chore: changes

.branchlet.json

@@ -1,37 +0,0 @@
-{
-  "worktreeCopyPatterns": [
-    ".env*",
-    ".vscode/**",
-    ".auth/**",
-    ".claude/**",
-    "autogpt_platform/.env*",
-    "autogpt_platform/backend/.env*",
-    "autogpt_platform/frontend/.env*",
-    "autogpt_platform/frontend/.auth/**",
-    "autogpt_platform/db/docker/.env*"
-  ],
-  "worktreeCopyIgnores": [
-    "**/node_modules/**",
-    "**/dist/**",
-    "**/.git/**",
-    "**/Thumbs.db",
-    "**/.DS_Store",
-    "**/.next/**",
-    "**/__pycache__/**",
-    "**/.ruff_cache/**",
-    "**/.pytest_cache/**",
-    "**/*.pyc",
-    "**/playwright-report/**",
-    "**/logs/**",
-    "**/site/**"
-  ],
-  "worktreePathTemplate": "$BASE_PATH.worktree",
-  "postCreateCmd": [
-    "cd autogpt_platform/autogpt_libs && poetry install",
-    "cd autogpt_platform/backend && poetry install && poetry run prisma generate",
-    "cd autogpt_platform/frontend && pnpm install",
-    "cd docs && pip install -r requirements.txt"
-  ],
-  "terminalCommand": "code .",
-  "deleteBranchWithWorktree": false
-}

.claude/skills/vercel-react-best-practices/SKILL.md

@@ -1,125 +0,0 @@
----
-name: vercel-react-best-practices
-description: React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
-license: MIT
-metadata:
-  author: vercel
-  version: "1.0.0"
----
-
-# Vercel React Best Practices
-
-Comprehensive performance optimization guide for React and Next.js applications, maintained by Vercel. Contains 45 rules across 8 categories, prioritized by impact to guide automated refactoring and code generation.
-
-## When to Apply
-
-Reference these guidelines when:
-- Writing new React components or Next.js pages
-- Implementing data fetching (client or server-side)
-- Reviewing code for performance issues
-- Refactoring existing React/Next.js code
-- Optimizing bundle size or load times
-
-## Rule Categories by Priority
-
-| Priority | Category | Impact | Prefix |
-|----------|----------|--------|--------|
-| 1 | Eliminating Waterfalls | CRITICAL | `async-` |
-| 2 | Bundle Size Optimization | CRITICAL | `bundle-` |
-| 3 | Server-Side Performance | HIGH | `server-` |
-| 4 | Client-Side Data Fetching | MEDIUM-HIGH | `client-` |
-| 5 | Re-render Optimization | MEDIUM | `rerender-` |
-| 6 | Rendering Performance | MEDIUM | `rendering-` |
-| 7 | JavaScript Performance | LOW-MEDIUM | `js-` |
-| 8 | Advanced Patterns | LOW | `advanced-` |
-
-## Quick Reference
-
-### 1. Eliminating Waterfalls (CRITICAL)
-
-- `async-defer-await` - Move await into branches where actually used
-- `async-parallel` - Use Promise.all() for independent operations
-- `async-dependencies` - Use better-all for partial dependencies
-- `async-api-routes` - Start promises early, await late in API routes
-- `async-suspense-boundaries` - Use Suspense to stream content
-
-### 2. Bundle Size Optimization (CRITICAL)
-
-- `bundle-barrel-imports` - Import directly, avoid barrel files
-- `bundle-dynamic-imports` - Use next/dynamic for heavy components
-- `bundle-defer-third-party` - Load analytics/logging after hydration
-- `bundle-conditional` - Load modules only when feature is activated
-- `bundle-preload` - Preload on hover/focus for perceived speed
-
-### 3. Server-Side Performance (HIGH)
-
-- `server-cache-react` - Use React.cache() for per-request deduplication
-- `server-cache-lru` - Use LRU cache for cross-request caching
-- `server-serialization` - Minimize data passed to client components
-- `server-parallel-fetching` - Restructure components to parallelize fetches
-- `server-after-nonblocking` - Use after() for non-blocking operations
-
-### 4. Client-Side Data Fetching (MEDIUM-HIGH)
-
-- `client-swr-dedup` - Use SWR for automatic request deduplication
-- `client-event-listeners` - Deduplicate global event listeners
-
-### 5. Re-render Optimization (MEDIUM)
-
-- `rerender-defer-reads` - Don't subscribe to state only used in callbacks
-- `rerender-memo` - Extract expensive work into memoized components
-- `rerender-dependencies` - Use primitive dependencies in effects
-- `rerender-derived-state` - Subscribe to derived booleans, not raw values
-- `rerender-functional-setstate` - Use functional setState for stable callbacks
-- `rerender-lazy-state-init` - Pass function to useState for expensive values
-- `rerender-transitions` - Use startTransition for non-urgent updates
-
-### 6. Rendering Performance (MEDIUM)
-
-- `rendering-animate-svg-wrapper` - Animate div wrapper, not SVG element
-- `rendering-content-visibility` - Use content-visibility for long lists
-- `rendering-hoist-jsx` - Extract static JSX outside components
-- `rendering-svg-precision` - Reduce SVG coordinate precision
-- `rendering-hydration-no-flicker` - Use inline script for client-only data
-- `rendering-activity` - Use Activity component for show/hide
-- `rendering-conditional-render` - Use ternary, not && for conditionals
-
-### 7. JavaScript Performance (LOW-MEDIUM)
-
-- `js-batch-dom-css` - Group CSS changes via classes or cssText
-- `js-index-maps` - Build Map for repeated lookups
-- `js-cache-property-access` - Cache object properties in loops
-- `js-cache-function-results` - Cache function results in module-level Map
-- `js-cache-storage` - Cache localStorage/sessionStorage reads
-- `js-combine-iterations` - Combine multiple filter/map into one loop
-- `js-length-check-first` - Check array length before expensive comparison
-- `js-early-exit` - Return early from functions
-- `js-hoist-regexp` - Hoist RegExp creation outside loops
-- `js-min-max-loop` - Use loop for min/max instead of sort
-- `js-set-map-lookups` - Use Set/Map for O(1) lookups
-- `js-tosorted-immutable` - Use toSorted() for immutability
-
-### 8. Advanced Patterns (LOW)
-
-- `advanced-event-handler-refs` - Store event handlers in refs
-- `advanced-use-latest` - useLatest for stable callback refs
-
-## How to Use
-
-Read individual rule files for detailed explanations and code examples:
-
-```
-rules/async-parallel.md
-rules/bundle-barrel-imports.md
-rules/_sections.md
-```
-
-Each rule file contains:
-- Brief explanation of why it matters
-- Incorrect code example with explanation
-- Correct code example with explanation
-- Additional context and references
-
-## Full Compiled Document
-
-For the complete guide with all rules expanded: `AGENTS.md`

.claude/skills/vercel-react-best-practices/rules/advanced-event-handler-refs.md

@@ -1,55 +0,0 @@
----
-title: Store Event Handlers in Refs
-impact: LOW
-impactDescription: stable subscriptions
-tags: advanced, hooks, refs, event-handlers, optimization
----
-
-## Store Event Handlers in Refs
-
-Store callbacks in refs when used in effects that shouldn't re-subscribe on callback changes.
-
-**Incorrect (re-subscribes on every render):**
-
-```tsx
-function useWindowEvent(event: string, handler: () => void) {
-  useEffect(() => {
-    window.addEventListener(event, handler)
-    return () => window.removeEventListener(event, handler)
-  }, [event, handler])
-}
-```
-
-**Correct (stable subscription):**
-
-```tsx
-function useWindowEvent(event: string, handler: () => void) {
-  const handlerRef = useRef(handler)
-  useEffect(() => {
-    handlerRef.current = handler
-  }, [handler])
-
-  useEffect(() => {
-    const listener = () => handlerRef.current()
-    window.addEventListener(event, listener)
-    return () => window.removeEventListener(event, listener)
-  }, [event])
-}
-```
-
-**Alternative: use `useEffectEvent` if you're on latest React:**
-
-```tsx
-import { useEffectEvent } from 'react'
-
-function useWindowEvent(event: string, handler: () => void) {
-  const onEvent = useEffectEvent(handler)
-
-  useEffect(() => {
-    window.addEventListener(event, onEvent)
-    return () => window.removeEventListener(event, onEvent)
-  }, [event])
-}
-```
-
-`useEffectEvent` provides a cleaner API for the same pattern: it creates a stable function reference that always calls the latest version of the handler.

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

@@ -1,49 +0,0 @@
----
-title: useLatest for Stable Callback Refs
-impact: LOW
-impactDescription: prevents effect re-runs
-tags: advanced, hooks, useLatest, refs, optimization
----
-
-## 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)
-  useEffect(() => {
-    ref.current = value
-  }, [value])
-  return ref
-}
-```
-
-**Incorrect (effect re-runs on every callback change):**
-
-```tsx
-function SearchInput({ onSearch }: { onSearch: (q: string) => void }) {
-  const [query, setQuery] = useState('')
-
-  useEffect(() => {
-    const timeout = setTimeout(() => onSearch(query), 300)
-    return () => clearTimeout(timeout)
-  }, [query, onSearch])
-}
-```
-
-**Correct (stable effect, fresh callback):**
-
-```tsx
-function SearchInput({ onSearch }: { onSearch: (q: string) => void }) {
-  const [query, setQuery] = useState('')
-  const onSearchRef = useLatest(onSearch)
-
-  useEffect(() => {
-    const timeout = setTimeout(() => onSearchRef.current(query), 300)
-    return () => clearTimeout(timeout)
-  }, [query])
-}
-```

.claude/skills/vercel-react-best-practices/rules/async-api-routes.md

@@ -1,38 +0,0 @@
----
-title: Prevent Waterfall Chains in API Routes
-impact: CRITICAL
-impactDescription: 2-10× improvement
-tags: api-routes, server-actions, waterfalls, parallelization
----
-
-## Prevent Waterfall Chains in API Routes
-
-In API routes and Server Actions, start independent operations immediately, even if you don't await them yet.
-
-**Incorrect (config waits for auth, data waits for both):**
-
-```typescript
-export async function GET(request: Request) {
-  const session = await auth()
-  const config = await fetchConfig()
-  const data = await fetchData(session.user.id)
-  return Response.json({ data, config })
-}
-```
-
-**Correct (auth and config start immediately):**
-
-```typescript
-export async function GET(request: Request) {
-  const sessionPromise = auth()
-  const configPromise = fetchConfig()
-  const session = await sessionPromise
-  const [config, data] = await Promise.all([
-    configPromise,
-    fetchData(session.user.id)
-  ])
-  return Response.json({ data, config })
-}
-```
-
-For operations with more complex dependency chains, use `better-all` to automatically maximize parallelism (see Dependency-Based Parallelization).

.claude/skills/vercel-react-best-practices/rules/async-defer-await.md

@@ -1,80 +0,0 @@
----
-title: Defer Await Until Needed
-impact: HIGH
-impactDescription: avoids blocking unused code paths
-tags: async, await, conditional, optimization
----
-
-## Defer Await Until Needed
-
-Move `await` operations into the branches where they're actually used to avoid blocking code paths that don't need them.
-
-**Incorrect (blocks both branches):**
-
-```typescript
-async function handleRequest(userId: string, skipProcessing: boolean) {
-  const userData = await fetchUserData(userId)
-  
-  if (skipProcessing) {
-    // Returns immediately but still waited for userData
-    return { skipped: true }
-  }
-  
-  // Only this branch uses userData
-  return processUserData(userData)
-}
-```
-
-**Correct (only blocks when needed):**
-
-```typescript
-async function handleRequest(userId: string, skipProcessing: boolean) {
-  if (skipProcessing) {
-    // Returns immediately without waiting
-    return { skipped: true }
-  }
-  
-  // Fetch only when needed
-  const userData = await fetchUserData(userId)
-  return processUserData(userData)
-}
-```
-
-**Another example (early return optimization):**
-
-```typescript
-// Incorrect: always fetches permissions
-async function updateResource(resourceId: string, userId: string) {
-  const permissions = await fetchPermissions(userId)
-  const resource = await getResource(resourceId)
-  
-  if (!resource) {
-    return { error: 'Not found' }
-  }
-  
-  if (!permissions.canEdit) {
-    return { error: 'Forbidden' }
-  }
-  
-  return await updateResourceData(resource, permissions)
-}
-
-// Correct: fetches only when needed
-async function updateResource(resourceId: string, userId: string) {
-  const resource = await getResource(resourceId)
-  
-  if (!resource) {
-    return { error: 'Not found' }
-  }
-  
-  const permissions = await fetchPermissions(userId)
-  
-  if (!permissions.canEdit) {
-    return { error: 'Forbidden' }
-  }
-  
-  return await updateResourceData(resource, permissions)
-}
-```
-
-This optimization is especially valuable when the skipped branch is frequently taken, or when the deferred operation is expensive.

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

@@ -1,36 +0,0 @@
----
-title: Dependency-Based Parallelization
-impact: CRITICAL
-impactDescription: 2-10× improvement
-tags: async, parallelization, dependencies, better-all
----
-
-## Dependency-Based Parallelization
-
-For operations with partial dependencies, use `better-all` to maximize parallelism. It automatically starts each task at the earliest possible moment.
-
-**Incorrect (profile waits for config unnecessarily):**
-
-```typescript
-const [user, config] = await Promise.all([
-  fetchUser(),
-  fetchConfig()
-])
-const profile = await fetchProfile(user.id)
-```
-
-**Correct (config and profile run in parallel):**
-
-```typescript
-import { all } from 'better-all'
-
-const { user, config, profile } = await all({
-  async user() { return fetchUser() },
-  async config() { return fetchConfig() },
-  async profile() {
-    return fetchProfile((await this.$.user).id)
-  }
-})
-```
-
-Reference: [https://github.com/shuding/better-all](https://github.com/shuding/better-all)

.claude/skills/vercel-react-best-practices/rules/async-parallel.md

@@ -1,28 +0,0 @@
----
-title: Promise.all() for Independent Operations
-impact: CRITICAL
-impactDescription: 2-10× improvement
-tags: async, parallelization, promises, waterfalls
----
-
-## Promise.all() for Independent Operations
-
-When async operations have no interdependencies, execute them concurrently using `Promise.all()`.
-
-**Incorrect (sequential execution, 3 round trips):**
-
-```typescript
-const user = await fetchUser()
-const posts = await fetchPosts()
-const comments = await fetchComments()
-```
-
-**Correct (parallel execution, 1 round trip):**
-
-```typescript
-const [user, posts, comments] = await Promise.all([
-  fetchUser(),
-  fetchPosts(),
-  fetchComments()
-])
-```

.claude/skills/vercel-react-best-practices/rules/async-suspense-boundaries.md

@@ -1,99 +0,0 @@
----
-title: Strategic Suspense Boundaries
-impact: HIGH
-impactDescription: faster initial paint
-tags: async, suspense, streaming, layout-shift
----
-
-## Strategic Suspense Boundaries
-
-Instead of awaiting data in async components before returning JSX, use Suspense boundaries to show the wrapper UI faster while data loads.
-
-**Incorrect (wrapper blocked by data fetching):**
-
-```tsx
-async function Page() {
-  const data = await fetchData() // Blocks entire page
-  
-  return (
-    <div>
-      <div>Sidebar</div>
-      <div>Header</div>
-      <div>
-        <DataDisplay data={data} />
-      </div>
-      <div>Footer</div>
-    </div>
-  )
-}
-```
-
-The entire layout waits for data even though only the middle section needs it.
-
-**Correct (wrapper shows immediately, data streams in):**
-
-```tsx
-function Page() {
-  return (
-    <div>
-      <div>Sidebar</div>
-      <div>Header</div>
-      <div>
-        <Suspense fallback={<Skeleton />}>
-          <DataDisplay />
-        </Suspense>
-      </div>
-      <div>Footer</div>
-    </div>
-  )
-}
-
-async function DataDisplay() {
-  const data = await fetchData() // Only blocks this component
-  return <div>{data.content}</div>
-}
-```
-
-Sidebar, Header, and Footer render immediately. Only DataDisplay waits for data.
-
-**Alternative (share promise across components):**
-
-```tsx
-function Page() {
-  // Start fetch immediately, but don't await
-  const dataPromise = fetchData()
-  
-  return (
-    <div>
-      <div>Sidebar</div>
-      <div>Header</div>
-      <Suspense fallback={<Skeleton />}>
-        <DataDisplay dataPromise={dataPromise} />
-        <DataSummary dataPromise={dataPromise} />
-      </Suspense>
-      <div>Footer</div>
-    </div>
-  )
-}
-
-function DataDisplay({ dataPromise }: { dataPromise: Promise<Data> }) {
-  const data = use(dataPromise) // Unwraps the promise
-  return <div>{data.content}</div>
-}
-
-function DataSummary({ dataPromise }: { dataPromise: Promise<Data> }) {
-  const data = use(dataPromise) // Reuses the same promise
-  return <div>{data.summary}</div>
-}
-```
-
-Both components share the same promise, so only one fetch occurs. Layout renders immediately while both components wait together.
-
-**When NOT to use this pattern:**
-
-- Critical data needed for layout decisions (affects positioning)
-- SEO-critical content above the fold
-- Small, fast queries where suspense overhead isn't worth it
-- When you want to avoid layout shift (loading → content jump)
-
-**Trade-off:** Faster initial paint vs potential layout shift. Choose based on your UX priorities.

.claude/skills/vercel-react-best-practices/rules/bundle-barrel-imports.md

@@ -1,59 +0,0 @@
----
-title: Avoid Barrel File Imports
-impact: CRITICAL
-impactDescription: 200-800ms import cost, slow builds
-tags: bundle, imports, tree-shaking, barrel-files, performance
----
-
-## Avoid Barrel File Imports
-
-Import directly from source files instead of barrel files to avoid loading thousands of unused modules. **Barrel files** are entry points that re-export multiple modules (e.g., `index.js` that does `export * from './module'`).
-
-Popular icon and component libraries can have **up to 10,000 re-exports** in their entry file. For many React packages, **it takes 200-800ms just to import them**, affecting both development speed and production cold starts.
-
-**Why tree-shaking doesn't help:** When a library is marked as external (not bundled), the bundler can't optimize it. If you bundle it to enable tree-shaking, builds become substantially slower analyzing the entire module graph.
-
-**Incorrect (imports entire library):**
-
-```tsx
-import { Check, X, Menu } from 'lucide-react'
-// Loads 1,583 modules, takes ~2.8s extra in dev
-// Runtime cost: 200-800ms on every cold start
-
-import { Button, TextField } from '@mui/material'
-// Loads 2,225 modules, takes ~4.2s extra in dev
-```
-
-**Correct (imports only what you need):**
-
-```tsx
-import Check from 'lucide-react/dist/esm/icons/check'
-import X from 'lucide-react/dist/esm/icons/x'
-import Menu from 'lucide-react/dist/esm/icons/menu'
-// Loads only 3 modules (~2KB vs ~1MB)
-
-import Button from '@mui/material/Button'
-import TextField from '@mui/material/TextField'
-// Loads only what you use
-```
-
-**Alternative (Next.js 13.5+):**
-
-```js
-// next.config.js - use optimizePackageImports
-module.exports = {
-  experimental: {
-    optimizePackageImports: ['lucide-react', '@mui/material']
-  }
-}
-
-// Then you can keep the ergonomic barrel imports:
-import { Check, X, Menu } from 'lucide-react'
-// Automatically transformed to direct imports at build time
-```
-
-Direct imports provide 15-70% faster dev boot, 28% faster builds, 40% faster cold starts, and significantly faster HMR.
-
-Libraries commonly affected: `lucide-react`, `@mui/material`, `@mui/icons-material`, `@tabler/icons-react`, `react-icons`, `@headlessui/react`, `@radix-ui/react-*`, `lodash`, `ramda`, `date-fns`, `rxjs`, `react-use`.
-
-Reference: [How we optimized package imports in Next.js](https://vercel.com/blog/how-we-optimized-package-imports-in-next-js)

.claude/skills/vercel-react-best-practices/rules/bundle-conditional.md

@@ -1,31 +0,0 @@
----
-title: Conditional Module Loading
-impact: HIGH
-impactDescription: loads large data only when needed
-tags: bundle, conditional-loading, lazy-loading
----
-
-## Conditional Module Loading
-
-Load large data or modules only when a feature is activated.
-
-**Example (lazy-load animation frames):**
-
-```tsx
-function AnimationPlayer({ enabled }: { enabled: boolean }) {
-  const [frames, setFrames] = useState<Frame[] | null>(null)
-
-  useEffect(() => {
-    if (enabled && !frames && typeof window !== 'undefined') {
-      import('./animation-frames.js')
-        .then(mod => setFrames(mod.frames))
-        .catch(() => setEnabled(false))
-    }
-  }, [enabled, frames])
-
-  if (!frames) return <Skeleton />
-  return <Canvas frames={frames} />
-}
-```
-
-The `typeof window !== 'undefined'` check prevents bundling this module for SSR, optimizing server bundle size and build speed.

.claude/skills/vercel-react-best-practices/rules/bundle-defer-third-party.md

@@ -1,49 +0,0 @@
----
-title: Defer Non-Critical Third-Party Libraries
-impact: MEDIUM
-impactDescription: loads after hydration
-tags: bundle, third-party, analytics, defer
----
-
-## Defer Non-Critical Third-Party Libraries
-
-Analytics, logging, and error tracking don't block user interaction. Load them after hydration.
-
-**Incorrect (blocks initial bundle):**
-
-```tsx
-import { Analytics } from '@vercel/analytics/react'
-
-export default function RootLayout({ children }) {
-  return (
-    <html>
-      <body>
-        {children}
-        <Analytics />
-      </body>
-    </html>
-  )
-}
-```
-
-**Correct (loads after hydration):**
-
-```tsx
-import dynamic from 'next/dynamic'
-
-const Analytics = dynamic(
-  () => import('@vercel/analytics/react').then(m => m.Analytics),
-  { ssr: false }
-)
-
-export default function RootLayout({ children }) {
-  return (
-    <html>
-      <body>
-        {children}
-        <Analytics />
-      </body>
-    </html>
-  )
-}
-```

.claude/skills/vercel-react-best-practices/rules/bundle-dynamic-imports.md

@@ -1,35 +0,0 @@
----
-title: Dynamic Imports for Heavy Components
-impact: CRITICAL
-impactDescription: directly affects TTI and LCP
-tags: bundle, dynamic-import, code-splitting, next-dynamic
----
-
-## Dynamic Imports for Heavy Components
-
-Use `next/dynamic` to lazy-load large components not needed on initial render.
-
-**Incorrect (Monaco bundles with main chunk ~300KB):**
-
-```tsx
-import { MonacoEditor } from './monaco-editor'
-
-function CodePanel({ code }: { code: string }) {
-  return <MonacoEditor value={code} />
-}
-```
-
-**Correct (Monaco loads on demand):**
-
-```tsx
-import dynamic from 'next/dynamic'
-
-const MonacoEditor = dynamic(
-  () => import('./monaco-editor').then(m => m.MonacoEditor),
-  { ssr: false }
-)
-
-function CodePanel({ code }: { code: string }) {
-  return <MonacoEditor value={code} />
-}
-```

.claude/skills/vercel-react-best-practices/rules/bundle-preload.md

@@ -1,50 +0,0 @@
----
-title: Preload Based on User Intent
-impact: MEDIUM
-impactDescription: reduces perceived latency
-tags: bundle, preload, user-intent, hover
----
-
-## Preload Based on User Intent
-
-Preload heavy bundles before they're needed to reduce perceived latency.
-
-**Example (preload on hover/focus):**
-
-```tsx
-function EditorButton({ onClick }: { onClick: () => void }) {
-  const preload = () => {
-    if (typeof window !== 'undefined') {
-      void import('./monaco-editor')
-    }
-  }
-
-  return (
-    <button
-      onMouseEnter={preload}
-      onFocus={preload}
-      onClick={onClick}
-    >
-      Open Editor
-    </button>
-  )
-}
-```
-
-**Example (preload when feature flag is enabled):**
-
-```tsx
-function FlagsProvider({ children, flags }: Props) {
-  useEffect(() => {
-    if (flags.editorEnabled && typeof window !== 'undefined') {
-      void import('./monaco-editor').then(mod => mod.init())
-    }
-  }, [flags.editorEnabled])
-
-  return <FlagsContext.Provider value={flags}>
-    {children}
-  </FlagsContext.Provider>
-}
-```
-
-The `typeof window !== 'undefined'` check prevents bundling preloaded modules for SSR, optimizing server bundle size and build speed.

.claude/skills/vercel-react-best-practices/rules/client-event-listeners.md

@@ -1,74 +0,0 @@
----
-title: Deduplicate Global Event Listeners
-impact: LOW
-impactDescription: single listener for N components
-tags: client, swr, event-listeners, subscription
----
-
-## Deduplicate Global Event Listeners
-
-Use `useSWRSubscription()` to share global event listeners across component instances.
-
-**Incorrect (N instances = N listeners):**
-
-```tsx
-function useKeyboardShortcut(key: string, callback: () => void) {
-  useEffect(() => {
-    const handler = (e: KeyboardEvent) => {
-      if (e.metaKey && e.key === key) {
-        callback()
-      }
-    }
-    window.addEventListener('keydown', handler)
-    return () => window.removeEventListener('keydown', handler)
-  }, [key, callback])
-}
-```
-
-When using the `useKeyboardShortcut` hook multiple times, each instance will register a new listener.
-
-**Correct (N instances = 1 listener):**
-
-```tsx
-import useSWRSubscription from 'swr/subscription'
-
-// Module-level Map to track callbacks per key
-const keyCallbacks = new Map<string, Set<() => void>>()
-
-function useKeyboardShortcut(key: string, callback: () => void) {
-  // Register this callback in the Map
-  useEffect(() => {
-    if (!keyCallbacks.has(key)) {
-      keyCallbacks.set(key, new Set())
-    }
-    keyCallbacks.get(key)!.add(callback)
-
-    return () => {
-      const set = keyCallbacks.get(key)
-      if (set) {
-        set.delete(callback)
-        if (set.size === 0) {
-          keyCallbacks.delete(key)
-        }
-      }
-    }
-  }, [key, callback])
-
-  useSWRSubscription('global-keydown', () => {
-    const handler = (e: KeyboardEvent) => {
-      if (e.metaKey && keyCallbacks.has(e.key)) {
-        keyCallbacks.get(e.key)!.forEach(cb => cb())
-      }
-    }
-    window.addEventListener('keydown', handler)
-    return () => window.removeEventListener('keydown', handler)
-  })
-}
-
-function Profile() {
-  // Multiple shortcuts will share the same listener
-  useKeyboardShortcut('p', () => { /* ... */ }) 
-  useKeyboardShortcut('k', () => { /* ... */ })
-  // ...
-}
-```

.claude/skills/vercel-react-best-practices/rules/client-swr-dedup.md

@@ -1,56 +0,0 @@
----
-title: Use SWR for Automatic Deduplication
-impact: MEDIUM-HIGH
-impactDescription: automatic deduplication
-tags: client, swr, deduplication, data-fetching
----
-
-## Use SWR for Automatic Deduplication
-
-SWR enables request deduplication, caching, and revalidation across component instances.
-
-**Incorrect (no deduplication, each instance fetches):**
-
-```tsx
-function UserList() {
-  const [users, setUsers] = useState([])
-  useEffect(() => {
-    fetch('/api/users')
-      .then(r => r.json())
-      .then(setUsers)
-  }, [])
-}
-```
-
-**Correct (multiple instances share one request):**
-
-```tsx
-import useSWR from 'swr'
-
-function UserList() {
-  const { data: users } = useSWR('/api/users', fetcher)
-}
-```
-
-**For immutable data:**
-
-```tsx
-import { useImmutableSWR } from '@/lib/swr'
-
-function StaticContent() {
-  const { data } = useImmutableSWR('/api/config', fetcher)
-}
-```
-
-**For mutations:**
-
-```tsx
-import { useSWRMutation } from 'swr/mutation'
-
-function UpdateButton() {
-  const { trigger } = useSWRMutation('/api/user', updateUser)
-  return <button onClick={() => trigger()}>Update</button>
-}
-```
-
-Reference: [https://swr.vercel.app](https://swr.vercel.app)

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

@@ -1,82 +0,0 @@
----
-title: Batch DOM CSS Changes
-impact: MEDIUM
-impactDescription: reduces reflows/repaints
-tags: javascript, dom, css, performance, reflow
----
-
-## Batch DOM CSS Changes
-
-Avoid changing styles one property at a time. Group multiple CSS changes together via classes or `cssText` to minimize browser reflows.
-
-**Incorrect (multiple reflows):**
-
-```typescript
-function updateElementStyles(element: HTMLElement) {
-  // Each line triggers a reflow
-  element.style.width = '100px'
-  element.style.height = '200px'
-  element.style.backgroundColor = 'blue'
-  element.style.border = '1px solid black'
-}
-```
-
-**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')
-}
-```
-
-**Correct (change cssText - single reflow):**
-
-```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.

.claude/skills/vercel-react-best-practices/rules/js-cache-function-results.md

@@ -1,80 +0,0 @@
----
-title: Cache Repeated Function Calls
-impact: MEDIUM
-impactDescription: avoid redundant computation
-tags: javascript, cache, memoization, performance
----
-
-## Cache Repeated Function Calls
-
-Use a module-level Map to cache function results when the same function is called repeatedly with the same inputs during render.
-
-**Incorrect (redundant computation):**
-
-```typescript
-function ProjectList({ projects }: { projects: Project[] }) {
-  return (
-    <div>
-      {projects.map(project => {
-        // slugify() called 100+ times for same project names
-        const slug = slugify(project.name)
-        
-        return <ProjectCard key={project.id} slug={slug} />
-      })}
-    </div>
-  )
-}
-```
-
-**Correct (cached results):**
-
-```typescript
-// Module-level cache
-const slugifyCache = new Map<string, string>()
-
-function cachedSlugify(text: string): string {
-  if (slugifyCache.has(text)) {
-    return slugifyCache.get(text)!
-  }
-  const result = slugify(text)
-  slugifyCache.set(text, result)
-  return result
-}
-
-function ProjectList({ projects }: { projects: Project[] }) {
-  return (
-    <div>
-      {projects.map(project => {
-        // Computed only once per unique project name
-        const slug = cachedSlugify(project.name)
-        
-        return <ProjectCard key={project.id} slug={slug} />
-      })}
-    </div>
-  )
-}
-```
-
-**Simpler pattern for single-value functions:**
-
-```typescript
-let isLoggedInCache: boolean | null = null
-
-function isLoggedIn(): boolean {
-  if (isLoggedInCache !== null) {
-    return isLoggedInCache
-  }
-  
-  isLoggedInCache = document.cookie.includes('auth=')
-  return isLoggedInCache
-}
-
-// Clear cache when auth changes
-function onAuthChange() {
-  isLoggedInCache = null
-}
-```
-
-Use a Map (not a hook) so it works everywhere: utilities, event handlers, not just React components.
-
-Reference: [How we made the Vercel Dashboard twice as fast](https://vercel.com/blog/how-we-made-the-vercel-dashboard-twice-as-fast)

.claude/skills/vercel-react-best-practices/rules/js-cache-property-access.md

@@ -1,28 +0,0 @@
----
-title: Cache Property Access in Loops
-impact: LOW-MEDIUM
-impactDescription: reduces lookups
-tags: javascript, loops, optimization, caching
----
-
-## Cache Property Access in Loops
-
-Cache object property lookups in hot paths.
-
-**Incorrect (3 lookups × N iterations):**
-
-```typescript
-for (let i = 0; i < arr.length; i++) {
-  process(obj.config.settings.value)
-}
-```
-
-**Correct (1 lookup total):**
-
-```typescript
-const value = obj.config.settings.value
-const len = arr.length
-for (let i = 0; i < len; i++) {
-  process(value)
-}
-```

.claude/skills/vercel-react-best-practices/rules/js-cache-storage.md

@@ -1,70 +0,0 @@
----
-title: Cache Storage API Calls
-impact: LOW-MEDIUM
-impactDescription: reduces expensive I/O
-tags: javascript, localStorage, storage, caching, performance
----
-
-## Cache Storage API Calls
-
-`localStorage`, `sessionStorage`, and `document.cookie` are synchronous and expensive. Cache reads in memory.
-
-**Incorrect (reads storage on every call):**
-
-```typescript
-function getTheme() {
-  return localStorage.getItem('theme') ?? 'light'
-}
-// Called 10 times = 10 storage reads
-```
-
-**Correct (Map cache):**
-
-```typescript
-const storageCache = new Map<string, string | null>()
-
-function getLocalStorage(key: string) {
-  if (!storageCache.has(key)) {
-    storageCache.set(key, localStorage.getItem(key))
-  }
-  return storageCache.get(key)
-}
-
-function setLocalStorage(key: string, value: string) {
-  localStorage.setItem(key, value)
-  storageCache.set(key, value)  // keep cache in sync
-}
-```
-
-Use a Map (not a hook) so it works everywhere: utilities, event handlers, not just React components.
-
-**Cookie caching:**
-
-```typescript
-let cookieCache: Record<string, string> | null = null
-
-function getCookie(name: string) {
-  if (!cookieCache) {
-    cookieCache = Object.fromEntries(
-      document.cookie.split('; ').map(c => c.split('='))
-    )
-  }
-  return cookieCache[name]
-}
-```
-
-**Important (invalidate on external changes):**
-
-If storage can change externally (another tab, server-set cookies), invalidate cache:
-
-```typescript
-window.addEventListener('storage', (e) => {
-  if (e.key) storageCache.delete(e.key)
-})
-
-document.addEventListener('visibilitychange', () => {
-  if (document.visibilityState === 'visible') {
-    storageCache.clear()
-  }
-})
-```

.claude/skills/vercel-react-best-practices/rules/js-combine-iterations.md

@@ -1,32 +0,0 @@
----
-title: Combine Multiple Array Iterations
-impact: LOW-MEDIUM
-impactDescription: reduces iterations
-tags: javascript, arrays, loops, performance
----
-
-## Combine Multiple Array Iterations
-
-Multiple `.filter()` or `.map()` calls iterate the array multiple times. Combine into one loop.
-
-**Incorrect (3 iterations):**
-
-```typescript
-const admins = users.filter(u => u.isAdmin)
-const testers = users.filter(u => u.isTester)
-const inactive = users.filter(u => !u.isActive)
-```
-
-**Correct (1 iteration):**
-
-```typescript
-const admins: User[] = []
-const testers: User[] = []
-const inactive: User[] = []
-
-for (const user of users) {
-  if (user.isAdmin) admins.push(user)
-  if (user.isTester) testers.push(user)
-  if (!user.isActive) inactive.push(user)
-}
-```

.claude/skills/vercel-react-best-practices/rules/js-early-exit.md

@@ -1,50 +0,0 @@
----
-title: Early Return from Functions
-impact: LOW-MEDIUM
-impactDescription: avoids unnecessary computation
-tags: javascript, functions, optimization, early-return
----
-
-## Early Return from Functions
-
-Return early when result is determined to skip unnecessary processing.
-
-**Incorrect (processes all items even after finding answer):**
-
-```typescript
-function validateUsers(users: User[]) {
-  let hasError = false
-  let errorMessage = ''
-  
-  for (const user of users) {
-    if (!user.email) {
-      hasError = true
-      errorMessage = 'Email required'
-    }
-    if (!user.name) {
-      hasError = true
-      errorMessage = 'Name required'
-    }
-    // Continues checking all users even after error found
-  }
-  
-  return hasError ? { valid: false, error: errorMessage } : { valid: true }
-}
-```
-
-**Correct (returns immediately on first error):**
-
-```typescript
-function validateUsers(users: User[]) {
-  for (const user of users) {
-    if (!user.email) {
-      return { valid: false, error: 'Email required' }
-    }
-    if (!user.name) {
-      return { valid: false, error: 'Name required' }
-    }
-  }
-
-  return { valid: true }
-}
-```

.claude/skills/vercel-react-best-practices/rules/js-hoist-regexp.md

@@ -1,45 +0,0 @@
----
-title: Hoist RegExp Creation
-impact: LOW-MEDIUM
-impactDescription: avoids recreation
-tags: javascript, regexp, optimization, memoization
----
-
-## Hoist RegExp Creation
-
-Don't create RegExp inside render. Hoist to module scope or memoize with `useMemo()`.
-
-**Incorrect (new RegExp every render):**
-
-```tsx
-function Highlighter({ text, query }: Props) {
-  const regex = new RegExp(`(${query})`, 'gi')
-  const parts = text.split(regex)
-  return <>{parts.map((part, i) => ...)}</>
-}
-```
-
-**Correct (memoize or hoist):**
-
-```tsx
-const EMAIL_REGEX = /^[^\s@]+@[^\s@]+\.[^\s@]+$/
-
-function Highlighter({ text, query }: Props) {
-  const regex = useMemo(
-    () => new RegExp(`(${escapeRegex(query)})`, 'gi'),
-    [query]
-  )
-  const parts = text.split(regex)
-  return <>{parts.map((part, i) => ...)}</>
-}
-```
-
-**Warning (global regex has mutable state):**
-
-Global regex (`/g`) has mutable `lastIndex` state:
-
-```typescript
-const regex = /foo/g
-regex.test('foo')  // true, lastIndex = 3
-regex.test('foo')  // false, lastIndex = 0
-```

.claude/skills/vercel-react-best-practices/rules/js-index-maps.md

@@ -1,37 +0,0 @@
----
-title: Build Index Maps for Repeated Lookups
-impact: LOW-MEDIUM
-impactDescription: 1M ops to 2K ops
-tags: javascript, map, indexing, optimization, performance
----
-
-## Build Index Maps for Repeated Lookups
-
-Multiple `.find()` calls by the same key should use a Map.
-
-**Incorrect (O(n) per lookup):**
-
-```typescript
-function processOrders(orders: Order[], users: User[]) {
-  return orders.map(order => ({
-    ...order,
-    user: users.find(u => u.id === order.userId)
-  }))
-}
-```
-
-**Correct (O(1) per lookup):**
-
-```typescript
-function processOrders(orders: Order[], users: User[]) {
-  const userById = new Map(users.map(u => [u.id, u]))
-
-  return orders.map(order => ({
-    ...order,
-    user: userById.get(order.userId)
-  }))
-}
-```
-
-Build map once (O(n)), then all lookups are O(1).
-For 1000 orders × 1000 users: 1M ops → 2K ops.

.claude/skills/vercel-react-best-practices/rules/js-length-check-first.md

@@ -1,49 +0,0 @@
----
-title: Early Length Check for Array Comparisons
-impact: MEDIUM-HIGH
-impactDescription: avoids expensive operations when lengths differ
-tags: javascript, arrays, performance, optimization, comparison
----
-
-## Early Length Check for Array Comparisons
-
-When comparing arrays with expensive operations (sorting, deep equality, serialization), check lengths first. If lengths differ, the arrays cannot be equal.
-
-In real-world applications, this optimization is especially valuable when the comparison runs in hot paths (event handlers, render loops).
-
-**Incorrect (always runs expensive comparison):**
-
-```typescript
-function hasChanges(current: string[], original: string[]) {
-  // Always sorts and joins, even when lengths differ
-  return current.sort().join() !== original.sort().join()
-}
-```
-
-Two O(n log n) sorts run even when `current.length` is 5 and `original.length` is 100. There is also overhead of joining the arrays and comparing the strings.
-
-**Correct (O(1) length check first):**
-
-```typescript
-function hasChanges(current: string[], original: string[]) {
-  // Early return if lengths differ
-  if (current.length !== original.length) {
-    return true
-  }
-  // Only sort/join when lengths match
-  const currentSorted = current.toSorted()
-  const originalSorted = original.toSorted()
-  for (let i = 0; i < currentSorted.length; i++) {
-    if (currentSorted[i] !== originalSorted[i]) {
-      return true
-    }
-  }
-  return false
-}
-```
-
-This new approach is more efficient because:
-- It avoids the overhead of sorting and joining the arrays when lengths differ
-- It avoids consuming memory for the joined strings (especially important for large arrays)
-- It avoids mutating the original arrays
-- It returns early when a difference is found

.claude/skills/vercel-react-best-practices/rules/js-min-max-loop.md

@@ -1,82 +0,0 @@
----
-title: Use Loop for Min/Max Instead of Sort
-impact: LOW
-impactDescription: O(n) instead of O(n log n)
-tags: javascript, arrays, performance, sorting, algorithms
----
-
-## Use Loop for Min/Max Instead of Sort
-
-Finding the smallest or largest element only requires a single pass through the array. Sorting is wasteful and slower.
-
-**Incorrect (O(n log n) - sort to find latest):**
-
-```typescript
-interface Project {
-  id: string
-  name: string
-  updatedAt: number
-}
-
-function getLatestProject(projects: Project[]) {
-  const sorted = [...projects].sort((a, b) => b.updatedAt - a.updatedAt)
-  return sorted[0]
-}
-```
-
-Sorts the entire array just to find the maximum value.
-
-**Incorrect (O(n log n) - sort for oldest and newest):**
-
-```typescript
-function getOldestAndNewest(projects: Project[]) {
-  const sorted = [...projects].sort((a, b) => a.updatedAt - b.updatedAt)
-  return { oldest: sorted[0], newest: sorted[sorted.length - 1] }
-}
-```
-
-Still sorts unnecessarily when only min/max are needed.
-
-**Correct (O(n) - single loop):**
-
-```typescript
-function getLatestProject(projects: Project[]) {
-  if (projects.length === 0) return null
-  
-  let latest = projects[0]
-  
-  for (let i = 1; i < projects.length; i++) {
-    if (projects[i].updatedAt > latest.updatedAt) {
-      latest = projects[i]
-    }
-  }
-  
-  return latest
-}
-
-function getOldestAndNewest(projects: Project[]) {
-  if (projects.length === 0) return { oldest: null, newest: null }
-  
-  let oldest = projects[0]
-  let newest = projects[0]
-  
-  for (let i = 1; i < projects.length; i++) {
-    if (projects[i].updatedAt < oldest.updatedAt) oldest = projects[i]
-    if (projects[i].updatedAt > newest.updatedAt) newest = projects[i]
-  }
-  
-  return { oldest, newest }
-}
-```
-
-Single pass through the array, no copying, no sorting.
-
-**Alternative (Math.min/Math.max for small arrays):**
-
-```typescript
-const numbers = [5, 2, 8, 1, 9]
-const min = Math.min(...numbers)
-const max = Math.max(...numbers)
-```
-
-This works for small arrays but can be slower for very large arrays due to spread operator limitations. Use the loop approach for reliability.

.claude/skills/vercel-react-best-practices/rules/js-set-map-lookups.md

@@ -1,24 +0,0 @@
----
-title: Use Set/Map for O(1) Lookups
-impact: LOW-MEDIUM
-impactDescription: O(n) to O(1)
-tags: javascript, set, map, data-structures, performance
----
-
-## Use Set/Map for O(1) Lookups
-
-Convert arrays to Set/Map for repeated membership checks.
-
-**Incorrect (O(n) per check):**
-
-```typescript
-const allowedIds = ['a', 'b', 'c', ...]
-items.filter(item => allowedIds.includes(item.id))
-```
-
-**Correct (O(1) per check):**
-
-```typescript
-const allowedIds = new Set(['a', 'b', 'c', ...])
-items.filter(item => allowedIds.has(item.id))
-```

.claude/skills/vercel-react-best-practices/rules/js-tosorted-immutable.md

@@ -1,57 +0,0 @@
----
-title: Use toSorted() Instead of sort() for Immutability
-impact: MEDIUM-HIGH
-impactDescription: prevents mutation bugs in React state
-tags: javascript, arrays, immutability, react, state, mutation
----
-
-## Use toSorted() Instead of sort() for Immutability
-
-`.sort()` mutates the array in place, which can cause bugs with React state and props. Use `.toSorted()` to create a new sorted array without mutation.
-
-**Incorrect (mutates original array):**
-
-```typescript
-function UserList({ users }: { users: User[] }) {
-  // Mutates the users prop array!
-  const sorted = useMemo(
-    () => users.sort((a, b) => a.name.localeCompare(b.name)),
-    [users]
-  )
-  return <div>{sorted.map(renderUser)}</div>
-}
-```
-
-**Correct (creates new array):**
-
-```typescript
-function UserList({ users }: { users: User[] }) {
-  // Creates new sorted array, original unchanged
-  const sorted = useMemo(
-    () => users.toSorted((a, b) => a.name.localeCompare(b.name)),
-    [users]
-  )
-  return <div>{sorted.map(renderUser)}</div>
-}
-```
-
-**Why this matters in React:**
-
-1. Props/state mutations break React's immutability model - React expects props and state to be treated as read-only
-2. Causes stale closure bugs - Mutating arrays inside closures (callbacks, effects) can lead to unexpected behavior
-
-**Browser support (fallback for older browsers):**
-
-`.toSorted()` is available in all modern browsers (Chrome 110+, Safari 16+, Firefox 115+, Node.js 20+). For older environments, use spread operator:
-
-```typescript
-// Fallback for older browsers
-const sorted = [...items].sort((a, b) => a.value - b.value)
-```
-
-**Other immutable array methods:**
-
-- `.toSorted()` - immutable sort
-- `.toReversed()` - immutable reverse
-- `.toSpliced()` - immutable splice
-- `.with()` - immutable element replacement

.claude/skills/vercel-react-best-practices/rules/rendering-activity.md

@@ -1,26 +0,0 @@
----
-title: Use Activity Component for Show/Hide
-impact: MEDIUM
-impactDescription: preserves state/DOM
-tags: rendering, activity, visibility, state-preservation
----
-
-## Use Activity Component for Show/Hide
-
-Use React's `<Activity>` to preserve state/DOM for expensive components that frequently toggle visibility.
-
-**Usage:**
-
-```tsx
-import { Activity } from 'react'
-
-function Dropdown({ isOpen }: Props) {
-  return (
-    <Activity mode={isOpen ? 'visible' : 'hidden'}>
-      <ExpensiveMenu />
-    </Activity>
-  )
-}
-```
-
-Avoids expensive re-renders and state loss.

.claude/skills/vercel-react-best-practices/rules/rendering-animate-svg-wrapper.md

@@ -1,47 +0,0 @@
----
-title: Animate SVG Wrapper Instead of SVG Element
-impact: LOW
-impactDescription: enables hardware acceleration
-tags: rendering, svg, css, animation, performance
----
-
-## Animate SVG Wrapper Instead of SVG Element
-
-Many browsers don't have hardware acceleration for CSS3 animations on SVG elements. Wrap SVG in a `<div>` and animate the wrapper instead.
-
-**Incorrect (animating SVG directly - no hardware acceleration):**
-
-```tsx
-function LoadingSpinner() {
-  return (
-    <svg 
-      className="animate-spin"
-      width="24" 
-      height="24" 
-      viewBox="0 0 24 24"
-    >
-      <circle cx="12" cy="12" r="10" stroke="currentColor" />
-    </svg>
-  )
-}
-```
-
-**Correct (animating wrapper div - hardware accelerated):**
-
-```tsx
-function LoadingSpinner() {
-  return (
-    <div className="animate-spin">
-      <svg 
-        width="24" 
-        height="24" 
-        viewBox="0 0 24 24"
-      >
-        <circle cx="12" cy="12" r="10" stroke="currentColor" />
-      </svg>
-    </div>
-  )
-}
-```
-
-This applies to all CSS transforms and transitions (`transform`, `opacity`, `translate`, `scale`, `rotate`). The wrapper div allows browsers to use GPU acceleration for smoother animations.

.claude/skills/vercel-react-best-practices/rules/rendering-conditional-render.md

@@ -1,40 +0,0 @@
----
-title: Use Explicit Conditional Rendering
-impact: LOW
-impactDescription: prevents rendering 0 or NaN
-tags: rendering, conditional, jsx, falsy-values
----
-
-## Use Explicit Conditional Rendering
-
-Use explicit ternary operators (`? :`) instead of `&&` for conditional rendering when the condition can be `0`, `NaN`, or other falsy values that render.
-
-**Incorrect (renders "0" when count is 0):**
-
-```tsx
-function Badge({ count }: { count: number }) {
-  return (
-    <div>
-      {count && <span className="badge">{count}</span>}
-    </div>
-  )
-}
-
-// When count = 0, renders: <div>0</div>
-// When count = 5, renders: <div><span class="badge">5</span></div>
-```
-
-**Correct (renders nothing when count is 0):**
-
-```tsx
-function Badge({ count }: { count: number }) {
-  return (
-    <div>
-      {count > 0 ? <span className="badge">{count}</span> : null}
-    </div>
-  )
-}
-
-// When count = 0, renders: <div></div>
-// When count = 5, renders: <div><span class="badge">5</span></div>
-```

.claude/skills/vercel-react-best-practices/rules/rendering-content-visibility.md

@@ -1,38 +0,0 @@
----
-title: CSS content-visibility for Long Lists
-impact: HIGH
-impactDescription: faster initial render
-tags: rendering, css, content-visibility, long-lists
----
-
-## CSS content-visibility for Long Lists
-
-Apply `content-visibility: auto` to defer off-screen rendering.
-
-**CSS:**
-
-```css
-.message-item {
-  content-visibility: auto;
-  contain-intrinsic-size: 0 80px;
-}
-```
-
-**Example:**
-
-```tsx
-function MessageList({ messages }: { messages: Message[] }) {
-  return (
-    <div className="overflow-y-auto h-screen">
-      {messages.map(msg => (
-        <div key={msg.id} className="message-item">
-          <Avatar user={msg.author} />
-          <div>{msg.content}</div>
-        </div>
-      ))}
-    </div>
-  )
-}
-```
-
-For 1000 messages, browser skips layout/paint for ~990 off-screen items (10× faster initial render).

.claude/skills/vercel-react-best-practices/rules/rendering-hoist-jsx.md

@@ -1,46 +0,0 @@
----
-title: Hoist Static JSX Elements
-impact: LOW
-impactDescription: avoids re-creation
-tags: rendering, jsx, static, optimization
----
-
-## Hoist Static JSX Elements
-
-Extract static JSX outside components to avoid re-creation.
-
-**Incorrect (recreates element every render):**
-
-```tsx
-function LoadingSkeleton() {
-  return <div className="animate-pulse h-20 bg-gray-200" />
-}
-
-function Container() {
-  return (
-    <div>
-      {loading && <LoadingSkeleton />}
-    </div>
-  )
-}
-```
-
-**Correct (reuses same element):**
-
-```tsx
-const loadingSkeleton = (
-  <div className="animate-pulse h-20 bg-gray-200" />
-)
-
-function Container() {
-  return (
-    <div>
-      {loading && loadingSkeleton}
-    </div>
-  )
-}
-```
-
-This is especially helpful for large and static SVG nodes, which can be expensive to recreate on every render.
-
-**Note:** If your project has [React Compiler](https://react.dev/learn/react-compiler) enabled, the compiler automatically hoists static JSX elements and optimizes component re-renders, making manual hoisting unnecessary.

.claude/skills/vercel-react-best-practices/rules/rendering-hydration-no-flicker.md

@@ -1,82 +0,0 @@
----
-title: Prevent Hydration Mismatch Without Flickering
-impact: MEDIUM
-impactDescription: avoids visual flicker and hydration errors
-tags: rendering, ssr, hydration, localStorage, flicker
----
-
-## Prevent Hydration Mismatch Without Flickering
-
-When rendering content that depends on client-side storage (localStorage, cookies), avoid both SSR breakage and post-hydration flickering by injecting a synchronous script that updates the DOM before React hydrates.
-
-**Incorrect (breaks SSR):**
-
-```tsx
-function ThemeWrapper({ children }: { children: ReactNode }) {
-  // localStorage is not available on server - throws error
-  const theme = localStorage.getItem('theme') || 'light'
-  
-  return (
-    <div className={theme}>
-      {children}
-    </div>
-  )
-}
-```
-
-Server-side rendering will fail because `localStorage` is undefined.
-
-**Incorrect (visual flickering):**
-
-```tsx
-function ThemeWrapper({ children }: { children: ReactNode }) {
-  const [theme, setTheme] = useState('light')
-  
-  useEffect(() => {
-    // Runs after hydration - causes visible flash
-    const stored = localStorage.getItem('theme')
-    if (stored) {
-      setTheme(stored)
-    }
-  }, [])
-  
-  return (
-    <div className={theme}>
-      {children}
-    </div>
-  )
-}
-```
-
-Component first renders with default value (`light`), then updates after hydration, causing a visible flash of incorrect content.
-
-**Correct (no flicker, no hydration mismatch):**
-
-```tsx
-function ThemeWrapper({ children }: { children: ReactNode }) {
-  return (
-    <>
-      <div id="theme-wrapper">
-        {children}
-      </div>
-      <script
-        dangerouslySetInnerHTML={{
-          __html: `
-            (function() {
-              try {
-                var theme = localStorage.getItem('theme') || 'light';
-                var el = document.getElementById('theme-wrapper');
-                if (el) el.className = theme;
-              } catch (e) {}
-            })();
-          `,
-        }}
-      />
-    </>
-  )
-}
-```
-
-The inline script executes synchronously before showing the element, ensuring the DOM already has the correct value. No flickering, no hydration mismatch.
-
-This pattern is especially useful for theme toggles, user preferences, authentication states, and any client-only data that should render immediately without flashing default values.

.claude/skills/vercel-react-best-practices/rules/rendering-svg-precision.md

@@ -1,28 +0,0 @@
----
-title: Optimize SVG Precision
-impact: LOW
-impactDescription: reduces file size
-tags: rendering, svg, optimization, svgo
----
-
-## Optimize SVG Precision
-
-Reduce SVG coordinate precision to decrease file size. The optimal precision depends on the viewBox size, but in general reducing precision should be considered.
-
-**Incorrect (excessive precision):**
-
-```svg
-<path d="M 10.293847 20.847362 L 30.938472 40.192837" />
-```
-
-**Correct (1 decimal place):**
-
-```svg
-<path d="M 10.3 20.8 L 30.9 40.2" />
-```
-
-**Automate with SVGO:**
-
-```bash
-npx svgo --precision=1 --multipass icon.svg
-```

.claude/skills/vercel-react-best-practices/rules/rerender-defer-reads.md

@@ -1,39 +0,0 @@
----
-title: Defer State Reads to Usage Point
-impact: MEDIUM
-impactDescription: avoids unnecessary subscriptions
-tags: rerender, searchParams, localStorage, optimization
----
-
-## Defer State Reads to Usage Point
-
-Don't subscribe to dynamic state (searchParams, localStorage) if you only read it inside callbacks.
-
-**Incorrect (subscribes to all searchParams changes):**
-
-```tsx
-function ShareButton({ chatId }: { chatId: string }) {
-  const searchParams = useSearchParams()
-
-  const handleShare = () => {
-    const ref = searchParams.get('ref')
-    shareChat(chatId, { ref })
-  }
-
-  return <button onClick={handleShare}>Share</button>
-}
-```
-
-**Correct (reads on demand, no subscription):**
-
-```tsx
-function ShareButton({ chatId }: { chatId: string }) {
-  const handleShare = () => {
-    const params = new URLSearchParams(window.location.search)
-    const ref = params.get('ref')
-    shareChat(chatId, { ref })
-  }
-
-  return <button onClick={handleShare}>Share</button>
-}
-```

.claude/skills/vercel-react-best-practices/rules/rerender-dependencies.md

@@ -1,45 +0,0 @@
----
-title: Narrow Effect Dependencies
-impact: LOW
-impactDescription: minimizes effect re-runs
-tags: rerender, useEffect, dependencies, optimization
----
-
-## Narrow Effect Dependencies
-
-Specify primitive dependencies instead of objects to minimize effect re-runs.
-
-**Incorrect (re-runs on any user field change):**
-
-```tsx
-useEffect(() => {
-  console.log(user.id)
-}, [user])
-```
-
-**Correct (re-runs only when id changes):**
-
-```tsx
-useEffect(() => {
-  console.log(user.id)
-}, [user.id])
-```
-
-**For derived state, compute outside effect:**
-
-```tsx
-// Incorrect: runs on width=767, 766, 765...
-useEffect(() => {
-  if (width < 768) {
-    enableMobileMode()
-  }
-}, [width])
-
-// Correct: runs only on boolean transition
-const isMobile = width < 768
-useEffect(() => {
-  if (isMobile) {
-    enableMobileMode()
-  }
-}, [isMobile])
-```

.claude/skills/vercel-react-best-practices/rules/rerender-derived-state.md

@@ -1,29 +0,0 @@
----
-title: Subscribe to Derived State
-impact: MEDIUM
-impactDescription: reduces re-render frequency
-tags: rerender, derived-state, media-query, optimization
----
-
-## Subscribe to Derived State
-
-Subscribe to derived boolean state instead of continuous values to reduce re-render frequency.
-
-**Incorrect (re-renders on every pixel change):**
-
-```tsx
-function Sidebar() {
-  const width = useWindowWidth()  // updates continuously
-  const isMobile = width < 768
-  return <nav className={isMobile ? 'mobile' : 'desktop'}>
-}
-```
-
-**Correct (re-renders only when boolean changes):**
-
-```tsx
-function Sidebar() {
-  const isMobile = useMediaQuery('(max-width: 767px)')
-  return <nav className={isMobile ? 'mobile' : 'desktop'}>
-}
-```

.claude/skills/vercel-react-best-practices/rules/rerender-functional-setstate.md

@@ -1,74 +0,0 @@
----
-title: Use Functional setState Updates
-impact: MEDIUM
-impactDescription: prevents stale closures and unnecessary callback recreations
-tags: react, hooks, useState, useCallback, callbacks, closures
----
-
-## Use Functional setState Updates
-
-When updating state based on the current state value, use the functional update form of setState instead of directly referencing the state variable. This prevents stale closures, eliminates unnecessary dependencies, and creates stable callback references.
-
-**Incorrect (requires state as dependency):**
-
-```tsx
-function TodoList() {
-  const [items, setItems] = useState(initialItems)
-  
-  // Callback must depend on items, recreated on every items change
-  const addItems = useCallback((newItems: Item[]) => {
-    setItems([...items, ...newItems])
-  }, [items])  // ❌ items dependency causes recreations
-  
-  // Risk of stale closure if dependency is forgotten
-  const removeItem = useCallback((id: string) => {
-    setItems(items.filter(item => item.id !== id))
-  }, [])  // ❌ Missing items dependency - will use stale items!
-  
-  return <ItemsEditor items={items} onAdd={addItems} onRemove={removeItem} />
-}
-```
-
-The first callback is recreated every time `items` changes, which can cause child components to re-render unnecessarily. The second callback has a stale closure bug—it will always reference the initial `items` value.
-
-**Correct (stable callbacks, no stale closures):**
-
-```tsx
-function TodoList() {
-  const [items, setItems] = useState(initialItems)
-  
-  // Stable callback, never recreated
-  const addItems = useCallback((newItems: Item[]) => {
-    setItems(curr => [...curr, ...newItems])
-  }, [])  // ✅ No dependencies needed
-  
-  // Always uses latest state, no stale closure risk
-  const removeItem = useCallback((id: string) => {
-    setItems(curr => curr.filter(item => item.id !== id))
-  }, [])  // ✅ Safe and stable
-  
-  return <ItemsEditor items={items} onAdd={addItems} onRemove={removeItem} />
-}
-```
-
-**Benefits:**
-
-1. **Stable callback references** - Callbacks don't need to be recreated when state changes
-2. **No stale closures** - Always operates on the latest state value
-3. **Fewer dependencies** - Simplifies dependency arrays and reduces memory leaks
-4. **Prevents bugs** - Eliminates the most common source of React closure bugs
-
-**When to use functional updates:**
-
-- Any setState that depends on the current state value
-- Inside useCallback/useMemo when state is needed
-- Event handlers that reference state
-- Async operations that update state
-
-**When direct updates are fine:**
-
-- Setting state to a static value: `setCount(0)`
-- Setting state from props/arguments only: `setName(newName)`
-- State doesn't depend on previous value
-
-**Note:** If your project has [React Compiler](https://react.dev/learn/react-compiler) enabled, the compiler can automatically optimize some cases, but functional updates are still recommended for correctness and to prevent stale closure bugs.

.claude/skills/vercel-react-best-practices/rules/rerender-lazy-state-init.md

@@ -1,58 +0,0 @@
----
-title: Use Lazy State Initialization
-impact: MEDIUM
-impactDescription: wasted computation on every render
-tags: react, hooks, useState, performance, initialization
----
-
-## Use Lazy State Initialization
-
-Pass a function to `useState` for expensive initial values. Without the function form, the initializer runs on every render even though the value is only used once.
-
-**Incorrect (runs on every render):**
-
-```tsx
-function FilteredList({ items }: { items: Item[] }) {
-  // buildSearchIndex() runs on EVERY render, even after initialization
-  const [searchIndex, setSearchIndex] = useState(buildSearchIndex(items))
-  const [query, setQuery] = useState('')
-  
-  // When query changes, buildSearchIndex runs again unnecessarily
-  return <SearchResults index={searchIndex} query={query} />
-}
-
-function UserProfile() {
-  // JSON.parse runs on every render
-  const [settings, setSettings] = useState(
-    JSON.parse(localStorage.getItem('settings') || '{}')
-  )
-  
-  return <SettingsForm settings={settings} onChange={setSettings} />
-}
-```
-
-**Correct (runs only once):**
-
-```tsx
-function FilteredList({ items }: { items: Item[] }) {
-  // buildSearchIndex() runs ONLY on initial render
-  const [searchIndex, setSearchIndex] = useState(() => buildSearchIndex(items))
-  const [query, setQuery] = useState('')
-  
-  return <SearchResults index={searchIndex} query={query} />
-}
-
-function UserProfile() {
-  // JSON.parse runs only on initial render
-  const [settings, setSettings] = useState(() => {
-    const stored = localStorage.getItem('settings')
-    return stored ? JSON.parse(stored) : {}
-  })
-  
-  return <SettingsForm settings={settings} onChange={setSettings} />
-}
-```
-
-Use lazy initialization when computing initial values from localStorage/sessionStorage, building data structures (indexes, maps), reading from the DOM, or performing heavy transformations.
-
-For simple primitives (`useState(0)`), direct references (`useState(props.value)`), or cheap literals (`useState({})`), the function form is unnecessary.

.claude/skills/vercel-react-best-practices/rules/rerender-memo.md

@@ -1,44 +0,0 @@
----
-title: Extract to Memoized Components
-impact: MEDIUM
-impactDescription: enables early returns
-tags: rerender, memo, useMemo, optimization
----
-
-## Extract to Memoized Components
-
-Extract expensive work into memoized components to enable early returns before computation.
-
-**Incorrect (computes avatar even when loading):**
-
-```tsx
-function Profile({ user, loading }: Props) {
-  const avatar = useMemo(() => {
-    const id = computeAvatarId(user)
-    return <Avatar id={id} />
-  }, [user])
-
-  if (loading) return <Skeleton />
-  return <div>{avatar}</div>
-}
-```
-
-**Correct (skips computation when loading):**
-
-```tsx
-const UserAvatar = memo(function UserAvatar({ user }: { user: User }) {
-  const id = useMemo(() => computeAvatarId(user), [user])
-  return <Avatar id={id} />
-})
-
-function Profile({ user, loading }: Props) {
-  if (loading) return <Skeleton />
-  return (
-    <div>
-      <UserAvatar user={user} />
-    </div>
-  )
-}
-```
-
-**Note:** If your project has [React Compiler](https://react.dev/learn/react-compiler) enabled, manual memoization with `memo()` and `useMemo()` is not necessary. The compiler automatically optimizes re-renders.

.claude/skills/vercel-react-best-practices/rules/rerender-transitions.md

@@ -1,40 +0,0 @@
----
-title: Use Transitions for Non-Urgent Updates
-impact: MEDIUM
-impactDescription: maintains UI responsiveness
-tags: rerender, transitions, startTransition, performance
----
-
-## Use Transitions for Non-Urgent Updates
-
-Mark frequent, non-urgent state updates as transitions to maintain UI responsiveness.
-
-**Incorrect (blocks UI on every scroll):**
-
-```tsx
-function ScrollTracker() {
-  const [scrollY, setScrollY] = useState(0)
-  useEffect(() => {
-    const handler = () => setScrollY(window.scrollY)
-    window.addEventListener('scroll', handler, { passive: true })
-    return () => window.removeEventListener('scroll', handler)
-  }, [])
-}
-```
-
-**Correct (non-blocking updates):**
-
-```tsx
-import { startTransition } from 'react'
-
-function ScrollTracker() {
-  const [scrollY, setScrollY] = useState(0)
-  useEffect(() => {
-    const handler = () => {
-      startTransition(() => setScrollY(window.scrollY))
-    }
-    window.addEventListener('scroll', handler, { passive: true })
-    return () => window.removeEventListener('scroll', handler)
-  }, [])
-}
-```

.claude/skills/vercel-react-best-practices/rules/server-after-nonblocking.md

@@ -1,73 +0,0 @@
----
-title: Use after() for Non-Blocking Operations
-impact: MEDIUM
-impactDescription: faster response times
-tags: server, async, logging, analytics, side-effects
----
-
-## Use after() for Non-Blocking Operations
-
-Use Next.js's `after()` to schedule work that should execute after a response is sent. This prevents logging, analytics, and other side effects from blocking the response.
-
-**Incorrect (blocks response):**
-
-```tsx
-import { logUserAction } from '@/app/utils'
-
-export async function POST(request: Request) {
-  // Perform mutation
-  await updateDatabase(request)
-  
-  // Logging blocks the response
-  const userAgent = request.headers.get('user-agent') || 'unknown'
-  await logUserAction({ userAgent })
-  
-  return new Response(JSON.stringify({ status: 'success' }), {
-    status: 200,
-    headers: { 'Content-Type': 'application/json' }
-  })
-}
-```
-
-**Correct (non-blocking):**
-
-```tsx
-import { after } from 'next/server'
-import { headers, cookies } from 'next/headers'
-import { logUserAction } from '@/app/utils'
-
-export async function POST(request: Request) {
-  // Perform mutation
-  await updateDatabase(request)
-  
-  // Log after response is sent
-  after(async () => {
-    const userAgent = (await headers()).get('user-agent') || 'unknown'
-    const sessionCookie = (await cookies()).get('session-id')?.value || 'anonymous'
-    
-    logUserAction({ sessionCookie, userAgent })
-  })
-  
-  return new Response(JSON.stringify({ status: 'success' }), {
-    status: 200,
-    headers: { 'Content-Type': 'application/json' }
-  })
-}
-```
-
-The response is sent immediately while logging happens in the background.
-
-**Common use cases:**
-
-- Analytics tracking
-- Audit logging
-- Sending notifications
-- Cache invalidation
-- Cleanup tasks
-
-**Important notes:**
-
-- `after()` runs even if the response fails or redirects
-- Works in Server Actions, Route Handlers, and Server Components
-
-Reference: [https://nextjs.org/docs/app/api-reference/functions/after](https://nextjs.org/docs/app/api-reference/functions/after)

.claude/skills/vercel-react-best-practices/rules/server-cache-lru.md

@@ -1,41 +0,0 @@
----
-title: Cross-Request LRU Caching
-impact: HIGH
-impactDescription: caches across requests
-tags: server, cache, lru, cross-request
----
-
-## Cross-Request LRU Caching
-
-`React.cache()` only works within one request. For data shared across sequential requests (user clicks button A then button B), use an LRU cache.
-
-**Implementation:**
-
-```typescript
-import { LRUCache } from 'lru-cache'
-
-const cache = new LRUCache<string, any>({
-  max: 1000,
-  ttl: 5 * 60 * 1000  // 5 minutes
-})
-
-export async function getUser(id: string) {
-  const cached = cache.get(id)
-  if (cached) return cached
-
-  const user = await db.user.findUnique({ where: { id } })
-  cache.set(id, user)
-  return user
-}
-
-// Request 1: DB query, result cached
-// Request 2: cache hit, no DB query
-```
-
-Use when sequential user actions hit multiple endpoints needing the same data within seconds.
-
-**With Vercel's [Fluid Compute](https://vercel.com/docs/fluid-compute):** LRU caching is especially effective because multiple concurrent requests can share the same function instance and cache. This means the cache persists across requests without needing external storage like Redis.
-
-**In traditional serverless:** Each invocation runs in isolation, so consider Redis for cross-process caching.
-
-Reference: [https://github.com/isaacs/node-lru-cache](https://github.com/isaacs/node-lru-cache)

.claude/skills/vercel-react-best-practices/rules/server-cache-react.md

@@ -1,26 +0,0 @@
----
-title: Per-Request Deduplication with React.cache()
-impact: MEDIUM
-impactDescription: deduplicates within request
-tags: server, cache, react-cache, deduplication
----
-
-## Per-Request Deduplication with React.cache()
-
-Use `React.cache()` for server-side request deduplication. Authentication and database queries benefit most.
-
-**Usage:**
-
-```typescript
-import { cache } from 'react'
-
-export const getCurrentUser = cache(async () => {
-  const session = await auth()
-  if (!session?.user?.id) return null
-  return await db.user.findUnique({
-    where: { id: session.user.id }
-  })
-})
-```
-
-Within a single request, multiple calls to `getCurrentUser()` execute the query only once.

.claude/skills/vercel-react-best-practices/rules/server-parallel-fetching.md

@@ -1,79 +0,0 @@
----
-title: Parallel Data Fetching with Component Composition
-impact: CRITICAL
-impactDescription: eliminates server-side waterfalls
-tags: server, rsc, parallel-fetching, composition
----
-
-## Parallel Data Fetching with Component Composition
-
-React Server Components execute sequentially within a tree. Restructure with composition to parallelize data fetching.
-
-**Incorrect (Sidebar waits for Page's fetch to complete):**
-
-```tsx
-export default async function Page() {
-  const header = await fetchHeader()
-  return (
-    <div>
-      <div>{header}</div>
-      <Sidebar />
-    </div>
-  )
-}
-
-async function Sidebar() {
-  const items = await fetchSidebarItems()
-  return <nav>{items.map(renderItem)}</nav>
-}
-```
-
-**Correct (both fetch simultaneously):**
-
-```tsx
-async function Header() {
-  const data = await fetchHeader()
-  return <div>{data}</div>
-}
-
-async function Sidebar() {
-  const items = await fetchSidebarItems()
-  return <nav>{items.map(renderItem)}</nav>
-}
-
-export default function Page() {
-  return (
-    <div>
-      <Header />
-      <Sidebar />
-    </div>
-  )
-}
-```
-
-**Alternative with children prop:**
-
-```tsx
-async function Layout({ children }: { children: ReactNode }) {
-  const header = await fetchHeader()
-  return (
-    <div>
-      <div>{header}</div>
-      {children}
-    </div>
-  )
-}
-
-async function Sidebar() {
-  const items = await fetchSidebarItems()
-  return <nav>{items.map(renderItem)}</nav>
-}
-
-export default function Page() {
-  return (
-    <Layout>
-      <Sidebar />
-    </Layout>
-  )
-}
-```

.claude/skills/vercel-react-best-practices/rules/server-serialization.md

@@ -1,38 +0,0 @@
----
-title: Minimize Serialization at RSC Boundaries
-impact: HIGH
-impactDescription: reduces data transfer size
-tags: server, rsc, serialization, props
----
-
-## Minimize Serialization at RSC Boundaries
-
-The React Server/Client boundary serializes all object properties into strings and embeds them in the HTML response and subsequent RSC requests. This serialized data directly impacts page weight and load time, so **size matters a lot**. Only pass fields that the client actually uses.
-
-**Incorrect (serializes all 50 fields):**
-
-```tsx
-async function Page() {
-  const user = await fetchUser()  // 50 fields
-  return <Profile user={user} />
-}
-
-'use client'
-function Profile({ user }: { user: User }) {
-  return <div>{user.name}</div>  // uses 1 field
-}
-```
-
-**Correct (serializes only 1 field):**
-
-```tsx
-async function Page() {
-  const user = await fetchUser()
-  return <Profile name={user.name} />
-}
-
-'use client'
-function Profile({ name }: { name: string }) {
-  return <div>{name}</div>
-}
-```

.dockerignore

@@ -1,9 +1,6 @@
 # Ignore everything by default, selectively add things to context
 *
 
-# Documentation (for embeddings/search)
-!docs/
-
 # Platform - Libs
 !autogpt_platform/autogpt_libs/autogpt_libs/
 !autogpt_platform/autogpt_libs/pyproject.toml
@@ -19,7 +16,6 @@
 !autogpt_platform/backend/poetry.lock
 !autogpt_platform/backend/README.md
 !autogpt_platform/backend/.env
-!autogpt_platform/backend/gen_prisma_types_stub.py
 
 # Platform - Market
 !autogpt_platform/market/market/

.github/copilot-instructions.md

@@ -12,7 +12,6 @@ This file provides comprehensive onboarding information for GitHub Copilot codin
 - **Infrastructure** - Docker configurations, CI/CD, and development tools
 
 **Primary Languages & Frameworks:**
-
 - **Backend**: Python 3.10-3.13, FastAPI, Prisma ORM, PostgreSQL, RabbitMQ
 - **Frontend**: TypeScript, Next.js 15, React, Tailwind CSS, Radix UI
 - **Development**: Docker, Poetry, pnpm, Playwright, Storybook
@@ -24,17 +23,15 @@ This file provides comprehensive onboarding information for GitHub Copilot codin
 **Always run these commands in the correct directory and in this order:**
 
 1. **Initial Setup** (required once):
-
    ```bash
    # Clone and enter repository
    git clone <repo> && cd AutoGPT
-
+   
    # Start all services (database, redis, rabbitmq, clamav)
    cd autogpt_platform && docker compose --profile local up deps --build --detach
    ```
 
 2. **Backend Setup** (always run before backend development):
-
    ```bash
    cd autogpt_platform/backend
    poetry install                    # Install dependencies
@@ -51,7 +48,6 @@ This file provides comprehensive onboarding information for GitHub Copilot codin
 ### Runtime Requirements
 
 **Critical:** Always ensure Docker services are running before starting development:
-
 ```bash
 cd autogpt_platform && docker compose --profile local up deps --build --detach
 ```
@@ -62,7 +58,6 @@ cd autogpt_platform && docker compose --profile local up deps --build --detach
 ### Development Commands
 
 **Backend Development:**
-
 ```bash
 cd autogpt_platform/backend
 poetry run serve                     # Start development server (port 8000)
@@ -73,7 +68,6 @@ poetry run lint                      # Lint code (ruff) - run after format
 ```
 
 **Frontend Development:**
-
 ```bash
 cd autogpt_platform/frontend
 pnpm dev                            # Start development server (port 3000) - use for active development
@@ -87,27 +81,23 @@ pnpm storybook                      # Start component development server
 ### Testing Strategy
 
 **Backend Tests:**
-
 - **Block Tests**: `poetry run pytest backend/blocks/test/test_block.py -xvs` (validates all blocks)
 - **Specific Block**: `poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[BlockName]' -xvs`
 - **Snapshot Tests**: Use `--snapshot-update` when output changes, always review with `git diff`
 
 **Frontend Tests:**
-
 - **E2E Tests**: Always run `pnpm dev` before `pnpm test` (Playwright requires running instance)
 - **Component Tests**: Use Storybook for isolated component development
 
 ### Critical Validation Steps
 
 **Before committing changes:**
-
 1. Run `poetry run format` (backend) and `pnpm format` (frontend)
 2. Ensure all tests pass in modified areas
 3. Verify Docker services are still running
 4. Check that database migrations apply cleanly
 
 **Common Issues & Workarounds:**
-
 - **Prisma issues**: Run `poetry run prisma generate` after schema changes
 - **Permission errors**: Ensure Docker has proper permissions
 - **Port conflicts**: Check the `docker-compose.yml` file for the current list of exposed ports. You can list all mapped ports with:
@@ -118,7 +108,6 @@ pnpm storybook                      # Start component development server
 ### Core Architecture
 
 **AutoGPT Platform** (`autogpt_platform/`):
-
 - `backend/` - FastAPI server with async support
   - `backend/backend/` - Core API logic
   - `backend/blocks/` - Agent execution blocks
@@ -132,7 +121,6 @@ pnpm storybook                      # Start component development server
 - `docker-compose.yml` - Development stack orchestration
 
 **Key Configuration Files:**
-
 - `pyproject.toml` - Python dependencies and tooling
 - `package.json` - Node.js dependencies and scripts
 - `schema.prisma` - Database schema and migrations
@@ -148,7 +136,6 @@ pnpm storybook                      # Start component development server
 ### Development Workflow
 
 **GitHub Actions**: Multiple CI/CD workflows in `.github/workflows/`
-
 - `platform-backend-ci.yml` - Backend testing and validation
 - `platform-frontend-ci.yml` - Frontend testing and validation
 - `platform-fullstack-ci.yml` - End-to-end integration tests
@@ -159,13 +146,11 @@ pnpm storybook                      # Start component development server
 ### Key Source Files
 
 **Backend Entry Points:**
-
 - `backend/backend/server/server.py` - FastAPI application setup
 - `backend/backend/data/` - Database models and user management
 - `backend/blocks/` - Agent execution blocks and logic
 
 **Frontend Entry Points:**
-
 - `frontend/src/app/layout.tsx` - Root application layout
 - `frontend/src/app/page.tsx` - Home page
 - `frontend/src/lib/supabase/` - Authentication and database client
@@ -175,7 +160,6 @@ pnpm storybook                      # Start component development server
 ### Agent Block System
 
 Agents are built using a visual block-based system where each block performs a single action. Blocks are defined in `backend/blocks/` and must include:
-
 - Block definition with input/output schemas
 - Execution logic with proper error handling
 - Tests validating functionality
@@ -183,7 +167,6 @@ Agents are built using a visual block-based system where each block performs a s
 ### Database & ORM
 
 **Prisma ORM** with PostgreSQL backend including pgvector for embeddings:
-
 - Schema in `schema.prisma`
 - Migrations in `backend/migrations/`
 - Always run `prisma migrate dev` and `prisma generate` after schema changes
@@ -191,15 +174,13 @@ Agents are built using a visual block-based system where each block performs a s
 ## Environment Configuration
 
 ### Configuration Files Priority Order
-
 1. **Backend**: `/backend/.env.default` → `/backend/.env` (user overrides)
-2. **Frontend**: `/frontend/.env.default` → `/frontend/.env` (user overrides)
+2. **Frontend**: `/frontend/.env.default` → `/frontend/.env` (user overrides)  
 3. **Platform**: `/.env.default` (Supabase/shared) → `/.env` (user overrides)
 4. Docker Compose `environment:` sections override file-based config
 5. Shell environment variables have highest precedence
 
 ### Docker Environment Setup
-
 - All services use hardcoded defaults (no `${VARIABLE}` substitutions)
 - The `env_file` directive loads variables INTO containers at runtime
 - Backend/Frontend services use YAML anchors for consistent configuration
@@ -208,7 +189,6 @@ Agents are built using a visual block-based system where each block performs a s
 ## Advanced Development Patterns
 
 ### Adding New Blocks
-
 1. Create file in `/backend/backend/blocks/`
 2. Inherit from `Block` base class with input/output schemas
 3. Implement `run` method with proper error handling
@@ -218,7 +198,6 @@ Agents are built using a visual block-based system where each block performs a s
 7. Consider how inputs/outputs connect with other blocks in graph editor
 
 ### API Development
-
 1. Update routes in `/backend/backend/server/routers/`
 2. Add/update Pydantic models in same directory
 3. Write tests alongside route files
@@ -226,76 +205,21 @@ Agents are built using a visual block-based system where each block performs a s
 5. Run `poetry run test` to verify changes
 
 ### Frontend Development
-
-**📖 Complete Frontend Guide**: See `autogpt_platform/frontend/CONTRIBUTING.md` and `autogpt_platform/frontend/.cursorrules` for comprehensive patterns and conventions.
-
-**Quick Reference:**
-
-**Component Structure:**
-
-- Separate render logic from data/behavior
-- Structure: `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
-- Exception: Small components (3-4 lines of logic) can be inline
-- Render-only components can be direct files without folders
-
-**Data Fetching:**
-
-- Use generated API hooks from `@/app/api/__generated__/endpoints/`
-- Generated via Orval from backend OpenAPI spec
-- Pattern: `use{Method}{Version}{OperationName}`
-- Example: `useGetV2ListLibraryAgents`
-- Regenerate with: `pnpm generate:api`
-- **Never** use deprecated `BackendAPI` or `src/lib/autogpt-server-api/*`
-
-**Code Conventions:**
-
-- Use function declarations for components and handlers (not arrow functions)
-- Only arrow functions for small inline lambdas (map, filter, etc.)
-- Components: `PascalCase`, Hooks: `camelCase` with `use` prefix
-- No barrel files or `index.ts` re-exports
-- Minimal comments (code should be self-documenting)
-
-**Styling:**
-
-- Use Tailwind CSS utilities only
-- Use design system components from `src/components/` (atoms, molecules, organisms)
-- Never use `src/components/__legacy__/*`
-- Only use Phosphor Icons (`@phosphor-icons/react`)
-- Prefer design tokens over hardcoded values
-
-**Error Handling:**
-
-- Render errors: Use `<ErrorCard />` component
-- Mutation errors: Display with toast notifications
-- Manual exceptions: Use `Sentry.captureException()`
-- Global error boundaries already configured
-
-**Testing:**
-
-- Add/update Storybook stories for UI components (`pnpm storybook`)
-- Run Playwright E2E tests with `pnpm test`
-- Verify in Chromatic after PR
-
-**Architecture:**
-
-- Default to client components ("use client")
-- Server components only for SEO or extreme TTFB needs
-- Use React Query for server state (via generated hooks)
-- Co-locate UI state in components/hooks
+1. Components in `/frontend/src/components/`
+2. Use existing UI components from `/frontend/src/components/ui/`
+3. Add Storybook stories for component development
+4. Test user-facing features with Playwright E2E tests
+5. Update protected routes in middleware when needed
 
 ### Security Guidelines
-
 **Cache Protection Middleware** (`/backend/backend/server/middleware/security.py`):
-
 - Default: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
 - Uses allow list approach for cacheable paths (static assets, health checks, public pages)
 - Prevents sensitive data caching in browsers/proxies
 - Add new cacheable endpoints to `CACHEABLE_PATHS`
 
 ### CI/CD Alignment
-
 The repository has comprehensive CI workflows that test:
-
 - **Backend**: Python 3.11-3.13, services (Redis/RabbitMQ/ClamAV), Prisma migrations, Poetry lock validation
 - **Frontend**: Node.js 21, pnpm, Playwright with Docker Compose stack, API schema validation
 - **Integration**: Full-stack type checking and E2E testing
@@ -305,7 +229,6 @@ Match these patterns when developing locally - the copilot setup environment mir
 ## Collaboration with Other AI Assistants
 
 This repository is actively developed with assistance from Claude (via CLAUDE.md files). When working on this codebase:
-
 - Check for existing CLAUDE.md files that provide additional context
 - Follow established patterns and conventions already in the codebase
 - Maintain consistency with existing code style and architecture
@@ -314,9 +237,8 @@ This repository is actively developed with assistance from Claude (via CLAUDE.md
 ## Trust These Instructions
 
 These instructions are comprehensive and tested. Only perform additional searches if:
-
 1. Information here is incomplete for your specific task
 2. You encounter errors not covered by the workarounds
 3. You need to understand implementation details not covered above
 
-For detailed platform development patterns, refer to `autogpt_platform/CLAUDE.md` and `AGENTS.md` in the repository root.
+For detailed platform development patterns, refer to `autogpt_platform/CLAUDE.md` and `AGENTS.md` in the repository root.

.github/workflows/claude-dependabot.yml

@@ -74,13 +74,13 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable

.github/workflows/claude.yml

@@ -44,12 +44,6 @@ jobs:
         with:
           fetch-depth: 1
 
-      - name: Free Disk Space (Ubuntu)
-        uses: jlumbroso/free-disk-space@v1.3.1
-        with:
-          large-packages: false  # slow
-          docker-images: false  # limited benefit
-
       # Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -90,13 +84,13 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable

.github/workflows/copilot-setup-steps.yml

@@ -72,13 +72,13 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -108,16 +108,6 @@ jobs:
       #   run: pnpm playwright install --with-deps chromium
 
       # Docker setup for development environment
-      - name: Free up disk space
-        run: |
-          # Remove large unused tools to free disk space for Docker builds
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /opt/hostedtoolcache/CodeQL
-          sudo docker system prune -af
-          df -h
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
@@ -309,4 +299,4 @@ jobs:
           echo "✅ AutoGPT Platform development environment setup complete!"
           echo "🚀 Ready for development with Docker services running"
           echo "📝 Backend server: poetry run serve (port 8000)"
-          echo "🌐 Frontend server: pnpm dev (port 3000)"
+          echo "🌐 Frontend server: pnpm dev (port 3000)"

.github/workflows/platform-backend-ci.yml

@@ -134,7 +134,7 @@ jobs:
         run: poetry install
 
       - name: Generate Prisma Client
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       - id: supabase
         name: Start Supabase
@@ -176,7 +176,7 @@ jobs:
           }
 
       - name: Run Database Migrations
-        run: poetry run prisma migrate deploy
+        run: poetry run prisma migrate dev --name updates
         env:
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
           DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}

.github/workflows/platform-frontend-ci.yml

@@ -11,11 +11,6 @@ on:
       - ".github/workflows/platform-frontend-ci.yml"
       - "autogpt_platform/frontend/**"
   merge_group:
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || format('{0}-{1}', github.ref, github.event.pull_request.number || github.sha) }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
 
 defaults:
   run:
@@ -35,7 +30,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -67,7 +62,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -102,7 +97,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -143,7 +138,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -152,14 +147,6 @@ jobs:
         run: |
           cp ../.env.default ../.env
 
-      - name: Copy backend .env and set OpenAI API key
-        run: |
-          cp ../backend/.env.default ../backend/.env
-          echo "OPENAI_INTERNAL_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> ../backend/.env
-        env:
-          # Used by E2E test data script to generate embeddings for approved store agents
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
@@ -235,25 +222,13 @@ jobs:
 
       - name: Run Playwright tests
         run: pnpm test:no-build
-        continue-on-error: false
 
-      - name: Upload Playwright report
-        if: always()
+      - name: Upload Playwright artifacts
+        if: failure()
         uses: actions/upload-artifact@v4
         with:
           name: playwright-report
           path: playwright-report
-          if-no-files-found: ignore
-          retention-days: 3
-
-      - name: Upload Playwright test results
-        if: always()
-        uses: actions/upload-artifact@v4
-        with:
-          name: playwright-test-results
-          path: test-results
-          if-no-files-found: ignore
-          retention-days: 3
 
       - name: Print Final Docker Compose logs
         if: always()

.github/workflows/platform-fullstack-ci.yml

@@ -12,10 +12,6 @@ on:
       - "autogpt_platform/**"
   merge_group:
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
 defaults:
   run:
     shell: bash
@@ -34,7 +30,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -70,7 +66,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable

.github/workflows/repo-close-stale-issues.yml

@@ -11,7 +11,7 @@ jobs:
   stale:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@v10
+      - uses: actions/stale@v9
         with:
           # operations-per-run: 5000
           stale-issue-message: >

.github/workflows/repo-pr-label.yml

@@ -61,6 +61,6 @@ jobs:
       pull-requests: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/labeler@v6
+      - uses: actions/labeler@v5
         with:
           sync-labels: true

.gitignore

@@ -178,4 +178,3 @@ autogpt_platform/backend/settings.py
 *.ign.*
 .test-contents
 .claude/settings.local.json
-/autogpt_platform/backend/logs

autogpt_platform/CLAUDE.md

@@ -63,9 +63,6 @@ poetry run pytest path/to/test.py --snapshot-update
 # Install dependencies
 cd frontend && pnpm i
 
-# Generate API client from OpenAPI spec
-pnpm generate:api
-
 # Start development server
 pnpm dev
 
@@ -78,23 +75,12 @@ pnpm storybook
 # Build production
 pnpm build
 
-# Format and lint
-pnpm format
-
 # Type checking
 pnpm types
 ```
 
-**📖 Complete Guide**: See `/frontend/CONTRIBUTING.md` and `/frontend/.cursorrules` for comprehensive frontend patterns.
+We have a components library in autogpt_platform/frontend/src/components/atoms that should be used when adding new pages and components. 
 
-**Key Frontend Conventions:**
-
-- Separate render logic from data/behavior in components
-- Use generated API hooks from `@/app/api/__generated__/endpoints/`
-- Use function declarations (not arrow functions) for components/handlers
-- Use design system components from `src/components/` (atoms, molecules, organisms)
-- Only use Phosphor Icons
-- Never use `src/components/__legacy__/*` or deprecated `BackendAPI`
 
 ## Architecture Overview
 
@@ -109,16 +95,11 @@ pnpm types
 
 ### Frontend Architecture
 
-- **Framework**: Next.js 15 App Router (client-first approach)
-- **Data Fetching**: Type-safe generated API hooks via Orval + React Query
-- **State Management**: React Query for server state, co-located UI state in components/hooks
-- **Component Structure**: Separate render logic (`.tsx`) from business logic (`use*.ts` hooks)
+- **Framework**: Next.js App Router with React Server Components
+- **State Management**: React hooks + Supabase client for real-time updates
 - **Workflow Builder**: Visual graph editor using @xyflow/react
-- **UI Components**: shadcn/ui (Radix UI primitives) with Tailwind CSS styling
-- **Icons**: Phosphor Icons only
+- **UI Components**: Radix UI primitives with Tailwind CSS styling
 - **Feature Flags**: LaunchDarkly integration
-- **Error Handling**: ErrorCard for render errors, toast for mutations, Sentry for exceptions
-- **Testing**: Playwright for E2E, Storybook for component development
 
 ### Key Concepts
 
@@ -172,7 +153,6 @@ Key models (defined in `/backend/schema.prisma`):
 **Adding a new block:**
 
 Follow the comprehensive [Block SDK Guide](../../../docs/content/platform/block-sdk-guide.md) which covers:
-
 - Provider configuration with `ProviderBuilder`
 - Block schema definition
 - Authentication (API keys, OAuth, webhooks)
@@ -180,7 +160,6 @@ Follow the comprehensive [Block SDK Guide](../../../docs/content/platform/block-
 - File organization
 
 Quick steps:
-
 1. Create new file in `/backend/backend/blocks/`
 2. Configure provider using `ProviderBuilder` in `_config.py`
 3. Inherit from `Block` base class
@@ -192,8 +171,6 @@ Quick steps:
 Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
 ex: do the inputs and outputs tie well together?
 
-If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
-
 **Modifying the API:**
 
 1. Update route in `/backend/backend/server/routers/`
@@ -203,20 +180,10 @@ If you get any pushback or hit complex block conditions check the new_blocks gui
 
 **Frontend feature development:**
 
-See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
-
-1. **Pages**: Create in `src/app/(platform)/feature-name/page.tsx`
-   - Add `usePageName.ts` hook for logic
-   - Put sub-components in local `components/` folder
-2. **Components**: Structure as `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
-   - Use design system components from `src/components/` (atoms, molecules, organisms)
-   - Never use `src/components/__legacy__/*`
-3. **Data fetching**: Use generated API hooks from `@/app/api/__generated__/endpoints/`
-   - Regenerate with `pnpm generate:api`
-   - Pattern: `use{Method}{Version}{OperationName}`
-4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
-5. **Testing**: Add Storybook stories for new components, Playwright for E2E
-6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
+1. Components go in `/frontend/src/components/`
+2. Use existing UI components from `/frontend/src/components/ui/`
+3. Add Storybook stories for new components
+4. Test with Playwright if user-facing
 
 ### Security Implementation
 

autogpt_platform/Makefile

@@ -1,4 +1,4 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
+.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend
 
 # Run just Supabase + Redis + RabbitMQ
 start-core:
@@ -6,15 +6,8 @@ start-core:
 
 # Stop core services
 stop-core:
-	docker compose stop 
+	docker compose stop deps
 
-reset-db:
-	docker compose stop db
-	rm -rf db/docker/volumes/db/data
-	cd backend && poetry run prisma migrate deploy
-	cd backend && poetry run prisma generate
-	cd backend && poetry run gen-prisma-stub
-	
 # View logs for core services
 logs-core:
 	docker compose logs -f deps
@@ -35,7 +28,6 @@ init-env:
 migrate:
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
-	cd backend && poetry run gen-prisma-stub
 
 run-backend:
 	cd backend && poetry run app
@@ -43,22 +35,13 @@ run-backend:
 run-frontend:
 	cd frontend && pnpm dev
 
-test-data:
-	cd backend && poetry run python test/test_data_creator.py
-
-load-store-agents:
-	cd backend && poetry run load-store-agents
-
 help:
 	@echo "Usage: make <target>"
 	@echo "Targets:"
 	@echo "  start-core - Start just the core services (Supabase, Redis, RabbitMQ) in background"
 	@echo "  stop-core - Stop the core services"
-	@echo "  reset-db - Reset the database by deleting the volume"
 	@echo "  logs-core - Tail the logs for core services"
 	@echo "  format - Format & lint backend (Python) and frontend (TypeScript) code"
 	@echo "  migrate - Run backend database migrations"
 	@echo "  run-backend - Run the backend FastAPI server"
-	@echo "  run-frontend - Run the frontend Next.js development server"
-	@echo "  test-data - Run the test data creator"
-	@echo "  load-store-agents - Load store agents from agents/ folder into test database"
+	@echo "  run-frontend - Run the frontend Next.js development server"

autogpt_platform/autogpt_libs/autogpt_libs/api_key/keysmith.py

@@ -57,9 +57,6 @@ class APIKeySmith:
 
     def hash_key(self, raw_key: str) -> tuple[str, str]:
         """Migrate a legacy hash to secure hash format."""
-        if not raw_key.startswith(self.PREFIX):
-            raise ValueError("Key without 'agpt_' prefix would fail validation")
-
         salt = self._generate_salt()
         hash = self._hash_key_with_salt(raw_key, salt)
         return hash, salt.hex()

autogpt_platform/autogpt_libs/autogpt_libs/auth/__init__.py

@@ -1,10 +1,5 @@
 from .config import verify_settings
-from .dependencies import (
-    get_optional_user_id,
-    get_user_id,
-    requires_admin_user,
-    requires_user,
-)
+from .dependencies import get_user_id, requires_admin_user, requires_user
 from .helpers import add_auth_responses_to_openapi
 from .models import User
 
@@ -13,7 +8,6 @@ __all__ = [
     "get_user_id",
     "requires_admin_user",
     "requires_user",
-    "get_optional_user_id",
     "add_auth_responses_to_openapi",
     "User",
 ]

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py

@@ -4,53 +4,11 @@ FastAPI dependency functions for JWT-based authentication and authorization.
 These are the high-level dependency functions used in route definitions.
 """
 
-import logging
-
 import fastapi
-from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
 
 from .jwt_utils import get_jwt_payload, verify_user
 from .models import User
 
-optional_bearer = HTTPBearer(auto_error=False)
-
-# Header name for admin impersonation
-IMPERSONATION_HEADER_NAME = "X-Act-As-User-Id"
-
-logger = logging.getLogger(__name__)
-
-
-def get_optional_user_id(
-    credentials: HTTPAuthorizationCredentials | None = fastapi.Security(
-        optional_bearer
-    ),
-) -> str | None:
-    """
-    Attempts to extract the user ID ("sub" claim) from a Bearer JWT if provided.
-
-    This dependency allows for both authenticated and anonymous access. If a valid bearer token is
-    supplied, it parses the JWT and extracts the user ID. If the token is missing or invalid, it returns None,
-    treating the request as anonymous.
-
-    Args:
-        credentials: Optional HTTPAuthorizationCredentials object from FastAPI Security dependency.
-
-    Returns:
-        The user ID (str) extracted from the JWT "sub" claim, or None if no valid token is present.
-    """
-    if not credentials:
-        return None
-
-    try:
-        # Parse JWT token to get user ID
-        from autogpt_libs.auth.jwt_utils import parse_jwt_token
-
-        payload = parse_jwt_token(credentials.credentials)
-        return payload.get("sub")
-    except Exception as e:
-        logger.debug(f"Auth token validation failed (anonymous access): {e}")
-        return None
-
 
 async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
@@ -74,44 +32,16 @@ async def requires_admin_user(
     return verify_user(jwt_payload, admin_only=True)
 
 
-async def get_user_id(
-    request: fastapi.Request, jwt_payload: dict = fastapi.Security(get_jwt_payload)
-) -> str:
+async def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
     """
     FastAPI dependency that returns the ID of the authenticated user.
 
-    Supports admin impersonation via X-Act-As-User-Id header:
-    - If the header is present and user is admin, returns the impersonated user ID
-    - Otherwise returns the authenticated user's own ID
-    - Logs all impersonation actions for audit trail
-
     Raises:
         HTTPException: 401 for authentication failures or missing user ID
-        HTTPException: 403 if non-admin tries to use impersonation
     """
-    # Get the authenticated user's ID from JWT
     user_id = jwt_payload.get("sub")
     if not user_id:
         raise fastapi.HTTPException(
             status_code=401, detail="User ID not found in token"
         )
-
-    # Check for admin impersonation header
-    impersonate_header = request.headers.get(IMPERSONATION_HEADER_NAME, "").strip()
-    if impersonate_header:
-        # Verify the authenticated user is an admin
-        authenticated_user = verify_user(jwt_payload, admin_only=False)
-        if authenticated_user.role != "admin":
-            raise fastapi.HTTPException(
-                status_code=403, detail="Only admin users can impersonate other users"
-            )
-
-        # Log the impersonation for audit trail
-        logger.info(
-            f"Admin impersonation: {authenticated_user.user_id} ({authenticated_user.email}) "
-            f"acting as user {impersonate_header} for requesting {request.method} {request.url}"
-        )
-
-        return impersonate_header
-
     return user_id

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py

@@ -4,10 +4,9 @@ Tests the full authentication flow from HTTP requests to user validation.
 """
 
 import os
-from unittest.mock import Mock
 
 import pytest
-from fastapi import FastAPI, HTTPException, Request, Security
+from fastapi import FastAPI, HTTPException, Security
 from fastapi.testclient import TestClient
 from pytest_mock import MockerFixture
 
@@ -46,7 +45,6 @@ class TestAuthDependencies:
         """Create a test client."""
         return TestClient(app)
 
-    @pytest.mark.asyncio
     async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user with valid JWT payload."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
@@ -60,7 +58,6 @@ class TestAuthDependencies:
         assert user.user_id == "user-123"
         assert user.role == "user"
 
-    @pytest.mark.asyncio
     async def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user accepts admin users."""
         jwt_payload = {
@@ -76,7 +73,6 @@ class TestAuthDependencies:
         assert user.user_id == "admin-456"
         assert user.role == "admin"
 
-    @pytest.mark.asyncio
     async def test_requires_user_missing_sub(self):
         """Test requires_user with missing user ID."""
         jwt_payload = {"role": "user", "email": "user@example.com"}
@@ -86,7 +82,6 @@ class TestAuthDependencies:
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    @pytest.mark.asyncio
     async def test_requires_user_empty_sub(self):
         """Test requires_user with empty user ID."""
         jwt_payload = {"sub": "", "role": "user"}
@@ -95,7 +90,6 @@ class TestAuthDependencies:
             await requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
 
-    @pytest.mark.asyncio
     async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
         """Test requires_admin_user with admin role."""
         jwt_payload = {
@@ -111,7 +105,6 @@ class TestAuthDependencies:
         assert user.user_id == "admin-789"
         assert user.role == "admin"
 
-    @pytest.mark.asyncio
     async def test_requires_admin_user_with_regular_user(self):
         """Test requires_admin_user rejects regular users."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
@@ -121,7 +114,6 @@ class TestAuthDependencies:
         assert exc_info.value.status_code == 403
         assert "Admin access required" in exc_info.value.detail
 
-    @pytest.mark.asyncio
     async def test_requires_admin_user_missing_role(self):
         """Test requires_admin_user with missing role."""
         jwt_payload = {"sub": "user-123", "email": "user@example.com"}
@@ -129,40 +121,31 @@ class TestAuthDependencies:
         with pytest.raises(KeyError):
             await requires_admin_user(jwt_payload)
 
-    @pytest.mark.asyncio
     async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
         """Test get_user_id extracts user ID correctly."""
-        request = Mock(spec=Request)
-        request.headers = {}
         jwt_payload = {"sub": "user-id-xyz", "role": "user"}
 
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user_id = await get_user_id(request, jwt_payload)
+        user_id = await get_user_id(jwt_payload)
         assert user_id == "user-id-xyz"
 
-    @pytest.mark.asyncio
     async def test_get_user_id_missing_sub(self):
         """Test get_user_id with missing user ID."""
-        request = Mock(spec=Request)
-        request.headers = {}
         jwt_payload = {"role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
+            await get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    @pytest.mark.asyncio
     async def test_get_user_id_none_sub(self):
         """Test get_user_id with None user ID."""
-        request = Mock(spec=Request)
-        request.headers = {}
         jwt_payload = {"sub": None, "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
+            await get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
 
 
@@ -187,7 +170,6 @@ class TestAuthDependenciesIntegration:
 
         return _create_token
 
-    @pytest.mark.asyncio
     async def test_endpoint_auth_enabled_no_token(self):
         """Test endpoints require token when auth is enabled."""
         app = FastAPI()
@@ -202,7 +184,6 @@ class TestAuthDependenciesIntegration:
         response = client.get("/test")
         assert response.status_code == 401
 
-    @pytest.mark.asyncio
     async def test_endpoint_with_valid_token(self, create_token):
         """Test endpoint with valid JWT token."""
         app = FastAPI()
@@ -222,7 +203,6 @@ class TestAuthDependenciesIntegration:
         assert response.status_code == 200
         assert response.json()["user_id"] == "test-user"
 
-    @pytest.mark.asyncio
     async def test_admin_endpoint_requires_admin_role(self, create_token):
         """Test admin endpoint rejects non-admin users."""
         app = FastAPI()
@@ -260,7 +240,6 @@ class TestAuthDependenciesIntegration:
 class TestAuthDependenciesEdgeCases:
     """Edge case tests for authentication dependencies."""
 
-    @pytest.mark.asyncio
     async def test_dependency_with_complex_payload(self):
         """Test dependencies handle complex JWT payloads."""
         complex_payload = {
@@ -284,7 +263,6 @@ class TestAuthDependenciesEdgeCases:
         admin = await requires_admin_user(complex_payload)
         assert admin.role == "admin"
 
-    @pytest.mark.asyncio
     async def test_dependency_with_unicode_in_payload(self):
         """Test dependencies handle unicode in JWT payloads."""
         unicode_payload = {
@@ -298,7 +276,6 @@ class TestAuthDependenciesEdgeCases:
         assert "😀" in user.user_id
         assert user.email == "测试@example.com"
 
-    @pytest.mark.asyncio
     async def test_dependency_with_null_values(self):
         """Test dependencies handle null values in payload."""
         null_payload = {
@@ -313,7 +290,6 @@ class TestAuthDependenciesEdgeCases:
         assert user.user_id == "user-123"
         assert user.email is None
 
-    @pytest.mark.asyncio
     async def test_concurrent_requests_isolation(self):
         """Test that concurrent requests don't interfere with each other."""
         payload1 = {"sub": "user-1", "role": "user"}
@@ -338,7 +314,6 @@ class TestAuthDependenciesEdgeCases:
             ({"sub": "user", "role": "user"}, "Admin access required", True),
         ],
     )
-    @pytest.mark.asyncio
     async def test_dependency_error_cases(
         self, payload, expected_error: str, admin_only: bool
     ):
@@ -350,7 +325,6 @@ class TestAuthDependenciesEdgeCases:
             verify_user(payload, admin_only=admin_only)
         assert expected_error in exc_info.value.detail
 
-    @pytest.mark.asyncio
     async def test_dependency_valid_user(self):
         """Test valid user case for dependency."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
@@ -359,196 +333,3 @@ class TestAuthDependenciesEdgeCases:
         # Valid case
         user = verify_user({"sub": "user", "role": "user"}, admin_only=False)
         assert user.user_id == "user"
-
-
-class TestAdminImpersonation:
-    """Test suite for admin user impersonation functionality."""
-
-    @pytest.mark.asyncio
-    async def test_admin_impersonation_success(self, mocker: MockerFixture):
-        """Test admin successfully impersonating another user."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "target-user-123"}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-456", email="admin@example.com", role="admin"
-        )
-
-        # Mock logger to verify audit logging
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should return the impersonated user ID
-        assert user_id == "target-user-123"
-
-        # Should log the impersonation attempt
-        mock_logger.info.assert_called_once()
-        log_call = mock_logger.info.call_args[0][0]
-        assert "Admin impersonation:" in log_call
-        assert "admin@example.com" in log_call
-        assert "target-user-123" in log_call
-
-    @pytest.mark.asyncio
-    async def test_non_admin_impersonation_attempt(self, mocker: MockerFixture):
-        """Test non-admin user attempting impersonation returns 403."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "target-user-123"}
-        jwt_payload = {
-            "sub": "regular-user",
-            "role": "user",
-            "email": "user@example.com",
-        }
-
-        # Mock verify_user to return regular user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="regular-user", email="user@example.com", role="user"
-        )
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
-
-        assert exc_info.value.status_code == 403
-        assert "Only admin users can impersonate other users" in exc_info.value.detail
-
-    @pytest.mark.asyncio
-    async def test_impersonation_empty_header(self, mocker: MockerFixture):
-        """Test impersonation with empty header falls back to regular user ID."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": ""}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should fall back to the admin's own user ID
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_missing_header(self, mocker: MockerFixture):
-        """Test normal behavior when impersonation header is missing."""
-        request = Mock(spec=Request)
-        request.headers = {}  # No impersonation header
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should return the admin's own user ID
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_audit_logging_details(self, mocker: MockerFixture):
-        """Test that impersonation audit logging includes all required details."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "victim-user-789"}
-        jwt_payload = {
-            "sub": "admin-999",
-            "role": "admin",
-            "email": "superadmin@company.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-999", email="superadmin@company.com", role="admin"
-        )
-
-        # Mock logger to capture audit trail
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Verify all audit details are logged
-        assert user_id == "victim-user-789"
-        mock_logger.info.assert_called_once()
-
-        log_message = mock_logger.info.call_args[0][0]
-        assert "Admin impersonation:" in log_message
-        assert "superadmin@company.com" in log_message
-        assert "victim-user-789" in log_message
-
-    @pytest.mark.asyncio
-    async def test_impersonation_header_case_sensitivity(self, mocker: MockerFixture):
-        """Test that impersonation header is case-sensitive."""
-        request = Mock(spec=Request)
-        # Use wrong case - should not trigger impersonation
-        request.headers = {"x-act-as-user-id": "target-user-123"}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should fall back to admin's own ID (header case mismatch)
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_with_whitespace_header(self, mocker: MockerFixture):
-        """Test impersonation with whitespace in header value."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "  target-user-123  "}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-456", email="admin@example.com", role="admin"
-        )
-
-        # Mock logger
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should strip whitespace and impersonate successfully
-        assert user_id == "target-user-123"
-        mock_logger.info.assert_called_once()

autogpt_platform/autogpt_libs/autogpt_libs/auth/helpers.py

@@ -1,25 +1,29 @@
 from fastapi import FastAPI
+from fastapi.openapi.utils import get_openapi
 
 from .jwt_utils import bearer_jwt_auth
 
 
 def add_auth_responses_to_openapi(app: FastAPI) -> None:
     """
-    Patch a FastAPI instance's `openapi()` method to add 401 responses
+    Set up custom OpenAPI schema generation that adds 401 responses
     to all authenticated endpoints.
 
     This is needed when using HTTPBearer with auto_error=False to get proper
     401 responses instead of 403, but FastAPI only automatically adds security
     responses when auto_error=True.
     """
-    # Wrap current method to allow stacking OpenAPI schema modifiers like this
-    wrapped_openapi = app.openapi
 
     def custom_openapi():
         if app.openapi_schema:
             return app.openapi_schema
 
-        openapi_schema = wrapped_openapi()
+        openapi_schema = get_openapi(
+            title=app.title,
+            version=app.version,
+            description=app.description,
+            routes=app.routes,
+        )
 
         # Add 401 response to all endpoints that have security requirements
         for path, methods in openapi_schema["paths"].items():

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -94,36 +94,42 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
     config = LoggingConfig()
     log_handlers: list[logging.Handler] = []
 
-    structured_logging = config.enable_cloud_logging or force_cloud_logging
-
     # Console output handlers
-    if not structured_logging:
-        stdout = logging.StreamHandler(stream=sys.stdout)
-        stdout.setLevel(config.level)
-        stdout.addFilter(BelowLevelFilter(logging.WARNING))
-        if config.level == logging.DEBUG:
-            stdout.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
-        else:
-            stdout.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
+    stdout = logging.StreamHandler(stream=sys.stdout)
+    stdout.setLevel(config.level)
+    stdout.addFilter(BelowLevelFilter(logging.WARNING))
+    if config.level == logging.DEBUG:
+        stdout.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
+    else:
+        stdout.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
 
-        stderr = logging.StreamHandler()
-        stderr.setLevel(logging.WARNING)
-        if config.level == logging.DEBUG:
-            stderr.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
-        else:
-            stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
+    stderr = logging.StreamHandler()
+    stderr.setLevel(logging.WARNING)
+    if config.level == logging.DEBUG:
+        stderr.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
+    else:
+        stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
 
-        log_handlers += [stdout, stderr]
+    log_handlers += [stdout, stderr]
 
     # Cloud logging setup
-    else:
-        # Use Google Cloud Structured Log Handler. Log entries are printed to stdout
-        # in a JSON format which is automatically picked up by Google Cloud Logging.
-        from google.cloud.logging.handlers import StructuredLogHandler
+    if config.enable_cloud_logging or force_cloud_logging:
+        import google.cloud.logging
+        from google.cloud.logging.handlers import CloudLoggingHandler
+        from google.cloud.logging_v2.handlers.transports import (
+            BackgroundThreadTransport,
+        )
 
-        structured_log_handler = StructuredLogHandler(stream=sys.stdout)
-        structured_log_handler.setLevel(config.level)
-        log_handlers.append(structured_log_handler)
+        client = google.cloud.logging.Client()
+        # Use BackgroundThreadTransport to prevent blocking the main thread
+        # and deadlocks when gRPC calls to Google Cloud Logging hang
+        cloud_handler = CloudLoggingHandler(
+            client,
+            name="autogpt_logs",
+            transport=BackgroundThreadTransport,
+        )
+        cloud_handler.setLevel(config.level)
+        log_handlers.append(cloud_handler)
 
     # File logging setup
     if config.enable_file_logging:
@@ -179,13 +185,7 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
 
     # Configure the root logger
     logging.basicConfig(
-        format=(
-            "%(levelname)s  %(message)s"
-            if structured_logging
-            else (
-                DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT
-            )
-        ),
+        format=DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT,
         level=config.level,
         handlers=log_handlers,
     )

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

@@ -0,0 +1,339 @@
+import asyncio
+import inspect
+import logging
+import threading
+import time
+from functools import wraps
+from typing import (
+    Any,
+    Callable,
+    ParamSpec,
+    Protocol,
+    TypeVar,
+    cast,
+    runtime_checkable,
+)
+
+P = ParamSpec("P")
+R = TypeVar("R")
+R_co = TypeVar("R_co", covariant=True)
+
+logger = logging.getLogger(__name__)
+
+
+def _make_hashable_key(
+    args: tuple[Any, ...], kwargs: dict[str, Any]
+) -> tuple[Any, ...]:
+    """
+    Convert args and kwargs into a hashable cache key.
+
+    Handles unhashable types like dict, list, set by converting them to
+    their sorted string representations.
+    """
+
+    def make_hashable(obj: Any) -> Any:
+        """Recursively convert an object to a hashable representation."""
+        if isinstance(obj, dict):
+            # Sort dict items to ensure consistent ordering
+            return (
+                "__dict__",
+                tuple(sorted((k, make_hashable(v)) for k, v in obj.items())),
+            )
+        elif isinstance(obj, (list, tuple)):
+            return ("__list__", tuple(make_hashable(item) for item in obj))
+        elif isinstance(obj, set):
+            return ("__set__", tuple(sorted(make_hashable(item) for item in obj)))
+        elif hasattr(obj, "__dict__"):
+            # Handle objects with __dict__ attribute
+            return ("__obj__", obj.__class__.__name__, make_hashable(obj.__dict__))
+        else:
+            # For basic hashable types (str, int, bool, None, etc.)
+            try:
+                hash(obj)
+                return obj
+            except TypeError:
+                # Fallback: convert to string representation
+                return ("__str__", str(obj))
+
+    hashable_args = tuple(make_hashable(arg) for arg in args)
+    hashable_kwargs = tuple(sorted((k, make_hashable(v)) for k, v in kwargs.items()))
+    return (hashable_args, hashable_kwargs)
+
+
+@runtime_checkable
+class CachedFunction(Protocol[P, R_co]):
+    """Protocol for cached functions with cache management methods."""
+
+    def cache_clear(self) -> None:
+        """Clear all cached entries."""
+        return None
+
+    def cache_info(self) -> dict[str, int | None]:
+        """Get cache statistics."""
+        return {}
+
+    def cache_delete(self, *args: P.args, **kwargs: P.kwargs) -> bool:
+        """Delete a specific cache entry by its arguments. Returns True if entry existed."""
+        return False
+
+    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
+        """Call the cached function."""
+        return None  # type: ignore
+
+
+def cached(
+    *,
+    maxsize: int = 128,
+    ttl_seconds: int | None = None,
+) -> Callable[[Callable], CachedFunction]:
+    """
+    Thundering herd safe cache decorator for both sync and async functions.
+
+    Uses double-checked locking to prevent multiple threads/coroutines from
+    executing the expensive operation simultaneously during cache misses.
+
+    Args:
+        func: The function to cache (when used without parentheses)
+        maxsize: Maximum number of cached entries
+        ttl_seconds: Time to live in seconds. If None, entries never expire
+
+    Returns:
+        Decorated function or decorator
+
+    Example:
+        @cache()  # Default: maxsize=128, no TTL
+        def expensive_sync_operation(param: str) -> dict:
+            return {"result": param}
+
+        @cache()  # Works with async too
+        async def expensive_async_operation(param: str) -> dict:
+            return {"result": param}
+
+        @cache(maxsize=1000, ttl_seconds=300)  # Custom maxsize and TTL
+        def another_operation(param: str) -> dict:
+            return {"result": param}
+    """
+
+    def decorator(target_func):
+        # Cache storage and per-event-loop locks
+        cache_storage = {}
+        _event_loop_locks = {}  # Maps event loop to its asyncio.Lock
+
+        if inspect.iscoroutinefunction(target_func):
+
+            def _get_cache_lock():
+                """Get or create an asyncio.Lock for the current event loop."""
+                try:
+                    loop = asyncio.get_running_loop()
+                except RuntimeError:
+                    # No event loop, use None as default key
+                    loop = None
+
+                if loop not in _event_loop_locks:
+                    return _event_loop_locks.setdefault(loop, asyncio.Lock())
+                return _event_loop_locks[loop]
+
+            @wraps(target_func)
+            async def async_wrapper(*args: P.args, **kwargs: P.kwargs):
+                key = _make_hashable_key(args, kwargs)
+                current_time = time.time()
+
+                # Fast path: check cache without lock
+                if key in cache_storage:
+                    if ttl_seconds is None:
+                        logger.debug(f"Cache hit for {target_func.__name__}")
+                        return cache_storage[key]
+                    else:
+                        cached_data = cache_storage[key]
+                        if isinstance(cached_data, tuple):
+                            result, timestamp = cached_data
+                            if current_time - timestamp < ttl_seconds:
+                                logger.debug(f"Cache hit for {target_func.__name__}")
+                                return result
+
+                # Slow path: acquire lock for cache miss/expiry
+                async with _get_cache_lock():
+                    # Double-check: another coroutine might have populated cache
+                    if key in cache_storage:
+                        if ttl_seconds is None:
+                            return cache_storage[key]
+                        else:
+                            cached_data = cache_storage[key]
+                            if isinstance(cached_data, tuple):
+                                result, timestamp = cached_data
+                                if current_time - timestamp < ttl_seconds:
+                                    return result
+
+                    # Cache miss - execute function
+                    logger.debug(f"Cache miss for {target_func.__name__}")
+                    result = await target_func(*args, **kwargs)
+
+                    # Store result
+                    if ttl_seconds is None:
+                        cache_storage[key] = result
+                    else:
+                        cache_storage[key] = (result, current_time)
+
+                    # Cleanup if needed
+                    if len(cache_storage) > maxsize:
+                        cutoff = maxsize // 2
+                        oldest_keys = (
+                            list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
+                        )
+                        for old_key in oldest_keys:
+                            cache_storage.pop(old_key, None)
+
+                    return result
+
+            wrapper = async_wrapper
+
+        else:
+            # Sync function with threading.Lock
+            cache_lock = threading.Lock()
+
+            @wraps(target_func)
+            def sync_wrapper(*args: P.args, **kwargs: P.kwargs):
+                key = _make_hashable_key(args, kwargs)
+                current_time = time.time()
+
+                # Fast path: check cache without lock
+                if key in cache_storage:
+                    if ttl_seconds is None:
+                        logger.debug(f"Cache hit for {target_func.__name__}")
+                        return cache_storage[key]
+                    else:
+                        cached_data = cache_storage[key]
+                        if isinstance(cached_data, tuple):
+                            result, timestamp = cached_data
+                            if current_time - timestamp < ttl_seconds:
+                                logger.debug(f"Cache hit for {target_func.__name__}")
+                                return result
+
+                # Slow path: acquire lock for cache miss/expiry
+                with cache_lock:
+                    # Double-check: another thread might have populated cache
+                    if key in cache_storage:
+                        if ttl_seconds is None:
+                            return cache_storage[key]
+                        else:
+                            cached_data = cache_storage[key]
+                            if isinstance(cached_data, tuple):
+                                result, timestamp = cached_data
+                                if current_time - timestamp < ttl_seconds:
+                                    return result
+
+                    # Cache miss - execute function
+                    logger.debug(f"Cache miss for {target_func.__name__}")
+                    result = target_func(*args, **kwargs)
+
+                    # Store result
+                    if ttl_seconds is None:
+                        cache_storage[key] = result
+                    else:
+                        cache_storage[key] = (result, current_time)
+
+                    # Cleanup if needed
+                    if len(cache_storage) > maxsize:
+                        cutoff = maxsize // 2
+                        oldest_keys = (
+                            list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
+                        )
+                        for old_key in oldest_keys:
+                            cache_storage.pop(old_key, None)
+
+                    return result
+
+            wrapper = sync_wrapper
+
+        # Add cache management methods
+        def cache_clear() -> None:
+            cache_storage.clear()
+
+        def cache_info() -> dict[str, int | None]:
+            return {
+                "size": len(cache_storage),
+                "maxsize": maxsize,
+                "ttl_seconds": ttl_seconds,
+            }
+
+        def cache_delete(*args, **kwargs) -> bool:
+            """Delete a specific cache entry. Returns True if entry existed."""
+            key = _make_hashable_key(args, kwargs)
+            if key in cache_storage:
+                del cache_storage[key]
+                return True
+            return False
+
+        setattr(wrapper, "cache_clear", cache_clear)
+        setattr(wrapper, "cache_info", cache_info)
+        setattr(wrapper, "cache_delete", cache_delete)
+
+        return cast(CachedFunction, wrapper)
+
+    return decorator
+
+
+def thread_cached(func):
+    """
+    Thread-local cache decorator for both sync and async functions.
+
+    Each thread gets its own cache, which is useful for request-scoped caching
+    in web applications where you want to cache within a single request but
+    not across requests.
+
+    Args:
+        func: The function to cache
+
+    Returns:
+        Decorated function with thread-local caching
+
+    Example:
+        @thread_cached
+        def expensive_operation(param: str) -> dict:
+            return {"result": param}
+
+        @thread_cached  # Works with async too
+        async def expensive_async_operation(param: str) -> dict:
+            return {"result": param}
+    """
+    thread_local = threading.local()
+
+    def _clear():
+        if hasattr(thread_local, "cache"):
+            del thread_local.cache
+
+    if inspect.iscoroutinefunction(func):
+
+        @wraps(func)
+        async def async_wrapper(*args, **kwargs):
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = _make_hashable_key(args, kwargs)
+            if key not in cache:
+                cache[key] = await func(*args, **kwargs)
+            return cache[key]
+
+        setattr(async_wrapper, "clear_cache", _clear)
+        return async_wrapper
+
+    else:
+
+        @wraps(func)
+        def sync_wrapper(*args, **kwargs):
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = _make_hashable_key(args, kwargs)
+            if key not in cache:
+                cache[key] = func(*args, **kwargs)
+            return cache[key]
+
+        setattr(sync_wrapper, "clear_cache", _clear)
+        return sync_wrapper
+
+
+def clear_thread_cache(func: Callable) -> None:
+    """Clear thread-local cache for a function."""
+    if clear := getattr(func, "clear_cache", None):
+        clear()

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py

@@ -16,7 +16,7 @@ from unittest.mock import Mock
 
 import pytest
 
-from backend.util.cache import cached, clear_thread_cache, thread_cached
+from autogpt_libs.utils.cache import cached, clear_thread_cache, thread_cached
 
 
 class TestThreadCached:
@@ -332,7 +332,7 @@ class TestCache:
         """Test basic sync caching functionality."""
         call_count = 0
 
-        @cached(ttl_seconds=300)
+        @cached()
         def expensive_sync_function(x: int, y: int = 0) -> int:
             nonlocal call_count
             call_count += 1
@@ -358,7 +358,7 @@ class TestCache:
         """Test basic async caching functionality."""
         call_count = 0
 
-        @cached(ttl_seconds=300)
+        @cached()
         async def expensive_async_function(x: int, y: int = 0) -> int:
             nonlocal call_count
             call_count += 1
@@ -385,7 +385,7 @@ class TestCache:
         call_count = 0
         results = []
 
-        @cached(ttl_seconds=300)
+        @cached()
         def slow_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
@@ -412,7 +412,7 @@ class TestCache:
         """Test that concurrent async calls don't cause thundering herd."""
         call_count = 0
 
-        @cached(ttl_seconds=300)
+        @cached()
         async def slow_async_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
@@ -508,7 +508,7 @@ class TestCache:
         """Test cache clearing functionality."""
         call_count = 0
 
-        @cached(ttl_seconds=300)
+        @cached()
         def clearable_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
@@ -537,7 +537,7 @@ class TestCache:
         """Test cache clearing functionality with async function."""
         call_count = 0
 
-        @cached(ttl_seconds=300)
+        @cached()
         async def async_clearable_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
@@ -567,7 +567,7 @@ class TestCache:
         """Test that cached async functions return actual results, not coroutines."""
         call_count = 0
 
-        @cached(ttl_seconds=300)
+        @cached()
         async def async_result_function(x: int) -> str:
             nonlocal call_count
             call_count += 1
@@ -593,7 +593,7 @@ class TestCache:
         """Test selective cache deletion functionality."""
         call_count = 0
 
-        @cached(ttl_seconds=300)
+        @cached()
         def deletable_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
@@ -636,7 +636,7 @@ class TestCache:
         """Test selective cache deletion functionality with async function."""
         call_count = 0
 
-        @cached(ttl_seconds=300)
+        @cached()
         async def async_deletable_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
@@ -674,450 +674,3 @@ class TestCache:
         # Try to delete non-existent entry
         was_deleted = async_deletable_function.cache_delete(99)
         assert was_deleted is False
-
-
-class TestSharedCache:
-    """Tests for shared_cache (Redis-backed) functionality."""
-
-    def test_sync_shared_cache_basic(self):
-        """Test basic shared cache functionality with sync function."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def shared_sync_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x + y
-
-        # Clear any existing cache
-        shared_sync_function.cache_clear()
-
-        # First call
-        result1 = shared_sync_function(10, 20)
-        assert result1 == 30
-        assert call_count == 1
-
-        # Second call - should use Redis cache
-        result2 = shared_sync_function(10, 20)
-        assert result2 == 30
-        assert call_count == 1
-
-        # Different args - should call function again
-        result3 = shared_sync_function(15, 25)
-        assert result3 == 40
-        assert call_count == 2
-
-        # Cleanup
-        shared_sync_function.cache_clear()
-
-    @pytest.mark.asyncio
-    async def test_async_shared_cache_basic(self):
-        """Test basic shared cache functionality with async function."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        async def shared_async_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return x + y
-
-        # Clear any existing cache
-        shared_async_function.cache_clear()
-
-        # First call
-        result1 = await shared_async_function(10, 20)
-        assert result1 == 30
-        assert call_count == 1
-
-        # Second call - should use Redis cache
-        result2 = await shared_async_function(10, 20)
-        assert result2 == 30
-        assert call_count == 1
-
-        # Different args - should call function again
-        result3 = await shared_async_function(15, 25)
-        assert result3 == 40
-        assert call_count == 2
-
-        # Cleanup
-        shared_async_function.cache_clear()
-
-    def test_shared_cache_ttl_refresh(self):
-        """Test TTL refresh functionality with shared cache."""
-        call_count = 0
-
-        @cached(ttl_seconds=2, shared_cache=True, refresh_ttl_on_get=True)
-        def ttl_refresh_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 10
-
-        # Clear any existing cache
-        ttl_refresh_function.cache_clear()
-
-        # First call
-        result1 = ttl_refresh_function(3)
-        assert result1 == 30
-        assert call_count == 1
-
-        # Wait 1 second
-        time.sleep(1)
-
-        # Second call - should refresh TTL and use cache
-        result2 = ttl_refresh_function(3)
-        assert result2 == 30
-        assert call_count == 1
-
-        # Wait another 1.5 seconds (total 2.5s from first call, 1.5s from second)
-        time.sleep(1.5)
-
-        # Third call - TTL should have been refreshed, so still cached
-        result3 = ttl_refresh_function(3)
-        assert result3 == 30
-        assert call_count == 1
-
-        # Wait 2.1 seconds - now it should expire
-        time.sleep(2.1)
-
-        # Fourth call - should call function again
-        result4 = ttl_refresh_function(3)
-        assert result4 == 30
-        assert call_count == 2
-
-        # Cleanup
-        ttl_refresh_function.cache_clear()
-
-    def test_shared_cache_without_ttl_refresh(self):
-        """Test that TTL doesn't refresh when refresh_ttl_on_get=False."""
-        call_count = 0
-
-        @cached(ttl_seconds=2, shared_cache=True, refresh_ttl_on_get=False)
-        def no_ttl_refresh_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 10
-
-        # Clear any existing cache
-        no_ttl_refresh_function.cache_clear()
-
-        # First call
-        result1 = no_ttl_refresh_function(4)
-        assert result1 == 40
-        assert call_count == 1
-
-        # Wait 1 second
-        time.sleep(1)
-
-        # Second call - should use cache but NOT refresh TTL
-        result2 = no_ttl_refresh_function(4)
-        assert result2 == 40
-        assert call_count == 1
-
-        # Wait another 1.1 seconds (total 2.1s from first call)
-        time.sleep(1.1)
-
-        # Third call - should have expired
-        result3 = no_ttl_refresh_function(4)
-        assert result3 == 40
-        assert call_count == 2
-
-        # Cleanup
-        no_ttl_refresh_function.cache_clear()
-
-    def test_shared_cache_complex_objects(self):
-        """Test caching complex objects with shared cache (pickle serialization)."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def complex_object_function(x: int) -> dict:
-            nonlocal call_count
-            call_count += 1
-            return {
-                "number": x,
-                "squared": x**2,
-                "nested": {"list": [1, 2, x], "tuple": (x, x * 2)},
-                "string": f"value_{x}",
-            }
-
-        # Clear any existing cache
-        complex_object_function.cache_clear()
-
-        # First call
-        result1 = complex_object_function(5)
-        assert result1["number"] == 5
-        assert result1["squared"] == 25
-        assert result1["nested"]["list"] == [1, 2, 5]
-        assert call_count == 1
-
-        # Second call - should use cache
-        result2 = complex_object_function(5)
-        assert result2 == result1
-        assert call_count == 1
-
-        # Cleanup
-        complex_object_function.cache_clear()
-
-    def test_shared_cache_info(self):
-        """Test cache_info for shared cache."""
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def info_shared_function(x: int) -> int:
-            return x * 2
-
-        # Clear any existing cache
-        info_shared_function.cache_clear()
-
-        # Check initial info
-        info = info_shared_function.cache_info()
-        assert info["size"] == 0
-        assert info["maxsize"] is None  # Redis manages size
-        assert info["ttl_seconds"] == 30
-
-        # Add some entries
-        info_shared_function(1)
-        info_shared_function(2)
-        info_shared_function(3)
-
-        info = info_shared_function.cache_info()
-        assert info["size"] == 3
-
-        # Cleanup
-        info_shared_function.cache_clear()
-
-    def test_shared_cache_delete(self):
-        """Test selective deletion with shared cache."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def delete_shared_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 3
-
-        # Clear any existing cache
-        delete_shared_function.cache_clear()
-
-        # Add entries
-        delete_shared_function(1)
-        delete_shared_function(2)
-        delete_shared_function(3)
-        assert call_count == 3
-
-        # Verify cached
-        delete_shared_function(1)
-        delete_shared_function(2)
-        assert call_count == 3
-
-        # Delete specific entry
-        was_deleted = delete_shared_function.cache_delete(2)
-        assert was_deleted is True
-
-        # Entry for x=2 should be gone
-        delete_shared_function(2)
-        assert call_count == 4
-
-        # Others should still be cached
-        delete_shared_function(1)
-        delete_shared_function(3)
-        assert call_count == 4
-
-        # Try to delete non-existent
-        was_deleted = delete_shared_function.cache_delete(99)
-        assert was_deleted is False
-
-        # Cleanup
-        delete_shared_function.cache_clear()
-
-    @pytest.mark.asyncio
-    async def test_async_shared_cache_thundering_herd(self):
-        """Test that shared cache prevents thundering herd for async functions."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        async def shared_slow_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.1)
-            return x * x
-
-        # Clear any existing cache
-        shared_slow_function.cache_clear()
-
-        # Launch multiple concurrent tasks
-        tasks = [shared_slow_function(8) for _ in range(10)]
-        results = await asyncio.gather(*tasks)
-
-        # All should return same result
-        assert all(r == 64 for r in results)
-        # Only one should have executed
-        assert call_count == 1
-
-        # Cleanup
-        shared_slow_function.cache_clear()
-
-    def test_shared_cache_clear_pattern(self):
-        """Test pattern-based cache clearing (Redis feature)."""
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def pattern_function(category: str, item: int) -> str:
-            return f"{category}_{item}"
-
-        # Clear any existing cache
-        pattern_function.cache_clear()
-
-        # Add various entries
-        pattern_function("fruit", 1)
-        pattern_function("fruit", 2)
-        pattern_function("vegetable", 1)
-        pattern_function("vegetable", 2)
-
-        info = pattern_function.cache_info()
-        assert info["size"] == 4
-
-        # Note: Pattern clearing with wildcards requires specific Redis scan
-        # implementation. The current code clears by pattern but needs
-        # adjustment for partial matching. For now, test full clear.
-        pattern_function.cache_clear()
-        info = pattern_function.cache_info()
-        assert info["size"] == 0
-
-    def test_shared_vs_local_cache_isolation(self):
-        """Test that shared and local caches are isolated."""
-        shared_count = 0
-        local_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def shared_function(x: int) -> int:
-            nonlocal shared_count
-            shared_count += 1
-            return x * 2
-
-        @cached(ttl_seconds=30, shared_cache=False)
-        def local_function(x: int) -> int:
-            nonlocal local_count
-            local_count += 1
-            return x * 2
-
-        # Clear caches
-        shared_function.cache_clear()
-        local_function.cache_clear()
-
-        # Call both with same args
-        shared_result = shared_function(5)
-        local_result = local_function(5)
-
-        assert shared_result == local_result == 10
-        assert shared_count == 1
-        assert local_count == 1
-
-        # Call again - both should use their respective caches
-        shared_function(5)
-        local_function(5)
-        assert shared_count == 1
-        assert local_count == 1
-
-        # Clear only shared cache
-        shared_function.cache_clear()
-
-        # Shared should recompute, local should still use cache
-        shared_function(5)
-        local_function(5)
-        assert shared_count == 2
-        assert local_count == 1
-
-        # Cleanup
-        shared_function.cache_clear()
-        local_function.cache_clear()
-
-    @pytest.mark.asyncio
-    async def test_shared_cache_concurrent_different_keys(self):
-        """Test that concurrent calls with different keys work correctly."""
-        call_counts = {}
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        async def multi_key_function(key: str) -> str:
-            if key not in call_counts:
-                call_counts[key] = 0
-            call_counts[key] += 1
-            await asyncio.sleep(0.05)
-            return f"result_{key}"
-
-        # Clear cache
-        multi_key_function.cache_clear()
-
-        # Launch concurrent tasks with different keys
-        keys = ["a", "b", "c", "d", "e"]
-        tasks = []
-        for key in keys:
-            # Multiple calls per key
-            tasks.extend([multi_key_function(key) for _ in range(3)])
-
-        results = await asyncio.gather(*tasks)
-
-        # Verify results
-        for i, key in enumerate(keys):
-            expected = f"result_{key}"
-            # Each key appears 3 times in results
-            key_results = results[i * 3 : (i + 1) * 3]
-            assert all(r == expected for r in key_results)
-
-        # Each key should only be computed once
-        for key in keys:
-            assert call_counts[key] == 1
-
-        # Cleanup
-        multi_key_function.cache_clear()
-
-    def test_shared_cache_performance_comparison(self):
-        """Compare performance of shared vs local cache."""
-        import statistics
-
-        shared_times = []
-        local_times = []
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def shared_perf_function(x: int) -> int:
-            time.sleep(0.01)  # Simulate work
-            return x * 2
-
-        @cached(ttl_seconds=30, shared_cache=False)
-        def local_perf_function(x: int) -> int:
-            time.sleep(0.01)  # Simulate work
-            return x * 2
-
-        # Clear caches
-        shared_perf_function.cache_clear()
-        local_perf_function.cache_clear()
-
-        # Warm up both caches
-        for i in range(5):
-            shared_perf_function(i)
-            local_perf_function(i)
-
-        # Measure cache hit times
-        for i in range(5):
-            # Shared cache hit
-            start = time.time()
-            shared_perf_function(i)
-            shared_times.append(time.time() - start)
-
-            # Local cache hit
-            start = time.time()
-            local_perf_function(i)
-            local_times.append(time.time() - start)
-
-        # Local cache should be faster (no Redis round-trip)
-        avg_shared = statistics.mean(shared_times)
-        avg_local = statistics.mean(local_times)
-
-        print(f"Avg shared cache hit time: {avg_shared:.6f}s")
-        print(f"Avg local cache hit time: {avg_local:.6f}s")
-
-        # Local should be significantly faster for cache hits
-        # Redis adds network latency even for cache hits
-        assert avg_local < avg_shared
-
-        # Cleanup
-        shared_perf_function.cache_clear()
-        local_perf_function.cache_clear()

autogpt_platform/backend/.env.default

@@ -58,13 +58,6 @@ V0_API_KEY=
 OPEN_ROUTER_API_KEY=
 NVIDIA_API_KEY=
 
-# Langfuse Prompt Management
-# Used for managing the CoPilot system prompt externally
-# Get credentials from https://cloud.langfuse.com or your self-hosted instance
-LANGFUSE_PUBLIC_KEY=
-LANGFUSE_SECRET_KEY=
-LANGFUSE_HOST=https://cloud.langfuse.com
-
 # OAuth Credentials
 # For the OAuth callback URL, use <your_frontend_url>/auth/integrations/oauth_callback,
 # e.g. http://localhost:3000/auth/integrations/oauth_callback
@@ -141,6 +134,13 @@ POSTMARK_WEBHOOK_TOKEN=
 # Error Tracking
 SENTRY_DSN=
 
+# Cloudflare Turnstile (CAPTCHA) Configuration
+# Get these from the Cloudflare Turnstile dashboard: https://dash.cloudflare.com/?to=/:account/turnstile
+# This is the backend secret key
+TURNSTILE_SECRET_KEY=
+# This is the verify URL
+TURNSTILE_VERIFY_URL=https://challenges.cloudflare.com/turnstile/v0/siteverify
+
 # Feature Flags
 LAUNCH_DARKLY_SDK_KEY=
 

autogpt_platform/backend/.gitignore

@@ -18,4 +18,3 @@ load-tests/results/
 load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
-migrations/*/rollback*.sql

autogpt_platform/backend/Dockerfile

@@ -47,9 +47,7 @@ RUN poetry install --no-ansi --no-root
 
 # Generate Prisma client
 COPY autogpt_platform/backend/schema.prisma ./
-COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
-COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
-RUN poetry run prisma generate && poetry run gen-prisma-stub
+RUN poetry run prisma generate
 
 FROM debian:13-slim AS server_dependencies
 
@@ -94,13 +92,11 @@ FROM server_dependencies AS migrate
 
 # Migration stage only needs schema and migrations - much lighter than full backend
 COPY autogpt_platform/backend/schema.prisma /app/autogpt_platform/backend/
-COPY autogpt_platform/backend/backend/data/partial_types.py /app/autogpt_platform/backend/backend/data/partial_types.py
 COPY autogpt_platform/backend/migrations /app/autogpt_platform/backend/migrations
 
 FROM server_dependencies AS server
 
 COPY autogpt_platform/backend /app/autogpt_platform/backend
-COPY docs /app/docs
 RUN poetry install --no-ansi --only-root
 
 ENV PORT=8000

autogpt_platform/backend/TESTING.md

@@ -108,7 +108,7 @@ import fastapi.testclient
 import pytest
 from pytest_snapshot.plugin import Snapshot
 
-from backend.api.features.myroute import router
+from backend.server.v2.myroute import router
 
 app = fastapi.FastAPI()
 app.include_router(router)
@@ -149,7 +149,7 @@ These provide the easiest way to set up authentication mocking in test modules:
 import fastapi
 import fastapi.testclient
 import pytest
-from backend.api.features.myroute import router
+from backend.server.v2.myroute import router
 
 app = fastapi.FastAPI()
 app.include_router(router)

autogpt_platform/backend/agents/StoreAgent_rows.csv

@@ -1,242 +0,0 @@
-listing_id,storeListingVersionId,slug,agent_name,agent_video,agent_image,featured,sub_heading,description,categories,useForOnboarding,is_available
-6e60a900-9d7d-490e-9af2-a194827ed632,d85882b8-633f-44ce-a315-c20a8c123d19,flux-ai-image-generator,Flux AI Image Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ca154dd1-140e-454c-91bd-2d8a00de3f08.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/577d995d-bc38-40a9-a23f-1f30f5774bdb.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/415db1b7-115c-43ab-bd6c-4e9f7ef95be1.jpg""]",false,Transform ideas into breathtaking images,"Transform ideas into breathtaking images with this AI-powered Image Generator. Using cutting-edge Flux AI technology, the tool crafts highly detailed, photorealistic visuals from simple text prompts. Perfect for artists, marketers, and content creators, this generator produces unique images tailored to user specifications. From fantastical scenes to lifelike portraits, users can unleash creativity with professional-quality results in seconds. Easy to use and endlessly versatile, bring imagination to life with the AI Image Generator today!","[""creative""]",false,true
-f11fc6e9-6166-4676-ac5d-f07127b270c1,c775f60d-b99f-418b-8fe0-53172258c3ce,youtube-transcription-scraper,YouTube Transcription Scraper,https://youtu.be/H8S3pU68lGE,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/65bce54b-0124-4b0d-9e3e-f9b89d0dc99e.jpg""]",false,Fetch the transcriptions from the most popular YouTube videos in your chosen topic,"Effortlessly gather transcriptions from multiple YouTube videos with this agent. It scrapes and compiles video transcripts into a clean, organized list, making it easy to extract insights, quotes, or content from various sources in one go. Ideal for researchers, content creators, and marketers looking to quickly analyze or repurpose video content.","[""writing""]",false,true
-17908889-b599-4010-8e4f-bed19b8f3446,6e16e65a-ad34-4108-b4fd-4a23fced5ea2,business-ownerceo-finder,Decision Maker Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/1020d94e-b6a2-4fa7-bbdf-2c218b0de563.jpg""]",false,Contact CEOs today,"Find the key decision-makers you need, fast.
-
-This agent identifies business owners or CEOs of local companies in any area you choose. Simply enter what kind of businesses you’re looking for and where, and it will:
-
-* Search the area and gather public information
-* Return names, roles, and contact details when available
-* Provide smart Google search suggestions if details aren’t found
-
-Perfect for:
-
-* B2B sales teams seeking verified leads
-* Recruiters sourcing local talent
-* Researchers looking to connect with business leaders
-
-Save hours of manual searching and get straight to the people who matter most.","[""business""]",true,true
-72beca1d-45ea-4403-a7ce-e2af168ee428,415b7352-0dc6-4214-9d87-0ad3751b711d,smart-meeting-brief,Smart Meeting Prep,https://youtu.be/9ydZR2hkxaY,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2f116ce1-63ae-4d39-a5cd-f514defc2b97.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0a71a60a-2263-4f12-9836-9c76ab49f155.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/95327695-9184-403c-907a-a9d3bdafa6a5.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2bc77788-790b-47d4-8a61-ce97b695e9f5.png""]",true,Business meeting briefings delivered daily,"Never walk into a meeting unprepared again. Every day at 4 pm, the Smart Meeting Prep Agent scans your calendar for tomorrow's external meetings. It reviews your past email exchanges, researches each participant's background and role, and compiles the insights into a concise briefing, so you can close your workday ready for tomorrow's calls.
-
-How It Works
-1. At 4 pm, the agent scans your calendar and identifies external meetings scheduled for the next day.
-2. It reviews recent email threads with each participant to surface key relationship history and communication context.
-3. It conducts online research to gather publicly available information on roles, company backgrounds, and relevant professional data.
-4. It produces a unified briefing for each participant, including past exchange highlights, profile notes, and strategic conversation points.","[""personal""]",true,true
-9fa5697a-617b-4fae-aea0-7dbbed279976,b8ceb480-a7a2-4c90-8513-181a49f7071f,automated-support-ai,Automated Support Agent,https://youtu.be/nBMfu_5sgDA,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ed56febc-2205-4179-9e7e-505d8500b66c.png""]",true,Automate up to 80 percent of inbound support emails,"Overview:
-Support teams spend countless hours on basic tickets. This agent automates repetitive customer support tasks. It reads incoming requests, researches your knowledge base, and responds automatically when confident. When unsure, it escalates to a human for final resolution.
-
-How it Works:
-New support emails are routed to the agent.
-The agent checks internal documentation for answers.
-It measures confidence in the answer found and either replies directly or escalates to a human.
-
-Business Value:
-Automating the easy 80 percent of support tickets allows your team to focus on high-value, complex customer issues, improving efficiency and response times.","[""business""]",false,true
-2bdac92b-a12c-4131-bb46-0e3b89f61413,31daf49d-31d3-476b-aa4c-099abc59b458,unspirational-poster-maker,Unspirational Poster Maker,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a490dac-27e5-405f-a4c4-8d1c55b85060.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d343fbb5-478c-4e38-94df-4337293b61f1.jpg""]",false,Because adulting is hard,"This witty AI agent generates hilariously relatable ""motivational"" posters that tackle the everyday struggles of procrastination, overthinking, and workplace chaos with a blend of absurdity and sarcasm. From goldfish facing impossible tasks to cats in existential crises, The Unspirational Poster Maker designs tongue-in-cheek graphics and captions that mock productivity clichés and embrace our collective struggles to ""get it together."" Perfect for adding a touch of humour to the workday, these posters remind us that sometimes, all we can do is laugh at the chaos.","[""creative""]",false,true
-9adf005e-2854-4cc7-98cf-f7103b92a7b7,a03b0d8c-4751-43d6-a54e-c3b7856ba4e3,ai-shortform-video-generator-create-viral-ready-content,AI Video Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/8d2670b9-fea5-4966-a597-0a4511bffdc3.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/aabe8aec-0110-4ce7-a259-4f86fe8fe07d.png""]",false,Create Viral-Ready Shorts Content in Seconds,"OVERVIEW
-Transform any trending headline or broad topic into a polished, vertical short-form video in a single run.  
-The agent automates research, scriptwriting, metadata creation, and Revid.ai rendering, returning one ready-to-publish MP4 plus its title, script and hashtags.
-
-HOW IT WORKS
-1. Input a topic or an exact news headline.  
-2. The agent fetches live search results and selects the most engaging related story.  
-3. Key facts are summarised into concise research notes.  
-4. Claude writes a 30–35 second script with visual cues, a three-second hook, tension loops, and a call-to-action.  
-5. GPT-4o generates an eye-catching title and one or two discoverability hashtags.  
-6. The script is sent to a state-of-the-art AI video generator to render a single 9:16 MP4 (default: 720 p, 30 fps, voice “Brian”, style “movingImage”, music “Bladerunner 2049”).  
-   – All voice, style and resolution settings can be adjusted in the Builder before you press ""Run"".  
-7. Output delivered: Title, Script, Hashtags, Video URL.
-
-KEY USE CASES
-- Broad-topic explainers (e.g. “Artificial Intelligence” or “Climate Tech”).  
-- Real-time newsjacking with a specific breaking headline.  
-- Product-launch spotlights and quick event recaps while interest is high.  
-
-BUSINESS VALUE
-- One-click speed: from idea to finished video in minutes.  
-- Consistent brand look: Revid presets keep voice, style and aspect ratio on spec.  
-- No-code workflow: marketers create social video without design or development queues.  
-- Cloud convenience: Auto-GPT Cloud users are pre-configured with all required keys.  
-  Self-hosted users simply add OpenAI, Anthropic, Perplexity (OpenRouter/Jina) and Revid keys once.
-
-IMPORTANT NOTES
-- The agent outputs exactly one video per execution. Run it again for additional shorts.  
-- Video rendering time varies; AI-generated footage may take several minutes.","[""writing""]",false,true
-864e48ef-fee5-42c1-b6a4-2ae139db9fc1,55d40473-0f31-4ada-9e40-d3a7139fcbd4,automated-blog-writer,Automated SEO Blog Writer,https://youtu.be/nKcDCbDVobs,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2dd5f95b-5b30-4bf8-a11b-bac776c5141a.jpg""]",true,"Automate research, writing, and publishing for high-ranking blog posts","Scale your blog with a fully automated content engine. The Automated SEO Blog Writer learns your brand voice, finds high-demand keywords, and creates SEO-optimized articles that attract organic traffic and boost visibility.
-
-How it works:
-
-1. Share your pitch, website, and values.
-2. The agent studies your site and uncovers proven SEO opportunities.
-3. It spends two hours researching and drafting each post.
-4. You set the cadence—publishing runs on autopilot.
-
-Business value: Consistently publish research-backed, optimized posts that build domain authority, rankings, and thought leadership while you focus on what matters most.
-
-Use cases:
-• Founders: Keep your blog active with no time drain.
-• Agencies: Deliver scalable SEO content for clients.
-• Strategists: Automate execution, focus on strategy.
-• Marketers: Drive steady organic growth.
-• Local businesses: Capture nearby search traffic.","[""writing""]",false,true
-6046f42e-eb84-406f-bae0-8e052064a4fa,a548e507-09a7-4b30-909c-f63fcda10fff,lead-finder-local-businesses,Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/abd6605f-d5f8-426b-af36-052e8ba5044f.webp""]",false,Auto-Prospect Like a Pro,"Turbo-charge your local lead generation with the AutoGPT Marketplace’s top Google Maps prospecting agent. “Lead Finder: Local Businesses” delivers verified, ready-to-contact prospects in any niche and city—so you can focus on closing, not searching.
-
-**WHAT IT DOES**
-• Searches Google Maps via the official API (no scraping)
-• Prompts like “dentists in Chicago” or “coffee shops near me”
-• Returns: Name, Website, Rating, Reviews, **Phone & Address**
-• Exports instantly to your CRM, sheet, or outreach workflow
-
-**WHY YOU’LL LOVE IT**
-✓ Hyper-targeted leads in minutes
-✓ Unlimited searches & locations
-✓ Zero CAPTCHAs or IP blocks
-✓ Works on AutoGPT Cloud or self-hosted (with your API key)
-✓ Cut prospecting time by 90%
-
-**PERFECT FOR**
-— Marketers & PPC agencies
-— SEO consultants & designers
-— SaaS founders & sales teams
-
-Stop scrolling directories—start filling your pipeline. Start now and let AI prospect while you profit.
-
-→ Click *Add to Library* and own your market today.","[""business""]",true,true
-f623c862-24e9-44fc-8ce8-d8282bb51ad2,eafa21d3-bf14-4f63-a97f-a5ee41df83b3,linkedin-post-generator,LinkedIn Post Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/297f6a8e-81a8-43e2-b106-c7ad4a5662df.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/fceebdc1-aef6-4000-97fc-4ef587f56bda.png""]",false,Auto‑craft LinkedIn gold,"Create research‑driven, high‑impact LinkedIn posts in minutes. This agent searches YouTube for the best videos on your chosen topic, pulls their transcripts, and distils the most valuable insights into a polished post ready for your company page or personal feed.  
-
-FEATURES  
-• Automated YouTube research – discovers and analyses top‑ranked videos so you don’t have to  
-• AI‑curated synthesis – combines multiple transcripts into one authoritative narrative  
-• Full creative control – adjust style, tone, objective, opinion, clarity, target word count and number of videos  
-• LinkedIn‑optimised output – hook, 2‑3 key points, CTA, strategic line breaks, 3‑5 hashtags, no markdown  
-• One‑click publish – returns a ready‑to‑post text block (≤1 300 characters)  
-
-HOW IT WORKS  
-1. Enter a topic and your preferred writing parameters.  
-2. The agent builds a YouTube search, fetches the page, and extracts the top N video URLs.  
-3. It pulls each transcript, then feeds them—plus your settings—into Claude 3.5 Sonnet.  
-4. The model writes a concise, engaging post designed for maximum LinkedIn engagement.  
-
-USE CASES  
-• Thought‑leadership updates backed by fresh video research  
-• Rapid industry summaries after major events, webinars, or conferences  
-• Consistent LinkedIn content for busy founders, marketers, and creators  
-
-WHY YOU’LL LOVE IT  
-Save hours of manual research, avoid surface‑level hot‑takes, and publish posts that showcase real expertise—without the heavy lift.","[""writing""]",true,true
-7d4120ad-b6b3-4419-8bdb-7dd7d350ef32,e7bb29a1-23c7-4fee-aa3b-5426174b8c52,youtube-to-linkedin-post-converter,YouTube to LinkedIn Post Converter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f084b326-a708-4396-be51-7ba59ad2ef32.png""]",false,Transform Your YouTube Videos into Engaging LinkedIn Posts with AI,"WHAT IT DOES: 
-This agent converts YouTube video content into a LinkedIn post by analyzing the video's transcript. It provides you with a tailored post that reflects the core ideas, key takeaways, and tone of the original video, optimizing it for engagement on LinkedIn.
-
-HOW IT WORKS:
-- You provide the URL to the YouTube video (required)
-- You can choose the structure for the LinkedIn post (e.g., Personal Achievement Story, Lesson Learned, Thought Leadership, etc.)
-- You can also select the tone (e.g., Inspirational, Analytical, Conversational, etc.)
-- The transcript of the video is analyzed by the GPT-4 model and the Claude 3.5 Sonnet model
-- The models extract key insights, memorable quotes, and the main points from the video
-- You’ll receive a LinkedIn post, formatted according to your chosen structure and tone, optimized for professional engagement
-
-INPUTS:
-- Source YouTube Video – Provide the URL to the YouTube video
-- Structure – Choose the post format (e.g., Personal Achievement Story, Thought Leadership, etc.)
-- Content – Specify the main message or idea of the post (e.g., Hot Take, Key Takeaways, etc.)
-- Tone – Select the tone for the post (e.g., Conversational, Inspirational, etc.)
-
-OUTPUT:
-- LinkedIn Post – A well-crafted, AI-generated LinkedIn post with a professional tone, based on the video content and your specified preferences
-
-Perfect for content creators, marketers, and professionals who want to repurpose YouTube videos for LinkedIn and boost their professional branding.","[""writing""]",false,true
-c61d6a83-ea48-4df8-b447-3da2d9fe5814,00fdd42c-a14c-4d19-a567-65374ea0e87f,personalized-morning-coffee-newsletter,Personal Newsletter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f4b38e4c-8166-4caf-9411-96c9c4c82d4c.png""]",false,Start your day with personalized AI newsletters that deliver credibility and context for every interest or mood.,"This Personal Newsletter Agent provides a bespoke daily digest on your favorite topics and tone. Whether you prefer industry insights, lighthearted reads, or breaking news, this agent crafts your own unique newsletter to keep you informed and entertained.
-
-
-How It Works
-1. Enter your favorite topics, industries, or areas of interest.
-2. Choose your tone—professional, casual, or humorous.
-3. Set your preferred delivery cadence: daily or weekly.
-4. The agent scans top sources and compiles 3–5 engaging stories, insights, and fun facts into a conversational newsletter.
-
-Skip the morning scroll and enjoy a thoughtfully curated newsletter designed just for you. Stay ahead of trends, spark creative ideas, and enjoy an effortless, informed start to your day.
-
-
-Use Cases
-• Executives: Get a daily digest of market updates and leadership insights.
-• Marketers: Receive curated creative trends and campaign inspiration.
-• Entrepreneurs: Stay updated on your industry without information overload.","[""research""]",true,true
-e2e49cfc-4a39-4d62-a6b3-c095f6d025ff,fc2c9976-0962-4625-a27b-d316573a9e7f,email-address-finder,Email Scout - Contact Finder Assistant,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/da8a690a-7a8b-4c1d-b6f8-e2f840c0205d.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a2ac25c-1609-4881-8140-e6da2421afb3.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/26179263-fe06-45bd-b6a0-0754660a0a46.jpg""]",false,Find contact details from name and location using AI search,"Finding someone's professional email address can be time-consuming and frustrating. Manual searching across multiple websites, social profiles, and business directories often leads to dead ends or outdated information.
-
-Email Scout automates this process by intelligently searching across publicly available sources when you provide a person's name and location. Simply input basic information like ""Tim Cook, USA"" or ""Sarah Smith, London"" and let the AI assistant do the work of finding potential contact details.
-
-Key Features:
-- Quick search from just name and location
-- Scans multiple public sources
-- Automated AI-powered search process
-- Easy to use with simple inputs
-
-Perfect for recruiters, business development professionals, researchers, and anyone needing to establish professional contact.
-
-Note: This tool searches only publicly available information. Search results depend on what contact information people have made public. Some searches may not yield results if the information isn't publicly accessible.","[""""]",false,true
-81bcc372-0922-4a36-bc35-f7b1e51d6939,e437cc95-e671-489d-b915-76561fba8c7f,ai-youtube-to-blog-converter,YouTube Video to SEO Blog Writer,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/239e5a41-2515-4e1c-96ef-31d0d37ecbeb.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/c7d96966-786f-4be6-ad7d-3a51c84efc0e.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0275a74c-e2c2-4e29-a6e4-3a616c3c35dd.png""]",false,One link. One click. One powerful blog post.,"Effortlessly transform your YouTube videos into high-quality, SEO-optimized blog posts.
-
-Your videos deserve a second life—in writing.  
-Make your content work twice as hard by repurposing it into engaging, searchable articles.
-
-Perfect for content creators, marketers, and bloggers, this tool analyzes video content and generates well-structured blog posts tailored to your tone, audience, and word count. Just paste a YouTube URL and let the AI handle the rest.
-
-FEATURES
-
-• CONTENT ANALYSIS  
-  Extracts key points from the video while preserving your message and intent.
-
-• CUSTOMIZABLE OUTPUT  
-  Select a tone that fits your audience: casual, professional, educational, or formal.
-
-• SEO OPTIMIZATION  
-  Automatically creates engaging titles and structured subheadings for better search visibility.
-
-• USER-FRIENDLY  
-  Repurpose your videos into written content to expand your reach and improve accessibility.
-
-Whether you're looking to grow your blog, boost SEO, or simply get more out of your content, the AI YouTube-to-Blog Converter makes it effortless.
-","[""writing""]",true,true
-5c3510d2-fc8b-4053-8e19-67f53c86eb1a,f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9,ai-webpage-copy-improver,AI Webpage Copy Improver,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d562d26f-5891-4b09-8859-fbb205972313.jpg""]",false,Boost Your Website's Search Engine Performance,"Elevate your web content with this powerful AI Webpage Copy Improver. Designed for marketers, SEO specialists, and web developers, this tool analyses and enhances website copy for maximum impact. Using advanced language models, it optimizes text for better clarity, SEO performance, and increased conversion rates. The AI examines your existing content, identifies areas for improvement, and generates refined copy that maintains your brand voice while boosting engagement. From homepage headlines to product descriptions, transform your web presence with AI-driven insights. Improve readability, incorporate targeted keywords, and craft compelling calls-to-action - all with the click of a button. Take your digital marketing to the next level with the AI Webpage Copy Improver.","[""marketing""]",true,true
-94d03bd3-7d44-4d47-b60c-edb2f89508d6,b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0,domain-name-finder,Domain Name Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/28545e09-b2b8-4916-b4c6-67f982510a78.jpeg""]",false,Instantly generate brand-ready domain names that are actually available,"Overview:
-Finding a domain name that fits your brand shouldn’t take hours of searching and failed checks. The Domain Name Finder Agent turns your pitch into hundreds of creative, brand-ready domain ideas—filtered by live availability so every result is actionable.
-
-How It Works
-1. Input your product pitch, company name, or core keywords.
-2. The agent analyzes brand tone, audience, and industry context.
-3. It generates a list of unique, memorable domains that match your criteria.
-4. All names are pre-filtered for real-time availability, so you can register immediately.
-
-
-Business Value
-Save hours of guesswork and eliminate dead ends. Accelerate brand launches, startup naming, and campaign creation with ready-to-claim domains.
-
-
-Key Use Cases
-• Startup Founders: Quickly find brand-ready domains for MVP launches or rebrands.
-• Marketers: Test name options across campaigns with instant availability data.
-• Entrepreneurs: Validate ideas faster with instant domain options.","[""business""]",false,true
-7a831906-daab-426f-9d66-bcf98d869426,516d813b-d1bc-470f-add7-c63a4b2c2bad,ai-function,AI Function,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/620e8117-2ee1-4384-89e6-c2ef4ec3d9c9.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/476259e2-5a79-4a7b-8e70-deeebfca70d7.png""]",false,Never Code Again,"AI FUNCTION MAGIC  
-Your AI‑powered assistant for turning plain‑English descriptions into working Python functions.  
-
-HOW IT WORKS  
-1. Describe what the function should do.  
-2. Specify the inputs it needs.  
-3. Receive the generated Python code.  
-
-FEATURES  
-- Effortless Function Generation: convert natural‑language specs into complete functions.  
-- Customizable Inputs: define the parameters that matter to you.  
-- Versatile Use Cases: simulate data, automate tasks, prototype ideas.  
-- Seamless Integration: add the generated function directly to your codebase.  
-
-EXAMPLE  
-Request: “Create a function that generates 20 examples of fake people, each with a name, date of birth, job title, and age.”  
-Input parameter: number_of_people (default 20)  
-Result: a list of dictionaries such as  
-[  
-  { ""name"": ""Emma Martinez"",  ""date_of_birth"": ""1992‑11‑03"", ""job_title"": ""Data Analyst"",        ""age"": 32 },  
-  { ""name"": ""Liam O’Connor"",  ""date_of_birth"": ""1985‑07‑19"", ""job_title"": ""Marketing Manager"",  ""age"": 39 },  
-  …18 more entries…  
-]","[""development""]",false,true

autogpt_platform/backend/agents/agent_31daf49d-31d3-476b-aa4c-099abc59b458.json

@@ -1,590 +0,0 @@
-{
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-  "version": 29,
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autogpt_platform/backend/agents/agent_d85882b8-633f-44ce-a315-c20a8c123d19.json

@@ -1,403 +0,0 @@
-{
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autogpt_platform/backend/agents/agent_f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9.json

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-          "gpt-4o-mini": "openai",
-          "gpt-5-2025-08-07": "openai",
-          "gpt-5-chat-latest": "openai",
-          "gpt-5-mini-2025-08-07": "openai",
-          "gpt-5-nano-2025-08-07": "openai",
-          "gpt-5.1-2025-11-13": "openai",
-          "gryphe/mythomax-l2-13b": "open_router",
-          "llama-3.1-8b-instant": "groq",
-          "llama-3.3-70b-versatile": "groq",
-          "llama3": "ollama",
-          "llama3.1:405b": "ollama",
-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
-          "meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
-          "nousresearch/hermes-3-llama-3.1-405b": "open_router",
-          "nousresearch/hermes-3-llama-3.1-70b": "open_router",
-          "nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
-          "o1": "openai",
-          "o1-mini": "openai",
-          "o3-2025-04-16": "openai",
-          "o3-mini": "openai",
-          "openai/gpt-oss-120b": "open_router",
-          "openai/gpt-oss-20b": "open_router",
-          "perplexity/sonar": "open_router",
-          "perplexity/sonar-deep-research": "open_router",
-          "perplexity/sonar-pro": "open_router",
-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
-          "x-ai/grok-4-fast": "open_router",
-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
-        },
-        "discriminator_values": [
-          "gpt-4o"
-        ]
-      }
-    },
-    "required": [
-      "jina_api_key_credentials",
-      "openai_api_key_credentials"
-    ],
-    "title": "AIWebpageCopyImproverCredentialsInputSchema",
-    "type": "object"
-  }
-}

autogpt_platform/backend/agents/agent_fc2c9976-0962-4625-a27b-d316573a9e7f.json

@@ -1,615 +0,0 @@
-{
-  "id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-  "version": 29,
-  "is_active": true,
-  "name": "Email Address Finder",
-  "description": "Input information of a business and find their email address",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Address",
-        "value": "USA"
-      },
-      "metadata": {
-        "position": {
-          "x": 1047.9357219838776,
-          "y": 1067.9123910370954
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
-          "source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
-          "sink_name": "values_#_ADDRESS",
-          "is_static": true
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "block_id": "3146e4fe-2cdd-4f29-bd12-0c9d5bb4deb0",
-      "input_default": {
-        "group": 1,
-        "pattern": "<email>(.*?)<\\/email>"
-      },
-      "metadata": {
-        "position": {
-          "x": 3381.2821481740634,
-          "y": 246.091098184158
-        }
-      },
-      "input_links": [
-        {
-          "id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
-          "source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "source_name": "response",
-          "sink_name": "text",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "b15b5143-27b7-486e-a166-4095e72e5235",
-          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-          "source_name": "negative",
-          "sink_name": "values_#_Result",
-          "is_static": false
-        },
-        {
-          "id": "23591872-3c6b-4562-87d3-5b6ade698e48",
-          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-          "source_name": "positive",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Email"
-      },
-      "metadata": {
-        "position": {
-          "x": 4525.4246310882,
-          "y": 246.36913665010354
-        }
-      },
-      "input_links": [
-        {
-          "id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
-          "source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-          "source_name": "output",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "23591872-3c6b-4562-87d3-5b6ade698e48",
-          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-          "source_name": "positive",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "block_id": "87840993-2053-44b7-8da4-187ad4ee518c",
-      "input_default": {},
-      "metadata": {
-        "position": {
-          "x": 2182.7499999999995,
-          "y": 242.00001144409185
-        }
-      },
-      "input_links": [
-        {
-          "id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
-          "source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "source_name": "output",
-          "sink_name": "query",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
-          "source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "results",
-          "sink_name": "prompt_values_#_WEBSITE_CONTENT",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Business Name",
-        "value": "Tim Cook"
-      },
-      "metadata": {
-        "position": {
-          "x": 1049.9704155272595,
-          "y": 244.49931152418344
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "946b522c-365f-4ee0-96f9-28863d9882ea",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
-          "sink_name": "values_#_NAME",
-          "is_static": true
-        },
-        {
-          "id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_BUSINESS_NAME",
-          "is_static": true
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-      "input_default": {
-        "format": "Email Address of {{NAME}}, {{ADDRESS}}",
-        "values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 1625.25,
-          "y": 243.25001144409185
-        }
-      },
-      "input_links": [
-        {
-          "id": "946b522c-365f-4ee0-96f9-28863d9882ea",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
-          "sink_name": "values_#_NAME",
-          "is_static": true
-        },
-        {
-          "id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
-          "source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
-          "sink_name": "values_#_ADDRESS",
-          "is_static": true
-        }
-      ],
-      "output_links": [
-        {
-          "id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
-          "source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "source_name": "output",
-          "sink_name": "query",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-      "input_default": {
-        "format": "Failed to find email. \nResult:\n{{RESULT}}",
-        "values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 3949.7493830805934,
-          "y": 705.209819698647
-        }
-      },
-      "input_links": [
-        {
-          "id": "b15b5143-27b7-486e-a166-4095e72e5235",
-          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-          "source_name": "negative",
-          "sink_name": "values_#_Result",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
-          "source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-          "source_name": "output",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "claude-sonnet-4-5-20250929",
-        "prompt": "<business_website>\n{{WEBSITE_CONTENT}}\n</business_website>\n\nExtract the Contact Email of {{BUSINESS_NAME}}.\n\nIf no email that can be used to contact {{BUSINESS_NAME}} is present, output `N/A`.\nDo not share any emails other than the email for this specific entity.\n\nIf multiple present pick the likely best one.\n\nRespond with the email (or N/A) inside <email></email> tags.\n\nExample Response:\n\n<thoughts_or_comments>\nThere were many emails present, but luckily one was for {{BUSINESS_NAME}} which I have included below.\n</thoughts_or_comments>\n<email>\nexample@email.com\n</email>",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 2774.879259081777,
-          "y": 243.3102035752969
-        }
-      },
-      "input_links": [
-        {
-          "id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_BUSINESS_NAME",
-          "is_static": true
-        },
-        {
-          "id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
-          "source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "results",
-          "sink_name": "prompt_values_#_WEBSITE_CONTENT",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
-          "source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "source_name": "response",
-          "sink_name": "text",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
-      "source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "source_name": "response",
-      "sink_name": "text",
-      "is_static": false
-    },
-    {
-      "id": "b15b5143-27b7-486e-a166-4095e72e5235",
-      "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "source_name": "negative",
-      "sink_name": "values_#_Result",
-      "is_static": false
-    },
-    {
-      "id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
-      "source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "source_name": "output",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "946b522c-365f-4ee0-96f9-28863d9882ea",
-      "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "source_name": "result",
-      "sink_name": "values_#_NAME",
-      "is_static": true
-    },
-    {
-      "id": "23591872-3c6b-4562-87d3-5b6ade698e48",
-      "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "source_name": "positive",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
-      "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_BUSINESS_NAME",
-      "is_static": true
-    },
-    {
-      "id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
-      "source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "source_name": "output",
-      "sink_name": "query",
-      "is_static": false
-    },
-    {
-      "id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
-      "source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-      "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "source_name": "result",
-      "sink_name": "values_#_ADDRESS",
-      "is_static": true
-    },
-    {
-      "id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
-      "source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "source_name": "results",
-      "sink_name": "prompt_values_#_WEBSITE_CONTENT",
-      "is_static": false
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2025-01-03T00:46:30.244Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Address": {
-        "advanced": false,
-        "secret": false,
-        "title": "Address",
-        "default": "USA"
-      },
-      "Business Name": {
-        "advanced": false,
-        "secret": false,
-        "title": "Business Name",
-        "default": "Tim Cook"
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Email": {
-        "advanced": false,
-        "secret": false,
-        "title": "Email"
-      }
-    },
-    "required": [
-      "Email"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "jina_api_key_credentials": {
-        "credentials_provider": [
-          "jina"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "jina",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.JINA: 'jina'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator_values": []
-      },
-      "anthropic_api_key_credentials": {
-        "credentials_provider": [
-          "anthropic"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "anthropic",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.ANTHROPIC: 'anthropic'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator": "model",
-        "discriminator_mapping": {
-          "Llama-3.3-70B-Instruct": "llama_api",
-          "Llama-3.3-8B-Instruct": "llama_api",
-          "Llama-4-Maverick-17B-128E-Instruct-FP8": "llama_api",
-          "Llama-4-Scout-17B-16E-Instruct-FP8": "llama_api",
-          "Qwen/Qwen2.5-72B-Instruct-Turbo": "aiml_api",
-          "amazon/nova-lite-v1": "open_router",
-          "amazon/nova-micro-v1": "open_router",
-          "amazon/nova-pro-v1": "open_router",
-          "claude-3-7-sonnet-20250219": "anthropic",
-          "claude-3-haiku-20240307": "anthropic",
-          "claude-haiku-4-5-20251001": "anthropic",
-          "claude-opus-4-1-20250805": "anthropic",
-          "claude-opus-4-20250514": "anthropic",
-          "claude-opus-4-5-20251101": "anthropic",
-          "claude-sonnet-4-20250514": "anthropic",
-          "claude-sonnet-4-5-20250929": "anthropic",
-          "cohere/command-r-08-2024": "open_router",
-          "cohere/command-r-plus-08-2024": "open_router",
-          "deepseek/deepseek-chat": "open_router",
-          "deepseek/deepseek-r1-0528": "open_router",
-          "dolphin-mistral:latest": "ollama",
-          "google/gemini-2.0-flash-001": "open_router",
-          "google/gemini-2.0-flash-lite-001": "open_router",
-          "google/gemini-2.5-flash": "open_router",
-          "google/gemini-2.5-flash-lite-preview-06-17": "open_router",
-          "google/gemini-2.5-pro-preview-03-25": "open_router",
-          "google/gemini-3-pro-preview": "open_router",
-          "gpt-3.5-turbo": "openai",
-          "gpt-4-turbo": "openai",
-          "gpt-4.1-2025-04-14": "openai",
-          "gpt-4.1-mini-2025-04-14": "openai",
-          "gpt-4o": "openai",
-          "gpt-4o-mini": "openai",
-          "gpt-5-2025-08-07": "openai",
-          "gpt-5-chat-latest": "openai",
-          "gpt-5-mini-2025-08-07": "openai",
-          "gpt-5-nano-2025-08-07": "openai",
-          "gpt-5.1-2025-11-13": "openai",
-          "gryphe/mythomax-l2-13b": "open_router",
-          "llama-3.1-8b-instant": "groq",
-          "llama-3.3-70b-versatile": "groq",
-          "llama3": "ollama",
-          "llama3.1:405b": "ollama",
-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
-          "meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
-          "nousresearch/hermes-3-llama-3.1-405b": "open_router",
-          "nousresearch/hermes-3-llama-3.1-70b": "open_router",
-          "nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
-          "o1": "openai",
-          "o1-mini": "openai",
-          "o3-2025-04-16": "openai",
-          "o3-mini": "openai",
-          "openai/gpt-oss-120b": "open_router",
-          "openai/gpt-oss-20b": "open_router",
-          "perplexity/sonar": "open_router",
-          "perplexity/sonar-deep-research": "open_router",
-          "perplexity/sonar-pro": "open_router",
-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
-          "x-ai/grok-4-fast": "open_router",
-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
-        },
-        "discriminator_values": [
-          "claude-sonnet-4-5-20250929"
-        ]
-      }
-    },
-    "required": [
-      "jina_api_key_credentials",
-      "anthropic_api_key_credentials"
-    ],
-    "title": "EmailAddressFinderCredentialsInputSchema",
-    "type": "object"
-  }
-}

autogpt_platform/backend/backend/api/external/middleware.py

@@ -1,107 +0,0 @@
-from fastapi import HTTPException, Security, status
-from fastapi.security import APIKeyHeader, HTTPAuthorizationCredentials, HTTPBearer
-from prisma.enums import APIKeyPermission
-
-from backend.data.auth.api_key import APIKeyInfo, validate_api_key
-from backend.data.auth.base import APIAuthorizationInfo
-from backend.data.auth.oauth import (
-    InvalidClientError,
-    InvalidTokenError,
-    OAuthAccessTokenInfo,
-    validate_access_token,
-)
-
-api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
-bearer_auth = HTTPBearer(auto_error=False)
-
-
-async def require_api_key(api_key: str | None = Security(api_key_header)) -> APIKeyInfo:
-    """Middleware for API key authentication only"""
-    if api_key is None:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED, detail="Missing API key"
-        )
-
-    api_key_obj = await validate_api_key(api_key)
-
-    if not api_key_obj:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key"
-        )
-
-    return api_key_obj
-
-
-async def require_access_token(
-    bearer: HTTPAuthorizationCredentials | None = Security(bearer_auth),
-) -> OAuthAccessTokenInfo:
-    """Middleware for OAuth access token authentication only"""
-    if bearer is None:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail="Missing Authorization header",
-        )
-
-    try:
-        token_info, _ = await validate_access_token(bearer.credentials)
-    except (InvalidClientError, InvalidTokenError) as e:
-        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail=str(e))
-
-    return token_info
-
-
-async def require_auth(
-    api_key: str | None = Security(api_key_header),
-    bearer: HTTPAuthorizationCredentials | None = Security(bearer_auth),
-) -> APIAuthorizationInfo:
-    """
-    Unified authentication middleware supporting both API keys and OAuth tokens.
-
-    Supports two authentication methods, which are checked in order:
-    1. X-API-Key header (existing API key authentication)
-    2. Authorization: Bearer <token> header (OAuth access token)
-
-    Returns:
-        APIAuthorizationInfo: base class of both APIKeyInfo and OAuthAccessTokenInfo.
-    """
-    # Try API key first
-    if api_key is not None:
-        api_key_info = await validate_api_key(api_key)
-        if api_key_info:
-            return api_key_info
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key"
-        )
-
-    # Try OAuth bearer token
-    if bearer is not None:
-        try:
-            token_info, _ = await validate_access_token(bearer.credentials)
-            return token_info
-        except (InvalidClientError, InvalidTokenError) as e:
-            raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail=str(e))
-
-    # No credentials provided
-    raise HTTPException(
-        status_code=status.HTTP_401_UNAUTHORIZED,
-        detail="Missing authentication. Provide API key or access token.",
-    )
-
-
-def require_permission(permission: APIKeyPermission):
-    """
-    Dependency function for checking specific permissions
-    (works with API keys and OAuth tokens)
-    """
-
-    async def check_permission(
-        auth: APIAuthorizationInfo = Security(require_auth),
-    ) -> APIAuthorizationInfo:
-        if permission not in auth.scopes:
-            raise HTTPException(
-                status_code=status.HTTP_403_FORBIDDEN,
-                detail=f"Missing required permission: {permission.value}",
-            )
-        return auth
-
-    return check_permission

autogpt_platform/backend/backend/api/external/v1/integrations.py

@@ -1,655 +0,0 @@
-"""
-External API endpoints for integrations and credentials.
-
-This module provides endpoints for external applications (like Autopilot) to:
-- Initiate OAuth flows with custom callback URLs
-- Complete OAuth flows by exchanging authorization codes
-- Create API key, user/password, and host-scoped credentials
-- List and manage user credentials
-"""
-
-import logging
-from typing import TYPE_CHECKING, Annotated, Any, Literal, Optional, Union
-from urllib.parse import urlparse
-
-from fastapi import APIRouter, Body, HTTPException, Path, Security, status
-from prisma.enums import APIKeyPermission
-from pydantic import BaseModel, Field, SecretStr
-
-from backend.api.external.middleware import require_permission
-from backend.api.features.integrations.models import get_all_provider_names
-from backend.data.auth.base import APIAuthorizationInfo
-from backend.data.model import (
-    APIKeyCredentials,
-    Credentials,
-    CredentialsType,
-    HostScopedCredentials,
-    OAuth2Credentials,
-    UserPasswordCredentials,
-)
-from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.integrations.oauth import CREDENTIALS_BY_PROVIDER, HANDLERS_BY_NAME
-from backend.integrations.providers import ProviderName
-from backend.util.settings import Settings
-
-if TYPE_CHECKING:
-    from backend.integrations.oauth import BaseOAuthHandler
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-creds_manager = IntegrationCredentialsManager()
-
-integrations_router = APIRouter(prefix="/integrations", tags=["integrations"])
-
-
-# ==================== Request/Response Models ==================== #
-
-
-class OAuthInitiateRequest(BaseModel):
-    """Request model for initiating an OAuth flow."""
-
-    callback_url: str = Field(
-        ..., description="The external app's callback URL for OAuth redirect"
-    )
-    scopes: list[str] = Field(
-        default_factory=list, description="OAuth scopes to request"
-    )
-    state_metadata: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Arbitrary metadata to echo back on completion",
-    )
-
-
-class OAuthInitiateResponse(BaseModel):
-    """Response model for OAuth initiation."""
-
-    login_url: str = Field(..., description="URL to redirect user for OAuth consent")
-    state_token: str = Field(..., description="State token for CSRF protection")
-    expires_at: int = Field(
-        ..., description="Unix timestamp when the state token expires"
-    )
-
-
-class OAuthCompleteRequest(BaseModel):
-    """Request model for completing an OAuth flow."""
-
-    code: str = Field(..., description="Authorization code from OAuth provider")
-    state_token: str = Field(..., description="State token from initiate request")
-
-
-class OAuthCompleteResponse(BaseModel):
-    """Response model for OAuth completion."""
-
-    credentials_id: str = Field(..., description="ID of the stored credentials")
-    provider: str = Field(..., description="Provider name")
-    type: str = Field(..., description="Credential type (oauth2)")
-    title: Optional[str] = Field(None, description="Credential title")
-    scopes: list[str] = Field(default_factory=list, description="Granted scopes")
-    username: Optional[str] = Field(None, description="Username from provider")
-    state_metadata: dict[str, Any] = Field(
-        default_factory=dict, description="Echoed metadata from initiate request"
-    )
-
-
-class CredentialSummary(BaseModel):
-    """Summary of a credential without sensitive data."""
-
-    id: str
-    provider: str
-    type: CredentialsType
-    title: Optional[str] = None
-    scopes: Optional[list[str]] = None
-    username: Optional[str] = None
-    host: Optional[str] = None
-
-
-class ProviderInfo(BaseModel):
-    """Information about an integration provider."""
-
-    name: str
-    supports_oauth: bool = False
-    supports_api_key: bool = False
-    supports_user_password: bool = False
-    supports_host_scoped: bool = False
-    default_scopes: list[str] = Field(default_factory=list)
-
-
-# ==================== Credential Creation Models ==================== #
-
-
-class CreateAPIKeyCredentialRequest(BaseModel):
-    """Request model for creating API key credentials."""
-
-    type: Literal["api_key"] = "api_key"
-    api_key: str = Field(..., description="The API key")
-    title: str = Field(..., description="A name for this credential")
-    expires_at: Optional[int] = Field(
-        None, description="Unix timestamp when the API key expires"
-    )
-
-
-class CreateUserPasswordCredentialRequest(BaseModel):
-    """Request model for creating username/password credentials."""
-
-    type: Literal["user_password"] = "user_password"
-    username: str = Field(..., description="Username")
-    password: str = Field(..., description="Password")
-    title: str = Field(..., description="A name for this credential")
-
-
-class CreateHostScopedCredentialRequest(BaseModel):
-    """Request model for creating host-scoped credentials."""
-
-    type: Literal["host_scoped"] = "host_scoped"
-    host: str = Field(..., description="Host/domain pattern to match")
-    headers: dict[str, str] = Field(..., description="Headers to include in requests")
-    title: str = Field(..., description="A name for this credential")
-
-
-# Union type for credential creation
-CreateCredentialRequest = Annotated[
-    CreateAPIKeyCredentialRequest
-    | CreateUserPasswordCredentialRequest
-    | CreateHostScopedCredentialRequest,
-    Field(discriminator="type"),
-]
-
-
-class CreateCredentialResponse(BaseModel):
-    """Response model for credential creation."""
-
-    id: str
-    provider: str
-    type: CredentialsType
-    title: Optional[str] = None
-
-
-# ==================== Helper Functions ==================== #
-
-
-def validate_callback_url(callback_url: str) -> bool:
-    """Validate that the callback URL is from an allowed origin."""
-    allowed_origins = settings.config.external_oauth_callback_origins
-
-    try:
-        parsed = urlparse(callback_url)
-        callback_origin = f"{parsed.scheme}://{parsed.netloc}"
-
-        for allowed in allowed_origins:
-            # Simple origin matching
-            if callback_origin == allowed:
-                return True
-
-        # Allow localhost with any port in development (proper hostname check)
-        if parsed.hostname == "localhost":
-            for allowed in allowed_origins:
-                allowed_parsed = urlparse(allowed)
-                if allowed_parsed.hostname == "localhost":
-                    return True
-
-        return False
-    except Exception:
-        return False
-
-
-def _get_oauth_handler_for_external(
-    provider_name: str, redirect_uri: str
-) -> "BaseOAuthHandler":
-    """Get an OAuth handler configured with an external redirect URI."""
-    # Ensure blocks are loaded so SDK providers are available
-    try:
-        from backend.blocks import load_all_blocks
-
-        load_all_blocks()
-    except Exception as e:
-        logger.warning(f"Failed to load blocks: {e}")
-
-    if provider_name not in HANDLERS_BY_NAME:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=f"Provider '{provider_name}' does not support OAuth",
-        )
-
-    # Check if this provider has custom OAuth credentials
-    oauth_credentials = CREDENTIALS_BY_PROVIDER.get(provider_name)
-
-    if oauth_credentials and not oauth_credentials.use_secrets:
-        import os
-
-        client_id = (
-            os.getenv(oauth_credentials.client_id_env_var)
-            if oauth_credentials.client_id_env_var
-            else None
-        )
-        client_secret = (
-            os.getenv(oauth_credentials.client_secret_env_var)
-            if oauth_credentials.client_secret_env_var
-            else None
-        )
-    else:
-        client_id = getattr(settings.secrets, f"{provider_name}_client_id", None)
-        client_secret = getattr(
-            settings.secrets, f"{provider_name}_client_secret", None
-        )
-
-    if not (client_id and client_secret):
-        logger.error(f"Attempt to use unconfigured {provider_name} OAuth integration")
-        raise HTTPException(
-            status_code=status.HTTP_501_NOT_IMPLEMENTED,
-            detail={
-                "message": f"Integration with provider '{provider_name}' is not configured.",
-                "hint": "Set client ID and secret in the application's deployment environment",
-            },
-        )
-
-    handler_class = HANDLERS_BY_NAME[provider_name]
-    return handler_class(
-        client_id=client_id,
-        client_secret=client_secret,
-        redirect_uri=redirect_uri,
-    )
-
-
-# ==================== Endpoints ==================== #
-
-
-@integrations_router.get("/providers", response_model=list[ProviderInfo])
-async def list_providers(
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.READ_INTEGRATIONS)
-    ),
-) -> list[ProviderInfo]:
-    """
-    List all available integration providers.
-
-    Returns a list of all providers with their supported credential types.
-    Most providers support API key credentials, and some also support OAuth.
-    """
-    # Ensure blocks are loaded
-    try:
-        from backend.blocks import load_all_blocks
-
-        load_all_blocks()
-    except Exception as e:
-        logger.warning(f"Failed to load blocks: {e}")
-
-    from backend.sdk.registry import AutoRegistry
-
-    providers = []
-    for name in get_all_provider_names():
-        supports_oauth = name in HANDLERS_BY_NAME
-        handler_class = HANDLERS_BY_NAME.get(name)
-        default_scopes = (
-            getattr(handler_class, "DEFAULT_SCOPES", []) if handler_class else []
-        )
-
-        # Check if provider has specific auth types from SDK registration
-        sdk_provider = AutoRegistry.get_provider(name)
-        if sdk_provider and sdk_provider.supported_auth_types:
-            supports_api_key = "api_key" in sdk_provider.supported_auth_types
-            supports_user_password = (
-                "user_password" in sdk_provider.supported_auth_types
-            )
-            supports_host_scoped = "host_scoped" in sdk_provider.supported_auth_types
-        else:
-            # Fallback for legacy providers
-            supports_api_key = True  # All providers can accept API keys
-            supports_user_password = name in ("smtp",)
-            supports_host_scoped = name == "http"
-
-        providers.append(
-            ProviderInfo(
-                name=name,
-                supports_oauth=supports_oauth,
-                supports_api_key=supports_api_key,
-                supports_user_password=supports_user_password,
-                supports_host_scoped=supports_host_scoped,
-                default_scopes=default_scopes,
-            )
-        )
-
-    return providers
-
-
-@integrations_router.post(
-    "/{provider}/oauth/initiate",
-    response_model=OAuthInitiateResponse,
-    summary="Initiate OAuth flow",
-)
-async def initiate_oauth(
-    provider: Annotated[str, Path(title="The OAuth provider")],
-    request: OAuthInitiateRequest,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.MANAGE_INTEGRATIONS)
-    ),
-) -> OAuthInitiateResponse:
-    """
-    Initiate an OAuth flow for an external application.
-
-    This endpoint allows external apps to start an OAuth flow with a custom
-    callback URL. The callback URL must be from an allowed origin configured
-    in the platform settings.
-
-    Returns a login URL to redirect the user to, along with a state token
-    for CSRF protection.
-    """
-    # Validate callback URL
-    if not validate_callback_url(request.callback_url):
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                f"Callback URL origin is not allowed. "
-                f"Allowed origins: {settings.config.external_oauth_callback_origins}",
-            ),
-        )
-
-    # Validate provider
-    try:
-        provider_name = ProviderName(provider)
-    except ValueError:
-        # Check if it's a dynamically registered provider
-        if provider not in HANDLERS_BY_NAME:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=f"Provider '{provider}' not found",
-            )
-        provider_name = provider
-
-    # Get OAuth handler with external callback URL
-    handler = _get_oauth_handler_for_external(
-        provider if isinstance(provider_name, str) else provider_name.value,
-        request.callback_url,
-    )
-
-    # Store state token with external flow metadata
-    # Note: initiated_by_api_key_id is only available for API key auth, not OAuth
-    api_key_id = getattr(auth, "id", None) if auth.type == "api_key" else None
-    state_token, code_challenge = await creds_manager.store.store_state_token(
-        user_id=auth.user_id,
-        provider=provider if isinstance(provider_name, str) else provider_name.value,
-        scopes=request.scopes,
-        callback_url=request.callback_url,
-        state_metadata=request.state_metadata,
-        initiated_by_api_key_id=api_key_id,
-    )
-
-    # Build login URL
-    login_url = handler.get_login_url(
-        request.scopes, state_token, code_challenge=code_challenge
-    )
-
-    # Calculate expiration (10 minutes from now)
-    from datetime import datetime, timedelta, timezone
-
-    expires_at = int((datetime.now(timezone.utc) + timedelta(minutes=10)).timestamp())
-
-    return OAuthInitiateResponse(
-        login_url=login_url,
-        state_token=state_token,
-        expires_at=expires_at,
-    )
-
-
-@integrations_router.post(
-    "/{provider}/oauth/complete",
-    response_model=OAuthCompleteResponse,
-    summary="Complete OAuth flow",
-)
-async def complete_oauth(
-    provider: Annotated[str, Path(title="The OAuth provider")],
-    request: OAuthCompleteRequest,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.MANAGE_INTEGRATIONS)
-    ),
-) -> OAuthCompleteResponse:
-    """
-    Complete an OAuth flow by exchanging the authorization code for tokens.
-
-    This endpoint should be called after the user has authorized the application
-    and been redirected back to the external app's callback URL with an
-    authorization code.
-    """
-    # Verify state token
-    valid_state = await creds_manager.store.verify_state_token(
-        auth.user_id, request.state_token, provider
-    )
-
-    if not valid_state:
-        logger.warning(f"Invalid or expired state token for provider {provider}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Invalid or expired state token",
-        )
-
-    # Verify this is an external flow (callback_url must be set)
-    if not valid_state.callback_url:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="State token was not created for external OAuth flow",
-        )
-
-    # Get OAuth handler with the original callback URL
-    handler = _get_oauth_handler_for_external(provider, valid_state.callback_url)
-
-    try:
-        scopes = valid_state.scopes
-        scopes = handler.handle_default_scopes(scopes)
-
-        credentials = await handler.exchange_code_for_tokens(
-            request.code, scopes, valid_state.code_verifier
-        )
-
-        # Handle Linear's space-separated scopes
-        if len(credentials.scopes) == 1 and " " in credentials.scopes[0]:
-            credentials.scopes = credentials.scopes[0].split(" ")
-
-        # Check scope mismatch
-        if not set(scopes).issubset(set(credentials.scopes)):
-            logger.warning(
-                f"Granted scopes {credentials.scopes} for provider {provider} "
-                f"do not include all requested scopes {scopes}"
-            )
-
-    except Exception as e:
-        logger.error(f"OAuth2 Code->Token exchange failed for provider {provider}: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"OAuth2 callback failed to exchange code for tokens: {str(e)}",
-        )
-
-    # Store credentials
-    await creds_manager.create(auth.user_id, credentials)
-
-    logger.info(f"Successfully completed external OAuth for provider {provider}")
-
-    return OAuthCompleteResponse(
-        credentials_id=credentials.id,
-        provider=credentials.provider,
-        type=credentials.type,
-        title=credentials.title,
-        scopes=credentials.scopes,
-        username=credentials.username,
-        state_metadata=valid_state.state_metadata,
-    )
-
-
-@integrations_router.get("/credentials", response_model=list[CredentialSummary])
-async def list_credentials(
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.READ_INTEGRATIONS)
-    ),
-) -> list[CredentialSummary]:
-    """
-    List all credentials for the authenticated user.
-
-    Returns metadata about each credential without exposing sensitive tokens.
-    """
-    credentials = await creds_manager.store.get_all_creds(auth.user_id)
-    return [
-        CredentialSummary(
-            id=cred.id,
-            provider=cred.provider,
-            type=cred.type,
-            title=cred.title,
-            scopes=cred.scopes if isinstance(cred, OAuth2Credentials) else None,
-            username=cred.username if isinstance(cred, OAuth2Credentials) else None,
-            host=cred.host if isinstance(cred, HostScopedCredentials) else None,
-        )
-        for cred in credentials
-    ]
-
-
-@integrations_router.get(
-    "/{provider}/credentials", response_model=list[CredentialSummary]
-)
-async def list_credentials_by_provider(
-    provider: Annotated[str, Path(title="The provider to list credentials for")],
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.READ_INTEGRATIONS)
-    ),
-) -> list[CredentialSummary]:
-    """
-    List credentials for a specific provider.
-    """
-    credentials = await creds_manager.store.get_creds_by_provider(
-        auth.user_id, provider
-    )
-    return [
-        CredentialSummary(
-            id=cred.id,
-            provider=cred.provider,
-            type=cred.type,
-            title=cred.title,
-            scopes=cred.scopes if isinstance(cred, OAuth2Credentials) else None,
-            username=cred.username if isinstance(cred, OAuth2Credentials) else None,
-            host=cred.host if isinstance(cred, HostScopedCredentials) else None,
-        )
-        for cred in credentials
-    ]
-
-
-@integrations_router.post(
-    "/{provider}/credentials",
-    response_model=CreateCredentialResponse,
-    status_code=status.HTTP_201_CREATED,
-    summary="Create credentials",
-)
-async def create_credential(
-    provider: Annotated[str, Path(title="The provider to create credentials for")],
-    request: Union[
-        CreateAPIKeyCredentialRequest,
-        CreateUserPasswordCredentialRequest,
-        CreateHostScopedCredentialRequest,
-    ] = Body(..., discriminator="type"),
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.MANAGE_INTEGRATIONS)
-    ),
-) -> CreateCredentialResponse:
-    """
-    Create non-OAuth credentials for a provider.
-
-    Supports creating:
-    - API key credentials (type: "api_key")
-    - Username/password credentials (type: "user_password")
-    - Host-scoped credentials (type: "host_scoped")
-
-    For OAuth credentials, use the OAuth initiate/complete flow instead.
-    """
-    # Validate provider exists
-    all_providers = get_all_provider_names()
-    if provider not in all_providers:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=f"Provider '{provider}' not found",
-        )
-
-    # Create the appropriate credential type
-    credentials: Credentials
-    if request.type == "api_key":
-        credentials = APIKeyCredentials(
-            provider=provider,
-            api_key=SecretStr(request.api_key),
-            title=request.title,
-            expires_at=request.expires_at,
-        )
-    elif request.type == "user_password":
-        credentials = UserPasswordCredentials(
-            provider=provider,
-            username=SecretStr(request.username),
-            password=SecretStr(request.password),
-            title=request.title,
-        )
-    elif request.type == "host_scoped":
-        # Convert string headers to SecretStr
-        secret_headers = {k: SecretStr(v) for k, v in request.headers.items()}
-        credentials = HostScopedCredentials(
-            provider=provider,
-            host=request.host,
-            headers=secret_headers,
-            title=request.title,
-        )
-    else:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Unsupported credential type: {request.type}",
-        )
-
-    # Store credentials
-    try:
-        await creds_manager.create(auth.user_id, credentials)
-    except Exception as e:
-        logger.error(f"Failed to store credentials: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to store credentials: {str(e)}",
-        )
-
-    logger.info(f"Created {request.type} credentials for provider {provider}")
-
-    return CreateCredentialResponse(
-        id=credentials.id,
-        provider=provider,
-        type=credentials.type,
-        title=credentials.title,
-    )
-
-
-class DeleteCredentialResponse(BaseModel):
-    """Response model for deleting a credential."""
-
-    deleted: bool = Field(..., description="Whether the credential was deleted")
-    credentials_id: str = Field(..., description="ID of the deleted credential")
-
-
-@integrations_router.delete(
-    "/{provider}/credentials/{cred_id}",
-    response_model=DeleteCredentialResponse,
-)
-async def delete_credential(
-    provider: Annotated[str, Path(title="The provider")],
-    cred_id: Annotated[str, Path(title="The credential ID to delete")],
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.DELETE_INTEGRATIONS)
-    ),
-) -> DeleteCredentialResponse:
-    """
-    Delete a credential.
-
-    Note: This does not revoke the tokens with the provider. For full cleanup,
-    use the main API's delete endpoint which handles webhook cleanup and
-    token revocation.
-    """
-    creds = await creds_manager.store.get_creds_by_id(auth.user_id, cred_id)
-    if not creds:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND, detail="Credentials not found"
-        )
-    if creds.provider != provider:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Credentials do not match the specified provider",
-        )
-
-    await creds_manager.delete(auth.user_id, cred_id)
-
-    return DeleteCredentialResponse(deleted=True, credentials_id=cred_id)

autogpt_platform/backend/backend/api/external/v1/routes.py

@@ -1,328 +0,0 @@
-import logging
-import urllib.parse
-from collections import defaultdict
-from typing import Annotated, Any, Literal, Optional, Sequence
-
-from fastapi import APIRouter, Body, HTTPException, Security
-from prisma.enums import AgentExecutionStatus, APIKeyPermission
-from pydantic import BaseModel, Field
-from typing_extensions import TypedDict
-
-import backend.api.features.store.cache as store_cache
-import backend.api.features.store.model as store_model
-import backend.data.block
-from backend.api.external.middleware import require_permission
-from backend.data import execution as execution_db
-from backend.data import graph as graph_db
-from backend.data import user as user_db
-from backend.data.auth.base import APIAuthorizationInfo
-from backend.data.block import BlockInput, CompletedBlockOutput
-from backend.executor.utils import add_graph_execution
-from backend.util.settings import Settings
-
-from .integrations import integrations_router
-from .tools import tools_router
-
-settings = Settings()
-logger = logging.getLogger(__name__)
-
-v1_router = APIRouter()
-
-v1_router.include_router(integrations_router)
-v1_router.include_router(tools_router)
-
-
-class UserInfoResponse(BaseModel):
-    id: str
-    name: Optional[str]
-    email: str
-    timezone: str = Field(
-        description="The user's last known timezone (e.g. 'Europe/Amsterdam'), "
-        "or 'not-set' if not set"
-    )
-
-
-@v1_router.get(
-    path="/me",
-    tags=["user", "meta"],
-)
-async def get_user_info(
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.IDENTITY)
-    ),
-) -> UserInfoResponse:
-    user = await user_db.get_user_by_id(auth.user_id)
-
-    return UserInfoResponse(
-        id=user.id,
-        name=user.name,
-        email=user.email,
-        timezone=user.timezone,
-    )
-
-
-@v1_router.get(
-    path="/blocks",
-    tags=["blocks"],
-    dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
-)
-async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
-    blocks = [block() for block in backend.data.block.get_blocks().values()]
-    return [b.to_dict() for b in blocks if not b.disabled]
-
-
-@v1_router.post(
-    path="/blocks/{block_id}/execute",
-    tags=["blocks"],
-    dependencies=[Security(require_permission(APIKeyPermission.EXECUTE_BLOCK))],
-)
-async def execute_graph_block(
-    block_id: str,
-    data: BlockInput,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.EXECUTE_BLOCK)
-    ),
-) -> CompletedBlockOutput:
-    obj = backend.data.block.get_block(block_id)
-    if not obj:
-        raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
-
-    output = defaultdict(list)
-    async for name, data in obj.execute(data):
-        output[name].append(data)
-    return output
-
-
-@v1_router.post(
-    path="/graphs/{graph_id}/execute/{graph_version}",
-    tags=["graphs"],
-)
-async def execute_graph(
-    graph_id: str,
-    graph_version: int,
-    node_input: Annotated[dict[str, Any], Body(..., embed=True, default_factory=dict)],
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.EXECUTE_GRAPH)
-    ),
-) -> dict[str, Any]:
-    try:
-        graph_exec = await add_graph_execution(
-            graph_id=graph_id,
-            user_id=auth.user_id,
-            inputs=node_input,
-            graph_version=graph_version,
-        )
-        return {"id": graph_exec.id}
-    except Exception as e:
-        msg = str(e).encode().decode("unicode_escape")
-        raise HTTPException(status_code=400, detail=msg)
-
-
-class ExecutionNode(TypedDict):
-    node_id: str
-    input: Any
-    output: dict[str, Any]
-
-
-class GraphExecutionResult(TypedDict):
-    execution_id: str
-    status: str
-    nodes: list[ExecutionNode]
-    output: Optional[list[dict[str, str]]]
-
-
-@v1_router.get(
-    path="/graphs/{graph_id}/executions/{graph_exec_id}/results",
-    tags=["graphs"],
-)
-async def get_graph_execution_results(
-    graph_id: str,
-    graph_exec_id: str,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.READ_GRAPH)
-    ),
-) -> GraphExecutionResult:
-    graph_exec = await execution_db.get_graph_execution(
-        user_id=auth.user_id,
-        execution_id=graph_exec_id,
-        include_node_executions=True,
-    )
-    if not graph_exec:
-        raise HTTPException(
-            status_code=404, detail=f"Graph execution #{graph_exec_id} not found."
-        )
-
-    if not await graph_db.get_graph(
-        graph_id=graph_exec.graph_id,
-        version=graph_exec.graph_version,
-        user_id=auth.user_id,
-    ):
-        raise HTTPException(status_code=404, detail=f"Graph #{graph_id} not found.")
-
-    return GraphExecutionResult(
-        execution_id=graph_exec_id,
-        status=graph_exec.status.value,
-        nodes=[
-            ExecutionNode(
-                node_id=node_exec.node_id,
-                input=node_exec.input_data.get("value", node_exec.input_data),
-                output={k: v for k, v in node_exec.output_data.items()},
-            )
-            for node_exec in graph_exec.node_executions
-        ],
-        output=(
-            [
-                {name: value}
-                for name, values in graph_exec.outputs.items()
-                for value in values
-            ]
-            if graph_exec.status == AgentExecutionStatus.COMPLETED
-            else None
-        ),
-    )
-
-
-##############################################
-############### Store Endpoints ##############
-##############################################
-
-
-@v1_router.get(
-    path="/store/agents",
-    tags=["store"],
-    dependencies=[Security(require_permission(APIKeyPermission.READ_STORE))],
-    response_model=store_model.StoreAgentsResponse,
-)
-async def get_store_agents(
-    featured: bool = False,
-    creator: str | None = None,
-    sorted_by: Literal["rating", "runs", "name", "updated_at"] | None = None,
-    search_query: str | None = None,
-    category: str | None = None,
-    page: int = 1,
-    page_size: int = 20,
-) -> store_model.StoreAgentsResponse:
-    """
-    Get a paginated list of agents from the store with optional filtering and sorting.
-
-    Args:
-        featured: Filter to only show featured agents
-        creator: Filter agents by creator username
-        sorted_by: Sort agents by "runs", "rating", "name", or "updated_at"
-        search_query: Search agents by name, subheading and description
-        category: Filter agents by category
-        page: Page number for pagination (default 1)
-        page_size: Number of agents per page (default 20)
-
-    Returns:
-        StoreAgentsResponse: Paginated list of agents matching the filters
-    """
-    if page < 1:
-        raise HTTPException(status_code=422, detail="Page must be greater than 0")
-
-    if page_size < 1:
-        raise HTTPException(status_code=422, detail="Page size must be greater than 0")
-
-    agents = await store_cache._get_cached_store_agents(
-        featured=featured,
-        creator=creator,
-        sorted_by=sorted_by,
-        search_query=search_query,
-        category=category,
-        page=page,
-        page_size=page_size,
-    )
-    return agents
-
-
-@v1_router.get(
-    path="/store/agents/{username}/{agent_name}",
-    tags=["store"],
-    dependencies=[Security(require_permission(APIKeyPermission.READ_STORE))],
-    response_model=store_model.StoreAgentDetails,
-)
-async def get_store_agent(
-    username: str,
-    agent_name: str,
-) -> store_model.StoreAgentDetails:
-    """
-    Get details of a specific store agent by username and agent name.
-
-    Args:
-        username: Creator's username
-        agent_name: Name/slug of the agent
-
-    Returns:
-        StoreAgentDetails: Detailed information about the agent
-    """
-    username = urllib.parse.unquote(username).lower()
-    agent_name = urllib.parse.unquote(agent_name).lower()
-    agent = await store_cache._get_cached_agent_details(
-        username=username, agent_name=agent_name
-    )
-    return agent
-
-
-@v1_router.get(
-    path="/store/creators",
-    tags=["store"],
-    dependencies=[Security(require_permission(APIKeyPermission.READ_STORE))],
-    response_model=store_model.CreatorsResponse,
-)
-async def get_store_creators(
-    featured: bool = False,
-    search_query: str | None = None,
-    sorted_by: Literal["agent_rating", "agent_runs", "num_agents"] | None = None,
-    page: int = 1,
-    page_size: int = 20,
-) -> store_model.CreatorsResponse:
-    """
-    Get a paginated list of store creators with optional filtering and sorting.
-
-    Args:
-        featured: Filter to only show featured creators
-        search_query: Search creators by profile description
-        sorted_by: Sort by "agent_rating", "agent_runs", or "num_agents"
-        page: Page number for pagination (default 1)
-        page_size: Number of creators per page (default 20)
-
-    Returns:
-        CreatorsResponse: Paginated list of creators matching the filters
-    """
-    if page < 1:
-        raise HTTPException(status_code=422, detail="Page must be greater than 0")
-
-    if page_size < 1:
-        raise HTTPException(status_code=422, detail="Page size must be greater than 0")
-
-    creators = await store_cache._get_cached_store_creators(
-        featured=featured,
-        search_query=search_query,
-        sorted_by=sorted_by,
-        page=page,
-        page_size=page_size,
-    )
-    return creators
-
-
-@v1_router.get(
-    path="/store/creators/{username}",
-    tags=["store"],
-    dependencies=[Security(require_permission(APIKeyPermission.READ_STORE))],
-    response_model=store_model.CreatorDetails,
-)
-async def get_store_creator(
-    username: str,
-) -> store_model.CreatorDetails:
-    """
-    Get details of a specific store creator by username.
-
-    Args:
-        username: Creator's username
-
-    Returns:
-        CreatorDetails: Detailed information about the creator
-    """
-    username = urllib.parse.unquote(username).lower()
-    creator = await store_cache._get_cached_creator_details(username=username)
-    return creator

autogpt_platform/backend/backend/api/external/v1/tools.py

@@ -1,152 +0,0 @@
-"""External API routes for chat tools - stateless HTTP endpoints.
-
-Note: These endpoints use ephemeral sessions that are not persisted to Redis.
-As a result, session-based rate limiting (max_agent_runs, max_agent_schedules)
-is not enforced for external API calls. Each request creates a fresh session
-with zeroed counters. Rate limiting for external API consumers should be
-handled separately (e.g., via API key quotas).
-"""
-
-import logging
-from typing import Any
-
-from fastapi import APIRouter, Security
-from prisma.enums import APIKeyPermission
-from pydantic import BaseModel, Field
-
-from backend.api.external.middleware import require_permission
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools import find_agent_tool, run_agent_tool
-from backend.api.features.chat.tools.models import ToolResponseBase
-from backend.data.auth.base import APIAuthorizationInfo
-
-logger = logging.getLogger(__name__)
-
-tools_router = APIRouter(prefix="/tools", tags=["tools"])
-
-# Note: We use Security() as a function parameter dependency (auth: APIAuthorizationInfo = Security(...))
-# rather than in the decorator's dependencies= list. This avoids duplicate permission checks
-# while still enforcing auth AND giving us access to auth for extracting user_id.
-
-
-# Request models
-class FindAgentRequest(BaseModel):
-    query: str = Field(..., description="Search query for finding agents")
-
-
-class RunAgentRequest(BaseModel):
-    """Request to run or schedule an agent.
-
-    The tool automatically handles the setup flow:
-    - First call returns available inputs so user can decide what values to use
-    - Returns missing credentials if user needs to configure them
-    - Executes when inputs are provided OR use_defaults=true
-    - Schedules execution if schedule_name and cron are provided
-    """
-
-    username_agent_slug: str = Field(
-        ...,
-        description="The marketplace agent slug (e.g., 'username/agent-name')",
-    )
-    inputs: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Dictionary of input values for the agent",
-    )
-    use_defaults: bool = Field(
-        default=False,
-        description="Set to true to run with default values (user must confirm)",
-    )
-    schedule_name: str | None = Field(
-        None,
-        description="Name for scheduled execution (triggers scheduling mode)",
-    )
-    cron: str | None = Field(
-        None,
-        description="Cron expression (5 fields: minute hour day month weekday)",
-    )
-    timezone: str = Field(
-        default="UTC",
-        description="IANA timezone (e.g., 'America/New_York', 'UTC')",
-    )
-
-
-def _create_ephemeral_session(user_id: str) -> ChatSession:
-    """Create an ephemeral session for stateless API requests."""
-    return ChatSession.new(user_id)
-
-
-@tools_router.post(
-    path="/find-agent",
-)
-async def find_agent(
-    request: FindAgentRequest,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.USE_TOOLS)
-    ),
-) -> dict[str, Any]:
-    """
-    Search for agents in the marketplace based on capabilities and user needs.
-
-    Args:
-        request: Search query for finding agents
-
-    Returns:
-        List of matching agents or no results response
-    """
-    session = _create_ephemeral_session(auth.user_id)
-    result = await find_agent_tool._execute(
-        user_id=auth.user_id,
-        session=session,
-        query=request.query,
-    )
-    return _response_to_dict(result)
-
-
-@tools_router.post(
-    path="/run-agent",
-)
-async def run_agent(
-    request: RunAgentRequest,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.USE_TOOLS)
-    ),
-) -> dict[str, Any]:
-    """
-    Run or schedule an agent from the marketplace.
-
-    The endpoint automatically handles the setup flow:
-    - Returns missing inputs if required fields are not provided
-    - Returns missing credentials if user needs to configure them
-    - Executes immediately if all requirements are met
-    - Schedules execution if schedule_name and cron are provided
-
-    For scheduled execution:
-    - Cron format: "minute hour day month weekday"
-    - Examples: "0 9 * * 1-5" (9am weekdays), "0 0 * * *" (daily at midnight)
-    - Timezone: Use IANA timezone names like "America/New_York"
-
-    Args:
-        request: Agent slug, inputs, and optional schedule config
-
-    Returns:
-        - setup_requirements: If inputs or credentials are missing
-        - execution_started: If agent was run or scheduled successfully
-        - error: If something went wrong
-    """
-    session = _create_ephemeral_session(auth.user_id)
-    result = await run_agent_tool._execute(
-        user_id=auth.user_id,
-        session=session,
-        username_agent_slug=request.username_agent_slug,
-        inputs=request.inputs,
-        use_defaults=request.use_defaults,
-        schedule_name=request.schedule_name or "",
-        cron=request.cron or "",
-        timezone=request.timezone,
-    )
-    return _response_to_dict(result)
-
-
-def _response_to_dict(result: ToolResponseBase) -> dict[str, Any]:
-    """Convert a tool response to a dictionary for JSON serialization."""
-    return result.model_dump()

autogpt_platform/backend/backend/api/features/admin/execution_analytics_routes.py

@@ -1,483 +0,0 @@
-import asyncio
-import logging
-from datetime import datetime
-from typing import Optional
-
-from autogpt_libs.auth import get_user_id, requires_admin_user
-from fastapi import APIRouter, HTTPException, Security
-from pydantic import BaseModel, Field
-
-from backend.blocks.llm import LlmModel
-from backend.data.analytics import (
-    AccuracyTrendsResponse,
-    get_accuracy_trends_and_alerts,
-)
-from backend.data.execution import (
-    ExecutionStatus,
-    GraphExecutionMeta,
-    get_graph_executions,
-    update_graph_execution_stats,
-)
-from backend.data.model import GraphExecutionStats
-from backend.executor.activity_status_generator import (
-    DEFAULT_SYSTEM_PROMPT,
-    DEFAULT_USER_PROMPT,
-    generate_activity_status_for_execution,
-)
-from backend.executor.manager import get_db_async_client
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-
-
-class ExecutionAnalyticsRequest(BaseModel):
-    graph_id: str = Field(..., description="Graph ID to analyze")
-    graph_version: Optional[int] = Field(None, description="Optional graph version")
-    user_id: Optional[str] = Field(None, description="Optional user ID filter")
-    created_after: Optional[datetime] = Field(
-        None, description="Optional created date lower bound"
-    )
-    model_name: str = Field("gpt-4o-mini", description="Model to use for generation")
-    batch_size: int = Field(
-        10, description="Batch size for concurrent processing", le=25, ge=1
-    )
-    system_prompt: Optional[str] = Field(
-        None, description="Custom system prompt (default: built-in prompt)"
-    )
-    user_prompt: Optional[str] = Field(
-        None,
-        description="Custom user prompt with {{GRAPH_NAME}} and {{EXECUTION_DATA}} placeholders (default: built-in prompt)",
-    )
-    skip_existing: bool = Field(
-        True,
-        description="Whether to skip executions that already have activity status and correctness score",
-    )
-
-
-class ExecutionAnalyticsResult(BaseModel):
-    agent_id: str
-    version_id: int
-    user_id: str
-    exec_id: str
-    summary_text: Optional[str]
-    score: Optional[float]
-    status: str  # "success", "failed", "skipped"
-    error_message: Optional[str] = None
-    started_at: Optional[datetime] = None
-    ended_at: Optional[datetime] = None
-
-
-class ExecutionAnalyticsResponse(BaseModel):
-    total_executions: int
-    processed_executions: int
-    successful_analytics: int
-    failed_analytics: int
-    skipped_executions: int
-    results: list[ExecutionAnalyticsResult]
-
-
-class ModelInfo(BaseModel):
-    value: str
-    label: str
-    provider: str
-
-
-class ExecutionAnalyticsConfig(BaseModel):
-    available_models: list[ModelInfo]
-    default_system_prompt: str
-    default_user_prompt: str
-    recommended_model: str
-
-
-class AccuracyTrendsRequest(BaseModel):
-    graph_id: str = Field(..., description="Graph ID to analyze", min_length=1)
-    user_id: Optional[str] = Field(None, description="Optional user ID filter")
-    days_back: int = Field(30, description="Number of days to look back", ge=7, le=90)
-    drop_threshold: float = Field(
-        10.0, description="Alert threshold percentage", ge=1.0, le=50.0
-    )
-    include_historical: bool = Field(
-        False, description="Include historical data for charts"
-    )
-
-
-router = APIRouter(
-    prefix="/admin",
-    tags=["admin", "execution_analytics"],
-    dependencies=[Security(requires_admin_user)],
-)
-
-
-@router.get(
-    "/execution_analytics/config",
-    response_model=ExecutionAnalyticsConfig,
-    summary="Get Execution Analytics Configuration",
-)
-async def get_execution_analytics_config(
-    admin_user_id: str = Security(get_user_id),
-):
-    """
-    Get the configuration for execution analytics including:
-    - Available AI models with metadata
-    - Default system and user prompts
-    - Recommended model selection
-    """
-    logger.info(f"Admin user {admin_user_id} requesting execution analytics config")
-
-    # Generate model list from LlmModel enum with provider information
-    available_models = []
-
-    # Function to generate friendly display names from model values
-    def generate_model_label(model: LlmModel) -> str:
-        """Generate a user-friendly label from the model enum value."""
-        value = model.value
-
-        # For all models, convert underscores/hyphens to spaces and title case
-        # e.g., "gpt-4-turbo" -> "GPT 4 Turbo", "claude-3-haiku-20240307" -> "Claude 3 Haiku"
-        parts = value.replace("_", "-").split("-")
-
-        # Handle provider prefixes (e.g., "google/", "x-ai/")
-        if "/" in value:
-            _, model_name = value.split("/", 1)
-            parts = model_name.replace("_", "-").split("-")
-
-        # Capitalize and format parts
-        formatted_parts = []
-        for part in parts:
-            # Skip date-like patterns - check for various date formats:
-            # - Long dates like "20240307" (8 digits)
-            # - Year components like "2024", "2025" (4 digit years >= 2020)
-            # - Month/day components like "04", "16" when they appear to be dates
-            if part.isdigit():
-                if len(part) >= 8:  # Long date format like "20240307"
-                    continue
-                elif len(part) == 4 and int(part) >= 2020:  # Year like "2024", "2025"
-                    continue
-                elif len(part) <= 2 and int(part) <= 31:  # Month/day like "04", "16"
-                    # Skip if this looks like a date component (basic heuristic)
-                    continue
-            # Keep version numbers as-is
-            if part.replace(".", "").isdigit():
-                formatted_parts.append(part)
-            # Capitalize normal words
-            else:
-                formatted_parts.append(
-                    part.upper()
-                    if part.upper() in ["GPT", "LLM", "API", "V0"]
-                    else part.capitalize()
-                )
-
-        model_name = " ".join(formatted_parts)
-
-        # Format provider name for better display
-        provider_name = model.provider.replace("_", " ").title()
-
-        # Return with provider prefix for clarity
-        return f"{provider_name}: {model_name}"
-
-    # Include all LlmModel values (no more filtering by hardcoded list)
-    recommended_model = LlmModel.GPT4O_MINI.value
-    for model in LlmModel:
-        label = generate_model_label(model)
-        # Add "(Recommended)" suffix to the recommended model
-        if model.value == recommended_model:
-            label += " (Recommended)"
-
-        available_models.append(
-            ModelInfo(
-                value=model.value,
-                label=label,
-                provider=model.provider,
-            )
-        )
-
-    # Sort models by provider and name for better UX
-    available_models.sort(key=lambda x: (x.provider, x.label))
-
-    return ExecutionAnalyticsConfig(
-        available_models=available_models,
-        default_system_prompt=DEFAULT_SYSTEM_PROMPT,
-        default_user_prompt=DEFAULT_USER_PROMPT,
-        recommended_model=recommended_model,
-    )
-
-
-@router.post(
-    "/execution_analytics",
-    response_model=ExecutionAnalyticsResponse,
-    summary="Generate Execution Analytics",
-)
-async def generate_execution_analytics(
-    request: ExecutionAnalyticsRequest,
-    admin_user_id: str = Security(get_user_id),
-):
-    """
-    Generate activity summaries and correctness scores for graph executions.
-
-    This endpoint:
-    1. Fetches all completed executions matching the criteria
-    2. Identifies executions missing activity_status or correctness_score
-    3. Generates missing data using AI in batches
-    4. Updates the database with new stats
-    5. Returns a detailed report of the analytics operation
-    """
-    logger.info(
-        f"Admin user {admin_user_id} starting execution analytics generation for graph {request.graph_id}"
-    )
-
-    try:
-        # Get database client
-        db_client = get_db_async_client()
-
-        # Fetch executions to process
-        executions = await get_graph_executions(
-            graph_id=request.graph_id,
-            graph_version=request.graph_version,
-            user_id=request.user_id,
-            created_time_gte=request.created_after,
-            statuses=[
-                ExecutionStatus.COMPLETED,
-                ExecutionStatus.FAILED,
-                ExecutionStatus.TERMINATED,
-            ],  # Only process finished executions
-        )
-
-        logger.info(
-            f"Found {len(executions)} total executions for graph {request.graph_id}"
-        )
-
-        # Filter executions that need analytics generation
-        executions_to_process = []
-        for execution in executions:
-            # Skip if we should skip existing analytics and both activity_status and correctness_score exist
-            if (
-                request.skip_existing
-                and execution.stats
-                and execution.stats.activity_status
-                and execution.stats.correctness_score is not None
-            ):
-                continue
-
-            # Add execution to processing list
-            executions_to_process.append(execution)
-
-        logger.info(
-            f"Found {len(executions_to_process)} executions needing analytics generation"
-        )
-
-        # Create results for ALL executions - processed and skipped
-        results = []
-        successful_count = 0
-        failed_count = 0
-
-        # Process executions that need analytics generation
-        if executions_to_process:
-            total_batches = len(
-                range(0, len(executions_to_process), request.batch_size)
-            )
-
-            for batch_idx, i in enumerate(
-                range(0, len(executions_to_process), request.batch_size)
-            ):
-                batch = executions_to_process[i : i + request.batch_size]
-                logger.info(
-                    f"Processing batch {batch_idx + 1}/{total_batches} with {len(batch)} executions"
-                )
-
-                batch_results = await _process_batch(batch, request, db_client)
-
-                for result in batch_results:
-                    results.append(result)
-                    if result.status == "success":
-                        successful_count += 1
-                    elif result.status == "failed":
-                        failed_count += 1
-
-                # Small delay between batches to avoid overwhelming the LLM API
-                if batch_idx < total_batches - 1:  # Don't delay after the last batch
-                    await asyncio.sleep(2)
-
-        # Add ALL executions to results (both processed and skipped)
-        for execution in executions:
-            # Skip if already processed (added to results above)
-            if execution in executions_to_process:
-                continue
-
-            results.append(
-                ExecutionAnalyticsResult(
-                    agent_id=execution.graph_id,
-                    version_id=execution.graph_version,
-                    user_id=execution.user_id,
-                    exec_id=execution.id,
-                    summary_text=(
-                        execution.stats.activity_status if execution.stats else None
-                    ),
-                    score=(
-                        execution.stats.correctness_score if execution.stats else None
-                    ),
-                    status="skipped",
-                    error_message=None,  # Not an error - just already processed
-                    started_at=execution.started_at,
-                    ended_at=execution.ended_at,
-                )
-            )
-
-        response = ExecutionAnalyticsResponse(
-            total_executions=len(executions),
-            processed_executions=len(executions_to_process),
-            successful_analytics=successful_count,
-            failed_analytics=failed_count,
-            skipped_executions=len(executions) - len(executions_to_process),
-            results=results,
-        )
-
-        logger.info(
-            f"Analytics generation completed: {successful_count} successful, {failed_count} failed, "
-            f"{response.skipped_executions} skipped"
-        )
-
-        return response
-
-    except Exception as e:
-        logger.exception(f"Error during execution analytics generation: {e}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-async def _process_batch(
-    executions, request: ExecutionAnalyticsRequest, db_client
-) -> list[ExecutionAnalyticsResult]:
-    """Process a batch of executions concurrently."""
-
-    if not settings.secrets.openai_internal_api_key:
-        raise HTTPException(status_code=500, detail="OpenAI API key not configured")
-
-    async def process_single_execution(execution) -> ExecutionAnalyticsResult:
-        try:
-            # Generate activity status and score using the specified model
-            # Convert stats to GraphExecutionStats if needed
-            if execution.stats:
-                if isinstance(execution.stats, GraphExecutionMeta.Stats):
-                    stats_for_generation = execution.stats.to_db()
-                else:
-                    # Already GraphExecutionStats
-                    stats_for_generation = execution.stats
-            else:
-                stats_for_generation = GraphExecutionStats()
-
-            activity_response = await generate_activity_status_for_execution(
-                graph_exec_id=execution.id,
-                graph_id=execution.graph_id,
-                graph_version=execution.graph_version,
-                execution_stats=stats_for_generation,
-                db_client=db_client,
-                user_id=execution.user_id,
-                execution_status=execution.status,
-                model_name=request.model_name,
-                skip_feature_flag=True,  # Admin endpoint bypasses feature flags
-                system_prompt=request.system_prompt or DEFAULT_SYSTEM_PROMPT,
-                user_prompt=request.user_prompt or DEFAULT_USER_PROMPT,
-                skip_existing=request.skip_existing,
-            )
-
-            if not activity_response:
-                return ExecutionAnalyticsResult(
-                    agent_id=execution.graph_id,
-                    version_id=execution.graph_version,
-                    user_id=execution.user_id,
-                    exec_id=execution.id,
-                    summary_text=None,
-                    score=None,
-                    status="skipped",
-                    error_message="Activity generation returned None",
-                    started_at=execution.started_at,
-                    ended_at=execution.ended_at,
-                )
-
-            # Update the execution stats
-            # Convert GraphExecutionMeta.Stats to GraphExecutionStats for DB compatibility
-            if execution.stats:
-                if isinstance(execution.stats, GraphExecutionMeta.Stats):
-                    updated_stats = execution.stats.to_db()
-                else:
-                    # Already GraphExecutionStats
-                    updated_stats = execution.stats
-            else:
-                updated_stats = GraphExecutionStats()
-
-            updated_stats.activity_status = activity_response["activity_status"]
-            updated_stats.correctness_score = activity_response["correctness_score"]
-
-            # Save to database with correct stats type
-            await update_graph_execution_stats(
-                graph_exec_id=execution.id, stats=updated_stats
-            )
-
-            return ExecutionAnalyticsResult(
-                agent_id=execution.graph_id,
-                version_id=execution.graph_version,
-                user_id=execution.user_id,
-                exec_id=execution.id,
-                summary_text=activity_response["activity_status"],
-                score=activity_response["correctness_score"],
-                status="success",
-                started_at=execution.started_at,
-                ended_at=execution.ended_at,
-            )
-
-        except Exception as e:
-            logger.exception(f"Error processing execution {execution.id}: {e}")
-            return ExecutionAnalyticsResult(
-                agent_id=execution.graph_id,
-                version_id=execution.graph_version,
-                user_id=execution.user_id,
-                exec_id=execution.id,
-                summary_text=None,
-                score=None,
-                status="failed",
-                error_message=str(e),
-                started_at=execution.started_at,
-                ended_at=execution.ended_at,
-            )
-
-    # Process all executions in the batch concurrently
-    return await asyncio.gather(
-        *[process_single_execution(execution) for execution in executions]
-    )
-
-
-@router.get(
-    "/execution_accuracy_trends",
-    response_model=AccuracyTrendsResponse,
-    summary="Get Execution Accuracy Trends and Alerts",
-)
-async def get_execution_accuracy_trends(
-    graph_id: str,
-    user_id: Optional[str] = None,
-    days_back: int = 30,
-    drop_threshold: float = 10.0,
-    include_historical: bool = False,
-    admin_user_id: str = Security(get_user_id),
-) -> AccuracyTrendsResponse:
-    """
-    Get execution accuracy trends with moving averages and alert detection.
-    Simple single-query approach.
-    """
-    logger.info(
-        f"Admin user {admin_user_id} requesting accuracy trends for graph {graph_id}"
-    )
-
-    try:
-        result = await get_accuracy_trends_and_alerts(
-            graph_id=graph_id,
-            days_back=days_back,
-            user_id=user_id,
-            drop_threshold=drop_threshold,
-            include_historical=include_historical,
-        )
-
-        return result
-
-    except Exception as e:
-        logger.exception(f"Error getting accuracy trends for graph {graph_id}: {e}")
-        raise HTTPException(status_code=500, detail=str(e))

autogpt_platform/backend/backend/api/features/analytics_test.py

@@ -1,340 +0,0 @@
-"""Tests for analytics API endpoints."""
-
-import json
-from unittest.mock import AsyncMock, Mock
-
-import fastapi
-import fastapi.testclient
-import pytest
-import pytest_mock
-from pytest_snapshot.plugin import Snapshot
-
-from .analytics import router as analytics_router
-
-app = fastapi.FastAPI()
-app.include_router(analytics_router)
-
-client = fastapi.testclient.TestClient(app)
-
-
-@pytest.fixture(autouse=True)
-def setup_app_auth(mock_jwt_user):
-    """Setup auth overrides for all tests in this module."""
-    from autogpt_libs.auth.jwt_utils import get_jwt_payload
-
-    app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
-    yield
-    app.dependency_overrides.clear()
-
-
-# =============================================================================
-# /log_raw_metric endpoint tests
-# =============================================================================
-
-
-def test_log_raw_metric_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-    test_user_id: str,
-) -> None:
-    """Test successful raw metric logging."""
-    mock_result = Mock(id="metric-123-uuid")
-    mock_log_metric = mocker.patch(
-        "backend.data.analytics.log_raw_metric",
-        new_callable=AsyncMock,
-        return_value=mock_result,
-    )
-
-    request_data = {
-        "metric_name": "page_load_time",
-        "metric_value": 2.5,
-        "data_string": "/dashboard",
-    }
-
-    response = client.post("/log_raw_metric", json=request_data)
-
-    assert response.status_code == 200, f"Unexpected response: {response.text}"
-    assert response.json() == "metric-123-uuid"
-
-    mock_log_metric.assert_called_once_with(
-        user_id=test_user_id,
-        metric_name="page_load_time",
-        metric_value=2.5,
-        data_string="/dashboard",
-    )
-
-    configured_snapshot.assert_match(
-        json.dumps({"metric_id": response.json()}, indent=2, sort_keys=True),
-        "analytics_log_metric_success",
-    )
-
-
-@pytest.mark.parametrize(
-    "metric_value,metric_name,data_string,test_id",
-    [
-        (100, "api_calls_count", "external_api", "integer_value"),
-        (0, "error_count", "no_errors", "zero_value"),
-        (-5.2, "temperature_delta", "cooling", "negative_value"),
-        (1.23456789, "precision_test", "float_precision", "float_precision"),
-        (999999999, "large_number", "max_value", "large_number"),
-        (0.0000001, "tiny_number", "min_value", "tiny_number"),
-    ],
-)
-def test_log_raw_metric_various_values(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-    metric_value: float,
-    metric_name: str,
-    data_string: str,
-    test_id: str,
-) -> None:
-    """Test raw metric logging with various metric values."""
-    mock_result = Mock(id=f"metric-{test_id}-uuid")
-    mocker.patch(
-        "backend.data.analytics.log_raw_metric",
-        new_callable=AsyncMock,
-        return_value=mock_result,
-    )
-
-    request_data = {
-        "metric_name": metric_name,
-        "metric_value": metric_value,
-        "data_string": data_string,
-    }
-
-    response = client.post("/log_raw_metric", json=request_data)
-
-    assert response.status_code == 200, f"Failed for {test_id}: {response.text}"
-
-    configured_snapshot.assert_match(
-        json.dumps(
-            {"metric_id": response.json(), "test_case": test_id},
-            indent=2,
-            sort_keys=True,
-        ),
-        f"analytics_metric_{test_id}",
-    )
-
-
-@pytest.mark.parametrize(
-    "invalid_data,expected_error",
-    [
-        ({}, "Field required"),
-        ({"metric_name": "test"}, "Field required"),
-        (
-            {"metric_name": "test", "metric_value": "not_a_number", "data_string": "x"},
-            "Input should be a valid number",
-        ),
-        (
-            {"metric_name": "", "metric_value": 1.0, "data_string": "test"},
-            "String should have at least 1 character",
-        ),
-        (
-            {"metric_name": "test", "metric_value": 1.0, "data_string": ""},
-            "String should have at least 1 character",
-        ),
-    ],
-    ids=[
-        "empty_request",
-        "missing_metric_value_and_data_string",
-        "invalid_metric_value_type",
-        "empty_metric_name",
-        "empty_data_string",
-    ],
-)
-def test_log_raw_metric_validation_errors(
-    invalid_data: dict,
-    expected_error: str,
-) -> None:
-    """Test validation errors for invalid metric requests."""
-    response = client.post("/log_raw_metric", json=invalid_data)
-
-    assert response.status_code == 422
-    error_detail = response.json()
-    assert "detail" in error_detail, f"Missing 'detail' in error: {error_detail}"
-
-    error_text = json.dumps(error_detail)
-    assert (
-        expected_error in error_text
-    ), f"Expected '{expected_error}' in error response: {error_text}"
-
-
-def test_log_raw_metric_service_error(
-    mocker: pytest_mock.MockFixture,
-    test_user_id: str,
-) -> None:
-    """Test error handling when analytics service fails."""
-    mocker.patch(
-        "backend.data.analytics.log_raw_metric",
-        new_callable=AsyncMock,
-        side_effect=Exception("Database connection failed"),
-    )
-
-    request_data = {
-        "metric_name": "test_metric",
-        "metric_value": 1.0,
-        "data_string": "test",
-    }
-
-    response = client.post("/log_raw_metric", json=request_data)
-
-    assert response.status_code == 500
-    error_detail = response.json()["detail"]
-    assert "Database connection failed" in error_detail["message"]
-    assert "hint" in error_detail
-
-
-# =============================================================================
-# /log_raw_analytics endpoint tests
-# =============================================================================
-
-
-def test_log_raw_analytics_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-    test_user_id: str,
-) -> None:
-    """Test successful raw analytics logging."""
-    mock_result = Mock(id="analytics-789-uuid")
-    mock_log_analytics = mocker.patch(
-        "backend.data.analytics.log_raw_analytics",
-        new_callable=AsyncMock,
-        return_value=mock_result,
-    )
-
-    request_data = {
-        "type": "user_action",
-        "data": {
-            "action": "button_click",
-            "button_id": "submit_form",
-            "timestamp": "2023-01-01T00:00:00Z",
-            "metadata": {"form_type": "registration", "fields_filled": 5},
-        },
-        "data_index": "button_click_submit_form",
-    }
-
-    response = client.post("/log_raw_analytics", json=request_data)
-
-    assert response.status_code == 200, f"Unexpected response: {response.text}"
-    assert response.json() == "analytics-789-uuid"
-
-    mock_log_analytics.assert_called_once_with(
-        test_user_id,
-        "user_action",
-        request_data["data"],
-        "button_click_submit_form",
-    )
-
-    configured_snapshot.assert_match(
-        json.dumps({"analytics_id": response.json()}, indent=2, sort_keys=True),
-        "analytics_log_analytics_success",
-    )
-
-
-def test_log_raw_analytics_complex_data(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test raw analytics logging with complex nested data structures."""
-    mock_result = Mock(id="analytics-complex-uuid")
-    mocker.patch(
-        "backend.data.analytics.log_raw_analytics",
-        new_callable=AsyncMock,
-        return_value=mock_result,
-    )
-
-    request_data = {
-        "type": "agent_execution",
-        "data": {
-            "agent_id": "agent_123",
-            "execution_id": "exec_456",
-            "status": "completed",
-            "duration_ms": 3500,
-            "nodes_executed": 15,
-            "blocks_used": [
-                {"block_id": "llm_block", "count": 3},
-                {"block_id": "http_block", "count": 5},
-                {"block_id": "code_block", "count": 2},
-            ],
-            "errors": [],
-            "metadata": {
-                "trigger": "manual",
-                "user_tier": "premium",
-                "environment": "production",
-            },
-        },
-        "data_index": "agent_123_exec_456",
-    }
-
-    response = client.post("/log_raw_analytics", json=request_data)
-
-    assert response.status_code == 200
-
-    configured_snapshot.assert_match(
-        json.dumps(
-            {"analytics_id": response.json(), "logged_data": request_data["data"]},
-            indent=2,
-            sort_keys=True,
-        ),
-        "analytics_log_analytics_complex_data",
-    )
-
-
-@pytest.mark.parametrize(
-    "invalid_data,expected_error",
-    [
-        ({}, "Field required"),
-        ({"type": "test"}, "Field required"),
-        (
-            {"type": "test", "data": "not_a_dict", "data_index": "test"},
-            "Input should be a valid dictionary",
-        ),
-        ({"type": "test", "data": {"key": "value"}}, "Field required"),
-    ],
-    ids=[
-        "empty_request",
-        "missing_data_and_data_index",
-        "invalid_data_type",
-        "missing_data_index",
-    ],
-)
-def test_log_raw_analytics_validation_errors(
-    invalid_data: dict,
-    expected_error: str,
-) -> None:
-    """Test validation errors for invalid analytics requests."""
-    response = client.post("/log_raw_analytics", json=invalid_data)
-
-    assert response.status_code == 422
-    error_detail = response.json()
-    assert "detail" in error_detail, f"Missing 'detail' in error: {error_detail}"
-
-    error_text = json.dumps(error_detail)
-    assert (
-        expected_error in error_text
-    ), f"Expected '{expected_error}' in error response: {error_text}"
-
-
-def test_log_raw_analytics_service_error(
-    mocker: pytest_mock.MockFixture,
-    test_user_id: str,
-) -> None:
-    """Test error handling when analytics service fails."""
-    mocker.patch(
-        "backend.data.analytics.log_raw_analytics",
-        new_callable=AsyncMock,
-        side_effect=Exception("Analytics DB unreachable"),
-    )
-
-    request_data = {
-        "type": "test_event",
-        "data": {"key": "value"},
-        "data_index": "test_index",
-    }
-
-    response = client.post("/log_raw_analytics", json=request_data)
-
-    assert response.status_code == 500
-    error_detail = response.json()["detail"]
-    assert "Analytics DB unreachable" in error_detail["message"]
-    assert "hint" in error_detail

autogpt_platform/backend/backend/api/features/builder/db.py

@@ -1,689 +0,0 @@
-import logging
-from dataclasses import dataclass
-from datetime import datetime, timedelta, timezone
-from difflib import SequenceMatcher
-from typing import Sequence
-
-import prisma
-
-import backend.api.features.library.db as library_db
-import backend.api.features.library.model as library_model
-import backend.api.features.store.db as store_db
-import backend.api.features.store.model as store_model
-import backend.data.block
-from backend.blocks import load_all_blocks
-from backend.blocks.llm import LlmModel
-from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
-from backend.data.db import query_raw_with_schema
-from backend.integrations.providers import ProviderName
-from backend.util.cache import cached
-from backend.util.models import Pagination
-
-from .model import (
-    BlockCategoryResponse,
-    BlockResponse,
-    BlockType,
-    CountResponse,
-    FilterType,
-    Provider,
-    ProviderResponse,
-    SearchEntry,
-)
-
-logger = logging.getLogger(__name__)
-llm_models = [name.name.lower().replace("_", " ") for name in LlmModel]
-
-MAX_LIBRARY_AGENT_RESULTS = 100
-MAX_MARKETPLACE_AGENT_RESULTS = 100
-MIN_SCORE_FOR_FILTERED_RESULTS = 10.0
-
-SearchResultItem = BlockInfo | library_model.LibraryAgent | store_model.StoreAgent
-
-
-@dataclass
-class _ScoredItem:
-    item: SearchResultItem
-    filter_type: FilterType
-    score: float
-    sort_key: str
-
-
-@dataclass
-class _SearchCacheEntry:
-    items: list[SearchResultItem]
-    total_items: dict[FilterType, int]
-
-
-def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse]:
-    categories: dict[BlockCategory, BlockCategoryResponse] = {}
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        # Skip disabled blocks
-        if block.disabled:
-            continue
-        # Skip blocks that don't have categories (all should have at least one)
-        if not block.categories:
-            continue
-
-        # Add block to the categories
-        for category in block.categories:
-            if category not in categories:
-                categories[category] = BlockCategoryResponse(
-                    name=category.name.lower(),
-                    total_blocks=0,
-                    blocks=[],
-                )
-
-            categories[category].total_blocks += 1
-
-            # Append if the category has less than the specified number of blocks
-            if len(categories[category].blocks) < category_blocks:
-                categories[category].blocks.append(block.get_info())
-
-    # Sort categories by name
-    return sorted(categories.values(), key=lambda x: x.name)
-
-
-def get_blocks(
-    *,
-    category: str | None = None,
-    type: BlockType | None = None,
-    provider: ProviderName | None = None,
-    page: int = 1,
-    page_size: int = 50,
-) -> BlockResponse:
-    """
-    Get blocks based on either category, type or provider.
-    Providing nothing fetches all block types.
-    """
-    # Only one of category, type, or provider can be specified
-    if (category and type) or (category and provider) or (type and provider):
-        raise ValueError("Only one of category, type, or provider can be specified")
-
-    blocks: list[AnyBlockSchema] = []
-    skip = (page - 1) * page_size
-    take = page_size
-    total = 0
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        # Skip disabled blocks
-        if block.disabled:
-            continue
-        # Skip blocks that don't match the category
-        if category and category not in {c.name.lower() for c in block.categories}:
-            continue
-        # Skip blocks that don't match the type
-        if (
-            (type == "input" and block.block_type.value != "Input")
-            or (type == "output" and block.block_type.value != "Output")
-            or (type == "action" and block.block_type.value in ("Input", "Output"))
-        ):
-            continue
-        # Skip blocks that don't match the provider
-        if provider:
-            credentials_info = block.input_schema.get_credentials_fields_info().values()
-            if not any(provider in info.provider for info in credentials_info):
-                continue
-
-        total += 1
-        if skip > 0:
-            skip -= 1
-            continue
-        if take > 0:
-            take -= 1
-            blocks.append(block)
-
-    return BlockResponse(
-        blocks=[b.get_info() for b in blocks],
-        pagination=Pagination(
-            total_items=total,
-            total_pages=(total + page_size - 1) // page_size,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
-def get_block_by_id(block_id: str) -> BlockInfo | None:
-    """
-    Get a specific block by its ID.
-    """
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.id == block_id:
-            return block.get_info()
-    return None
-
-
-async def update_search(user_id: str, search: SearchEntry) -> str:
-    """
-    Upsert a search request for the user and return the search ID.
-    """
-    if search.search_id:
-        # Update existing search
-        await prisma.models.BuilderSearchHistory.prisma().update(
-            where={
-                "id": search.search_id,
-            },
-            data={
-                "searchQuery": search.search_query or "",
-                "filter": search.filter or [],  # type: ignore
-                "byCreator": search.by_creator or [],
-            },
-        )
-        return search.search_id
-    else:
-        # Create new search
-        new_search = await prisma.models.BuilderSearchHistory.prisma().create(
-            data={
-                "userId": user_id,
-                "searchQuery": search.search_query or "",
-                "filter": search.filter or [],  # type: ignore
-                "byCreator": search.by_creator or [],
-            }
-        )
-        return new_search.id
-
-
-async def get_recent_searches(user_id: str, limit: int = 5) -> list[SearchEntry]:
-    """
-    Get the user's most recent search requests.
-    """
-    searches = await prisma.models.BuilderSearchHistory.prisma().find_many(
-        where={
-            "userId": user_id,
-        },
-        order={
-            "updatedAt": "desc",
-        },
-        take=limit,
-    )
-    return [
-        SearchEntry(
-            search_query=s.searchQuery,
-            filter=s.filter,  # type: ignore
-            by_creator=s.byCreator,
-            search_id=s.id,
-        )
-        for s in searches
-    ]
-
-
-async def get_sorted_search_results(
-    *,
-    user_id: str,
-    search_query: str | None,
-    filters: Sequence[FilterType],
-    by_creator: Sequence[str] | None = None,
-) -> _SearchCacheEntry:
-    normalized_filters: tuple[FilterType, ...] = tuple(sorted(set(filters or [])))
-    normalized_creators: tuple[str, ...] = tuple(sorted(set(by_creator or [])))
-    return await _build_cached_search_results(
-        user_id=user_id,
-        search_query=search_query or "",
-        filters=normalized_filters,
-        by_creator=normalized_creators,
-    )
-
-
-@cached(ttl_seconds=300, shared_cache=True)
-async def _build_cached_search_results(
-    user_id: str,
-    search_query: str,
-    filters: tuple[FilterType, ...],
-    by_creator: tuple[str, ...],
-) -> _SearchCacheEntry:
-    normalized_query = (search_query or "").strip().lower()
-
-    include_blocks = "blocks" in filters
-    include_integrations = "integrations" in filters
-    include_library_agents = "my_agents" in filters
-    include_marketplace_agents = "marketplace_agents" in filters
-
-    scored_items: list[_ScoredItem] = []
-    total_items: dict[FilterType, int] = {
-        "blocks": 0,
-        "integrations": 0,
-        "marketplace_agents": 0,
-        "my_agents": 0,
-    }
-
-    block_results, block_total, integration_total = _collect_block_results(
-        normalized_query=normalized_query,
-        include_blocks=include_blocks,
-        include_integrations=include_integrations,
-    )
-    scored_items.extend(block_results)
-    total_items["blocks"] = block_total
-    total_items["integrations"] = integration_total
-
-    if include_library_agents:
-        library_response = await library_db.list_library_agents(
-            user_id=user_id,
-            search_term=search_query or None,
-            page=1,
-            page_size=MAX_LIBRARY_AGENT_RESULTS,
-        )
-        total_items["my_agents"] = library_response.pagination.total_items
-        scored_items.extend(
-            _build_library_items(
-                agents=library_response.agents,
-                normalized_query=normalized_query,
-            )
-        )
-
-    if include_marketplace_agents:
-        marketplace_response = await store_db.get_store_agents(
-            creators=list(by_creator) or None,
-            search_query=search_query or None,
-            page=1,
-            page_size=MAX_MARKETPLACE_AGENT_RESULTS,
-        )
-        total_items["marketplace_agents"] = marketplace_response.pagination.total_items
-        scored_items.extend(
-            _build_marketplace_items(
-                agents=marketplace_response.agents,
-                normalized_query=normalized_query,
-            )
-        )
-
-    sorted_items = sorted(
-        scored_items,
-        key=lambda entry: (-entry.score, entry.sort_key, entry.filter_type),
-    )
-
-    return _SearchCacheEntry(
-        items=[entry.item for entry in sorted_items],
-        total_items=total_items,
-    )
-
-
-def _collect_block_results(
-    *,
-    normalized_query: str,
-    include_blocks: bool,
-    include_integrations: bool,
-) -> tuple[list[_ScoredItem], int, int]:
-    results: list[_ScoredItem] = []
-    block_count = 0
-    integration_count = 0
-
-    if not include_blocks and not include_integrations:
-        return results, block_count, integration_count
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.disabled:
-            continue
-
-        block_info = block.get_info()
-        credentials = list(block.input_schema.get_credentials_fields().values())
-        is_integration = len(credentials) > 0
-
-        if is_integration and not include_integrations:
-            continue
-        if not is_integration and not include_blocks:
-            continue
-
-        score = _score_block(block, block_info, normalized_query)
-        if not _should_include_item(score, normalized_query):
-            continue
-
-        filter_type: FilterType = "integrations" if is_integration else "blocks"
-        if is_integration:
-            integration_count += 1
-        else:
-            block_count += 1
-
-        results.append(
-            _ScoredItem(
-                item=block_info,
-                filter_type=filter_type,
-                score=score,
-                sort_key=_get_item_name(block_info),
-            )
-        )
-
-    return results, block_count, integration_count
-
-
-def _build_library_items(
-    *,
-    agents: list[library_model.LibraryAgent],
-    normalized_query: str,
-) -> list[_ScoredItem]:
-    results: list[_ScoredItem] = []
-
-    for agent in agents:
-        score = _score_library_agent(agent, normalized_query)
-        if not _should_include_item(score, normalized_query):
-            continue
-
-        results.append(
-            _ScoredItem(
-                item=agent,
-                filter_type="my_agents",
-                score=score,
-                sort_key=_get_item_name(agent),
-            )
-        )
-
-    return results
-
-
-def _build_marketplace_items(
-    *,
-    agents: list[store_model.StoreAgent],
-    normalized_query: str,
-) -> list[_ScoredItem]:
-    results: list[_ScoredItem] = []
-
-    for agent in agents:
-        score = _score_store_agent(agent, normalized_query)
-        if not _should_include_item(score, normalized_query):
-            continue
-
-        results.append(
-            _ScoredItem(
-                item=agent,
-                filter_type="marketplace_agents",
-                score=score,
-                sort_key=_get_item_name(agent),
-            )
-        )
-
-    return results
-
-
-def get_providers(
-    query: str = "",
-    page: int = 1,
-    page_size: int = 50,
-) -> ProviderResponse:
-    providers = []
-    query = query.lower()
-
-    skip = (page - 1) * page_size
-    take = page_size
-
-    all_providers = _get_all_providers()
-
-    for provider in all_providers.values():
-        if (
-            query not in provider.name.value.lower()
-            and query not in provider.description.lower()
-        ):
-            continue
-        if skip > 0:
-            skip -= 1
-            continue
-        if take > 0:
-            take -= 1
-            providers.append(provider)
-
-    total = len(all_providers)
-
-    return ProviderResponse(
-        providers=providers,
-        pagination=Pagination(
-            total_items=total,
-            total_pages=(total + page_size - 1) // page_size,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
-async def get_counts(user_id: str) -> CountResponse:
-    my_agents = await prisma.models.LibraryAgent.prisma().count(
-        where={
-            "userId": user_id,
-            "isDeleted": False,
-            "isArchived": False,
-        }
-    )
-    counts = await _get_static_counts()
-    return CountResponse(
-        my_agents=my_agents,
-        **counts,
-    )
-
-
-@cached(ttl_seconds=3600)
-async def _get_static_counts():
-    """
-    Get counts of blocks, integrations, and marketplace agents.
-    This is cached to avoid unnecessary database queries and calculations.
-    """
-    all_blocks = 0
-    input_blocks = 0
-    action_blocks = 0
-    output_blocks = 0
-    integrations = 0
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.disabled:
-            continue
-
-        all_blocks += 1
-
-        if block.block_type.value == "Input":
-            input_blocks += 1
-        elif block.block_type.value == "Output":
-            output_blocks += 1
-        else:
-            action_blocks += 1
-
-        credentials = list(block.input_schema.get_credentials_fields().values())
-        if len(credentials) > 0:
-            integrations += 1
-
-    marketplace_agents = await prisma.models.StoreAgent.prisma().count()
-
-    return {
-        "all_blocks": all_blocks,
-        "input_blocks": input_blocks,
-        "action_blocks": action_blocks,
-        "output_blocks": output_blocks,
-        "integrations": integrations,
-        "marketplace_agents": marketplace_agents,
-    }
-
-
-def _matches_llm_model(schema_cls: type[BlockSchema], query: str) -> bool:
-    for field in schema_cls.model_fields.values():
-        if field.annotation == LlmModel:
-            # Check if query matches any value in llm_models
-            if any(query in name for name in llm_models):
-                return True
-    return False
-
-
-def _score_block(
-    block: AnyBlockSchema,
-    block_info: BlockInfo,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = block_info.name.lower()
-    description = block_info.description.lower()
-    score = _score_primary_fields(name, description, normalized_query)
-
-    category_text = " ".join(
-        category.get("category", "").lower() for category in block_info.categories
-    )
-    score += _score_additional_field(category_text, normalized_query, 12, 6)
-
-    credentials_info = block.input_schema.get_credentials_fields_info().values()
-    provider_names = [
-        provider.value.lower()
-        for info in credentials_info
-        for provider in info.provider
-    ]
-    provider_text = " ".join(provider_names)
-    score += _score_additional_field(provider_text, normalized_query, 15, 6)
-
-    if _matches_llm_model(block.input_schema, normalized_query):
-        score += 20
-
-    return score
-
-
-def _score_library_agent(
-    agent: library_model.LibraryAgent,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = agent.name.lower()
-    description = (agent.description or "").lower()
-    instructions = (agent.instructions or "").lower()
-
-    score = _score_primary_fields(name, description, normalized_query)
-    score += _score_additional_field(instructions, normalized_query, 15, 6)
-    score += _score_additional_field(
-        agent.creator_name.lower(), normalized_query, 10, 5
-    )
-
-    return score
-
-
-def _score_store_agent(
-    agent: store_model.StoreAgent,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = agent.agent_name.lower()
-    description = agent.description.lower()
-    sub_heading = agent.sub_heading.lower()
-
-    score = _score_primary_fields(name, description, normalized_query)
-    score += _score_additional_field(sub_heading, normalized_query, 12, 6)
-    score += _score_additional_field(agent.creator.lower(), normalized_query, 10, 5)
-
-    return score
-
-
-def _score_primary_fields(name: str, description: str, query: str) -> float:
-    score = 0.0
-    if name == query:
-        score += 120
-    elif name.startswith(query):
-        score += 90
-    elif query in name:
-        score += 60
-
-    score += SequenceMatcher(None, name, query).ratio() * 50
-    if description:
-        if query in description:
-            score += 30
-        score += SequenceMatcher(None, description, query).ratio() * 25
-    return score
-
-
-def _score_additional_field(
-    value: str,
-    query: str,
-    contains_weight: float,
-    similarity_weight: float,
-) -> float:
-    if not value or not query:
-        return 0.0
-
-    score = 0.0
-    if query in value:
-        score += contains_weight
-    score += SequenceMatcher(None, value, query).ratio() * similarity_weight
-    return score
-
-
-def _should_include_item(score: float, normalized_query: str) -> bool:
-    if not normalized_query:
-        return True
-    return score >= MIN_SCORE_FOR_FILTERED_RESULTS
-
-
-def _get_item_name(item: SearchResultItem) -> str:
-    if isinstance(item, BlockInfo):
-        return item.name.lower()
-    if isinstance(item, library_model.LibraryAgent):
-        return item.name.lower()
-    return item.agent_name.lower()
-
-
-@cached(ttl_seconds=3600)
-def _get_all_providers() -> dict[ProviderName, Provider]:
-    providers: dict[ProviderName, Provider] = {}
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.disabled:
-            continue
-
-        credentials_info = block.input_schema.get_credentials_fields_info().values()
-        for info in credentials_info:
-            for provider in info.provider:  # provider is a ProviderName enum member
-                if provider in providers:
-                    providers[provider].integration_count += 1
-                else:
-                    providers[provider] = Provider(
-                        name=provider, description="", integration_count=1
-                    )
-    return providers
-
-
-@cached(ttl_seconds=3600)
-async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
-    suggested_blocks = []
-    # Sum the number of executions for each block type
-    # Prisma cannot group by nested relations, so we do a raw query
-    # Calculate the cutoff timestamp
-    timestamp_threshold = datetime.now(timezone.utc) - timedelta(days=30)
-
-    results = await query_raw_with_schema(
-        """
-        SELECT
-            agent_node."agentBlockId" AS block_id,
-            COUNT(execution.id) AS execution_count
-        FROM {schema_prefix}"AgentNodeExecution" execution
-        JOIN {schema_prefix}"AgentNode" agent_node ON execution."agentNodeId" = agent_node.id
-        WHERE execution."endedTime" >= $1::timestamp
-        GROUP BY agent_node."agentBlockId"
-        ORDER BY execution_count DESC;
-        """,
-        timestamp_threshold,
-    )
-
-    # Get the top blocks based on execution count
-    # But ignore Input and Output blocks
-    blocks: list[tuple[BlockInfo, int]] = []
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.disabled or block.block_type in (
-            backend.data.block.BlockType.INPUT,
-            backend.data.block.BlockType.OUTPUT,
-            backend.data.block.BlockType.AGENT,
-        ):
-            continue
-        # Find the execution count for this block
-        execution_count = next(
-            (row["execution_count"] for row in results if row["block_id"] == block.id),
-            0,
-        )
-        blocks.append((block.get_info(), execution_count))
-    # Sort blocks by execution count
-    blocks.sort(key=lambda x: x[1], reverse=True)
-
-    suggested_blocks = [block[0] for block in blocks]
-
-    # Return the top blocks
-    return suggested_blocks[:count]

autogpt_platform/backend/backend/api/features/chat/config.py

@@ -1,90 +0,0 @@
-"""Configuration management for chat system."""
-
-import os
-
-from pydantic import Field, field_validator
-from pydantic_settings import BaseSettings
-
-
-class ChatConfig(BaseSettings):
-    """Configuration for the chat system."""
-
-    # OpenAI API Configuration
-    model: str = Field(
-        default="anthropic/claude-opus-4.5", description="Default model to use"
-    )
-    title_model: str = Field(
-        default="openai/gpt-4o-mini",
-        description="Model to use for generating session titles (should be fast/cheap)",
-    )
-    api_key: str | None = Field(default=None, description="OpenAI API key")
-    base_url: str | None = Field(
-        default="https://openrouter.ai/api/v1",
-        description="Base URL for API (e.g., for OpenRouter)",
-    )
-
-    # Session TTL Configuration - 12 hours
-    session_ttl: int = Field(default=43200, description="Session TTL in seconds")
-
-    # Streaming Configuration
-    max_context_messages: int = Field(
-        default=50, ge=1, le=200, description="Maximum context messages"
-    )
-
-    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
-    max_retries: int = Field(default=3, description="Maximum number of retries")
-    max_agent_runs: int = Field(default=3, description="Maximum number of agent runs")
-    max_agent_schedules: int = Field(
-        default=3, description="Maximum number of agent schedules"
-    )
-
-    # Langfuse Prompt Management Configuration
-    # Note: Langfuse credentials are in Settings().secrets (settings.py)
-    langfuse_prompt_name: str = Field(
-        default="CoPilot Prompt",
-        description="Name of the prompt in Langfuse to fetch",
-    )
-
-    @field_validator("api_key", mode="before")
-    @classmethod
-    def get_api_key(cls, v):
-        """Get API key from environment if not provided."""
-        if v is None:
-            # Try to get from environment variables
-            # First check for CHAT_API_KEY (Pydantic prefix)
-            v = os.getenv("CHAT_API_KEY")
-            if not v:
-                # Fall back to OPEN_ROUTER_API_KEY
-                v = os.getenv("OPEN_ROUTER_API_KEY")
-            if not v:
-                # Fall back to OPENAI_API_KEY
-                v = os.getenv("OPENAI_API_KEY")
-        return v
-
-    @field_validator("base_url", mode="before")
-    @classmethod
-    def get_base_url(cls, v):
-        """Get base URL from environment if not provided."""
-        if v is None:
-            # Check for OpenRouter or custom base URL
-            v = os.getenv("CHAT_BASE_URL")
-            if not v:
-                v = os.getenv("OPENROUTER_BASE_URL")
-            if not v:
-                v = os.getenv("OPENAI_BASE_URL")
-            if not v:
-                v = "https://openrouter.ai/api/v1"
-        return v
-
-    # Prompt paths for different contexts
-    PROMPT_PATHS: dict[str, str] = {
-        "default": "prompts/chat_system.md",
-        "onboarding": "prompts/onboarding_system.md",
-    }
-
-    class Config:
-        """Pydantic config."""
-
-        env_file = ".env"
-        env_file_encoding = "utf-8"
-        extra = "ignore"  # Ignore extra environment variables

autogpt_platform/backend/backend/api/features/chat/db.py

@@ -1,249 +0,0 @@
-"""Database operations for chat sessions."""
-
-import asyncio
-import logging
-from datetime import UTC, datetime
-from typing import Any, cast
-
-from prisma.models import ChatMessage as PrismaChatMessage
-from prisma.models import ChatSession as PrismaChatSession
-from prisma.types import (
-    ChatMessageCreateInput,
-    ChatSessionCreateInput,
-    ChatSessionUpdateInput,
-    ChatSessionWhereInput,
-)
-
-from backend.data.db import transaction
-from backend.util.json import SafeJson
-
-logger = logging.getLogger(__name__)
-
-
-async def get_chat_session(session_id: str) -> PrismaChatSession | None:
-    """Get a chat session by ID from the database."""
-    session = await PrismaChatSession.prisma().find_unique(
-        where={"id": session_id},
-        include={"Messages": True},
-    )
-    if session and session.Messages:
-        # Sort messages by sequence in Python - Prisma Python client doesn't support
-        # order_by in include clauses (unlike Prisma JS), so we sort after fetching
-        session.Messages.sort(key=lambda m: m.sequence)
-    return session
-
-
-async def create_chat_session(
-    session_id: str,
-    user_id: str,
-) -> PrismaChatSession:
-    """Create a new chat session in the database."""
-    data = ChatSessionCreateInput(
-        id=session_id,
-        userId=user_id,
-        credentials=SafeJson({}),
-        successfulAgentRuns=SafeJson({}),
-        successfulAgentSchedules=SafeJson({}),
-    )
-    return await PrismaChatSession.prisma().create(
-        data=data,
-        include={"Messages": True},
-    )
-
-
-async def update_chat_session(
-    session_id: str,
-    credentials: dict[str, Any] | None = None,
-    successful_agent_runs: dict[str, Any] | None = None,
-    successful_agent_schedules: dict[str, Any] | None = None,
-    total_prompt_tokens: int | None = None,
-    total_completion_tokens: int | None = None,
-    title: str | None = None,
-) -> PrismaChatSession | None:
-    """Update a chat session's metadata."""
-    data: ChatSessionUpdateInput = {"updatedAt": datetime.now(UTC)}
-
-    if credentials is not None:
-        data["credentials"] = SafeJson(credentials)
-    if successful_agent_runs is not None:
-        data["successfulAgentRuns"] = SafeJson(successful_agent_runs)
-    if successful_agent_schedules is not None:
-        data["successfulAgentSchedules"] = SafeJson(successful_agent_schedules)
-    if total_prompt_tokens is not None:
-        data["totalPromptTokens"] = total_prompt_tokens
-    if total_completion_tokens is not None:
-        data["totalCompletionTokens"] = total_completion_tokens
-    if title is not None:
-        data["title"] = title
-
-    session = await PrismaChatSession.prisma().update(
-        where={"id": session_id},
-        data=data,
-        include={"Messages": True},
-    )
-    if session and session.Messages:
-        # Sort in Python - Prisma Python doesn't support order_by in include clauses
-        session.Messages.sort(key=lambda m: m.sequence)
-    return session
-
-
-async def add_chat_message(
-    session_id: str,
-    role: str,
-    sequence: int,
-    content: str | None = None,
-    name: str | None = None,
-    tool_call_id: str | None = None,
-    refusal: str | None = None,
-    tool_calls: list[dict[str, Any]] | None = None,
-    function_call: dict[str, Any] | None = None,
-) -> PrismaChatMessage:
-    """Add a message to a chat session."""
-    # Build input dict dynamically rather than using ChatMessageCreateInput directly
-    # because Prisma's TypedDict validation rejects optional fields set to None.
-    # We only include fields that have values, then cast at the end.
-    data: dict[str, Any] = {
-        "Session": {"connect": {"id": session_id}},
-        "role": role,
-        "sequence": sequence,
-    }
-
-    # Add optional string fields
-    if content is not None:
-        data["content"] = content
-    if name is not None:
-        data["name"] = name
-    if tool_call_id is not None:
-        data["toolCallId"] = tool_call_id
-    if refusal is not None:
-        data["refusal"] = refusal
-
-    # Add optional JSON fields only when they have values
-    if tool_calls is not None:
-        data["toolCalls"] = SafeJson(tool_calls)
-    if function_call is not None:
-        data["functionCall"] = SafeJson(function_call)
-
-    # Run message create and session timestamp update in parallel for lower latency
-    _, message = await asyncio.gather(
-        PrismaChatSession.prisma().update(
-            where={"id": session_id},
-            data={"updatedAt": datetime.now(UTC)},
-        ),
-        PrismaChatMessage.prisma().create(data=cast(ChatMessageCreateInput, data)),
-    )
-    return message
-
-
-async def add_chat_messages_batch(
-    session_id: str,
-    messages: list[dict[str, Any]],
-    start_sequence: int,
-) -> list[PrismaChatMessage]:
-    """Add multiple messages to a chat session in a batch.
-
-    Uses a transaction for atomicity - if any message creation fails,
-    the entire batch is rolled back.
-    """
-    if not messages:
-        return []
-
-    created_messages = []
-
-    async with transaction() as tx:
-        for i, msg in enumerate(messages):
-            # Build input dict dynamically rather than using ChatMessageCreateInput
-            # directly because Prisma's TypedDict validation rejects optional fields
-            # set to None. We only include fields that have values, then cast.
-            data: dict[str, Any] = {
-                "Session": {"connect": {"id": session_id}},
-                "role": msg["role"],
-                "sequence": start_sequence + i,
-            }
-
-            # Add optional string fields
-            if msg.get("content") is not None:
-                data["content"] = msg["content"]
-            if msg.get("name") is not None:
-                data["name"] = msg["name"]
-            if msg.get("tool_call_id") is not None:
-                data["toolCallId"] = msg["tool_call_id"]
-            if msg.get("refusal") is not None:
-                data["refusal"] = msg["refusal"]
-
-            # Add optional JSON fields only when they have values
-            if msg.get("tool_calls") is not None:
-                data["toolCalls"] = SafeJson(msg["tool_calls"])
-            if msg.get("function_call") is not None:
-                data["functionCall"] = SafeJson(msg["function_call"])
-
-            created = await PrismaChatMessage.prisma(tx).create(
-                data=cast(ChatMessageCreateInput, data)
-            )
-            created_messages.append(created)
-
-        # Update session's updatedAt timestamp within the same transaction.
-        # Note: Token usage (total_prompt_tokens, total_completion_tokens) is updated
-        # separately via update_chat_session() after streaming completes.
-        await PrismaChatSession.prisma(tx).update(
-            where={"id": session_id},
-            data={"updatedAt": datetime.now(UTC)},
-        )
-
-    return created_messages
-
-
-async def get_user_chat_sessions(
-    user_id: str,
-    limit: int = 50,
-    offset: int = 0,
-) -> list[PrismaChatSession]:
-    """Get chat sessions for a user, ordered by most recent."""
-    return await PrismaChatSession.prisma().find_many(
-        where={"userId": user_id},
-        order={"updatedAt": "desc"},
-        take=limit,
-        skip=offset,
-    )
-
-
-async def get_user_session_count(user_id: str) -> int:
-    """Get the total number of chat sessions for a user."""
-    return await PrismaChatSession.prisma().count(where={"userId": user_id})
-
-
-async def delete_chat_session(session_id: str, user_id: str | None = None) -> bool:
-    """Delete a chat session and all its messages.
-
-    Args:
-        session_id: The session ID to delete.
-        user_id: If provided, validates that the session belongs to this user
-            before deletion. This prevents unauthorized deletion of other
-            users' sessions.
-
-    Returns:
-        True if deleted successfully, False otherwise.
-    """
-    try:
-        # Build typed where clause with optional user_id validation
-        where_clause: ChatSessionWhereInput = {"id": session_id}
-        if user_id is not None:
-            where_clause["userId"] = user_id
-
-        result = await PrismaChatSession.prisma().delete_many(where=where_clause)
-        if result == 0:
-            logger.warning(
-                f"No session deleted for {session_id} "
-                f"(user_id validation: {user_id is not None})"
-            )
-            return False
-        return True
-    except Exception as e:
-        logger.error(f"Failed to delete chat session {session_id}: {e}")
-        return False
-
-
-async def get_chat_session_message_count(session_id: str) -> int:
-    """Get the number of messages in a chat session."""
-    count = await PrismaChatMessage.prisma().count(where={"sessionId": session_id})
-    return count