Format

.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-ci-failure-auto-fix.yml

@@ -93,5 +93,5 @@ jobs:
 
             Error logs:
             ${{ toJSON(fromJSON(steps.failure_details.outputs.result).errorLogs) }}
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           claude_args: "--allowedTools 'Edit,MultiEdit,Write,Read,Glob,Grep,LS,Bash(git:*),Bash(bun:*),Bash(npm:*),Bash(npx:*),Bash(gh:*)'"

.github/workflows/claude-dependabot.yml

@@ -7,7 +7,7 @@
 # - Provide actionable recommendations for the development team
 #
 # Triggered on: Dependabot PRs (opened, synchronize)
-# Requirements: CLAUDE_CODE_OAUTH_TOKEN secret must be configured
+# Requirements: ANTHROPIC_API_KEY secret must be configured
 
 name: Claude Dependabot PR Review
 
@@ -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
@@ -308,10 +308,10 @@ jobs:
         id: claude_review
         uses: anthropics/claude-code-action@v1
         with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           claude_args: |
             --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*)"
-          prompt: |
+          custom_system_prompt: |
             You are Claude, an AI assistant specialized in reviewing Dependabot dependency update PRs. 
             
             Your primary tasks are:

.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
@@ -323,9 +317,8 @@ jobs:
         id: claude
         uses: anthropics/claude-code-action@v1
         with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           claude_args: |
-            --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*), Bash(gh pr edit:*)"
-            --model opus
+            --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*)"
           additional_permissions: |
             actions: read

.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/docs-block-sync.yml

@@ -1,78 +0,0 @@
-name: Block Documentation Sync Check
-
-on:
-  push:
-    branches: [master, dev]
-    paths:
-      - "autogpt_platform/backend/backend/blocks/**"
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/scripts/generate_block_docs.py"
-      - ".github/workflows/docs-block-sync.yml"
-  pull_request:
-    branches: [master, dev]
-    paths:
-      - "autogpt_platform/backend/backend/blocks/**"
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/scripts/generate_block_docs.py"
-      - ".github/workflows/docs-block-sync.yml"
-
-jobs:
-  check-docs-sync:
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Check block documentation is in sync
-        working-directory: autogpt_platform/backend
-        run: |
-          echo "Checking if block documentation is in sync with code..."
-          poetry run python scripts/generate_block_docs.py --check
-
-      - name: Show diff if out of sync
-        if: failure()
-        working-directory: autogpt_platform/backend
-        run: |
-          echo "::error::Block documentation is out of sync with code!"
-          echo ""
-          echo "To fix this, run the following command locally:"
-          echo "  cd autogpt_platform/backend && poetry run python scripts/generate_block_docs.py"
-          echo ""
-          echo "Then commit the updated documentation files."
-          echo ""
-          echo "Regenerating docs to show diff..."
-          poetry run python scripts/generate_block_docs.py
-          echo ""
-          echo "Changes detected:"
-          git diff ../../docs/integrations/ || true

.github/workflows/docs-claude-review.yml

@@ -1,95 +0,0 @@
-name: Claude Block Docs Review
-
-on:
-  pull_request:
-    types: [opened, synchronize]
-    paths:
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/backend/blocks/**"
-
-jobs:
-  claude-review:
-    # Only run for PRs from members/collaborators
-    if: |
-      github.event.pull_request.author_association == 'OWNER' ||
-      github.event.pull_request.author_association == 'MEMBER' ||
-      github.event.pull_request.author_association == 'COLLABORATOR'
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    permissions:
-      contents: read
-      pull-requests: write
-      id-token: write
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Run Claude Code Review
-        uses: anthropics/claude-code-action@v1
-        with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          claude_args: |
-            --allowedTools "Read,Glob,Grep,Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*)"
-          prompt: |
-            You are reviewing a PR that modifies block documentation or block code for AutoGPT.
-
-            ## Your Task
-            Review the changes in this PR and provide constructive feedback. Focus on:
-
-            1. **Documentation Accuracy**: For any block code changes, verify that:
-               - Input/output tables in docs match the actual block schemas
-               - Description text accurately reflects what the block does
-               - Any new blocks have corresponding documentation
-
-            2. **Manual Content Quality**: Check manual sections (marked with `<!-- MANUAL: -->` markers):
-               - "How it works" sections should have clear technical explanations
-               - "Possible use case" sections should have practical, real-world examples
-               - Content should be helpful for users trying to understand the blocks
-
-            3. **Template Compliance**: Ensure docs follow the standard template:
-               - What it is (brief intro)
-               - What it does (description)
-               - How it works (technical explanation)
-               - Inputs table
-               - Outputs table
-               - Possible use case
-
-            4. **Cross-references**: Check that links and anchors are correct
-
-            ## Review Process
-            1. First, get the PR diff to see what changed: `gh pr diff ${{ github.event.pull_request.number }}`
-            2. Read any modified block files to understand the implementation
-            3. Read corresponding documentation files to verify accuracy
-            4. Provide your feedback as a PR comment
-
-            Be constructive and specific. If everything looks good, say so!
-            If there are issues, explain what's wrong and suggest how to fix it.

.github/workflows/docs-enhance.yml

@@ -1,194 +0,0 @@
-name: Enhance Block Documentation
-
-on:
-  workflow_dispatch:
-    inputs:
-      block_pattern:
-        description: 'Block file pattern to enhance (e.g., "google/*.md" or "*" for all blocks)'
-        required: true
-        default: '*'
-        type: string
-      dry_run:
-        description: 'Dry run mode - show proposed changes without committing'
-        type: boolean
-        default: true
-      max_blocks:
-        description: 'Maximum number of blocks to process (0 for unlimited)'
-        type: number
-        default: 10
-
-jobs:
-  enhance-docs:
-    runs-on: ubuntu-latest
-    timeout-minutes: 45
-    permissions:
-      contents: write
-      pull-requests: write
-      id-token: write
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Run Claude Enhancement
-        uses: anthropics/claude-code-action@v1
-        with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          claude_args: |
-            --allowedTools "Read,Edit,Glob,Grep,Write,Bash(git:*),Bash(gh:*),Bash(find:*),Bash(ls:*)"
-          prompt: |
-            You are enhancing block documentation for AutoGPT. Your task is to improve the MANUAL sections
-            of block documentation files by reading the actual block implementations and writing helpful content.
-
-            ## Configuration
-            - Block pattern: ${{ inputs.block_pattern }}
-            - Dry run: ${{ inputs.dry_run }}
-            - Max blocks to process: ${{ inputs.max_blocks }}
-
-            ## Your Task
-
-            1. **Find Documentation Files**
-               Find block documentation files matching the pattern in `docs/integrations/`
-               Pattern: ${{ inputs.block_pattern }}
-
-               Use: `find docs/integrations -name "*.md" -type f`
-
-            2. **For Each Documentation File** (up to ${{ inputs.max_blocks }} files):
-
-               a. Read the documentation file
-
-               b. Identify which block(s) it documents (look for the block class name)
-
-               c. Find and read the corresponding block implementation in `autogpt_platform/backend/backend/blocks/`
-
-               d. Improve the MANUAL sections:
-
-                  **"How it works" section** (within `<!-- MANUAL: how_it_works -->` markers):
-                  - Explain the technical flow of the block
-                  - Describe what APIs or services it connects to
-                  - Note any important configuration or prerequisites
-                  - Keep it concise but informative (2-4 paragraphs)
-
-                  **"Possible use case" section** (within `<!-- MANUAL: use_case -->` markers):
-                  - Provide 2-3 practical, real-world examples
-                  - Make them specific and actionable
-                  - Show how this block could be used in an automation workflow
-
-            3. **Important Rules**
-               - ONLY modify content within `<!-- MANUAL: -->` and `<!-- END MANUAL -->` markers
-               - Do NOT modify auto-generated sections (inputs/outputs tables, descriptions)
-               - Keep content accurate based on the actual block implementation
-               - Write for users who may not be technical experts
-
-            4. **Output**
-               ${{ inputs.dry_run == true && 'DRY RUN MODE: Show proposed changes for each file but do NOT actually edit the files. Describe what you would change.' || 'LIVE MODE: Actually edit the files to improve the documentation.' }}
-
-            ## Example Improvements
-
-            **Before (How it works):**
-            ```
-            _Add technical explanation here._
-            ```
-
-            **After (How it works):**
-            ```
-            This block connects to the GitHub API to retrieve issue information. When executed,
-            it authenticates using your GitHub credentials and fetches issue details including
-            title, body, labels, and assignees.
-
-            The block requires a valid GitHub OAuth connection with repository access permissions.
-            It supports both public and private repositories you have access to.
-            ```
-
-            **Before (Possible use case):**
-            ```
-            _Add practical use case examples here._
-            ```
-
-            **After (Possible use case):**
-            ```
-            **Customer Support Automation**: Monitor a GitHub repository for new issues with
-            the "bug" label, then automatically create a ticket in your support system and
-            notify the on-call engineer via Slack.
-
-            **Release Notes Generation**: When a new release is published, gather all closed
-            issues since the last release and generate a summary for your changelog.
-            ```
-
-            Begin by finding and listing the documentation files to process.
-
-      - name: Create PR with enhanced documentation
-        if: ${{ inputs.dry_run == false }}
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          # Check if there are changes
-          if git diff --quiet docs/integrations/; then
-            echo "No changes to commit"
-            exit 0
-          fi
-
-          # Configure git
-          git config user.name "github-actions[bot]"
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-
-          # Create branch and commit
-          BRANCH_NAME="docs/enhance-blocks-$(date +%Y%m%d-%H%M%S)"
-          git checkout -b "$BRANCH_NAME"
-          git add docs/integrations/
-          git commit -m "docs: enhance block documentation with LLM-generated content
-
-          Pattern: ${{ inputs.block_pattern }}
-          Max blocks: ${{ inputs.max_blocks }}
-
-          🤖 Generated with [Claude Code](https://claude.com/claude-code)
-
-          Co-Authored-By: Claude <noreply@anthropic.com>"
-
-          # Push and create PR
-          git push -u origin "$BRANCH_NAME"
-          gh pr create \
-            --title "docs: LLM-enhanced block documentation" \
-            --body "## Summary
-          This PR contains LLM-enhanced documentation for block files matching pattern: \`${{ inputs.block_pattern }}\`
-
-          The following manual sections were improved:
-          - **How it works**: Technical explanations based on block implementations
-          - **Possible use case**: Practical, real-world examples
-
-          ## Review Checklist
-          - [ ] Content is accurate based on block implementations
-          - [ ] Examples are practical and helpful
-          - [ ] No auto-generated sections were modified
-
-          ---
-          🤖 Generated with [Claude Code](https://claude.com/claude-code)" \
-            --base dev

.github/workflows/platform-autgpt-deploy-prod.yml

@@ -3,7 +3,6 @@ name: AutoGPT Platform - Deploy Prod Environment
 on:
   release:
     types: [published]
-  workflow_dispatch:
 
 permissions:
   contents: 'read'
@@ -18,8 +17,6 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-        with:
-          ref: ${{ github.ref_name || 'master' }}
 
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -39,7 +36,7 @@ jobs:
           DATABASE_URL: ${{ secrets.BACKEND_DATABASE_URL }}
           DIRECT_URL: ${{ secrets.BACKEND_DATABASE_URL }}
 
-
+          
   trigger:
     needs: migrate
     runs-on: ubuntu-latest
@@ -50,5 +47,4 @@ jobs:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
           event-type: build_deploy_prod
-          client-payload: |
-            {"ref": "${{ github.ref_name || 'master' }}", "repository": "${{ github.repository }}"}
+          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'

.github/workflows/platform-autogpt-deploy-dev.yaml

@@ -5,13 +5,6 @@ on:
     branches: [ dev ]
     paths:
       - 'autogpt_platform/**'
-  workflow_dispatch:
-    inputs:
-      git_ref:
-        description: 'Git ref (branch/tag) of AutoGPT to deploy'
-        required: true
-        default: 'master'
-        type: string
 
 permissions:
   contents: 'read'
@@ -26,8 +19,6 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.git_ref || github.ref_name }}
 
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -57,4 +48,4 @@ jobs:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
           event-type: build_deploy_dev
-          client-payload: '{"ref": "${{ github.event.inputs.git_ref || github.ref }}", "repository": "${{ github.repository }}"}'
+          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'

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

@@ -37,7 +37,9 @@ jobs:
 
     services:
       redis:
-        image: redis:latest
+        image: bitnami/redis:6.2
+        env:
+          REDIS_PASSWORD: testpassword
         ports:
           - 6379:6379
       rabbitmq:
@@ -134,7 +136,7 @@ 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 +178,7 @@ 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 }}
@@ -202,6 +204,7 @@ jobs:
           JWT_VERIFY_KEY: ${{ steps.supabase.outputs.JWT_SECRET }}
           REDIS_HOST: "localhost"
           REDIS_PORT: "6379"
+          REDIS_PASSWORD: "testpassword"
           ENCRYPTION_KEY: "dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw=" # DO NOT USE IN PRODUCTION!!
 
     env:

.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

.pr_agent.toml

@@ -1,3 +1,6 @@
+[pr_reviewer]
+num_code_suggestions=0
+
 [pr_code_suggestions]
 commitable_code_suggestions=false
 num_code_suggestions=0

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

@@ -1,64 +0,0 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
-
-# Run just Supabase + Redis + RabbitMQ
-start-core:
-	docker compose up -d deps
-
-# Stop core services
-stop-core:
-	docker compose stop 
-
-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
-
-# Run formatting and linting for backend and frontend
-format:
-	cd backend && poetry run format
-	cd frontend && pnpm format
-	cd frontend && pnpm lint
-
-init-env:
-	cp -n .env.default .env || true
-	cd backend && cp -n .env.default .env || true
-	cd frontend && cp -n .env.default .env || true
-
-
-# Run migrations for backend
-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
-
-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"

autogpt_platform/README.md

@@ -38,37 +38,6 @@ To run the AutoGPT Platform, follow these steps:
 
 4. After all the services are in ready state, open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
 
-### Running Just Core services
-
-You can now run the following to enable just the core services.
-
-```
-# For help
-make help
-
-# Run just Supabase + Redis + RabbitMQ
-make start-core
-
-# Stop core services
-make stop-core
-
-# View logs from core services 
-make logs-core
-
-# Run formatting and linting for backend and frontend
-make format
-
-# Run migrations for backend database
-make migrate
-
-# Run backend server
-make run-backend
-
-# Run frontend development server
-make run-frontend
-
-```
-
 ### Docker Compose Commands
 
 Here are some useful Docker Compose commands for managing your AutoGPT Platform:

autogpt_platform/autogpt_libs/autogpt_libs/api_key/key_manager.py

@@ -0,0 +1,35 @@
+import hashlib
+import secrets
+from typing import NamedTuple
+
+
+class APIKeyContainer(NamedTuple):
+    """Container for API key parts."""
+
+    raw: str
+    prefix: str
+    postfix: str
+    hash: str
+
+
+class APIKeyManager:
+    PREFIX: str = "agpt_"
+    PREFIX_LENGTH: int = 8
+    POSTFIX_LENGTH: int = 8
+
+    def generate_api_key(self) -> APIKeyContainer:
+        """Generate a new API key with all its parts."""
+        raw_key = f"{self.PREFIX}{secrets.token_urlsafe(32)}"
+        return APIKeyContainer(
+            raw=raw_key,
+            prefix=raw_key[: self.PREFIX_LENGTH],
+            postfix=raw_key[-self.POSTFIX_LENGTH :],
+            hash=hashlib.sha256(raw_key.encode()).hexdigest(),
+        )
+
+    def verify_api_key(self, provided_key: str, stored_hash: str) -> bool:
+        """Verify if a provided API key matches the stored hash."""
+        if not provided_key.startswith(self.PREFIX):
+            return False
+        provided_hash = hashlib.sha256(provided_key.encode()).hexdigest()
+        return secrets.compare_digest(provided_hash, stored_hash)

autogpt_platform/autogpt_libs/autogpt_libs/api_key/keysmith.py

@@ -1,81 +0,0 @@
-import hashlib
-import secrets
-from typing import NamedTuple
-
-from cryptography.hazmat.primitives.kdf.scrypt import Scrypt
-
-
-class APIKeyContainer(NamedTuple):
-    """Container for API key parts."""
-
-    key: str
-    head: str
-    tail: str
-    hash: str
-    salt: str
-
-
-class APIKeySmith:
-    PREFIX: str = "agpt_"
-    HEAD_LENGTH: int = 8
-    TAIL_LENGTH: int = 8
-
-    def generate_key(self) -> APIKeyContainer:
-        """Generate a new API key with secure hashing."""
-        raw_key = f"{self.PREFIX}{secrets.token_urlsafe(32)}"
-        hash, salt = self.hash_key(raw_key)
-
-        return APIKeyContainer(
-            key=raw_key,
-            head=raw_key[: self.HEAD_LENGTH],
-            tail=raw_key[-self.TAIL_LENGTH :],
-            hash=hash,
-            salt=salt,
-        )
-
-    def verify_key(
-        self, provided_key: str, known_hash: str, known_salt: str | None = None
-    ) -> bool:
-        """
-        Verify an API key against a known hash (+ salt).
-        Supports verifying both legacy SHA256 and secure Scrypt hashes.
-        """
-        if not provided_key.startswith(self.PREFIX):
-            return False
-
-        # Handle legacy SHA256 hashes (migration support)
-        if known_salt is None:
-            legacy_hash = hashlib.sha256(provided_key.encode()).hexdigest()
-            return secrets.compare_digest(legacy_hash, known_hash)
-
-        try:
-            salt_bytes = bytes.fromhex(known_salt)
-            provided_hash = self._hash_key_with_salt(provided_key, salt_bytes)
-            return secrets.compare_digest(provided_hash, known_hash)
-        except (ValueError, TypeError):
-            return False
-
-    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()
-
-    def _generate_salt(self) -> bytes:
-        """Generate a random salt for hashing."""
-        return secrets.token_bytes(32)
-
-    def _hash_key_with_salt(self, raw_key: str, salt: bytes) -> str:
-        """Hash API key using Scrypt with salt."""
-        kdf = Scrypt(
-            length=32,
-            salt=salt,
-            n=2**14,  # CPU/memory cost parameter
-            r=8,  # Block size parameter
-            p=1,  # Parallelization parameter
-        )
-        key_hash = kdf.derive(raw_key.encode())
-        return key_hash.hex()

autogpt_platform/autogpt_libs/autogpt_libs/api_key/test_keysmith.py

@@ -1,79 +0,0 @@
-import hashlib
-
-from autogpt_libs.api_key.keysmith import APIKeySmith
-
-
-def test_generate_api_key():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    assert key.key.startswith(keysmith.PREFIX)
-    assert key.head == key.key[: keysmith.HEAD_LENGTH]
-    assert key.tail == key.key[-keysmith.TAIL_LENGTH :]
-    assert len(key.hash) == 64  # 32 bytes hex encoded
-    assert len(key.salt) == 64  # 32 bytes hex encoded
-
-
-def test_verify_new_secure_key():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    # Test correct key validates
-    assert keysmith.verify_key(key.key, key.hash, key.salt) is True
-
-    # Test wrong key fails
-    wrong_key = f"{keysmith.PREFIX}wrongkey123"
-    assert keysmith.verify_key(wrong_key, key.hash, key.salt) is False
-
-
-def test_verify_legacy_key():
-    keysmith = APIKeySmith()
-    legacy_key = f"{keysmith.PREFIX}legacykey123"
-    legacy_hash = hashlib.sha256(legacy_key.encode()).hexdigest()
-
-    # Test legacy key validates without salt
-    assert keysmith.verify_key(legacy_key, legacy_hash) is True
-
-    # Test wrong legacy key fails
-    wrong_key = f"{keysmith.PREFIX}wronglegacy"
-    assert keysmith.verify_key(wrong_key, legacy_hash) is False
-
-
-def test_rehash_existing_key():
-    keysmith = APIKeySmith()
-    legacy_key = f"{keysmith.PREFIX}migratekey123"
-
-    # Migrate the legacy key
-    new_hash, new_salt = keysmith.hash_key(legacy_key)
-
-    # Verify migrated key works
-    assert keysmith.verify_key(legacy_key, new_hash, new_salt) is True
-
-    # Verify different key fails with migrated hash
-    wrong_key = f"{keysmith.PREFIX}wrongkey"
-    assert keysmith.verify_key(wrong_key, new_hash, new_salt) is False
-
-
-def test_invalid_key_prefix():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    # Test key without proper prefix fails
-    invalid_key = "invalid_prefix_key"
-    assert keysmith.verify_key(invalid_key, key.hash, key.salt) is False
-
-
-def test_secure_hash_requires_salt():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    # Secure hash without salt should fail
-    assert keysmith.verify_key(key.key, key.hash) is False
-
-
-def test_invalid_salt_format():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    # Invalid salt format should fail gracefully
-    assert keysmith.verify_key(key.key, key.hash, "invalid_hex") is False

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

@@ -4,55 +4,13 @@ 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:
+def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
     FastAPI dependency that requires a valid authenticated user.
 
@@ -62,9 +20,7 @@ async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -
     return verify_user(jwt_payload, admin_only=False)
 
 
-async def requires_admin_user(
-    jwt_payload: dict = fastapi.Security(get_jwt_payload),
-) -> User:
+def requires_admin_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
     FastAPI dependency that requires a valid admin user.
 
@@ -74,44 +30,16 @@ 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:
+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
 
@@ -46,8 +45,7 @@ class TestAuthDependencies:
         """Create a test client."""
         return TestClient(app)
 
-    @pytest.mark.asyncio
-    async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
+    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"}
 
@@ -55,13 +53,12 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_user(jwt_payload)
+        user = requires_user(jwt_payload)
         assert isinstance(user, User)
         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):
+    def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user accepts admin users."""
         jwt_payload = {
             "sub": "admin-456",
@@ -72,31 +69,28 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_user(jwt_payload)
+        user = requires_user(jwt_payload)
         assert user.user_id == "admin-456"
         assert user.role == "admin"
 
-    @pytest.mark.asyncio
-    async def test_requires_user_missing_sub(self):
+    def test_requires_user_missing_sub(self):
         """Test requires_user with missing user ID."""
         jwt_payload = {"role": "user", "email": "user@example.com"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_user(jwt_payload)
+            requires_user(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_requires_user_empty_sub(self):
+    def test_requires_user_empty_sub(self):
         """Test requires_user with empty user ID."""
         jwt_payload = {"sub": "", "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_user(jwt_payload)
+            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):
+    def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
         """Test requires_admin_user with admin role."""
         jwt_payload = {
             "sub": "admin-789",
@@ -107,62 +101,51 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_admin_user(jwt_payload)
+        user = requires_admin_user(jwt_payload)
         assert user.user_id == "admin-789"
         assert user.role == "admin"
 
-    @pytest.mark.asyncio
-    async def test_requires_admin_user_with_regular_user(self):
+    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"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_admin_user(jwt_payload)
+            requires_admin_user(jwt_payload)
         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):
+    def test_requires_admin_user_missing_role(self):
         """Test requires_admin_user with missing role."""
         jwt_payload = {"sub": "user-123", "email": "user@example.com"}
 
         with pytest.raises(KeyError):
-            await requires_admin_user(jwt_payload)
+            requires_admin_user(jwt_payload)
 
-    @pytest.mark.asyncio
-    async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
+    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 = get_user_id(jwt_payload)
         assert user_id == "user-id-xyz"
 
-    @pytest.mark.asyncio
-    async def test_get_user_id_missing_sub(self):
+    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)
+            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):
+    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)
+            get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
 
 
@@ -187,8 +170,7 @@ class TestAuthDependenciesIntegration:
 
         return _create_token
 
-    @pytest.mark.asyncio
-    async def test_endpoint_auth_enabled_no_token(self):
+    def test_endpoint_auth_enabled_no_token(self):
         """Test endpoints require token when auth is enabled."""
         app = FastAPI()
 
@@ -202,8 +184,7 @@ class TestAuthDependenciesIntegration:
         response = client.get("/test")
         assert response.status_code == 401
 
-    @pytest.mark.asyncio
-    async def test_endpoint_with_valid_token(self, create_token):
+    def test_endpoint_with_valid_token(self, create_token):
         """Test endpoint with valid JWT token."""
         app = FastAPI()
 
@@ -222,8 +203,7 @@ 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):
+    def test_admin_endpoint_requires_admin_role(self, create_token):
         """Test admin endpoint rejects non-admin users."""
         app = FastAPI()
 
@@ -260,8 +240,7 @@ class TestAuthDependenciesIntegration:
 class TestAuthDependenciesEdgeCases:
     """Edge case tests for authentication dependencies."""
 
-    @pytest.mark.asyncio
-    async def test_dependency_with_complex_payload(self):
+    def test_dependency_with_complex_payload(self):
         """Test dependencies handle complex JWT payloads."""
         complex_payload = {
             "sub": "user-123",
@@ -277,15 +256,14 @@ class TestAuthDependenciesEdgeCases:
             "exp": 9999999999,
         }
 
-        user = await requires_user(complex_payload)
+        user = requires_user(complex_payload)
         assert user.user_id == "user-123"
         assert user.email == "test@example.com"
 
-        admin = await requires_admin_user(complex_payload)
+        admin = requires_admin_user(complex_payload)
         assert admin.role == "admin"
 
-    @pytest.mark.asyncio
-    async def test_dependency_with_unicode_in_payload(self):
+    def test_dependency_with_unicode_in_payload(self):
         """Test dependencies handle unicode in JWT payloads."""
         unicode_payload = {
             "sub": "user-😀-123",
@@ -294,12 +272,11 @@ class TestAuthDependenciesEdgeCases:
             "name": "日本語",
         }
 
-        user = await requires_user(unicode_payload)
+        user = requires_user(unicode_payload)
         assert "😀" in user.user_id
         assert user.email == "测试@example.com"
 
-    @pytest.mark.asyncio
-    async def test_dependency_with_null_values(self):
+    def test_dependency_with_null_values(self):
         """Test dependencies handle null values in payload."""
         null_payload = {
             "sub": "user-123",
@@ -309,19 +286,18 @@ class TestAuthDependenciesEdgeCases:
             "metadata": None,
         }
 
-        user = await requires_user(null_payload)
+        user = requires_user(null_payload)
         assert user.user_id == "user-123"
         assert user.email is None
 
-    @pytest.mark.asyncio
-    async def test_concurrent_requests_isolation(self):
+    def test_concurrent_requests_isolation(self):
         """Test that concurrent requests don't interfere with each other."""
         payload1 = {"sub": "user-1", "role": "user"}
         payload2 = {"sub": "user-2", "role": "admin"}
 
         # Simulate concurrent processing
-        user1 = await requires_user(payload1)
-        user2 = await requires_admin_user(payload2)
+        user1 = requires_user(payload1)
+        user2 = requires_admin_user(payload2)
 
         assert user1.user_id == "user-1"
         assert user2.user_id == "user-2"
@@ -338,8 +314,7 @@ class TestAuthDependenciesEdgeCases:
             ({"sub": "user", "role": "user"}, "Admin access required", True),
         ],
     )
-    @pytest.mark.asyncio
-    async def test_dependency_error_cases(
+    def test_dependency_error_cases(
         self, payload, expected_error: str, admin_only: bool
     ):
         """Test that errors propagate correctly through dependencies."""
@@ -350,8 +325,7 @@ 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):
+    def test_dependency_valid_user(self):
         """Test valid user case for dependency."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
         from autogpt_libs.auth.jwt_utils import verify_user
@@ -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/auth/jwt_utils.py

@@ -16,7 +16,7 @@ bearer_jwt_auth = HTTPBearer(
 )
 
 
-async def get_jwt_payload(
+def get_jwt_payload(
     credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
 ) -> dict[str, Any]:
     """

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils_test.py

@@ -116,32 +116,32 @@ def test_parse_jwt_token_missing_audience():
     assert "Invalid token" in str(exc_info.value)
 
 
-async def test_get_jwt_payload_with_valid_token():
+def test_get_jwt_payload_with_valid_token():
     """Test extracting JWT payload with valid bearer token."""
     token = create_token(TEST_USER_PAYLOAD)
     credentials = HTTPAuthorizationCredentials(scheme="Bearer", credentials=token)
 
-    result = await jwt_utils.get_jwt_payload(credentials)
+    result = jwt_utils.get_jwt_payload(credentials)
     assert result["sub"] == "test-user-id"
     assert result["role"] == "user"
 
 
-async def test_get_jwt_payload_no_credentials():
+def test_get_jwt_payload_no_credentials():
     """Test JWT payload when no credentials provided."""
     with pytest.raises(HTTPException) as exc_info:
-        await jwt_utils.get_jwt_payload(None)
+        jwt_utils.get_jwt_payload(None)
     assert exc_info.value.status_code == 401
     assert "Authorization header is missing" in exc_info.value.detail
 
 
-async def test_get_jwt_payload_invalid_token():
+def test_get_jwt_payload_invalid_token():
     """Test JWT payload extraction with invalid token."""
     credentials = HTTPAuthorizationCredentials(
         scheme="Bearer", credentials="invalid.token.here"
     )
 
     with pytest.raises(HTTPException) as exc_info:
-        await jwt_utils.get_jwt_payload(credentials)
+        jwt_utils.get_jwt_payload(credentials)
     assert exc_info.value.status_code == 401
     assert "Invalid token" in exc_info.value.detail
 

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -4,7 +4,6 @@ import logging
 import os
 import socket
 import sys
-from logging.handlers import RotatingFileHandler
 from pathlib import Path
 
 from pydantic import Field, field_validator
@@ -94,36 +93,42 @@ 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:
@@ -134,13 +139,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
         print(f"Log directory: {config.log_dir}")
 
         # Activity log handler (INFO and above)
-        # Security fix: Use RotatingFileHandler with size limits to prevent disk exhaustion
-        activity_log_handler = RotatingFileHandler(
-            config.log_dir / LOG_FILE,
-            mode="a",
-            encoding="utf-8",
-            maxBytes=10 * 1024 * 1024,  # 10MB per file
-            backupCount=3,  # Keep 3 backup files (40MB total)
+        activity_log_handler = logging.FileHandler(
+            config.log_dir / LOG_FILE, "a", "utf-8"
         )
         activity_log_handler.setLevel(config.level)
         activity_log_handler.setFormatter(
@@ -150,13 +150,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
 
         if config.level == logging.DEBUG:
             # Debug log handler (all levels)
-            # Security fix: Use RotatingFileHandler with size limits
-            debug_log_handler = RotatingFileHandler(
-                config.log_dir / DEBUG_LOG_FILE,
-                mode="a",
-                encoding="utf-8",
-                maxBytes=10 * 1024 * 1024,  # 10MB per file
-                backupCount=3,  # Keep 3 backup files (40MB total)
+            debug_log_handler = logging.FileHandler(
+                config.log_dir / DEBUG_LOG_FILE, "a", "utf-8"
             )
             debug_log_handler.setLevel(logging.DEBUG)
             debug_log_handler.setFormatter(
@@ -165,13 +160,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
             log_handlers.append(debug_log_handler)
 
         # Error log handler (ERROR and above)
-        # Security fix: Use RotatingFileHandler with size limits
-        error_log_handler = RotatingFileHandler(
-            config.log_dir / ERROR_LOG_FILE,
-            mode="a",
-            encoding="utf-8",
-            maxBytes=10 * 1024 * 1024,  # 10MB per file
-            backupCount=3,  # Keep 3 backup files (40MB total)
+        error_log_handler = logging.FileHandler(
+            config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
         )
         error_log_handler.setLevel(logging.ERROR)
         error_log_handler.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True))
@@ -179,13 +169,7 @@ 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/rate_limit/config.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from pydantic import Field
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
@@ -15,8 +13,8 @@ class RateLimitSettings(BaseSettings):
         default="6379", description="Redis port", validation_alias="REDIS_PORT"
     )
 
-    redis_password: Optional[str] = Field(
-        default=None,
+    redis_password: str = Field(
+        default="password",
         description="Redis password",
         validation_alias="REDIS_PASSWORD",
     )

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/limiter.py

@@ -11,7 +11,7 @@ class RateLimiter:
         self,
         redis_host: str = RATE_LIMIT_SETTINGS.redis_host,
         redis_port: str = RATE_LIMIT_SETTINGS.redis_port,
-        redis_password: str | None = RATE_LIMIT_SETTINGS.redis_password,
+        redis_password: str = RATE_LIMIT_SETTINGS.redis_password,
         requests_per_minute: int = RATE_LIMIT_SETTINGS.requests_per_minute,
     ):
         self.redis = Redis(

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

@@ -0,0 +1,266 @@
+import inspect
+import logging
+import threading
+import time
+from functools import wraps
+from typing import (
+    Awaitable,
+    Callable,
+    ParamSpec,
+    Protocol,
+    Tuple,
+    TypeVar,
+    cast,
+    overload,
+    runtime_checkable,
+)
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+logger = logging.getLogger(__name__)
+
+
+@overload
+def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    pass
+
+
+@overload
+def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
+    pass
+
+
+def thread_cached(
+    func: Callable[P, R] | Callable[P, Awaitable[R]],
+) -> Callable[P, R] | Callable[P, Awaitable[R]]:
+    thread_local = threading.local()
+
+    def _clear():
+        if hasattr(thread_local, "cache"):
+            del thread_local.cache
+
+    if inspect.iscoroutinefunction(func):
+
+        async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = (args, tuple(sorted(kwargs.items())))
+            if key not in cache:
+                cache[key] = await cast(Callable[P, Awaitable[R]], func)(
+                    *args, **kwargs
+                )
+            return cache[key]
+
+        setattr(async_wrapper, "clear_cache", _clear)
+        return async_wrapper
+
+    else:
+
+        def sync_wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = (args, tuple(sorted(kwargs.items())))
+            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:
+    if clear := getattr(func, "clear_cache", None):
+        clear()
+
+
+FuncT = TypeVar("FuncT")
+
+
+R_co = TypeVar("R_co", covariant=True)
+
+
+@runtime_checkable
+class AsyncCachedFunction(Protocol[P, R_co]):
+    """Protocol for async 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 {}
+
+    async def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
+        """Call the cached function."""
+        return None  # type: ignore
+
+
+def async_ttl_cache(
+    maxsize: int = 128, ttl_seconds: int | None = None
+) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
+    """
+    TTL (Time To Live) cache decorator for async functions.
+
+    Similar to functools.lru_cache but works with async functions and includes optional TTL.
+
+    Args:
+        maxsize: Maximum number of cached entries
+        ttl_seconds: Time to live in seconds. If None, entries never expire (like lru_cache)
+
+    Returns:
+        Decorator function
+
+    Example:
+        # With TTL
+        @async_ttl_cache(maxsize=1000, ttl_seconds=300)
+        async def api_call(param: str) -> dict:
+            return {"result": param}
+
+        # Without TTL (permanent cache like lru_cache)
+        @async_ttl_cache(maxsize=1000)
+        async def expensive_computation(param: str) -> dict:
+            return {"result": param}
+    """
+
+    def decorator(
+        async_func: Callable[P, Awaitable[R]],
+    ) -> AsyncCachedFunction[P, R]:
+        # Cache storage - use union type to handle both cases
+        cache_storage: dict[tuple, R | Tuple[R, float]] = {}
+
+        @wraps(async_func)
+        async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            # Create cache key from arguments
+            key = (args, tuple(sorted(kwargs.items())))
+            current_time = time.time()
+
+            # Check if we have a valid cached entry
+            if key in cache_storage:
+                if ttl_seconds is None:
+                    # No TTL - return cached result directly
+                    logger.debug(
+                        f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
+                    )
+                    return cast(R, cache_storage[key])
+                else:
+                    # With TTL - check expiration
+                    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 {async_func.__name__} with key: {str(key)[:50]}"
+                            )
+                            return cast(R, result)
+                        else:
+                            # Expired entry
+                            del cache_storage[key]
+                            logger.debug(
+                                f"Cache entry expired for {async_func.__name__}"
+                            )
+
+            # Cache miss or expired - fetch fresh data
+            logger.debug(
+                f"Cache miss for {async_func.__name__} with key: {str(key)[:50]}"
+            )
+            result = await async_func(*args, **kwargs)
+
+            # Store in cache
+            if ttl_seconds is None:
+                cache_storage[key] = result
+            else:
+                cache_storage[key] = (result, current_time)
+
+            # Simple cleanup when cache gets too large
+            if len(cache_storage) > maxsize:
+                # Remove oldest entries (simple FIFO cleanup)
+                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)
+                logger.debug(
+                    f"Cache cleanup: removed {len(oldest_keys)} entries for {async_func.__name__}"
+                )
+
+            return result
+
+        # Add cache management methods (similar to functools.lru_cache)
+        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,
+            }
+
+        # Attach methods to wrapper
+        setattr(wrapper, "cache_clear", cache_clear)
+        setattr(wrapper, "cache_info", cache_info)
+
+        return cast(AsyncCachedFunction[P, R], wrapper)
+
+    return decorator
+
+
+@overload
+def async_cache(
+    func: Callable[P, Awaitable[R]],
+) -> AsyncCachedFunction[P, R]:
+    pass
+
+
+@overload
+def async_cache(
+    func: None = None,
+    *,
+    maxsize: int = 128,
+) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
+    pass
+
+
+def async_cache(
+    func: Callable[P, Awaitable[R]] | None = None,
+    *,
+    maxsize: int = 128,
+) -> (
+    AsyncCachedFunction[P, R]
+    | Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]
+):
+    """
+    Process-level cache decorator for async functions (no TTL).
+
+    Similar to functools.lru_cache but works with async functions.
+    This is a convenience wrapper around async_ttl_cache with ttl_seconds=None.
+
+    Args:
+        func: The async function to cache (when used without parentheses)
+        maxsize: Maximum number of cached entries
+
+    Returns:
+        Decorated function or decorator
+
+    Example:
+        # Without parentheses (uses default maxsize=128)
+        @async_cache
+        async def get_data(param: str) -> dict:
+            return {"result": param}
+
+        # With parentheses and custom maxsize
+        @async_cache(maxsize=1000)
+        async def expensive_computation(param: str) -> dict:
+            # Expensive computation here
+            return {"result": param}
+    """
+    if func is None:
+        # Called with parentheses @async_cache() or @async_cache(maxsize=...)
+        return async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
+    else:
+        # Called without parentheses @async_cache
+        decorator = async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
+        return decorator(func)

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py

@@ -0,0 +1,705 @@
+"""Tests for the @thread_cached decorator.
+
+This module tests the thread-local caching functionality including:
+- Basic caching for sync and async functions
+- Thread isolation (each thread has its own cache)
+- Cache clearing functionality
+- Exception handling (exceptions are not cached)
+- Argument handling (positional vs keyword arguments)
+"""
+
+import asyncio
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor
+from unittest.mock import Mock
+
+import pytest
+
+from autogpt_libs.utils.cache import (
+    async_cache,
+    async_ttl_cache,
+    clear_thread_cache,
+    thread_cached,
+)
+
+
+class TestThreadCached:
+    def test_sync_function_caching(self):
+        call_count = 0
+
+        @thread_cached
+        def expensive_function(x: int, y: int = 0) -> int:
+            nonlocal call_count
+            call_count += 1
+            return x + y
+
+        assert expensive_function(1, 2) == 3
+        assert call_count == 1
+
+        assert expensive_function(1, 2) == 3
+        assert call_count == 1
+
+        assert expensive_function(1, y=2) == 3
+        assert call_count == 2
+
+        assert expensive_function(2, 3) == 5
+        assert call_count == 3
+
+        assert expensive_function(1) == 1
+        assert call_count == 4
+
+    @pytest.mark.asyncio
+    async def test_async_function_caching(self):
+        call_count = 0
+
+        @thread_cached
+        async def expensive_async_function(x: int, y: int = 0) -> int:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.01)
+            return x + y
+
+        assert await expensive_async_function(1, 2) == 3
+        assert call_count == 1
+
+        assert await expensive_async_function(1, 2) == 3
+        assert call_count == 1
+
+        assert await expensive_async_function(1, y=2) == 3
+        assert call_count == 2
+
+        assert await expensive_async_function(2, 3) == 5
+        assert call_count == 3
+
+    def test_thread_isolation(self):
+        call_count = 0
+        results = {}
+
+        @thread_cached
+        def thread_specific_function(x: int) -> str:
+            nonlocal call_count
+            call_count += 1
+            return f"{threading.current_thread().name}-{x}"
+
+        def worker(thread_id: int):
+            result1 = thread_specific_function(1)
+            result2 = thread_specific_function(1)
+            result3 = thread_specific_function(2)
+            results[thread_id] = (result1, result2, result3)
+
+        with ThreadPoolExecutor(max_workers=3) as executor:
+            futures = [executor.submit(worker, i) for i in range(3)]
+            for future in futures:
+                future.result()
+
+        assert call_count >= 2
+
+        for thread_id, (r1, r2, r3) in results.items():
+            assert r1 == r2
+            assert r1 != r3
+
+    @pytest.mark.asyncio
+    async def test_async_thread_isolation(self):
+        call_count = 0
+        results = {}
+
+        @thread_cached
+        async def async_thread_specific_function(x: int) -> str:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.01)
+            return f"{threading.current_thread().name}-{x}"
+
+        async def async_worker(worker_id: int):
+            result1 = await async_thread_specific_function(1)
+            result2 = await async_thread_specific_function(1)
+            result3 = await async_thread_specific_function(2)
+            results[worker_id] = (result1, result2, result3)
+
+        tasks = [async_worker(i) for i in range(3)]
+        await asyncio.gather(*tasks)
+
+        for worker_id, (r1, r2, r3) in results.items():
+            assert r1 == r2
+            assert r1 != r3
+
+    def test_clear_cache_sync(self):
+        call_count = 0
+
+        @thread_cached
+        def clearable_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            return x * 2
+
+        assert clearable_function(5) == 10
+        assert call_count == 1
+
+        assert clearable_function(5) == 10
+        assert call_count == 1
+
+        clear_thread_cache(clearable_function)
+
+        assert clearable_function(5) == 10
+        assert call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_clear_cache_async(self):
+        call_count = 0
+
+        @thread_cached
+        async def clearable_async_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.01)
+            return x * 2
+
+        assert await clearable_async_function(5) == 10
+        assert call_count == 1
+
+        assert await clearable_async_function(5) == 10
+        assert call_count == 1
+
+        clear_thread_cache(clearable_async_function)
+
+        assert await clearable_async_function(5) == 10
+        assert call_count == 2
+
+    def test_simple_arguments(self):
+        call_count = 0
+
+        @thread_cached
+        def simple_function(a: str, b: int, c: str = "default") -> str:
+            nonlocal call_count
+            call_count += 1
+            return f"{a}-{b}-{c}"
+
+        # First call with all positional args
+        result1 = simple_function("test", 42, "custom")
+        assert call_count == 1
+
+        # Same args, all positional - should hit cache
+        result2 = simple_function("test", 42, "custom")
+        assert call_count == 1
+        assert result1 == result2
+
+        # Same values but last arg as keyword - creates different cache key
+        result3 = simple_function("test", 42, c="custom")
+        assert call_count == 2
+        assert result1 == result3  # Same result, different cache entry
+
+        # Different value - new cache entry
+        result4 = simple_function("test", 43, "custom")
+        assert call_count == 3
+        assert result1 != result4
+
+    def test_positional_vs_keyword_args(self):
+        """Test that positional and keyword arguments create different cache entries."""
+        call_count = 0
+
+        @thread_cached
+        def func(a: int, b: int = 10) -> str:
+            nonlocal call_count
+            call_count += 1
+            return f"result-{a}-{b}"
+
+        # All positional
+        result1 = func(1, 2)
+        assert call_count == 1
+        assert result1 == "result-1-2"
+
+        # Same values, but second arg as keyword
+        result2 = func(1, b=2)
+        assert call_count == 2  # Different cache key!
+        assert result2 == "result-1-2"  # Same result
+
+        # Verify both are cached separately
+        func(1, 2)  # Uses first cache entry
+        assert call_count == 2
+
+        func(1, b=2)  # Uses second cache entry
+        assert call_count == 2
+
+    def test_exception_handling(self):
+        call_count = 0
+
+        @thread_cached
+        def failing_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            if x < 0:
+                raise ValueError("Negative value")
+            return x * 2
+
+        assert failing_function(5) == 10
+        assert call_count == 1
+
+        with pytest.raises(ValueError):
+            failing_function(-1)
+        assert call_count == 2
+
+        with pytest.raises(ValueError):
+            failing_function(-1)
+        assert call_count == 3
+
+        assert failing_function(5) == 10
+        assert call_count == 3
+
+    @pytest.mark.asyncio
+    async def test_async_exception_handling(self):
+        call_count = 0
+
+        @thread_cached
+        async def async_failing_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.01)
+            if x < 0:
+                raise ValueError("Negative value")
+            return x * 2
+
+        assert await async_failing_function(5) == 10
+        assert call_count == 1
+
+        with pytest.raises(ValueError):
+            await async_failing_function(-1)
+        assert call_count == 2
+
+        with pytest.raises(ValueError):
+            await async_failing_function(-1)
+        assert call_count == 3
+
+    def test_sync_caching_performance(self):
+        @thread_cached
+        def slow_function(x: int) -> int:
+            print(f"slow_function called with x={x}")
+            time.sleep(0.1)
+            return x * 2
+
+        start = time.time()
+        result1 = slow_function(5)
+        first_call_time = time.time() - start
+        print(f"First call took {first_call_time:.4f} seconds")
+
+        start = time.time()
+        result2 = slow_function(5)
+        second_call_time = time.time() - start
+        print(f"Second call took {second_call_time:.4f} seconds")
+
+        assert result1 == result2 == 10
+        assert first_call_time > 0.09
+        assert second_call_time < 0.01
+
+    @pytest.mark.asyncio
+    async def test_async_caching_performance(self):
+        @thread_cached
+        async def slow_async_function(x: int) -> int:
+            print(f"slow_async_function called with x={x}")
+            await asyncio.sleep(0.1)
+            return x * 2
+
+        start = time.time()
+        result1 = await slow_async_function(5)
+        first_call_time = time.time() - start
+        print(f"First async call took {first_call_time:.4f} seconds")
+
+        start = time.time()
+        result2 = await slow_async_function(5)
+        second_call_time = time.time() - start
+        print(f"Second async call took {second_call_time:.4f} seconds")
+
+        assert result1 == result2 == 10
+        assert first_call_time > 0.09
+        assert second_call_time < 0.01
+
+    def test_with_mock_objects(self):
+        mock = Mock(return_value=42)
+
+        @thread_cached
+        def function_using_mock(x: int) -> int:
+            return mock(x)
+
+        assert function_using_mock(1) == 42
+        assert mock.call_count == 1
+
+        assert function_using_mock(1) == 42
+        assert mock.call_count == 1
+
+        assert function_using_mock(2) == 42
+        assert mock.call_count == 2
+
+
+class TestAsyncTTLCache:
+    """Tests for the @async_ttl_cache decorator."""
+
+    @pytest.mark.asyncio
+    async def test_basic_caching(self):
+        """Test basic caching functionality."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def cached_function(x: int, y: int = 0) -> int:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.01)  # Simulate async work
+            return x + y
+
+        # First call
+        result1 = await cached_function(1, 2)
+        assert result1 == 3
+        assert call_count == 1
+
+        # Second call with same args - should use cache
+        result2 = await cached_function(1, 2)
+        assert result2 == 3
+        assert call_count == 1  # No additional call
+
+        # Different args - should call function again
+        result3 = await cached_function(2, 3)
+        assert result3 == 5
+        assert call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_ttl_expiration(self):
+        """Test that cache entries expire after TTL."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
+        async def short_lived_cache(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            return x * 2
+
+        # First call
+        result1 = await short_lived_cache(5)
+        assert result1 == 10
+        assert call_count == 1
+
+        # Second call immediately - should use cache
+        result2 = await short_lived_cache(5)
+        assert result2 == 10
+        assert call_count == 1
+
+        # Wait for TTL to expire
+        await asyncio.sleep(1.1)
+
+        # Third call after expiration - should call function again
+        result3 = await short_lived_cache(5)
+        assert result3 == 10
+        assert call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_cache_info(self):
+        """Test cache info functionality."""
+
+        @async_ttl_cache(maxsize=5, ttl_seconds=300)
+        async def info_test_function(x: int) -> int:
+            return x * 3
+
+        # Check initial cache info
+        info = info_test_function.cache_info()
+        assert info["size"] == 0
+        assert info["maxsize"] == 5
+        assert info["ttl_seconds"] == 300
+
+        # Add an entry
+        await info_test_function(1)
+        info = info_test_function.cache_info()
+        assert info["size"] == 1
+
+    @pytest.mark.asyncio
+    async def test_cache_clear(self):
+        """Test cache clearing functionality."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def clearable_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            return x * 4
+
+        # First call
+        result1 = await clearable_function(2)
+        assert result1 == 8
+        assert call_count == 1
+
+        # Second call - should use cache
+        result2 = await clearable_function(2)
+        assert result2 == 8
+        assert call_count == 1
+
+        # Clear cache
+        clearable_function.cache_clear()
+
+        # Third call after clear - should call function again
+        result3 = await clearable_function(2)
+        assert result3 == 8
+        assert call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_maxsize_cleanup(self):
+        """Test that cache cleans up when maxsize is exceeded."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=3, ttl_seconds=60)
+        async def size_limited_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            return x**2
+
+        # Fill cache to maxsize
+        await size_limited_function(1)  # call_count: 1
+        await size_limited_function(2)  # call_count: 2
+        await size_limited_function(3)  # call_count: 3
+
+        info = size_limited_function.cache_info()
+        assert info["size"] == 3
+
+        # Add one more entry - should trigger cleanup
+        await size_limited_function(4)  # call_count: 4
+
+        # Cache size should be reduced (cleanup removes oldest entries)
+        info = size_limited_function.cache_info()
+        assert info["size"] is not None and info["size"] <= 3  # Should be cleaned up
+
+    @pytest.mark.asyncio
+    async def test_argument_variations(self):
+        """Test caching with different argument patterns."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def arg_test_function(a: int, b: str = "default", *, c: int = 100) -> str:
+            nonlocal call_count
+            call_count += 1
+            return f"{a}-{b}-{c}"
+
+        # Different ways to call with same logical arguments
+        result1 = await arg_test_function(1, "test", c=200)
+        assert call_count == 1
+
+        # Same arguments, same order - should use cache
+        result2 = await arg_test_function(1, "test", c=200)
+        assert call_count == 1
+        assert result1 == result2
+
+        # Different arguments - should call function
+        result3 = await arg_test_function(2, "test", c=200)
+        assert call_count == 2
+        assert result1 != result3
+
+    @pytest.mark.asyncio
+    async def test_exception_handling(self):
+        """Test that exceptions are not cached."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def exception_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            if x < 0:
+                raise ValueError("Negative value not allowed")
+            return x * 2
+
+        # Successful call - should be cached
+        result1 = await exception_function(5)
+        assert result1 == 10
+        assert call_count == 1
+
+        # Same successful call - should use cache
+        result2 = await exception_function(5)
+        assert result2 == 10
+        assert call_count == 1
+
+        # Exception call - should not be cached
+        with pytest.raises(ValueError):
+            await exception_function(-1)
+        assert call_count == 2
+
+        # Same exception call - should call again (not cached)
+        with pytest.raises(ValueError):
+            await exception_function(-1)
+        assert call_count == 3
+
+    @pytest.mark.asyncio
+    async def test_concurrent_calls(self):
+        """Test caching behavior with concurrent calls."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def concurrent_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.05)  # Simulate work
+            return x * x
+
+        # Launch concurrent calls with same arguments
+        tasks = [concurrent_function(3) for _ in range(5)]
+        results = await asyncio.gather(*tasks)
+
+        # All results should be the same
+        assert all(result == 9 for result in results)
+
+        # Note: Due to race conditions, call_count might be up to 5 for concurrent calls
+        # This tests that the cache doesn't break under concurrent access
+        assert 1 <= call_count <= 5
+
+
+class TestAsyncCache:
+    """Tests for the @async_cache decorator (no TTL)."""
+
+    @pytest.mark.asyncio
+    async def test_basic_caching_no_ttl(self):
+        """Test basic caching functionality without TTL."""
+        call_count = 0
+
+        @async_cache(maxsize=10)
+        async def cached_function(x: int, y: int = 0) -> int:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.01)  # Simulate async work
+            return x + y
+
+        # First call
+        result1 = await cached_function(1, 2)
+        assert result1 == 3
+        assert call_count == 1
+
+        # Second call with same args - should use cache
+        result2 = await cached_function(1, 2)
+        assert result2 == 3
+        assert call_count == 1  # No additional call
+
+        # Third call after some time - should still use cache (no TTL)
+        await asyncio.sleep(0.05)
+        result3 = await cached_function(1, 2)
+        assert result3 == 3
+        assert call_count == 1  # Still no additional call
+
+        # Different args - should call function again
+        result4 = await cached_function(2, 3)
+        assert result4 == 5
+        assert call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_no_ttl_vs_ttl_behavior(self):
+        """Test the difference between TTL and no-TTL caching."""
+        ttl_call_count = 0
+        no_ttl_call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
+        async def ttl_function(x: int) -> int:
+            nonlocal ttl_call_count
+            ttl_call_count += 1
+            return x * 2
+
+        @async_cache(maxsize=10)  # No TTL
+        async def no_ttl_function(x: int) -> int:
+            nonlocal no_ttl_call_count
+            no_ttl_call_count += 1
+            return x * 2
+
+        # First calls
+        await ttl_function(5)
+        await no_ttl_function(5)
+        assert ttl_call_count == 1
+        assert no_ttl_call_count == 1
+
+        # Wait for TTL to expire
+        await asyncio.sleep(1.1)
+
+        # Second calls after TTL expiry
+        await ttl_function(5)  # Should call function again (TTL expired)
+        await no_ttl_function(5)  # Should use cache (no TTL)
+        assert ttl_call_count == 2  # TTL function called again
+        assert no_ttl_call_count == 1  # No-TTL function still cached
+
+    @pytest.mark.asyncio
+    async def test_async_cache_info(self):
+        """Test cache info for no-TTL cache."""
+
+        @async_cache(maxsize=5)
+        async def info_test_function(x: int) -> int:
+            return x * 3
+
+        # Check initial cache info
+        info = info_test_function.cache_info()
+        assert info["size"] == 0
+        assert info["maxsize"] == 5
+        assert info["ttl_seconds"] is None  # No TTL
+
+        # Add an entry
+        await info_test_function(1)
+        info = info_test_function.cache_info()
+        assert info["size"] == 1
+
+
+class TestTTLOptional:
+    """Tests for optional TTL functionality."""
+
+    @pytest.mark.asyncio
+    async def test_ttl_none_behavior(self):
+        """Test that ttl_seconds=None works like no TTL."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=None)
+        async def no_ttl_via_none(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            return x**2
+
+        # First call
+        result1 = await no_ttl_via_none(3)
+        assert result1 == 9
+        assert call_count == 1
+
+        # Wait (would expire if there was TTL)
+        await asyncio.sleep(0.1)
+
+        # Second call - should still use cache
+        result2 = await no_ttl_via_none(3)
+        assert result2 == 9
+        assert call_count == 1  # No additional call
+
+        # Check cache info
+        info = no_ttl_via_none.cache_info()
+        assert info["ttl_seconds"] is None
+
+    @pytest.mark.asyncio
+    async def test_cache_options_comparison(self):
+        """Test different cache options work as expected."""
+        ttl_calls = 0
+        no_ttl_calls = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # With TTL
+        async def ttl_function(x: int) -> int:
+            nonlocal ttl_calls
+            ttl_calls += 1
+            return x * 10
+
+        @async_cache(maxsize=10)  # Process-level cache (no TTL)
+        async def process_function(x: int) -> int:
+            nonlocal no_ttl_calls
+            no_ttl_calls += 1
+            return x * 10
+
+        # Both should cache initially
+        await ttl_function(3)
+        await process_function(3)
+        assert ttl_calls == 1
+        assert no_ttl_calls == 1
+
+        # Immediate second calls - both should use cache
+        await ttl_function(3)
+        await process_function(3)
+        assert ttl_calls == 1
+        assert no_ttl_calls == 1
+
+        # Wait for TTL to expire
+        await asyncio.sleep(1.1)
+
+        # After TTL expiry
+        await ttl_function(3)  # Should call function again
+        await process_function(3)  # Should still use cache
+        assert ttl_calls == 2  # TTL cache expired, called again
+        assert no_ttl_calls == 1  # Process cache never expires

autogpt_platform/autogpt_libs/poetry.lock

@@ -1002,18 +1002,6 @@ dynamodb = ["boto3 (>=1.9.71)"]
 redis = ["redis (>=2.10.5)"]
 test-filesource = ["pyyaml (>=5.3.1)", "watchdog (>=3.0.0)"]
 
-[[package]]
-name = "nodeenv"
-version = "1.9.1"
-description = "Node.js virtual environment builder"
-optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
-groups = ["dev"]
-files = [
-    {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"},
-    {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
-]
-
 [[package]]
 name = "opentelemetry-api"
 version = "1.35.0"
@@ -1359,27 +1347,6 @@ files = [
     {file = "pyrfc3339-2.0.1.tar.gz", hash = "sha256:e47843379ea35c1296c3b6c67a948a1a490ae0584edfcbdea0eaffb5dd29960b"},
 ]
 
-[[package]]
-name = "pyright"
-version = "1.1.404"
-description = "Command line wrapper for pyright"
-optional = false
-python-versions = ">=3.7"
-groups = ["dev"]
-files = [
-    {file = "pyright-1.1.404-py3-none-any.whl", hash = "sha256:c7b7ff1fdb7219c643079e4c3e7d4125f0dafcc19d253b47e898d130ea426419"},
-    {file = "pyright-1.1.404.tar.gz", hash = "sha256:455e881a558ca6be9ecca0b30ce08aa78343ecc031d37a198ffa9a7a1abeb63e"},
-]
-
-[package.dependencies]
-nodeenv = ">=1.6.0"
-typing-extensions = ">=4.1"
-
-[package.extras]
-all = ["nodejs-wheel-binaries", "twine (>=3.4.1)"]
-dev = ["twine (>=3.4.1)"]
-nodejs = ["nodejs-wheel-binaries"]
-
 [[package]]
 name = "pytest"
 version = "8.4.1"
@@ -1773,6 +1740,7 @@ files = [
     {file = "typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"},
     {file = "typing_extensions-4.14.1.tar.gz", hash = "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36"},
 ]
+markers = {dev = "python_version < \"3.11\""}
 
 [[package]]
 name = "typing-inspection"
@@ -1929,4 +1897,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "0c40b63c3c921846cf05ccfb4e685d4959854b29c2c302245f9832e20aac6954"
+content-hash = "d841f62f95180f6ad63ce82ed8e62aa201b9bf89242cc9299ae0f26ff1f72136"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -9,7 +9,6 @@ packages = [{ include = "autogpt_libs" }]
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 colorama = "^0.4.6"
-cryptography = "^45.0"
 expiringdict = "^1.2.2"
 fastapi = "^0.116.1"
 google-cloud-logging = "^3.12.1"
@@ -22,12 +21,11 @@ supabase = "^2.16.0"
 uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]
-pyright = "^1.1.404"
+ruff = "^0.12.11"
 pytest = "^8.4.1"
 pytest-asyncio = "^1.1.0"
 pytest-mock = "^3.14.1"
 pytest-cov = "^6.2.1"
-ruff = "^0.12.11"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.env.default

@@ -21,7 +21,7 @@ PRISMA_SCHEMA="postgres/schema.prisma"
 # Redis Configuration
 REDIS_HOST=localhost
 REDIS_PORT=6379
-# REDIS_PASSWORD=
+REDIS_PASSWORD=password
 
 # RabbitMQ Credentials
 RABBITMQ_DEFAULT_USER=rabbitmq_user_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
@@ -73,11 +66,6 @@ LANGFUSE_HOST=https://cloud.langfuse.com
 GITHUB_CLIENT_ID=
 GITHUB_CLIENT_SECRET=
 
-# Notion OAuth App server credentials - https://developers.notion.com/docs/authorization
-# Configure a public integration
-NOTION_CLIENT_ID=
-NOTION_CLIENT_SECRET=
-
 # Google OAuth App server credentials - https://console.cloud.google.com/apis/credentials, and enable gmail api and set scopes
 # https://console.cloud.google.com/apis/credentials/consent ?project=<your_project_id>
 # You'll need to add/enable the following scopes (minimum):
@@ -141,6 +129,13 @@ 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

@@ -9,13 +9,4 @@ secrets/*
 !secrets/.gitkeep
 
 *.ignore.*
-*.ign.*
-
-# Load test results and reports
-load-tests/*_RESULTS.md
-load-tests/*_REPORT.md
-load-tests/results/
-load-tests/*.json
-load-tests/*.log
-load-tests/node_modules/*
-migrations/*/rollback*.sql
+*.ign.*

autogpt_platform/backend/Dockerfile

@@ -9,15 +9,8 @@ WORKDIR /app
 
 RUN echo 'Acquire::http::Pipeline-Depth 0;\nAcquire::http::No-Cache true;\nAcquire::BrokenProxy true;\n' > /etc/apt/apt.conf.d/99fixbadproxy
 
-# Install Node.js repository key and setup
+# Update package list and install Python and build dependencies
 RUN apt-get update --allow-releaseinfo-change --fix-missing \
-    && apt-get install -y curl ca-certificates gnupg \
-    && mkdir -p /etc/apt/keyrings \
-    && curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg \
-    && echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" | tee /etc/apt/sources.list.d/nodesource.list
-
-# Update package list and install Python, Node.js, and build dependencies
-RUN apt-get update \
     && apt-get install -y \
     python3.13 \
     python3.13-dev \
@@ -27,9 +20,7 @@ RUN apt-get update \
     libpq5 \
     libz-dev \
     libssl-dev \
-    postgresql-client \
-    nodejs \
-    && rm -rf /var/lib/apt/lists/*
+    postgresql-client
 
 ENV POETRY_HOME=/opt/poetry
 ENV POETRY_NO_INTERACTION=1
@@ -47,9 +38,7 @@ 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
 
@@ -65,18 +54,13 @@ ENV PATH=/opt/poetry/bin:$PATH
 # Install Python without upgrading system-managed packages
 RUN apt-get update && apt-get install -y \
     python3.13 \
-    python3-pip \
-    && rm -rf /var/lib/apt/lists/*
+    python3-pip
 
 # Copy only necessary files from builder
 COPY --from=builder /app /app
 COPY --from=builder /usr/local/lib/python3* /usr/local/lib/python3*
 COPY --from=builder /usr/local/bin/poetry /usr/local/bin/poetry
-# Copy Node.js installation for Prisma
-COPY --from=builder /usr/bin/node /usr/bin/node
-COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
-COPY --from=builder /usr/bin/npm /usr/bin/npm
-COPY --from=builder /usr/bin/npx /usr/bin/npx
+# Copy Prisma binaries
 COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
 
 ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
@@ -94,13 +78,11 @@ 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 @@
-{
-  "id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-  "version": 29,
-  "is_active": false,
-  "name": "Unspirational Poster Maker",
-  "description": "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 clich\u00e9s 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.",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Generated Image",
-        "description": "The resulting generated image ready for you to review and post."
-      },
-      "metadata": {
-        "position": {
-          "x": 2329.937006807125,
-          "y": 80.49068076698347
-        }
-      },
-      "input_links": [
-        {
-          "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
-          "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "20845dda-91de-4508-8077-0504b1a5ae03",
-          "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
-          "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
-          "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Theme",
-        "value": "Cooking"
-      },
-      "metadata": {
-        "position": {
-          "x": -1219.5966324967521,
-          "y": 80.50339731789956
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
-          "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
-          "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_THEME",
-          "is_static": true
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-      "input_default": {
-        "upscale": "No Upscale"
-      },
-      "metadata": {
-        "position": {
-          "x": 1132.373897280427,
-          "y": 88.44610377514573
-        }
-      },
-      "input_links": [
-        {
-          "id": "54588c74-e090-4e49-89e4-844b9952a585",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "20845dda-91de-4508-8077-0504b1a5ae03",
-          "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-      "input_default": {
-        "upscale": "No Upscale"
-      },
-      "metadata": {
-        "position": {
-          "x": 590.7543882245375,
-          "y": 85.69546832466654
-        }
-      },
-      "input_links": [
-        {
-          "id": "66646786-3006-4417-a6b7-0158f2603d1d",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
-          "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-      "input_default": {
-        "upscale": "No Upscale"
-      },
-      "metadata": {
-        "position": {
-          "x": 60.48904654237981,
-          "y": 86.06183359510214
-        }
-      },
-      "input_links": [
-        {
-          "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
-          "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-      "input_default": {
-        "prompt": "A cat sprawled dramatically across an important-looking document during a work-from-home meeting, making direct eye contact with the camera while knocking over a coffee mug in slow motion. Text Overlay: \"Chaos is a career path. Be the obstacle everyone has to work around.\"",
-        "upscale": "No Upscale"
-      },
-      "metadata": {
-        "position": {
-          "x": 1668.3572666956795,
-          "y": 89.69665262457966
-        }
-      },
-      "input_links": [
-        {
-          "id": "509b7587-1940-4a06-808d-edde9a74f400",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
-          "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "gpt-4o",
-        "prompt": "<example_output>\nA photo of a sloth lounging on a desk, with its head resting on a keyboard. The keyboard is on top of a laptop with a blank spreadsheet open. A to-do list is placed beside the laptop, with the top item written as \"Do literally anything\". There is a text overlay that says \"If you can't outwork them, outnap them.\".\n</example_output>\n\nCreate a relatable satirical, snarky, user-deprecating motivational style image based on the theme: \"{{THEME}}\".\n\nOutput only the image description and caption, without any additional commentary or formatting.",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": -561.1139207164056,
-          "y": 78.60434452403524
-        }
-      },
-      "input_links": [
-        {
-          "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
-          "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
-          "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_THEME",
-          "is_static": true
-        }
-      ],
-      "output_links": [
-        {
-          "id": "54588c74-e090-4e49-89e4-844b9952a585",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        },
-        {
-          "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        },
-        {
-          "id": "509b7587-1940-4a06-808d-edde9a74f400",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        },
-        {
-          "id": "66646786-3006-4417-a6b7-0158f2603d1d",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "66646786-3006-4417-a6b7-0158f2603d1d",
-      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    },
-    {
-      "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
-      "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
-      "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "20845dda-91de-4508-8077-0504b1a5ae03",
-      "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
-      "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
-      "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_THEME",
-      "is_static": true
-    },
-    {
-      "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
-      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    },
-    {
-      "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
-      "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "54588c74-e090-4e49-89e4-844b9952a585",
-      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    },
-    {
-      "id": "509b7587-1940-4a06-808d-edde9a74f400",
-      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2024-12-20T19:58:34.390Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Theme": {
-        "advanced": false,
-        "secret": false,
-        "title": "Theme",
-        "default": "Cooking"
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Generated Image": {
-        "advanced": false,
-        "secret": false,
-        "title": "Generated Image",
-        "description": "The resulting generated image ready for you to review and post."
-      }
-    },
-    "required": [
-      "Generated Image"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "ideogram_api_key_credentials": {
-        "credentials_provider": [
-          "ideogram"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "ideogram",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.IDEOGRAM: 'ideogram'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator_values": []
-      },
-      "openai_api_key_credentials": {
-        "credentials_provider": [
-          "openai"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "openai",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], 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": [
-          "gpt-4o"
-        ]
-      }
-    },
-    "required": [
-      "ideogram_api_key_credentials",
-      "openai_api_key_credentials"
-    ],
-    "title": "UnspirationalPosterMakerCredentialsInputSchema",
-    "type": "object"
-  }
-}

autogpt_platform/backend/agents/agent_516d813b-d1bc-470f-add7-c63a4b2c2bad.json

@@ -1,447 +0,0 @@
-{
-  "id": "622849a7-5848-4838-894d-01f8f07e3fad",
-  "version": 18,
-  "is_active": true,
-  "name": "AI Function",
-  "description": "## AI-Powered Function Magic: Never code again!\nProvide a description of a python function and your inputs and AI will provide the results.",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "return",
-        "title": null,
-        "value": null,
-        "format": "",
-        "secret": false,
-        "advanced": false,
-        "description": "The value returned by the function"
-      },
-      "metadata": {
-        "position": {
-          "x": 1598.8622921127233,
-          "y": 291.59140862204725
-        }
-      },
-      "input_links": [
-        {
-          "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
-          "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
-          "source_name": "response",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "o3-mini",
-        "retry": 3,
-        "prompt": "{{ARGS}}",
-        "sys_prompt": "You are now the following python function:\n\n```\n# {{DESCRIPTION}}\n{{FUNCTION}}\n```\n\nThe user will provide your input arguments.\nOnly respond with your `return` value.\nDo not include any commentary or additional text in your response. \nDo not include ``` backticks or any other decorators.",
-        "ollama_host": "localhost:11434",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 995,
-          "y": 290.50000000000006
-        }
-      },
-      "input_links": [
-        {
-          "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
-          "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_FUNCTION",
-          "is_static": true
-        },
-        {
-          "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
-          "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_ARGS",
-          "is_static": true
-        },
-        {
-          "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
-          "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_DESCRIPTION",
-          "is_static": true
-        }
-      ],
-      "output_links": [
-        {
-          "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
-          "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
-          "source_name": "response",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
-      "block_id": "7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
-      "input_default": {
-        "name": "Function Definition",
-        "title": null,
-        "value": "def fake_people(n: int) -> list[dict]:",
-        "secret": false,
-        "advanced": false,
-        "description": "The function definition (text). This is what you would type on the first line of the function when programming.\n\ne.g \"def fake_people(n: int) -> list[dict]:\"",
-        "placeholder_values": []
-      },
-      "metadata": {
-        "position": {
-          "x": -672.6908629664215,
-          "y": 302.42044359789116
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
-          "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_FUNCTION",
-          "is_static": true
-        }
-      ],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "844530de-2354-46d8-b748-67306b7bbca1",
-      "block_id": "7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
-      "input_default": {
-        "name": "Arguments",
-        "title": null,
-        "value": "20",
-        "secret": false,
-        "advanced": false,
-        "description": "The function's inputs\n\ne.g \"20\"",
-        "placeholder_values": []
-      },
-      "metadata": {
-        "position": {
-          "x": -158.1623599617334,
-          "y": 295.410856928333
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
-          "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_ARGS",
-          "is_static": true
-        }
-      ],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
-      "block_id": "90a56ffb-7024-4b2b-ab50-e26c5e5ab8ba",
-      "input_default": {
-        "name": "Description",
-        "title": null,
-        "value": "Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.",
-        "secret": false,
-        "advanced": false,
-        "description": "Describe what the function does.\n\ne.g \"Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.\"",
-        "placeholder_values": []
-      },
-      "metadata": {
-        "position": {
-          "x": 374.4548658057796,
-          "y": 290.3779121974126
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
-          "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_DESCRIPTION",
-          "is_static": true
-        }
-      ],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
-      "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
-      "source_name": "response",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
-      "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
-      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_DESCRIPTION",
-      "is_static": true
-    },
-    {
-      "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
-      "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
-      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_ARGS",
-      "is_static": true
-    },
-    {
-      "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
-      "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
-      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_FUNCTION",
-      "is_static": true
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2025-04-19T17:10:48.857Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Function Definition": {
-        "advanced": false,
-        "anyOf": [
-          {
-            "format": "short-text",
-            "type": "string"
-          },
-          {
-            "type": "null"
-          }
-        ],
-        "secret": false,
-        "title": "Function Definition",
-        "description": "The function definition (text). This is what you would type on the first line of the function when programming.\n\ne.g \"def fake_people(n: int) -> list[dict]:\"",
-        "default": "def fake_people(n: int) -> list[dict]:"
-      },
-      "Arguments": {
-        "advanced": false,
-        "anyOf": [
-          {
-            "format": "short-text",
-            "type": "string"
-          },
-          {
-            "type": "null"
-          }
-        ],
-        "secret": false,
-        "title": "Arguments",
-        "description": "The function's inputs\n\ne.g \"20\"",
-        "default": "20"
-      },
-      "Description": {
-        "advanced": false,
-        "anyOf": [
-          {
-            "format": "long-text",
-            "type": "string"
-          },
-          {
-            "type": "null"
-          }
-        ],
-        "secret": false,
-        "title": "Description",
-        "description": "Describe what the function does.\n\ne.g \"Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.\"",
-        "default": "Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age."
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "return": {
-        "advanced": false,
-        "secret": false,
-        "title": "return",
-        "description": "The value returned by the function"
-      }
-    },
-    "required": [
-      "return"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "openai_api_key_credentials": {
-        "credentials_provider": [
-          "openai"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "openai",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], 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": [
-          "o3-mini"
-        ]
-      }
-    },
-    "required": [
-      "openai_api_key_credentials"
-    ],
-    "title": "AIFunctionCredentialsInputSchema",
-    "type": "object"
-  }
-}