )
+
+    return value
+
+
 class CreateAppPayload(BaseModel):
     name: str = Field(..., min_length=1, description="App name")
     description: str | None = Field(default=None, description="App description (max 400 chars)", max_length=400)
@@ -81,6 +118,11 @@ class CreateAppPayload(BaseModel):
     icon: str | None = Field(default=None, description="Icon")
     icon_background: str | None = Field(default=None, description="Icon background color")
 
+    @field_validator("name", "description", mode="before")
+    @classmethod
+    def validate_xss_safe(cls, value: str | None, info) -> str | None:
+        return _validate_xss_safe(value, info.field_name)
+
 
 class UpdateAppPayload(BaseModel):
     name: str = Field(..., min_length=1, description="App name")
@@ -91,6 +133,11 @@ class UpdateAppPayload(BaseModel):
     use_icon_as_answer_icon: bool | None = Field(default=None, description="Use icon as answer icon")
     max_active_requests: int | None = Field(default=None, description="Maximum active requests")
 
+    @field_validator("name", "description", mode="before")
+    @classmethod
+    def validate_xss_safe(cls, value: str | None, info) -> str | None:
+        return _validate_xss_safe(value, info.field_name)
+
 
 class CopyAppPayload(BaseModel):
     name: str | None = Field(default=None, description="Name for the copied app")
@@ -99,6 +146,11 @@ class CopyAppPayload(BaseModel):
     icon: str | None = Field(default=None, description="Icon")
     icon_background: str | None = Field(default=None, description="Icon background color")
 
+    @field_validator("name", "description", mode="before")
+    @classmethod
+    def validate_xss_safe(cls, value: str | None, info) -> str | None:
+        return _validate_xss_safe(value, info.field_name)
+
 
 class AppExportQuery(BaseModel):
     include_secret: bool = Field(default=False, description="Include secrets in export")
@@ -134,124 +186,292 @@ class AppTracePayload(BaseModel):
         return value
 
 
-def reg(cls: type[BaseModel]):
-    console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
+JSONValue: TypeAlias = Any
 
 
-reg(AppListQuery)
-reg(CreateAppPayload)
-reg(UpdateAppPayload)
-reg(CopyAppPayload)
-reg(AppExportQuery)
-reg(AppNamePayload)
-reg(AppIconPayload)
-reg(AppSiteStatusPayload)
-reg(AppApiStatusPayload)
-reg(AppTracePayload)
+class ResponseModel(BaseModel):
+    model_config = ConfigDict(
+        from_attributes=True,
+        extra="ignore",
+        populate_by_name=True,
+        serialize_by_alias=True,
+        protected_namespaces=(),
+    )
 
-# Register models for flask_restx to avoid dict type issues in Swagger
-# Register base models first
-tag_model = console_ns.model("Tag", tag_fields)
 
-workflow_partial_model = console_ns.model("WorkflowPartial", _workflow_partial_fields_dict)
+def _to_timestamp(value: datetime | int | None) -> int | None:
+    if isinstance(value, datetime):
+        return int(value.timestamp())
+    return value
 
-model_config_model = console_ns.model("ModelConfig", model_config_fields)
 
-model_config_partial_model = console_ns.model("ModelConfigPartial", model_config_partial_fields)
+def _build_icon_url(icon_type: str | IconType | None, icon: str | None) -> str | None:
+    if icon is None or icon_type is None:
+        return None
+    icon_type_value = icon_type.value if isinstance(icon_type, IconType) else str(icon_type)
+    if icon_type_value.lower() != IconType.IMAGE.value:
+        return None
+    return file_helpers.get_signed_file_url(icon)
 
-deleted_tool_model = console_ns.model("DeletedTool", deleted_tool_fields)
 
-site_model = console_ns.model("Site", site_fields)
+class Tag(ResponseModel):
+    id: str
+    name: str
+    type: str
 
-app_partial_model = console_ns.model(
-    "AppPartial",
-    {
-        "id": fields.String,
-        "name": fields.String,
-        "max_active_requests": fields.Raw(),
-        "description": fields.String(attribute="desc_or_prompt"),
-        "mode": fields.String(attribute="mode_compatible_with_agent"),
-        "icon_type": fields.String,
-        "icon": fields.String,
-        "icon_background": fields.String,
-        "icon_url": AppIconUrlField,
-        "model_config": fields.Nested(model_config_partial_model, attribute="app_model_config", allow_null=True),
-        "workflow": fields.Nested(workflow_partial_model, allow_null=True),
-        "use_icon_as_answer_icon": fields.Boolean,
-        "created_by": fields.String,
-        "created_at": TimestampField,
-        "updated_by": fields.String,
-        "updated_at": TimestampField,
-        "tags": fields.List(fields.Nested(tag_model)),
-        "access_mode": fields.String,
-        "create_user_name": fields.String,
-        "author_name": fields.String,
-        "has_draft_trigger": fields.Boolean,
-    },
-)
 
-app_detail_model = console_ns.model(
-    "AppDetail",
-    {
-        "id": fields.String,
-        "name": fields.String,
-        "description": fields.String,
-        "mode": fields.String(attribute="mode_compatible_with_agent"),
-        "icon": fields.String,
-        "icon_background": fields.String,
-        "enable_site": fields.Boolean,
-        "enable_api": fields.Boolean,
-        "model_config": fields.Nested(model_config_model, attribute="app_model_config", allow_null=True),
-        "workflow": fields.Nested(workflow_partial_model, allow_null=True),
-        "tracing": fields.Raw,
-        "use_icon_as_answer_icon": fields.Boolean,
-        "created_by": fields.String,
-        "created_at": TimestampField,
-        "updated_by": fields.String,
-        "updated_at": TimestampField,
-        "access_mode": fields.String,
-        "tags": fields.List(fields.Nested(tag_model)),
-    },
-)
+class WorkflowPartial(ResponseModel):
+    id: str
+    created_by: str | None = None
+    created_at: int | None = None
+    updated_by: str | None = None
+    updated_at: int | None = None
 
-app_detail_with_site_model = console_ns.model(
-    "AppDetailWithSite",
-    {
-        "id": fields.String,
-        "name": fields.String,
-        "description": fields.String,
-        "mode": fields.String(attribute="mode_compatible_with_agent"),
-        "icon_type": fields.String,
-        "icon": fields.String,
-        "icon_background": fields.String,
-        "icon_url": AppIconUrlField,
-        "enable_site": fields.Boolean,
-        "enable_api": fields.Boolean,
-        "model_config": fields.Nested(model_config_model, attribute="app_model_config", allow_null=True),
-        "workflow": fields.Nested(workflow_partial_model, allow_null=True),
-        "api_base_url": fields.String,
-        "use_icon_as_answer_icon": fields.Boolean,
-        "max_active_requests": fields.Integer,
-        "created_by": fields.String,
-        "created_at": TimestampField,
-        "updated_by": fields.String,
-        "updated_at": TimestampField,
-        "deleted_tools": fields.List(fields.Nested(deleted_tool_model)),
-        "access_mode": fields.String,
-        "tags": fields.List(fields.Nested(tag_model)),
-        "site": fields.Nested(site_model),
-    },
-)
+    @field_validator("created_at", "updated_at", mode="before")
+    @classmethod
+    def _normalize_timestamp(cls, value: datetime | int | None) -> int | None:
+        return _to_timestamp(value)
 
-app_pagination_model = console_ns.model(
-    "AppPagination",
-    {
-        "page": fields.Integer,
-        "limit": fields.Integer(attribute="per_page"),
-        "total": fields.Integer,
-        "has_more": fields.Boolean(attribute="has_next"),
-        "data": fields.List(fields.Nested(app_partial_model), attribute="items"),
-    },
+
+class ModelConfigPartial(ResponseModel):
+    model: JSONValue | None = Field(default=None, validation_alias=AliasChoices("model_dict", "model"))
+    pre_prompt: str | None = None
+    created_by: str | None = None
+    created_at: int | None = None
+    updated_by: str | None = None
+    updated_at: int | None = None
+
+    @field_validator("created_at", "updated_at", mode="before")
+    @classmethod
+    def _normalize_timestamp(cls, value: datetime | int | None) -> int | None:
+        return _to_timestamp(value)
+
+
+class ModelConfig(ResponseModel):
+    opening_statement: str | None = None
+    suggested_questions: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("suggested_questions_list", "suggested_questions")
+    )
+    suggested_questions_after_answer: JSONValue | None = Field(
+        default=None,
+        validation_alias=AliasChoices("suggested_questions_after_answer_dict", "suggested_questions_after_answer"),
+    )
+    speech_to_text: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("speech_to_text_dict", "speech_to_text")
+    )
+    text_to_speech: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("text_to_speech_dict", "text_to_speech")
+    )
+    retriever_resource: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("retriever_resource_dict", "retriever_resource")
+    )
+    annotation_reply: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("annotation_reply_dict", "annotation_reply")
+    )
+    more_like_this: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("more_like_this_dict", "more_like_this")
+    )
+    sensitive_word_avoidance: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("sensitive_word_avoidance_dict", "sensitive_word_avoidance")
+    )
+    external_data_tools: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("external_data_tools_list", "external_data_tools")
+    )
+    model: JSONValue | None = Field(default=None, validation_alias=AliasChoices("model_dict", "model"))
+    user_input_form: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("user_input_form_list", "user_input_form")
+    )
+    dataset_query_variable: str | None = None
+    pre_prompt: str | None = None
+    agent_mode: JSONValue | None = Field(default=None, validation_alias=AliasChoices("agent_mode_dict", "agent_mode"))
+    prompt_type: str | None = None
+    chat_prompt_config: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("chat_prompt_config_dict", "chat_prompt_config")
+    )
+    completion_prompt_config: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("completion_prompt_config_dict", "completion_prompt_config")
+    )
+    dataset_configs: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("dataset_configs_dict", "dataset_configs")
+    )
+    file_upload: JSONValue | None = Field(
+        default=None, validation_alias=AliasChoices("file_upload_dict", "file_upload")
+    )
+    created_by: str | None = None
+    created_at: int | None = None
+    updated_by: str | None = None
+    updated_at: int | None = None
+
+    @field_validator("created_at", "updated_at", mode="before")
+    @classmethod
+    def _normalize_timestamp(cls, value: datetime | int | None) -> int | None:
+        return _to_timestamp(value)
+
+
+class Site(ResponseModel):
+    access_token: str | None = Field(default=None, validation_alias="code")
+    code: str | None = None
+    title: str | None = None
+    icon_type: str | IconType | None = None
+    icon: str | None = None
+    icon_background: str | None = None
+    description: str | None = None
+    default_language: str | None = None
+    chat_color_theme: str | None = None
+    chat_color_theme_inverted: bool | None = None
+    customize_domain: str | None = None
+    copyright: str | None = None
+    privacy_policy: str | None = None
+    custom_disclaimer: str | None = None
+    customize_token_strategy: str | None = None
+    prompt_public: bool | None = None
+    app_base_url: str | None = None
+    show_workflow_steps: bool | None = None
+    use_icon_as_answer_icon: bool | None = None
+    created_by: str | None = None
+    created_at: int | None = None
+    updated_by: str | None = None
+    updated_at: int | None = None
+
+    @computed_field(return_type=str | None)  # type: ignore
+    @property
+    def icon_url(self) -> str | None:
+        return _build_icon_url(self.icon_type, self.icon)
+
+    @field_validator("icon_type", mode="before")
+    @classmethod
+    def _normalize_icon_type(cls, value: str | IconType | None) -> str | None:
+        if isinstance(value, IconType):
+            return value.value
+        return value
+
+    @field_validator("created_at", "updated_at", mode="before")
+    @classmethod
+    def _normalize_timestamp(cls, value: datetime | int | None) -> int | None:
+        return _to_timestamp(value)
+
+
+class DeletedTool(ResponseModel):
+    type: str
+    tool_name: str
+    provider_id: str
+
+
+class AppPartial(ResponseModel):
+    id: str
+    name: str
+    max_active_requests: int | None = None
+    description: str | None = Field(default=None, validation_alias=AliasChoices("desc_or_prompt", "description"))
+    mode: str = Field(validation_alias="mode_compatible_with_agent")
+    icon_type: str | None = None
+    icon: str | None = None
+    icon_background: str | None = None
+    model_config_: ModelConfigPartial | None = Field(
+        default=None,
+        validation_alias=AliasChoices("app_model_config", "model_config"),
+        alias="model_config",
+    )
+    workflow: WorkflowPartial | None = None
+    use_icon_as_answer_icon: bool | None = None
+    created_by: str | None = None
+    created_at: int | None = None
+    updated_by: str | None = None
+    updated_at: int | None = None
+    tags: list[Tag] = Field(default_factory=list)
+    access_mode: str | None = None
+    create_user_name: str | None = None
+    author_name: str | None = None
+    has_draft_trigger: bool | None = None
+
+    @computed_field(return_type=str | None)  # type: ignore
+    @property
+    def icon_url(self) -> str | None:
+        return _build_icon_url(self.icon_type, self.icon)
+
+    @field_validator("created_at", "updated_at", mode="before")
+    @classmethod
+    def _normalize_timestamp(cls, value: datetime | int | None) -> int | None:
+        return _to_timestamp(value)
+
+
+class AppDetail(ResponseModel):
+    id: str
+    name: str
+    description: str | None = None
+    mode: str = Field(validation_alias="mode_compatible_with_agent")
+    icon: str | None = None
+    icon_background: str | None = None
+    enable_site: bool
+    enable_api: bool
+    model_config_: ModelConfig | None = Field(
+        default=None,
+        validation_alias=AliasChoices("app_model_config", "model_config"),
+        alias="model_config",
+    )
+    workflow: WorkflowPartial | None = None
+    tracing: JSONValue | None = None
+    use_icon_as_answer_icon: bool | None = None
+    created_by: str | None = None
+    created_at: int | None = None
+    updated_by: str | None = None
+    updated_at: int | None = None
+    access_mode: str | None = None
+    tags: list[Tag] = Field(default_factory=list)
+
+    @field_validator("created_at", "updated_at", mode="before")
+    @classmethod
+    def _normalize_timestamp(cls, value: datetime | int | None) -> int | None:
+        return _to_timestamp(value)
+
+
+class AppDetailWithSite(AppDetail):
+    icon_type: str | None = None
+    api_base_url: str | None = None
+    max_active_requests: int | None = None
+    deleted_tools: list[DeletedTool] = Field(default_factory=list)
+    site: Site | None = None
+
+    @computed_field(return_type=str | None)  # type: ignore
+    @property
+    def icon_url(self) -> str | None:
+        return _build_icon_url(self.icon_type, self.icon)
+
+
+class AppPagination(ResponseModel):
+    page: int
+    limit: int = Field(validation_alias=AliasChoices("per_page", "limit"))
+    total: int
+    has_more: bool = Field(validation_alias=AliasChoices("has_next", "has_more"))
+    data: list[AppPartial] = Field(validation_alias=AliasChoices("items", "data"))
+
+
+class AppExportResponse(ResponseModel):
+    data: str
+
+
+register_schema_models(
+    console_ns,
+    AppListQuery,
+    CreateAppPayload,
+    UpdateAppPayload,
+    CopyAppPayload,
+    AppExportQuery,
+    AppNamePayload,
+    AppIconPayload,
+    AppSiteStatusPayload,
+    AppApiStatusPayload,
+    AppTracePayload,
+    Tag,
+    WorkflowPartial,
+    ModelConfigPartial,
+    ModelConfig,
+    Site,
+    DeletedTool,
+    AppPartial,
+    AppDetail,
+    AppDetailWithSite,
+    AppPagination,
+    AppExportResponse,
 )
 
 
@@ -260,7 +480,7 @@ class AppListApi(Resource):
     @console_ns.doc("list_apps")
     @console_ns.doc(description="Get list of applications with pagination and filtering")
     @console_ns.expect(console_ns.models[AppListQuery.__name__])
-    @console_ns.response(200, "Success", app_pagination_model)
+    @console_ns.response(200, "Success", console_ns.models[AppPagination.__name__])
     @setup_required
     @login_required
     @account_initialization_required
@@ -276,7 +496,8 @@ class AppListApi(Resource):
         app_service = AppService()
         app_pagination = app_service.get_paginate_apps(current_user.id, current_tenant_id, args_dict)
         if not app_pagination:
-            return {"data": [], "total": 0, "page": 1, "limit": 20, "has_more": False}
+            empty = AppPagination(page=args.page, limit=args.limit, total=0, has_more=False, data=[])
+            return empty.model_dump(mode="json"), 200
 
         if FeatureService.get_system_features().webapp_auth.enabled:
             app_ids = [str(app.id) for app in app_pagination.items]
@@ -320,18 +541,18 @@ class AppListApi(Resource):
         for app in app_pagination.items:
             app.has_draft_trigger = str(app.id) in draft_trigger_app_ids
 
-        return marshal(app_pagination, app_pagination_model), 200
+        pagination_model = AppPagination.model_validate(app_pagination, from_attributes=True)
+        return pagination_model.model_dump(mode="json"), 200
 
     @console_ns.doc("create_app")
     @console_ns.doc(description="Create a new application")
     @console_ns.expect(console_ns.models[CreateAppPayload.__name__])
-    @console_ns.response(201, "App created successfully", app_detail_model)
+    @console_ns.response(201, "App created successfully", console_ns.models[AppDetail.__name__])
     @console_ns.response(403, "Insufficient permissions")
     @console_ns.response(400, "Invalid request parameters")
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(app_detail_model)
     @cloud_edition_billing_resource_check("apps")
     @edit_permission_required
     def post(self):
@@ -341,8 +562,8 @@ class AppListApi(Resource):
 
         app_service = AppService()
         app = app_service.create_app(current_tenant_id, args.model_dump(), current_user)
-
-        return app, 201
+        app_detail = AppDetail.model_validate(app, from_attributes=True)
+        return app_detail.model_dump(mode="json"), 201
 
 
 @console_ns.route("/apps/<uuid:app_id>")
@@ -350,13 +571,12 @@ class AppApi(Resource):
     @console_ns.doc("get_app_detail")
     @console_ns.doc(description="Get application details")
     @console_ns.doc(params={"app_id": "Application ID"})
-    @console_ns.response(200, "Success", app_detail_with_site_model)
+    @console_ns.response(200, "Success", console_ns.models[AppDetailWithSite.__name__])
     @setup_required
     @login_required
     @account_initialization_required
     @enterprise_license_required
-    @get_app_model
-    @marshal_with(app_detail_with_site_model)
+    @get_app_model(mode=None)
     def get(self, app_model):
         """Get app detail"""
         app_service = AppService()
@@ -367,21 +587,21 @@ class AppApi(Resource):
             app_setting = EnterpriseService.WebAppAuth.get_app_access_mode_by_id(app_id=str(app_model.id))
             app_model.access_mode = app_setting.access_mode
 
-        return app_model
+        response_model = AppDetailWithSite.model_validate(app_model, from_attributes=True)
+        return response_model.model_dump(mode="json")
 
     @console_ns.doc("update_app")
     @console_ns.doc(description="Update application details")
     @console_ns.doc(params={"app_id": "Application ID"})
     @console_ns.expect(console_ns.models[UpdateAppPayload.__name__])
-    @console_ns.response(200, "App updated successfully", app_detail_with_site_model)
+    @console_ns.response(200, "App updated successfully", console_ns.models[AppDetailWithSite.__name__])
     @console_ns.response(403, "Insufficient permissions")
     @console_ns.response(400, "Invalid request parameters")
     @setup_required
     @login_required
     @account_initialization_required
-    @get_app_model
+    @get_app_model(mode=None)
     @edit_permission_required
-    @marshal_with(app_detail_with_site_model)
     def put(self, app_model):
         """Update app"""
         args = UpdateAppPayload.model_validate(console_ns.payload)
@@ -398,8 +618,8 @@ class AppApi(Resource):
             "max_active_requests": args.max_active_requests or 0,
         }
         app_model = app_service.update_app(app_model, args_dict)
-
-        return app_model
+        response_model = AppDetailWithSite.model_validate(app_model, from_attributes=True)
+        return response_model.model_dump(mode="json")
 
     @console_ns.doc("delete_app")
     @console_ns.doc(description="Delete application")
@@ -425,14 +645,13 @@ class AppCopyApi(Resource):
     @console_ns.doc(description="Create a copy of an existing application")
     @console_ns.doc(params={"app_id": "Application ID to copy"})
     @console_ns.expect(console_ns.models[CopyAppPayload.__name__])
-    @console_ns.response(201, "App copied successfully", app_detail_with_site_model)
+    @console_ns.response(201, "App copied successfully", console_ns.models[AppDetailWithSite.__name__])
     @console_ns.response(403, "Insufficient permissions")
     @setup_required
     @login_required
     @account_initialization_required
-    @get_app_model
+    @get_app_model(mode=None)
     @edit_permission_required
-    @marshal_with(app_detail_with_site_model)
     def post(self, app_model):
         """Copy app"""
         # The role of the current user in the ta table must be admin, owner, or editor
@@ -458,7 +677,8 @@ class AppCopyApi(Resource):
             stmt = select(App).where(App.id == result.app_id)
             app = session.scalar(stmt)
 
-        return app, 201
+        response_model = AppDetailWithSite.model_validate(app, from_attributes=True)
+        return response_model.model_dump(mode="json"), 201
 
 
 @console_ns.route("/apps/<uuid:app_id>/export")
@@ -467,11 +687,7 @@ class AppExportApi(Resource):
     @console_ns.doc(description="Export application configuration as DSL")
     @console_ns.doc(params={"app_id": "Application ID to export"})
     @console_ns.expect(console_ns.models[AppExportQuery.__name__])
-    @console_ns.response(
-        200,
-        "App exported successfully",
-        console_ns.model("AppExportResponse", {"data": fields.String(description="DSL export data")}),
-    )
+    @console_ns.response(200, "App exported successfully", console_ns.models[AppExportResponse.__name__])
     @console_ns.response(403, "Insufficient permissions")
     @get_app_model
     @setup_required
@@ -482,13 +698,14 @@ class AppExportApi(Resource):
         """Export app"""
         args = AppExportQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore
 
-        return {
-            "data": AppDslService.export_dsl(
+        payload = AppExportResponse(
+            data=AppDslService.export_dsl(
                 app_model=app_model,
                 include_secret=args.include_secret,
                 workflow_id=args.workflow_id,
             )
-        }
+        )
+        return payload.model_dump(mode="json")
 
 
 @console_ns.route("/apps/<uuid:app_id>/name")
@@ -497,20 +714,19 @@ class AppNameApi(Resource):
     @console_ns.doc(description="Check if app name is available")
     @console_ns.doc(params={"app_id": "Application ID"})
     @console_ns.expect(console_ns.models[AppNamePayload.__name__])
-    @console_ns.response(200, "Name availability checked")
+    @console_ns.response(200, "Name availability checked", console_ns.models[AppDetail.__name__])
     @setup_required
     @login_required
     @account_initialization_required
-    @get_app_model
-    @marshal_with(app_detail_model)
+    @get_app_model(mode=None)
     @edit_permission_required
     def post(self, app_model):
         args = AppNamePayload.model_validate(console_ns.payload)
 
         app_service = AppService()
         app_model = app_service.update_app_name(app_model, args.name)
-
-        return app_model
+        response_model = AppDetail.model_validate(app_model, from_attributes=True)
+        return response_model.model_dump(mode="json")
 
 
 @console_ns.route("/apps/<uuid:app_id>/icon")
@@ -524,16 +740,15 @@ class AppIconApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @get_app_model
-    @marshal_with(app_detail_model)
+    @get_app_model(mode=None)
     @edit_permission_required
     def post(self, app_model):
         args = AppIconPayload.model_validate(console_ns.payload or {})
 
         app_service = AppService()
         app_model = app_service.update_app_icon(app_model, args.icon or "", args.icon_background or "")
-
-        return app_model
+        response_model = AppDetail.model_validate(app_model, from_attributes=True)
+        return response_model.model_dump(mode="json")
 
 
 @console_ns.route("/apps/<uuid:app_id>/site-enable")
@@ -542,21 +757,20 @@ class AppSiteStatus(Resource):
     @console_ns.doc(description="Enable or disable app site")
     @console_ns.doc(params={"app_id": "Application ID"})
     @console_ns.expect(console_ns.models[AppSiteStatusPayload.__name__])
-    @console_ns.response(200, "Site status updated successfully", app_detail_model)
+    @console_ns.response(200, "Site status updated successfully", console_ns.models[AppDetail.__name__])
     @console_ns.response(403, "Insufficient permissions")
     @setup_required
     @login_required
     @account_initialization_required
-    @get_app_model
-    @marshal_with(app_detail_model)
+    @get_app_model(mode=None)
     @edit_permission_required
     def post(self, app_model):
         args = AppSiteStatusPayload.model_validate(console_ns.payload)
 
         app_service = AppService()
         app_model = app_service.update_app_site_status(app_model, args.enable_site)
-
-        return app_model
+        response_model = AppDetail.model_validate(app_model, from_attributes=True)
+        return response_model.model_dump(mode="json")
 
 
 @console_ns.route("/apps/<uuid:app_id>/api-enable")
@@ -565,21 +779,20 @@ class AppApiStatus(Resource):
     @console_ns.doc(description="Enable or disable app API")
     @console_ns.doc(params={"app_id": "Application ID"})
     @console_ns.expect(console_ns.models[AppApiStatusPayload.__name__])
-    @console_ns.response(200, "API status updated successfully", app_detail_model)
+    @console_ns.response(200, "API status updated successfully", console_ns.models[AppDetail.__name__])
     @console_ns.response(403, "Insufficient permissions")
     @setup_required
     @login_required
     @is_admin_or_owner_required
     @account_initialization_required
-    @get_app_model
-    @marshal_with(app_detail_model)
+    @get_app_model(mode=None)
     def post(self, app_model):
         args = AppApiStatusPayload.model_validate(console_ns.payload)
 
         app_service = AppService()
         app_model = app_service.update_app_api_status(app_model, args.enable_api)
-
-        return app_model
+        response_model = AppDetail.model_validate(app_model, from_attributes=True)
+        return response_model.model_dump(mode="json")
 
 
 @console_ns.route("/apps/<uuid:app_id>/trace")

api/controllers/console/app/conversation.py

@@ -13,7 +13,6 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, edit_permission_required, setup_required
 from core.app.entities.app_invoke_entities import InvokeFrom
 from extensions.ext_database import db
-from fields.conversation_fields import MessageTextField
 from fields.raws import FilesContainedField
 from libs.datetime_utils import naive_utc_now, parse_time_range
 from libs.helper import TimestampField
@@ -177,6 +176,12 @@ annotation_hit_history_model = console_ns.model(
     },
 )
 
+
+class MessageTextField(fields.Raw):
+    def format(self, value):
+        return value[0]["text"] if value else ""
+
+
 # Simple message detail model
 simple_message_detail_model = console_ns.model(
     "SimpleMessageDetail",
@@ -343,10 +348,13 @@ class CompletionConversationApi(Resource):
         )
 
         if args.keyword:
+            from libs.helper import escape_like_pattern
+
+            escaped_keyword = escape_like_pattern(args.keyword)
             query = query.join(Message, Message.conversation_id == Conversation.id).where(
                 or_(
-                    Message.query.ilike(f"%{args.keyword}%"),
-                    Message.answer.ilike(f"%{args.keyword}%"),
+                    Message.query.ilike(f"%{escaped_keyword}%", escape="\\"),
+                    Message.answer.ilike(f"%{escaped_keyword}%", escape="\\"),
                 )
             )
 
@@ -455,7 +463,10 @@ class ChatConversationApi(Resource):
         query = sa.select(Conversation).where(Conversation.app_id == app_model.id, Conversation.is_deleted.is_(False))
 
         if args.keyword:
-            keyword_filter = f"%{args.keyword}%"
+            from libs.helper import escape_like_pattern
+
+            escaped_keyword = escape_like_pattern(args.keyword)
+            keyword_filter = f"%{escaped_keyword}%"
             query = (
                 query.join(
                     Message,
@@ -464,11 +475,11 @@ class ChatConversationApi(Resource):
                 .join(subquery, subquery.c.conversation_id == Conversation.id)
                 .where(
                     or_(
-                        Message.query.ilike(keyword_filter),
-                        Message.answer.ilike(keyword_filter),
-                        Conversation.name.ilike(keyword_filter),
-                        Conversation.introduction.ilike(keyword_filter),
-                        subquery.c.from_end_user_session_id.ilike(keyword_filter),
+                        Message.query.ilike(keyword_filter, escape="\\"),
+                        Message.answer.ilike(keyword_filter, escape="\\"),
+                        Conversation.name.ilike(keyword_filter, escape="\\"),
+                        Conversation.introduction.ilike(keyword_filter, escape="\\"),
+                        subquery.c.from_end_user_session_id.ilike(keyword_filter, escape="\\"),
                     ),
                 )
                 .group_by(Conversation.id)

api/controllers/console/app/message.py

@@ -202,6 +202,7 @@ message_detail_model = console_ns.model(
         "status": fields.String,
         "error": fields.String,
         "parent_message_id": fields.String,
+        "generation_detail": fields.Raw,
     },
 )
 

api/controllers/console/auth/login.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 import flask_login
 from flask import make_response, request
 from flask_restx import Resource
@@ -96,14 +98,13 @@ class LoginApi(Resource):
         if is_login_error_rate_limit:
             raise EmailPasswordLoginLimitError()
 
-        # TODO: why invitation is re-assigned with different type?
-        invitation = args.invite_token  # type: ignore
-        if invitation:
-            invitation = RegisterService.get_invitation_if_token_valid(None, args.email, invitation)  # type: ignore
+        invitation_data: dict[str, Any] | None = None
+        if args.invite_token:
+            invitation_data = RegisterService.get_invitation_if_token_valid(None, args.email, args.invite_token)
 
         try:
-            if invitation:
-                data = invitation.get("data", {})  # type: ignore
+            if invitation_data:
+                data = invitation_data.get("data", {})
                 invitee_email = data.get("email") if data else None
                 if invitee_email != args.email:
                     raise InvalidEmailError()

api/controllers/console/auth/oauth.py

@@ -124,7 +124,7 @@ class OAuthCallback(Resource):
             return redirect(f"{dify_config.CONSOLE_WEB_URL}/signin/invite-settings?invite_token={invite_token}")
 
         try:
-            account = _generate_account(provider, user_info)
+            account, oauth_new_user = _generate_account(provider, user_info)
         except AccountNotFoundError:
             return redirect(f"{dify_config.CONSOLE_WEB_URL}/signin?message=Account not found.")
         except (WorkSpaceNotFoundError, WorkSpaceNotAllowedCreateError):
@@ -159,7 +159,10 @@ class OAuthCallback(Resource):
             ip_address=extract_remote_ip(request),
         )
 
-        response = redirect(f"{dify_config.CONSOLE_WEB_URL}")
+        base_url = dify_config.CONSOLE_WEB_URL
+        query_char = "&" if "?" in base_url else "?"
+        target_url = f"{base_url}{query_char}oauth_new_user={str(oauth_new_user).lower()}"
+        response = redirect(target_url)
 
         set_access_token_to_cookie(request, response, token_pair.access_token)
         set_refresh_token_to_cookie(request, response, token_pair.refresh_token)
@@ -177,9 +180,10 @@ def _get_account_by_openid_or_email(provider: str, user_info: OAuthUserInfo) ->
     return account
 
 
-def _generate_account(provider: str, user_info: OAuthUserInfo):
+def _generate_account(provider: str, user_info: OAuthUserInfo) -> tuple[Account, bool]:
     # Get account by openid or email.
     account = _get_account_by_openid_or_email(provider, user_info)
+    oauth_new_user = False
 
     if account:
         tenants = TenantService.get_join_tenants(account)
@@ -193,6 +197,7 @@ def _generate_account(provider: str, user_info: OAuthUserInfo):
                 tenant_was_created.send(new_tenant)
 
     if not account:
+        oauth_new_user = True
         if not FeatureService.get_system_features().is_allow_register:
             if dify_config.BILLING_ENABLED and BillingService.is_email_in_freeze(user_info.email):
                 raise AccountRegisterError(
@@ -220,4 +225,4 @@ def _generate_account(provider: str, user_info: OAuthUserInfo):
     # Link account
     AccountService.link_account_integrate(provider, user_info.id, account)
 
-    return account
+    return account, oauth_new_user

api/controllers/console/billing/billing.py

@@ -1,8 +1,9 @@
 import base64
+from typing import Literal
 
 from flask import request
 from flask_restx import Resource, fields
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field
 from werkzeug.exceptions import BadRequest
 
 from controllers.console import console_ns
@@ -15,22 +16,8 @@ DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 
 
 class SubscriptionQuery(BaseModel):
-    plan: str = Field(..., description="Subscription plan")
-    interval: str = Field(..., description="Billing interval")
-
-    @field_validator("plan")
-    @classmethod
-    def validate_plan(cls, value: str) -> str:
-        if value not in [CloudPlan.PROFESSIONAL, CloudPlan.TEAM]:
-            raise ValueError("Invalid plan")
-        return value
-
-    @field_validator("interval")
-    @classmethod
-    def validate_interval(cls, value: str) -> str:
-        if value not in {"month", "year"}:
-            raise ValueError("Invalid interval")
-        return value
+    plan: Literal[CloudPlan.PROFESSIONAL, CloudPlan.TEAM] = Field(..., description="Subscription plan")
+    interval: Literal["month", "year"] = Field(..., description="Billing interval")
 
 
 class PartnerTenantsPayload(BaseModel):

api/controllers/console/datasets/datasets_document.py

@@ -751,12 +751,12 @@ class DocumentApi(DocumentResource):
         elif metadata == "without":
             dataset_process_rules = DatasetService.get_process_rules(dataset_id)
             document_process_rules = document.dataset_process_rule.to_dict() if document.dataset_process_rule else {}
-            data_source_info = document.data_source_detail_dict
             response = {
                 "id": document.id,
                 "position": document.position,
                 "data_source_type": document.data_source_type,
-                "data_source_info": data_source_info,
+                "data_source_info": document.data_source_info_dict,
+                "data_source_detail_dict": document.data_source_detail_dict,
                 "dataset_process_rule_id": document.dataset_process_rule_id,
                 "dataset_process_rule": dataset_process_rules,
                 "document_process_rule": document_process_rules,
@@ -784,12 +784,12 @@ class DocumentApi(DocumentResource):
         else:
             dataset_process_rules = DatasetService.get_process_rules(dataset_id)
             document_process_rules = document.dataset_process_rule.to_dict() if document.dataset_process_rule else {}
-            data_source_info = document.data_source_detail_dict
             response = {
                 "id": document.id,
                 "position": document.position,
                 "data_source_type": document.data_source_type,
-                "data_source_info": data_source_info,
+                "data_source_info": document.data_source_info_dict,
+                "data_source_detail_dict": document.data_source_detail_dict,
                 "dataset_process_rule_id": document.dataset_process_rule_id,
                 "dataset_process_rule": dataset_process_rules,
                 "document_process_rule": document_process_rules,

api/controllers/console/datasets/datasets_segments.py

@@ -3,10 +3,12 @@ import uuid
 from flask import request
 from flask_restx import Resource, marshal
 from pydantic import BaseModel, Field
-from sqlalchemy import select
+from sqlalchemy import String, cast, func, or_, select
+from sqlalchemy.dialects.postgresql import JSONB
 from werkzeug.exceptions import Forbidden, NotFound
 
 import services
+from configs import dify_config
 from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.app.error import ProviderNotInitializeError
@@ -28,6 +30,7 @@ from core.model_runtime.entities.model_entities import ModelType
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from fields.segment_fields import child_chunk_fields, segment_fields
+from libs.helper import escape_like_pattern
 from libs.login import current_account_with_tenant, login_required
 from models.dataset import ChildChunk, DocumentSegment
 from models.model import UploadFile
@@ -143,7 +146,31 @@ class DatasetDocumentSegmentListApi(Resource):
             query = query.where(DocumentSegment.hit_count >= hit_count_gte)
 
         if keyword:
-            query = query.where(DocumentSegment.content.ilike(f"%{keyword}%"))
+            # Escape special characters in keyword to prevent SQL injection via LIKE wildcards
+            escaped_keyword = escape_like_pattern(keyword)
+            # Search in both content and keywords fields
+            # Use database-specific methods for JSON array search
+            if dify_config.SQLALCHEMY_DATABASE_URI_SCHEME == "postgresql":
+                # PostgreSQL: Use jsonb_array_elements_text to properly handle Unicode/Chinese text
+                keywords_condition = func.array_to_string(
+                    func.array(
+                        select(func.jsonb_array_elements_text(cast(DocumentSegment.keywords, JSONB)))
+                        .correlate(DocumentSegment)
+                        .scalar_subquery()
+                    ),
+                    ",",
+                ).ilike(f"%{escaped_keyword}%", escape="\\")
+            else:
+                # MySQL: Cast JSON to string for pattern matching
+                # MySQL stores Chinese text directly in JSON without Unicode escaping
+                keywords_condition = cast(DocumentSegment.keywords, String).ilike(f"%{escaped_keyword}%", escape="\\")
+
+            query = query.where(
+                or_(
+                    DocumentSegment.content.ilike(f"%{escaped_keyword}%", escape="\\"),
+                    keywords_condition,
+                )
+            )
 
         if args.enabled.lower() != "all":
             if args.enabled.lower() == "true":

api/controllers/console/datasets/hit_testing_base.py

@@ -1,7 +1,7 @@
 import logging
 from typing import Any
 
-from flask_restx import marshal, reqparse
+from flask_restx import marshal
 from pydantic import BaseModel, Field
 from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
 
@@ -56,15 +56,10 @@ class DatasetsHitTestingBase:
         HitTestingService.hit_testing_args_check(args)
 
     @staticmethod
-    def parse_args():
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("query", type=str, required=False, location="json")
-            .add_argument("attachment_ids", type=list, required=False, location="json")
-            .add_argument("retrieval_model", type=dict, required=False, location="json")
-            .add_argument("external_retrieval_model", type=dict, required=False, location="json")
-        )
-        return parser.parse_args()
+    def parse_args(payload: dict[str, Any]) -> dict[str, Any]:
+        """Validate and return hit-testing arguments from an incoming payload."""
+        hit_testing_payload = HitTestingPayload.model_validate(payload or {})
+        return hit_testing_payload.model_dump(exclude_none=True)
 
     @staticmethod
     def perform_hit_testing(dataset, args):

api/controllers/console/datasets/rag_pipeline/rag_pipeline_workflow.py

@@ -355,7 +355,7 @@ class PublishedRagPipelineRunApi(Resource):
                 pipeline=pipeline,
                 user=current_user,
                 args=args,
-                invoke_from=InvokeFrom.DEBUGGER if payload.is_preview else InvokeFrom.PUBLISHED,
+                invoke_from=InvokeFrom.DEBUGGER if payload.is_preview else InvokeFrom.PUBLISHED_PIPELINE,
                 streaming=streaming,
             )
 

api/controllers/console/explore/conversation.py

@@ -1,8 +1,7 @@
 from typing import Any
 
 from flask import request
-from flask_restx import marshal_with
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, Field, TypeAdapter, model_validator
 from sqlalchemy.orm import Session
 from werkzeug.exceptions import NotFound
 
@@ -11,7 +10,11 @@ from controllers.console.explore.error import NotChatAppError
 from controllers.console.explore.wraps import InstalledAppResource
 from core.app.entities.app_invoke_entities import InvokeFrom
 from extensions.ext_database import db
-from fields.conversation_fields import conversation_infinite_scroll_pagination_fields, simple_conversation_fields
+from fields.conversation_fields import (
+    ConversationInfiniteScrollPagination,
+    ResultResponse,
+    SimpleConversation,
+)
 from libs.helper import UUIDStrOrEmpty
 from libs.login import current_user
 from models import Account
@@ -49,7 +52,6 @@ register_schema_models(console_ns, ConversationListQuery, ConversationRenamePayl
     endpoint="installed_app_conversations",
 )
 class ConversationListApi(InstalledAppResource):
-    @marshal_with(conversation_infinite_scroll_pagination_fields)
     @console_ns.expect(console_ns.models[ConversationListQuery.__name__])
     def get(self, installed_app):
         app_model = installed_app.app
@@ -73,7 +75,7 @@ class ConversationListApi(InstalledAppResource):
             if not isinstance(current_user, Account):
                 raise ValueError("current_user must be an Account instance")
             with Session(db.engine) as session:
-                return WebConversationService.pagination_by_last_id(
+                pagination = WebConversationService.pagination_by_last_id(
                     session=session,
                     app_model=app_model,
                     user=current_user,
@@ -82,6 +84,13 @@ class ConversationListApi(InstalledAppResource):
                     invoke_from=InvokeFrom.EXPLORE,
                     pinned=args.pinned,
                 )
+                adapter = TypeAdapter(SimpleConversation)
+                conversations = [adapter.validate_python(item, from_attributes=True) for item in pagination.data]
+                return ConversationInfiniteScrollPagination(
+                    limit=pagination.limit,
+                    has_more=pagination.has_more,
+                    data=conversations,
+                ).model_dump(mode="json")
         except LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 
@@ -105,7 +114,7 @@ class ConversationApi(InstalledAppResource):
         except ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
 
-        return {"result": "success"}, 204
+        return ResultResponse(result="success").model_dump(mode="json"), 204
 
 
 @console_ns.route(
@@ -113,7 +122,6 @@ class ConversationApi(InstalledAppResource):
     endpoint="installed_app_conversation_rename",
 )
 class ConversationRenameApi(InstalledAppResource):
-    @marshal_with(simple_conversation_fields)
     @console_ns.expect(console_ns.models[ConversationRenamePayload.__name__])
     def post(self, installed_app, c_id):
         app_model = installed_app.app
@@ -128,9 +136,14 @@ class ConversationRenameApi(InstalledAppResource):
         try:
             if not isinstance(current_user, Account):
                 raise ValueError("current_user must be an Account instance")
-            return ConversationService.rename(
+            conversation = ConversationService.rename(
                 app_model, conversation_id, current_user, payload.name, payload.auto_generate
             )
+            return (
+                TypeAdapter(SimpleConversation)
+                .validate_python(conversation, from_attributes=True)
+                .model_dump(mode="json")
+            )
         except ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
 
@@ -155,7 +168,7 @@ class ConversationPinApi(InstalledAppResource):
         except ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")
 
 
 @console_ns.route(
@@ -174,4 +187,4 @@ class ConversationUnPinApi(InstalledAppResource):
             raise ValueError("current_user must be an Account instance")
         WebConversationService.unpin(app_model, conversation_id, current_user)
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")

api/controllers/console/explore/message.py

@@ -1,10 +1,8 @@
 import logging
 from typing import Literal
-from uuid import UUID
 
 from flask import request
-from flask_restx import marshal_with
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, TypeAdapter
 from werkzeug.exceptions import InternalServerError, NotFound
 
 from controllers.common.schema import register_schema_models
@@ -24,8 +22,10 @@ from controllers.console.explore.wraps import InstalledAppResource
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
-from fields.message_fields import message_infinite_scroll_pagination_fields
+from fields.conversation_fields import ResultResponse
+from fields.message_fields import MessageInfiniteScrollPagination, MessageListItem, SuggestedQuestionsResponse
 from libs import helper
+from libs.helper import UUIDStrOrEmpty
 from libs.login import current_account_with_tenant
 from models.model import AppMode
 from services.app_generate_service import AppGenerateService
@@ -44,8 +44,8 @@ logger = logging.getLogger(__name__)
 
 
 class MessageListQuery(BaseModel):
-    conversation_id: UUID
-    first_id: UUID | None = None
+    conversation_id: UUIDStrOrEmpty
+    first_id: UUIDStrOrEmpty | None = None
     limit: int = Field(default=20, ge=1, le=100)
 
 
@@ -66,7 +66,6 @@ register_schema_models(console_ns, MessageListQuery, MessageFeedbackPayload, Mor
     endpoint="installed_app_messages",
 )
 class MessageListApi(InstalledAppResource):
-    @marshal_with(message_infinite_scroll_pagination_fields)
     @console_ns.expect(console_ns.models[MessageListQuery.__name__])
     def get(self, installed_app):
         current_user, _ = current_account_with_tenant()
@@ -78,13 +77,20 @@ class MessageListApi(InstalledAppResource):
         args = MessageListQuery.model_validate(request.args.to_dict())
 
         try:
-            return MessageService.pagination_by_first_id(
+            pagination = MessageService.pagination_by_first_id(
                 app_model,
                 current_user,
                 str(args.conversation_id),
                 str(args.first_id) if args.first_id else None,
                 args.limit,
             )
+            adapter = TypeAdapter(MessageListItem)
+            items = [adapter.validate_python(message, from_attributes=True) for message in pagination.data]
+            return MessageInfiniteScrollPagination(
+                limit=pagination.limit,
+                has_more=pagination.has_more,
+                data=items,
+            ).model_dump(mode="json")
         except ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except FirstMessageNotExistsError:
@@ -116,7 +122,7 @@ class MessageFeedbackApi(InstalledAppResource):
         except MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")
 
 
 @console_ns.route(
@@ -201,4 +207,4 @@ class MessageSuggestedQuestionApi(InstalledAppResource):
             logger.exception("internal server error.")
             raise InternalServerError()
 
-        return {"data": questions}
+        return SuggestedQuestionsResponse(data=questions).model_dump(mode="json")

api/controllers/console/explore/parameter.py

@@ -1,5 +1,3 @@
-from flask_restx import marshal_with
-
 from controllers.common import fields
 from controllers.console import console_ns
 from controllers.console.app.error import AppUnavailableError
@@ -13,7 +11,6 @@ from services.app_service import AppService
 class AppParameterApi(InstalledAppResource):
     """Resource for app variables."""
 
-    @marshal_with(fields.parameters_fields)
     def get(self, installed_app: InstalledApp):
         """Retrieve app parameters."""
         app_model = installed_app.app
@@ -37,7 +34,8 @@ class AppParameterApi(InstalledAppResource):
 
             user_input_form = features_dict.get("user_input_form", [])
 
-        return get_parameters_from_feature_dict(features_dict=features_dict, user_input_form=user_input_form)
+        parameters = get_parameters_from_feature_dict(features_dict=features_dict, user_input_form=user_input_form)
+        return fields.Parameters.model_validate(parameters).model_dump(mode="json")
 
 
 @console_ns.route("/installed-apps/<uuid:installed_app_id>/meta", endpoint="installed_app_meta")

api/controllers/console/explore/saved_message.py

@@ -1,55 +1,33 @@
-from uuid import UUID
-
 from flask import request
-from flask_restx import fields, marshal_with
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, TypeAdapter
 from werkzeug.exceptions import NotFound
 
 from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.explore.error import NotCompletionAppError
 from controllers.console.explore.wraps import InstalledAppResource
-from fields.conversation_fields import message_file_fields
-from libs.helper import TimestampField
+from fields.conversation_fields import ResultResponse
+from fields.message_fields import SavedMessageInfiniteScrollPagination, SavedMessageItem
+from libs.helper import UUIDStrOrEmpty
 from libs.login import current_account_with_tenant
 from services.errors.message import MessageNotExistsError
 from services.saved_message_service import SavedMessageService
 
 
 class SavedMessageListQuery(BaseModel):
-    last_id: UUID | None = None
+    last_id: UUIDStrOrEmpty | None = None
     limit: int = Field(default=20, ge=1, le=100)
 
 
 class SavedMessageCreatePayload(BaseModel):
-    message_id: UUID
+    message_id: UUIDStrOrEmpty
 
 
 register_schema_models(console_ns, SavedMessageListQuery, SavedMessageCreatePayload)
 
 
-feedback_fields = {"rating": fields.String}
-
-message_fields = {
-    "id": fields.String,
-    "inputs": fields.Raw,
-    "query": fields.String,
-    "answer": fields.String,
-    "message_files": fields.List(fields.Nested(message_file_fields)),
-    "feedback": fields.Nested(feedback_fields, attribute="user_feedback", allow_null=True),
-    "created_at": TimestampField,
-}
-
-
 @console_ns.route("/installed-apps/<uuid:installed_app_id>/saved-messages", endpoint="installed_app_saved_messages")
 class SavedMessageListApi(InstalledAppResource):
-    saved_message_infinite_scroll_pagination_fields = {
-        "limit": fields.Integer,
-        "has_more": fields.Boolean,
-        "data": fields.List(fields.Nested(message_fields)),
-    }
-
-    @marshal_with(saved_message_infinite_scroll_pagination_fields)
     @console_ns.expect(console_ns.models[SavedMessageListQuery.__name__])
     def get(self, installed_app):
         current_user, _ = current_account_with_tenant()
@@ -59,12 +37,19 @@ class SavedMessageListApi(InstalledAppResource):
 
         args = SavedMessageListQuery.model_validate(request.args.to_dict())
 
-        return SavedMessageService.pagination_by_last_id(
+        pagination = SavedMessageService.pagination_by_last_id(
             app_model,
             current_user,
             str(args.last_id) if args.last_id else None,
             args.limit,
         )
+        adapter = TypeAdapter(SavedMessageItem)
+        items = [adapter.validate_python(message, from_attributes=True) for message in pagination.data]
+        return SavedMessageInfiniteScrollPagination(
+            limit=pagination.limit,
+            has_more=pagination.has_more,
+            data=items,
+        ).model_dump(mode="json")
 
     @console_ns.expect(console_ns.models[SavedMessageCreatePayload.__name__])
     def post(self, installed_app):
@@ -80,7 +65,7 @@ class SavedMessageListApi(InstalledAppResource):
         except MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")
 
 
 @console_ns.route(
@@ -98,4 +83,4 @@ class SavedMessageApi(InstalledAppResource):
 
         SavedMessageService.delete(app_model, current_user, message_id)
 
-        return {"result": "success"}, 204
+        return ResultResponse(result="success").model_dump(mode="json"), 204

api/controllers/console/files.py

@@ -1,7 +1,7 @@
 from typing import Literal
 
 from flask import request
-from flask_restx import Resource, marshal_with
+from flask_restx import Resource
 from werkzeug.exceptions import Forbidden
 
 import services
@@ -15,18 +15,21 @@ from controllers.common.errors import (
     TooManyFilesError,
     UnsupportedFileTypeError,
 )
+from controllers.common.schema import register_schema_models
 from controllers.console.wraps import (
     account_initialization_required,
     cloud_edition_billing_resource_check,
     setup_required,
 )
 from extensions.ext_database import db
-from fields.file_fields import file_fields, upload_config_fields
+from fields.file_fields import FileResponse, UploadConfig
 from libs.login import current_account_with_tenant, login_required
 from services.file_service import FileService
 
 from . import console_ns
 
+register_schema_models(console_ns, UploadConfig, FileResponse)
+
 PREVIEW_WORDS_LIMIT = 3000
 
 
@@ -35,26 +38,27 @@ class FileApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(upload_config_fields)
+    @console_ns.response(200, "Success", console_ns.models[UploadConfig.__name__])
     def get(self):
-        return {
-            "file_size_limit": dify_config.UPLOAD_FILE_SIZE_LIMIT,
-            "batch_count_limit": dify_config.UPLOAD_FILE_BATCH_LIMIT,
-            "file_upload_limit": dify_config.BATCH_UPLOAD_LIMIT,
-            "image_file_size_limit": dify_config.UPLOAD_IMAGE_FILE_SIZE_LIMIT,
-            "video_file_size_limit": dify_config.UPLOAD_VIDEO_FILE_SIZE_LIMIT,
-            "audio_file_size_limit": dify_config.UPLOAD_AUDIO_FILE_SIZE_LIMIT,
-            "workflow_file_upload_limit": dify_config.WORKFLOW_FILE_UPLOAD_LIMIT,
-            "image_file_batch_limit": dify_config.IMAGE_FILE_BATCH_LIMIT,
-            "single_chunk_attachment_limit": dify_config.SINGLE_CHUNK_ATTACHMENT_LIMIT,
-            "attachment_image_file_size_limit": dify_config.ATTACHMENT_IMAGE_FILE_SIZE_LIMIT,
-        }, 200
+        config = UploadConfig(
+            file_size_limit=dify_config.UPLOAD_FILE_SIZE_LIMIT,
+            batch_count_limit=dify_config.UPLOAD_FILE_BATCH_LIMIT,
+            file_upload_limit=dify_config.BATCH_UPLOAD_LIMIT,
+            image_file_size_limit=dify_config.UPLOAD_IMAGE_FILE_SIZE_LIMIT,
+            video_file_size_limit=dify_config.UPLOAD_VIDEO_FILE_SIZE_LIMIT,
+            audio_file_size_limit=dify_config.UPLOAD_AUDIO_FILE_SIZE_LIMIT,
+            workflow_file_upload_limit=dify_config.WORKFLOW_FILE_UPLOAD_LIMIT,
+            image_file_batch_limit=dify_config.IMAGE_FILE_BATCH_LIMIT,
+            single_chunk_attachment_limit=dify_config.SINGLE_CHUNK_ATTACHMENT_LIMIT,
+            attachment_image_file_size_limit=dify_config.ATTACHMENT_IMAGE_FILE_SIZE_LIMIT,
+        )
+        return config.model_dump(mode="json"), 200
 
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(file_fields)
     @cloud_edition_billing_resource_check("documents")
+    @console_ns.response(201, "File uploaded successfully", console_ns.models[FileResponse.__name__])
     def post(self):
         current_user, _ = current_account_with_tenant()
         source_str = request.form.get("source")
@@ -90,7 +94,8 @@ class FileApi(Resource):
         except services.errors.file.BlockedFileExtensionError as blocked_extension_error:
             raise BlockedFileExtensionError(blocked_extension_error.description)
 
-        return upload_file, 201
+        response = FileResponse.model_validate(upload_file, from_attributes=True)
+        return response.model_dump(mode="json"), 201
 
 
 @console_ns.route("/files/<uuid:file_id>/preview")

api/controllers/console/remote_files.py

@@ -1,7 +1,7 @@
 import urllib.parse
 
 import httpx
-from flask_restx import Resource, marshal_with
+from flask_restx import Resource
 from pydantic import BaseModel, Field
 
 import services
@@ -11,19 +11,22 @@ from controllers.common.errors import (
     RemoteFileUploadError,
     UnsupportedFileTypeError,
 )
+from controllers.common.schema import register_schema_models
 from core.file import helpers as file_helpers
 from core.helper import ssrf_proxy
 from extensions.ext_database import db
-from fields.file_fields import file_fields_with_signed_url, remote_file_info_fields
+from fields.file_fields import FileWithSignedUrl, RemoteFileInfo
 from libs.login import current_account_with_tenant
 from services.file_service import FileService
 
 from . import console_ns
 
+register_schema_models(console_ns, RemoteFileInfo, FileWithSignedUrl)
+
 
 @console_ns.route("/remote-files/<path:url>")
 class RemoteFileInfoApi(Resource):
-    @marshal_with(remote_file_info_fields)
+    @console_ns.response(200, "Remote file info", console_ns.models[RemoteFileInfo.__name__])
     def get(self, url):
         decoded_url = urllib.parse.unquote(url)
         resp = ssrf_proxy.head(decoded_url)
@@ -31,10 +34,11 @@ class RemoteFileInfoApi(Resource):
             # failed back to get method
             resp = ssrf_proxy.get(decoded_url, timeout=3)
         resp.raise_for_status()
-        return {
-            "file_type": resp.headers.get("Content-Type", "application/octet-stream"),
-            "file_length": int(resp.headers.get("Content-Length", 0)),
-        }
+        info = RemoteFileInfo(
+            file_type=resp.headers.get("Content-Type", "application/octet-stream"),
+            file_length=int(resp.headers.get("Content-Length", 0)),
+        )
+        return info.model_dump(mode="json")
 
 
 class RemoteFileUploadPayload(BaseModel):
@@ -50,7 +54,7 @@ console_ns.schema_model(
 @console_ns.route("/remote-files/upload")
 class RemoteFileUploadApi(Resource):
     @console_ns.expect(console_ns.models[RemoteFileUploadPayload.__name__])
-    @marshal_with(file_fields_with_signed_url)
+    @console_ns.response(201, "Remote file uploaded", console_ns.models[FileWithSignedUrl.__name__])
     def post(self):
         args = RemoteFileUploadPayload.model_validate(console_ns.payload)
         url = args.url
@@ -85,13 +89,14 @@ class RemoteFileUploadApi(Resource):
         except services.errors.file.UnsupportedFileTypeError:
             raise UnsupportedFileTypeError()
 
-        return {
-            "id": upload_file.id,
-            "name": upload_file.name,
-            "size": upload_file.size,
-            "extension": upload_file.extension,
-            "url": file_helpers.get_signed_file_url(upload_file_id=upload_file.id),
-            "mime_type": upload_file.mime_type,
-            "created_by": upload_file.created_by,
-            "created_at": upload_file.created_at,
-        }, 201
+        payload = FileWithSignedUrl(
+            id=upload_file.id,
+            name=upload_file.name,
+            size=upload_file.size,
+            extension=upload_file.extension,
+            url=file_helpers.get_signed_file_url(upload_file_id=upload_file.id),
+            mime_type=upload_file.mime_type,
+            created_by=upload_file.created_by,
+            created_at=int(upload_file.created_at.timestamp()),
+        )
+        return payload.model_dump(mode="json"), 201

api/controllers/console/workspace/account.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from datetime import datetime
 from typing import Literal
 
@@ -99,7 +101,7 @@ class AccountPasswordPayload(BaseModel):
     repeat_new_password: str
 
     @model_validator(mode="after")
-    def check_passwords_match(self) -> "AccountPasswordPayload":
+    def check_passwords_match(self) -> AccountPasswordPayload:
         if self.new_password != self.repeat_new_password:
             raise RepeatPasswordNotMatchError()
         return self

api/controllers/console/workspace/load_balancing_config.py

@@ -1,6 +1,8 @@
-from flask_restx import Resource, reqparse
+from flask_restx import Resource
+from pydantic import BaseModel
 from werkzeug.exceptions import Forbidden
 
+from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.wraps import account_initialization_required, setup_required
 from core.model_runtime.entities.model_entities import ModelType
@@ -10,10 +12,20 @@ from models import TenantAccountRole
 from services.model_load_balancing_service import ModelLoadBalancingService
 
 
+class LoadBalancingCredentialPayload(BaseModel):
+    model: str
+    model_type: ModelType
+    credentials: dict[str, object]
+
+
+register_schema_models(console_ns, LoadBalancingCredentialPayload)
+
+
 @console_ns.route(
     "/workspaces/current/model-providers/<path:provider>/models/load-balancing-configs/credentials-validate"
 )
 class LoadBalancingCredentialsValidateApi(Resource):
+    @console_ns.expect(console_ns.models[LoadBalancingCredentialPayload.__name__])
     @setup_required
     @login_required
     @account_initialization_required
@@ -24,20 +36,7 @@ class LoadBalancingCredentialsValidateApi(Resource):
 
         tenant_id = current_tenant_id
 
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("model", type=str, required=True, nullable=False, location="json")
-            .add_argument(
-                "model_type",
-                type=str,
-                required=True,
-                nullable=False,
-                choices=[mt.value for mt in ModelType],
-                location="json",
-            )
-            .add_argument("credentials", type=dict, required=True, nullable=False, location="json")
-        )
-        args = parser.parse_args()
+        payload = LoadBalancingCredentialPayload.model_validate(console_ns.payload or {})
 
         # validate model load balancing credentials
         model_load_balancing_service = ModelLoadBalancingService()
@@ -49,9 +48,9 @@ class LoadBalancingCredentialsValidateApi(Resource):
             model_load_balancing_service.validate_load_balancing_credentials(
                 tenant_id=tenant_id,
                 provider=provider,
-                model=args["model"],
-                model_type=args["model_type"],
-                credentials=args["credentials"],
+                model=payload.model,
+                model_type=payload.model_type,
+                credentials=payload.credentials,
             )
         except CredentialsValidateFailedError as ex:
             result = False
@@ -69,6 +68,7 @@ class LoadBalancingCredentialsValidateApi(Resource):
     "/workspaces/current/model-providers/<path:provider>/models/load-balancing-configs/<string:config_id>/credentials-validate"
 )
 class LoadBalancingConfigCredentialsValidateApi(Resource):
+    @console_ns.expect(console_ns.models[LoadBalancingCredentialPayload.__name__])
     @setup_required
     @login_required
     @account_initialization_required
@@ -79,20 +79,7 @@ class LoadBalancingConfigCredentialsValidateApi(Resource):
 
         tenant_id = current_tenant_id
 
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("model", type=str, required=True, nullable=False, location="json")
-            .add_argument(
-                "model_type",
-                type=str,
-                required=True,
-                nullable=False,
-                choices=[mt.value for mt in ModelType],
-                location="json",
-            )
-            .add_argument("credentials", type=dict, required=True, nullable=False, location="json")
-        )
-        args = parser.parse_args()
+        payload = LoadBalancingCredentialPayload.model_validate(console_ns.payload or {})
 
         # validate model load balancing config credentials
         model_load_balancing_service = ModelLoadBalancingService()
@@ -104,9 +91,9 @@ class LoadBalancingConfigCredentialsValidateApi(Resource):
             model_load_balancing_service.validate_load_balancing_credentials(
                 tenant_id=tenant_id,
                 provider=provider,
-                model=args["model"],
-                model_type=args["model_type"],
-                credentials=args["credentials"],
+                model=payload.model,
+                model_type=payload.model_type,
+                credentials=payload.credentials,
                 config_id=config_id,
             )
         except CredentialsValidateFailedError as ex:

api/controllers/console/workspace/tool_providers.py

@@ -1,4 +1,5 @@
 import io
+import logging
 from urllib.parse import urlparse
 
 from flask import make_response, redirect, request, send_file
@@ -17,8 +18,8 @@ from controllers.console.wraps import (
     is_admin_or_owner_required,
     setup_required,
 )
+from core.db.session_factory import session_factory
 from core.entities.mcp_provider import MCPAuthentication, MCPConfiguration
-from core.helper.tool_provider_cache import ToolProviderListCache
 from core.mcp.auth.auth_flow import auth, handle_callback
 from core.mcp.error import MCPAuthError, MCPError, MCPRefreshTokenError
 from core.mcp.mcp_client import MCPClient
@@ -40,6 +41,8 @@ from services.tools.tools_manage_service import ToolCommonService
 from services.tools.tools_transform_service import ToolTransformService
 from services.tools.workflow_tools_manage_service import WorkflowToolManageService
 
+logger = logging.getLogger(__name__)
+
 
 def is_valid_url(url: str) -> bool:
     if not url:
@@ -945,8 +948,8 @@ class ToolProviderMCPApi(Resource):
         configuration = MCPConfiguration.model_validate(args["configuration"])
         authentication = MCPAuthentication.model_validate(args["authentication"]) if args["authentication"] else None
 
-        # Create provider in transaction
-        with Session(db.engine) as session, session.begin():
+        # 1) Create provider in a short transaction (no network I/O inside)
+        with session_factory.create_session() as session, session.begin():
             service = MCPToolManageService(session=session)
             result = service.create_provider(
                 tenant_id=tenant_id,
@@ -962,8 +965,26 @@ class ToolProviderMCPApi(Resource):
                 authentication=authentication,
             )
 
-        # Invalidate cache AFTER transaction commits to avoid holding locks during Redis operations
-        ToolProviderListCache.invalidate_cache(tenant_id)
+        # 2) Try to fetch tools immediately after creation so they appear without a second save.
+        #    Perform network I/O outside any DB session to avoid holding locks.
+        try:
+            reconnect = MCPToolManageService.reconnect_with_url(
+                server_url=args["server_url"],
+                headers=args.get("headers") or {},
+                timeout=configuration.timeout,
+                sse_read_timeout=configuration.sse_read_timeout,
+            )
+            # Update just-created provider with authed/tools in a new short transaction
+            with session_factory.create_session() as session, session.begin():
+                service = MCPToolManageService(session=session)
+                db_provider = service.get_provider(provider_id=result.id, tenant_id=tenant_id)
+                db_provider.authed = reconnect.authed
+                db_provider.tools = reconnect.tools
+
+                result = ToolTransformService.mcp_provider_to_user_provider(db_provider, for_list=True)
+        except Exception:
+            # Best-effort: if initial fetch fails (e.g., auth required), return created provider as-is
+            logger.warning("Failed to fetch MCP tools after creation", exc_info=True)
 
         return jsonable_encoder(result)
 
@@ -1011,9 +1032,6 @@ class ToolProviderMCPApi(Resource):
                 validation_result=validation_result,
             )
 
-        # Invalidate cache AFTER transaction commits to avoid holding locks during Redis operations
-        ToolProviderListCache.invalidate_cache(current_tenant_id)
-
         return {"result": "success"}
 
     @console_ns.expect(parser_mcp_delete)
@@ -1028,9 +1046,6 @@ class ToolProviderMCPApi(Resource):
             service = MCPToolManageService(session=session)
             service.delete_provider(tenant_id=current_tenant_id, provider_id=args["provider_id"])
 
-        # Invalidate cache AFTER transaction commits to avoid holding locks during Redis operations
-        ToolProviderListCache.invalidate_cache(current_tenant_id)
-
         return {"result": "success"}
 
 
@@ -1081,8 +1096,6 @@ class ToolMCPAuthApi(Resource):
                         credentials=provider_entity.credentials,
                         authed=True,
                     )
-                # Invalidate cache after updating credentials
-                ToolProviderListCache.invalidate_cache(tenant_id)
                 return {"result": "success"}
         except MCPAuthError as e:
             try:
@@ -1096,22 +1109,16 @@ class ToolMCPAuthApi(Resource):
                 with Session(db.engine) as session, session.begin():
                     service = MCPToolManageService(session=session)
                     response = service.execute_auth_actions(auth_result)
-                    # Invalidate cache after auth actions may have updated provider state
-                    ToolProviderListCache.invalidate_cache(tenant_id)
                     return response
             except MCPRefreshTokenError as e:
                 with Session(db.engine) as session, session.begin():
                     service = MCPToolManageService(session=session)
                     service.clear_provider_credentials(provider_id=provider_id, tenant_id=tenant_id)
-                # Invalidate cache after clearing credentials
-                ToolProviderListCache.invalidate_cache(tenant_id)
                 raise ValueError(f"Failed to refresh token, please try to authorize again: {e}") from e
         except (MCPError, ValueError) as e:
             with Session(db.engine) as session, session.begin():
                 service = MCPToolManageService(session=session)
                 service.clear_provider_credentials(provider_id=provider_id, tenant_id=tenant_id)
-            # Invalidate cache after clearing credentials
-            ToolProviderListCache.invalidate_cache(tenant_id)
             raise ValueError(f"Failed to connect to MCP server: {e}") from e
 
 

api/controllers/console/workspace/trigger_providers.py

@@ -4,12 +4,11 @@ from typing import Any
 
 from flask import make_response, redirect, request
 from flask_restx import Resource, reqparse
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 from sqlalchemy.orm import Session
 from werkzeug.exceptions import BadRequest, Forbidden
 
 from configs import dify_config
-from constants import HIDDEN_VALUE, UNKNOWN_VALUE
 from controllers.web.error import NotFoundError
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.plugin.entities.plugin_daemon import CredentialType
@@ -44,6 +43,12 @@ class TriggerSubscriptionUpdateRequest(BaseModel):
     parameters: Mapping[str, Any] | None = Field(default=None, description="The parameters for the subscription")
     properties: Mapping[str, Any] | None = Field(default=None, description="The properties for the subscription")
 
+    @model_validator(mode="after")
+    def check_at_least_one_field(self):
+        if all(v is None for v in (self.name, self.credentials, self.parameters, self.properties)):
+            raise ValueError("At least one of name, credentials, parameters, or properties must be provided")
+        return self
+
 
 class TriggerSubscriptionVerifyRequest(BaseModel):
     """Request payload for verifying subscription credentials."""
@@ -333,7 +338,7 @@ class TriggerSubscriptionUpdateApi(Resource):
         user = current_user
         assert user.current_tenant_id is not None
 
-        args = TriggerSubscriptionUpdateRequest.model_validate(console_ns.payload)
+        request = TriggerSubscriptionUpdateRequest.model_validate(console_ns.payload)
 
         subscription = TriggerProviderService.get_subscription_by_id(
             tenant_id=user.current_tenant_id,
@@ -345,50 +350,32 @@ class TriggerSubscriptionUpdateApi(Resource):
         provider_id = TriggerProviderID(subscription.provider_id)
 
         try:
-            # rename only
-            if (
-                args.name is not None
-                and args.credentials is None
-                and args.parameters is None
-                and args.properties is None
-            ):
+            # For rename only, just update the name
+            rename = request.name is not None and not any((request.credentials, request.parameters, request.properties))
+            # When credential type is UNAUTHORIZED, it indicates the subscription was manually created
+            # For Manually created subscription, they dont have credentials, parameters
+            # They only have name and properties(which is input by user)
+            manually_created = subscription.credential_type == CredentialType.UNAUTHORIZED
+            if rename or manually_created:
                 TriggerProviderService.update_trigger_subscription(
                     tenant_id=user.current_tenant_id,
                     subscription_id=subscription_id,
-                    name=args.name,
+                    name=request.name,
+                    properties=request.properties,
                 )
                 return 200
 
-            # rebuild for create automatically by the provider
-            match subscription.credential_type:
-                case CredentialType.UNAUTHORIZED:
-                    TriggerProviderService.update_trigger_subscription(
-                        tenant_id=user.current_tenant_id,
-                        subscription_id=subscription_id,
-                        name=args.name,
-                        properties=args.properties,
-                    )
-                    return 200
-                case CredentialType.API_KEY | CredentialType.OAUTH2:
-                    if args.credentials:
-                        new_credentials: dict[str, Any] = {
-                            key: value if value != HIDDEN_VALUE else subscription.credentials.get(key, UNKNOWN_VALUE)
-                            for key, value in args.credentials.items()
-                        }
-                    else:
-                        new_credentials = subscription.credentials
-
-                    TriggerProviderService.rebuild_trigger_subscription(
-                        tenant_id=user.current_tenant_id,
-                        name=args.name,
-                        provider_id=provider_id,
-                        subscription_id=subscription_id,
-                        credentials=new_credentials,
-                        parameters=args.parameters or subscription.parameters,
-                    )
-                    return 200
-                case _:
-                    raise BadRequest("Invalid credential type")
+            # For the rest cases(API_KEY, OAUTH2)
+            # we need to call third party provider(e.g. GitHub) to rebuild the subscription
+            TriggerProviderService.rebuild_trigger_subscription(
+                tenant_id=user.current_tenant_id,
+                name=request.name,
+                provider_id=provider_id,
+                subscription_id=subscription_id,
+                credentials=request.credentials or subscription.credentials,
+                parameters=request.parameters or subscription.parameters,
+            )
+            return 200
         except ValueError as e:
             raise BadRequest(str(e))
         except Exception as e:

api/controllers/files/upload.py

@@ -4,18 +4,18 @@ from flask import request
 from flask_restx import Resource
 from flask_restx.api import HTTPStatus
 from pydantic import BaseModel, Field
-from werkzeug.datastructures import FileStorage
 from werkzeug.exceptions import Forbidden
 
 import services
 from core.file.helpers import verify_plugin_file_signature
 from core.tools.tool_file_manager import ToolFileManager
-from fields.file_fields import build_file_model
+from fields.file_fields import FileResponse
 
 from ..common.errors import (
     FileTooLargeError,
     UnsupportedFileTypeError,
 )
+from ..common.schema import register_schema_models
 from ..console.wraps import setup_required
 from ..files import files_ns
 from ..inner_api.plugin.wraps import get_user
@@ -35,6 +35,8 @@ files_ns.schema_model(
     PluginUploadQuery.__name__, PluginUploadQuery.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0)
 )
 
+register_schema_models(files_ns, FileResponse)
+
 
 @files_ns.route("/upload/for-plugin")
 class PluginUploadFileApi(Resource):
@@ -51,7 +53,7 @@ class PluginUploadFileApi(Resource):
             415: "Unsupported file type",
         }
     )
-    @files_ns.marshal_with(build_file_model(files_ns), code=HTTPStatus.CREATED)
+    @files_ns.response(HTTPStatus.CREATED, "File uploaded", files_ns.models[FileResponse.__name__])
     def post(self):
         """Upload a file for plugin usage.
 
@@ -69,7 +71,7 @@ class PluginUploadFileApi(Resource):
         """
         args = PluginUploadQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore
 
-        file: FileStorage | None = request.files.get("file")
+        file = request.files.get("file")
         if file is None:
             raise Forbidden("File is required.")
 
@@ -80,8 +82,8 @@ class PluginUploadFileApi(Resource):
         user_id = args.user_id
         user = get_user(tenant_id, user_id)
 
-        filename: str | None = file.filename
-        mimetype: str | None = file.mimetype
+        filename = file.filename
+        mimetype = file.mimetype
 
         if not filename or not mimetype:
             raise Forbidden("Invalid request.")
@@ -111,22 +113,22 @@ class PluginUploadFileApi(Resource):
             preview_url = ToolFileManager.sign_file(tool_file_id=tool_file.id, extension=extension)
 
             # Create a dictionary with all the necessary attributes
-            result = {
-                "id": tool_file.id,
-                "user_id": tool_file.user_id,
-                "tenant_id": tool_file.tenant_id,
-                "conversation_id": tool_file.conversation_id,
-                "file_key": tool_file.file_key,
-                "mimetype": tool_file.mimetype,
-                "original_url": tool_file.original_url,
-                "name": tool_file.name,
-                "size": tool_file.size,
-                "mime_type": mimetype,
-                "extension": extension,
-                "preview_url": preview_url,
-            }
+            result = FileResponse(
+                id=tool_file.id,
+                name=tool_file.name,
+                size=tool_file.size,
+                extension=extension,
+                mime_type=mimetype,
+                preview_url=preview_url,
+                source_url=tool_file.original_url,
+                original_url=tool_file.original_url,
+                user_id=tool_file.user_id,
+                tenant_id=tool_file.tenant_id,
+                conversation_id=tool_file.conversation_id,
+                file_key=tool_file.file_key,
+            )
 
-            return result, 201
+            return result.model_dump(mode="json"), 201
         except services.errors.file.FileTooLargeError as file_too_large_error:
             raise FileTooLargeError(file_too_large_error.description)
         except services.errors.file.UnsupportedFileTypeError:

api/controllers/service_api/app/annotation.py

@@ -1,7 +1,7 @@
 from typing import Literal
 
 from flask import request
-from flask_restx import Api, Namespace, Resource, fields
+from flask_restx import Namespace, Resource, fields
 from flask_restx.api import HTTPStatus
 from pydantic import BaseModel, Field
 
@@ -92,7 +92,7 @@ annotation_list_fields = {
 }
 
 
-def build_annotation_list_model(api_or_ns: Api | Namespace):
+def build_annotation_list_model(api_or_ns: Namespace):
     """Build the annotation list model for the API or Namespace."""
     copied_annotation_list_fields = annotation_list_fields.copy()
     copied_annotation_list_fields["data"] = fields.List(fields.Nested(build_annotation_model(api_or_ns)))

api/controllers/service_api/app/app.py

@@ -1,6 +1,6 @@
 from flask_restx import Resource
 
-from controllers.common.fields import build_parameters_model
+from controllers.common.fields import Parameters
 from controllers.service_api import service_api_ns
 from controllers.service_api.app.error import AppUnavailableError
 from controllers.service_api.wraps import validate_app_token
@@ -23,7 +23,6 @@ class AppParameterApi(Resource):
         }
     )
     @validate_app_token
-    @service_api_ns.marshal_with(build_parameters_model(service_api_ns))
     def get(self, app_model: App):
         """Retrieve app parameters.
 
@@ -45,7 +44,8 @@ class AppParameterApi(Resource):
 
             user_input_form = features_dict.get("user_input_form", [])
 
-        return get_parameters_from_feature_dict(features_dict=features_dict, user_input_form=user_input_form)
+        parameters = get_parameters_from_feature_dict(features_dict=features_dict, user_input_form=user_input_form)
+        return Parameters.model_validate(parameters).model_dump(mode="json")
 
 
 @service_api_ns.route("/meta")

api/controllers/service_api/app/conversation.py

@@ -3,8 +3,7 @@ from uuid import UUID
 
 from flask import request
 from flask_restx import Resource
-from flask_restx._http import HTTPStatus
-from pydantic import BaseModel, Field, field_validator, model_validator
+from pydantic import BaseModel, Field, TypeAdapter, field_validator, model_validator
 from sqlalchemy.orm import Session
 from werkzeug.exceptions import BadRequest, NotFound
 
@@ -16,9 +15,9 @@ from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate
 from core.app.entities.app_invoke_entities import InvokeFrom
 from extensions.ext_database import db
 from fields.conversation_fields import (
-    build_conversation_delete_model,
-    build_conversation_infinite_scroll_pagination_model,
-    build_simple_conversation_model,
+    ConversationDelete,
+    ConversationInfiniteScrollPagination,
+    SimpleConversation,
 )
 from fields.conversation_variable_fields import (
     build_conversation_variable_infinite_scroll_pagination_model,
@@ -105,7 +104,6 @@ class ConversationApi(Resource):
         }
     )
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY))
-    @service_api_ns.marshal_with(build_conversation_infinite_scroll_pagination_model(service_api_ns))
     def get(self, app_model: App, end_user: EndUser):
         """List all conversations for the current user.
 
@@ -120,7 +118,7 @@ class ConversationApi(Resource):
 
         try:
             with Session(db.engine) as session:
-                return ConversationService.pagination_by_last_id(
+                pagination = ConversationService.pagination_by_last_id(
                     session=session,
                     app_model=app_model,
                     user=end_user,
@@ -129,6 +127,13 @@ class ConversationApi(Resource):
                     invoke_from=InvokeFrom.SERVICE_API,
                     sort_by=query_args.sort_by,
                 )
+                adapter = TypeAdapter(SimpleConversation)
+                conversations = [adapter.validate_python(item, from_attributes=True) for item in pagination.data]
+                return ConversationInfiniteScrollPagination(
+                    limit=pagination.limit,
+                    has_more=pagination.has_more,
+                    data=conversations,
+                ).model_dump(mode="json")
         except services.errors.conversation.LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 
@@ -146,7 +151,6 @@ class ConversationDetailApi(Resource):
         }
     )
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
-    @service_api_ns.marshal_with(build_conversation_delete_model(service_api_ns), code=HTTPStatus.NO_CONTENT)
     def delete(self, app_model: App, end_user: EndUser, c_id):
         """Delete a specific conversation."""
         app_mode = AppMode.value_of(app_model.mode)
@@ -159,7 +163,7 @@ class ConversationDetailApi(Resource):
             ConversationService.delete(app_model, conversation_id, end_user)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
-        return {"result": "success"}, 204
+        return ConversationDelete(result="success").model_dump(mode="json"), 204
 
 
 @service_api_ns.route("/conversations/<uuid:c_id>/name")
@@ -176,7 +180,6 @@ class ConversationRenameApi(Resource):
         }
     )
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
-    @service_api_ns.marshal_with(build_simple_conversation_model(service_api_ns))
     def post(self, app_model: App, end_user: EndUser, c_id):
         """Rename a conversation or auto-generate a name."""
         app_mode = AppMode.value_of(app_model.mode)
@@ -188,7 +191,14 @@ class ConversationRenameApi(Resource):
         payload = ConversationRenamePayload.model_validate(service_api_ns.payload or {})
 
         try:
-            return ConversationService.rename(app_model, conversation_id, end_user, payload.name, payload.auto_generate)
+            conversation = ConversationService.rename(
+                app_model, conversation_id, end_user, payload.name, payload.auto_generate
+            )
+            return (
+                TypeAdapter(SimpleConversation)
+                .validate_python(conversation, from_attributes=True)
+                .model_dump(mode="json")
+            )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
 

api/controllers/service_api/app/file.py

@@ -10,13 +10,16 @@ from controllers.common.errors import (
     TooManyFilesError,
     UnsupportedFileTypeError,
 )
+from controllers.common.schema import register_schema_models
 from controllers.service_api import service_api_ns
 from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
 from extensions.ext_database import db
-from fields.file_fields import build_file_model
+from fields.file_fields import FileResponse
 from models import App, EndUser
 from services.file_service import FileService
 
+register_schema_models(service_api_ns, FileResponse)
+
 
 @service_api_ns.route("/files/upload")
 class FileApi(Resource):
@@ -31,8 +34,8 @@ class FileApi(Resource):
             415: "Unsupported file type",
         }
     )
-    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.FORM))
-    @service_api_ns.marshal_with(build_file_model(service_api_ns), code=HTTPStatus.CREATED)
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.FORM))  # type: ignore
+    @service_api_ns.response(HTTPStatus.CREATED, "File uploaded", service_api_ns.models[FileResponse.__name__])
     def post(self, app_model: App, end_user: EndUser):
         """Upload a file for use in conversations.
 
@@ -64,4 +67,5 @@ class FileApi(Resource):
         except services.errors.file.UnsupportedFileTypeError:
             raise UnsupportedFileTypeError()
 
-        return upload_file, 201
+        response = FileResponse.model_validate(upload_file, from_attributes=True)
+        return response.model_dump(mode="json"), 201

api/controllers/service_api/app/message.py

@@ -1,11 +1,10 @@
-import json
 import logging
 from typing import Literal
 from uuid import UUID
 
 from flask import request
-from flask_restx import Namespace, Resource, fields
-from pydantic import BaseModel, Field
+from flask_restx import Resource
+from pydantic import BaseModel, Field, TypeAdapter
 from werkzeug.exceptions import BadRequest, InternalServerError, NotFound
 
 import services
@@ -14,10 +13,8 @@ from controllers.service_api import service_api_ns
 from controllers.service_api.app.error import NotChatAppError
 from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
 from core.app.entities.app_invoke_entities import InvokeFrom
-from fields.conversation_fields import build_message_file_model
-from fields.message_fields import build_agent_thought_model, build_feedback_model
-from fields.raws import FilesContainedField
-from libs.helper import TimestampField
+from fields.conversation_fields import ResultResponse
+from fields.message_fields import MessageInfiniteScrollPagination, MessageListItem
 from models.model import App, AppMode, EndUser
 from services.errors.message import (
     FirstMessageNotExistsError,
@@ -48,49 +45,6 @@ class FeedbackListQuery(BaseModel):
 register_schema_models(service_api_ns, MessageListQuery, MessageFeedbackPayload, FeedbackListQuery)
 
 
-def build_message_model(api_or_ns: Namespace):
-    """Build the message model for the API or Namespace."""
-    # First build the nested models
-    feedback_model = build_feedback_model(api_or_ns)
-    agent_thought_model = build_agent_thought_model(api_or_ns)
-    message_file_model = build_message_file_model(api_or_ns)
-
-    # Then build the message fields with nested models
-    message_fields = {
-        "id": fields.String,
-        "conversation_id": fields.String,
-        "parent_message_id": fields.String,
-        "inputs": FilesContainedField,
-        "query": fields.String,
-        "answer": fields.String(attribute="re_sign_file_url_answer"),
-        "message_files": fields.List(fields.Nested(message_file_model)),
-        "feedback": fields.Nested(feedback_model, attribute="user_feedback", allow_null=True),
-        "retriever_resources": fields.Raw(
-            attribute=lambda obj: json.loads(obj.message_metadata).get("retriever_resources", [])
-            if obj.message_metadata
-            else []
-        ),
-        "created_at": TimestampField,
-        "agent_thoughts": fields.List(fields.Nested(agent_thought_model)),
-        "status": fields.String,
-        "error": fields.String,
-    }
-    return api_or_ns.model("Message", message_fields)
-
-
-def build_message_infinite_scroll_pagination_model(api_or_ns: Namespace):
-    """Build the message infinite scroll pagination model for the API or Namespace."""
-    # Build the nested message model first
-    message_model = build_message_model(api_or_ns)
-
-    message_infinite_scroll_pagination_fields = {
-        "limit": fields.Integer,
-        "has_more": fields.Boolean,
-        "data": fields.List(fields.Nested(message_model)),
-    }
-    return api_or_ns.model("MessageInfiniteScrollPagination", message_infinite_scroll_pagination_fields)
-
-
 @service_api_ns.route("/messages")
 class MessageListApi(Resource):
     @service_api_ns.expect(service_api_ns.models[MessageListQuery.__name__])
@@ -104,7 +58,6 @@ class MessageListApi(Resource):
         }
     )
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY))
-    @service_api_ns.marshal_with(build_message_infinite_scroll_pagination_model(service_api_ns))
     def get(self, app_model: App, end_user: EndUser):
         """List messages in a conversation.
 
@@ -119,9 +72,16 @@ class MessageListApi(Resource):
         first_id = str(query_args.first_id) if query_args.first_id else None
 
         try:
-            return MessageService.pagination_by_first_id(
+            pagination = MessageService.pagination_by_first_id(
                 app_model, end_user, conversation_id, first_id, query_args.limit
             )
+            adapter = TypeAdapter(MessageListItem)
+            items = [adapter.validate_python(message, from_attributes=True) for message in pagination.data]
+            return MessageInfiniteScrollPagination(
+                limit=pagination.limit,
+                has_more=pagination.has_more,
+                data=items,
+            ).model_dump(mode="json")
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except FirstMessageNotExistsError:
@@ -162,7 +122,7 @@ class MessageFeedbackApi(Resource):
         except MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")
 
 
 @service_api_ns.route("/app/feedbacks")

api/controllers/service_api/app/site.py

@@ -1,7 +1,7 @@
 from flask_restx import Resource
 from werkzeug.exceptions import Forbidden
 
-from controllers.common.fields import build_site_model
+from controllers.common.fields import Site as SiteResponse
 from controllers.service_api import service_api_ns
 from controllers.service_api.wraps import validate_app_token
 from extensions.ext_database import db
@@ -23,7 +23,6 @@ class AppSiteApi(Resource):
         }
     )
     @validate_app_token
-    @service_api_ns.marshal_with(build_site_model(service_api_ns))
     def get(self, app_model: App):
         """Retrieve app site info.
 
@@ -38,4 +37,4 @@ class AppSiteApi(Resource):
         if app_model.tenant.status == TenantStatus.ARCHIVE:
             raise Forbidden()
 
-        return site
+        return SiteResponse.model_validate(site).model_dump(mode="json")

api/controllers/service_api/app/workflow.py

@@ -3,7 +3,7 @@ from typing import Any, Literal
 
 from dateutil.parser import isoparse
 from flask import request
-from flask_restx import Api, Namespace, Resource, fields
+from flask_restx import Namespace, Resource, fields
 from pydantic import BaseModel, Field
 from sqlalchemy.orm import Session, sessionmaker
 from werkzeug.exceptions import BadRequest, InternalServerError, NotFound
@@ -78,7 +78,7 @@ workflow_run_fields = {
 }
 
 
-def build_workflow_run_model(api_or_ns: Api | Namespace):
+def build_workflow_run_model(api_or_ns: Namespace):
     """Build the workflow run model for the API or Namespace."""
     return api_or_ns.model("WorkflowRun", workflow_run_fields)
 

api/controllers/service_api/dataset/dataset.py

@@ -13,7 +13,6 @@ from controllers.service_api.dataset.error import DatasetInUseError, DatasetName
 from controllers.service_api.wraps import (
     DatasetApiResource,
     cloud_edition_billing_rate_limit_check,
-    validate_dataset_token,
 )
 from core.model_runtime.entities.model_entities import ModelType
 from core.provider_manager import ProviderManager
@@ -460,9 +459,8 @@ class DatasetTagsApi(DatasetApiResource):
             401: "Unauthorized - invalid API token",
         }
     )
-    @validate_dataset_token
     @service_api_ns.marshal_with(build_dataset_tag_fields(service_api_ns))
-    def get(self, _, dataset_id):
+    def get(self, _):
         """Get all knowledge type tags."""
         assert isinstance(current_user, Account)
         cid = current_user.current_tenant_id
@@ -482,8 +480,7 @@ class DatasetTagsApi(DatasetApiResource):
         }
     )
     @service_api_ns.marshal_with(build_dataset_tag_fields(service_api_ns))
-    @validate_dataset_token
-    def post(self, _, dataset_id):
+    def post(self, _):
         """Add a knowledge type tag."""
         assert isinstance(current_user, Account)
         if not (current_user.has_edit_permission or current_user.is_dataset_editor):
@@ -506,8 +503,7 @@ class DatasetTagsApi(DatasetApiResource):
         }
     )
     @service_api_ns.marshal_with(build_dataset_tag_fields(service_api_ns))
-    @validate_dataset_token
-    def patch(self, _, dataset_id):
+    def patch(self, _):
         assert isinstance(current_user, Account)
         if not (current_user.has_edit_permission or current_user.is_dataset_editor):
             raise Forbidden()
@@ -533,9 +529,8 @@ class DatasetTagsApi(DatasetApiResource):
             403: "Forbidden - insufficient permissions",
         }
     )
-    @validate_dataset_token
     @edit_permission_required
-    def delete(self, _, dataset_id):
+    def delete(self, _):
         """Delete a knowledge type tag."""
         payload = TagDeletePayload.model_validate(service_api_ns.payload or {})
         TagService.delete_tag(payload.tag_id)
@@ -555,8 +550,7 @@ class DatasetTagBindingApi(DatasetApiResource):
             403: "Forbidden - insufficient permissions",
         }
     )
-    @validate_dataset_token
-    def post(self, _, dataset_id):
+    def post(self, _):
         # The role of the current user in the ta table must be admin, owner, editor, or dataset_operator
         assert isinstance(current_user, Account)
         if not (current_user.has_edit_permission or current_user.is_dataset_editor):
@@ -580,8 +574,7 @@ class DatasetTagUnbindingApi(DatasetApiResource):
             403: "Forbidden - insufficient permissions",
         }
     )
-    @validate_dataset_token
-    def post(self, _, dataset_id):
+    def post(self, _):
         # The role of the current user in the ta table must be admin, owner, editor, or dataset_operator
         assert isinstance(current_user, Account)
         if not (current_user.has_edit_permission or current_user.is_dataset_editor):
@@ -604,7 +597,6 @@ class DatasetTagsBindingStatusApi(DatasetApiResource):
             401: "Unauthorized - invalid API token",
         }
     )
-    @validate_dataset_token
     def get(self, _, *args, **kwargs):
         """Get all knowledge type tags."""
         dataset_id = kwargs.get("dataset_id")

api/controllers/service_api/dataset/hit_testing.py

@@ -24,7 +24,7 @@ class HitTestingApi(DatasetApiResource, DatasetsHitTestingBase):
         dataset_id_str = str(dataset_id)
 
         dataset = self.get_and_validate_dataset(dataset_id_str)
-        args = self.parse_args()
+        args = self.parse_args(service_api_ns.payload)
         self.hit_testing_args_check(args)
 
         return self.perform_hit_testing(dataset, args)

api/controllers/service_api/dataset/rag_pipeline/rag_pipeline_workflow.py

@@ -174,7 +174,7 @@ class PipelineRunApi(DatasetApiResource):
                 pipeline=pipeline,
                 user=current_user,
                 args=payload.model_dump(),
-                invoke_from=InvokeFrom.PUBLISHED if payload.is_published else InvokeFrom.DEBUGGER,
+                invoke_from=InvokeFrom.PUBLISHED_PIPELINE if payload.is_published else InvokeFrom.DEBUGGER,
                 streaming=payload.response_mode == "streaming",
             )
 

api/controllers/web/app.py

@@ -1,7 +1,7 @@
 import logging
 
 from flask import request
-from flask_restx import Resource, marshal_with
+from flask_restx import Resource
 from pydantic import BaseModel, ConfigDict, Field
 from werkzeug.exceptions import Unauthorized
 
@@ -50,7 +50,6 @@ class AppParameterApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(fields.parameters_fields)
     def get(self, app_model: App, end_user):
         """Retrieve app parameters."""
         if app_model.mode in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
@@ -69,7 +68,8 @@ class AppParameterApi(WebApiResource):
 
             user_input_form = features_dict.get("user_input_form", [])
 
-        return get_parameters_from_feature_dict(features_dict=features_dict, user_input_form=user_input_form)
+        parameters = get_parameters_from_feature_dict(features_dict=features_dict, user_input_form=user_input_form)
+        return fields.Parameters.model_validate(parameters).model_dump(mode="json")
 
 
 @web_ns.route("/meta")

api/controllers/web/conversation.py

@@ -1,14 +1,21 @@
-from flask_restx import fields, marshal_with, reqparse
-from flask_restx.inputs import int_range
+from typing import Literal
+
+from flask import request
+from pydantic import BaseModel, Field, TypeAdapter, field_validator, model_validator
 from sqlalchemy.orm import Session
 from werkzeug.exceptions import NotFound
 
+from controllers.common.schema import register_schema_models
 from controllers.web import web_ns
 from controllers.web.error import NotChatAppError
 from controllers.web.wraps import WebApiResource
 from core.app.entities.app_invoke_entities import InvokeFrom
 from extensions.ext_database import db
-from fields.conversation_fields import conversation_infinite_scroll_pagination_fields, simple_conversation_fields
+from fields.conversation_fields import (
+    ConversationInfiniteScrollPagination,
+    ResultResponse,
+    SimpleConversation,
+)
 from libs.helper import uuid_value
 from models.model import AppMode
 from services.conversation_service import ConversationService
@@ -16,6 +23,35 @@ from services.errors.conversation import ConversationNotExistsError, LastConvers
 from services.web_conversation_service import WebConversationService
 
 
+class ConversationListQuery(BaseModel):
+    last_id: str | None = None
+    limit: int = Field(default=20, ge=1, le=100)
+    pinned: bool | None = None
+    sort_by: Literal["created_at", "-created_at", "updated_at", "-updated_at"] = "-updated_at"
+
+    @field_validator("last_id")
+    @classmethod
+    def validate_last_id(cls, value: str | None) -> str | None:
+        if value is None:
+            return value
+        return uuid_value(value)
+
+
+class ConversationRenamePayload(BaseModel):
+    name: str | None = None
+    auto_generate: bool = False
+
+    @model_validator(mode="after")
+    def validate_name_requirement(self):
+        if not self.auto_generate:
+            if self.name is None or not self.name.strip():
+                raise ValueError("name is required when auto_generate is false")
+        return self
+
+
+register_schema_models(web_ns, ConversationListQuery, ConversationRenamePayload)
+
+
 @web_ns.route("/conversations")
 class ConversationListApi(WebApiResource):
     @web_ns.doc("Get Conversation List")
@@ -54,54 +90,39 @@ class ConversationListApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(conversation_infinite_scroll_pagination_fields)
     def get(self, app_model, end_user):
         app_mode = AppMode.value_of(app_model.mode)
         if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("last_id", type=uuid_value, location="args")
-            .add_argument("limit", type=int_range(1, 100), required=False, default=20, location="args")
-            .add_argument("pinned", type=str, choices=["true", "false", None], location="args")
-            .add_argument(
-                "sort_by",
-                type=str,
-                choices=["created_at", "-created_at", "updated_at", "-updated_at"],
-                required=False,
-                default="-updated_at",
-                location="args",
-            )
-        )
-        args = parser.parse_args()
-
-        pinned = None
-        if "pinned" in args and args["pinned"] is not None:
-            pinned = args["pinned"] == "true"
+        raw_args = request.args.to_dict()
+        query = ConversationListQuery.model_validate(raw_args)
 
         try:
             with Session(db.engine) as session:
-                return WebConversationService.pagination_by_last_id(
+                pagination = WebConversationService.pagination_by_last_id(
                     session=session,
                     app_model=app_model,
                     user=end_user,
-                    last_id=args["last_id"],
-                    limit=args["limit"],
+                    last_id=query.last_id,
+                    limit=query.limit,
                     invoke_from=InvokeFrom.WEB_APP,
-                    pinned=pinned,
-                    sort_by=args["sort_by"],
+                    pinned=query.pinned,
+                    sort_by=query.sort_by,
                 )
+                adapter = TypeAdapter(SimpleConversation)
+                conversations = [adapter.validate_python(item, from_attributes=True) for item in pagination.data]
+                return ConversationInfiniteScrollPagination(
+                    limit=pagination.limit,
+                    has_more=pagination.has_more,
+                    data=conversations,
+                ).model_dump(mode="json")
         except LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 
 
 @web_ns.route("/conversations/<uuid:c_id>")
 class ConversationApi(WebApiResource):
-    delete_response_fields = {
-        "result": fields.String,
-    }
-
     @web_ns.doc("Delete Conversation")
     @web_ns.doc(description="Delete a specific conversation.")
     @web_ns.doc(params={"c_id": {"description": "Conversation UUID", "type": "string", "required": True}})
@@ -115,7 +136,6 @@ class ConversationApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(delete_response_fields)
     def delete(self, app_model, end_user, c_id):
         app_mode = AppMode.value_of(app_model.mode)
         if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
@@ -126,7 +146,7 @@ class ConversationApi(WebApiResource):
             ConversationService.delete(app_model, conversation_id, end_user)
         except ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
-        return {"result": "success"}, 204
+        return ResultResponse(result="success").model_dump(mode="json"), 204
 
 
 @web_ns.route("/conversations/<uuid:c_id>/name")
@@ -155,7 +175,6 @@ class ConversationRenameApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(simple_conversation_fields)
     def post(self, app_model, end_user, c_id):
         app_mode = AppMode.value_of(app_model.mode)
         if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
@@ -163,25 +182,23 @@ class ConversationRenameApi(WebApiResource):
 
         conversation_id = str(c_id)
 
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("name", type=str, required=False, location="json")
-            .add_argument("auto_generate", type=bool, required=False, default=False, location="json")
-        )
-        args = parser.parse_args()
+        payload = ConversationRenamePayload.model_validate(web_ns.payload or {})
 
         try:
-            return ConversationService.rename(app_model, conversation_id, end_user, args["name"], args["auto_generate"])
+            conversation = ConversationService.rename(
+                app_model, conversation_id, end_user, payload.name, payload.auto_generate
+            )
+            return (
+                TypeAdapter(SimpleConversation)
+                .validate_python(conversation, from_attributes=True)
+                .model_dump(mode="json")
+            )
         except ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
 
 
 @web_ns.route("/conversations/<uuid:c_id>/pin")
 class ConversationPinApi(WebApiResource):
-    pin_response_fields = {
-        "result": fields.String,
-    }
-
     @web_ns.doc("Pin Conversation")
     @web_ns.doc(description="Pin a specific conversation to keep it at the top of the list.")
     @web_ns.doc(params={"c_id": {"description": "Conversation UUID", "type": "string", "required": True}})
@@ -195,7 +212,6 @@ class ConversationPinApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(pin_response_fields)
     def patch(self, app_model, end_user, c_id):
         app_mode = AppMode.value_of(app_model.mode)
         if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
@@ -208,15 +224,11 @@ class ConversationPinApi(WebApiResource):
         except ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")
 
 
 @web_ns.route("/conversations/<uuid:c_id>/unpin")
 class ConversationUnPinApi(WebApiResource):
-    unpin_response_fields = {
-        "result": fields.String,
-    }
-
     @web_ns.doc("Unpin Conversation")
     @web_ns.doc(description="Unpin a specific conversation to remove it from the top of the list.")
     @web_ns.doc(params={"c_id": {"description": "Conversation UUID", "type": "string", "required": True}})
@@ -230,7 +242,6 @@ class ConversationUnPinApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(unpin_response_fields)
     def patch(self, app_model, end_user, c_id):
         app_mode = AppMode.value_of(app_model.mode)
         if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
@@ -239,4 +250,4 @@ class ConversationUnPinApi(WebApiResource):
         conversation_id = str(c_id)
         WebConversationService.unpin(app_model, conversation_id, end_user)
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")

api/controllers/web/files.py

@@ -1,5 +1,4 @@
 from flask import request
-from flask_restx import marshal_with
 
 import services
 from controllers.common.errors import (
@@ -9,12 +8,15 @@ from controllers.common.errors import (
     TooManyFilesError,
     UnsupportedFileTypeError,
 )
+from controllers.common.schema import register_schema_models
 from controllers.web import web_ns
 from controllers.web.wraps import WebApiResource
 from extensions.ext_database import db
-from fields.file_fields import build_file_model
+from fields.file_fields import FileResponse
 from services.file_service import FileService
 
+register_schema_models(web_ns, FileResponse)
+
 
 @web_ns.route("/files/upload")
 class FileApi(WebApiResource):
@@ -28,7 +30,7 @@ class FileApi(WebApiResource):
             415: "Unsupported file type",
         }
     )
-    @marshal_with(build_file_model(web_ns))
+    @web_ns.response(201, "File uploaded successfully", web_ns.models[FileResponse.__name__])
     def post(self, app_model, end_user):
         """Upload a file for use in web applications.
 
@@ -81,4 +83,5 @@ class FileApi(WebApiResource):
         except services.errors.file.UnsupportedFileTypeError:
             raise UnsupportedFileTypeError()
 
-        return upload_file, 201
+        response = FileResponse.model_validate(upload_file, from_attributes=True)
+        return response.model_dump(mode="json"), 201

api/controllers/web/message.py

@@ -2,8 +2,7 @@ import logging
 from typing import Literal
 
 from flask import request
-from flask_restx import fields, marshal_with
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, TypeAdapter, field_validator
 from werkzeug.exceptions import InternalServerError, NotFound
 
 from controllers.common.schema import register_schema_models
@@ -22,11 +21,10 @@ from controllers.web.wraps import WebApiResource
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
-from fields.conversation_fields import message_file_fields
-from fields.message_fields import agent_thought_fields, feedback_fields, retriever_resource_fields
-from fields.raws import FilesContainedField
+from fields.conversation_fields import ResultResponse
+from fields.message_fields import SuggestedQuestionsResponse, WebMessageInfiniteScrollPagination, WebMessageListItem
 from libs import helper
-from libs.helper import TimestampField, uuid_value
+from libs.helper import uuid_value
 from models.model import AppMode
 from services.app_generate_service import AppGenerateService
 from services.errors.app import MoreLikeThisDisabledError
@@ -70,29 +68,6 @@ register_schema_models(web_ns, MessageListQuery, MessageFeedbackPayload, Message
 
 @web_ns.route("/messages")
 class MessageListApi(WebApiResource):
-    message_fields = {
-        "id": fields.String,
-        "conversation_id": fields.String,
-        "parent_message_id": fields.String,
-        "inputs": FilesContainedField,
-        "query": fields.String,
-        "answer": fields.String(attribute="re_sign_file_url_answer"),
-        "message_files": fields.List(fields.Nested(message_file_fields)),
-        "feedback": fields.Nested(feedback_fields, attribute="user_feedback", allow_null=True),
-        "retriever_resources": fields.List(fields.Nested(retriever_resource_fields)),
-        "created_at": TimestampField,
-        "agent_thoughts": fields.List(fields.Nested(agent_thought_fields)),
-        "metadata": fields.Raw(attribute="message_metadata_dict"),
-        "status": fields.String,
-        "error": fields.String,
-    }
-
-    message_infinite_scroll_pagination_fields = {
-        "limit": fields.Integer,
-        "has_more": fields.Boolean,
-        "data": fields.List(fields.Nested(message_fields)),
-    }
-
     @web_ns.doc("Get Message List")
     @web_ns.doc(description="Retrieve paginated list of messages from a conversation in a chat application.")
     @web_ns.doc(
@@ -121,7 +96,6 @@ class MessageListApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(message_infinite_scroll_pagination_fields)
     def get(self, app_model, end_user):
         app_mode = AppMode.value_of(app_model.mode)
         if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
@@ -131,9 +105,16 @@ class MessageListApi(WebApiResource):
         query = MessageListQuery.model_validate(raw_args)
 
         try:
-            return MessageService.pagination_by_first_id(
+            pagination = MessageService.pagination_by_first_id(
                 app_model, end_user, query.conversation_id, query.first_id, query.limit
             )
+            adapter = TypeAdapter(WebMessageListItem)
+            items = [adapter.validate_python(message, from_attributes=True) for message in pagination.data]
+            return WebMessageInfiniteScrollPagination(
+                limit=pagination.limit,
+                has_more=pagination.has_more,
+                data=items,
+            ).model_dump(mode="json")
         except ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except FirstMessageNotExistsError:
@@ -142,10 +123,6 @@ class MessageListApi(WebApiResource):
 
 @web_ns.route("/messages/<uuid:message_id>/feedbacks")
 class MessageFeedbackApi(WebApiResource):
-    feedback_response_fields = {
-        "result": fields.String,
-    }
-
     @web_ns.doc("Create Message Feedback")
     @web_ns.doc(description="Submit feedback (like/dislike) for a specific message.")
     @web_ns.doc(params={"message_id": {"description": "Message UUID", "type": "string", "required": True}})
@@ -170,7 +147,6 @@ class MessageFeedbackApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(feedback_response_fields)
     def post(self, app_model, end_user, message_id):
         message_id = str(message_id)
 
@@ -187,7 +163,7 @@ class MessageFeedbackApi(WebApiResource):
         except MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")
 
 
 @web_ns.route("/messages/<uuid:message_id>/more-like-this")
@@ -247,10 +223,6 @@ class MessageMoreLikeThisApi(WebApiResource):
 
 @web_ns.route("/messages/<uuid:message_id>/suggested-questions")
 class MessageSuggestedQuestionApi(WebApiResource):
-    suggested_questions_response_fields = {
-        "data": fields.List(fields.String),
-    }
-
     @web_ns.doc("Get Suggested Questions")
     @web_ns.doc(description="Get suggested follow-up questions after a message (chat apps only).")
     @web_ns.doc(params={"message_id": {"description": "Message UUID", "type": "string", "required": True}})
@@ -264,7 +236,6 @@ class MessageSuggestedQuestionApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(suggested_questions_response_fields)
     def get(self, app_model, end_user, message_id):
         app_mode = AppMode.value_of(app_model.mode)
         if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
@@ -277,7 +248,6 @@ class MessageSuggestedQuestionApi(WebApiResource):
                 app_model=app_model, user=end_user, message_id=message_id, invoke_from=InvokeFrom.WEB_APP
             )
             # questions is a list of strings, not a list of Message objects
-            # so we can directly return it
         except MessageNotExistsError:
             raise NotFound("Message not found")
         except ConversationNotExistsError:
@@ -296,4 +266,4 @@ class MessageSuggestedQuestionApi(WebApiResource):
             logger.exception("internal server error.")
             raise InternalServerError()
 
-        return {"data": questions}
+        return SuggestedQuestionsResponse(data=questions).model_dump(mode="json")

api/controllers/web/remote_files.py

@@ -1,7 +1,6 @@
 import urllib.parse
 
 import httpx
-from flask_restx import marshal_with
 from pydantic import BaseModel, Field, HttpUrl
 
 import services
@@ -14,7 +13,7 @@ from controllers.common.errors import (
 from core.file import helpers as file_helpers
 from core.helper import ssrf_proxy
 from extensions.ext_database import db
-from fields.file_fields import build_file_with_signed_url_model, build_remote_file_info_model
+from fields.file_fields import FileWithSignedUrl, RemoteFileInfo
 from services.file_service import FileService
 
 from ..common.schema import register_schema_models
@@ -26,7 +25,7 @@ class RemoteFileUploadPayload(BaseModel):
     url: HttpUrl = Field(description="Remote file URL")
 
 
-register_schema_models(web_ns, RemoteFileUploadPayload)
+register_schema_models(web_ns, RemoteFileUploadPayload, RemoteFileInfo, FileWithSignedUrl)
 
 
 @web_ns.route("/remote-files/<path:url>")
@@ -41,7 +40,7 @@ class RemoteFileInfoApi(WebApiResource):
             500: "Failed to fetch remote file",
         }
     )
-    @marshal_with(build_remote_file_info_model(web_ns))
+    @web_ns.response(200, "Remote file info", web_ns.models[RemoteFileInfo.__name__])
     def get(self, app_model, end_user, url):
         """Get information about a remote file.
 
@@ -65,10 +64,11 @@ class RemoteFileInfoApi(WebApiResource):
             # failed back to get method
             resp = ssrf_proxy.get(decoded_url, timeout=3)
         resp.raise_for_status()
-        return {
-            "file_type": resp.headers.get("Content-Type", "application/octet-stream"),
-            "file_length": int(resp.headers.get("Content-Length", -1)),
-        }
+        info = RemoteFileInfo(
+            file_type=resp.headers.get("Content-Type", "application/octet-stream"),
+            file_length=int(resp.headers.get("Content-Length", -1)),
+        )
+        return info.model_dump(mode="json")
 
 
 @web_ns.route("/remote-files/upload")
@@ -84,7 +84,7 @@ class RemoteFileUploadApi(WebApiResource):
             500: "Failed to fetch remote file",
         }
     )
-    @marshal_with(build_file_with_signed_url_model(web_ns))
+    @web_ns.response(201, "Remote file uploaded", web_ns.models[FileWithSignedUrl.__name__])
     def post(self, app_model, end_user):
         """Upload a file from a remote URL.
 
@@ -139,13 +139,14 @@ class RemoteFileUploadApi(WebApiResource):
         except services.errors.file.UnsupportedFileTypeError:
             raise UnsupportedFileTypeError
 
-        return {
-            "id": upload_file.id,
-            "name": upload_file.name,
-            "size": upload_file.size,
-            "extension": upload_file.extension,
-            "url": file_helpers.get_signed_file_url(upload_file_id=upload_file.id),
-            "mime_type": upload_file.mime_type,
-            "created_by": upload_file.created_by,
-            "created_at": upload_file.created_at,
-        }, 201
+        payload1 = FileWithSignedUrl(
+            id=upload_file.id,
+            name=upload_file.name,
+            size=upload_file.size,
+            extension=upload_file.extension,
+            url=file_helpers.get_signed_file_url(upload_file_id=upload_file.id),
+            mime_type=upload_file.mime_type,
+            created_by=upload_file.created_by,
+            created_at=int(upload_file.created_at.timestamp()),
+        )
+        return payload1.model_dump(mode="json"), 201

api/controllers/web/saved_message.py

@@ -1,40 +1,32 @@
-from flask_restx import fields, marshal_with, reqparse
-from flask_restx.inputs import int_range
+from flask import request
+from pydantic import BaseModel, Field, TypeAdapter
 from werkzeug.exceptions import NotFound
 
+from controllers.common.schema import register_schema_models
 from controllers.web import web_ns
 from controllers.web.error import NotCompletionAppError
 from controllers.web.wraps import WebApiResource
-from fields.conversation_fields import message_file_fields
-from libs.helper import TimestampField, uuid_value
+from fields.conversation_fields import ResultResponse
+from fields.message_fields import SavedMessageInfiniteScrollPagination, SavedMessageItem
+from libs.helper import UUIDStrOrEmpty
 from services.errors.message import MessageNotExistsError
 from services.saved_message_service import SavedMessageService
 
-feedback_fields = {"rating": fields.String}
 
-message_fields = {
-    "id": fields.String,
-    "inputs": fields.Raw,
-    "query": fields.String,
-    "answer": fields.String,
-    "message_files": fields.List(fields.Nested(message_file_fields)),
-    "feedback": fields.Nested(feedback_fields, attribute="user_feedback", allow_null=True),
-    "created_at": TimestampField,
-}
+class SavedMessageListQuery(BaseModel):
+    last_id: UUIDStrOrEmpty | None = None
+    limit: int = Field(default=20, ge=1, le=100)
+
+
+class SavedMessageCreatePayload(BaseModel):
+    message_id: UUIDStrOrEmpty
+
+
+register_schema_models(web_ns, SavedMessageListQuery, SavedMessageCreatePayload)
 
 
 @web_ns.route("/saved-messages")
 class SavedMessageListApi(WebApiResource):
-    saved_message_infinite_scroll_pagination_fields = {
-        "limit": fields.Integer,
-        "has_more": fields.Boolean,
-        "data": fields.List(fields.Nested(message_fields)),
-    }
-
-    post_response_fields = {
-        "result": fields.String,
-    }
-
     @web_ns.doc("Get Saved Messages")
     @web_ns.doc(description="Retrieve paginated list of saved messages for a completion application.")
     @web_ns.doc(
@@ -58,19 +50,21 @@ class SavedMessageListApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(saved_message_infinite_scroll_pagination_fields)
     def get(self, app_model, end_user):
         if app_model.mode != "completion":
             raise NotCompletionAppError()
 
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("last_id", type=uuid_value, location="args")
-            .add_argument("limit", type=int_range(1, 100), required=False, default=20, location="args")
-        )
-        args = parser.parse_args()
+        raw_args = request.args.to_dict()
+        query = SavedMessageListQuery.model_validate(raw_args)
 
-        return SavedMessageService.pagination_by_last_id(app_model, end_user, args["last_id"], args["limit"])
+        pagination = SavedMessageService.pagination_by_last_id(app_model, end_user, query.last_id, query.limit)
+        adapter = TypeAdapter(SavedMessageItem)
+        items = [adapter.validate_python(message, from_attributes=True) for message in pagination.data]
+        return SavedMessageInfiniteScrollPagination(
+            limit=pagination.limit,
+            has_more=pagination.has_more,
+            data=items,
+        ).model_dump(mode="json")
 
     @web_ns.doc("Save Message")
     @web_ns.doc(description="Save a specific message for later reference.")
@@ -89,28 +83,22 @@ class SavedMessageListApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(post_response_fields)
     def post(self, app_model, end_user):
         if app_model.mode != "completion":
             raise NotCompletionAppError()
 
-        parser = reqparse.RequestParser().add_argument("message_id", type=uuid_value, required=True, location="json")
-        args = parser.parse_args()
+        payload = SavedMessageCreatePayload.model_validate(web_ns.payload or {})
 
         try:
-            SavedMessageService.save(app_model, end_user, args["message_id"])
+            SavedMessageService.save(app_model, end_user, payload.message_id)
         except MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 
-        return {"result": "success"}
+        return ResultResponse(result="success").model_dump(mode="json")
 
 
 @web_ns.route("/saved-messages/<uuid:message_id>")
 class SavedMessageApi(WebApiResource):
-    delete_response_fields = {
-        "result": fields.String,
-    }
-
     @web_ns.doc("Delete Saved Message")
     @web_ns.doc(description="Remove a message from saved messages.")
     @web_ns.doc(params={"message_id": {"description": "Message UUID to delete", "type": "string", "required": True}})
@@ -124,7 +112,6 @@ class SavedMessageApi(WebApiResource):
             500: "Internal Server Error",
         }
     )
-    @marshal_with(delete_response_fields)
     def delete(self, app_model, end_user, message_id):
         message_id = str(message_id)
 
@@ -133,4 +120,4 @@ class SavedMessageApi(WebApiResource):
 
         SavedMessageService.delete(app_model, end_user, message_id)
 
-        return {"result": "success"}, 204
+        return ResultResponse(result="success").model_dump(mode="json"), 204

api/core/agent/agent_app_runner.py

@@ -0,0 +1,380 @@
+import logging
+from collections.abc import Generator
+from copy import deepcopy
+from typing import Any
+
+from core.agent.base_agent_runner import BaseAgentRunner
+from core.agent.entities import AgentEntity, AgentLog, AgentResult
+from core.agent.patterns.strategy_factory import StrategyFactory
+from core.app.apps.base_app_queue_manager import PublishFrom
+from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
+from core.file import file_manager
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageContentType,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
+from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
+from core.tools.__base.tool import Tool
+from core.tools.entities.tool_entities import ToolInvokeMeta
+from core.tools.tool_engine import ToolEngine
+from models.model import Message
+
+logger = logging.getLogger(__name__)
+
+
+class AgentAppRunner(BaseAgentRunner):
+    def _create_tool_invoke_hook(self, message: Message):
+        """
+        Create a tool invoke hook that uses ToolEngine.agent_invoke.
+        This hook handles file creation and returns proper meta information.
+        """
+        # Get trace manager from app generate entity
+        trace_manager = self.application_generate_entity.trace_manager
+
+        def tool_invoke_hook(
+            tool: Tool, tool_args: dict[str, Any], tool_name: str
+        ) -> tuple[str, list[str], ToolInvokeMeta]:
+            """Hook that uses agent_invoke for proper file and meta handling."""
+            tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
+                tool=tool,
+                tool_parameters=tool_args,
+                user_id=self.user_id,
+                tenant_id=self.tenant_id,
+                message=message,
+                invoke_from=self.application_generate_entity.invoke_from,
+                agent_tool_callback=self.agent_callback,
+                trace_manager=trace_manager,
+                app_id=self.application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                conversation_id=self.conversation.id,
+            )
+
+            # Publish files and track IDs
+            for message_file_id in message_files:
+                self.queue_manager.publish(
+                    QueueMessageFileEvent(message_file_id=message_file_id),
+                    PublishFrom.APPLICATION_MANAGER,
+                )
+                self._current_message_file_ids.append(message_file_id)
+
+            return tool_invoke_response, message_files, tool_invoke_meta
+
+        return tool_invoke_hook
+
+    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
+        """
+        Run Agent application
+        """
+        self.query = query
+        app_generate_entity = self.application_generate_entity
+
+        app_config = self.app_config
+        assert app_config is not None, "app_config is required"
+        assert app_config.agent is not None, "app_config.agent is required"
+
+        # convert tools into ModelRuntime Tool format
+        tool_instances, _ = self._init_prompt_tools()
+
+        assert app_config.agent
+
+        # Create tool invoke hook for agent_invoke
+        tool_invoke_hook = self._create_tool_invoke_hook(message)
+
+        # Get instruction for ReAct strategy
+        instruction = self.app_config.prompt_template.simple_prompt_template or ""
+
+        # Use factory to create appropriate strategy
+        strategy = StrategyFactory.create_strategy(
+            model_features=self.model_features,
+            model_instance=self.model_instance,
+            tools=list(tool_instances.values()),
+            files=list(self.files),
+            max_iterations=app_config.agent.max_iteration,
+            context=self.build_execution_context(),
+            agent_strategy=self.config.strategy,
+            tool_invoke_hook=tool_invoke_hook,
+            instruction=instruction,
+        )
+
+        # Initialize state variables
+        current_agent_thought_id = None
+        has_published_thought = False
+        current_tool_name: str | None = None
+        self._current_message_file_ids: list[str] = []
+
+        # organize prompt messages
+        prompt_messages = self._organize_prompt_messages()
+
+        # Run strategy
+        generator = strategy.run(
+            prompt_messages=prompt_messages,
+            model_parameters=app_generate_entity.model_conf.parameters,
+            stop=app_generate_entity.model_conf.stop,
+            stream=True,
+        )
+
+        # Consume generator and collect result
+        result: AgentResult | None = None
+        try:
+            while True:
+                try:
+                    output = next(generator)
+                except StopIteration as e:
+                    # Generator finished, get the return value
+                    result = e.value
+                    break
+
+                if isinstance(output, LLMResultChunk):
+                    # Handle LLM chunk
+                    if current_agent_thought_id and not has_published_thought:
+                        self.queue_manager.publish(
+                            QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                            PublishFrom.APPLICATION_MANAGER,
+                        )
+                        has_published_thought = True
+
+                    yield output
+
+                elif isinstance(output, AgentLog):
+                    # Handle Agent Log using log_type for type-safe dispatch
+                    if output.status == AgentLog.LogStatus.START:
+                        if output.log_type == AgentLog.LogType.ROUND:
+                            # Start of a new round
+                            message_file_ids: list[str] = []
+                            current_agent_thought_id = self.create_agent_thought(
+                                message_id=message.id,
+                                message="",
+                                tool_name="",
+                                tool_input="",
+                                messages_ids=message_file_ids,
+                            )
+                            has_published_thought = False
+
+                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Tool call start - extract data from structured fields
+                            current_tool_name = output.data.get("tool_name", "")
+                            tool_input = output.data.get("tool_args", {})
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=current_tool_name,
+                                tool_input=tool_input,
+                                thought=None,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=None,
+                                messages_ids=[],
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                    elif output.status == AgentLog.LogStatus.SUCCESS:
+                        if output.log_type == AgentLog.LogType.THOUGHT:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            thought_text = output.data.get("thought")
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=thought_text,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=None,
+                                messages_ids=[],
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Tool call finished
+                            tool_output = output.data.get("output")
+                            # Get meta from strategy output (now properly populated)
+                            tool_meta = output.data.get("meta")
+
+                            # Wrap tool_meta with tool_name as key (required by agent_service)
+                            if tool_meta and current_tool_name:
+                                tool_meta = {current_tool_name: tool_meta}
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=None,
+                                observation=tool_output,
+                                tool_invoke_meta=tool_meta,
+                                answer=None,
+                                messages_ids=self._current_message_file_ids,
+                            )
+                            # Clear message file ids after saving
+                            self._current_message_file_ids = []
+                            current_tool_name = None
+
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                        elif output.log_type == AgentLog.LogType.ROUND:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Round finished - save LLM usage and answer
+                            llm_usage = output.metadata.get(AgentLog.LogMetadata.LLM_USAGE)
+                            llm_result = output.data.get("llm_result")
+                            final_answer = output.data.get("final_answer")
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=llm_result,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=final_answer,
+                                messages_ids=[],
+                                llm_usage=llm_usage,
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+        except Exception:
+            # Re-raise any other exceptions
+            raise
+
+        # Process final result
+        if isinstance(result, AgentResult):
+            final_answer = result.text
+            usage = result.usage or LLMUsage.empty_usage()
+
+            # Publish end event
+            self.queue_manager.publish(
+                QueueMessageEndEvent(
+                    llm_result=LLMResult(
+                        model=self.model_instance.model,
+                        prompt_messages=prompt_messages,
+                        message=AssistantPromptMessage(content=final_answer),
+                        usage=usage,
+                        system_fingerprint="",
+                    )
+                ),
+                PublishFrom.APPLICATION_MANAGER,
+            )
+
+    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Initialize system message
+        """
+        if not prompt_template:
+            return prompt_messages or []
+
+        prompt_messages = prompt_messages or []
+
+        if prompt_messages and isinstance(prompt_messages[0], SystemPromptMessage):
+            prompt_messages[0] = SystemPromptMessage(content=prompt_template)
+            return prompt_messages
+
+        if not prompt_messages:
+            return [SystemPromptMessage(content=prompt_template)]
+
+        prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
+        return prompt_messages
+
+    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Organize user query
+        """
+        if self.files:
+            # get image detail config
+            image_detail_config = (
+                self.application_generate_entity.file_upload_config.image_config.detail
+                if (
+                    self.application_generate_entity.file_upload_config
+                    and self.application_generate_entity.file_upload_config.image_config
+                )
+                else None
+            )
+            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+            for file in self.files:
+                prompt_message_contents.append(
+                    file_manager.to_prompt_message_content(
+                        file,
+                        image_detail_config=image_detail_config,
+                    )
+                )
+            prompt_message_contents.append(TextPromptMessageContent(data=query))
+
+            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+        else:
+            prompt_messages.append(UserPromptMessage(content=query))
+
+        return prompt_messages
+
+    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        As for now, gpt supports both fc and vision at the first iteration.
+        We need to remove the image messages from the prompt messages at the first iteration.
+        """
+        prompt_messages = deepcopy(prompt_messages)
+
+        for prompt_message in prompt_messages:
+            if isinstance(prompt_message, UserPromptMessage):
+                if isinstance(prompt_message.content, list):
+                    prompt_message.content = "\n".join(
+                        [
+                            content.data
+                            if content.type == PromptMessageContentType.TEXT
+                            else "[image]"
+                            if content.type == PromptMessageContentType.IMAGE
+                            else "[file]"
+                            for content in prompt_message.content
+                        ]
+                    )
+
+        return prompt_messages
+
+    def _organize_prompt_messages(self):
+        # For ReAct strategy, use the agent prompt template
+        if self.config.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT and self.config.prompt:
+            prompt_template = self.config.prompt.first_prompt
+        else:
+            prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
+
+        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
+        query_prompt_messages = self._organize_user_query(self.query or "", [])
+
+        self.history_prompt_messages = AgentHistoryPromptTransform(
+            model_config=self.model_config,
+            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
+            history_messages=self.history_prompt_messages,
+            memory=self.memory,
+        ).get_prompt()
+
+        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
+        if len(self._current_thoughts) != 0:
+            # clear messages after the first iteration
+            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
+        return prompt_messages

api/core/agent/base_agent_runner.py

@@ -5,7 +5,7 @@ from typing import Union, cast
 
 from sqlalchemy import select
 
-from core.agent.entities import AgentEntity, AgentToolEntity
+from core.agent.entities import AgentEntity, AgentToolEntity, ExecutionContext
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager
@@ -114,9 +114,20 @@ class BaseAgentRunner(AppRunner):
         features = model_schema.features if model_schema and model_schema.features else []
         self.stream_tool_call = ModelFeature.STREAM_TOOL_CALL in features
         self.files = application_generate_entity.files if ModelFeature.VISION in features else []
+        self.model_features = features
         self.query: str | None = ""
         self._current_thoughts: list[PromptMessage] = []
 
+    def build_execution_context(self) -> ExecutionContext:
+        """Build execution context."""
+        return ExecutionContext(
+            user_id=self.user_id,
+            app_id=self.app_config.app_id,
+            conversation_id=self.conversation.id,
+            message_id=self.message.id,
+            tenant_id=self.tenant_id,
+        )
+
     def _repack_app_generate_entity(
         self, app_generate_entity: AgentChatAppGenerateEntity
     ) -> AgentChatAppGenerateEntity:

api/core/agent/cot_agent_runner.py

@@ -1,431 +0,0 @@
-import json
-import logging
-from abc import ABC, abstractmethod
-from collections.abc import Generator, Mapping, Sequence
-from typing import Any
-
-from core.agent.base_agent_runner import BaseAgentRunner
-from core.agent.entities import AgentScratchpadUnit
-from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
-from core.app.apps.base_app_queue_manager import PublishFrom
-from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    PromptMessageTool,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-from core.ops.ops_trace_manager import TraceQueueManager
-from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
-from core.tools.__base.tool import Tool
-from core.tools.entities.tool_entities import ToolInvokeMeta
-from core.tools.tool_engine import ToolEngine
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class CotAgentRunner(BaseAgentRunner, ABC):
-    _is_first_iteration = True
-    _ignore_observation_providers = ["wenxin"]
-    _historic_prompt_messages: list[PromptMessage]
-    _agent_scratchpad: list[AgentScratchpadUnit]
-    _instruction: str
-    _query: str
-    _prompt_messages_tools: Sequence[PromptMessageTool]
-
-    def run(
-        self,
-        message: Message,
-        query: str,
-        inputs: Mapping[str, str],
-    ) -> Generator:
-        """
-        Run Cot agent application
-        """
-
-        app_generate_entity = self.application_generate_entity
-        self._repack_app_generate_entity(app_generate_entity)
-        self._init_react_state(query)
-
-        trace_manager = app_generate_entity.trace_manager
-
-        # check model mode
-        if "Observation" not in app_generate_entity.model_conf.stop:
-            if app_generate_entity.model_conf.provider not in self._ignore_observation_providers:
-                app_generate_entity.model_conf.stop.append("Observation")
-
-        app_config = self.app_config
-        assert app_config.agent
-
-        # init instruction
-        inputs = inputs or {}
-        instruction = app_config.prompt_template.simple_prompt_template or ""
-        self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
-
-        iteration_step = 1
-        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
-
-        # convert tools into ModelRuntime Tool format
-        tool_instances, prompt_messages_tools = self._init_prompt_tools()
-        self._prompt_messages_tools = prompt_messages_tools
-
-        function_call_state = True
-        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
-        final_answer = ""
-        prompt_messages: list = []  # Initialize prompt_messages
-        agent_thought_id = ""  # Initialize agent_thought_id
-
-        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
-            if not final_llm_usage_dict["usage"]:
-                final_llm_usage_dict["usage"] = usage
-            else:
-                llm_usage = final_llm_usage_dict["usage"]
-                llm_usage.prompt_tokens += usage.prompt_tokens
-                llm_usage.completion_tokens += usage.completion_tokens
-                llm_usage.total_tokens += usage.total_tokens
-                llm_usage.prompt_price += usage.prompt_price
-                llm_usage.completion_price += usage.completion_price
-                llm_usage.total_price += usage.total_price
-
-        model_instance = self.model_instance
-
-        while function_call_state and iteration_step <= max_iteration_steps:
-            # continue to run until there is not any tool call
-            function_call_state = False
-
-            if iteration_step == max_iteration_steps:
-                # the last iteration, remove all tools
-                self._prompt_messages_tools = []
-
-            message_file_ids: list[str] = []
-
-            agent_thought_id = self.create_agent_thought(
-                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
-            )
-
-            if iteration_step > 1:
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            # recalc llm max tokens
-            prompt_messages = self._organize_prompt_messages()
-            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
-            # invoke model
-            chunks = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters=app_generate_entity.model_conf.parameters,
-                tools=[],
-                stop=app_generate_entity.model_conf.stop,
-                stream=True,
-                user=self.user_id,
-                callbacks=[],
-            )
-
-            usage_dict: dict[str, LLMUsage | None] = {}
-            react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks, usage_dict)
-            scratchpad = AgentScratchpadUnit(
-                agent_response="",
-                thought="",
-                action_str="",
-                observation="",
-                action=None,
-            )
-
-            # publish agent thought if it's first iteration
-            if iteration_step == 1:
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            for chunk in react_chunks:
-                if isinstance(chunk, AgentScratchpadUnit.Action):
-                    action = chunk
-                    # detect action
-                    assert scratchpad.agent_response is not None
-                    scratchpad.agent_response += json.dumps(chunk.model_dump())
-                    scratchpad.action_str = json.dumps(chunk.model_dump())
-                    scratchpad.action = action
-                else:
-                    assert scratchpad.agent_response is not None
-                    scratchpad.agent_response += chunk
-                    assert scratchpad.thought is not None
-                    scratchpad.thought += chunk
-                    yield LLMResultChunk(
-                        model=self.model_config.model,
-                        prompt_messages=prompt_messages,
-                        system_fingerprint="",
-                        delta=LLMResultChunkDelta(index=0, message=AssistantPromptMessage(content=chunk), usage=None),
-                    )
-
-            assert scratchpad.thought is not None
-            scratchpad.thought = scratchpad.thought.strip() or "I am thinking about how to help you"
-            self._agent_scratchpad.append(scratchpad)
-
-            # get llm usage
-            if "usage" in usage_dict:
-                if usage_dict["usage"] is not None:
-                    increase_usage(llm_usage, usage_dict["usage"])
-            else:
-                usage_dict["usage"] = LLMUsage.empty_usage()
-
-            self.save_agent_thought(
-                agent_thought_id=agent_thought_id,
-                tool_name=(scratchpad.action.action_name if scratchpad.action and not scratchpad.is_final() else ""),
-                tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
-                tool_invoke_meta={},
-                thought=scratchpad.thought or "",
-                observation="",
-                answer=scratchpad.agent_response or "",
-                messages_ids=[],
-                llm_usage=usage_dict["usage"],
-            )
-
-            if not scratchpad.is_final():
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            if not scratchpad.action:
-                # failed to extract action, return final answer directly
-                final_answer = ""
-            else:
-                if scratchpad.action.action_name.lower() == "final answer":
-                    # action is final answer, return final answer directly
-                    try:
-                        if isinstance(scratchpad.action.action_input, dict):
-                            final_answer = json.dumps(scratchpad.action.action_input, ensure_ascii=False)
-                        elif isinstance(scratchpad.action.action_input, str):
-                            final_answer = scratchpad.action.action_input
-                        else:
-                            final_answer = f"{scratchpad.action.action_input}"
-                    except TypeError:
-                        final_answer = f"{scratchpad.action.action_input}"
-                else:
-                    function_call_state = True
-                    # action is tool call, invoke tool
-                    tool_invoke_response, tool_invoke_meta = self._handle_invoke_action(
-                        action=scratchpad.action,
-                        tool_instances=tool_instances,
-                        message_file_ids=message_file_ids,
-                        trace_manager=trace_manager,
-                    )
-                    scratchpad.observation = tool_invoke_response
-                    scratchpad.agent_response = tool_invoke_response
-
-                    self.save_agent_thought(
-                        agent_thought_id=agent_thought_id,
-                        tool_name=scratchpad.action.action_name,
-                        tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
-                        thought=scratchpad.thought or "",
-                        observation={scratchpad.action.action_name: tool_invoke_response},
-                        tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
-                        answer=scratchpad.agent_response,
-                        messages_ids=message_file_ids,
-                        llm_usage=usage_dict["usage"],
-                    )
-
-                    self.queue_manager.publish(
-                        QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                    )
-
-                # update prompt tool message
-                for prompt_tool in self._prompt_messages_tools:
-                    self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
-
-            iteration_step += 1
-
-        yield LLMResultChunk(
-            model=model_instance.model,
-            prompt_messages=prompt_messages,
-            delta=LLMResultChunkDelta(
-                index=0, message=AssistantPromptMessage(content=final_answer), usage=llm_usage["usage"]
-            ),
-            system_fingerprint="",
-        )
-
-        # save agent thought
-        self.save_agent_thought(
-            agent_thought_id=agent_thought_id,
-            tool_name="",
-            tool_input={},
-            tool_invoke_meta={},
-            thought=final_answer,
-            observation={},
-            answer=final_answer,
-            messages_ids=[],
-        )
-        # publish end event
-        self.queue_manager.publish(
-            QueueMessageEndEvent(
-                llm_result=LLMResult(
-                    model=model_instance.model,
-                    prompt_messages=prompt_messages,
-                    message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
-                    system_fingerprint="",
-                )
-            ),
-            PublishFrom.APPLICATION_MANAGER,
-        )
-
-    def _handle_invoke_action(
-        self,
-        action: AgentScratchpadUnit.Action,
-        tool_instances: Mapping[str, Tool],
-        message_file_ids: list[str],
-        trace_manager: TraceQueueManager | None = None,
-    ) -> tuple[str, ToolInvokeMeta]:
-        """
-        handle invoke action
-        :param action: action
-        :param tool_instances: tool instances
-        :param message_file_ids: message file ids
-        :param trace_manager: trace manager
-        :return: observation, meta
-        """
-        # action is tool call, invoke tool
-        tool_call_name = action.action_name
-        tool_call_args = action.action_input
-        tool_instance = tool_instances.get(tool_call_name)
-
-        if not tool_instance:
-            answer = f"there is not a tool named {tool_call_name}"
-            return answer, ToolInvokeMeta.error_instance(answer)
-
-        if isinstance(tool_call_args, str):
-            try:
-                tool_call_args = json.loads(tool_call_args)
-            except json.JSONDecodeError:
-                pass
-
-        # invoke tool
-        tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
-            tool=tool_instance,
-            tool_parameters=tool_call_args,
-            user_id=self.user_id,
-            tenant_id=self.tenant_id,
-            message=self.message,
-            invoke_from=self.application_generate_entity.invoke_from,
-            agent_tool_callback=self.agent_callback,
-            trace_manager=trace_manager,
-        )
-
-        # publish files
-        for message_file_id in message_files:
-            # publish message file
-            self.queue_manager.publish(
-                QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
-            )
-            # add message file ids
-            message_file_ids.append(message_file_id)
-
-        return tool_invoke_response, tool_invoke_meta
-
-    def _convert_dict_to_action(self, action: dict) -> AgentScratchpadUnit.Action:
-        """
-        convert dict to action
-        """
-        return AgentScratchpadUnit.Action(action_name=action["action"], action_input=action["action_input"])
-
-    def _fill_in_inputs_from_external_data_tools(self, instruction: str, inputs: Mapping[str, Any]) -> str:
-        """
-        fill in inputs from external data tools
-        """
-        for key, value in inputs.items():
-            try:
-                instruction = instruction.replace(f"{{{{{key}}}}}", str(value))
-            except Exception:
-                continue
-
-        return instruction
-
-    def _init_react_state(self, query):
-        """
-        init agent scratchpad
-        """
-        self._query = query
-        self._agent_scratchpad = []
-        self._historic_prompt_messages = self._organize_historic_prompt_messages()
-
-    @abstractmethod
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        organize prompt messages
-        """
-
-    def _format_assistant_message(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
-        """
-        format assistant message
-        """
-        message = ""
-        for scratchpad in agent_scratchpad:
-            if scratchpad.is_final():
-                message += f"Final Answer: {scratchpad.agent_response}"
-            else:
-                message += f"Thought: {scratchpad.thought}\n\n"
-                if scratchpad.action_str:
-                    message += f"Action: {scratchpad.action_str}\n\n"
-                if scratchpad.observation:
-                    message += f"Observation: {scratchpad.observation}\n\n"
-
-        return message
-
-    def _organize_historic_prompt_messages(
-        self, current_session_messages: list[PromptMessage] | None = None
-    ) -> list[PromptMessage]:
-        """
-        organize historic prompt messages
-        """
-        result: list[PromptMessage] = []
-        scratchpads: list[AgentScratchpadUnit] = []
-        current_scratchpad: AgentScratchpadUnit | None = None
-
-        for message in self.history_prompt_messages:
-            if isinstance(message, AssistantPromptMessage):
-                if not current_scratchpad:
-                    assert isinstance(message.content, str)
-                    current_scratchpad = AgentScratchpadUnit(
-                        agent_response=message.content,
-                        thought=message.content or "I am thinking about how to help you",
-                        action_str="",
-                        action=None,
-                        observation=None,
-                    )
-                    scratchpads.append(current_scratchpad)
-                if message.tool_calls:
-                    try:
-                        current_scratchpad.action = AgentScratchpadUnit.Action(
-                            action_name=message.tool_calls[0].function.name,
-                            action_input=json.loads(message.tool_calls[0].function.arguments),
-                        )
-                        current_scratchpad.action_str = json.dumps(current_scratchpad.action.to_dict())
-                    except Exception:
-                        logger.exception("Failed to parse tool call from assistant message")
-            elif isinstance(message, ToolPromptMessage):
-                if current_scratchpad:
-                    assert isinstance(message.content, str)
-                    current_scratchpad.observation = message.content
-                else:
-                    raise NotImplementedError("expected str type")
-            elif isinstance(message, UserPromptMessage):
-                if scratchpads:
-                    result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
-                    scratchpads = []
-                    current_scratchpad = None
-
-                result.append(message)
-
-        if scratchpads:
-            result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
-
-        historic_prompts = AgentHistoryPromptTransform(
-            model_config=self.model_config,
-            prompt_messages=current_session_messages or [],
-            history_messages=result,
-            memory=self.memory,
-        ).get_prompt()
-        return historic_prompts

api/core/agent/cot_chat_agent_runner.py

@@ -1,118 +0,0 @@
-import json
-
-from core.agent.cot_agent_runner import CotAgentRunner
-from core.file import file_manager
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from core.model_runtime.utils.encoders import jsonable_encoder
-
-
-class CotChatAgentRunner(CotAgentRunner):
-    def _organize_system_prompt(self) -> SystemPromptMessage:
-        """
-        Organize system prompt
-        """
-        assert self.app_config.agent
-        assert self.app_config.agent.prompt
-
-        prompt_entity = self.app_config.agent.prompt
-        if not prompt_entity:
-            raise ValueError("Agent prompt configuration is not set")
-        first_prompt = prompt_entity.first_prompt
-
-        system_prompt = (
-            first_prompt.replace("{{instruction}}", self._instruction)
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
-            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
-        )
-
-        return SystemPromptMessage(content=system_prompt)
-
-    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Organize user query
-        """
-        if self.files:
-            # get image detail config
-            image_detail_config = (
-                self.application_generate_entity.file_upload_config.image_config.detail
-                if (
-                    self.application_generate_entity.file_upload_config
-                    and self.application_generate_entity.file_upload_config.image_config
-                )
-                else None
-            )
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-
-            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
-            for file in self.files:
-                prompt_message_contents.append(
-                    file_manager.to_prompt_message_content(
-                        file,
-                        image_detail_config=image_detail_config,
-                    )
-                )
-            prompt_message_contents.append(TextPromptMessageContent(data=query))
-
-            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-        else:
-            prompt_messages.append(UserPromptMessage(content=query))
-
-        return prompt_messages
-
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        Organize
-        """
-        # organize system prompt
-        system_message = self._organize_system_prompt()
-
-        # organize current assistant messages
-        agent_scratchpad = self._agent_scratchpad
-        if not agent_scratchpad:
-            assistant_messages = []
-        else:
-            assistant_message = AssistantPromptMessage(content="")
-            assistant_message.content = ""  # FIXME: type check tell mypy that assistant_message.content is str
-            for unit in agent_scratchpad:
-                if unit.is_final():
-                    assert isinstance(assistant_message.content, str)
-                    assistant_message.content += f"Final Answer: {unit.agent_response}"
-                else:
-                    assert isinstance(assistant_message.content, str)
-                    assistant_message.content += f"Thought: {unit.thought}\n\n"
-                    if unit.action_str:
-                        assistant_message.content += f"Action: {unit.action_str}\n\n"
-                    if unit.observation:
-                        assistant_message.content += f"Observation: {unit.observation}\n\n"
-
-            assistant_messages = [assistant_message]
-
-        # query messages
-        query_messages = self._organize_user_query(self._query, [])
-
-        if assistant_messages:
-            # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages(
-                [system_message, *query_messages, *assistant_messages, UserPromptMessage(content="continue")]
-            )
-            messages = [
-                system_message,
-                *historic_messages,
-                *query_messages,
-                *assistant_messages,
-                UserPromptMessage(content="continue"),
-            ]
-        else:
-            # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages([system_message, *query_messages])
-            messages = [system_message, *historic_messages, *query_messages]
-
-        # join all messages
-        return messages

api/core/agent/cot_completion_agent_runner.py

@@ -1,87 +0,0 @@
-import json
-
-from core.agent.cot_agent_runner import CotAgentRunner
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.utils.encoders import jsonable_encoder
-
-
-class CotCompletionAgentRunner(CotAgentRunner):
-    def _organize_instruction_prompt(self) -> str:
-        """
-        Organize instruction prompt
-        """
-        if self.app_config.agent is None:
-            raise ValueError("Agent configuration is not set")
-        prompt_entity = self.app_config.agent.prompt
-        if prompt_entity is None:
-            raise ValueError("prompt entity is not set")
-        first_prompt = prompt_entity.first_prompt
-
-        system_prompt = (
-            first_prompt.replace("{{instruction}}", self._instruction)
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
-            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
-        )
-
-        return system_prompt
-
-    def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] | None = None) -> str:
-        """
-        Organize historic prompt
-        """
-        historic_prompt_messages = self._organize_historic_prompt_messages(current_session_messages)
-        historic_prompt = ""
-
-        for message in historic_prompt_messages:
-            if isinstance(message, UserPromptMessage):
-                historic_prompt += f"Question: {message.content}\n\n"
-            elif isinstance(message, AssistantPromptMessage):
-                if isinstance(message.content, str):
-                    historic_prompt += message.content + "\n\n"
-                elif isinstance(message.content, list):
-                    for content in message.content:
-                        if not isinstance(content, TextPromptMessageContent):
-                            continue
-                        historic_prompt += content.data
-
-        return historic_prompt
-
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        Organize prompt messages
-        """
-        # organize system prompt
-        system_prompt = self._organize_instruction_prompt()
-
-        # organize historic prompt messages
-        historic_prompt = self._organize_historic_prompt()
-
-        # organize current assistant messages
-        agent_scratchpad = self._agent_scratchpad
-        assistant_prompt = ""
-        for unit in agent_scratchpad or []:
-            if unit.is_final():
-                assistant_prompt += f"Final Answer: {unit.agent_response}"
-            else:
-                assistant_prompt += f"Thought: {unit.thought}\n\n"
-                if unit.action_str:
-                    assistant_prompt += f"Action: {unit.action_str}\n\n"
-                if unit.observation:
-                    assistant_prompt += f"Observation: {unit.observation}\n\n"
-
-        # query messages
-        query_prompt = f"Question: {self._query}"
-
-        # join all messages
-        prompt = (
-            system_prompt.replace("{{historic_messages}}", historic_prompt)
-            .replace("{{agent_scratchpad}}", assistant_prompt)
-            .replace("{{query}}", query_prompt)
-        )
-
-        return [UserPromptMessage(content=prompt)]

api/core/agent/entities.py

@@ -1,3 +1,5 @@
+import uuid
+from collections.abc import Mapping
 from enum import StrEnum
 from typing import Any, Union
 
@@ -92,3 +94,96 @@ class AgentInvokeMessage(ToolInvokeMessage):
     """
 
     pass
+
+
+class ExecutionContext(BaseModel):
+    """Execution context containing trace and audit information.
+
+    This context carries all the IDs and metadata that are not part of
+    the core business logic but needed for tracing, auditing, and
+    correlation purposes.
+    """
+
+    user_id: str | None = None
+    app_id: str | None = None
+    conversation_id: str | None = None
+    message_id: str | None = None
+    tenant_id: str | None = None
+
+    @classmethod
+    def create_minimal(cls, user_id: str | None = None) -> "ExecutionContext":
+        """Create a minimal context with only essential fields."""
+        return cls(user_id=user_id)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for passing to legacy code."""
+        return {
+            "user_id": self.user_id,
+            "app_id": self.app_id,
+            "conversation_id": self.conversation_id,
+            "message_id": self.message_id,
+            "tenant_id": self.tenant_id,
+        }
+
+    def with_updates(self, **kwargs) -> "ExecutionContext":
+        """Create a new context with updated fields."""
+        data = self.to_dict()
+        data.update(kwargs)
+
+        return ExecutionContext(
+            user_id=data.get("user_id"),
+            app_id=data.get("app_id"),
+            conversation_id=data.get("conversation_id"),
+            message_id=data.get("message_id"),
+            tenant_id=data.get("tenant_id"),
+        )
+
+
+class AgentLog(BaseModel):
+    """
+    Agent Log.
+    """
+
+    class LogType(StrEnum):
+        """Type of agent log entry."""
+
+        ROUND = "round"  # A complete iteration round
+        THOUGHT = "thought"  # LLM thinking/reasoning
+        TOOL_CALL = "tool_call"  # Tool invocation
+
+    class LogMetadata(StrEnum):
+        STARTED_AT = "started_at"
+        FINISHED_AT = "finished_at"
+        ELAPSED_TIME = "elapsed_time"
+        TOTAL_PRICE = "total_price"
+        TOTAL_TOKENS = "total_tokens"
+        PROVIDER = "provider"
+        CURRENCY = "currency"
+        LLM_USAGE = "llm_usage"
+        ICON = "icon"
+        ICON_DARK = "icon_dark"
+
+    class LogStatus(StrEnum):
+        START = "start"
+        ERROR = "error"
+        SUCCESS = "success"
+
+    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="The id of the log")
+    label: str = Field(..., description="The label of the log")
+    log_type: LogType = Field(..., description="The type of the log")
+    parent_id: str | None = Field(default=None, description="Leave empty for root log")
+    error: str | None = Field(default=None, description="The error message")
+    status: LogStatus = Field(..., description="The status of the log")
+    data: Mapping[str, Any] = Field(..., description="Detailed log data")
+    metadata: Mapping[LogMetadata, Any] = Field(default={}, description="The metadata of the log")
+
+
+class AgentResult(BaseModel):
+    """
+    Agent execution result.
+    """
+
+    text: str = Field(default="", description="The generated text")
+    files: list[Any] = Field(default_factory=list, description="Files produced during execution")
+    usage: Any | None = Field(default=None, description="LLM usage statistics")
+    finish_reason: str | None = Field(default=None, description="Reason for completion")

api/core/agent/fc_agent_runner.py

@@ -1,465 +0,0 @@
-import json
-import logging
-from collections.abc import Generator
-from copy import deepcopy
-from typing import Any, Union
-
-from core.agent.base_agent_runner import BaseAgentRunner
-from core.app.apps.base_app_queue_manager import PublishFrom
-from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
-from core.file import file_manager
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    LLMResult,
-    LLMResultChunk,
-    LLMResultChunkDelta,
-    LLMUsage,
-    PromptMessage,
-    PromptMessageContentType,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
-from core.tools.entities.tool_entities import ToolInvokeMeta
-from core.tools.tool_engine import ToolEngine
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class FunctionCallAgentRunner(BaseAgentRunner):
-    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
-        """
-        Run FunctionCall agent application
-        """
-        self.query = query
-        app_generate_entity = self.application_generate_entity
-
-        app_config = self.app_config
-        assert app_config is not None, "app_config is required"
-        assert app_config.agent is not None, "app_config.agent is required"
-
-        # convert tools into ModelRuntime Tool format
-        tool_instances, prompt_messages_tools = self._init_prompt_tools()
-
-        assert app_config.agent
-
-        iteration_step = 1
-        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
-
-        # continue to run until there is not any tool call
-        function_call_state = True
-        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
-        final_answer = ""
-        prompt_messages: list = []  # Initialize prompt_messages
-
-        # get tracing instance
-        trace_manager = app_generate_entity.trace_manager
-
-        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
-            if not final_llm_usage_dict["usage"]:
-                final_llm_usage_dict["usage"] = usage
-            else:
-                llm_usage = final_llm_usage_dict["usage"]
-                llm_usage.prompt_tokens += usage.prompt_tokens
-                llm_usage.completion_tokens += usage.completion_tokens
-                llm_usage.total_tokens += usage.total_tokens
-                llm_usage.prompt_price += usage.prompt_price
-                llm_usage.completion_price += usage.completion_price
-                llm_usage.total_price += usage.total_price
-
-        model_instance = self.model_instance
-
-        while function_call_state and iteration_step <= max_iteration_steps:
-            function_call_state = False
-
-            if iteration_step == max_iteration_steps:
-                # the last iteration, remove all tools
-                prompt_messages_tools = []
-
-            message_file_ids: list[str] = []
-            agent_thought_id = self.create_agent_thought(
-                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
-            )
-
-            # recalc llm max tokens
-            prompt_messages = self._organize_prompt_messages()
-            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
-            # invoke model
-            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters=app_generate_entity.model_conf.parameters,
-                tools=prompt_messages_tools,
-                stop=app_generate_entity.model_conf.stop,
-                stream=self.stream_tool_call,
-                user=self.user_id,
-                callbacks=[],
-            )
-
-            tool_calls: list[tuple[str, str, dict[str, Any]]] = []
-
-            # save full response
-            response = ""
-
-            # save tool call names and inputs
-            tool_call_names = ""
-            tool_call_inputs = ""
-
-            current_llm_usage = None
-
-            if isinstance(chunks, Generator):
-                is_first_chunk = True
-                for chunk in chunks:
-                    if is_first_chunk:
-                        self.queue_manager.publish(
-                            QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                        )
-                        is_first_chunk = False
-                    # check if there is any tool call
-                    if self.check_tool_calls(chunk):
-                        function_call_state = True
-                        tool_calls.extend(self.extract_tool_calls(chunk) or [])
-                        tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
-                        try:
-                            tool_call_inputs = json.dumps(
-                                {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
-                            )
-                        except TypeError:
-                            # fallback: force ASCII to handle non-serializable objects
-                            tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
-
-                    if chunk.delta.message and chunk.delta.message.content:
-                        if isinstance(chunk.delta.message.content, list):
-                            for content in chunk.delta.message.content:
-                                response += content.data
-                        else:
-                            response += str(chunk.delta.message.content)
-
-                    if chunk.delta.usage:
-                        increase_usage(llm_usage, chunk.delta.usage)
-                        current_llm_usage = chunk.delta.usage
-
-                    yield chunk
-            else:
-                result = chunks
-                # check if there is any tool call
-                if self.check_blocking_tool_calls(result):
-                    function_call_state = True
-                    tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
-                    tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
-                    try:
-                        tool_call_inputs = json.dumps(
-                            {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
-                        )
-                    except TypeError:
-                        # fallback: force ASCII to handle non-serializable objects
-                        tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
-
-                if result.usage:
-                    increase_usage(llm_usage, result.usage)
-                    current_llm_usage = result.usage
-
-                if result.message and result.message.content:
-                    if isinstance(result.message.content, list):
-                        for content in result.message.content:
-                            response += content.data
-                    else:
-                        response += str(result.message.content)
-
-                if not result.message.content:
-                    result.message.content = ""
-
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-                yield LLMResultChunk(
-                    model=model_instance.model,
-                    prompt_messages=result.prompt_messages,
-                    system_fingerprint=result.system_fingerprint,
-                    delta=LLMResultChunkDelta(
-                        index=0,
-                        message=result.message,
-                        usage=result.usage,
-                    ),
-                )
-
-            assistant_message = AssistantPromptMessage(content="", tool_calls=[])
-            if tool_calls:
-                assistant_message.tool_calls = [
-                    AssistantPromptMessage.ToolCall(
-                        id=tool_call[0],
-                        type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name=tool_call[1], arguments=json.dumps(tool_call[2], ensure_ascii=False)
-                        ),
-                    )
-                    for tool_call in tool_calls
-                ]
-            else:
-                assistant_message.content = response
-
-            self._current_thoughts.append(assistant_message)
-
-            # save thought
-            self.save_agent_thought(
-                agent_thought_id=agent_thought_id,
-                tool_name=tool_call_names,
-                tool_input=tool_call_inputs,
-                thought=response,
-                tool_invoke_meta=None,
-                observation=None,
-                answer=response,
-                messages_ids=[],
-                llm_usage=current_llm_usage,
-            )
-            self.queue_manager.publish(
-                QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-            )
-
-            final_answer += response + "\n"
-
-            # call tools
-            tool_responses = []
-            for tool_call_id, tool_call_name, tool_call_args in tool_calls:
-                tool_instance = tool_instances.get(tool_call_name)
-                if not tool_instance:
-                    tool_response = {
-                        "tool_call_id": tool_call_id,
-                        "tool_call_name": tool_call_name,
-                        "tool_response": f"there is not a tool named {tool_call_name}",
-                        "meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict(),
-                    }
-                else:
-                    # invoke tool
-                    tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
-                        tool=tool_instance,
-                        tool_parameters=tool_call_args,
-                        user_id=self.user_id,
-                        tenant_id=self.tenant_id,
-                        message=self.message,
-                        invoke_from=self.application_generate_entity.invoke_from,
-                        agent_tool_callback=self.agent_callback,
-                        trace_manager=trace_manager,
-                        app_id=self.application_generate_entity.app_config.app_id,
-                        message_id=self.message.id,
-                        conversation_id=self.conversation.id,
-                    )
-                    # publish files
-                    for message_file_id in message_files:
-                        # publish message file
-                        self.queue_manager.publish(
-                            QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
-                        )
-                        # add message file ids
-                        message_file_ids.append(message_file_id)
-
-                    tool_response = {
-                        "tool_call_id": tool_call_id,
-                        "tool_call_name": tool_call_name,
-                        "tool_response": tool_invoke_response,
-                        "meta": tool_invoke_meta.to_dict(),
-                    }
-
-                tool_responses.append(tool_response)
-                if tool_response["tool_response"] is not None:
-                    self._current_thoughts.append(
-                        ToolPromptMessage(
-                            content=str(tool_response["tool_response"]),
-                            tool_call_id=tool_call_id,
-                            name=tool_call_name,
-                        )
-                    )
-
-            if len(tool_responses) > 0:
-                # save agent thought
-                self.save_agent_thought(
-                    agent_thought_id=agent_thought_id,
-                    tool_name="",
-                    tool_input="",
-                    thought="",
-                    tool_invoke_meta={
-                        tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
-                    },
-                    observation={
-                        tool_response["tool_call_name"]: tool_response["tool_response"]
-                        for tool_response in tool_responses
-                    },
-                    answer="",
-                    messages_ids=message_file_ids,
-                )
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            # update prompt tool
-            for prompt_tool in prompt_messages_tools:
-                self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
-
-            iteration_step += 1
-
-        # publish end event
-        self.queue_manager.publish(
-            QueueMessageEndEvent(
-                llm_result=LLMResult(
-                    model=model_instance.model,
-                    prompt_messages=prompt_messages,
-                    message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
-                    system_fingerprint="",
-                )
-            ),
-            PublishFrom.APPLICATION_MANAGER,
-        )
-
-    def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
-        """
-        Check if there is any tool call in llm result chunk
-        """
-        if llm_result_chunk.delta.message.tool_calls:
-            return True
-        return False
-
-    def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
-        """
-        Check if there is any blocking tool call in llm result
-        """
-        if llm_result.message.tool_calls:
-            return True
-        return False
-
-    def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
-        """
-        Extract tool calls from llm result chunk
-
-        Returns:
-            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
-        """
-        tool_calls = []
-        for prompt_message in llm_result_chunk.delta.message.tool_calls:
-            args = {}
-            if prompt_message.function.arguments != "":
-                args = json.loads(prompt_message.function.arguments)
-
-            tool_calls.append(
-                (
-                    prompt_message.id,
-                    prompt_message.function.name,
-                    args,
-                )
-            )
-
-        return tool_calls
-
-    def extract_blocking_tool_calls(self, llm_result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
-        """
-        Extract blocking tool calls from llm result
-
-        Returns:
-            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
-        """
-        tool_calls = []
-        for prompt_message in llm_result.message.tool_calls:
-            args = {}
-            if prompt_message.function.arguments != "":
-                args = json.loads(prompt_message.function.arguments)
-
-            tool_calls.append(
-                (
-                    prompt_message.id,
-                    prompt_message.function.name,
-                    args,
-                )
-            )
-
-        return tool_calls
-
-    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Initialize system message
-        """
-        if not prompt_messages and prompt_template:
-            return [
-                SystemPromptMessage(content=prompt_template),
-            ]
-
-        if prompt_messages and not isinstance(prompt_messages[0], SystemPromptMessage) and prompt_template:
-            prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
-
-        return prompt_messages or []
-
-    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Organize user query
-        """
-        if self.files:
-            # get image detail config
-            image_detail_config = (
-                self.application_generate_entity.file_upload_config.image_config.detail
-                if (
-                    self.application_generate_entity.file_upload_config
-                    and self.application_generate_entity.file_upload_config.image_config
-                )
-                else None
-            )
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-
-            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
-            for file in self.files:
-                prompt_message_contents.append(
-                    file_manager.to_prompt_message_content(
-                        file,
-                        image_detail_config=image_detail_config,
-                    )
-                )
-            prompt_message_contents.append(TextPromptMessageContent(data=query))
-
-            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-        else:
-            prompt_messages.append(UserPromptMessage(content=query))
-
-        return prompt_messages
-
-    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        As for now, gpt supports both fc and vision at the first iteration.
-        We need to remove the image messages from the prompt messages at the first iteration.
-        """
-        prompt_messages = deepcopy(prompt_messages)
-
-        for prompt_message in prompt_messages:
-            if isinstance(prompt_message, UserPromptMessage):
-                if isinstance(prompt_message.content, list):
-                    prompt_message.content = "\n".join(
-                        [
-                            content.data
-                            if content.type == PromptMessageContentType.TEXT
-                            else "[image]"
-                            if content.type == PromptMessageContentType.IMAGE
-                            else "[file]"
-                            for content in prompt_message.content
-                        ]
-                    )
-
-        return prompt_messages
-
-    def _organize_prompt_messages(self):
-        prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
-        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
-        query_prompt_messages = self._organize_user_query(self.query or "", [])
-
-        self.history_prompt_messages = AgentHistoryPromptTransform(
-            model_config=self.model_config,
-            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
-            history_messages=self.history_prompt_messages,
-            memory=self.memory,
-        ).get_prompt()
-
-        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
-        if len(self._current_thoughts) != 0:
-            # clear messages after the first iteration
-            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
-        return prompt_messages

api/core/agent/patterns/README.md

@@ -0,0 +1,55 @@
+# Agent Patterns
+
+A unified agent pattern module that powers both Agent V2 workflow nodes and agent applications. Strategies share a common execution contract while adapting to model capabilities and tool availability.
+
+## Overview
+
+The module applies a strategy pattern around LLM/tool orchestration. `StrategyFactory` auto-selects the best implementation based on model features or an explicit agent strategy, and each strategy streams logs and usage consistently.
+
+## Key Features
+
+- **Dual strategies**
+  - `FunctionCallStrategy`: uses native LLM function/tool calling when the model exposes `TOOL_CALL`, `MULTI_TOOL_CALL`, or `STREAM_TOOL_CALL`.
+  - `ReActStrategy`: ReAct (reasoning + acting) flow driven by `CotAgentOutputParser`, used when function calling is unavailable or explicitly requested.
+- **Explicit or auto selection**
+  - `StrategyFactory.create_strategy` prefers an explicit `AgentEntity.Strategy` (FUNCTION_CALLING or CHAIN_OF_THOUGHT).
+  - Otherwise it falls back to function calling when tool-call features exist, or ReAct when they do not.
+- **Unified execution contract**
+  - `AgentPattern.run` yields streaming `AgentLog` entries and `LLMResultChunk` data, returning an `AgentResult` with text, files, usage, and `finish_reason`.
+  - Iterations are configurable and hard-capped at 99 rounds; the last round forces a final answer by withholding tools.
+- **Tool handling and hooks**
+  - Tools convert to `PromptMessageTool` objects before invocation.
+  - Optional `tool_invoke_hook` lets callers override tool execution (e.g., agent apps) while workflow runs use `ToolEngine.generic_invoke`.
+  - Tool outputs support text, links, JSON, variables, blobs, retriever resources, and file attachments; `target=="self"` files are reloaded into model context, others are returned as outputs.
+- **File-aware arguments**
+  - Tool args accept `[File: <id>]` or `[Files: <id1, id2>]` placeholders that resolve to `File` objects before invocation, enabling models to reference uploaded files safely.
+- **ReAct prompt shaping**
+  - System prompts replace `{{instruction}}`, `{{tools}}`, and `{{tool_names}}` placeholders.
+  - Adds `Observation` to stop sequences and appends scratchpad text so the model sees prior Thought/Action/Observation history.
+- **Observability and accounting**
+  - Standardized `AgentLog` entries for rounds, model thoughts, and tool calls, including usage aggregation (`LLMUsage`) across streaming and non-streaming paths.
+
+## Architecture
+
+```
+agent/patterns/
+├── base.py              # Shared utilities: logging, usage, tool invocation, file handling
+├── function_call.py     # Native function-calling loop with tool execution
+├── react.py             # ReAct loop with CoT parsing and scratchpad wiring
+└── strategy_factory.py  # Strategy selection by model features or explicit override
+```
+
+## Usage
+
+- For auto-selection:
+  - Call `StrategyFactory.create_strategy(model_features, model_instance, context, tools, files, ...)` and run the returned strategy with prompt messages and model params.
+- For explicit behavior:
+  - Pass `agent_strategy=AgentEntity.Strategy.FUNCTION_CALLING` to force native calls (falls back to ReAct if unsupported), or `CHAIN_OF_THOUGHT` to force ReAct.
+- Both strategies stream chunks and logs; collect the generator output until it returns an `AgentResult`.
+
+## Integration Points
+
+- **Model runtime**: delegates to `ModelInstance.invoke_llm` for both streaming and non-streaming calls.
+- **Tool system**: defaults to `ToolEngine.generic_invoke`, with `tool_invoke_hook` for custom callers.
+- **Files**: flows through `File` objects for tool inputs/outputs and model-context attachments.
+- **Execution context**: `ExecutionContext` fields (user/app/conversation/message) propagate to tool invocations and logging.

api/core/agent/patterns/__init__.py

@@ -0,0 +1,19 @@
+"""Agent patterns module.
+
+This module provides different strategies for agent execution:
+- FunctionCallStrategy: Uses native function/tool calling
+- ReActStrategy: Uses ReAct (Reasoning + Acting) approach
+- StrategyFactory: Factory for creating strategies based on model features
+"""
+
+from .base import AgentPattern
+from .function_call import FunctionCallStrategy
+from .react import ReActStrategy
+from .strategy_factory import StrategyFactory
+
+__all__ = [
+    "AgentPattern",
+    "FunctionCallStrategy",
+    "ReActStrategy",
+    "StrategyFactory",
+]

api/core/agent/patterns/base.py

@@ -0,0 +1,474 @@
+"""Base class for agent strategies."""
+
+from __future__ import annotations
+
+import json
+import re
+import time
+from abc import ABC, abstractmethod
+from collections.abc import Callable, Generator
+from typing import TYPE_CHECKING, Any
+
+from core.agent.entities import AgentLog, AgentResult, ExecutionContext
+from core.file import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.message_entities import TextPromptMessageContent
+from core.tools.entities.tool_entities import ToolInvokeMessage, ToolInvokeMeta
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+# Type alias for tool invoke hook
+# Returns: (response_content, message_file_ids, tool_invoke_meta)
+ToolInvokeHook = Callable[["Tool", dict[str, Any], str], tuple[str, list[str], ToolInvokeMeta]]
+
+
+class AgentPattern(ABC):
+    """Base class for agent execution strategies."""
+
+    def __init__(
+        self,
+        model_instance: ModelInstance,
+        tools: list[Tool],
+        context: ExecutionContext,
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        files: list[File] = [],
+        tool_invoke_hook: ToolInvokeHook | None = None,
+    ):
+        """Initialize the agent strategy."""
+        self.model_instance = model_instance
+        self.tools = tools
+        self.context = context
+        self.max_iterations = min(max_iterations, 99)  # Cap at 99 iterations
+        self.workflow_call_depth = workflow_call_depth
+        self.files: list[File] = files
+        self.tool_invoke_hook = tool_invoke_hook
+
+    @abstractmethod
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the agent strategy."""
+        pass
+
+    def _accumulate_usage(self, total_usage: dict[str, Any], delta_usage: LLMUsage) -> None:
+        """Accumulate LLM usage statistics."""
+        if not total_usage.get("usage"):
+            # Create a copy to avoid modifying the original
+            total_usage["usage"] = LLMUsage(
+                prompt_tokens=delta_usage.prompt_tokens,
+                prompt_unit_price=delta_usage.prompt_unit_price,
+                prompt_price_unit=delta_usage.prompt_price_unit,
+                prompt_price=delta_usage.prompt_price,
+                completion_tokens=delta_usage.completion_tokens,
+                completion_unit_price=delta_usage.completion_unit_price,
+                completion_price_unit=delta_usage.completion_price_unit,
+                completion_price=delta_usage.completion_price,
+                total_tokens=delta_usage.total_tokens,
+                total_price=delta_usage.total_price,
+                currency=delta_usage.currency,
+                latency=delta_usage.latency,
+            )
+        else:
+            current: LLMUsage = total_usage["usage"]
+            current.prompt_tokens += delta_usage.prompt_tokens
+            current.completion_tokens += delta_usage.completion_tokens
+            current.total_tokens += delta_usage.total_tokens
+            current.prompt_price += delta_usage.prompt_price
+            current.completion_price += delta_usage.completion_price
+            current.total_price += delta_usage.total_price
+
+    def _extract_content(self, content: Any) -> str:
+        """Extract text content from message content."""
+        if isinstance(content, list):
+            # Content items are PromptMessageContentUnionTypes
+            text_parts = []
+            for c in content:
+                # Check if it's a TextPromptMessageContent (which has data attribute)
+                if isinstance(c, TextPromptMessageContent):
+                    text_parts.append(c.data)
+            return "".join(text_parts)
+        return str(content)
+
+    def _has_tool_calls(self, chunk: LLMResultChunk) -> bool:
+        """Check if chunk contains tool calls."""
+        # LLMResultChunk always has delta attribute
+        return bool(chunk.delta.message and chunk.delta.message.tool_calls)
+
+    def _has_tool_calls_result(self, result: LLMResult) -> bool:
+        """Check if result contains tool calls (non-streaming)."""
+        # LLMResult always has message attribute
+        return bool(result.message and result.message.tool_calls)
+
+    def _extract_tool_calls(self, chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
+        """Extract tool calls from streaming chunk."""
+        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+        if chunk.delta.message and chunk.delta.message.tool_calls:
+            for tool_call in chunk.delta.message.tool_calls:
+                if tool_call.function:
+                    try:
+                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
+                    except json.JSONDecodeError:
+                        args = {}
+                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
+        return tool_calls
+
+    def _extract_tool_calls_result(self, result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
+        """Extract tool calls from non-streaming result."""
+        tool_calls = []
+        if result.message and result.message.tool_calls:
+            for tool_call in result.message.tool_calls:
+                if tool_call.function:
+                    try:
+                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
+                    except json.JSONDecodeError:
+                        args = {}
+                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
+        return tool_calls
+
+    def _extract_text_from_message(self, message: PromptMessage) -> str:
+        """Extract text content from a prompt message."""
+        # PromptMessage always has content attribute
+        content = message.content
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            # Extract text from content list
+            text_parts = []
+            for item in content:
+                if isinstance(item, TextPromptMessageContent):
+                    text_parts.append(item.data)
+            return " ".join(text_parts)
+        return ""
+
+    def _get_tool_metadata(self, tool_instance: Tool) -> dict[AgentLog.LogMetadata, Any]:
+        """Get metadata for a tool including provider and icon info."""
+        from core.tools.tool_manager import ToolManager
+
+        metadata: dict[AgentLog.LogMetadata, Any] = {}
+        if tool_instance.entity and tool_instance.entity.identity:
+            identity = tool_instance.entity.identity
+            if identity.provider:
+                metadata[AgentLog.LogMetadata.PROVIDER] = identity.provider
+
+            # Get icon using ToolManager for proper URL generation
+            tenant_id = self.context.tenant_id
+            if tenant_id and identity.provider:
+                try:
+                    provider_type = tool_instance.tool_provider_type()
+                    icon = ToolManager.get_tool_icon(tenant_id, provider_type, identity.provider)
+                    if isinstance(icon, str):
+                        metadata[AgentLog.LogMetadata.ICON] = icon
+                    elif isinstance(icon, dict):
+                        # Handle icon dict with background/content or light/dark variants
+                        metadata[AgentLog.LogMetadata.ICON] = icon
+                except Exception:
+                    # Fallback to identity.icon if ToolManager fails
+                    if identity.icon:
+                        metadata[AgentLog.LogMetadata.ICON] = identity.icon
+            elif identity.icon:
+                metadata[AgentLog.LogMetadata.ICON] = identity.icon
+        return metadata
+
+    def _create_log(
+        self,
+        label: str,
+        log_type: AgentLog.LogType,
+        status: AgentLog.LogStatus,
+        data: dict[str, Any] | None = None,
+        parent_id: str | None = None,
+        extra_metadata: dict[AgentLog.LogMetadata, Any] | None = None,
+    ) -> AgentLog:
+        """Create a new AgentLog with standard metadata."""
+        metadata: dict[AgentLog.LogMetadata, Any] = {
+            AgentLog.LogMetadata.STARTED_AT: time.perf_counter(),
+        }
+        if extra_metadata:
+            metadata.update(extra_metadata)
+
+        return AgentLog(
+            label=label,
+            log_type=log_type,
+            status=status,
+            data=data or {},
+            parent_id=parent_id,
+            metadata=metadata,
+        )
+
+    def _finish_log(
+        self,
+        log: AgentLog,
+        data: dict[str, Any] | None = None,
+        usage: LLMUsage | None = None,
+    ) -> AgentLog:
+        """Finish an AgentLog by updating its status and metadata."""
+        log.status = AgentLog.LogStatus.SUCCESS
+
+        if data is not None:
+            log.data = data
+
+        # Calculate elapsed time
+        started_at = log.metadata.get(AgentLog.LogMetadata.STARTED_AT, time.perf_counter())
+        finished_at = time.perf_counter()
+
+        # Update metadata
+        log.metadata = {
+            **log.metadata,
+            AgentLog.LogMetadata.FINISHED_AT: finished_at,
+            # Calculate elapsed time in seconds
+            AgentLog.LogMetadata.ELAPSED_TIME: round(finished_at - started_at, 4),
+        }
+
+        # Add usage information if provided
+        if usage:
+            log.metadata.update(
+                {
+                    AgentLog.LogMetadata.TOTAL_PRICE: usage.total_price,
+                    AgentLog.LogMetadata.CURRENCY: usage.currency,
+                    AgentLog.LogMetadata.TOTAL_TOKENS: usage.total_tokens,
+                    AgentLog.LogMetadata.LLM_USAGE: usage,
+                }
+            )
+
+        return log
+
+    def _replace_file_references(self, tool_args: dict[str, Any]) -> dict[str, Any]:
+        """
+        Replace file references in tool arguments with actual File objects.
+
+        Args:
+            tool_args: Dictionary of tool arguments
+
+        Returns:
+            Updated tool arguments with file references replaced
+        """
+        # Process each argument in the dictionary
+        processed_args: dict[str, Any] = {}
+        for key, value in tool_args.items():
+            processed_args[key] = self._process_file_reference(value)
+        return processed_args
+
+    def _process_file_reference(self, data: Any) -> Any:
+        """
+        Recursively process data to replace file references.
+        Supports both single file [File: file_id] and multiple files [Files: file_id1, file_id2, ...].
+
+        Args:
+            data: The data to process (can be dict, list, str, or other types)
+
+        Returns:
+            Processed data with file references replaced
+        """
+        single_file_pattern = re.compile(r"^\[File:\s*([^\]]+)\]$")
+        multiple_files_pattern = re.compile(r"^\[Files:\s*([^\]]+)\]$")
+
+        if isinstance(data, dict):
+            # Process dictionary recursively
+            return {key: self._process_file_reference(value) for key, value in data.items()}
+        elif isinstance(data, list):
+            # Process list recursively
+            return [self._process_file_reference(item) for item in data]
+        elif isinstance(data, str):
+            # Check for single file pattern [File: file_id]
+            single_match = single_file_pattern.match(data.strip())
+            if single_match:
+                file_id = single_match.group(1).strip()
+                # Find the file in self.files
+                for file in self.files:
+                    if file.id and str(file.id) == file_id:
+                        return file
+                # If file not found, return original value
+                return data
+
+            # Check for multiple files pattern [Files: file_id1, file_id2, ...]
+            multiple_match = multiple_files_pattern.match(data.strip())
+            if multiple_match:
+                file_ids_str = multiple_match.group(1).strip()
+                # Split by comma and strip whitespace
+                file_ids = [fid.strip() for fid in file_ids_str.split(",")]
+
+                # Find all matching files
+                matched_files: list[File] = []
+                for file_id in file_ids:
+                    for file in self.files:
+                        if file.id and str(file.id) == file_id:
+                            matched_files.append(file)
+                            break
+
+                # Return list of files if any were found, otherwise return original
+                return matched_files or data
+
+            return data
+        else:
+            # Return other types as-is
+            return data
+
+    def _create_text_chunk(self, text: str, prompt_messages: list[PromptMessage]) -> LLMResultChunk:
+        """Create a text chunk for streaming."""
+        return LLMResultChunk(
+            model=self.model_instance.model,
+            prompt_messages=prompt_messages,
+            delta=LLMResultChunkDelta(
+                index=0,
+                message=AssistantPromptMessage(content=text),
+                usage=None,
+            ),
+            system_fingerprint="",
+        )
+
+    def _invoke_tool(
+        self,
+        tool_instance: Tool,
+        tool_args: dict[str, Any],
+        tool_name: str,
+    ) -> tuple[str, list[File], ToolInvokeMeta | None]:
+        """
+        Invoke a tool and collect its response.
+
+        Args:
+            tool_instance: The tool instance to invoke
+            tool_args: Tool arguments
+            tool_name: Name of the tool
+
+        Returns:
+            Tuple of (response_content, tool_files, tool_invoke_meta)
+        """
+        # Process tool_args to replace file references with actual File objects
+        tool_args = self._replace_file_references(tool_args)
+
+        # If a tool invoke hook is set, use it instead of generic_invoke
+        if self.tool_invoke_hook:
+            response_content, _, tool_invoke_meta = self.tool_invoke_hook(tool_instance, tool_args, tool_name)
+            # Note: message_file_ids are stored in DB, we don't convert them to File objects here
+            # The caller (AgentAppRunner) handles file publishing
+            return response_content, [], tool_invoke_meta
+
+        # Default: use generic_invoke for workflow scenarios
+        # Import here to avoid circular import
+        from core.tools.tool_engine import DifyWorkflowCallbackHandler, ToolEngine
+
+        tool_response = ToolEngine().generic_invoke(
+            tool=tool_instance,
+            tool_parameters=tool_args,
+            user_id=self.context.user_id or "",
+            workflow_tool_callback=DifyWorkflowCallbackHandler(),
+            workflow_call_depth=self.workflow_call_depth,
+            app_id=self.context.app_id,
+            conversation_id=self.context.conversation_id,
+            message_id=self.context.message_id,
+        )
+
+        # Collect response and files
+        response_content = ""
+        tool_files: list[File] = []
+
+        for response in tool_response:
+            if response.type == ToolInvokeMessage.MessageType.TEXT:
+                assert isinstance(response.message, ToolInvokeMessage.TextMessage)
+                response_content += response.message.text
+
+            elif response.type == ToolInvokeMessage.MessageType.LINK:
+                # Handle link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Link: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.IMAGE:
+                # Handle image URL messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Image: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK:
+                # Handle image link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Image: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.BINARY_LINK:
+                # Handle binary file link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    filename = response.meta.get("filename", "file") if response.meta else "file"
+                    response_content += f"[File: {filename} - {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.JSON:
+                # Handle JSON messages
+                if isinstance(response.message, ToolInvokeMessage.JsonMessage):
+                    response_content += json.dumps(response.message.json_object, ensure_ascii=False, indent=2)
+
+            elif response.type == ToolInvokeMessage.MessageType.BLOB:
+                # Handle blob messages - convert to text representation
+                if isinstance(response.message, ToolInvokeMessage.BlobMessage):
+                    mime_type = (
+                        response.meta.get("mime_type", "application/octet-stream")
+                        if response.meta
+                        else "application/octet-stream"
+                    )
+                    size = len(response.message.blob)
+                    response_content += f"[Binary data: {mime_type}, size: {size} bytes]"
+
+            elif response.type == ToolInvokeMessage.MessageType.VARIABLE:
+                # Handle variable messages
+                if isinstance(response.message, ToolInvokeMessage.VariableMessage):
+                    var_name = response.message.variable_name
+                    var_value = response.message.variable_value
+                    if isinstance(var_value, str):
+                        response_content += var_value
+                    else:
+                        response_content += f"[Variable {var_name}: {json.dumps(var_value, ensure_ascii=False)}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.BLOB_CHUNK:
+                # Handle blob chunk messages - these are parts of a larger blob
+                if isinstance(response.message, ToolInvokeMessage.BlobChunkMessage):
+                    response_content += f"[Blob chunk {response.message.sequence}: {len(response.message.blob)} bytes]"
+
+            elif response.type == ToolInvokeMessage.MessageType.RETRIEVER_RESOURCES:
+                # Handle retriever resources messages
+                if isinstance(response.message, ToolInvokeMessage.RetrieverResourceMessage):
+                    response_content += response.message.context
+
+            elif response.type == ToolInvokeMessage.MessageType.FILE:
+                # Extract file from meta
+                if response.meta and "file" in response.meta:
+                    file = response.meta["file"]
+                    if isinstance(file, File):
+                        # Check if file is for model or tool output
+                        if response.meta.get("target") == "self":
+                            # File is for model - add to files for next prompt
+                            self.files.append(file)
+                            response_content += f"File '{file.filename}' has been loaded into your context."
+                        else:
+                            # File is tool output
+                            tool_files.append(file)
+
+        return response_content, tool_files, None
+
+    def _find_tool_by_name(self, tool_name: str) -> Tool | None:
+        """Find a tool instance by its name."""
+        for tool in self.tools:
+            if tool.entity.identity.name == tool_name:
+                return tool
+        return None
+
+    def _convert_tools_to_prompt_format(self) -> list[PromptMessageTool]:
+        """Convert tools to prompt message format."""
+        prompt_tools: list[PromptMessageTool] = []
+        for tool in self.tools:
+            prompt_tools.append(tool.to_prompt_message_tool())
+        return prompt_tools
+
+    def _update_usage_with_empty(self, llm_usage: dict[str, Any]) -> None:
+        """Initialize usage tracking with empty usage if not set."""
+        if "usage" not in llm_usage or llm_usage["usage"] is None:
+            llm_usage["usage"] = LLMUsage.empty_usage()

api/core/agent/patterns/function_call.py

@@ -0,0 +1,299 @@
+"""Function Call strategy implementation."""
+
+import json
+from collections.abc import Generator
+from typing import Any, Union
+
+from core.agent.entities import AgentLog, AgentResult
+from core.file import File
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageTool,
+    ToolPromptMessage,
+)
+from core.tools.entities.tool_entities import ToolInvokeMeta
+
+from .base import AgentPattern
+
+
+class FunctionCallStrategy(AgentPattern):
+    """Function Call strategy using model's native tool calling capability."""
+
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the function call agent strategy."""
+        # Convert tools to prompt format
+        prompt_tools: list[PromptMessageTool] = self._convert_tools_to_prompt_format()
+
+        # Initialize tracking
+        iteration_step: int = 1
+        max_iterations: int = self.max_iterations + 1
+        function_call_state: bool = True
+        total_usage: dict[str, LLMUsage | None] = {"usage": None}
+        messages: list[PromptMessage] = list(prompt_messages)  # Create mutable copy
+        final_text: str = ""
+        finish_reason: str | None = None
+        output_files: list[File] = []  # Track files produced by tools
+
+        while function_call_state and iteration_step <= max_iterations:
+            function_call_state = False
+            round_log = self._create_log(
+                label=f"ROUND {iteration_step}",
+                log_type=AgentLog.LogType.ROUND,
+                status=AgentLog.LogStatus.START,
+                data={},
+            )
+            yield round_log
+            # On last iteration, remove tools to force final answer
+            current_tools: list[PromptMessageTool] = [] if iteration_step == max_iterations else prompt_tools
+            model_log = self._create_log(
+                label=f"{self.model_instance.model} Thought",
+                log_type=AgentLog.LogType.THOUGHT,
+                status=AgentLog.LogStatus.START,
+                data={},
+                parent_id=round_log.id,
+                extra_metadata={
+                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
+                },
+            )
+            yield model_log
+
+            # Track usage for this round only
+            round_usage: dict[str, LLMUsage | None] = {"usage": None}
+
+            # Invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
+                prompt_messages=messages,
+                model_parameters=model_parameters,
+                tools=current_tools,
+                stop=stop,
+                stream=stream,
+                user=self.context.user_id,
+                callbacks=[],
+            )
+
+            # Process response
+            tool_calls, response_content, chunk_finish_reason = yield from self._handle_chunks(
+                chunks, round_usage, model_log
+            )
+            messages.append(self._create_assistant_message(response_content, tool_calls))
+
+            # Accumulate to total usage
+            round_usage_value = round_usage.get("usage")
+            if round_usage_value:
+                self._accumulate_usage(total_usage, round_usage_value)
+
+            # Update final text if no tool calls (this is likely the final answer)
+            if not tool_calls:
+                final_text = response_content
+
+            # Update finish reason
+            if chunk_finish_reason:
+                finish_reason = chunk_finish_reason
+
+            # Process tool calls
+            tool_outputs: dict[str, str] = {}
+            if tool_calls:
+                function_call_state = True
+                # Execute tools
+                for tool_call_id, tool_name, tool_args in tool_calls:
+                    tool_response, tool_files, _ = yield from self._handle_tool_call(
+                        tool_name, tool_args, tool_call_id, messages, round_log
+                    )
+                    tool_outputs[tool_name] = tool_response
+                    # Track files produced by tools
+                    output_files.extend(tool_files)
+            yield self._finish_log(
+                round_log,
+                data={
+                    "llm_result": response_content,
+                    "tool_calls": [
+                        {"name": tc[1], "args": tc[2], "output": tool_outputs.get(tc[1], "")} for tc in tool_calls
+                    ]
+                    if tool_calls
+                    else [],
+                    "final_answer": final_text if not function_call_state else None,
+                },
+                usage=round_usage.get("usage"),
+            )
+            iteration_step += 1
+
+        # Return final result
+        from core.agent.entities import AgentResult
+
+        return AgentResult(
+            text=final_text,
+            files=output_files,
+            usage=total_usage.get("usage") or LLMUsage.empty_usage(),
+            finish_reason=finish_reason,
+        )
+
+    def _handle_chunks(
+        self,
+        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
+        llm_usage: dict[str, LLMUsage | None],
+        start_log: AgentLog,
+    ) -> Generator[
+        LLMResultChunk | AgentLog,
+        None,
+        tuple[list[tuple[str, str, dict[str, Any]]], str, str | None],
+    ]:
+        """Handle LLM response chunks and extract tool calls and content.
+
+        Returns a tuple of (tool_calls, response_content, finish_reason).
+        """
+        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+        response_content: str = ""
+        finish_reason: str | None = None
+        if isinstance(chunks, Generator):
+            # Streaming response
+            for chunk in chunks:
+                # Extract tool calls
+                if self._has_tool_calls(chunk):
+                    tool_calls.extend(self._extract_tool_calls(chunk))
+
+                # Extract content
+                if chunk.delta.message and chunk.delta.message.content:
+                    response_content += self._extract_content(chunk.delta.message.content)
+
+                # Track usage
+                if chunk.delta.usage:
+                    self._accumulate_usage(llm_usage, chunk.delta.usage)
+
+                # Capture finish reason
+                if chunk.delta.finish_reason:
+                    finish_reason = chunk.delta.finish_reason
+
+                yield chunk
+        else:
+            # Non-streaming response
+            result: LLMResult = chunks
+
+            if self._has_tool_calls_result(result):
+                tool_calls.extend(self._extract_tool_calls_result(result))
+
+            if result.message and result.message.content:
+                response_content += self._extract_content(result.message.content)
+
+            if result.usage:
+                self._accumulate_usage(llm_usage, result.usage)
+
+            # Convert to streaming format
+            yield LLMResultChunk(
+                model=result.model,
+                prompt_messages=result.prompt_messages,
+                delta=LLMResultChunkDelta(index=0, message=result.message, usage=result.usage),
+            )
+        yield self._finish_log(
+            start_log,
+            data={
+                "result": response_content,
+            },
+            usage=llm_usage.get("usage"),
+        )
+        return tool_calls, response_content, finish_reason
+
+    def _create_assistant_message(
+        self, content: str, tool_calls: list[tuple[str, str, dict[str, Any]]] | None = None
+    ) -> AssistantPromptMessage:
+        """Create assistant message with tool calls."""
+        if tool_calls is None:
+            return AssistantPromptMessage(content=content)
+        return AssistantPromptMessage(
+            content=content or "",
+            tool_calls=[
+                AssistantPromptMessage.ToolCall(
+                    id=tc[0],
+                    type="function",
+                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tc[1], arguments=json.dumps(tc[2])),
+                )
+                for tc in tool_calls
+            ],
+        )
+
+    def _handle_tool_call(
+        self,
+        tool_name: str,
+        tool_args: dict[str, Any],
+        tool_call_id: str,
+        messages: list[PromptMessage],
+        round_log: AgentLog,
+    ) -> Generator[AgentLog, None, tuple[str, list[File], ToolInvokeMeta | None]]:
+        """Handle a single tool call and return response with files and meta."""
+        # Find tool
+        tool_instance = self._find_tool_by_name(tool_name)
+        if not tool_instance:
+            raise ValueError(f"Tool {tool_name} not found")
+
+        # Get tool metadata (provider, icon, etc.)
+        tool_metadata = self._get_tool_metadata(tool_instance)
+
+        # Create tool call log
+        tool_call_log = self._create_log(
+            label=f"CALL {tool_name}",
+            log_type=AgentLog.LogType.TOOL_CALL,
+            status=AgentLog.LogStatus.START,
+            data={
+                "tool_call_id": tool_call_id,
+                "tool_name": tool_name,
+                "tool_args": tool_args,
+            },
+            parent_id=round_log.id,
+            extra_metadata=tool_metadata,
+        )
+        yield tool_call_log
+
+        # Invoke tool using base class method with error handling
+        try:
+            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args, tool_name)
+
+            yield self._finish_log(
+                tool_call_log,
+                data={
+                    **tool_call_log.data,
+                    "output": response_content,
+                    "files": len(tool_files),
+                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
+                },
+            )
+            final_content = response_content or "Tool executed successfully"
+            # Add tool response to messages
+            messages.append(
+                ToolPromptMessage(
+                    content=final_content,
+                    tool_call_id=tool_call_id,
+                    name=tool_name,
+                )
+            )
+            return response_content, tool_files, tool_invoke_meta
+        except Exception as e:
+            # Tool invocation failed, yield error log
+            error_message = str(e)
+            tool_call_log.status = AgentLog.LogStatus.ERROR
+            tool_call_log.error = error_message
+            tool_call_log.data = {
+                **tool_call_log.data,
+                "error": error_message,
+            }
+            yield tool_call_log
+
+            # Add error message to conversation
+            error_content = f"Tool execution failed: {error_message}"
+            messages.append(
+                ToolPromptMessage(
+                    content=error_content,
+                    tool_call_id=tool_call_id,
+                    name=tool_name,
+                )
+            )
+            return error_content, [], None

api/core/agent/patterns/react.py

@@ -0,0 +1,418 @@
+"""ReAct strategy implementation."""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Generator
+from typing import TYPE_CHECKING, Any, Union
+
+from core.agent.entities import AgentLog, AgentResult, AgentScratchpadUnit, ExecutionContext
+from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
+from core.file import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    PromptMessage,
+    SystemPromptMessage,
+)
+
+from .base import AgentPattern, ToolInvokeHook
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+
+class ReActStrategy(AgentPattern):
+    """ReAct strategy using reasoning and acting approach."""
+
+    def __init__(
+        self,
+        model_instance: ModelInstance,
+        tools: list[Tool],
+        context: ExecutionContext,
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        files: list[File] = [],
+        tool_invoke_hook: ToolInvokeHook | None = None,
+        instruction: str = "",
+    ):
+        """Initialize the ReAct strategy with instruction support."""
+        super().__init__(
+            model_instance=model_instance,
+            tools=tools,
+            context=context,
+            max_iterations=max_iterations,
+            workflow_call_depth=workflow_call_depth,
+            files=files,
+            tool_invoke_hook=tool_invoke_hook,
+        )
+        self.instruction = instruction
+
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the ReAct agent strategy."""
+        # Initialize tracking
+        agent_scratchpad: list[AgentScratchpadUnit] = []
+        iteration_step: int = 1
+        max_iterations: int = self.max_iterations + 1
+        react_state: bool = True
+        total_usage: dict[str, Any] = {"usage": None}
+        output_files: list[File] = []  # Track files produced by tools
+        final_text: str = ""
+        finish_reason: str | None = None
+
+        # Add "Observation" to stop sequences
+        if "Observation" not in stop:
+            stop = stop.copy()
+            stop.append("Observation")
+
+        while react_state and iteration_step <= max_iterations:
+            react_state = False
+            round_log = self._create_log(
+                label=f"ROUND {iteration_step}",
+                log_type=AgentLog.LogType.ROUND,
+                status=AgentLog.LogStatus.START,
+                data={},
+            )
+            yield round_log
+
+            # Build prompt with/without tools based on iteration
+            include_tools = iteration_step < max_iterations
+            current_messages = self._build_prompt_with_react_format(
+                prompt_messages, agent_scratchpad, include_tools, self.instruction
+            )
+
+            model_log = self._create_log(
+                label=f"{self.model_instance.model} Thought",
+                log_type=AgentLog.LogType.THOUGHT,
+                status=AgentLog.LogStatus.START,
+                data={},
+                parent_id=round_log.id,
+                extra_metadata={
+                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
+                },
+            )
+            yield model_log
+
+            # Track usage for this round only
+            round_usage: dict[str, Any] = {"usage": None}
+
+            # Use current messages directly (files are handled by base class if needed)
+            messages_to_use = current_messages
+
+            # Invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
+                prompt_messages=messages_to_use,
+                model_parameters=model_parameters,
+                stop=stop,
+                stream=stream,
+                user=self.context.user_id or "",
+                callbacks=[],
+            )
+
+            # Process response
+            scratchpad, chunk_finish_reason = yield from self._handle_chunks(
+                chunks, round_usage, model_log, current_messages
+            )
+            agent_scratchpad.append(scratchpad)
+
+            # Accumulate to total usage
+            round_usage_value = round_usage.get("usage")
+            if round_usage_value:
+                self._accumulate_usage(total_usage, round_usage_value)
+
+            # Update finish reason
+            if chunk_finish_reason:
+                finish_reason = chunk_finish_reason
+
+            # Check if we have an action to execute
+            if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
+                react_state = True
+                # Execute tool
+                observation, tool_files = yield from self._handle_tool_call(
+                    scratchpad.action, current_messages, round_log
+                )
+                scratchpad.observation = observation
+                # Track files produced by tools
+                output_files.extend(tool_files)
+
+                # Add observation to scratchpad for display
+                yield self._create_text_chunk(f"\nObservation: {observation}\n", current_messages)
+            else:
+                # Extract final answer
+                if scratchpad.action and scratchpad.action.action_input:
+                    final_answer = scratchpad.action.action_input
+                    if isinstance(final_answer, dict):
+                        final_answer = json.dumps(final_answer, ensure_ascii=False)
+                    final_text = str(final_answer)
+                elif scratchpad.thought:
+                    # If no action but we have thought, use thought as final answer
+                    final_text = scratchpad.thought
+
+            yield self._finish_log(
+                round_log,
+                data={
+                    "thought": scratchpad.thought,
+                    "action": scratchpad.action_str if scratchpad.action else None,
+                    "observation": scratchpad.observation or None,
+                    "final_answer": final_text if not react_state else None,
+                },
+                usage=round_usage.get("usage"),
+            )
+            iteration_step += 1
+
+        # Return final result
+
+        from core.agent.entities import AgentResult
+
+        return AgentResult(
+            text=final_text, files=output_files, usage=total_usage.get("usage"), finish_reason=finish_reason
+        )
+
+    def _build_prompt_with_react_format(
+        self,
+        original_messages: list[PromptMessage],
+        agent_scratchpad: list[AgentScratchpadUnit],
+        include_tools: bool = True,
+        instruction: str = "",
+    ) -> list[PromptMessage]:
+        """Build prompt messages with ReAct format."""
+        # Copy messages to avoid modifying original
+        messages = list(original_messages)
+
+        # Find and update the system prompt that should already exist
+        system_prompt_found = False
+        for i, msg in enumerate(messages):
+            if isinstance(msg, SystemPromptMessage):
+                system_prompt_found = True
+                # The system prompt from frontend already has the template, just replace placeholders
+
+                # Format tools
+                tools_str = ""
+                tool_names = []
+                if include_tools and self.tools:
+                    # Convert tools to prompt message tools format
+                    prompt_tools = [tool.to_prompt_message_tool() for tool in self.tools]
+                    tool_names = [tool.name for tool in prompt_tools]
+
+                    # Format tools as JSON for comprehensive information
+                    from core.model_runtime.utils.encoders import jsonable_encoder
+
+                    tools_str = json.dumps(jsonable_encoder(prompt_tools), indent=2)
+                    tool_names_str = ", ".join(f'"{name}"' for name in tool_names)
+                else:
+                    tools_str = "No tools available"
+                    tool_names_str = ""
+
+                # Replace placeholders in the existing system prompt
+                updated_content = msg.content
+                assert isinstance(updated_content, str)
+                updated_content = updated_content.replace("{{instruction}}", instruction)
+                updated_content = updated_content.replace("{{tools}}", tools_str)
+                updated_content = updated_content.replace("{{tool_names}}", tool_names_str)
+
+                # Create new SystemPromptMessage with updated content
+                messages[i] = SystemPromptMessage(content=updated_content)
+                break
+
+        # If no system prompt found, that's unexpected but add scratchpad anyway
+        if not system_prompt_found:
+            # This shouldn't happen if frontend is working correctly
+            pass
+
+        # Format agent scratchpad
+        scratchpad_str = ""
+        if agent_scratchpad:
+            scratchpad_parts: list[str] = []
+            for unit in agent_scratchpad:
+                if unit.thought:
+                    scratchpad_parts.append(f"Thought: {unit.thought}")
+                if unit.action_str:
+                    scratchpad_parts.append(f"Action:\n```\n{unit.action_str}\n```")
+                if unit.observation:
+                    scratchpad_parts.append(f"Observation: {unit.observation}")
+            scratchpad_str = "\n".join(scratchpad_parts)
+
+        # If there's a scratchpad, append it to the last message
+        if scratchpad_str:
+            messages.append(AssistantPromptMessage(content=scratchpad_str))
+
+        return messages
+
+    def _handle_chunks(
+        self,
+        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
+        llm_usage: dict[str, Any],
+        model_log: AgentLog,
+        current_messages: list[PromptMessage],
+    ) -> Generator[
+        LLMResultChunk | AgentLog,
+        None,
+        tuple[AgentScratchpadUnit, str | None],
+    ]:
+        """Handle LLM response chunks and extract action/thought.
+
+        Returns a tuple of (scratchpad_unit, finish_reason).
+        """
+        usage_dict: dict[str, Any] = {}
+
+        # Convert non-streaming to streaming format if needed
+        if isinstance(chunks, LLMResult):
+            # Create a generator from the LLMResult
+            def result_to_chunks() -> Generator[LLMResultChunk, None, None]:
+                yield LLMResultChunk(
+                    model=chunks.model,
+                    prompt_messages=chunks.prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=chunks.message,
+                        usage=chunks.usage,
+                        finish_reason=None,  # LLMResult doesn't have finish_reason, only streaming chunks do
+                    ),
+                    system_fingerprint=chunks.system_fingerprint or "",
+                )
+
+            streaming_chunks = result_to_chunks()
+        else:
+            streaming_chunks = chunks
+
+        react_chunks = CotAgentOutputParser.handle_react_stream_output(streaming_chunks, usage_dict)
+
+        # Initialize scratchpad unit
+        scratchpad = AgentScratchpadUnit(
+            agent_response="",
+            thought="",
+            action_str="",
+            observation="",
+            action=None,
+        )
+
+        finish_reason: str | None = None
+
+        # Process chunks
+        for chunk in react_chunks:
+            if isinstance(chunk, AgentScratchpadUnit.Action):
+                # Action detected
+                action_str = json.dumps(chunk.model_dump())
+                scratchpad.agent_response = (scratchpad.agent_response or "") + action_str
+                scratchpad.action_str = action_str
+                scratchpad.action = chunk
+
+                yield self._create_text_chunk(json.dumps(chunk.model_dump()), current_messages)
+            else:
+                # Text chunk
+                chunk_text = str(chunk)
+                scratchpad.agent_response = (scratchpad.agent_response or "") + chunk_text
+                scratchpad.thought = (scratchpad.thought or "") + chunk_text
+
+                yield self._create_text_chunk(chunk_text, current_messages)
+
+        # Update usage
+        if usage_dict.get("usage"):
+            if llm_usage.get("usage"):
+                self._accumulate_usage(llm_usage, usage_dict["usage"])
+            else:
+                llm_usage["usage"] = usage_dict["usage"]
+
+        # Clean up thought
+        scratchpad.thought = (scratchpad.thought or "").strip() or "I am thinking about how to help you"
+
+        # Finish model log
+        yield self._finish_log(
+            model_log,
+            data={
+                "thought": scratchpad.thought,
+                "action": scratchpad.action_str if scratchpad.action else None,
+            },
+            usage=llm_usage.get("usage"),
+        )
+
+        return scratchpad, finish_reason
+
+    def _handle_tool_call(
+        self,
+        action: AgentScratchpadUnit.Action,
+        prompt_messages: list[PromptMessage],
+        round_log: AgentLog,
+    ) -> Generator[AgentLog, None, tuple[str, list[File]]]:
+        """Handle tool call and return observation with files."""
+        tool_name = action.action_name
+        tool_args: dict[str, Any] | str = action.action_input
+
+        # Find tool instance first to get metadata
+        tool_instance = self._find_tool_by_name(tool_name)
+        tool_metadata = self._get_tool_metadata(tool_instance) if tool_instance else {}
+
+        # Start tool log with tool metadata
+        tool_log = self._create_log(
+            label=f"CALL {tool_name}",
+            log_type=AgentLog.LogType.TOOL_CALL,
+            status=AgentLog.LogStatus.START,
+            data={
+                "tool_name": tool_name,
+                "tool_args": tool_args,
+            },
+            parent_id=round_log.id,
+            extra_metadata=tool_metadata,
+        )
+        yield tool_log
+
+        if not tool_instance:
+            # Finish tool log with error
+            yield self._finish_log(
+                tool_log,
+                data={
+                    **tool_log.data,
+                    "error": f"Tool {tool_name} not found",
+                },
+            )
+            return f"Tool {tool_name} not found", []
+
+        # Ensure tool_args is a dict
+        tool_args_dict: dict[str, Any]
+        if isinstance(tool_args, str):
+            try:
+                tool_args_dict = json.loads(tool_args)
+            except json.JSONDecodeError:
+                tool_args_dict = {"input": tool_args}
+        elif not isinstance(tool_args, dict):
+            tool_args_dict = {"input": str(tool_args)}
+        else:
+            tool_args_dict = tool_args
+
+        # Invoke tool using base class method with error handling
+        try:
+            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args_dict, tool_name)
+
+            # Finish tool log
+            yield self._finish_log(
+                tool_log,
+                data={
+                    **tool_log.data,
+                    "output": response_content,
+                    "files": len(tool_files),
+                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
+                },
+            )
+
+            return response_content or "Tool executed successfully", tool_files
+        except Exception as e:
+            # Tool invocation failed, yield error log
+            error_message = str(e)
+            tool_log.status = AgentLog.LogStatus.ERROR
+            tool_log.error = error_message
+            tool_log.data = {
+                **tool_log.data,
+                "error": error_message,
+            }
+            yield tool_log
+
+            return f"Tool execution failed: {error_message}", []

api/core/agent/patterns/strategy_factory.py

@@ -0,0 +1,107 @@
+"""Strategy factory for creating agent strategies."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from core.agent.entities import AgentEntity, ExecutionContext
+from core.file.models import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.model_entities import ModelFeature
+
+from .base import AgentPattern, ToolInvokeHook
+from .function_call import FunctionCallStrategy
+from .react import ReActStrategy
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+
+class StrategyFactory:
+    """Factory for creating agent strategies based on model features."""
+
+    # Tool calling related features
+    TOOL_CALL_FEATURES = {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL}
+
+    @staticmethod
+    def create_strategy(
+        model_features: list[ModelFeature],
+        model_instance: ModelInstance,
+        context: ExecutionContext,
+        tools: list[Tool],
+        files: list[File],
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        agent_strategy: AgentEntity.Strategy | None = None,
+        tool_invoke_hook: ToolInvokeHook | None = None,
+        instruction: str = "",
+    ) -> AgentPattern:
+        """
+        Create an appropriate strategy based on model features.
+
+        Args:
+            model_features: List of model features/capabilities
+            model_instance: Model instance to use
+            context: Execution context containing trace/audit information
+            tools: Available tools
+            files: Available files
+            max_iterations: Maximum iterations for the strategy
+            workflow_call_depth: Depth of workflow calls
+            agent_strategy: Optional explicit strategy override
+            tool_invoke_hook: Optional hook for custom tool invocation (e.g., agent_invoke)
+            instruction: Optional instruction for ReAct strategy
+
+        Returns:
+            AgentStrategy instance
+        """
+        # If explicit strategy is provided and it's Function Calling, try to use it if supported
+        if agent_strategy == AgentEntity.Strategy.FUNCTION_CALLING:
+            if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
+                return FunctionCallStrategy(
+                    model_instance=model_instance,
+                    context=context,
+                    tools=tools,
+                    files=files,
+                    max_iterations=max_iterations,
+                    workflow_call_depth=workflow_call_depth,
+                    tool_invoke_hook=tool_invoke_hook,
+                )
+            # Fallback to ReAct if FC is requested but not supported
+
+        # If explicit strategy is Chain of Thought (ReAct)
+        if agent_strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
+            return ReActStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+                instruction=instruction,
+            )
+
+        # Default auto-selection logic
+        if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
+            # Model supports native function calling
+            return FunctionCallStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+            )
+        else:
+            # Use ReAct strategy for models without function calling
+            return ReActStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+                instruction=instruction,
+            )

api/core/app/apps/advanced_chat/app_runner.py

@@ -20,6 +20,8 @@ from core.app.entities.queue_entities import (
     QueueTextChunkEvent,
 )
 from core.app.features.annotation_reply.annotation_reply import AnnotationReplyFeature
+from core.app.layers.conversation_variable_persist_layer import ConversationVariablePersistenceLayer
+from core.db.session_factory import session_factory
 from core.moderation.base import ModerationError
 from core.moderation.input_moderation import InputModeration
 from core.variables.variables import VariableUnion
@@ -40,6 +42,7 @@ from models import Workflow
 from models.enums import UserFrom
 from models.model import App, Conversation, Message, MessageAnnotation
 from models.workflow import ConversationVariable
+from services.conversation_variable_updater import ConversationVariableUpdater
 
 logger = logging.getLogger(__name__)
 
@@ -200,6 +203,10 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         )
 
         workflow_entry.graph_engine.layer(persistence_layer)
+        conversation_variable_layer = ConversationVariablePersistenceLayer(
+            ConversationVariableUpdater(session_factory.get_session_maker())
+        )
+        workflow_entry.graph_engine.layer(conversation_variable_layer)
         for layer in self._graph_engine_layers:
             workflow_entry.graph_engine.layer(layer)
 

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -4,6 +4,7 @@ import re
 import time
 from collections.abc import Callable, Generator, Mapping
 from contextlib import contextmanager
+from dataclasses import dataclass, field
 from threading import Thread
 from typing import Any, Union
 
@@ -19,6 +20,7 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
 )
 from core.app.entities.queue_entities import (
+    ChunkType,
     MessageQueueMessage,
     QueueAdvancedChatMessageEndEvent,
     QueueAgentLogEvent,
@@ -70,13 +72,122 @@ from core.workflow.runtime import GraphRuntimeState
 from core.workflow.system_variable import SystemVariable
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models import Account, Conversation, EndUser, Message, MessageFile
+from models import Account, Conversation, EndUser, LLMGenerationDetail, Message, MessageFile
 from models.enums import CreatorUserRole
 from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
 
+@dataclass
+class StreamEventBuffer:
+    """
+    Buffer for recording stream events in order to reconstruct the generation sequence.
+    Records the exact order of text chunks, thoughts, and tool calls as they stream.
+    """
+
+    # Accumulated reasoning content (each thought block is a separate element)
+    reasoning_content: list[str] = field(default_factory=list)
+    # Current reasoning buffer (accumulates until we see a different event type)
+    _current_reasoning: str = ""
+    # Tool calls with their details
+    tool_calls: list[dict] = field(default_factory=list)
+    # Tool call ID to index mapping for updating results
+    _tool_call_id_map: dict[str, int] = field(default_factory=dict)
+    # Sequence of events in stream order
+    sequence: list[dict] = field(default_factory=list)
+    # Current position in answer text
+    _content_position: int = 0
+    # Track last event type to detect transitions
+    _last_event_type: str | None = None
+
+    def _flush_current_reasoning(self) -> None:
+        """Flush accumulated reasoning to the list and add to sequence."""
+        if self._current_reasoning.strip():
+            self.reasoning_content.append(self._current_reasoning.strip())
+            self.sequence.append({"type": "reasoning", "index": len(self.reasoning_content) - 1})
+            self._current_reasoning = ""
+
+    def record_text_chunk(self, text: str) -> None:
+        """Record a text chunk event."""
+        if not text:
+            return
+
+        # Flush any pending reasoning first
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+        text_len = len(text)
+        start_pos = self._content_position
+
+        # If last event was also content, extend it; otherwise create new
+        if self.sequence and self.sequence[-1].get("type") == "content":
+            self.sequence[-1]["end"] = start_pos + text_len
+        else:
+            self.sequence.append({"type": "content", "start": start_pos, "end": start_pos + text_len})
+
+        self._content_position += text_len
+        self._last_event_type = "content"
+
+    def record_thought_chunk(self, text: str) -> None:
+        """Record a thought/reasoning chunk event."""
+        if not text:
+            return
+
+        # Accumulate thought content
+        self._current_reasoning += text
+        self._last_event_type = "thought"
+
+    def record_tool_call(self, tool_call_id: str, tool_name: str, tool_arguments: str) -> None:
+        """Record a tool call event."""
+        if not tool_call_id:
+            return
+
+        # Flush any pending reasoning first
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+        # Check if this tool call already exists (we might get multiple chunks)
+        if tool_call_id in self._tool_call_id_map:
+            idx = self._tool_call_id_map[tool_call_id]
+            # Update arguments if provided
+            if tool_arguments:
+                self.tool_calls[idx]["arguments"] = tool_arguments
+        else:
+            # New tool call
+            tool_call = {
+                "id": tool_call_id or "",
+                "name": tool_name or "",
+                "arguments": tool_arguments or "",
+                "result": "",
+                "elapsed_time": None,
+            }
+            self.tool_calls.append(tool_call)
+            idx = len(self.tool_calls) - 1
+            self._tool_call_id_map[tool_call_id] = idx
+            self.sequence.append({"type": "tool_call", "index": idx})
+
+        self._last_event_type = "tool_call"
+
+    def record_tool_result(self, tool_call_id: str, result: str, tool_elapsed_time: float | None = None) -> None:
+        """Record a tool result event (update existing tool call)."""
+        if not tool_call_id:
+            return
+        if tool_call_id in self._tool_call_id_map:
+            idx = self._tool_call_id_map[tool_call_id]
+            self.tool_calls[idx]["result"] = result
+            self.tool_calls[idx]["elapsed_time"] = tool_elapsed_time
+
+    def finalize(self) -> None:
+        """Finalize the buffer, flushing any pending data."""
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+    def has_data(self) -> bool:
+        """Check if there's any meaningful data recorded."""
+        return bool(self.reasoning_content or self.tool_calls or self.sequence)
+
+
 class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
     """
     AdvancedChatAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
@@ -144,6 +255,8 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         self._workflow_run_id: str = ""
         self._draft_var_saver_factory = draft_var_saver_factory
         self._graph_runtime_state: GraphRuntimeState | None = None
+        # Stream event buffer for recording generation sequence
+        self._stream_buffer = StreamEventBuffer()
         self._seed_graph_runtime_state_from_queue_manager()
 
     def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
@@ -358,6 +471,25 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if node_finish_resp:
             yield node_finish_resp
 
+        # For ANSWER nodes, check if we need to send a message_replace event
+        # Only send if the final output differs from the accumulated task_state.answer
+        # This happens when variables were updated by variable_assigner during workflow execution
+        if event.node_type == NodeType.ANSWER and event.outputs:
+            final_answer = event.outputs.get("answer")
+            if final_answer is not None and final_answer != self._task_state.answer:
+                logger.info(
+                    "ANSWER node final output '%s' differs from accumulated answer '%s', sending message_replace event",
+                    final_answer,
+                    self._task_state.answer,
+                )
+                # Update the task state answer
+                self._task_state.answer = str(final_answer)
+                # Send message_replace event to update the UI
+                yield self._message_cycle_manager.message_replace_to_stream_response(
+                    answer=str(final_answer),
+                    reason="variable_update",
+                )
+
     def _handle_node_failed_events(
         self,
         event: Union[QueueNodeFailedEvent, QueueNodeExceptionEvent],
@@ -383,7 +515,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         queue_message: Union[WorkflowQueueMessage, MessageQueueMessage] | None = None,
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
-        """Handle text chunk events."""
+        """Handle text chunk events and record to stream buffer for sequence reconstruction."""
         delta_text = event.text
         if delta_text is None:
             return
@@ -405,9 +537,52 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        self._task_state.answer += delta_text
+        tool_call = event.tool_call
+        tool_result = event.tool_result
+        tool_payload = tool_call or tool_result
+        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else ""
+        tool_name = tool_payload.name if tool_payload and tool_payload.name else ""
+        tool_arguments = tool_call.arguments if tool_call and tool_call.arguments else ""
+        tool_files = tool_result.files if tool_result else []
+        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
+        tool_icon = tool_payload.icon if tool_payload else None
+        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
+        # Record stream event based on chunk type
+        chunk_type = event.chunk_type or ChunkType.TEXT
+        match chunk_type:
+            case ChunkType.TEXT:
+                self._stream_buffer.record_text_chunk(delta_text)
+                self._task_state.answer += delta_text
+            case ChunkType.THOUGHT:
+                # Reasoning should not be part of final answer text
+                self._stream_buffer.record_thought_chunk(delta_text)
+            case ChunkType.TOOL_CALL:
+                self._stream_buffer.record_tool_call(
+                    tool_call_id=tool_call_id,
+                    tool_name=tool_name,
+                    tool_arguments=tool_arguments,
+                )
+            case ChunkType.TOOL_RESULT:
+                self._stream_buffer.record_tool_result(
+                    tool_call_id=tool_call_id,
+                    result=delta_text,
+                    tool_elapsed_time=tool_elapsed_time,
+                )
+                self._task_state.answer += delta_text
+            case _:
+                pass
         yield self._message_cycle_manager.message_to_stream_response(
-            answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
+            answer=delta_text,
+            message_id=self._message_id,
+            from_variable_selector=event.from_variable_selector,
+            chunk_type=event.chunk_type.value if event.chunk_type else None,
+            tool_call_id=tool_call_id or None,
+            tool_name=tool_name or None,
+            tool_arguments=tool_arguments or None,
+            tool_files=tool_files,
+            tool_elapsed_time=tool_elapsed_time,
+            tool_icon=tool_icon,
+            tool_icon_dark=tool_icon_dark,
         )
 
     def _handle_iteration_start_event(
@@ -775,6 +950,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
 
         # If there are assistant files, remove markdown image links from answer
         answer_text = self._task_state.answer
+        answer_text = self._strip_think_blocks(answer_text)
         if self._recorded_files:
             # Remove markdown image links since we're storing files separately
             answer_text = re.sub(r"!\[.*?\]\(.*?\)", "", answer_text).strip()
@@ -826,6 +1002,54 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         ]
         session.add_all(message_files)
 
+        # Save generation detail (reasoning/tool calls/sequence) from stream buffer
+        self._save_generation_detail(session=session, message=message)
+
+    @staticmethod
+    def _strip_think_blocks(text: str) -> str:
+        """Remove <think>...</think> blocks (including their content) from text."""
+        if not text or "<think" not in text.lower():
+            return text
+
+        clean_text = re.sub(r"<think[^>]*>.*?</think>", "", text, flags=re.IGNORECASE | re.DOTALL)
+        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
+        return clean_text
+
+    def _save_generation_detail(self, *, session: Session, message: Message) -> None:
+        """
+        Save LLM generation detail for Chatflow using stream event buffer.
+        The buffer records the exact order of events as they streamed,
+        allowing accurate reconstruction of the generation sequence.
+        """
+        # Finalize the stream buffer to flush any pending data
+        self._stream_buffer.finalize()
+
+        # Only save if there's meaningful data
+        if not self._stream_buffer.has_data():
+            return
+
+        reasoning_content = self._stream_buffer.reasoning_content
+        tool_calls = self._stream_buffer.tool_calls
+        sequence = self._stream_buffer.sequence
+
+        # Check if generation detail already exists for this message
+        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
+
+        if existing:
+            existing.reasoning_content = json.dumps(reasoning_content) if reasoning_content else None
+            existing.tool_calls = json.dumps(tool_calls) if tool_calls else None
+            existing.sequence = json.dumps(sequence) if sequence else None
+        else:
+            generation_detail = LLMGenerationDetail(
+                tenant_id=self._application_generate_entity.app_config.tenant_id,
+                app_id=self._application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                reasoning_content=json.dumps(reasoning_content) if reasoning_content else None,
+                tool_calls=json.dumps(tool_calls) if tool_calls else None,
+                sequence=json.dumps(sequence) if sequence else None,
+            )
+            session.add(generation_detail)
+
     def _seed_graph_runtime_state_from_queue_manager(self) -> None:
         """Bootstrap the cached runtime state from the queue manager when present."""
         candidate = self._base_task_pipeline.queue_manager.graph_runtime_state

api/core/app/apps/agent_chat/app_runner.py

@@ -3,10 +3,8 @@ from typing import cast
 
 from sqlalchemy import select
 
-from core.agent.cot_chat_agent_runner import CotChatAgentRunner
-from core.agent.cot_completion_agent_runner import CotCompletionAgentRunner
+from core.agent.agent_app_runner import AgentAppRunner
 from core.agent.entities import AgentEntity
-from core.agent.fc_agent_runner import FunctionCallAgentRunner
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.apps.base_app_runner import AppRunner
@@ -14,8 +12,7 @@ from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity
 from core.app.entities.queue_entities import QueueAnnotationReplyEvent
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
-from core.model_runtime.entities.llm_entities import LLMMode
-from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
+from core.model_runtime.entities.model_entities import ModelFeature
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.moderation.base import ModerationError
 from extensions.ext_database import db
@@ -194,22 +191,7 @@ class AgentChatAppRunner(AppRunner):
             raise ValueError("Message not found")
         db.session.close()
 
-        runner_cls: type[FunctionCallAgentRunner] | type[CotChatAgentRunner] | type[CotCompletionAgentRunner]
-        # start agent runner
-        if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
-            # check LLM mode
-            if model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT:
-                runner_cls = CotChatAgentRunner
-            elif model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.COMPLETION:
-                runner_cls = CotCompletionAgentRunner
-            else:
-                raise ValueError(f"Invalid LLM mode: {model_schema.model_properties.get(ModelPropertyKey.MODE)}")
-        elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
-            runner_cls = FunctionCallAgentRunner
-        else:
-            raise ValueError(f"Invalid agent strategy: {agent_entity.strategy}")
-
-        runner = runner_cls(
+        runner = AgentAppRunner(
             tenant_id=app_config.tenant_id,
             application_generate_entity=application_generate_entity,
             conversation=conversation_result,

api/core/app/apps/base_app_queue_manager.py

@@ -90,6 +90,7 @@ class AppQueueManager:
         """
         self._clear_task_belong_cache()
         self._q.put(None)
+        self._graph_runtime_state = None  # Release reference to allow GC to reclaim memory
 
     def _clear_task_belong_cache(self) -> None:
         """

api/core/app/apps/common/workflow_response_converter.py

@@ -671,7 +671,7 @@ class WorkflowResponseConverter:
             task_id=task_id,
             data=AgentLogStreamResponse.Data(
                 node_execution_id=event.node_execution_id,
-                id=event.id,
+                message_id=event.id,
                 parent_id=event.parent_id,
                 label=event.label,
                 error=event.error,