fix: account deletion api

Co-authored-by: CodingOnStar <hanxujiang@dify.ai>

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

@@ -4,6 +4,7 @@ Quick validation script for skills - minimal version
 """
 
 import sys
+import os
 import re
 import yaml
 from pathlib import Path

.agents/skills/vercel-react-best-practices/AGENTS.md

@@ -33,13 +33,11 @@ Comprehensive performance optimization guide for React and Next.js applications,
    - 2.4 [Dynamic Imports for Heavy Components](#24-dynamic-imports-for-heavy-components)
    - 2.5 [Preload Based on User Intent](#25-preload-based-on-user-intent)
 3. [Server-Side Performance](#3-server-side-performance) — **HIGH**
-   - 3.1 [Authenticate Server Actions Like API Routes](#31-authenticate-server-actions-like-api-routes)
-   - 3.2 [Avoid Duplicate Serialization in RSC Props](#32-avoid-duplicate-serialization-in-rsc-props)
-   - 3.3 [Cross-Request LRU Caching](#33-cross-request-lru-caching)
-   - 3.4 [Minimize Serialization at RSC Boundaries](#34-minimize-serialization-at-rsc-boundaries)
-   - 3.5 [Parallel Data Fetching with Component Composition](#35-parallel-data-fetching-with-component-composition)
-   - 3.6 [Per-Request Deduplication with React.cache()](#36-per-request-deduplication-with-reactcache)
-   - 3.7 [Use after() for Non-Blocking Operations](#37-use-after-for-non-blocking-operations)
+   - 3.1 [Cross-Request LRU Caching](#31-cross-request-lru-caching)
+   - 3.2 [Minimize Serialization at RSC Boundaries](#32-minimize-serialization-at-rsc-boundaries)
+   - 3.3 [Parallel Data Fetching with Component Composition](#33-parallel-data-fetching-with-component-composition)
+   - 3.4 [Per-Request Deduplication with React.cache()](#34-per-request-deduplication-with-reactcache)
+   - 3.5 [Use after() for Non-Blocking Operations](#35-use-after-for-non-blocking-operations)
 4. [Client-Side Data Fetching](#4-client-side-data-fetching) — **MEDIUM-HIGH**
    - 4.1 [Deduplicate Global Event Listeners](#41-deduplicate-global-event-listeners)
    - 4.2 [Use Passive Event Listeners for Scrolling Performance](#42-use-passive-event-listeners-for-scrolling-performance)
@@ -570,158 +568,7 @@ The `typeof window !== 'undefined'` check prevents bundling preloaded modules fo
 
 Optimizing server-side rendering and data fetching eliminates server-side waterfalls and reduces response times.
 
-### 3.1 Authenticate Server Actions Like API Routes
-
-**Impact: CRITICAL (prevents unauthorized access to server mutations)**
-
-Server Actions (functions with `"use server"`) are exposed as public endpoints, just like API routes. Always verify authentication and authorization **inside** each Server Action—do not rely solely on middleware, layout guards, or page-level checks, as Server Actions can be invoked directly.
-
-Next.js documentation explicitly states: "Treat Server Actions with the same security considerations as public-facing API endpoints, and verify if the user is allowed to perform a mutation."
-
-**Incorrect: no authentication check**
-
-```typescript
-'use server'
-
-export async function deleteUser(userId: string) {
-  // Anyone can call this! No auth check
-  await db.user.delete({ where: { id: userId } })
-  return { success: true }
-}
-```
-
-**Correct: authentication inside the action**
-
-```typescript
-'use server'
-
-import { verifySession } from '@/lib/auth'
-import { unauthorized } from '@/lib/errors'
-
-export async function deleteUser(userId: string) {
-  // Always check auth inside the action
-  const session = await verifySession()
-  
-  if (!session) {
-    throw unauthorized('Must be logged in')
-  }
-  
-  // Check authorization too
-  if (session.user.role !== 'admin' && session.user.id !== userId) {
-    throw unauthorized('Cannot delete other users')
-  }
-  
-  await db.user.delete({ where: { id: userId } })
-  return { success: true }
-}
-```
-
-**With input validation:**
-
-```typescript
-'use server'
-
-import { verifySession } from '@/lib/auth'
-import { z } from 'zod'
-
-const updateProfileSchema = z.object({
-  userId: z.string().uuid(),
-  name: z.string().min(1).max(100),
-  email: z.string().email()
-})
-
-export async function updateProfile(data: unknown) {
-  // Validate input first
-  const validated = updateProfileSchema.parse(data)
-  
-  // Then authenticate
-  const session = await verifySession()
-  if (!session) {
-    throw new Error('Unauthorized')
-  }
-  
-  // Then authorize
-  if (session.user.id !== validated.userId) {
-    throw new Error('Can only update own profile')
-  }
-  
-  // Finally perform the mutation
-  await db.user.update({
-    where: { id: validated.userId },
-    data: {
-      name: validated.name,
-      email: validated.email
-    }
-  })
-  
-  return { success: true }
-}
-```
-
-Reference: [https://nextjs.org/docs/app/guides/authentication](https://nextjs.org/docs/app/guides/authentication)
-
-### 3.2 Avoid Duplicate Serialization in RSC Props
-
-**Impact: LOW (reduces network payload by avoiding duplicate serialization)**
-
-RSC→client serialization deduplicates by object reference, not value. Same reference = serialized once; new reference = serialized again. Do transformations (`.toSorted()`, `.filter()`, `.map()`) in client, not server.
-
-**Incorrect: duplicates array**
-
-```tsx
-// RSC: sends 6 strings (2 arrays × 3 items)
-<ClientList usernames={usernames} usernamesOrdered={usernames.toSorted()} />
-```
-
-**Correct: sends 3 strings**
-
-```tsx
-// RSC: send once
-<ClientList usernames={usernames} />
-
-// Client: transform there
-'use client'
-const sorted = useMemo(() => [...usernames].sort(), [usernames])
-```
-
-**Nested deduplication behavior:**
-
-```tsx
-// string[] - duplicates everything
-usernames={['a','b']} sorted={usernames.toSorted()} // sends 4 strings
-
-// object[] - duplicates array structure only
-users={[{id:1},{id:2}]} sorted={users.toSorted()} // sends 2 arrays + 2 unique objects (not 4)
-```
-
-Deduplication works recursively. Impact varies by data type:
-
-- `string[]`, `number[]`, `boolean[]`: **HIGH impact** - array + all primitives fully duplicated
-
-- `object[]`: **LOW impact** - array duplicated, but nested objects deduplicated by reference
-
-**Operations breaking deduplication: create new references**
-
-- Arrays: `.toSorted()`, `.filter()`, `.map()`, `.slice()`, `[...arr]`
-
-- Objects: `{...obj}`, `Object.assign()`, `structuredClone()`, `JSON.parse(JSON.stringify())`
-
-**More examples:**
-
-```tsx
-// ❌ Bad
-<C users={users} active={users.filter(u => u.active)} />
-<C product={product} productName={product.name} />
-
-// ✅ Good
-<C users={users} />
-<C product={product} />
-// Do filtering/destructuring in client
-```
-
-**Exception:** Pass derived data when transformation is expensive or client doesn't need original.
-
-### 3.3 Cross-Request LRU Caching
+### 3.1 Cross-Request LRU Caching
 
 **Impact: HIGH (caches across requests)**
 
@@ -758,7 +605,7 @@ Use when sequential user actions hit multiple endpoints needing the same data wi
 
 Reference: [https://github.com/isaacs/node-lru-cache](https://github.com/isaacs/node-lru-cache)
 
-### 3.4 Minimize Serialization at RSC Boundaries
+### 3.2 Minimize Serialization at RSC Boundaries
 
 **Impact: HIGH (reduces data transfer size)**
 
@@ -792,7 +639,7 @@ function Profile({ name }: { name: string }) {
 }
 ```
 
-### 3.5 Parallel Data Fetching with Component Composition
+### 3.3 Parallel Data Fetching with Component Composition
 
 **Impact: CRITICAL (eliminates server-side waterfalls)**
 
@@ -871,7 +718,7 @@ export default function Page() {
 }
 ```
 
-### 3.6 Per-Request Deduplication with React.cache()
+### 3.4 Per-Request Deduplication with React.cache()
 
 **Impact: MEDIUM (deduplicates within request)**
 
@@ -937,7 +784,7 @@ Use `React.cache()` to deduplicate these operations across your component tree.
 
 Reference: [https://react.dev/reference/react/cache](https://react.dev/reference/react/cache)
 
-### 3.7 Use after() for Non-Blocking Operations
+### 3.5 Use after() for Non-Blocking Operations
 
 **Impact: MEDIUM (faster response times)**
 
@@ -1868,32 +1715,79 @@ Micro-optimizations for hot paths can add up to meaningful improvements.
 
 **Impact: MEDIUM (reduces reflows/repaints)**
 
-Avoid interleaving style writes with layout reads. When you read a layout property (like `offsetWidth`, `getBoundingClientRect()`, or `getComputedStyle()`) between style changes, the browser is forced to trigger a synchronous reflow.
+Avoid changing styles one property at a time. Group multiple CSS changes together via classes or `cssText` to minimize browser reflows.
 
-**Incorrect: interleaved reads and writes force reflows**
+**Incorrect: multiple reflows**
 
 ```typescript
 function updateElementStyles(element: HTMLElement) {
+  // Each line triggers a reflow
   element.style.width = '100px'
-  const width = element.offsetWidth  // Forces reflow
   element.style.height = '200px'
-  const height = element.offsetHeight  // Forces another reflow
+  element.style.backgroundColor = 'blue'
+  element.style.border = '1px solid black'
 }
 ```
 
-**Correct: batch writes, then read once**
+**Correct: add class - single reflow**
 
 ```typescript
+// CSS file
+.highlighted-box {
+  width: 100px;
+  height: 200px;
+  background-color: blue;
+  border: 1px solid black;
+}
+
+// JavaScript
 function updateElementStyles(element: HTMLElement) {
   element.classList.add('highlighted-box')
-
-  const { width, height } = element.getBoundingClientRect()
 }
 ```
 
-**Better: use CSS classes**
+**Correct: change cssText - single reflow**
 
-Prefer CSS classes over inline styles when possible. CSS files are cached by the browser, and classes provide better separation of concerns and are easier to maintain.
+```typescript
+function updateElementStyles(element: HTMLElement) {
+  element.style.cssText = `
+    width: 100px;
+    height: 200px;
+    background-color: blue;
+    border: 1px solid black;
+  `
+}
+```
+
+**React example:**
+
+```tsx
+// Incorrect: changing styles one by one
+function Box({ isHighlighted }: { isHighlighted: boolean }) {
+  const ref = useRef<HTMLDivElement>(null)
+  
+  useEffect(() => {
+    if (ref.current && isHighlighted) {
+      ref.current.style.width = '100px'
+      ref.current.style.height = '200px'
+      ref.current.style.backgroundColor = 'blue'
+    }
+  }, [isHighlighted])
+  
+  return <div ref={ref}>Content</div>
+}
+
+// Correct: toggle class
+function Box({ isHighlighted }: { isHighlighted: boolean }) {
+  return (
+    <div className={isHighlighted ? 'highlighted-box' : ''}>
+      Content
+    </div>
+  )
+}
+```
+
+Prefer CSS classes over inline styles when possible. Classes are cached by the browser and provide better separation of concerns.
 
 ### 7.2 Build Index Maps for Repeated Lookups
 
@@ -2150,7 +2044,7 @@ function hasChanges(current: string[], original: string[]) {
   if (current.length !== original.length) {
     return true
   }
-  // Only sort when lengths match
+  // Only sort/join when lengths match
   const currentSorted = current.toSorted()
   const originalSorted = original.toSorted()
   for (let i = 0; i < currentSorted.length; i++) {
@@ -2335,7 +2229,7 @@ const min = Math.min(...numbers)
 const max = Math.max(...numbers)
 ```
 
-This works for small arrays, but can be slower or just throw an error for very large arrays due to spread operator limitations. Maximal array length is approximately 124000 in Chrome 143 and 638000 in Safari 18; exact numbers may vary - see [the fiddle](https://jsfiddle.net/qw1jabsx/4/). Use the loop approach for reliability.
+This works for small arrays but can be slower for very large arrays due to spread operator limitations. Use the loop approach for reliability.
 
 ### 7.11 Use Set/Map for O(1) Lookups
 
@@ -2431,7 +2325,7 @@ Store callbacks in refs when used in effects that shouldn't re-subscribe on call
 **Incorrect: re-subscribes on every render**
 
 ```tsx
-function useWindowEvent(event: string, handler: (e) => void) {
+function useWindowEvent(event: string, handler: () => void) {
   useEffect(() => {
     window.addEventListener(event, handler)
     return () => window.removeEventListener(event, handler)
@@ -2444,7 +2338,7 @@ function useWindowEvent(event: string, handler: (e) => void) {
 ```tsx
 import { useEffectEvent } from 'react'
 
-function useWindowEvent(event: string, handler: (e) => void) {
+function useWindowEvent(event: string, handler: () => void) {
   const onEvent = useEffectEvent(handler)
 
   useEffect(() => {
@@ -2469,7 +2363,7 @@ Access latest values in callbacks without adding them to dependency arrays. Prev
 ```typescript
 function useLatest<T>(value: T) {
   const ref = useRef(value)
-  useLayoutEffect(() => {
+  useEffect(() => {
     ref.current = value
   }, [value])
   return ref

.agents/skills/vercel-react-best-practices/rules/advanced-use-latest.md

@@ -1,14 +1,26 @@
 ---
-title: useEffectEvent for Stable Callback Refs
+title: useLatest for Stable Callback Refs
 impact: LOW
 impactDescription: prevents effect re-runs
-tags: advanced, hooks, useEffectEvent, refs, optimization
+tags: advanced, hooks, useLatest, refs, optimization
 ---
 
-## useEffectEvent for Stable Callback Refs
+## useLatest for Stable Callback Refs
 
 Access latest values in callbacks without adding them to dependency arrays. Prevents effect re-runs while avoiding stale closures.
 
+**Implementation:**
+
+```typescript
+function useLatest<T>(value: T) {
+  const ref = useRef(value)
+  useLayoutEffect(() => {
+    ref.current = value
+  }, [value])
+  return ref
+}
+```
+
 **Incorrect (effect re-runs on every callback change):**
 
 ```tsx
@@ -22,17 +34,15 @@ function SearchInput({ onSearch }: { onSearch: (q: string) => void }) {
 }
 ```
 
-**Correct (using React's useEffectEvent):**
+**Correct (stable effect, fresh callback):**
 
 ```tsx
-import { useEffectEvent } from 'react';
-
 function SearchInput({ onSearch }: { onSearch: (q: string) => void }) {
   const [query, setQuery] = useState('')
-  const onSearchEvent = useEffectEvent(onSearch)
+  const onSearchRef = useLatest(onSearch)
 
   useEffect(() => {
-    const timeout = setTimeout(() => onSearchEvent(query), 300)
+    const timeout = setTimeout(() => onSearchRef.current(query), 300)
     return () => clearTimeout(timeout)
   }, [query])
 }

.agents/skills/vercel-react-best-practices/rules/async-dependencies.md

@@ -33,19 +33,4 @@ const { user, config, profile } = await all({
 })
 ```
 
-**Alternative without extra dependencies:**
-
-We can also create all the promises first, and do `Promise.all()` at the end.
-
-```typescript
-const userPromise = fetchUser()
-const profilePromise = userPromise.then(user => fetchProfile(user.id))
-
-const [user, config, profile] = await Promise.all([
-  userPromise,
-  fetchConfig(),
-  profilePromise
-])
-```
-
 Reference: [https://github.com/shuding/better-all](https://github.com/shuding/better-all)

.agents/skills/vercel-react-best-practices/rules/js-batch-dom-css.md

@@ -1,28 +1,18 @@
 ---
-title: Avoid Layout Thrashing
+title: Batch DOM CSS Changes
 impact: MEDIUM
-impactDescription: prevents forced synchronous layouts and reduces performance bottlenecks
-tags: javascript, dom, css, performance, reflow, layout-thrashing
+impactDescription: reduces reflows/repaints
+tags: javascript, dom, css, performance, reflow
 ---
 
-## Avoid Layout Thrashing
+## Batch DOM CSS Changes
 
 Avoid interleaving style writes with layout reads. When you read a layout property (like `offsetWidth`, `getBoundingClientRect()`, or `getComputedStyle()`) between style changes, the browser is forced to trigger a synchronous reflow.
 
-**This is OK (browser batches style changes):**
+**Incorrect (interleaved reads and writes force reflows):**
+
 ```typescript
 function updateElementStyles(element: HTMLElement) {
-  // Each line invalidates style, but browser batches the recalculation
-  element.style.width = '100px'
-  element.style.height = '200px'
-  element.style.backgroundColor = 'blue'
-  element.style.border = '1px solid black'
-}
-```
-
-**Incorrect (interleaved reads and writes force reflows):**
-```typescript
-function layoutThrashing(element: HTMLElement) {
   element.style.width = '100px'
   const width = element.offsetWidth  // Forces reflow
   element.style.height = '200px'
@@ -31,6 +21,7 @@ function layoutThrashing(element: HTMLElement) {
 ```
 
 **Correct (batch writes, then read once):**
+
 ```typescript
 function updateElementStyles(element: HTMLElement) {
   // Batch all writes together
@@ -44,21 +35,8 @@ function updateElementStyles(element: HTMLElement) {
 }
 ```
 
-**Correct (batch reads, then writes):**
-```typescript
-function avoidThrashing(element: HTMLElement) {
-  // Read phase - all layout queries first
-  const rect1 = element.getBoundingClientRect()
-  const offsetWidth = element.offsetWidth
-  const offsetHeight = element.offsetHeight
-  
-  // Write phase - all style changes after
-  element.style.width = '100px'
-  element.style.height = '200px'
-}
-```
-
 **Better: use CSS classes**
+
 ```css
 .highlighted-box {
   width: 100px;
@@ -67,41 +45,13 @@ function avoidThrashing(element: HTMLElement) {
   border: 1px solid black;
 }
 ```
+
 ```typescript
 function updateElementStyles(element: HTMLElement) {
   element.classList.add('highlighted-box')
-  
+
   const { width, height } = element.getBoundingClientRect()
 }
 ```
 
-**React example:**
-```tsx
-// Incorrect: interleaving style changes with layout queries
-function Box({ isHighlighted }: { isHighlighted: boolean }) {
-  const ref = useRef<HTMLDivElement>(null)
-  
-  useEffect(() => {
-    if (ref.current && isHighlighted) {
-      ref.current.style.width = '100px'
-      const width = ref.current.offsetWidth // Forces layout
-      ref.current.style.height = '200px'
-    }
-  }, [isHighlighted])
-  
-  return <div ref={ref}>Content</div>
-}
-
-// Correct: toggle class
-function Box({ isHighlighted }: { isHighlighted: boolean }) {
-  return (
-    <div className={isHighlighted ? 'highlighted-box' : ''}>
-      Content
-    </div>
-  )
-}
-```
-
-Prefer CSS classes over inline styles when possible. CSS files are cached by the browser, and classes provide better separation of concerns and are easier to maintain.
-
-See [this gist](https://gist.github.com/paulirish/5d52fb081b3570c81e3a) and [CSS Triggers](https://csstriggers.com/) for more information on layout-forcing operations.
+Prefer CSS classes over inline styles when possible. CSS files are cached by the browser, and classes provide better separation of concerns and are easier to maintain.

.agents/skills/vercel-react-best-practices/rules/rerender-memo-with-default-value.md

@@ -1,38 +0,0 @@
----
-
-title: Extract Default Non-primitive Parameter Value from Memoized Component to Constant
-impact: MEDIUM
-impactDescription: restores memoization by using a constant for default value
-tags: rerender, memo, optimization
-
----
-
-## Extract Default Non-primitive Parameter Value from Memoized Component to Constant
-
-When memoized component has a default value for some non-primitive optional parameter, such as an array, function, or object, calling the component without that parameter results in broken memoization. This is because new value instances are created on every rerender, and they do not pass strict equality comparison in `memo()`.
-
-To address this issue, extract the default value into a constant.
-
-**Incorrect (`onClick` has different values on every rerender):**
-
-```tsx
-const UserAvatar = memo(function UserAvatar({ onClick = () => {} }: { onClick?: () => void }) {
-  // ...
-})
-
-// Used without optional onClick
-<UserAvatar />
-```
-
-**Correct (stable default value):**
-
-```tsx
-const NOOP = () => {};
-
-const UserAvatar = memo(function UserAvatar({ onClick = NOOP }: { onClick?: () => void }) {
-  // ...
-})
-
-// Used without optional onClick
-<UserAvatar />
-```

.agents/skills/vercel-react-best-practices/rules/rerender-simple-expression-in-memo.md

@@ -1,35 +0,0 @@
----
-title: Do not wrap a simple expression with a primitive result type in useMemo
-impact: LOW-MEDIUM
-impactDescription: wasted computation on every render
-tags: rerender, useMemo, optimization
----
-
-## Do not wrap a simple expression with a primitive result type in useMemo
-
-When an expression is simple (few logical or arithmetical operators) and has a primitive result type (boolean, number, string), do not wrap it in `useMemo`.
-Calling `useMemo` and comparing hook dependencies may consume more resources than the expression itself.
-
-**Incorrect:**
-
-```tsx
-function Header({ user, notifications }: Props) {
-  const isLoading = useMemo(() => {
-    return user.isLoading || notifications.isLoading
-  }, [user.isLoading, notifications.isLoading])
-
-  if (isLoading) return <Skeleton />
-  // return some markup
-}
-```
-
-**Correct:**
-
-```tsx
-function Header({ user, notifications }: Props) {
-  const isLoading = user.isLoading || notifications.isLoading
-
-  if (isLoading) return <Skeleton />
-  // return some markup
-}
-```

.agents/skills/vercel-react-best-practices/rules/server-auth-actions.md

@@ -1,96 +0,0 @@
----
-title: Authenticate Server Actions Like API Routes
-impact: CRITICAL
-impactDescription: prevents unauthorized access to server mutations
-tags: server, server-actions, authentication, security, authorization
----
-
-## Authenticate Server Actions Like API Routes
-
-**Impact: CRITICAL (prevents unauthorized access to server mutations)**
-
-Server Actions (functions with `"use server"`) are exposed as public endpoints, just like API routes. Always verify authentication and authorization **inside** each Server Action—do not rely solely on middleware, layout guards, or page-level checks, as Server Actions can be invoked directly.
-
-Next.js documentation explicitly states: "Treat Server Actions with the same security considerations as public-facing API endpoints, and verify if the user is allowed to perform a mutation."
-
-**Incorrect (no authentication check):**
-
-```typescript
-'use server'
-
-export async function deleteUser(userId: string) {
-  // Anyone can call this! No auth check
-  await db.user.delete({ where: { id: userId } })
-  return { success: true }
-}
-```
-
-**Correct (authentication inside the action):**
-
-```typescript
-'use server'
-
-import { verifySession } from '@/lib/auth'
-import { unauthorized } from '@/lib/errors'
-
-export async function deleteUser(userId: string) {
-  // Always check auth inside the action
-  const session = await verifySession()
-  
-  if (!session) {
-    throw unauthorized('Must be logged in')
-  }
-  
-  // Check authorization too
-  if (session.user.role !== 'admin' && session.user.id !== userId) {
-    throw unauthorized('Cannot delete other users')
-  }
-  
-  await db.user.delete({ where: { id: userId } })
-  return { success: true }
-}
-```
-
-**With input validation:**
-
-```typescript
-'use server'
-
-import { verifySession } from '@/lib/auth'
-import { z } from 'zod'
-
-const updateProfileSchema = z.object({
-  userId: z.string().uuid(),
-  name: z.string().min(1).max(100),
-  email: z.string().email()
-})
-
-export async function updateProfile(data: unknown) {
-  // Validate input first
-  const validated = updateProfileSchema.parse(data)
-  
-  // Then authenticate
-  const session = await verifySession()
-  if (!session) {
-    throw new Error('Unauthorized')
-  }
-  
-  // Then authorize
-  if (session.user.id !== validated.userId) {
-    throw new Error('Can only update own profile')
-  }
-  
-  // Finally perform the mutation
-  await db.user.update({
-    where: { id: validated.userId },
-    data: {
-      name: validated.name,
-      email: validated.email
-    }
-  })
-  
-  return { success: true }
-}
-```
-
-Reference: [https://nextjs.org/docs/app/guides/authentication](https://nextjs.org/docs/app/guides/authentication)

.agents/skills/vercel-react-best-practices/rules/server-dedup-props.md

@@ -1,65 +0,0 @@
----
-title: Avoid Duplicate Serialization in RSC Props
-impact: LOW
-impactDescription: reduces network payload by avoiding duplicate serialization
-tags: server, rsc, serialization, props, client-components
----
-
-## Avoid Duplicate Serialization in RSC Props
-
-**Impact: LOW (reduces network payload by avoiding duplicate serialization)**
-
-RSC→client serialization deduplicates by object reference, not value. Same reference = serialized once; new reference = serialized again. Do transformations (`.toSorted()`, `.filter()`, `.map()`) in client, not server.
-
-**Incorrect (duplicates array):**
-
-```tsx
-// RSC: sends 6 strings (2 arrays × 3 items)
-<ClientList usernames={usernames} usernamesOrdered={usernames.toSorted()} />
-```
-
-**Correct (sends 3 strings):**
-
-```tsx
-// RSC: send once
-<ClientList usernames={usernames} />
-
-// Client: transform there
-'use client'
-const sorted = useMemo(() => [...usernames].sort(), [usernames])
-```
-
-**Nested deduplication behavior:**
-
-Deduplication works recursively. Impact varies by data type:
-
-- `string[]`, `number[]`, `boolean[]`: **HIGH impact** - array + all primitives fully duplicated
-- `object[]`: **LOW impact** - array duplicated, but nested objects deduplicated by reference
-
-```tsx
-// string[] - duplicates everything
-usernames={['a','b']} sorted={usernames.toSorted()} // sends 4 strings
-
-// object[] - duplicates array structure only
-users={[{id:1},{id:2}]} sorted={users.toSorted()} // sends 2 arrays + 2 unique objects (not 4)
-```
-
-**Operations breaking deduplication (create new references):**
-
-- Arrays: `.toSorted()`, `.filter()`, `.map()`, `.slice()`, `[...arr]`
-- Objects: `{...obj}`, `Object.assign()`, `structuredClone()`, `JSON.parse(JSON.stringify())`
-
-**More examples:**
-
-```tsx
-// ❌ Bad
-<C users={users} active={users.filter(u => u.active)} />
-<C product={product} productName={product.name} />
-
-// ✅ Good
-<C users={users} />
-<C product={product} />
-// Do filtering/destructuring in client
-```
-
-**Exception:** Pass derived data when transformation is expensive or client doesn't need original.

.github/workflows/autofix.yml

@@ -79,6 +79,29 @@ jobs:
           find . -name "*.py" -type f -exec sed -i.bak -E 's/"([^"]+)" \| None/Optional["\1"]/g; s/'"'"'([^'"'"']+)'"'"' \| None/Optional['"'"'\1'"'"']/g' {} \;
           find . -name "*.py.bak" -type f -delete
 
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          package_json_file: web/package.json
+          run_install: false
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: 24
+          cache: pnpm
+          cache-dependency-path: ./web/pnpm-lock.yaml
+
+      - name: Install web dependencies
+        run: |
+          cd web
+          pnpm install --frozen-lockfile
+
+      - name: ESLint autofix
+        run: |
+          cd web
+          pnpm lint:fix || true
+
       # mdformat breaks YAML front matter in markdown files. Add --exclude for directories containing YAML front matter.
       - name: mdformat
         run: |

.github/workflows/style.yml

@@ -125,7 +125,7 @@ jobs:
       - name: Web type check
         if: steps.changed-files.outputs.any_changed == 'true'
         working-directory: ./web
-        run: pnpm run type-check:tsgo
+        run: pnpm run type-check
 
       - name: Web dead code check
         if: steps.changed-files.outputs.any_changed == 'true'

.gitignore

@@ -209,7 +209,6 @@ api/.vscode
 .history
 
 .idea/
-web/migration/
 
 # pnpm
 /.pnpm-store

api/.env.example

@@ -717,13 +717,3 @@ SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_SIZE=1000
 SANDBOX_EXPIRED_RECORDS_RETENTION_DAYS=30
 SANDBOX_EXPIRED_RECORDS_CLEAN_TASK_LOCK_TTL=90000
 
-# Sandbox Dify CLI configuration
-# Directory containing dify CLI binaries (dify-cli-<os>-<arch>). Defaults to api/bin when unset.
-SANDBOX_DIFY_CLI_ROOT=
-
-# CLI API URL for sandbox (dify-sandbox or e2b) to call back to Dify API.
-# This URL must be accessible from the sandbox environment.
-# For local development: use http://localhost:5001 or http://127.0.0.1:5001
-# For Docker deployment: use http://api:5001 (internal Docker network)
-# For external sandbox (e.g., e2b): use a publicly accessible URL
-CLI_API_URL=http://localhost:5001

api/.importlinter

@@ -27,7 +27,9 @@ ignore_imports =
     core.workflow.nodes.iteration.iteration_node -> core.workflow.graph_events
     core.workflow.nodes.loop.loop_node -> core.workflow.graph_events
 
-    core.workflow.nodes.node_factory -> core.workflow.graph
+    core.workflow.nodes.iteration.iteration_node -> core.app.workflow.node_factory
+    core.workflow.nodes.loop.loop_node -> core.app.workflow.node_factory
+
     core.workflow.nodes.iteration.iteration_node -> core.workflow.graph_engine
     core.workflow.nodes.iteration.iteration_node -> core.workflow.graph
     core.workflow.nodes.iteration.iteration_node -> core.workflow.graph_engine.command_channels
@@ -57,6 +59,252 @@ ignore_imports =
     core.workflow.graph_engine.manager -> extensions.ext_redis
     core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_redis
 
+[importlinter:contract:workflow-external-imports]
+name = Workflow External Imports
+type = forbidden
+source_modules =
+    core.workflow
+forbidden_modules =
+    configs
+    controllers
+    extensions
+    models
+    services
+    tasks
+    core.agent
+    core.app
+    core.base
+    core.callback_handler
+    core.datasource
+    core.db
+    core.entities
+    core.errors
+    core.extension
+    core.external_data_tool
+    core.file
+    core.helper
+    core.hosting_configuration
+    core.indexing_runner
+    core.llm_generator
+    core.logging
+    core.mcp
+    core.memory
+    core.model_manager
+    core.moderation
+    core.ops
+    core.plugin
+    core.prompt
+    core.provider_manager
+    core.rag
+    core.repositories
+    core.schemas
+    core.tools
+    core.trigger
+    core.variables
+ignore_imports =
+    core.workflow.nodes.loop.loop_node -> core.app.workflow.node_factory
+    core.workflow.graph_engine.command_channels.redis_channel -> extensions.ext_redis
+    core.workflow.graph_engine.layers.observability -> configs
+    core.workflow.graph_engine.layers.observability -> extensions.otel.runtime
+    core.workflow.graph_engine.layers.persistence -> core.ops.ops_trace_manager
+    core.workflow.graph_engine.worker_management.worker_pool -> configs
+    core.workflow.nodes.agent.agent_node -> core.model_manager
+    core.workflow.nodes.agent.agent_node -> core.provider_manager
+    core.workflow.nodes.agent.agent_node -> core.tools.tool_manager
+    core.workflow.nodes.code.code_node -> core.helper.code_executor.code_executor
+    core.workflow.nodes.datasource.datasource_node -> models.model
+    core.workflow.nodes.datasource.datasource_node -> models.tools
+    core.workflow.nodes.datasource.datasource_node -> services.datasource_provider_service
+    core.workflow.nodes.document_extractor.node -> configs
+    core.workflow.nodes.document_extractor.node -> core.file.file_manager
+    core.workflow.nodes.document_extractor.node -> core.helper.ssrf_proxy
+    core.workflow.nodes.http_request.entities -> configs
+    core.workflow.nodes.http_request.executor -> configs
+    core.workflow.nodes.http_request.executor -> core.file.file_manager
+    core.workflow.nodes.http_request.node -> configs
+    core.workflow.nodes.http_request.node -> core.tools.tool_file_manager
+    core.workflow.nodes.iteration.iteration_node -> core.app.workflow.node_factory
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> core.rag.index_processor.index_processor_factory
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.rag.datasource.retrieval_service
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.rag.retrieval.dataset_retrieval
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> models.dataset
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> services.feature_service
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.model_runtime.model_providers.__base.large_language_model
+    core.workflow.nodes.llm.llm_utils -> configs
+    core.workflow.nodes.llm.llm_utils -> core.app.entities.app_invoke_entities
+    core.workflow.nodes.llm.llm_utils -> core.file.models
+    core.workflow.nodes.llm.llm_utils -> core.model_manager
+    core.workflow.nodes.llm.llm_utils -> core.model_runtime.model_providers.__base.large_language_model
+    core.workflow.nodes.llm.llm_utils -> models.model
+    core.workflow.nodes.llm.llm_utils -> models.provider
+    core.workflow.nodes.llm.llm_utils -> services.credit_pool_service
+    core.workflow.nodes.llm.node -> core.tools.signature
+    core.workflow.nodes.template_transform.template_transform_node -> configs
+    core.workflow.nodes.tool.tool_node -> core.callback_handler.workflow_tool_callback_handler
+    core.workflow.nodes.tool.tool_node -> core.tools.tool_engine
+    core.workflow.nodes.tool.tool_node -> core.tools.tool_manager
+    core.workflow.workflow_entry -> configs
+    core.workflow.workflow_entry -> models.workflow
+    core.workflow.nodes.agent.agent_node -> core.agent.entities
+    core.workflow.nodes.agent.agent_node -> core.agent.plugin_entities
+    core.workflow.graph_engine.layers.persistence -> core.app.entities.app_invoke_entities
+    core.workflow.nodes.base.node -> core.app.entities.app_invoke_entities
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> core.app.entities.app_invoke_entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.app.app_config.entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.app.entities.app_invoke_entities
+    core.workflow.nodes.llm.node -> core.app.entities.app_invoke_entities
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.app.entities.app_invoke_entities
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.advanced_prompt_transform
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.simple_prompt_transform
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.model_runtime.model_providers.__base.large_language_model
+    core.workflow.nodes.question_classifier.question_classifier_node -> core.app.entities.app_invoke_entities
+    core.workflow.nodes.question_classifier.question_classifier_node -> core.prompt.advanced_prompt_transform
+    core.workflow.nodes.question_classifier.question_classifier_node -> core.prompt.simple_prompt_transform
+    core.workflow.nodes.start.entities -> core.app.app_config.entities
+    core.workflow.nodes.start.start_node -> core.app.app_config.entities
+    core.workflow.workflow_entry -> core.app.apps.exc
+    core.workflow.workflow_entry -> core.app.entities.app_invoke_entities
+    core.workflow.workflow_entry -> core.app.workflow.node_factory
+    core.workflow.nodes.datasource.datasource_node -> core.datasource.datasource_manager
+    core.workflow.nodes.datasource.datasource_node -> core.datasource.utils.message_transformer
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.entities.agent_entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.entities.model_entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.model_manager
+    core.workflow.nodes.llm.llm_utils -> core.entities.provider_entities
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.model_manager
+    core.workflow.nodes.question_classifier.question_classifier_node -> core.model_manager
+    core.workflow.node_events.node -> core.file
+    core.workflow.nodes.agent.agent_node -> core.file
+    core.workflow.nodes.datasource.datasource_node -> core.file
+    core.workflow.nodes.datasource.datasource_node -> core.file.enums
+    core.workflow.nodes.document_extractor.node -> core.file
+    core.workflow.nodes.http_request.executor -> core.file.enums
+    core.workflow.nodes.http_request.node -> core.file
+    core.workflow.nodes.http_request.node -> core.file.file_manager
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.file.models
+    core.workflow.nodes.list_operator.node -> core.file
+    core.workflow.nodes.llm.file_saver -> core.file
+    core.workflow.nodes.llm.llm_utils -> core.variables.segments
+    core.workflow.nodes.llm.node -> core.file
+    core.workflow.nodes.llm.node -> core.file.file_manager
+    core.workflow.nodes.llm.node -> core.file.models
+    core.workflow.nodes.loop.entities -> core.variables.types
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.file
+    core.workflow.nodes.protocols -> core.file
+    core.workflow.nodes.question_classifier.question_classifier_node -> core.file.models
+    core.workflow.nodes.tool.tool_node -> core.file
+    core.workflow.nodes.tool.tool_node -> core.tools.utils.message_transformer
+    core.workflow.nodes.tool.tool_node -> models
+    core.workflow.nodes.trigger_webhook.node -> core.file
+    core.workflow.runtime.variable_pool -> core.file
+    core.workflow.runtime.variable_pool -> core.file.file_manager
+    core.workflow.system_variable -> core.file.models
+    core.workflow.utils.condition.processor -> core.file
+    core.workflow.utils.condition.processor -> core.file.file_manager
+    core.workflow.workflow_entry -> core.file.models
+    core.workflow.workflow_type_encoder -> core.file.models
+    core.workflow.nodes.agent.agent_node -> models.model
+    core.workflow.nodes.code.code_node -> core.helper.code_executor.code_node_provider
+    core.workflow.nodes.code.code_node -> core.helper.code_executor.javascript.javascript_code_provider
+    core.workflow.nodes.code.code_node -> core.helper.code_executor.python3.python3_code_provider
+    core.workflow.nodes.code.entities -> core.helper.code_executor.code_executor
+    core.workflow.nodes.datasource.datasource_node -> core.variables.variables
+    core.workflow.nodes.http_request.executor -> core.helper.ssrf_proxy
+    core.workflow.nodes.http_request.node -> core.helper.ssrf_proxy
+    core.workflow.nodes.llm.file_saver -> core.helper.ssrf_proxy
+    core.workflow.nodes.llm.node -> core.helper.code_executor
+    core.workflow.nodes.template_transform.template_renderer -> core.helper.code_executor.code_executor
+    core.workflow.nodes.llm.node -> core.llm_generator.output_parser.errors
+    core.workflow.nodes.llm.node -> core.llm_generator.output_parser.structured_output
+    core.workflow.nodes.llm.node -> core.model_manager
+    core.workflow.graph_engine.layers.persistence -> core.ops.entities.trace_entity
+    core.workflow.nodes.agent.entities -> core.prompt.entities.advanced_prompt_entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.prompt.simple_prompt_transform
+    core.workflow.nodes.llm.entities -> core.prompt.entities.advanced_prompt_entities
+    core.workflow.nodes.llm.llm_utils -> core.prompt.entities.advanced_prompt_entities
+    core.workflow.nodes.llm.node -> core.prompt.entities.advanced_prompt_entities
+    core.workflow.nodes.llm.node -> core.prompt.utils.prompt_message_util
+    core.workflow.nodes.parameter_extractor.entities -> core.prompt.entities.advanced_prompt_entities
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.entities.advanced_prompt_entities
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.utils.prompt_message_util
+    core.workflow.nodes.question_classifier.entities -> core.prompt.entities.advanced_prompt_entities
+    core.workflow.nodes.question_classifier.question_classifier_node -> core.prompt.utils.prompt_message_util
+    core.workflow.nodes.knowledge_index.entities -> core.rag.retrieval.retrieval_methods
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> core.rag.retrieval.retrieval_methods
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> models.dataset
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.rag.retrieval.retrieval_methods
+    core.workflow.nodes.llm.node -> models.dataset
+    core.workflow.nodes.agent.agent_node -> core.tools.utils.message_transformer
+    core.workflow.nodes.llm.file_saver -> core.tools.signature
+    core.workflow.nodes.llm.file_saver -> core.tools.tool_file_manager
+    core.workflow.nodes.tool.tool_node -> core.tools.errors
+    core.workflow.conversation_variable_updater -> core.variables
+    core.workflow.graph_engine.entities.commands -> core.variables.variables
+    core.workflow.nodes.agent.agent_node -> core.variables.segments
+    core.workflow.nodes.answer.answer_node -> core.variables
+    core.workflow.nodes.code.code_node -> core.variables.segments
+    core.workflow.nodes.code.code_node -> core.variables.types
+    core.workflow.nodes.code.entities -> core.variables.types
+    core.workflow.nodes.datasource.datasource_node -> core.variables.segments
+    core.workflow.nodes.document_extractor.node -> core.variables
+    core.workflow.nodes.document_extractor.node -> core.variables.segments
+    core.workflow.nodes.http_request.executor -> core.variables.segments
+    core.workflow.nodes.http_request.node -> core.variables.segments
+    core.workflow.nodes.iteration.iteration_node -> core.variables
+    core.workflow.nodes.iteration.iteration_node -> core.variables.segments
+    core.workflow.nodes.iteration.iteration_node -> core.variables.variables
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.variables
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.variables.segments
+    core.workflow.nodes.list_operator.node -> core.variables
+    core.workflow.nodes.list_operator.node -> core.variables.segments
+    core.workflow.nodes.llm.node -> core.variables
+    core.workflow.nodes.loop.loop_node -> core.variables
+    core.workflow.nodes.parameter_extractor.entities -> core.variables.types
+    core.workflow.nodes.parameter_extractor.exc -> core.variables.types
+    core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.variables.types
+    core.workflow.nodes.tool.tool_node -> core.variables.segments
+    core.workflow.nodes.tool.tool_node -> core.variables.variables
+    core.workflow.nodes.trigger_webhook.node -> core.variables.types
+    core.workflow.nodes.trigger_webhook.node -> core.variables.variables
+    core.workflow.nodes.variable_aggregator.entities -> core.variables.types
+    core.workflow.nodes.variable_aggregator.variable_aggregator_node -> core.variables.segments
+    core.workflow.nodes.variable_assigner.common.helpers -> core.variables
+    core.workflow.nodes.variable_assigner.common.helpers -> core.variables.consts
+    core.workflow.nodes.variable_assigner.common.helpers -> core.variables.types
+    core.workflow.nodes.variable_assigner.v1.node -> core.variables
+    core.workflow.nodes.variable_assigner.v2.helpers -> core.variables
+    core.workflow.nodes.variable_assigner.v2.node -> core.variables
+    core.workflow.nodes.variable_assigner.v2.node -> core.variables.consts
+    core.workflow.runtime.graph_runtime_state_protocol -> core.variables.segments
+    core.workflow.runtime.read_only_wrappers -> core.variables.segments
+    core.workflow.runtime.variable_pool -> core.variables
+    core.workflow.runtime.variable_pool -> core.variables.consts
+    core.workflow.runtime.variable_pool -> core.variables.segments
+    core.workflow.runtime.variable_pool -> core.variables.variables
+    core.workflow.utils.condition.processor -> core.variables
+    core.workflow.utils.condition.processor -> core.variables.segments
+    core.workflow.variable_loader -> core.variables
+    core.workflow.variable_loader -> core.variables.consts
+    core.workflow.workflow_type_encoder -> core.variables
+    core.workflow.graph_engine.manager -> extensions.ext_redis
+    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.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_redis
+    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.workflow_entry -> extensions.otel.runtime
+    core.workflow.nodes.agent.agent_node -> models
+    core.workflow.nodes.base.node -> models.enums
+    core.workflow.nodes.llm.llm_utils -> models.provider_ids
+    core.workflow.nodes.llm.node -> models.model
+    core.workflow.workflow_entry -> models.enums
+    core.workflow.nodes.agent.agent_node -> services
+    core.workflow.nodes.tool.tool_node -> services
+
 [importlinter:contract:rsc]
 name = RSC
 type = layers

api/README.md

@@ -1,6 +1,6 @@
 # Dify Backend API
 
-## Usage
+## Setup and Run
 
 > [!IMPORTANT]
 >
@@ -8,48 +8,77 @@
 > [`uv`](https://docs.astral.sh/uv/) as the package manager
 > for Dify API backend service.
 
-1. Start the docker-compose stack
+`uv` and `pnpm` are required to run the setup and development commands below.
 
-   The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
+### Using scripts (recommended)
+
+The scripts resolve paths relative to their location, so you can run them from anywhere.
+
+1. Run setup (copies env files and installs dependencies).
 
    ```bash
-   cd ../docker
-   cp middleware.env.example middleware.env
-   # change the profile to mysql if you are not using postgres,change the profile to other vector database if you are not using weaviate
-   docker compose -f docker-compose.middleware.yaml --profile postgresql --profile weaviate -p dify up -d
-   cd ../api
+   ./dev/setup
    ```
 
-1. Copy `.env.example` to `.env`
+1. Review `api/.env`, `web/.env.local`, and `docker/middleware.env` values (see the `SECRET_KEY` note below).
 
-   ```cli
-   cp .env.example .env
+1. Start middleware (PostgreSQL/Redis/Weaviate).
+
+   ```bash
+   ./dev/start-docker-compose
    ```
 
-> [!IMPORTANT]
->
-> When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., `example.com`). The frontend and backend must be under the same top-level domain in order to share authentication cookies.
+1. Start backend (runs migrations first).
 
-1. Generate a `SECRET_KEY` in the `.env` file.
-
-   bash for Linux
-
-   ```bash for Linux
-   sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
+   ```bash
+   ./dev/start-api
    ```
 
-   bash for Mac
+1. Start Dify [web](../web) service.
 
-   ```bash for Mac
-   secret_key=$(openssl rand -base64 42)
-   sed -i '' "/^SECRET_KEY=/c\\
-   SECRET_KEY=${secret_key}" .env
+   ```bash
+   ./dev/start-web
    ```
 
-1. Create environment.
+1. Set up your application by visiting `http://localhost:3000`.
 
-   Dify API service uses [UV](https://docs.astral.sh/uv/) to manage dependencies.
-   First, you need to add the uv package manager, if you don't have it already.
+1. Optional: start the worker service (async tasks, runs from `api`).
+
+   ```bash
+   ./dev/start-worker
+   ```
+
+1. Optional: start Celery Beat (scheduled tasks).
+
+   ```bash
+   ./dev/start-beat
+   ```
+
+### Manual commands
+
+<details>
+<summary>Show manual setup and run steps</summary>
+
+These commands assume you start from the repository root.
+
+1. Start the docker-compose stack.
+
+   The backend requires middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
+
+   ```bash
+   cp docker/middleware.env.example docker/middleware.env
+   # Use mysql or another vector database profile if you are not using postgres/weaviate.
+   docker compose -f docker/docker-compose.middleware.yaml --profile postgresql --profile weaviate -p dify up -d
+   ```
+
+1. Copy env files.
+
+   ```bash
+   cp api/.env.example api/.env
+   cp web/.env.example web/.env.local
+   ```
+
+1. Install UV if needed.
 
    ```bash
    pip install uv
@@ -57,60 +86,96 @@
    brew install uv
    ```
 
-1. Install dependencies
+1. Install API dependencies.
 
    ```bash
-   uv sync --dev
+   cd api
+   uv sync --group dev
    ```
 
-1. Run migrate
-
-   Before the first launch, migrate the database to the latest version.
+1. Install web dependencies.
 
    ```bash
+   cd web
+   pnpm install
+   cd ..
+   ```
+
+1. Start backend (runs migrations first, in a new terminal).
+
+   ```bash
+   cd api
    uv run flask db upgrade
-   ```
-
-1. Start backend
-
-   ```bash
    uv run flask run --host 0.0.0.0 --port=5001 --debug
    ```
 
-1. Start Dify [web](../web) service.
+1. Start Dify [web](../web) service (in a new terminal).
 
-1. Setup your application by visiting `http://localhost:3000`.
+   ```bash
+   cd web
+   pnpm dev:inspect
+   ```
 
-1. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
+1. Set up your application by visiting `http://localhost:3000`.
 
-```bash
-uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
-```
+1. Optional: start the worker service (async tasks, in a new terminal).
 
-Additionally, if you want to debug the celery scheduled tasks, you can run the following command in another terminal to start the beat service:
+   ```bash
+   cd api
+   uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
+   ```
 
-```bash
-uv run celery -A app.celery beat
-```
+1. Optional: start Celery Beat (scheduled tasks, in a new terminal).
+
+   ```bash
+   cd api
+   uv run celery -A app.celery beat
+   ```
+
+</details>
+
+### Environment notes
+
+> [!IMPORTANT]
+>
+> When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., `example.com`). The frontend and backend must be under the same top-level domain in order to share authentication cookies.
+
+- Generate a `SECRET_KEY` in the `.env` file.
+
+  bash for Linux
+
+  ```bash
+  sed -i "/^SECRET_KEY=/c\\SECRET_KEY=$(openssl rand -base64 42)" .env
+  ```
+
+  bash for Mac
+
+  ```bash
+  secret_key=$(openssl rand -base64 42)
+  sed -i '' "/^SECRET_KEY=/c\\
+  SECRET_KEY=${secret_key}" .env
+  ```
 
 ## Testing
 
 1. Install dependencies for both the backend and the test environment
 
    ```bash
-   uv sync --dev
+   cd api
+   uv sync --group dev
    ```
 
 1. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`, more can check [Claude.md](../CLAUDE.md)
 
    ```bash
+   cd api
    uv run pytest                           # Run all tests
    uv run pytest tests/unit_tests/         # Unit tests only
    uv run pytest tests/integration_tests/  # Integration tests
 
    # Code quality
-   ../dev/reformat               # Run all formatters and linters
-   uv run ruff check --fix ./    # Fix linting issues
-   uv run ruff format ./         # Format code
-   uv run basedpyright .         # Type checking
+   ./dev/reformat               # Run all formatters and linters
+   uv run ruff check --fix ./   # Fix linting issues
+   uv run ruff format ./        # Format code
+   uv run basedpyright .        # Type checking
    ```

api/agent-notes/core/app_assets/packager/zip_packager.py.md

@@ -1,14 +0,0 @@
-# Zip Packager Notes
-
-## Purpose
-- Builds a ZIP archive of asset contents stored via the configured storage backend.
-
-## Key Decisions
-- Packaging writes assets into an in-memory zip buffer returned as bytes.
-- Asset fetch + zip writing are executed via a thread pool with a lock guarding `ZipFile` writes.
-
-## Edge Cases
-- ZIP writes are serialized by the lock; storage reads still run in parallel.
-
-## Tests/Verification
-- None yet.

api/agent-notes/core/virtual_environment/providers/e2b_sandbox.py.md

@@ -1,16 +0,0 @@
-# E2B Sandbox Provider Notes
-
-## Purpose
-- Implements the E2B-backed `VirtualEnvironment` provider and bootstraps sandbox metadata, file I/O, and command execution.
-
-## Key Decisions
-- Sandbox metadata is gathered during `_construct_environment` using the E2B SDK before returning `Metadata`.
-- Architecture/OS detection uses a single `uname -m -s` call split by whitespace to reduce round-trips.
-- Command execution streams stdout/stderr through `QueueTransportReadCloser`; stdin is unsupported.
-
-## Edge Cases
-- `release_environment` raises when sandbox termination fails.
-- `execute_command` runs in a background thread; consumers must read stdout/stderr until EOF.
-
-## Tests/Verification
-- None yet. Add targeted service tests when behavior changes.

api/app_factory.py

@@ -81,6 +81,7 @@ def initialize_extensions(app: DifyApp):
         ext_commands,
         ext_compress,
         ext_database,
+        ext_fastopenapi,
         ext_forward_refs,
         ext_hosting_provider,
         ext_import_modules,
@@ -128,6 +129,7 @@ def initialize_extensions(app: DifyApp):
         ext_proxy_fix,
         ext_blueprints,
         ext_commands,
+        ext_fastopenapi,
         ext_otel,
         ext_request_logging,
         ext_session_factory,

api/commands.py

@@ -23,8 +23,7 @@ from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.index_processor.constant.built_in_field import BuiltInField
 from core.rag.models.document import Document
-from core.sandbox import SandboxBuilder, SandboxType
-from core.tools.utils.system_encryption import encrypt_system_params
+from core.tools.utils.system_oauth_encryption import encrypt_system_oauth_params
 from events.app_event import app_was_created
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -951,6 +950,346 @@ def clean_workflow_runs(
     )
 
 
+@click.command(
+    "archive-workflow-runs",
+    help="Archive workflow runs for paid plan tenants to S3-compatible storage.",
+)
+@click.option("--tenant-ids", default=None, help="Optional comma-separated tenant IDs for grayscale rollout.")
+@click.option("--before-days", default=90, show_default=True, help="Archive runs older than N days.")
+@click.option(
+    "--from-days-ago",
+    default=None,
+    type=click.IntRange(min=0),
+    help="Lower bound in days ago (older). Must be paired with --to-days-ago.",
+)
+@click.option(
+    "--to-days-ago",
+    default=None,
+    type=click.IntRange(min=0),
+    help="Upper bound in days ago (newer). Must be paired with --from-days-ago.",
+)
+@click.option(
+    "--start-from",
+    type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+    default=None,
+    help="Archive runs created at or after this timestamp (UTC if no timezone).",
+)
+@click.option(
+    "--end-before",
+    type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+    default=None,
+    help="Archive runs created before this timestamp (UTC if no timezone).",
+)
+@click.option("--batch-size", default=100, show_default=True, help="Batch size for processing.")
+@click.option("--workers", default=1, show_default=True, type=int, help="Concurrent workflow runs to archive.")
+@click.option("--limit", default=None, type=int, help="Maximum number of runs to archive.")
+@click.option("--dry-run", is_flag=True, help="Preview without archiving.")
+@click.option("--delete-after-archive", is_flag=True, help="Delete runs and related data after archiving.")
+def archive_workflow_runs(
+    tenant_ids: str | None,
+    before_days: int,
+    from_days_ago: int | None,
+    to_days_ago: int | None,
+    start_from: datetime.datetime | None,
+    end_before: datetime.datetime | None,
+    batch_size: int,
+    workers: int,
+    limit: int | None,
+    dry_run: bool,
+    delete_after_archive: bool,
+):
+    """
+    Archive workflow runs for paid plan tenants older than the specified days.
+
+    This command archives the following tables to storage:
+    - workflow_node_executions
+    - workflow_node_execution_offload
+    - workflow_pauses
+    - workflow_pause_reasons
+    - workflow_trigger_logs
+
+    The workflow_runs and workflow_app_logs tables are preserved for UI listing.
+    """
+    from services.retention.workflow_run.archive_paid_plan_workflow_run import WorkflowRunArchiver
+
+    run_started_at = datetime.datetime.now(datetime.UTC)
+    click.echo(
+        click.style(
+            f"Starting workflow run archiving at {run_started_at.isoformat()}.",
+            fg="white",
+        )
+    )
+
+    if (start_from is None) ^ (end_before is None):
+        click.echo(click.style("start-from and end-before must be provided together.", fg="red"))
+        return
+
+    if (from_days_ago is None) ^ (to_days_ago is None):
+        click.echo(click.style("from-days-ago and to-days-ago must be provided together.", fg="red"))
+        return
+
+    if from_days_ago is not None and to_days_ago is not None:
+        if start_from or end_before:
+            click.echo(click.style("Choose either day offsets or explicit dates, not both.", fg="red"))
+            return
+        if from_days_ago <= to_days_ago:
+            click.echo(click.style("from-days-ago must be greater than to-days-ago.", fg="red"))
+            return
+        now = datetime.datetime.now()
+        start_from = now - datetime.timedelta(days=from_days_ago)
+        end_before = now - datetime.timedelta(days=to_days_ago)
+        before_days = 0
+
+    if start_from and end_before and start_from >= end_before:
+        click.echo(click.style("start-from must be earlier than end-before.", fg="red"))
+        return
+    if workers < 1:
+        click.echo(click.style("workers must be at least 1.", fg="red"))
+        return
+
+    archiver = WorkflowRunArchiver(
+        days=before_days,
+        batch_size=batch_size,
+        start_from=start_from,
+        end_before=end_before,
+        workers=workers,
+        tenant_ids=[tid.strip() for tid in tenant_ids.split(",")] if tenant_ids else None,
+        limit=limit,
+        dry_run=dry_run,
+        delete_after_archive=delete_after_archive,
+    )
+    summary = archiver.run()
+    click.echo(
+        click.style(
+            f"Summary: processed={summary.total_runs_processed}, archived={summary.runs_archived}, "
+            f"skipped={summary.runs_skipped}, failed={summary.runs_failed}, "
+            f"time={summary.total_elapsed_time:.2f}s",
+            fg="cyan",
+        )
+    )
+
+    run_finished_at = datetime.datetime.now(datetime.UTC)
+    elapsed = run_finished_at - run_started_at
+    click.echo(
+        click.style(
+            f"Workflow run archiving completed. start={run_started_at.isoformat()} "
+            f"end={run_finished_at.isoformat()} duration={elapsed}",
+            fg="green",
+        )
+    )
+
+
+@click.command(
+    "restore-workflow-runs",
+    help="Restore archived workflow runs from S3-compatible storage.",
+)
+@click.option(
+    "--tenant-ids",
+    required=False,
+    help="Tenant IDs (comma-separated).",
+)
+@click.option("--run-id", required=False, help="Workflow run ID to restore.")
+@click.option(
+    "--start-from",
+    type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+    default=None,
+    help="Optional lower bound (inclusive) for created_at; must be paired with --end-before.",
+)
+@click.option(
+    "--end-before",
+    type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+    default=None,
+    help="Optional upper bound (exclusive) for created_at; must be paired with --start-from.",
+)
+@click.option("--workers", default=1, show_default=True, type=int, help="Concurrent workflow runs to restore.")
+@click.option("--limit", type=int, default=100, show_default=True, help="Maximum number of runs to restore.")
+@click.option("--dry-run", is_flag=True, help="Preview without restoring.")
+def restore_workflow_runs(
+    tenant_ids: str | None,
+    run_id: str | None,
+    start_from: datetime.datetime | None,
+    end_before: datetime.datetime | None,
+    workers: int,
+    limit: int,
+    dry_run: bool,
+):
+    """
+    Restore an archived workflow run from storage to the database.
+
+    This restores the following tables:
+    - workflow_node_executions
+    - workflow_node_execution_offload
+    - workflow_pauses
+    - workflow_pause_reasons
+    - workflow_trigger_logs
+    """
+    from services.retention.workflow_run.restore_archived_workflow_run import WorkflowRunRestore
+
+    parsed_tenant_ids = None
+    if tenant_ids:
+        parsed_tenant_ids = [tid.strip() for tid in tenant_ids.split(",") if tid.strip()]
+        if not parsed_tenant_ids:
+            raise click.BadParameter("tenant-ids must not be empty")
+
+    if (start_from is None) ^ (end_before is None):
+        raise click.UsageError("--start-from and --end-before must be provided together.")
+    if run_id is None and (start_from is None or end_before is None):
+        raise click.UsageError("--start-from and --end-before are required for batch restore.")
+    if workers < 1:
+        raise click.BadParameter("workers must be at least 1")
+
+    start_time = datetime.datetime.now(datetime.UTC)
+    click.echo(
+        click.style(
+            f"Starting restore of workflow run {run_id} at {start_time.isoformat()}.",
+            fg="white",
+        )
+    )
+
+    restorer = WorkflowRunRestore(dry_run=dry_run, workers=workers)
+    if run_id:
+        results = [restorer.restore_by_run_id(run_id)]
+    else:
+        assert start_from is not None
+        assert end_before is not None
+        results = restorer.restore_batch(
+            parsed_tenant_ids,
+            start_date=start_from,
+            end_date=end_before,
+            limit=limit,
+        )
+
+    end_time = datetime.datetime.now(datetime.UTC)
+    elapsed = end_time - start_time
+
+    successes = sum(1 for result in results if result.success)
+    failures = len(results) - successes
+
+    if failures == 0:
+        click.echo(
+            click.style(
+                f"Restore completed successfully. success={successes} duration={elapsed}",
+                fg="green",
+            )
+        )
+    else:
+        click.echo(
+            click.style(
+                f"Restore completed with failures. success={successes} failed={failures} duration={elapsed}",
+                fg="red",
+            )
+        )
+
+
+@click.command(
+    "delete-archived-workflow-runs",
+    help="Delete archived workflow runs from the database.",
+)
+@click.option(
+    "--tenant-ids",
+    required=False,
+    help="Tenant IDs (comma-separated).",
+)
+@click.option("--run-id", required=False, help="Workflow run ID to delete.")
+@click.option(
+    "--start-from",
+    type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+    default=None,
+    help="Optional lower bound (inclusive) for created_at; must be paired with --end-before.",
+)
+@click.option(
+    "--end-before",
+    type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+    default=None,
+    help="Optional upper bound (exclusive) for created_at; must be paired with --start-from.",
+)
+@click.option("--limit", type=int, default=100, show_default=True, help="Maximum number of runs to delete.")
+@click.option("--dry-run", is_flag=True, help="Preview without deleting.")
+def delete_archived_workflow_runs(
+    tenant_ids: str | None,
+    run_id: str | None,
+    start_from: datetime.datetime | None,
+    end_before: datetime.datetime | None,
+    limit: int,
+    dry_run: bool,
+):
+    """
+    Delete archived workflow runs from the database.
+    """
+    from services.retention.workflow_run.delete_archived_workflow_run import ArchivedWorkflowRunDeletion
+
+    parsed_tenant_ids = None
+    if tenant_ids:
+        parsed_tenant_ids = [tid.strip() for tid in tenant_ids.split(",") if tid.strip()]
+        if not parsed_tenant_ids:
+            raise click.BadParameter("tenant-ids must not be empty")
+
+    if (start_from is None) ^ (end_before is None):
+        raise click.UsageError("--start-from and --end-before must be provided together.")
+    if run_id is None and (start_from is None or end_before is None):
+        raise click.UsageError("--start-from and --end-before are required for batch delete.")
+
+    start_time = datetime.datetime.now(datetime.UTC)
+    target_desc = f"workflow run {run_id}" if run_id else "workflow runs"
+    click.echo(
+        click.style(
+            f"Starting delete of {target_desc} at {start_time.isoformat()}.",
+            fg="white",
+        )
+    )
+
+    deleter = ArchivedWorkflowRunDeletion(dry_run=dry_run)
+    if run_id:
+        results = [deleter.delete_by_run_id(run_id)]
+    else:
+        assert start_from is not None
+        assert end_before is not None
+        results = deleter.delete_batch(
+            parsed_tenant_ids,
+            start_date=start_from,
+            end_date=end_before,
+            limit=limit,
+        )
+
+    for result in results:
+        if result.success:
+            click.echo(
+                click.style(
+                    f"{'[DRY RUN] Would delete' if dry_run else 'Deleted'} "
+                    f"workflow run {result.run_id} (tenant={result.tenant_id})",
+                    fg="green",
+                )
+            )
+        else:
+            click.echo(
+                click.style(
+                    f"Failed to delete workflow run {result.run_id}: {result.error}",
+                    fg="red",
+                )
+            )
+
+    end_time = datetime.datetime.now(datetime.UTC)
+    elapsed = end_time - start_time
+
+    successes = sum(1 for result in results if result.success)
+    failures = len(results) - successes
+
+    if failures == 0:
+        click.echo(
+            click.style(
+                f"Delete completed successfully. success={successes} duration={elapsed}",
+                fg="green",
+            )
+        )
+    else:
+        click.echo(
+            click.style(
+                f"Delete completed with failures. success={successes} failed={failures} duration={elapsed}",
+                fg="red",
+            )
+        )
+
+
 @click.option("-f", "--force", is_flag=True, help="Skip user confirmation and force the command to execute.")
 @click.command("clear-orphaned-file-records", help="Clear orphaned file records.")
 def clear_orphaned_file_records(force: bool):
@@ -1246,7 +1585,7 @@ def remove_orphaned_files_on_storage(force: bool):
             click.echo(click.style(f"- Scanning files on storage path {storage_path}", fg="white"))
             files = storage.scan(path=storage_path, files=True, directories=False)
             all_files_on_storage.extend(files)
-        except FileNotFoundError:
+        except FileNotFoundError as e:
             click.echo(click.style(f"  -> Skipping path {storage_path} as it does not exist.", fg="yellow"))
             continue
         except Exception as e:
@@ -1494,57 +1833,6 @@ def file_usage(
             click.echo(click.style(f"Use --offset {offset + limit} to see next page", fg="white"))
 
 
-@click.command("setup-sandbox-system-config", help="Setup system-level sandbox provider configuration.")
-@click.option(
-    "--provider-type", prompt=True, type=click.Choice(["e2b", "docker", "local"]), help="Sandbox provider type"
-)
-@click.option("--config", prompt=True, help='Configuration JSON (e.g., {"api_key": "xxx"} for e2b)')
-def setup_sandbox_system_config(provider_type: str, config: str):
-    """
-    Setup system-level sandbox provider configuration.
-
-    Examples:
-        flask setup-sandbox-system-config --provider-type e2b --config '{"api_key": "e2b_xxx"}'
-        flask setup-sandbox-system-config --provider-type docker --config '{"docker_sock": "unix:///var/run/docker.sock"}'
-        flask setup-sandbox-system-config --provider-type local --config '{}'
-    """
-    from models.sandbox import SandboxProviderSystemConfig
-
-    try:
-        click.echo(click.style(f"Validating config: {config}", fg="yellow"))
-        config_dict = TypeAdapter(dict[str, Any]).validate_json(config)
-        click.echo(click.style("Config validated successfully.", fg="green"))
-
-        click.echo(click.style(f"Validating config schema for provider type: {provider_type}", fg="yellow"))
-        SandboxBuilder.validate(SandboxType(provider_type), config_dict)
-        click.echo(click.style("Config schema validated successfully.", fg="green"))
-
-        click.echo(click.style("Encrypting config...", fg="yellow"))
-        click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        encrypted_config = encrypt_system_params(config_dict)
-        click.echo(click.style("Config encrypted successfully.", fg="green"))
-    except Exception as e:
-        click.echo(click.style(f"Error validating/encrypting config: {str(e)}", fg="red"))
-        return
-
-    deleted_count = db.session.query(SandboxProviderSystemConfig).filter_by(provider_type=provider_type).delete()
-    if deleted_count > 0:
-        click.echo(
-            click.style(
-                f"Deleted {deleted_count} existing system config for provider type: {provider_type}", fg="yellow"
-            )
-        )
-
-    system_config = SandboxProviderSystemConfig(
-        provider_type=provider_type,
-        encrypted_config=encrypted_config,
-    )
-    db.session.add(system_config)
-    db.session.commit()
-    click.echo(click.style(f"Sandbox system config setup successfully. id: {system_config.id}", fg="green"))
-    click.echo(click.style(f"Provider type: {provider_type}", fg="green"))
-
-
 @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")
@@ -1564,7 +1852,7 @@ def setup_system_tool_oauth_client(provider, client_params):
 
         click.echo(click.style(f"Encrypting client params: {client_params}", fg="yellow"))
         click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        oauth_client_params = encrypt_system_params(client_params_dict)
+        oauth_client_params = encrypt_system_oauth_params(client_params_dict)
         click.echo(click.style("Client params encrypted successfully.", fg="green"))
     except Exception as e:
         click.echo(click.style(f"Error parsing client params: {str(e)}", fg="red"))
@@ -1613,7 +1901,7 @@ def setup_system_trigger_oauth_client(provider, client_params):
 
         click.echo(click.style(f"Encrypting client params: {client_params}", fg="yellow"))
         click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        oauth_client_params = encrypt_system_params(client_params_dict)
+        oauth_client_params = encrypt_system_oauth_params(client_params_dict)
         click.echo(click.style("Client params encrypted successfully.", fg="green"))
     except Exception as e:
         click.echo(click.style(f"Error parsing client params: {str(e)}", fg="red"))

api/configs/app_config.py

@@ -2,7 +2,6 @@ import logging
 from pathlib import Path
 from typing import Any
 
-from pydantic import Field
 from pydantic.fields import FieldInfo
 from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, SettingsConfigDict, TomlConfigSettingsSource
 
@@ -83,14 +82,6 @@ class DifyConfig(
         extra="ignore",
     )
 
-    SANDBOX_DIFY_CLI_ROOT: str | None = Field(
-        default=None,
-        description=(
-            "Filesystem directory containing dify CLI binaries named dify-cli-<os>-<arch>. "
-            "Defaults to api/bin when unset."
-        ),
-    )
-
     # Before adding any config,
     # please consider to arrange it in the proper config group of existed or added
     # for better readability and maintainability.

api/configs/feature/__init__.py

@@ -244,17 +244,6 @@ class PluginConfig(BaseSettings):
     )
 
 
-class CliApiConfig(BaseSettings):
-    """
-    Configuration for CLI API (for dify-cli to call back from external sandbox environments)
-    """
-
-    CLI_API_URL: str = Field(
-        description="CLI API URL for external sandbox (e.g., e2b) to call back.",
-        default="http://localhost:5001",
-    )
-
-
 class MarketplaceConfig(BaseSettings):
     """
     Configuration for marketplace
@@ -1334,7 +1323,6 @@ class FeatureConfig(
     TriggerConfig,
     AsyncWorkflowConfig,
     PluginConfig,
-    CliApiConfig,
     MarketplaceConfig,
     DataSetConfig,
     EndpointConfig,

api/context/flask_app_context.py

@@ -3,6 +3,7 @@ Flask App Context - Flask implementation of AppContext interface.
 """
 
 import contextvars
+import threading
 from collections.abc import Generator
 from contextlib import contextmanager
 from typing import Any, final
@@ -118,6 +119,7 @@ class FlaskExecutionContext:
         self._context_vars = context_vars
         self._user = user
         self._flask_app = flask_app
+        self._local = threading.local()
 
     @property
     def app_context(self) -> FlaskAppContext:
@@ -136,47 +138,39 @@ class FlaskExecutionContext:
 
     def __enter__(self) -> "FlaskExecutionContext":
         """Enter the Flask execution context."""
-        # Restore context variables
+        # Restore non-Flask context variables to avoid leaking Flask tokens across threads
         for var, val in self._context_vars.items():
             var.set(val)
 
-        # Save current user from g if available
-        saved_user = None
-        if hasattr(g, "_login_user"):
-            saved_user = g._login_user
-
         # Enter Flask app context
-        self._cm = self._app_context.enter()
-        self._cm.__enter__()
+        cm = self._app_context.enter()
+        self._local.cm = cm
+        cm.__enter__()
 
         # Restore user in new app context
-        if saved_user is not None:
-            g._login_user = saved_user
+        if self._user is not None:
+            g._login_user = self._user
 
         return self
 
     def __exit__(self, *args: Any) -> None:
         """Exit the Flask execution context."""
-        if hasattr(self, "_cm"):
-            self._cm.__exit__(*args)
+        cm = getattr(self._local, "cm", None)
+        if cm is not None:
+            cm.__exit__(*args)
 
     @contextmanager
     def enter(self) -> Generator[None, None, None]:
         """Enter Flask execution context as context manager."""
-        # Restore context variables
+        # Restore non-Flask context variables to avoid leaking Flask tokens across threads
         for var, val in self._context_vars.items():
             var.set(val)
 
-        # Save current user from g if available
-        saved_user = None
-        if hasattr(g, "_login_user"):
-            saved_user = g._login_user
-
         # Enter Flask app context
         with self._flask_app.app_context():
             # Restore user in new app context
-            if saved_user is not None:
-                g._login_user = saved_user
+            if self._user is not None:
+                g._login_user = self._user
             yield
 
 

api/controllers/cli_api/__init__.py

@@ -1,27 +0,0 @@
-from flask import Blueprint
-from flask_restx import Namespace
-
-from libs.external_api import ExternalApi
-
-bp = Blueprint("cli_api", __name__, url_prefix="/cli/api")
-
-api = ExternalApi(
-    bp,
-    version="1.0",
-    title="CLI API",
-    description="APIs for Dify CLI to call back from external sandbox environments (e.g., e2b)",
-)
-
-# Create namespace
-cli_api_ns = Namespace("cli_api", description="CLI API operations", path="/")
-
-from .plugin import plugin as _plugin
-
-api.add_namespace(cli_api_ns)
-
-__all__ = [
-    "_plugin",
-    "api",
-    "bp",
-    "cli_api_ns",
-]

api/controllers/cli_api/plugin/plugin.py

@@ -1,137 +0,0 @@
-from flask_restx import Resource
-
-from controllers.cli_api import cli_api_ns
-from controllers.cli_api.plugin.wraps import get_cli_user_tenant, plugin_data
-from controllers.cli_api.wraps import cli_api_only
-from controllers.console.wraps import setup_required
-from core.file.helpers import get_signed_file_url_for_plugin
-from core.plugin.backwards_invocation.app import PluginAppBackwardsInvocation
-from core.plugin.backwards_invocation.base import BaseBackwardsInvocationResponse
-from core.plugin.backwards_invocation.model import PluginModelBackwardsInvocation
-from core.plugin.backwards_invocation.tool import PluginToolBackwardsInvocation
-from core.plugin.entities.request import (
-    RequestInvokeApp,
-    RequestInvokeLLM,
-    RequestInvokeTool,
-    RequestRequestUploadFile,
-)
-from core.tools.entities.tool_entities import ToolProviderType
-from libs.helper import length_prefixed_response
-from models import Account, Tenant
-from models.model import EndUser
-
-
-@cli_api_ns.route("/invoke/llm")
-class CliInvokeLLMApi(Resource):
-    @get_cli_user_tenant
-    @setup_required
-    @cli_api_only
-    @plugin_data(payload_type=RequestInvokeLLM)
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeLLM):
-        def generator():
-            response = PluginModelBackwardsInvocation.invoke_llm(user_model.id, tenant_model, payload)
-            return PluginModelBackwardsInvocation.convert_to_event_stream(response)
-
-        return length_prefixed_response(0xF, generator())
-
-
-@cli_api_ns.route("/invoke/tool")
-class CliInvokeToolApi(Resource):
-    @get_cli_user_tenant
-    @setup_required
-    @cli_api_only
-    @plugin_data(payload_type=RequestInvokeTool)
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeTool):
-        def generator():
-            return PluginToolBackwardsInvocation.convert_to_event_stream(
-                PluginToolBackwardsInvocation.invoke_tool(
-                    tenant_id=tenant_model.id,
-                    user_id=user_model.id,
-                    tool_type=ToolProviderType.value_of(payload.tool_type),
-                    provider=payload.provider,
-                    tool_name=payload.tool,
-                    tool_parameters=payload.tool_parameters,
-                    credential_id=payload.credential_id,
-                ),
-            )
-
-        return length_prefixed_response(0xF, generator())
-
-
-@cli_api_ns.route("/invoke/app")
-class CliInvokeAppApi(Resource):
-    @get_cli_user_tenant
-    @setup_required
-    @cli_api_only
-    @plugin_data(payload_type=RequestInvokeApp)
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeApp):
-        response = PluginAppBackwardsInvocation.invoke_app(
-            app_id=payload.app_id,
-            user_id=user_model.id,
-            tenant_id=tenant_model.id,
-            conversation_id=payload.conversation_id,
-            query=payload.query,
-            stream=payload.response_mode == "streaming",
-            inputs=payload.inputs,
-            files=payload.files,
-        )
-
-        return length_prefixed_response(0xF, PluginAppBackwardsInvocation.convert_to_event_stream(response))
-
-
-@cli_api_ns.route("/upload/file/request")
-class CliUploadFileRequestApi(Resource):
-    @get_cli_user_tenant
-    @setup_required
-    @cli_api_only
-    @plugin_data(payload_type=RequestRequestUploadFile)
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestRequestUploadFile):
-        # generate signed url
-        url = get_signed_file_url_for_plugin(
-            filename=payload.filename,
-            mimetype=payload.mimetype,
-            tenant_id=tenant_model.id,
-            user_id=user_model.id,
-        )
-        return BaseBackwardsInvocationResponse(data={"url": url}).model_dump()
-
-
-@cli_api_ns.route("/fetch/tools/list")
-class CliFetchToolsListApi(Resource):
-    @get_cli_user_tenant
-    @setup_required
-    @cli_api_only
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant):
-        from sqlalchemy.orm import Session
-
-        from extensions.ext_database import db
-        from services.tools.api_tools_manage_service import ApiToolManageService
-        from services.tools.builtin_tools_manage_service import BuiltinToolManageService
-        from services.tools.mcp_tools_manage_service import MCPToolManageService
-        from services.tools.workflow_tools_manage_service import WorkflowToolManageService
-
-        providers = []
-
-        # Get builtin tools
-        builtin_providers = BuiltinToolManageService.list_builtin_tools(user_model.id, tenant_model.id)
-        for provider in builtin_providers:
-            providers.append(provider.to_dict())
-
-        # Get API tools
-        api_providers = ApiToolManageService.list_api_tools(tenant_model.id)
-        for provider in api_providers:
-            providers.append(provider.to_dict())
-
-        # Get workflow tools
-        workflow_providers = WorkflowToolManageService.list_tenant_workflow_tools(user_model.id, tenant_model.id)
-        for provider in workflow_providers:
-            providers.append(provider.to_dict())
-
-        # Get MCP tools
-        with Session(db.engine) as session:
-            mcp_service = MCPToolManageService(session)
-            mcp_providers = mcp_service.list_providers(tenant_id=tenant_model.id, for_list=True)
-            for provider in mcp_providers:
-                providers.append(provider.to_dict())
-
-        return BaseBackwardsInvocationResponse(data={"providers": providers}).model_dump()

api/controllers/cli_api/plugin/wraps.py

@@ -1,146 +0,0 @@
-from collections.abc import Callable
-from functools import wraps
-from typing import ParamSpec, TypeVar
-
-from flask import current_app, request
-from flask_login import user_logged_in
-from pydantic import BaseModel
-from sqlalchemy.orm import Session
-
-from core.session.cli_api import CliApiSession, CliApiSessionManager
-from extensions.ext_database import db
-from libs.login import current_user
-from models.account import Tenant
-from models.model import DefaultEndUserSessionID, EndUser
-
-P = ParamSpec("P")
-R = TypeVar("R")
-
-
-class TenantUserPayload(BaseModel):
-    tenant_id: str
-    user_id: str
-
-
-def get_user(tenant_id: str, user_id: str | None) -> EndUser:
-    """
-    Get current user
-
-    NOTE: user_id is not trusted, it could be maliciously set to any value.
-    As a result, it could only be considered as an end user id.
-    """
-    if not user_id:
-        user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
-    is_anonymous = user_id == DefaultEndUserSessionID.DEFAULT_SESSION_ID
-    try:
-        with Session(db.engine) as session:
-            user_model = None
-
-            if is_anonymous:
-                user_model = (
-                    session.query(EndUser)
-                    .where(
-                        EndUser.session_id == user_id,
-                        EndUser.tenant_id == tenant_id,
-                    )
-                    .first()
-                )
-            else:
-                user_model = (
-                    session.query(EndUser)
-                    .where(
-                        EndUser.id == user_id,
-                        EndUser.tenant_id == tenant_id,
-                    )
-                    .first()
-                )
-
-            if not user_model:
-                user_model = EndUser(
-                    tenant_id=tenant_id,
-                    type="service_api",
-                    is_anonymous=is_anonymous,
-                    session_id=user_id,
-                )
-                session.add(user_model)
-                session.commit()
-                session.refresh(user_model)
-
-    except Exception:
-        raise ValueError("user not found")
-
-    return user_model
-
-
-def get_cli_user_tenant(view_func: Callable[P, R]):
-    @wraps(view_func)
-    def decorated_view(*args: P.args, **kwargs: P.kwargs):
-        session_id = request.headers.get("X-Cli-Api-Session-Id")
-
-        if session_id:
-            session: CliApiSession | None = CliApiSessionManager().get(session_id)
-            if not session:
-                raise ValueError("session not found")
-            user_id = session.user_id
-            tenant_id = session.tenant_id
-
-        else:
-            payload = TenantUserPayload.model_validate(request.get_json(silent=True) or {})
-            user_id = payload.user_id
-            tenant_id = payload.tenant_id
-
-        if not tenant_id:
-            raise ValueError("tenant_id is required")
-
-        if not user_id:
-            user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
-
-        try:
-            tenant_model = (
-                db.session.query(Tenant)
-                .where(
-                    Tenant.id == tenant_id,
-                )
-                .first()
-            )
-        except Exception:
-            raise ValueError("tenant not found")
-
-        if not tenant_model:
-            raise ValueError("tenant not found")
-
-        kwargs["tenant_model"] = tenant_model
-
-        user = get_user(tenant_id, user_id)
-        kwargs["user_model"] = user
-
-        current_app.login_manager._update_request_context_with_user(user)  # type: ignore
-        user_logged_in.send(current_app._get_current_object(), user=current_user)  # type: ignore
-
-        return view_func(*args, **kwargs)
-
-    return decorated_view
-
-
-def plugin_data(view: Callable[P, R] | None = None, *, payload_type: type[BaseModel]):
-    def decorator(view_func: Callable[P, R]):
-        def decorated_view(*args: P.args, **kwargs: P.kwargs):
-            try:
-                data = request.get_json()
-            except Exception:
-                raise ValueError("invalid json")
-
-            try:
-                payload = payload_type.model_validate(data)
-            except Exception as e:
-                raise ValueError(f"invalid payload: {str(e)}")
-
-            kwargs["payload"] = payload
-            return view_func(*args, **kwargs)
-
-        return decorated_view
-
-    if view is None:
-        return decorator
-    else:
-        return decorator(view)

api/controllers/cli_api/wraps.py

@@ -1,54 +0,0 @@
-import hashlib
-import hmac
-import time
-from collections.abc import Callable
-from functools import wraps
-from typing import ParamSpec, TypeVar
-
-from flask import abort, request
-
-from core.session.cli_api import CliApiSessionManager
-
-P = ParamSpec("P")
-R = TypeVar("R")
-
-SIGNATURE_TTL_SECONDS = 300
-
-
-def _verify_signature(session_secret: str, timestamp: str, body: bytes, signature: str) -> bool:
-    expected = hmac.new(
-        session_secret.encode(),
-        f"{timestamp}.".encode() + body,
-        hashlib.sha256,
-    ).hexdigest()
-    return hmac.compare_digest(f"sha256={expected}", signature)
-
-
-def cli_api_only(view: Callable[P, R]):
-    @wraps(view)
-    def decorated(*args: P.args, **kwargs: P.kwargs):
-        session_id = request.headers.get("X-Cli-Api-Session-Id")
-        timestamp = request.headers.get("X-Cli-Api-Timestamp")
-        signature = request.headers.get("X-Cli-Api-Signature")
-
-        if not session_id or not timestamp or not signature:
-            abort(401)
-
-        try:
-            ts = int(timestamp)
-            if abs(time.time() - ts) > SIGNATURE_TTL_SECONDS:
-                abort(401)
-        except ValueError:
-            abort(401)
-
-        session = CliApiSessionManager().get(session_id)
-        if not session:
-            abort(401)
-
-        body = request.get_data()
-        if not _verify_signature(session.secret, timestamp, body, signature):
-            abort(401)
-
-        return view(*args, **kwargs)
-
-    return decorated

api/controllers/console/__init__.py

@@ -50,7 +50,6 @@ from .app import (
     agent,
     annotation,
     app,
-    app_asset,
     audio,
     completion,
     conversation,
@@ -129,7 +128,6 @@ from .workspace import (
     model_providers,
     models,
     plugin,
-    sandbox_providers,
     tool_providers,
     trigger_providers,
     workspace,
@@ -148,7 +146,6 @@ __all__ = [
     "api",
     "apikey",
     "app",
-    "app_asset",
     "audio",
     "banner",
     "billing",
@@ -197,7 +194,6 @@ __all__ = [
     "rag_pipeline_import",
     "rag_pipeline_workflow",
     "recommended_app",
-    "sandbox_providers",
     "saved_message",
     "setup",
     "site",

api/controllers/console/app/app_asset.py

@@ -1,274 +0,0 @@
-from flask import request
-from flask_restx import Resource
-from pydantic import BaseModel, Field, field_validator
-
-from controllers.console import console_ns
-from controllers.console.app.error import (
-    AppAssetFileRequiredError,
-    AppAssetNodeNotFoundError,
-    AppAssetPathConflictError,
-)
-from controllers.console.app.wraps import get_app_model
-from controllers.console.wraps import account_initialization_required, setup_required
-from libs.login import current_account_with_tenant, login_required
-from models import App
-from models.model import AppMode
-from services.app_asset_service import AppAssetService
-from services.errors.app_asset import (
-    AppAssetNodeNotFoundError as ServiceNodeNotFoundError,
-)
-from services.errors.app_asset import (
-    AppAssetParentNotFoundError,
-)
-from services.errors.app_asset import (
-    AppAssetPathConflictError as ServicePathConflictError,
-)
-
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
-
-
-class CreateFolderPayload(BaseModel):
-    name: str = Field(..., min_length=1, max_length=255)
-    parent_id: str | None = None
-
-
-class CreateFilePayload(BaseModel):
-    name: str = Field(..., min_length=1, max_length=255)
-    parent_id: str | None = None
-
-    @field_validator("name", mode="before")
-    @classmethod
-    def strip_name(cls, v: str) -> str:
-        return v.strip() if isinstance(v, str) else v
-
-    @field_validator("parent_id", mode="before")
-    @classmethod
-    def empty_to_none(cls, v: str | None) -> str | None:
-        return v or None
-
-
-class UpdateFileContentPayload(BaseModel):
-    content: str
-
-
-class RenameNodePayload(BaseModel):
-    name: str = Field(..., min_length=1, max_length=255)
-
-
-class MoveNodePayload(BaseModel):
-    parent_id: str | None = None
-
-
-class ReorderNodePayload(BaseModel):
-    after_node_id: str | None = Field(default=None, description="Place after this node, None for first position")
-
-
-def reg(cls: type[BaseModel]) -> None:
-    console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
-
-
-reg(CreateFolderPayload)
-reg(CreateFilePayload)
-reg(UpdateFileContentPayload)
-reg(RenameNodePayload)
-reg(MoveNodePayload)
-reg(ReorderNodePayload)
-
-
-@console_ns.route("/apps/<string:app_id>/assets/tree")
-class AppAssetTreeResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def get(self, app_model: App):
-        current_user, _ = current_account_with_tenant()
-        tree = AppAssetService.get_asset_tree(app_model, current_user.id)
-        return {"children": [view.model_dump() for view in tree.transform()]}
-
-
-@console_ns.route("/apps/<string:app_id>/assets/folders")
-class AppAssetFolderResource(Resource):
-    @console_ns.expect(console_ns.models[CreateFolderPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App):
-        current_user, _ = current_account_with_tenant()
-        payload = CreateFolderPayload.model_validate(console_ns.payload or {})
-
-        try:
-            node = AppAssetService.create_folder(app_model, current_user.id, payload.name, payload.parent_id)
-            return node.model_dump(), 201
-        except AppAssetParentNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except ServicePathConflictError:
-            raise AppAssetPathConflictError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/files")
-class AppAssetFileResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App):
-        current_user, _ = current_account_with_tenant()
-
-        file = request.files.get("file")
-        if not file:
-            raise AppAssetFileRequiredError()
-
-        payload = CreateFilePayload.model_validate(request.form.to_dict())
-        content = file.read()
-
-        try:
-            node = AppAssetService.create_file(app_model, current_user.id, payload.name, content, payload.parent_id)
-            return node.model_dump(), 201
-        except AppAssetParentNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except ServicePathConflictError:
-            raise AppAssetPathConflictError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/files/<string:node_id>")
-class AppAssetFileDetailResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def get(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        try:
-            content = AppAssetService.get_file_content(app_model, current_user.id, node_id)
-            return {"content": content.decode("utf-8", errors="replace")}
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-
-    @console_ns.expect(console_ns.models[UpdateFileContentPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def put(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-
-        file = request.files.get("file")
-        if file:
-            content = file.read()
-        else:
-            payload = UpdateFileContentPayload.model_validate(console_ns.payload or {})
-            content = payload.content.encode("utf-8")
-
-        try:
-            node = AppAssetService.update_file_content(app_model, current_user.id, node_id, content)
-            return node.model_dump()
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>")
-class AppAssetNodeResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def delete(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        try:
-            AppAssetService.delete_node(app_model, current_user.id, node_id)
-            return {"result": "success"}, 200
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/rename")
-class AppAssetNodeRenameResource(Resource):
-    @console_ns.expect(console_ns.models[RenameNodePayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        payload = RenameNodePayload.model_validate(console_ns.payload or {})
-
-        try:
-            node = AppAssetService.rename_node(app_model, current_user.id, node_id, payload.name)
-            return node.model_dump()
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except ServicePathConflictError:
-            raise AppAssetPathConflictError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/move")
-class AppAssetNodeMoveResource(Resource):
-    @console_ns.expect(console_ns.models[MoveNodePayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        payload = MoveNodePayload.model_validate(console_ns.payload or {})
-
-        try:
-            node = AppAssetService.move_node(app_model, current_user.id, node_id, payload.parent_id)
-            return node.model_dump()
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except AppAssetParentNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except ServicePathConflictError:
-            raise AppAssetPathConflictError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/reorder")
-class AppAssetNodeReorderResource(Resource):
-    @console_ns.expect(console_ns.models[ReorderNodePayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        payload = ReorderNodePayload.model_validate(console_ns.payload or {})
-
-        try:
-            node = AppAssetService.reorder_node(app_model, current_user.id, node_id, payload.after_node_id)
-            return node.model_dump()
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/publish")
-class AppAssetPublishResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App):
-        current_user, _ = current_account_with_tenant()
-        published = AppAssetService.publish(app_model, current_user.id)
-        return {
-            "id": published.id,
-            "version": published.version,
-            "asset_tree": published.asset_tree.model_dump(),
-        }, 201
-
-
-@console_ns.route("/apps/<string:app_id>/assets/files/<string:node_id>/download-url")
-class AppAssetFileDownloadUrlResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def get(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        try:
-            download_url = AppAssetService.get_file_download_url(app_model, current_user.id, node_id)
-            return {"download_url": download_url}
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()

api/controllers/console/app/error.py

@@ -82,13 +82,13 @@ class ProviderNotSupportSpeechToTextError(BaseHTTPException):
 class DraftWorkflowNotExist(BaseHTTPException):
     error_code = "draft_workflow_not_exist"
     description = "Draft workflow need to be initialized."
-    code = 400
+    code = 404
 
 
 class DraftWorkflowNotSync(BaseHTTPException):
     error_code = "draft_workflow_not_sync"
     description = "Workflow graph might have been modified, please refresh and resubmit."
-    code = 400
+    code = 409
 
 
 class TracingConfigNotExist(BaseHTTPException):
@@ -110,6 +110,8 @@ class TracingConfigCheckError(BaseHTTPException):
 
 
 class InvokeRateLimitError(BaseHTTPException):
+    """Raised when the Invoke returns rate limit error."""
+
     error_code = "rate_limit_error"
     description = "Rate Limit Error"
     code = 429
@@ -119,21 +121,3 @@ class NeedAddIdsError(BaseHTTPException):
     error_code = "need_add_ids"
     description = "Need to add ids."
     code = 400
-
-
-class AppAssetNodeNotFoundError(BaseHTTPException):
-    error_code = "app_asset_node_not_found"
-    description = "App asset node not found."
-    code = 404
-
-
-class AppAssetFileRequiredError(BaseHTTPException):
-    error_code = "app_asset_file_required"
-    description = "File is required."
-    code = 400
-
-
-class AppAssetPathConflictError(BaseHTTPException):
-    error_code = "app_asset_path_conflict"
-    description = "Path already exists."
-    code = 409

api/controllers/console/app/generator.py

@@ -55,35 +55,6 @@ class InstructionTemplatePayload(BaseModel):
     type: str = Field(..., description="Instruction template type")
 
 
-class ContextGeneratePayload(BaseModel):
-    """Payload for generating extractor code node."""
-
-    workflow_id: str = Field(..., description="Workflow ID")
-    node_id: str = Field(..., description="Current tool/llm node ID")
-    parameter_name: str = Field(..., description="Parameter name to generate code for")
-    language: str = Field(default="python3", description="Code language (python3/javascript)")
-    prompt_messages: list[dict[str, Any]] = Field(
-        ..., description="Multi-turn conversation history, last message is the current instruction"
-    )
-    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
-
-
-class SuggestedQuestionsPayload(BaseModel):
-    """Payload for generating suggested questions."""
-
-    workflow_id: str = Field(..., description="Workflow ID")
-    node_id: str = Field(..., description="Current tool/llm node ID")
-    parameter_name: str = Field(..., description="Parameter name")
-    language: str = Field(
-        default="English", description="Language for generated questions (e.g. English, Chinese, Japanese)"
-    )
-    model_config_data: dict[str, Any] | None = Field(
-        default=None,
-        alias="model_config",
-        description="Model configuration (optional, uses system default if not provided)",
-    )
-
-
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -93,8 +64,6 @@ reg(RuleCodeGeneratePayload)
 reg(RuleStructuredOutputPayload)
 reg(InstructionGeneratePayload)
 reg(InstructionTemplatePayload)
-reg(ContextGeneratePayload)
-reg(SuggestedQuestionsPayload)
 
 
 @console_ns.route("/rule-generate")
@@ -309,74 +278,3 @@ class InstructionGenerationTemplateApi(Resource):
                 return {"data": INSTRUCTION_GENERATE_TEMPLATE_CODE}
             case _:
                 raise ValueError(f"Invalid type: {args.type}")
-
-
-@console_ns.route("/context-generate")
-class ContextGenerateApi(Resource):
-    @console_ns.doc("generate_with_context")
-    @console_ns.doc(description="Generate with multi-turn conversation context")
-    @console_ns.expect(console_ns.models[ContextGeneratePayload.__name__])
-    @console_ns.response(200, "Content generated successfully")
-    @console_ns.response(400, "Invalid request parameters or workflow not found")
-    @console_ns.response(402, "Provider quota exceeded")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self):
-        from core.llm_generator.utils import deserialize_prompt_messages
-
-        args = ContextGeneratePayload.model_validate(console_ns.payload)
-        _, current_tenant_id = current_account_with_tenant()
-
-        prompt_messages = deserialize_prompt_messages(args.prompt_messages)
-
-        try:
-            return LLMGenerator.generate_with_context(
-                tenant_id=current_tenant_id,
-                workflow_id=args.workflow_id,
-                node_id=args.node_id,
-                parameter_name=args.parameter_name,
-                language=args.language,
-                prompt_messages=prompt_messages,
-                model_config=args.model_config_data,
-            )
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-        except QuotaExceededError:
-            raise ProviderQuotaExceededError()
-        except ModelCurrentlyNotSupportError:
-            raise ProviderModelCurrentlyNotSupportError()
-        except InvokeError as e:
-            raise CompletionRequestError(e.description)
-
-
-@console_ns.route("/context-generate/suggested-questions")
-class SuggestedQuestionsApi(Resource):
-    @console_ns.doc("generate_suggested_questions")
-    @console_ns.doc(description="Generate suggested questions for context generation")
-    @console_ns.expect(console_ns.models[SuggestedQuestionsPayload.__name__])
-    @console_ns.response(200, "Questions generated successfully")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self):
-        args = SuggestedQuestionsPayload.model_validate(console_ns.payload)
-        _, current_tenant_id = current_account_with_tenant()
-
-        try:
-            return LLMGenerator.generate_suggested_questions(
-                tenant_id=current_tenant_id,
-                workflow_id=args.workflow_id,
-                node_id=args.node_id,
-                parameter_name=args.parameter_name,
-                language=args.language,
-                model_config=args.model_config_data,
-            )
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-        except QuotaExceededError:
-            raise ProviderQuotaExceededError()
-        except ModelCurrentlyNotSupportError:
-            raise ProviderModelCurrentlyNotSupportError()
-        except InvokeError as e:
-            raise CompletionRequestError(e.description)

api/controllers/console/app/message.py

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

api/controllers/console/app/workflow.py

@@ -46,8 +46,6 @@ from models.workflow import Workflow
 from services.app_generate_service import AppGenerateService
 from services.errors.app import WorkflowHashNotEqualError
 from services.errors.llm import InvokeRateLimitError
-from services.workflow.entities import NestedNodeGraphRequest, NestedNodeParameterSchema
-from services.workflow.nested_node_graph_service import NestedNodeGraphService
 from services.workflow_service import DraftWorkflowDeletionError, WorkflowInUseError, WorkflowService
 
 logger = logging.getLogger(__name__)
@@ -190,15 +188,6 @@ class DraftWorkflowTriggerRunAllPayload(BaseModel):
     node_ids: list[str]
 
 
-class NestedNodeGraphPayload(BaseModel):
-    """Request payload for generating nested node graph."""
-
-    parent_node_id: str = Field(description="ID of the parent node that uses the extracted value")
-    parameter_key: str = Field(description="Key of the parameter being extracted")
-    context_source: list[str] = Field(description="Variable selector for the context source")
-    parameter_schema: dict[str, Any] = Field(description="Schema of the parameter to extract")
-
-
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -216,7 +205,6 @@ reg(WorkflowListQuery)
 reg(WorkflowUpdatePayload)
 reg(DraftWorkflowTriggerRunPayload)
 reg(DraftWorkflowTriggerRunAllPayload)
-reg(NestedNodeGraphPayload)
 
 
 # TODO(QuantumGhost): Refactor existing node run API to handle file parameter parsing
@@ -482,7 +470,7 @@ class AdvancedChatDraftRunLoopNodeApi(Resource):
         Run draft workflow loop node
         """
         current_user, _ = current_account_with_tenant()
-        args = LoopNodeRunPayload.model_validate(console_ns.payload or {}).model_dump(exclude_none=True)
+        args = LoopNodeRunPayload.model_validate(console_ns.payload or {})
 
         try:
             response = AppGenerateService.generate_single_loop(
@@ -520,7 +508,7 @@ class WorkflowDraftRunLoopNodeApi(Resource):
         Run draft workflow loop node
         """
         current_user, _ = current_account_with_tenant()
-        args = LoopNodeRunPayload.model_validate(console_ns.payload or {}).model_dump(exclude_none=True)
+        args = LoopNodeRunPayload.model_validate(console_ns.payload or {})
 
         try:
             response = AppGenerateService.generate_single_loop(
@@ -1011,6 +999,7 @@ class DraftWorkflowTriggerRunApi(Resource):
             if not event:
                 return jsonable_encoder({"status": "waiting", "retry_in": LISTENING_RETRY_IN})
             workflow_args = dict(event.workflow_args)
+
             workflow_args[SKIP_PREPARE_USER_INPUTS_KEY] = True
             return helper.compact_generate_response(
                 AppGenerateService.generate(
@@ -1159,6 +1148,7 @@ class DraftWorkflowTriggerRunAllApi(Resource):
 
         try:
             workflow_args = dict(trigger_debug_event.workflow_args)
+
             workflow_args[SKIP_PREPARE_USER_INPUTS_KEY] = True
             response = AppGenerateService.generate(
                 app_model=app_model,
@@ -1178,54 +1168,3 @@ class DraftWorkflowTriggerRunAllApi(Resource):
                     "status": "error",
                 }
             ), 400
-
-
-@console_ns.route("/apps/<uuid:app_id>/workflows/draft/nested-node-graph")
-class NestedNodeGraphApi(Resource):
-    """
-    API for generating Nested Node LLM graph structures.
-
-    This endpoint creates a complete graph structure containing an LLM node
-    configured to extract values from list[PromptMessage] variables.
-    """
-
-    @console_ns.doc("generate_nested_node_graph")
-    @console_ns.doc(description="Generate a Nested Node LLM graph structure")
-    @console_ns.doc(params={"app_id": "Application ID"})
-    @console_ns.expect(console_ns.models[NestedNodeGraphPayload.__name__])
-    @console_ns.response(200, "Nested node graph generated successfully")
-    @console_ns.response(400, "Invalid request parameters")
-    @console_ns.response(403, "Permission denied")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    @edit_permission_required
-    def post(self, app_model: App):
-        """
-        Generate a Nested Node LLM graph structure.
-
-        Returns a complete graph structure containing a single LLM node
-        configured for extracting values from list[PromptMessage] context.
-        """
-
-        payload = NestedNodeGraphPayload.model_validate(console_ns.payload or {})
-
-        parameter_schema = NestedNodeParameterSchema(
-            name=payload.parameter_schema.get("name", payload.parameter_key),
-            type=payload.parameter_schema.get("type", "string"),
-            description=payload.parameter_schema.get("description", ""),
-        )
-
-        request = NestedNodeGraphRequest(
-            parent_node_id=payload.parent_node_id,
-            parameter_key=payload.parameter_key,
-            context_source=payload.context_source,
-            parameter_schema=parameter_schema,
-        )
-
-        with Session(db.engine) as session:
-            service = NestedNodeGraphService(session)
-            response = service.generate_nested_node_graph(tenant_id=app_model.tenant_id, request=request)
-
-        return response.model_dump()

api/controllers/console/app/workflow_app_log.py

@@ -11,7 +11,10 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, setup_required
 from core.workflow.enums import WorkflowExecutionStatus
 from extensions.ext_database import db
-from fields.workflow_app_log_fields import build_workflow_app_log_pagination_model
+from fields.workflow_app_log_fields import (
+    build_workflow_app_log_pagination_model,
+    build_workflow_archived_log_pagination_model,
+)
 from libs.login import login_required
 from models import App
 from models.model import AppMode
@@ -61,6 +64,7 @@ console_ns.schema_model(
 
 # Register model for flask_restx to avoid dict type issues in Swagger
 workflow_app_log_pagination_model = build_workflow_app_log_pagination_model(console_ns)
+workflow_archived_log_pagination_model = build_workflow_archived_log_pagination_model(console_ns)
 
 
 @console_ns.route("/apps/<uuid:app_id>/workflow-app-logs")
@@ -99,3 +103,33 @@ class WorkflowAppLogApi(Resource):
             )
 
             return workflow_app_log_pagination
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflow-archived-logs")
+class WorkflowArchivedLogApi(Resource):
+    @console_ns.doc("get_workflow_archived_logs")
+    @console_ns.doc(description="Get workflow archived execution logs")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.expect(console_ns.models[WorkflowAppLogQuery.__name__])
+    @console_ns.response(200, "Workflow archived logs retrieved successfully", workflow_archived_log_pagination_model)
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.WORKFLOW])
+    @marshal_with(workflow_archived_log_pagination_model)
+    def get(self, app_model: App):
+        """
+        Get workflow archived logs
+        """
+        args = WorkflowAppLogQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore
+
+        workflow_app_service = WorkflowAppService()
+        with Session(db.engine) as session:
+            workflow_app_log_pagination = workflow_app_service.get_paginate_workflow_archive_logs(
+                session=session,
+                app_model=app_model,
+                page=args.page,
+                limit=args.limit,
+            )
+
+            return workflow_app_log_pagination

api/controllers/console/app/workflow_draft_variable.py

@@ -16,15 +16,14 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, edit_permission_required, setup_required
 from controllers.web.error import InvalidArgumentError, NotFoundError
 from core.file import helpers as file_helpers
-from core.sandbox.manager import SandboxManager
 from core.variables.segment_group import SegmentGroup
-from core.variables.segments import ArrayFileSegment, ArrayPromptMessageSegment, FileSegment, Segment
+from core.variables.segments import ArrayFileSegment, FileSegment, Segment
 from core.variables.types import SegmentType
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID, SYSTEM_VARIABLE_NODE_ID
 from extensions.ext_database import db
 from factories.file_factory import build_from_mapping, build_from_mappings
 from factories.variable_factory import build_segment_with_type
-from libs.login import current_account_with_tenant, login_required
+from libs.login import login_required
 from models import App, AppMode
 from models.workflow import WorkflowDraftVariable
 from services.workflow_draft_variable_service import WorkflowDraftVariableList, WorkflowDraftVariableService
@@ -59,8 +58,6 @@ def _convert_values_to_json_serializable_object(value: Segment):
         return value.value.model_dump()
     elif isinstance(value, ArrayFileSegment):
         return [i.model_dump() for i in value.value]
-    elif isinstance(value, ArrayPromptMessageSegment):
-        return value.to_object()
     elif isinstance(value, SegmentGroup):
         return [_convert_values_to_json_serializable_object(i) for i in value.value]
     else:
@@ -250,9 +247,6 @@ class WorkflowVariableCollectionApi(Resource):
     @console_ns.response(204, "Workflow variables deleted successfully")
     @_api_prerequisite
     def delete(self, app_model: App):
-        # FIXME(Mairuis): move to SandboxArtifactService
-        current_user, _ = current_account_with_tenant()
-        SandboxManager.delete_storage(app_model.tenant_id, current_user.id)
         draft_var_srv = WorkflowDraftVariableService(
             session=db.session(),
         )

api/controllers/console/app/workflow_run.py

@@ -1,12 +1,15 @@
+from datetime import UTC, datetime, timedelta
 from typing import Literal, cast
 
 from flask import request
 from flask_restx import Resource, fields, marshal_with
 from pydantic import BaseModel, Field, field_validator
+from sqlalchemy import select
 
 from controllers.console import console_ns
 from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, setup_required
+from extensions.ext_database import db
 from fields.end_user_fields import simple_end_user_fields
 from fields.member_fields import simple_account_fields
 from fields.workflow_run_fields import (
@@ -19,14 +22,17 @@ from fields.workflow_run_fields import (
     workflow_run_node_execution_list_fields,
     workflow_run_pagination_fields,
 )
+from libs.archive_storage import ArchiveStorageNotConfiguredError, get_archive_storage
 from libs.custom_inputs import time_duration
 from libs.helper import uuid_value
 from libs.login import current_user, login_required
-from models import Account, App, AppMode, EndUser, WorkflowRunTriggeredFrom
+from models import Account, App, AppMode, EndUser, WorkflowArchiveLog, WorkflowRunTriggeredFrom
+from services.retention.workflow_run.constants import ARCHIVE_BUNDLE_NAME
 from services.workflow_run_service import WorkflowRunService
 
 # Workflow run status choices for filtering
 WORKFLOW_RUN_STATUS_CHOICES = ["running", "succeeded", "failed", "stopped", "partial-succeeded"]
+EXPORT_SIGNED_URL_EXPIRE_SECONDS = 3600
 
 # Register models for flask_restx to avoid dict type issues in Swagger
 # Register in dependency order: base models first, then dependent models
@@ -93,6 +99,15 @@ workflow_run_node_execution_list_model = console_ns.model(
     "WorkflowRunNodeExecutionList", workflow_run_node_execution_list_fields_copy
 )
 
+workflow_run_export_fields = console_ns.model(
+    "WorkflowRunExport",
+    {
+        "status": fields.String(description="Export status: success/failed"),
+        "presigned_url": fields.String(description="Pre-signed URL for download", required=False),
+        "presigned_url_expires_at": fields.String(description="Pre-signed URL expiration time", required=False),
+    },
+)
+
 DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 
 
@@ -181,6 +196,56 @@ class AdvancedChatAppWorkflowRunListApi(Resource):
         return result
 
 
+@console_ns.route("/apps/<uuid:app_id>/workflow-runs/<uuid:run_id>/export")
+class WorkflowRunExportApi(Resource):
+    @console_ns.doc("get_workflow_run_export_url")
+    @console_ns.doc(description="Generate a download URL for an archived workflow run.")
+    @console_ns.doc(params={"app_id": "Application ID", "run_id": "Workflow run ID"})
+    @console_ns.response(200, "Export URL generated", workflow_run_export_fields)
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model()
+    def get(self, app_model: App, run_id: str):
+        tenant_id = str(app_model.tenant_id)
+        app_id = str(app_model.id)
+        run_id_str = str(run_id)
+
+        run_created_at = db.session.scalar(
+            select(WorkflowArchiveLog.run_created_at)
+            .where(
+                WorkflowArchiveLog.tenant_id == tenant_id,
+                WorkflowArchiveLog.app_id == app_id,
+                WorkflowArchiveLog.workflow_run_id == run_id_str,
+            )
+            .limit(1)
+        )
+        if not run_created_at:
+            return {"code": "archive_log_not_found", "message": "workflow run archive not found"}, 404
+
+        prefix = (
+            f"{tenant_id}/app_id={app_id}/year={run_created_at.strftime('%Y')}/"
+            f"month={run_created_at.strftime('%m')}/workflow_run_id={run_id_str}"
+        )
+        archive_key = f"{prefix}/{ARCHIVE_BUNDLE_NAME}"
+
+        try:
+            archive_storage = get_archive_storage()
+        except ArchiveStorageNotConfiguredError as e:
+            return {"code": "archive_storage_not_configured", "message": str(e)}, 500
+
+        presigned_url = archive_storage.generate_presigned_url(
+            archive_key,
+            expires_in=EXPORT_SIGNED_URL_EXPIRE_SECONDS,
+        )
+        expires_at = datetime.now(UTC) + timedelta(seconds=EXPORT_SIGNED_URL_EXPIRE_SECONDS)
+        return {
+            "status": "success",
+            "presigned_url": presigned_url,
+            "presigned_url_expires_at": expires_at.isoformat(),
+        }, 200
+
+
 @console_ns.route("/apps/<uuid:app_id>/advanced-chat/workflow-runs/count")
 class AdvancedChatAppWorkflowRunCountApi(Resource):
     @console_ns.doc("get_advanced_chat_workflow_runs_count")

api/controllers/console/feature.py

@@ -1,6 +1,7 @@
 from flask_restx import Resource, fields
+from werkzeug.exceptions import Unauthorized
 
-from libs.login import current_account_with_tenant, login_required
+from libs.login import current_account_with_tenant, current_user, login_required
 from services.feature_service import FeatureService
 
 from . import console_ns
@@ -39,5 +40,21 @@ class SystemFeatureApi(Resource):
         ),
     )
     def get(self):
-        """Get system-wide feature configuration"""
-        return FeatureService.get_system_features().model_dump()
+        """Get system-wide feature configuration
+
+        NOTE: This endpoint is unauthenticated by design, as it provides system features
+        data required for dashboard initialization.
+
+        Authentication would create circular dependency (can't login without dashboard loading).
+
+        Only non-sensitive configuration data should be returned by this endpoint.
+        """
+        # NOTE(QuantumGhost): ideally we should access `current_user.is_authenticated`
+        # without a try-catch. However, due to the implementation of user loader (the `load_user_from_request`
+        # in api/extensions/ext_login.py), accessing `current_user.is_authenticated` will
+        # raise `Unauthorized` exception if authentication token is not provided.
+        try:
+            is_authenticated = current_user.is_authenticated
+        except Unauthorized:
+            is_authenticated = False
+        return FeatureService.get_system_features(is_authenticated=is_authenticated).model_dump()

api/controllers/console/ping.py

@@ -1,17 +1,17 @@
-from flask_restx import Resource, fields
+from pydantic import BaseModel, Field
 
-from . import console_ns
+from controllers.fastopenapi import console_router
 
 
-@console_ns.route("/ping")
-class PingApi(Resource):
-    @console_ns.doc("health_check")
-    @console_ns.doc(description="Health check endpoint for connection testing")
-    @console_ns.response(
-        200,
-        "Success",
-        console_ns.model("PingResponse", {"result": fields.String(description="Health check result", example="pong")}),
-    )
-    def get(self):
-        """Health check endpoint for connection testing"""
-        return {"result": "pong"}
+class PingResponse(BaseModel):
+    result: str = Field(description="Health check result", examples=["pong"])
+
+
+@console_router.get(
+    "/ping",
+    response_model=PingResponse,
+    tags=["console"],
+)
+def ping() -> PingResponse:
+    """Health check endpoint for connection testing."""
+    return PingResponse(result="pong")

api/controllers/console/workspace/dsl.py

@@ -1,65 +0,0 @@
-import json
-
-import httpx
-import yaml
-from flask_restx import Resource, reqparse
-from sqlalchemy.orm import Session
-from werkzeug.exceptions import Forbidden
-
-from controllers.console import console_ns
-from controllers.console.wraps import account_initialization_required, setup_required
-from core.plugin.impl.exc import PluginPermissionDeniedError
-from extensions.ext_database import db
-from libs.login import current_account_with_tenant, login_required
-from models.model import App
-from models.workflow import Workflow
-from services.app_dsl_service import AppDslService
-
-
-@console_ns.route("/workspaces/current/dsl/predict")
-class DSLPredictApi(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self):
-        user, _ = current_account_with_tenant()
-        if not user.is_admin_or_owner:
-            raise Forbidden()
-
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("app_id", type=str, required=True, location="json")
-            .add_argument("current_node_id", type=str, required=True, location="json")
-        )
-        args = parser.parse_args()
-
-        app_id: str = args["app_id"]
-        current_node_id: str = args["current_node_id"]
-
-        with Session(db.engine) as session:
-            app = session.query(App).filter_by(id=app_id).first()
-            workflow = session.query(Workflow).filter_by(app_id=app_id, version=Workflow.VERSION_DRAFT).first()
-
-        if not app:
-            raise ValueError("App not found")
-        if not workflow:
-            raise ValueError("Workflow not found")
-
-        try:
-            i = 0
-            for node_id, _ in workflow.walk_nodes():
-                if node_id == current_node_id:
-                    break
-                i += 1
-
-            dsl = yaml.safe_load(AppDslService.export_dsl(app_model=app))
-
-            response = httpx.post(
-                "http://spark-832c:8000/predict",
-                json={"graph_data": dsl, "source_node_index": i},
-            )
-            return {
-                "nodes": json.loads(response.json()),
-            }
-        except PluginPermissionDeniedError as e:
-            raise ValueError(e.description) from e

api/controllers/console/workspace/sandbox_providers.py

@@ -1,103 +0,0 @@
-import logging
-
-from flask_restx import Resource, fields, reqparse
-
-from controllers.console import console_ns
-from controllers.console.wraps import account_initialization_required, setup_required
-from core.model_runtime.utils.encoders import jsonable_encoder
-from libs.login import current_account_with_tenant, login_required
-from services.sandbox.sandbox_provider_service import SandboxProviderService
-
-logger = logging.getLogger(__name__)
-
-
-@console_ns.route("/workspaces/current/sandbox-providers")
-class SandboxProviderListApi(Resource):
-    @console_ns.doc("list_sandbox_providers")
-    @console_ns.doc(description="Get list of available sandbox providers with configuration status")
-    @console_ns.response(200, "Success", fields.List(fields.Raw(description="Sandbox provider information")))
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def get(self):
-        _, current_tenant_id = current_account_with_tenant()
-        providers = SandboxProviderService.list_providers(current_tenant_id)
-        return jsonable_encoder([p.model_dump() for p in providers])
-
-
-config_parser = reqparse.RequestParser()
-config_parser.add_argument("config", type=dict, required=True, location="json")
-config_parser.add_argument("activate", type=bool, required=False, default=False, location="json")
-
-
-@console_ns.route("/workspaces/current/sandbox-provider/<string:provider_type>/config")
-class SandboxProviderConfigApi(Resource):
-    @console_ns.doc("save_sandbox_provider_config")
-    @console_ns.doc(description="Save or update configuration for a sandbox provider")
-    @console_ns.expect(config_parser)
-    @console_ns.response(200, "Success")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self, provider_type: str):
-        _, current_tenant_id = current_account_with_tenant()
-        args = config_parser.parse_args()
-
-        try:
-            result = SandboxProviderService.save_config(
-                tenant_id=current_tenant_id,
-                provider_type=provider_type,
-                config=args["config"],
-                activate=args["activate"],
-            )
-            return result
-        except ValueError as e:
-            return {"message": str(e)}, 400
-
-    @console_ns.doc("delete_sandbox_provider_config")
-    @console_ns.doc(description="Delete configuration for a sandbox provider")
-    @console_ns.response(200, "Success")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def delete(self, provider_type: str):
-        _, current_tenant_id = current_account_with_tenant()
-
-        try:
-            result = SandboxProviderService.delete_config(
-                tenant_id=current_tenant_id,
-                provider_type=provider_type,
-            )
-            return result
-        except ValueError as e:
-            return {"message": str(e)}, 400
-
-
-activate_parser = reqparse.RequestParser()
-activate_parser.add_argument("type", type=str, required=True, location="json")
-
-
-@console_ns.route("/workspaces/current/sandbox-provider/<string:provider_type>/activate")
-class SandboxProviderActivateApi(Resource):
-    """Activate a sandbox provider."""
-
-    @console_ns.doc("activate_sandbox_provider")
-    @console_ns.doc(description="Activate a sandbox provider for the current workspace")
-    @console_ns.response(200, "Success")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self, provider_type: str):
-        """Activate a sandbox provider."""
-        _, current_tenant_id = current_account_with_tenant()
-
-        try:
-            args = activate_parser.parse_args()
-            result = SandboxProviderService.activate_provider(
-                tenant_id=current_tenant_id,
-                provider_type=provider_type,
-                type=args["type"],
-            )
-            return result
-        except ValueError as e:
-            return {"message": str(e)}, 400

api/controllers/fastopenapi.py

@@ -0,0 +1,3 @@
+from fastopenapi.routers import FlaskRouter
+
+console_router = FlaskRouter()

api/controllers/files/__init__.py

@@ -14,7 +14,7 @@ api = ExternalApi(
 
 files_ns = Namespace("files", description="File operations", path="/")
 
-from . import image_preview, storage_download, tool_files, upload
+from . import image_preview, tool_files, upload
 
 api.add_namespace(files_ns)
 
@@ -23,7 +23,6 @@ __all__ = [
     "bp",
     "files_ns",
     "image_preview",
-    "storage_download",
     "tool_files",
     "upload",
 ]

api/controllers/files/storage_download.py

@@ -1,56 +0,0 @@
-from urllib.parse import quote, unquote
-
-from flask import Response, request
-from flask_restx import Resource
-from pydantic import BaseModel, Field
-from werkzeug.exceptions import Forbidden, NotFound
-
-from controllers.files import files_ns
-from extensions.ext_storage import storage
-from extensions.storage.file_presign_storage import FilePresignStorage
-
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
-
-
-class StorageDownloadQuery(BaseModel):
-    timestamp: str = Field(..., description="Unix timestamp used in the signature")
-    nonce: str = Field(..., description="Random string for signature")
-    sign: str = Field(..., description="HMAC signature")
-
-
-files_ns.schema_model(
-    StorageDownloadQuery.__name__,
-    StorageDownloadQuery.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
-)
-
-
-@files_ns.route("/storage/<path:filename>/download")
-class StorageFileDownloadApi(Resource):
-    def get(self, filename: str):
-        filename = unquote(filename)
-
-        args = StorageDownloadQuery.model_validate(request.args.to_dict(flat=True))
-
-        if not FilePresignStorage.verify_signature(
-            filename=filename,
-            timestamp=args.timestamp,
-            nonce=args.nonce,
-            sign=args.sign,
-        ):
-            raise Forbidden("Invalid or expired download link")
-
-        try:
-            generator = storage.load_stream(filename)
-        except FileNotFoundError:
-            raise NotFound("File not found")
-
-        encoded_filename = quote(filename.split("/")[-1])
-
-        return Response(
-            generator,
-            mimetype="application/octet-stream",
-            direct_passthrough=True,
-            headers={
-                "Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}",
-            },
-        )

api/controllers/inner_api/plugin/plugin.py

@@ -448,53 +448,3 @@ class PluginFetchAppInfoApi(Resource):
         return BaseBackwardsInvocationResponse(
             data=PluginAppBackwardsInvocation.fetch_app_info(payload.app_id, tenant_model.id)
         ).model_dump()
-
-
-@inner_api_ns.route("/fetch/tools/list")
-class PluginFetchToolsListApi(Resource):
-    @get_user_tenant
-    @setup_required
-    @plugin_inner_api_only
-    @inner_api_ns.doc("plugin_fetch_tools_list")
-    @inner_api_ns.doc(description="Fetch all available tools through plugin interface")
-    @inner_api_ns.doc(
-        responses={
-            200: "Tools list retrieved successfully",
-            401: "Unauthorized - invalid API key",
-            404: "Service not available",
-        }
-    )
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant):
-        from sqlalchemy.orm import Session
-
-        from extensions.ext_database import db
-        from services.tools.api_tools_manage_service import ApiToolManageService
-        from services.tools.builtin_tools_manage_service import BuiltinToolManageService
-        from services.tools.mcp_tools_manage_service import MCPToolManageService
-        from services.tools.workflow_tools_manage_service import WorkflowToolManageService
-
-        providers = []
-
-        # Get builtin tools
-        builtin_providers = BuiltinToolManageService.list_builtin_tools(user_model.id, tenant_model.id)
-        for provider in builtin_providers:
-            providers.append(provider.to_dict())
-
-        # Get API tools
-        api_providers = ApiToolManageService.list_api_tools(tenant_model.id)
-        for provider in api_providers:
-            providers.append(provider.to_dict())
-
-        # Get workflow tools
-        workflow_providers = WorkflowToolManageService.list_tenant_workflow_tools(user_model.id, tenant_model.id)
-        for provider in workflow_providers:
-            providers.append(provider.to_dict())
-
-        # Get MCP tools
-        with Session(db.engine) as session:
-            mcp_service = MCPToolManageService(session)
-            mcp_providers = mcp_service.list_providers(tenant_id=tenant_model.id, for_list=True)
-            for provider in mcp_providers:
-                providers.append(provider.to_dict())
-
-        return BaseBackwardsInvocationResponse(data={"providers": providers}).model_dump()

api/controllers/inner_api/plugin/wraps.py

@@ -75,6 +75,7 @@ def get_user_tenant(view_func: Callable[P, R]):
     @wraps(view_func)
     def decorated_view(*args: P.args, **kwargs: P.kwargs):
         payload = TenantUserPayload.model_validate(request.get_json(silent=True) or {})
+
         user_id = payload.user_id
         tenant_id = payload.tenant_id
 

api/controllers/inner_api/wraps.py

@@ -5,15 +5,14 @@ from hashlib import sha1
 from hmac import new as hmac_new
 from typing import ParamSpec, TypeVar
 
+P = ParamSpec("P")
+R = TypeVar("R")
 from flask import abort, request
 
 from configs import dify_config
 from extensions.ext_database import db
 from models.model import EndUser
 
-P = ParamSpec("P")
-R = TypeVar("R")
-
 
 def billing_inner_api_only(view: Callable[P, R]):
     @wraps(view)
@@ -89,11 +88,11 @@ def plugin_inner_api_only(view: Callable[P, R]):
         if not dify_config.PLUGIN_DAEMON_KEY:
             abort(404)
 
-        # validate using inner api key
+        # get header 'X-Inner-Api-Key'
         inner_api_key = request.headers.get("X-Inner-Api-Key")
-        if inner_api_key and inner_api_key == dify_config.INNER_API_KEY_FOR_PLUGIN:
-            return view(*args, **kwargs)
+        if not inner_api_key or inner_api_key != dify_config.INNER_API_KEY_FOR_PLUGIN:
+            abort(404)
 
-        abort(401)
+        return view(*args, **kwargs)
 
     return decorated

api/controllers/service_api/dataset/document.py

@@ -261,17 +261,6 @@ class DocumentAddByFileApi(DatasetApiResource):
     @cloud_edition_billing_rate_limit_check("knowledge", "dataset")
     def post(self, tenant_id, dataset_id):
         """Create document by upload file."""
-        args = {}
-        if "data" in request.form:
-            args = json.loads(request.form["data"])
-        if "doc_form" not in args:
-            args["doc_form"] = "text_model"
-        if "doc_language" not in args:
-            args["doc_language"] = "English"
-
-        # get dataset info
-        dataset_id = str(dataset_id)
-        tenant_id = str(tenant_id)
         dataset = db.session.query(Dataset).where(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
 
         if not dataset:
@@ -280,6 +269,18 @@ class DocumentAddByFileApi(DatasetApiResource):
         if dataset.provider == "external":
             raise ValueError("External datasets are not supported.")
 
+        args = {}
+        if "data" in request.form:
+            args = json.loads(request.form["data"])
+        if "doc_form" not in args:
+            args["doc_form"] = dataset.chunk_structure or "text_model"
+        if "doc_language" not in args:
+            args["doc_language"] = "English"
+
+        # get dataset info
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+
         indexing_technique = args.get("indexing_technique") or dataset.indexing_technique
         if not indexing_technique:
             raise ValueError("indexing_technique is required.")
@@ -370,17 +371,6 @@ class DocumentUpdateByFileApi(DatasetApiResource):
     @cloud_edition_billing_rate_limit_check("knowledge", "dataset")
     def post(self, tenant_id, dataset_id, document_id):
         """Update document by upload file."""
-        args = {}
-        if "data" in request.form:
-            args = json.loads(request.form["data"])
-        if "doc_form" not in args:
-            args["doc_form"] = "text_model"
-        if "doc_language" not in args:
-            args["doc_language"] = "English"
-
-        # get dataset info
-        dataset_id = str(dataset_id)
-        tenant_id = str(tenant_id)
         dataset = db.session.query(Dataset).where(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
 
         if not dataset:
@@ -389,6 +379,18 @@ class DocumentUpdateByFileApi(DatasetApiResource):
         if dataset.provider == "external":
             raise ValueError("External datasets are not supported.")
 
+        args = {}
+        if "data" in request.form:
+            args = json.loads(request.form["data"])
+        if "doc_form" not in args:
+            args["doc_form"] = dataset.chunk_structure or "text_model"
+        if "doc_language" not in args:
+            args["doc_language"] = "English"
+
+        # get dataset info
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+
         # indexing_technique is already set in dataset since this is an update
         args["indexing_technique"] = dataset.indexing_technique
 

api/controllers/web/feature.py

@@ -17,5 +17,15 @@ class SystemFeatureApi(Resource):
 
         Returns:
             dict: System feature configuration object
+
+        This endpoint is akin to the `SystemFeatureApi` endpoint in api/controllers/console/feature.py,
+        except it is intended for use by the web app, instead of the console dashboard.
+
+        NOTE: This endpoint is unauthenticated by design, as it provides system features
+        data required for webapp initialization.
+
+        Authentication would create circular dependency (can't authenticate without webapp loading).
+
+        Only non-sensitive configuration data should be returned by this endpoint.
         """
         return FeatureService.get_system_features().model_dump()

api/controllers/web/login.py

@@ -1,9 +1,11 @@
 from flask import make_response, request
-from flask_restx import Resource, reqparse
+from flask_restx import Resource
 from jwt import InvalidTokenError
+from pydantic import BaseModel, Field, field_validator
 
 import services
 from configs import dify_config
+from controllers.common.schema import register_schema_models
 from controllers.console.auth.error import (
     AuthenticationFailedError,
     EmailCodeError,
@@ -18,7 +20,7 @@ from controllers.console.wraps import (
 )
 from controllers.web import web_ns
 from controllers.web.wraps import decode_jwt_token
-from libs.helper import email
+from libs.helper import EmailStr
 from libs.passport import PassportService
 from libs.password import valid_password
 from libs.token import (
@@ -30,10 +32,35 @@ from services.app_service import AppService
 from services.webapp_auth_service import WebAppAuthService
 
 
+class LoginPayload(BaseModel):
+    email: EmailStr
+    password: str
+
+    @field_validator("password")
+    @classmethod
+    def validate_password(cls, value: str) -> str:
+        return valid_password(value)
+
+
+class EmailCodeLoginSendPayload(BaseModel):
+    email: EmailStr
+    language: str | None = None
+
+
+class EmailCodeLoginVerifyPayload(BaseModel):
+    email: EmailStr
+    code: str
+    token: str = Field(min_length=1)
+
+
+register_schema_models(web_ns, LoginPayload, EmailCodeLoginSendPayload, EmailCodeLoginVerifyPayload)
+
+
 @web_ns.route("/login")
 class LoginApi(Resource):
     """Resource for web app email/password login."""
 
+    @web_ns.expect(web_ns.models[LoginPayload.__name__])
     @setup_required
     @only_edition_enterprise
     @web_ns.doc("web_app_login")
@@ -50,15 +77,10 @@ class LoginApi(Resource):
     @decrypt_password_field
     def post(self):
         """Authenticate user and login."""
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("email", type=email, required=True, location="json")
-            .add_argument("password", type=valid_password, required=True, location="json")
-        )
-        args = parser.parse_args()
+        payload = LoginPayload.model_validate(web_ns.payload or {})
 
         try:
-            account = WebAppAuthService.authenticate(args["email"], args["password"])
+            account = WebAppAuthService.authenticate(payload.email, payload.password)
         except services.errors.account.AccountLoginError:
             raise AccountBannedError()
         except services.errors.account.AccountPasswordError:
@@ -145,6 +167,7 @@ class EmailCodeLoginSendEmailApi(Resource):
     @only_edition_enterprise
     @web_ns.doc("send_email_code_login")
     @web_ns.doc(description="Send email verification code for login")
+    @web_ns.expect(web_ns.models[EmailCodeLoginSendPayload.__name__])
     @web_ns.doc(
         responses={
             200: "Email code sent successfully",
@@ -153,19 +176,14 @@ class EmailCodeLoginSendEmailApi(Resource):
         }
     )
     def post(self):
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("email", type=email, required=True, location="json")
-            .add_argument("language", type=str, required=False, location="json")
-        )
-        args = parser.parse_args()
+        payload = EmailCodeLoginSendPayload.model_validate(web_ns.payload or {})
 
-        if args["language"] is not None and args["language"] == "zh-Hans":
+        if payload.language == "zh-Hans":
             language = "zh-Hans"
         else:
             language = "en-US"
 
-        account = WebAppAuthService.get_user_through_email(args["email"])
+        account = WebAppAuthService.get_user_through_email(payload.email)
         if account is None:
             raise AuthenticationFailedError()
         else:
@@ -179,6 +197,7 @@ class EmailCodeLoginApi(Resource):
     @only_edition_enterprise
     @web_ns.doc("verify_email_code_login")
     @web_ns.doc(description="Verify email code and complete login")
+    @web_ns.expect(web_ns.models[EmailCodeLoginVerifyPayload.__name__])
     @web_ns.doc(
         responses={
             200: "Email code verified and login successful",
@@ -189,17 +208,11 @@ class EmailCodeLoginApi(Resource):
     )
     @decrypt_code_field
     def post(self):
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("email", type=str, required=True, location="json")
-            .add_argument("code", type=str, required=True, location="json")
-            .add_argument("token", type=str, required=True, location="json")
-        )
-        args = parser.parse_args()
+        payload = EmailCodeLoginVerifyPayload.model_validate(web_ns.payload or {})
 
-        user_email = args["email"].lower()
+        user_email = payload.email.lower()
 
-        token_data = WebAppAuthService.get_email_code_login_data(args["token"])
+        token_data = WebAppAuthService.get_email_code_login_data(payload.token)
         if token_data is None:
             raise InvalidTokenError()
 
@@ -210,10 +223,10 @@ class EmailCodeLoginApi(Resource):
         if normalized_token_email != user_email:
             raise InvalidEmailError()
 
-        if token_data["code"] != args["code"]:
+        if token_data["code"] != payload.code:
             raise EmailCodeError()
 
-        WebAppAuthService.revoke_email_code_login_token(args["token"])
+        WebAppAuthService.revoke_email_code_login_token(payload.token)
         account = WebAppAuthService.get_user_through_email(token_email)
         if not account:
             raise AuthenticationFailedError()

api/controllers/web/workflow.py

@@ -1,8 +1,10 @@
 import logging
+from typing import Any
 
-from flask_restx import reqparse
+from pydantic import BaseModel, Field
 from werkzeug.exceptions import InternalServerError
 
+from controllers.common.schema import register_schema_models
 from controllers.web import web_ns
 from controllers.web.error import (
     CompletionRequestError,
@@ -27,19 +29,22 @@ from models.model import App, AppMode, EndUser
 from services.app_generate_service import AppGenerateService
 from services.errors.llm import InvokeRateLimitError
 
+
+class WorkflowRunPayload(BaseModel):
+    inputs: dict[str, Any] = Field(description="Input variables for the workflow")
+    files: list[dict[str, Any]] | None = Field(default=None, description="Files to be processed by the workflow")
+
+
 logger = logging.getLogger(__name__)
 
+register_schema_models(web_ns, WorkflowRunPayload)
+
 
 @web_ns.route("/workflows/run")
 class WorkflowRunApi(WebApiResource):
     @web_ns.doc("Run Workflow")
     @web_ns.doc(description="Execute a workflow with provided inputs and files.")
-    @web_ns.doc(
-        params={
-            "inputs": {"description": "Input variables for the workflow", "type": "object", "required": True},
-            "files": {"description": "Files to be processed by the workflow", "type": "array", "required": False},
-        }
-    )
+    @web_ns.expect(web_ns.models[WorkflowRunPayload.__name__])
     @web_ns.doc(
         responses={
             200: "Success",
@@ -58,12 +63,8 @@ class WorkflowRunApi(WebApiResource):
         if app_mode != AppMode.WORKFLOW:
             raise NotWorkflowAppError()
 
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("inputs", type=dict, required=True, nullable=False, location="json")
-            .add_argument("files", type=list, required=False, location="json")
-        )
-        args = parser.parse_args()
+        payload = WorkflowRunPayload.model_validate(web_ns.payload or {})
+        args = payload.model_dump(exclude_none=True)
 
         try:
             response = AppGenerateService.generate(

api/core/agent/agent_app_runner.py

@@ -1,380 +0,0 @@
-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

@@ -6,7 +6,7 @@ from typing import Union, cast
 
 from sqlalchemy import select
 
-from core.agent.entities import AgentEntity, AgentToolEntity, ExecutionContext
+from core.agent.entities import AgentEntity, AgentToolEntity
 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
@@ -116,20 +116,9 @@ 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

@@ -0,0 +1,437 @@
+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 core.workflow.nodes.agent.exc import AgentMaxIterationError
+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)
+
+            # Check if max iteration is reached and model still wants to call tools
+            if iteration_step == max_iteration_steps and scratchpad.action:
+                if scratchpad.action.action_name.lower() != "final answer":
+                    raise AgentMaxIterationError(app_config.agent.max_iteration)
+
+            # 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

@@ -0,0 +1,118 @@
+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

@@ -0,0 +1,87 @@
+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

@@ -1,5 +1,3 @@
-import uuid
-from collections.abc import Mapping
 from enum import StrEnum
 from typing import Any, Union
 
@@ -94,96 +92,3 @@ 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

@@ -0,0 +1,468 @@
+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 core.workflow.nodes.agent.exc import AgentMaxIterationError
+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=response, 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
+                ]
+
+            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"
+
+            # Check if max iteration is reached and model still wants to call tools
+            if iteration_step == max_iteration_steps and tool_calls:
+                raise AgentMaxIterationError(app_config.agent.max_iteration)
+
+            # 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

@@ -1,55 +0,0 @@
-# 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

@@ -1,19 +0,0 @@
-"""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

@@ -1,474 +0,0 @@
-"""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

@@ -1,299 +0,0 @@
-"""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

@@ -1,418 +0,0 @@
-"""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

@@ -1,107 +0,0 @@
-"""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_generator.py

@@ -1,9 +1,11 @@
+from __future__ import annotations
+
 import contextvars
 import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping
-from typing import Any, Literal, Union, overload
+from typing import TYPE_CHECKING, Any, Literal, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -13,6 +15,9 @@ from sqlalchemy.orm import Session, sessionmaker
 import contexts
 from configs import dify_config
 from constants import UUID_NIL
+
+if TYPE_CHECKING:
+    from controllers.console.app.workflow import LoopNodeRunPayload
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfigManager
 from core.app.apps.advanced_chat.app_runner import AdvancedChatAppRunner
@@ -24,13 +29,11 @@ from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
 from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
-from core.app.layers.sandbox_layer import SandboxLayer
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
 from core.repositories import DifyCoreRepositoryFactory
-from core.sandbox import Sandbox, SandboxManager
 from core.workflow.repositories.draft_variable_repository import (
     DraftVariableSaverFactory,
 )
@@ -42,9 +45,7 @@ from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, Conversation, EndUser, Message, Workflow, WorkflowNodeExecutionTriggeredFrom
 from models.enums import WorkflowRunTriggeredFrom
-from models.workflow_features import WorkflowFeatures
 from services.conversation_service import ConversationService
-from services.sandbox.sandbox_provider_service import SandboxProviderService
 from services.workflow_draft_variable_service import (
     DraftVarLoader,
     WorkflowDraftVariableService,
@@ -308,7 +309,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         workflow: Workflow,
         node_id: str,
         user: Account | EndUser,
-        args: Mapping,
+        args: LoopNodeRunPayload,
         streaming: bool = True,
     ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], Any, None]:
         """
@@ -324,7 +325,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         if not node_id:
             raise ValueError("node_id is required")
 
-        if args.get("inputs") is None:
+        if args.inputs is None:
             raise ValueError("inputs is required")
 
         # convert to app config
@@ -342,7 +343,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             stream=streaming,
             invoke_from=InvokeFrom.DEBUGGER,
             extras={"auto_generate_conversation_name": False},
-            single_loop_run=AdvancedChatAppGenerateEntity.SingleLoopRunEntity(node_id=node_id, inputs=args["inputs"]),
+            single_loop_run=AdvancedChatAppGenerateEntity.SingleLoopRunEntity(node_id=node_id, inputs=args.inputs),
         )
         contexts.plugin_tool_providers.set({})
         contexts.plugin_tool_providers_lock.set(threading.Lock())
@@ -516,31 +517,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 if workflow is None:
                     raise ValueError("Workflow not found")
 
-                sandbox: Sandbox | None = None
-                graph_engine_layers: tuple = ()
-                if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
-                    sandbox_provider = SandboxProviderService.get_sandbox_provider(
-                        application_generate_entity.app_config.tenant_id
-                    )
-                    if workflow.version == Workflow.VERSION_DRAFT:
-                        sandbox = SandboxManager.create_draft(
-                            tenant_id=application_generate_entity.app_config.tenant_id,
-                            app_id=application_generate_entity.app_config.app_id,
-                            user_id=application_generate_entity.user_id,
-                            sandbox_provider=sandbox_provider,
-                        )
-                    else:
-                        if application_generate_entity.workflow_run_id is None:
-                            raise ValueError("workflow_run_id is required when sandbox is enabled")
-                        sandbox = SandboxManager.create(
-                            tenant_id=application_generate_entity.app_config.tenant_id,
-                            app_id=application_generate_entity.app_config.app_id,
-                            user_id=application_generate_entity.user_id,
-                            workflow_execution_id=application_generate_entity.workflow_run_id,
-                            sandbox_provider=sandbox_provider,
-                        )
-                    graph_engine_layers = (SandboxLayer(sandbox=sandbox),)
-
                 # Determine system_user_id based on invocation source
                 is_external_api_call = application_generate_entity.invoke_from in {
                     InvokeFrom.WEB_APP,
@@ -571,8 +547,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 app=app,
                 workflow_execution_repository=workflow_execution_repository,
                 workflow_node_execution_repository=workflow_node_execution_repository,
-                graph_engine_layers=graph_engine_layers,
-                sandbox=sandbox,
             )
 
             try:

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

@@ -24,7 +24,6 @@ from core.app.layers.conversation_variable_persist_layer import ConversationVari
 from core.db.session_factory import session_factory
 from core.moderation.base import ModerationError
 from core.moderation.input_moderation import InputModeration
-from core.sandbox import Sandbox
 from core.variables.variables import Variable
 from core.workflow.enums import WorkflowType
 from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
@@ -67,7 +66,6 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        sandbox: Sandbox | None = None,
     ):
         super().__init__(
             queue_manager=queue_manager,
@@ -84,7 +82,6 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         self._app = app
         self._workflow_execution_repository = workflow_execution_repository
         self._workflow_node_execution_repository = workflow_node_execution_repository
-        self._sandbox = sandbox
 
     @trace_span(WorkflowAppRunnerHandler)
     def run(self):
@@ -159,10 +156,6 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
 
             # init graph
             graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.time())
-
-            if self._sandbox:
-                graph_runtime_state.set_sandbox(self._sandbox)
-
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,
                 graph_runtime_state=graph_runtime_state,

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

@@ -82,7 +82,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -110,7 +110,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -120,6 +120,6 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
 
             yield response_chunk

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

@@ -4,7 +4,6 @@ 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
 
@@ -20,7 +19,6 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
 )
 from core.app.entities.queue_entities import (
-    ChunkType,
     MessageQueueMessage,
     QueueAdvancedChatMessageEndEvent,
     QueueAgentLogEvent,
@@ -72,131 +70,13 @@ 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, LLMGenerationDetail, Message, MessageFile
+from models import Account, Conversation, EndUser, 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,
-        tool_icon: str | dict | None = None,
-        tool_icon_dark: str | dict | None = None,
-    ) -> 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,
-                "icon": tool_icon,
-                "icon_dark": tool_icon_dark,
-            }
-            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.
@@ -264,8 +144,6 @@ 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]]:
@@ -505,7 +383,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         queue_message: Union[WorkflowQueueMessage, MessageQueueMessage] | None = None,
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
-        """Handle text chunk events and record to stream buffer for sequence reconstruction."""
+        """Handle text chunk events."""
         delta_text = event.text
         if delta_text is None:
             return
@@ -527,53 +405,9 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        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,
-                    tool_icon=tool_icon,
-                    tool_icon_dark=tool_icon_dark,
-                )
-            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,
-                )
-            case _:
-                pass
+        self._task_state.answer += delta_text
         yield self._message_cycle_manager.message_to_stream_response(
-            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,
+            answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
         )
 
     def _handle_iteration_start_event(
@@ -941,7 +775,6 @@ 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()
@@ -993,54 +826,6 @@ 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

@@ -3,8 +3,10 @@ from typing import cast
 
 from sqlalchemy import select
 
-from core.agent.agent_app_runner import AgentAppRunner
+from core.agent.cot_chat_agent_runner import CotChatAgentRunner
+from core.agent.cot_completion_agent_runner import CotCompletionAgentRunner
 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
@@ -12,7 +14,8 @@ 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.model_entities import ModelFeature
+from core.model_runtime.entities.llm_entities import LLMMode
+from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.moderation.base import ModerationError
 from extensions.ext_database import db
@@ -191,7 +194,22 @@ class AgentChatAppRunner(AppRunner):
             raise ValueError("Message not found")
         db.session.close()
 
-        runner = AgentAppRunner(
+        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(
             tenant_id=app_config.tenant_id,
             application_generate_entity=application_generate_entity,
             conversation=conversation_result,

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

@@ -81,7 +81,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
 
             yield response_chunk

api/core/app/apps/chat/generate_response_converter.py

@@ -81,7 +81,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
 
             yield response_chunk

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

@@ -70,8 +70,6 @@ class _NodeSnapshot:
     """Empty string means the node is not executing inside an iteration."""
     loop_id: str = ""
     """Empty string means the node is not executing inside a loop."""
-    parent_node_id: str = ""
-    """Empty string means the node is not an nested node (extractor node)."""
 
 
 class WorkflowResponseConverter:
@@ -133,7 +131,6 @@ class WorkflowResponseConverter:
             start_at=event.start_at,
             iteration_id=event.in_iteration_id or "",
             loop_id=event.in_loop_id or "",
-            parent_node_id=event.in_parent_node_id or "",
         )
         node_execution_id = NodeExecutionId(event.node_execution_id)
         self._node_snapshots[node_execution_id] = snapshot
@@ -290,7 +287,6 @@ class WorkflowResponseConverter:
                 created_at=int(snapshot.start_at.timestamp()),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
-                parent_node_id=event.in_parent_node_id,
                 agent_strategy=event.agent_strategy,
             ),
         )
@@ -377,7 +373,6 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
-                parent_node_id=event.in_parent_node_id,
             ),
         )
 
@@ -427,7 +422,6 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
-                parent_node_id=event.in_parent_node_id,
                 retry_index=event.retry_index,
             ),
         )
@@ -677,7 +671,7 @@ class WorkflowResponseConverter:
             task_id=task_id,
             data=AgentLogStreamResponse.Data(
                 node_execution_id=event.node_execution_id,
-                message_id=event.id,
+                id=event.id,
                 parent_id=event.parent_id,
                 label=event.label,
                 error=event.error,

api/core/app/apps/completion/generate_response_converter.py

@@ -79,7 +79,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -106,7 +106,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
                 metadata = sub_stream_response_dict.get("metadata", {})
                 if not isinstance(metadata, dict):
                     metadata = {}
@@ -116,6 +116,6 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
 
             yield response_chunk

api/core/app/apps/pipeline/generate_response_converter.py

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(cast(dict, data))
             else:
-                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump())
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(cast(dict, sub_stream_response.to_ignore_detail_dict()))
             else:
-                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump())
             yield response_chunk

api/core/app/apps/pipeline/pipeline_runner.py

@@ -9,13 +9,13 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
     RagPipelineGenerateEntity,
 )
+from core.app.workflow.node_factory import DifyNodeFactory
 from core.variables.variables import RAGPipelineVariable, RAGPipelineVariableInput
 from core.workflow.entities.graph_init_params import GraphInitParams
 from core.workflow.enums import WorkflowType
 from core.workflow.graph import Graph
 from core.workflow.graph_engine.layers.persistence import PersistenceWorkflowInfo, WorkflowPersistenceLayer
 from core.workflow.graph_events import GraphEngineEvent, GraphRunFailedEvent
-from core.workflow.nodes.node_factory import DifyNodeFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
 from core.workflow.runtime import GraphRuntimeState, VariablePool

api/core/app/apps/workflow/app_generator.py

@@ -1,9 +1,11 @@
+from __future__ import annotations
+
 import contextvars
 import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, Literal, Union, overload
+from typing import TYPE_CHECKING, Any, Literal, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -23,13 +25,11 @@ from core.app.apps.workflow.generate_response_converter import WorkflowAppGenera
 from core.app.apps.workflow.generate_task_pipeline import WorkflowAppGenerateTaskPipeline
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import WorkflowAppBlockingResponse, WorkflowAppStreamResponse
-from core.app.layers.sandbox_layer import SandboxLayer
 from core.db.session_factory import session_factory
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.repositories import DifyCoreRepositoryFactory
-from core.sandbox import Sandbox, SandboxManager
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
@@ -40,10 +40,11 @@ from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, EndUser, Workflow, WorkflowNodeExecutionTriggeredFrom
 from models.enums import WorkflowRunTriggeredFrom
-from models.workflow_features import WorkflowFeatures
-from services.sandbox.sandbox_provider_service import SandboxProviderService
 from services.workflow_draft_variable_service import DraftVarLoader, WorkflowDraftVariableService
 
+if TYPE_CHECKING:
+    from controllers.console.app.workflow import LoopNodeRunPayload
+
 SKIP_PREPARE_USER_INPUTS_KEY = "_skip_prepare_user_inputs"
 
 logger = logging.getLogger(__name__)
@@ -385,7 +386,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         workflow: Workflow,
         node_id: str,
         user: Account | EndUser,
-        args: Mapping[str, Any],
+        args: LoopNodeRunPayload,
         streaming: bool = True,
     ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], None, None]:
         """
@@ -401,7 +402,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         if not node_id:
             raise ValueError("node_id is required")
 
-        if args.get("inputs") is None:
+        if args.inputs is None:
             raise ValueError("inputs is required")
 
         # convert to app config
@@ -417,7 +418,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             stream=streaming,
             invoke_from=InvokeFrom.DEBUGGER,
             extras={"auto_generate_conversation_name": False},
-            single_loop_run=WorkflowAppGenerateEntity.SingleLoopRunEntity(node_id=node_id, inputs=args["inputs"]),
+            single_loop_run=WorkflowAppGenerateEntity.SingleLoopRunEntity(node_id=node_id, inputs=args.inputs or {}),
             workflow_execution_id=str(uuid.uuid4()),
         )
         contexts.plugin_tool_providers.set({})
@@ -492,31 +493,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 if workflow is None:
                     raise ValueError("Workflow not found")
 
-                sandbox: Sandbox | None = None
-                if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
-                    sandbox_provider = SandboxProviderService.get_sandbox_provider(
-                        application_generate_entity.app_config.tenant_id
-                    )
-                    if workflow.version == Workflow.VERSION_DRAFT:
-                        sandbox = SandboxManager.create_draft(
-                            tenant_id=application_generate_entity.app_config.tenant_id,
-                            app_id=application_generate_entity.app_config.app_id,
-                            user_id=application_generate_entity.user_id,
-                            sandbox_provider=sandbox_provider,
-                        )
-                    else:
-                        sandbox = SandboxManager.create(
-                            tenant_id=application_generate_entity.app_config.tenant_id,
-                            app_id=application_generate_entity.app_config.app_id,
-                            user_id=application_generate_entity.user_id,
-                            workflow_execution_id=application_generate_entity.workflow_execution_id,
-                            sandbox_provider=sandbox_provider,
-                        )
-                    graph_engine_layers = (
-                        *graph_engine_layers,
-                        SandboxLayer(sandbox=sandbox),
-                    )
-
                 # Determine system_user_id based on invocation source
                 is_external_api_call = application_generate_entity.invoke_from in {
                     InvokeFrom.WEB_APP,
@@ -541,7 +517,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 workflow_node_execution_repository=workflow_node_execution_repository,
                 root_node_id=root_node_id,
                 graph_engine_layers=graph_engine_layers,
-                sandbox=sandbox,
             )
 
             try:

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

@@ -7,7 +7,6 @@ from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.apps.workflow.app_config_manager import WorkflowAppConfig
 from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
-from core.sandbox import Sandbox
 from core.workflow.enums import WorkflowType
 from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
@@ -43,7 +42,6 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        sandbox: Sandbox | None = None,
     ):
         super().__init__(
             queue_manager=queue_manager,
@@ -57,7 +55,6 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         self._root_node_id = root_node_id
         self._workflow_execution_repository = workflow_execution_repository
         self._workflow_node_execution_repository = workflow_node_execution_repository
-        self._sandbox = sandbox
 
     @trace_span(WorkflowAppRunnerHandler)
     def run(self):
@@ -102,9 +99,6 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
 
             graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
 
-            if self._sandbox:
-                graph_runtime_state.set_sandbox(self._sandbox)
-
             # init graph
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,

api/core/app/apps/workflow/generate_response_converter.py

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk

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

@@ -13,7 +13,6 @@ from core.app.apps.common.workflow_response_converter import WorkflowResponseCon
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.queue_entities import (
     AppQueueEvent,
-    ChunkType,
     MessageQueueMessage,
     QueueAgentLogEvent,
     QueueErrorEvent,
@@ -484,33 +483,11 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if delta_text is None:
             return
 
-        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 None
-        tool_name = tool_payload.name if tool_payload and tool_payload.name else None
-        tool_arguments = tool_call.arguments if tool_call else None
-        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
-        tool_files = tool_result.files if tool_result else []
-        tool_icon = tool_payload.icon if tool_payload else None
-        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
-
         # only publish tts message at text chunk streaming
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        yield self._text_chunk_to_stream_response(
-            text=delta_text,
-            from_variable_selector=event.from_variable_selector,
-            chunk_type=event.chunk_type,
-            tool_call_id=tool_call_id,
-            tool_name=tool_name,
-            tool_arguments=tool_arguments,
-            tool_files=tool_files,
-            tool_elapsed_time=tool_elapsed_time,
-            tool_icon=tool_icon,
-            tool_icon_dark=tool_icon_dark,
-        )
+        yield self._text_chunk_to_stream_response(delta_text, from_variable_selector=event.from_variable_selector)
 
     def _handle_agent_log_event(self, event: QueueAgentLogEvent, **kwargs) -> Generator[StreamResponse, None, None]:
         """Handle agent log events."""
@@ -673,61 +650,16 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         session.add(workflow_app_log)
 
     def _text_chunk_to_stream_response(
-        self,
-        text: str,
-        from_variable_selector: list[str] | None = None,
-        chunk_type: ChunkType | None = None,
-        tool_call_id: str | None = None,
-        tool_name: str | None = None,
-        tool_arguments: str | None = None,
-        tool_files: list[str] | None = None,
-        tool_error: str | None = None,
-        tool_elapsed_time: float | None = None,
-        tool_icon: str | dict | None = None,
-        tool_icon_dark: str | dict | None = None,
+        self, text: str, from_variable_selector: list[str] | None = None
     ) -> TextChunkStreamResponse:
         """
         Handle completed event.
         :param text: text
         :return:
         """
-        from core.app.entities.task_entities import ChunkType as ResponseChunkType
-
-        response_chunk_type = ResponseChunkType(chunk_type.value) if chunk_type else ResponseChunkType.TEXT
-
-        data = TextChunkStreamResponse.Data(
-            text=text,
-            from_variable_selector=from_variable_selector,
-            chunk_type=response_chunk_type,
-        )
-
-        if response_chunk_type == ResponseChunkType.TOOL_CALL:
-            data = data.model_copy(
-                update={
-                    "tool_call_id": tool_call_id,
-                    "tool_name": tool_name,
-                    "tool_arguments": tool_arguments,
-                    "tool_icon": tool_icon,
-                    "tool_icon_dark": tool_icon_dark,
-                }
-            )
-        elif response_chunk_type == ResponseChunkType.TOOL_RESULT:
-            data = data.model_copy(
-                update={
-                    "tool_call_id": tool_call_id,
-                    "tool_name": tool_name,
-                    "tool_arguments": tool_arguments,
-                    "tool_files": tool_files,
-                    "tool_error": tool_error,
-                    "tool_elapsed_time": tool_elapsed_time,
-                    "tool_icon": tool_icon,
-                    "tool_icon_dark": tool_icon_dark,
-                }
-            )
-
         response = TextChunkStreamResponse(
             task_id=self._application_generate_entity.task_id,
-            data=data,
+            data=TextChunkStreamResponse.Data(text=text, from_variable_selector=from_variable_selector),
         )
 
         return response

api/core/app/apps/workflow_app_runner.py

@@ -25,6 +25,7 @@ from core.app.entities.queue_entities import (
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
+from core.app.workflow.node_factory import DifyNodeFactory
 from core.workflow.entities import GraphInitParams
 from core.workflow.graph import Graph
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
@@ -53,7 +54,6 @@ from core.workflow.graph_events import (
 )
 from core.workflow.graph_events.graph import GraphRunAbortedEvent
 from core.workflow.nodes import NodeType
-from core.workflow.nodes.node_factory import DifyNodeFactory
 from core.workflow.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
 from core.workflow.runtime import GraphRuntimeState, VariablePool
 from core.workflow.system_variable import SystemVariable
@@ -166,18 +166,22 @@ class WorkflowBasedAppRunner:
 
         # Determine which type of single node execution and get graph/variable_pool
         if single_iteration_run:
-            graph, variable_pool = self._get_graph_and_variable_pool_of_single_iteration(
+            graph, variable_pool = self._get_graph_and_variable_pool_for_single_node_run(
                 workflow=workflow,
                 node_id=single_iteration_run.node_id,
                 user_inputs=dict(single_iteration_run.inputs),
                 graph_runtime_state=graph_runtime_state,
+                node_type_filter_key="iteration_id",
+                node_type_label="iteration",
             )
         elif single_loop_run:
-            graph, variable_pool = self._get_graph_and_variable_pool_of_single_loop(
+            graph, variable_pool = self._get_graph_and_variable_pool_for_single_node_run(
                 workflow=workflow,
                 node_id=single_loop_run.node_id,
                 user_inputs=dict(single_loop_run.inputs),
                 graph_runtime_state=graph_runtime_state,
+                node_type_filter_key="loop_id",
+                node_type_label="loop",
             )
         else:
             raise ValueError("Neither single_iteration_run nor single_loop_run is specified")
@@ -314,44 +318,6 @@ class WorkflowBasedAppRunner:
 
         return graph, variable_pool
 
-    def _get_graph_and_variable_pool_of_single_iteration(
-        self,
-        workflow: Workflow,
-        node_id: str,
-        user_inputs: dict[str, Any],
-        graph_runtime_state: GraphRuntimeState,
-    ) -> tuple[Graph, VariablePool]:
-        """
-        Get variable pool of single iteration
-        """
-        return self._get_graph_and_variable_pool_for_single_node_run(
-            workflow=workflow,
-            node_id=node_id,
-            user_inputs=user_inputs,
-            graph_runtime_state=graph_runtime_state,
-            node_type_filter_key="iteration_id",
-            node_type_label="iteration",
-        )
-
-    def _get_graph_and_variable_pool_of_single_loop(
-        self,
-        workflow: Workflow,
-        node_id: str,
-        user_inputs: dict[str, Any],
-        graph_runtime_state: GraphRuntimeState,
-    ) -> tuple[Graph, VariablePool]:
-        """
-        Get variable pool of single loop
-        """
-        return self._get_graph_and_variable_pool_for_single_node_run(
-            workflow=workflow,
-            node_id=node_id,
-            user_inputs=user_inputs,
-            graph_runtime_state=graph_runtime_state,
-            node_type_filter_key="loop_id",
-            node_type_label="loop",
-        )
-
     def _handle_event(self, workflow_entry: WorkflowEntry, event: GraphEngineEvent):
         """
         Handle event
@@ -385,7 +351,6 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_parent_node_id=event.in_parent_node_id,
                     inputs=inputs,
                     process_data=process_data,
                     outputs=outputs,
@@ -406,7 +371,6 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_parent_node_id=event.in_parent_node_id,
                     agent_strategy=event.agent_strategy,
                     provider_type=event.provider_type,
                     provider_id=event.provider_id,
@@ -430,7 +394,6 @@ class WorkflowBasedAppRunner:
                     execution_metadata=execution_metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunFailedEvent):
@@ -447,7 +410,6 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunExceptionEvent):
@@ -464,25 +426,15 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunStreamChunkEvent):
-            from core.app.entities.queue_entities import ChunkType as QueueChunkType
-
-            if event.is_final and not event.chunk:
-                return
-
             self._publish_event(
                 QueueTextChunkEvent(
                     text=event.chunk,
                     from_variable_selector=list(event.selector),
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    chunk_type=QueueChunkType(event.chunk_type.value),
-                    tool_call=event.tool_call,
-                    tool_result=event.tool_result,
-                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunRetrieverResourceEvent):
@@ -491,7 +443,6 @@ class WorkflowBasedAppRunner:
                     retriever_resources=event.retriever_resources,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunAgentLogEvent):

api/core/app/entities/app_asset_entities.py

@@ -1,294 +0,0 @@
-from __future__ import annotations
-
-from collections import defaultdict
-from collections.abc import Generator
-from enum import StrEnum
-
-from pydantic import BaseModel, Field
-
-
-class AssetNodeType(StrEnum):
-    FILE = "file"
-    FOLDER = "folder"
-
-
-class AppAssetNode(BaseModel):
-    id: str = Field(description="Unique identifier for the node")
-    node_type: AssetNodeType = Field(description="Type of node: file or folder")
-    name: str = Field(description="Name of the file or folder")
-    parent_id: str | None = Field(default=None, description="Parent folder ID, None for root level")
-    order: int = Field(default=0, description="Sort order within parent folder, lower values first")
-    extension: str = Field(default="", description="File extension without dot, empty for folders")
-    size: int = Field(default=0, description="File size in bytes, 0 for folders")
-    checksum: str = Field(default="", description="SHA-256 checksum of file content, empty for folders")
-
-    @classmethod
-    def create_folder(cls, node_id: str, name: str, parent_id: str | None = None) -> AppAssetNode:
-        return cls(id=node_id, node_type=AssetNodeType.FOLDER, name=name, parent_id=parent_id)
-
-    @classmethod
-    def create_file(
-        cls, node_id: str, name: str, parent_id: str | None = None, size: int = 0, checksum: str = ""
-    ) -> AppAssetNode:
-        return cls(
-            id=node_id,
-            node_type=AssetNodeType.FILE,
-            name=name,
-            parent_id=parent_id,
-            extension=name.rsplit(".", 1)[-1] if "." in name else "",
-            size=size,
-            checksum=checksum,
-        )
-
-
-class AppAssetNodeView(BaseModel):
-    id: str = Field(description="Unique identifier for the node")
-    node_type: str = Field(description="Type of node: 'file' or 'folder'")
-    name: str = Field(description="Name of the file or folder")
-    path: str = Field(description="Full path from root, e.g. '/folder/file.txt'")
-    extension: str = Field(default="", description="File extension without dot")
-    size: int = Field(default=0, description="File size in bytes")
-    checksum: str = Field(default="", description="SHA-256 checksum of file content")
-    children: list[AppAssetNodeView] = Field(default_factory=list, description="Child nodes for folders")
-
-
-class TreeNodeNotFoundError(Exception):
-    """Tree internal: node not found"""
-
-    pass
-
-
-class TreeParentNotFoundError(Exception):
-    """Tree internal: parent folder not found"""
-
-    pass
-
-
-class TreePathConflictError(Exception):
-    """Tree internal: path already exists"""
-
-    pass
-
-
-class AppAssetFileTree(BaseModel):
-    """
-    File tree structure for app assets using adjacency list pattern.
-
-    Design:
-    - Storage: Flat list with parent_id references (adjacency list)
-    - Path: Computed dynamically via get_path(), not stored
-    - Order: Integer field for user-defined sorting within each folder
-    - API response: transform() builds nested tree with computed paths
-
-    Why adjacency list over nested tree or materialized path:
-    - Simpler CRUD: move/rename only updates one node's parent_id
-    - No path cascade: renaming parent doesn't require updating all descendants
-    - JSON-friendly: flat list serializes cleanly to database JSON column
-    - Trade-off: path lookup is O(depth), acceptable for typical file trees
-    """
-
-    nodes: list[AppAssetNode] = Field(default_factory=list, description="Flat list of all nodes in the tree")
-
-    def get(self, node_id: str) -> AppAssetNode | None:
-        return next((n for n in self.nodes if n.id == node_id), None)
-
-    def get_children(self, parent_id: str | None) -> list[AppAssetNode]:
-        return [n for n in self.nodes if n.parent_id == parent_id]
-
-    def has_child_named(self, parent_id: str | None, name: str) -> bool:
-        return any(n.name == name and n.parent_id == parent_id for n in self.nodes)
-
-    def get_path(self, node_id: str) -> str:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-        parts: list[str] = []
-        current: AppAssetNode | None = node
-        while current:
-            parts.append(current.name)
-            current = self.get(current.parent_id) if current.parent_id else None
-        return "/".join(reversed(parts))
-
-    def relative_path(self, a: AppAssetNode, b: AppAssetNode) -> str:
-        """
-        Calculate relative path from node a to node b for Markdown references.
-        Path is computed from a's parent directory (where the file resides).
-
-        Examples:
-            /foo/a.md -> /foo/b.md         => ./b.md
-            /foo/a.md -> /foo/sub/b.md     => ./sub/b.md
-            /foo/sub/a.md -> /foo/b.md     => ../b.md
-            /foo/sub/deep/a.md -> /foo/b.md => ../../b.md
-        """
-
-        def get_ancestor_ids(node_id: str | None) -> list[str]:
-            chain: list[str] = []
-            current_id = node_id
-            while current_id:
-                chain.append(current_id)
-                node = self.get(current_id)
-                current_id = node.parent_id if node else None
-            return chain
-
-        a_dir_ancestors = get_ancestor_ids(a.parent_id)
-        b_ancestors = [b.id] + get_ancestor_ids(b.parent_id)
-        a_dir_set = set(a_dir_ancestors)
-
-        lca_id: str | None = None
-        lca_index_in_b = -1
-        for idx, ancestor_id in enumerate(b_ancestors):
-            if ancestor_id in a_dir_set or (a.parent_id is None and b_ancestors[idx:] == []):
-                lca_id = ancestor_id
-                lca_index_in_b = idx
-                break
-
-        if a.parent_id is None:
-            steps_up = 0
-            lca_index_in_b = len(b_ancestors)
-        elif lca_id is None:
-            steps_up = len(a_dir_ancestors)
-            lca_index_in_b = len(b_ancestors)
-        else:
-            steps_up = 0
-            for ancestor_id in a_dir_ancestors:
-                if ancestor_id == lca_id:
-                    break
-                steps_up += 1
-
-        path_down: list[str] = []
-        for i in range(lca_index_in_b - 1, -1, -1):
-            node = self.get(b_ancestors[i])
-            if node:
-                path_down.append(node.name)
-
-        if steps_up == 0:
-            return "./" + "/".join(path_down)
-
-        parts: list[str] = [".."] * steps_up + path_down
-        return "/".join(parts)
-
-    def get_descendant_ids(self, node_id: str) -> list[str]:
-        result: list[str] = []
-        stack = [node_id]
-        while stack:
-            current_id = stack.pop()
-            for child in self.nodes:
-                if child.parent_id == current_id:
-                    result.append(child.id)
-                    stack.append(child.id)
-        return result
-
-    def add(self, node: AppAssetNode) -> AppAssetNode:
-        if self.get(node.id):
-            raise TreePathConflictError(node.id)
-        if self.has_child_named(node.parent_id, node.name):
-            raise TreePathConflictError(node.name)
-        if node.parent_id:
-            parent = self.get(node.parent_id)
-            if not parent or parent.node_type != AssetNodeType.FOLDER:
-                raise TreeParentNotFoundError(node.parent_id)
-        siblings = self.get_children(node.parent_id)
-        node.order = max((s.order for s in siblings), default=-1) + 1
-        self.nodes.append(node)
-        return node
-
-    def update(self, node_id: str, size: int, checksum: str) -> AppAssetNode:
-        node = self.get(node_id)
-        if not node or node.node_type != AssetNodeType.FILE:
-            raise TreeNodeNotFoundError(node_id)
-        node.size = size
-        node.checksum = checksum
-        return node
-
-    def rename(self, node_id: str, new_name: str) -> AppAssetNode:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-        if node.name != new_name and self.has_child_named(node.parent_id, new_name):
-            raise TreePathConflictError(new_name)
-        node.name = new_name
-        if node.node_type == AssetNodeType.FILE:
-            node.extension = new_name.rsplit(".", 1)[-1] if "." in new_name else ""
-        return node
-
-    def move(self, node_id: str, new_parent_id: str | None) -> AppAssetNode:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-        if new_parent_id:
-            parent = self.get(new_parent_id)
-            if not parent or parent.node_type != AssetNodeType.FOLDER:
-                raise TreeParentNotFoundError(new_parent_id)
-        if self.has_child_named(new_parent_id, node.name):
-            raise TreePathConflictError(node.name)
-        node.parent_id = new_parent_id
-        siblings = self.get_children(new_parent_id)
-        node.order = max((s.order for s in siblings if s.id != node_id), default=-1) + 1
-        return node
-
-    def reorder(self, node_id: str, after_node_id: str | None) -> AppAssetNode:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-
-        siblings = sorted(self.get_children(node.parent_id), key=lambda x: x.order)
-        siblings = [s for s in siblings if s.id != node_id]
-
-        if after_node_id is None:
-            insert_idx = 0
-        else:
-            after_node = self.get(after_node_id)
-            if not after_node or after_node.parent_id != node.parent_id:
-                raise TreeNodeNotFoundError(after_node_id)
-            insert_idx = next((i for i, s in enumerate(siblings) if s.id == after_node_id), -1) + 1
-
-        siblings.insert(insert_idx, node)
-        for idx, sibling in enumerate(siblings):
-            sibling.order = idx
-
-        return node
-
-    def remove(self, node_id: str) -> list[str]:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-        ids_to_remove = [node_id] + self.get_descendant_ids(node_id)
-        self.nodes = [n for n in self.nodes if n.id not in ids_to_remove]
-        return ids_to_remove
-
-    def walk_files(self) -> Generator[AppAssetNode, None, None]:
-        return (n for n in self.nodes if n.node_type == AssetNodeType.FILE)
-
-    def transform(self) -> list[AppAssetNodeView]:
-        by_parent: dict[str | None, list[AppAssetNode]] = defaultdict(list)
-        for n in self.nodes:
-            by_parent[n.parent_id].append(n)
-
-        for children in by_parent.values():
-            children.sort(key=lambda x: x.order)
-
-        paths: dict[str, str] = {}
-        tree_views: dict[str, AppAssetNodeView] = {}
-
-        def build_view(node: AppAssetNode, parent_path: str) -> None:
-            path = f"{parent_path}/{node.name}"
-            paths[node.id] = path
-            child_views: list[AppAssetNodeView] = []
-            for child in by_parent.get(node.id, []):
-                build_view(child, path)
-                child_views.append(tree_views[child.id])
-            tree_views[node.id] = AppAssetNodeView(
-                id=node.id,
-                node_type=node.node_type.value,
-                name=node.name,
-                path=path,
-                extension=node.extension,
-                size=node.size,
-                checksum=node.checksum,
-                children=child_views,
-            )
-
-        for root_node in by_parent.get(None, []):
-            build_view(root_node, "")
-
-        return [tree_views[n.id] for n in by_parent.get(None, [])]

api/core/app/entities/app_invoke_entities.py

@@ -36,9 +36,6 @@ class InvokeFrom(StrEnum):
     # this is used for plugin trigger and webhook trigger.
     TRIGGER = "trigger"
 
-    # AGENT indicates that this invocation is from an agent.
-    AGENT = "agent"
-
     # EXPLORE indicates that this invocation is from
     # the workflow (or chatflow) explore page.
     EXPLORE = "explore"

api/core/app/entities/llm_generation_entities.py

@@ -1,72 +0,0 @@
-"""
-LLM Generation Detail entities.
-
-Defines the structure for storing and transmitting LLM generation details
-including reasoning content, tool calls, and their sequence.
-"""
-
-from typing import Literal
-
-from pydantic import BaseModel, Field
-
-
-class ContentSegment(BaseModel):
-    """Represents a content segment in the generation sequence."""
-
-    type: Literal["content"] = "content"
-    start: int = Field(..., description="Start position in the text")
-    end: int = Field(..., description="End position in the text")
-
-
-class ReasoningSegment(BaseModel):
-    """Represents a reasoning segment in the generation sequence."""
-
-    type: Literal["reasoning"] = "reasoning"
-    index: int = Field(..., description="Index into reasoning_content array")
-
-
-class ToolCallSegment(BaseModel):
-    """Represents a tool call segment in the generation sequence."""
-
-    type: Literal["tool_call"] = "tool_call"
-    index: int = Field(..., description="Index into tool_calls array")
-
-
-SequenceSegment = ContentSegment | ReasoningSegment | ToolCallSegment
-
-
-class ToolCallDetail(BaseModel):
-    """Represents a tool call with its arguments and result."""
-
-    id: str = Field(default="", description="Unique identifier for the tool call")
-    name: str = Field(..., description="Name of the tool")
-    arguments: str = Field(default="", description="JSON string of tool arguments")
-    result: str = Field(default="", description="Result from the tool execution")
-    elapsed_time: float | None = Field(default=None, description="Elapsed time in seconds")
-    icon: str | dict | None = Field(default=None, description="Icon of the tool")
-    icon_dark: str | dict | None = Field(default=None, description="Dark theme icon of the tool")
-
-
-class LLMGenerationDetailData(BaseModel):
-    """
-    Domain model for LLM generation detail.
-
-    Contains the structured data for reasoning content, tool calls,
-    and their display sequence.
-    """
-
-    reasoning_content: list[str] = Field(default_factory=list, description="List of reasoning segments")
-    tool_calls: list[ToolCallDetail] = Field(default_factory=list, description="List of tool call details")
-    sequence: list[SequenceSegment] = Field(default_factory=list, description="Display order of segments")
-
-    def is_empty(self) -> bool:
-        """Check if there's any meaningful generation detail."""
-        return not self.reasoning_content and not self.tool_calls
-
-    def to_response_dict(self) -> dict:
-        """Convert to dictionary for API response."""
-        return {
-            "reasoning_content": self.reasoning_content,
-            "tool_calls": [tc.model_dump() for tc in self.tool_calls],
-            "sequence": [seg.model_dump() for seg in self.sequence],
-        }

api/core/app/entities/queue_entities.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities import AgentNodeStrategyInit, ToolCall, ToolResult
+from core.workflow.entities import AgentNodeStrategyInit
 from core.workflow.enums import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes import NodeType
 
@@ -177,17 +177,6 @@ class QueueLoopCompletedEvent(AppQueueEvent):
     error: str | None = None
 
 
-class ChunkType(StrEnum):
-    """Stream chunk type for LLM-related events."""
-
-    TEXT = "text"  # Normal text streaming
-    TOOL_CALL = "tool_call"  # Tool call arguments streaming
-    TOOL_RESULT = "tool_result"  # Tool execution result
-    THOUGHT = "thought"  # Agent thinking process (ReAct)
-    THOUGHT_START = "thought_start"  # Agent thought start
-    THOUGHT_END = "thought_end"  # Agent thought end
-
-
 class QueueTextChunkEvent(AppQueueEvent):
     """
     QueueTextChunkEvent entity
@@ -201,18 +190,6 @@ class QueueTextChunkEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_parent_node_id: str | None = None
-    """parent node id if this is an extractor node event"""
-
-    # Extended fields for Agent/Tool streaming
-    chunk_type: ChunkType = ChunkType.TEXT
-    """type of the chunk"""
-
-    # Tool streaming payloads
-    tool_call: ToolCall | None = None
-    """structured tool call info"""
-    tool_result: ToolResult | None = None
-    """structured tool result info"""
 
 
 class QueueAgentMessageEvent(AppQueueEvent):
@@ -252,8 +229,6 @@ class QueueRetrieverResourcesEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_parent_node_id: str | None = None
-    """parent node id if this is an extractor node event"""
 
 
 class QueueAnnotationReplyEvent(AppQueueEvent):
@@ -331,8 +306,6 @@ class QueueNodeStartedEvent(AppQueueEvent):
     node_run_index: int = 1  # FIXME(-LAN-): may not used
     in_iteration_id: str | None = None
     in_loop_id: str | None = None
-    in_parent_node_id: str | None = None
-    """parent node id if this is an extractor node event"""
     start_at: datetime
     agent_strategy: AgentNodeStrategyInit | None = None
 
@@ -355,8 +328,6 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_parent_node_id: str | None = None
-    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -412,8 +383,6 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_parent_node_id: str | None = None
-    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -438,8 +407,6 @@ class QueueNodeFailedEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_parent_node_id: str | None = None
-    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)

api/core/app/entities/task_entities.py

@@ -113,38 +113,6 @@ class MessageStreamResponse(StreamResponse):
     answer: str
     from_variable_selector: list[str] | None = None
 
-    # Extended fields for Agent/Tool streaming (imported at runtime to avoid circular import)
-    chunk_type: str | None = None
-    """type of the chunk: text, tool_call, tool_result, thought"""
-
-    # Tool call fields (when chunk_type == "tool_call")
-    tool_call_id: str | None = None
-    """unique identifier for this tool call"""
-    tool_name: str | None = None
-    """name of the tool being called"""
-    tool_arguments: str | None = None
-    """accumulated tool arguments JSON"""
-
-    # Tool result fields (when chunk_type == "tool_result")
-    tool_files: list[str] | None = None
-    """file IDs produced by tool"""
-    tool_error: str | None = None
-    """error message if tool failed"""
-    tool_elapsed_time: float | None = None
-    """elapsed time spent executing the tool"""
-    tool_icon: str | dict | None = None
-    """icon of the tool"""
-    tool_icon_dark: str | dict | None = None
-    """dark theme icon of the tool"""
-
-    def model_dump(self, *args, **kwargs) -> dict[str, object]:
-        kwargs.setdefault("exclude_none", True)
-        return super().model_dump(*args, **kwargs)
-
-    def model_dump_json(self, *args, **kwargs) -> str:
-        kwargs.setdefault("exclude_none", True)
-        return super().model_dump_json(*args, **kwargs)
-
 
 class MessageAudioStreamResponse(StreamResponse):
     """
@@ -294,7 +262,6 @@ class NodeStartStreamResponse(StreamResponse):
         extras: dict[str, object] = Field(default_factory=dict)
         iteration_id: str | None = None
         loop_id: str | None = None
-        parent_node_id: str | None = None
         agent_strategy: AgentNodeStrategyInit | None = None
 
     event: StreamEvent = StreamEvent.NODE_STARTED
@@ -318,7 +285,6 @@ class NodeStartStreamResponse(StreamResponse):
                 "extras": {},
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
-                "parent_node_id": self.data.parent_node_id,
             },
         }
 
@@ -354,7 +320,6 @@ class NodeFinishStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
-        parent_node_id: str | None = None
 
     event: StreamEvent = StreamEvent.NODE_FINISHED
     workflow_run_id: str
@@ -384,7 +349,6 @@ class NodeFinishStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
-                "parent_node_id": self.data.parent_node_id,
             },
         }
 
@@ -420,7 +384,6 @@ class NodeRetryStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
-        parent_node_id: str | None = None
         retry_index: int = 0
 
     event: StreamEvent = StreamEvent.NODE_RETRY
@@ -451,7 +414,6 @@ class NodeRetryStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
-                "parent_node_id": self.data.parent_node_id,
                 "retry_index": self.data.retry_index,
             },
         }
@@ -620,17 +582,6 @@ class LoopNodeCompletedStreamResponse(StreamResponse):
     data: Data
 
 
-class ChunkType(StrEnum):
-    """Stream chunk type for LLM-related events."""
-
-    TEXT = "text"  # Normal text streaming
-    TOOL_CALL = "tool_call"  # Tool call arguments streaming
-    TOOL_RESULT = "tool_result"  # Tool execution result
-    THOUGHT = "thought"  # Agent thinking process (ReAct)
-    THOUGHT_START = "thought_start"  # Agent thought start
-    THOUGHT_END = "thought_end"  # Agent thought end
-
-
 class TextChunkStreamResponse(StreamResponse):
     """
     TextChunkStreamResponse entity
@@ -644,36 +595,6 @@ class TextChunkStreamResponse(StreamResponse):
         text: str
         from_variable_selector: list[str] | None = None
 
-        # Extended fields for Agent/Tool streaming
-        chunk_type: ChunkType = ChunkType.TEXT
-        """type of the chunk"""
-
-        # Tool call fields (when chunk_type == TOOL_CALL)
-        tool_call_id: str | None = None
-        """unique identifier for this tool call"""
-        tool_name: str | None = None
-        """name of the tool being called"""
-        tool_arguments: str | None = None
-        """accumulated tool arguments JSON"""
-
-        # Tool result fields (when chunk_type == TOOL_RESULT)
-        tool_files: list[str] | None = None
-        """file IDs produced by tool"""
-        tool_error: str | None = None
-        """error message if tool failed"""
-
-        # Tool elapsed time fields (when chunk_type == TOOL_RESULT)
-        tool_elapsed_time: float | None = None
-        """elapsed time spent executing the tool"""
-
-        def model_dump(self, *args, **kwargs) -> dict[str, object]:
-            kwargs.setdefault("exclude_none", True)
-            return super().model_dump(*args, **kwargs)
-
-        def model_dump_json(self, *args, **kwargs) -> str:
-            kwargs.setdefault("exclude_none", True)
-            return super().model_dump_json(*args, **kwargs)
-
     event: StreamEvent = StreamEvent.TEXT_CHUNK
     data: Data
 
@@ -822,7 +743,7 @@ class AgentLogStreamResponse(StreamResponse):
         """
 
         node_execution_id: str
-        message_id: str
+        id: str
         label: str
         parent_id: str | None = None
         error: str | None = None

api/core/app/layers/sandbox_layer.py

@@ -1,22 +0,0 @@
-import logging
-
-from core.sandbox import Sandbox
-from core.workflow.graph_engine.layers.base import GraphEngineLayer
-from core.workflow.graph_events.base import GraphEngineEvent
-
-logger = logging.getLogger(__name__)
-
-
-class SandboxLayer(GraphEngineLayer):
-    def __init__(self, sandbox: Sandbox) -> None:
-        super().__init__()
-        self._sandbox = sandbox
-
-    def on_graph_start(self) -> None:
-        pass
-
-    def on_event(self, event: GraphEngineEvent) -> None:
-        pass
-
-    def on_graph_end(self, error: Exception | None) -> None:
-        self._sandbox.release()

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -1,5 +1,4 @@
 import logging
-import re
 import time
 from collections.abc import Generator
 from threading import Thread
@@ -59,7 +58,7 @@ from core.prompt.utils.prompt_template_parser import PromptTemplateParser
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models.model import AppMode, Conversation, LLMGenerationDetail, Message, MessageAgentThought
+from models.model import AppMode, Conversation, Message, MessageAgentThought
 
 logger = logging.getLogger(__name__)
 
@@ -69,8 +68,6 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
     EasyUIBasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
-    _THINK_PATTERN = re.compile(r"<think[^>]*>(.*?)</think>", re.IGNORECASE | re.DOTALL)
-
     _task_state: EasyUITaskState
     _application_generate_entity: Union[ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity]
 
@@ -412,136 +409,11 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
                 )
             )
 
-        # Save LLM generation detail if there's reasoning_content
-        self._save_generation_detail(session=session, message=message, llm_result=llm_result)
-
         message_was_created.send(
             message,
             application_generate_entity=self._application_generate_entity,
         )
 
-    def _save_generation_detail(self, *, session: Session, message: Message, llm_result: LLMResult) -> None:
-        """
-        Save LLM generation detail for Completion/Chat/Agent-Chat applications.
-        For Agent-Chat, also merges MessageAgentThought records.
-        """
-        import json
-
-        reasoning_list: list[str] = []
-        tool_calls_list: list[dict] = []
-        sequence: list[dict] = []
-        answer = message.answer or ""
-
-        # Check if this is Agent-Chat mode by looking for agent thoughts
-        agent_thoughts = (
-            session.query(MessageAgentThought)
-            .filter_by(message_id=message.id)
-            .order_by(MessageAgentThought.position.asc())
-            .all()
-        )
-
-        if agent_thoughts:
-            # Agent-Chat mode: merge MessageAgentThought records
-            content_pos = 0
-            cleaned_answer_parts: list[str] = []
-            for thought in agent_thoughts:
-                # Add thought/reasoning
-                if thought.thought:
-                    reasoning_text = thought.thought
-                    if "<think" in reasoning_text.lower():
-                        clean_text, extracted_reasoning = self._split_reasoning_from_answer(reasoning_text)
-                        if extracted_reasoning:
-                            reasoning_text = extracted_reasoning
-                            thought.thought = clean_text or extracted_reasoning
-                    reasoning_list.append(reasoning_text)
-                    sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
-
-                # Add tool calls
-                if thought.tool:
-                    tool_calls_list.append(
-                        {
-                            "name": thought.tool,
-                            "arguments": thought.tool_input or "",
-                            "result": thought.observation or "",
-                        }
-                    )
-                    sequence.append({"type": "tool_call", "index": len(tool_calls_list) - 1})
-
-                # Add answer content if present
-                if thought.answer:
-                    content_text = thought.answer
-                    if "<think" in content_text.lower():
-                        clean_answer, extracted_reasoning = self._split_reasoning_from_answer(content_text)
-                        if extracted_reasoning:
-                            reasoning_list.append(extracted_reasoning)
-                            sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
-                        content_text = clean_answer
-                        thought.answer = clean_answer or content_text
-
-                    if content_text:
-                        start = content_pos
-                        end = content_pos + len(content_text)
-                        sequence.append({"type": "content", "start": start, "end": end})
-                        content_pos = end
-                        cleaned_answer_parts.append(content_text)
-
-            if cleaned_answer_parts:
-                merged_answer = "".join(cleaned_answer_parts)
-                message.answer = merged_answer
-                llm_result.message.content = merged_answer
-        else:
-            # Completion/Chat mode: use reasoning_content from llm_result
-            reasoning_content = llm_result.reasoning_content
-            if not reasoning_content and answer:
-                # Extract reasoning from <think> blocks and clean the final answer
-                clean_answer, reasoning_content = self._split_reasoning_from_answer(answer)
-                if reasoning_content:
-                    answer = clean_answer
-                    llm_result.message.content = clean_answer
-                    llm_result.reasoning_content = reasoning_content
-                    message.answer = clean_answer
-            if reasoning_content:
-                reasoning_list = [reasoning_content]
-                # Content comes first, then reasoning
-                if answer:
-                    sequence.append({"type": "content", "start": 0, "end": len(answer)})
-                sequence.append({"type": "reasoning", "index": 0})
-
-        # Only save if there's meaningful generation detail
-        if not reasoning_list and not tool_calls_list:
-            return
-
-        # Check if generation detail already exists
-        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
-
-        if existing:
-            existing.reasoning_content = json.dumps(reasoning_list) if reasoning_list else None
-            existing.tool_calls = json.dumps(tool_calls_list) if tool_calls_list 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_list) if reasoning_list else None,
-                tool_calls=json.dumps(tool_calls_list) if tool_calls_list else None,
-                sequence=json.dumps(sequence) if sequence else None,
-            )
-            session.add(generation_detail)
-
-    @classmethod
-    def _split_reasoning_from_answer(cls, text: str) -> tuple[str, str]:
-        """
-        Extract reasoning segments from <think> blocks and return (clean_text, reasoning).
-        """
-        matches = cls._THINK_PATTERN.findall(text)
-        reasoning_content = "\n".join(match.strip() for match in matches) if matches else ""
-
-        clean_text = cls._THINK_PATTERN.sub("", text)
-        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
-
-        return clean_text, reasoning_content or ""
-
     def _handle_stop(self, event: QueueStopEvent):
         """
         Handle stop.

api/core/app/task_pipeline/message_cycle_manager.py

@@ -232,31 +232,15 @@ class MessageCycleManager:
         answer: str,
         message_id: str,
         from_variable_selector: list[str] | None = None,
-        chunk_type: str | None = None,
-        tool_call_id: str | None = None,
-        tool_name: str | None = None,
-        tool_arguments: str | None = None,
-        tool_files: list[str] | None = None,
-        tool_error: str | None = None,
-        tool_elapsed_time: float | None = None,
-        tool_icon: str | dict | None = None,
-        tool_icon_dark: str | dict | None = None,
         event_type: StreamEvent | None = None,
     ) -> MessageStreamResponse:
         """
         Message to stream response.
         :param answer: answer
         :param message_id: message id
-        :param from_variable_selector: from variable selector
-        :param chunk_type: type of the chunk (text, function_call, tool_result, thought)
-        :param tool_call_id: unique identifier for this tool call
-        :param tool_name: name of the tool being called
-        :param tool_arguments: accumulated tool arguments JSON
-        :param tool_files: file IDs produced by tool
-        :param tool_error: error message if tool failed
         :return:
         """
-        response = MessageStreamResponse(
+        return MessageStreamResponse(
             task_id=self._application_generate_entity.task_id,
             id=message_id,
             answer=answer,
@@ -264,35 +248,6 @@ class MessageCycleManager:
             event=event_type or StreamEvent.MESSAGE,
         )
 
-        if chunk_type:
-            response = response.model_copy(update={"chunk_type": chunk_type})
-
-        if chunk_type == "tool_call":
-            response = response.model_copy(
-                update={
-                    "tool_call_id": tool_call_id,
-                    "tool_name": tool_name,
-                    "tool_arguments": tool_arguments,
-                    "tool_icon": tool_icon,
-                    "tool_icon_dark": tool_icon_dark,
-                }
-            )
-        elif chunk_type == "tool_result":
-            response = response.model_copy(
-                update={
-                    "tool_call_id": tool_call_id,
-                    "tool_name": tool_name,
-                    "tool_arguments": tool_arguments,
-                    "tool_files": tool_files,
-                    "tool_error": tool_error,
-                    "tool_elapsed_time": tool_elapsed_time,
-                    "tool_icon": tool_icon,
-                    "tool_icon_dark": tool_icon_dark,
-                }
-            )
-
-        return response
-
     def message_replace_to_stream_response(self, answer: str, reason: str = "") -> MessageReplaceStreamResponse:
         """
         Message replace to stream response.

api/core/app/workflow/__init__.py

@@ -0,0 +1,3 @@
+from .node_factory import DifyNodeFactory
+
+__all__ = ["DifyNodeFactory"]

api/core/app/workflow/node_factory.py

@@ -15,6 +15,7 @@ from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.code.code_node import CodeNode
 from core.workflow.nodes.code.limits import CodeNodeLimits
 from core.workflow.nodes.http_request.node import HttpRequestNode
+from core.workflow.nodes.node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
 from core.workflow.nodes.protocols import FileManagerProtocol, HttpClientProtocol
 from core.workflow.nodes.template_transform.template_renderer import (
     CodeExecutorJinja2TemplateRenderer,
@@ -23,8 +24,6 @@ from core.workflow.nodes.template_transform.template_renderer import (
 from core.workflow.nodes.template_transform.template_transform_node import TemplateTransformNode
 from libs.typing import is_str, is_str_dict
 
-from .node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
-
 if TYPE_CHECKING:
     from core.workflow.entities import GraphInitParams
     from core.workflow.runtime import GraphRuntimeState

api/core/app_assets/__init__.py

@@ -1,23 +0,0 @@
-from .constants import AppAssetsAttrs
-from .entities import (
-    AssetItem,
-    FileAsset,
-    SkillAsset,
-)
-from .packager import AssetPackager, ZipPackager
-from .parser import AssetItemParser, AssetParser, FileAssetParser, SkillAssetParser
-from .paths import AssetPaths
-
-__all__ = [
-    "AppAssetsAttrs",
-    "AssetItem",
-    "AssetItemParser",
-    "AssetPackager",
-    "AssetParser",
-    "AssetPaths",
-    "FileAsset",
-    "FileAssetParser",
-    "SkillAsset",
-    "SkillAssetParser",
-    "ZipPackager",
-]

api/core/app_assets/builder/__init__.py

@@ -1,12 +0,0 @@
-from .base import AssetBuilder, BuildContext
-from .file_builder import FileBuilder
-from .pipeline import AssetBuildPipeline
-from .skill_builder import SkillBuilder
-
-__all__ = [
-    "AssetBuildPipeline",
-    "AssetBuilder",
-    "BuildContext",
-    "FileBuilder",
-    "SkillBuilder",
-]

api/core/app_assets/builder/base.py

@@ -1,20 +0,0 @@
-from dataclasses import dataclass
-from typing import Protocol
-
-from core.app.entities.app_asset_entities import AppAssetFileTree, AppAssetNode
-from core.app_assets.entities import AssetItem
-
-
-@dataclass
-class BuildContext:
-    tenant_id: str
-    app_id: str
-    build_id: str
-
-
-class AssetBuilder(Protocol):
-    def accept(self, node: AppAssetNode) -> bool: ...
-
-    def collect(self, node: AppAssetNode, path: str, ctx: BuildContext) -> None: ...
-
-    def build(self, tree: AppAssetFileTree, ctx: BuildContext) -> list[AssetItem]: ...

api/core/app_assets/builder/file_builder.py

@@ -1,30 +0,0 @@
-from core.app.entities.app_asset_entities import AppAssetFileTree, AppAssetNode
-from core.app_assets.entities import AssetItem, FileAsset
-from core.app_assets.paths import AssetPaths
-
-from .base import BuildContext
-
-
-class FileBuilder:
-    _nodes: list[tuple[AppAssetNode, str]]
-
-    def __init__(self) -> None:
-        self._nodes = []
-
-    def accept(self, node: AppAssetNode) -> bool:
-        return True
-
-    def collect(self, node: AppAssetNode, path: str, ctx: BuildContext) -> None:
-        self._nodes.append((node, path))
-
-    def build(self, tree: AppAssetFileTree, ctx: BuildContext) -> list[AssetItem]:
-        return [
-            FileAsset(
-                asset_id=node.id,
-                path=path,
-                file_name=node.name,
-                extension=node.extension or "",
-                storage_key=AssetPaths.draft_file(ctx.tenant_id, ctx.app_id, node.id),
-            )
-            for node, path in self._nodes
-        ]

api/core/app_assets/builder/pipeline.py

@@ -1,29 +0,0 @@
-from core.app.entities.app_asset_entities import AppAssetFileTree
-from core.app_assets.builder.file_builder import FileBuilder
-from core.app_assets.builder.skill_builder import SkillBuilder
-from core.app_assets.entities import AssetItem
-
-from .base import AssetBuilder, BuildContext
-
-
-class AssetBuildPipeline:
-    _builders: list[AssetBuilder]
-
-    def __init__(self, builders: list[AssetBuilder] | None = None) -> None:
-        self._builders = builders or [SkillBuilder(), FileBuilder()]
-
-    def build_all(self, tree: AppAssetFileTree, ctx: BuildContext) -> list[AssetItem]:
-        # 1. Distribute: each node goes to first accepting builder
-        for node in tree.walk_files():
-            path = tree.get_path(node.id)
-            for builder in self._builders:
-                if builder.accept(node):
-                    builder.collect(node, path, ctx)
-                    break
-
-        # 2. Each builder builds its collected nodes
-        results: list[AssetItem] = []
-        for builder in self._builders:
-            results.extend(builder.build(tree, ctx))
-
-        return results

api/core/app_assets/builder/skill_builder.py

@@ -1,113 +0,0 @@
-import json
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import dataclass
-
-from core.app.entities.app_asset_entities import AppAssetFileTree, AppAssetNode
-from core.app_assets.entities import AssetItem, FileAsset
-from core.app_assets.paths import AssetPaths
-from core.skill.entities.skill_document import SkillDocument
-from core.skill.skill_compiler import SkillCompiler
-from core.skill.skill_manager import SkillManager
-from extensions.ext_storage import storage
-
-from .base import BuildContext
-
-
-@dataclass
-class _LoadedSkill:
-    node: AppAssetNode
-    path: str
-    content: str
-    metadata: dict
-
-
-@dataclass
-class _CompiledSkill:
-    node: AppAssetNode
-    path: str
-    resolved_key: str
-    content_bytes: bytes
-
-
-class SkillBuilder:
-    _nodes: list[tuple[AppAssetNode, str]]
-    _max_workers: int
-
-    def __init__(self, max_workers: int = 8) -> None:
-        self._nodes = []
-        self._max_workers = max_workers
-
-    def accept(self, node: AppAssetNode) -> bool:
-        return node.extension == "md"
-
-    def collect(self, node: AppAssetNode, path: str, ctx: BuildContext) -> None:
-        self._nodes.append((node, path))
-
-    def build(self, tree: AppAssetFileTree, ctx: BuildContext) -> list[AssetItem]:
-        if not self._nodes:
-            return []
-
-        # 1. Load all skills (parallel IO)
-        loaded = self._load_all(ctx)
-
-        # 2. Compile all skills (CPU-bound, single thread)
-        documents = [SkillDocument(skill_id=s.node.id, content=s.content, metadata=s.metadata) for s in loaded]
-        artifact_set = SkillCompiler().compile_all(documents, tree, ctx.build_id)
-
-        SkillManager.save_bundle(ctx.tenant_id, ctx.app_id, ctx.build_id, artifact_set)
-
-        # 4. Prepare compiled skills for upload
-        to_upload: list[_CompiledSkill] = []
-        for skill in loaded:
-            artifact = artifact_set.get(skill.node.id)
-            if artifact is None:
-                continue
-            resolved_key = AssetPaths.build_resolved_file(ctx.tenant_id, ctx.app_id, ctx.build_id, skill.node.id)
-            to_upload.append(
-                _CompiledSkill(
-                    node=skill.node,
-                    path=skill.path,
-                    resolved_key=resolved_key,
-                    content_bytes=artifact.content.encode("utf-8"),
-                )
-            )
-
-        # 5. Upload all compiled skills (parallel IO)
-        self._upload_all(to_upload)
-
-        # 6. Return FileAssets
-        return [
-            FileAsset(
-                asset_id=s.node.id,
-                path=s.path,
-                file_name=s.node.name,
-                extension=s.node.extension or "",
-                storage_key=s.resolved_key,
-            )
-            for s in to_upload
-        ]
-
-    def _load_all(self, ctx: BuildContext) -> list[_LoadedSkill]:
-        def load_one(node: AppAssetNode, path: str) -> _LoadedSkill:
-            draft_key = AssetPaths.draft_file(ctx.tenant_id, ctx.app_id, node.id)
-            try:
-                data = json.loads(storage.load_once(draft_key))
-                content = data.get("content", "") if isinstance(data, dict) else ""
-                metadata = data.get("metadata", {}) if isinstance(data, dict) else {}
-            except Exception:
-                content = ""
-                metadata = {}
-            return _LoadedSkill(node=node, path=path, content=content, metadata=metadata)
-
-        with ThreadPoolExecutor(max_workers=self._max_workers) as executor:
-            futures = [executor.submit(load_one, node, path) for node, path in self._nodes]
-            return [f.result() for f in futures]
-
-    def _upload_all(self, skills: list[_CompiledSkill]) -> None:
-        def upload_one(skill: _CompiledSkill) -> None:
-            storage.save(skill.resolved_key, skill.content_bytes)
-
-        with ThreadPoolExecutor(max_workers=self._max_workers) as executor:
-            futures = [executor.submit(upload_one, skill) for skill in skills]
-            for f in futures:
-                f.result()

api/core/app_assets/constants.py

@@ -1,7 +0,0 @@
-from core.app.entities.app_asset_entities import AppAssetFileTree
-from libs.attr_map import AttrKey
-
-
-class AppAssetsAttrs:
-    # Skill artifact set
-    FILE_TREE = AttrKey("file_tree", AppAssetFileTree)