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5 Commits

Author SHA1 Message Date
Bently
82bddd885b Merge branch 'dev' into update-install-scripts 2025-08-19 15:57:45 +01:00
Bentlybro
c71406af8b Simplify setup scripts and remove Sentry prompts
Refactored Windows and Linux setup scripts to streamline prerequisite checks, repository detection, and service startup. Removed Sentry configuration and related prompts for a simpler setup experience. Updated user messaging and improved error handling for common Docker issues.
2025-08-13 18:07:22 +01:00
Bently
468d1af802 Merge branch 'dev' into update-install-scripts 2025-08-08 12:47:12 +01:00
Bentlybro
a2c88c7786 Refactor setup scripts for improved reliability and clarity
Reworked both Windows (.bat) and Unix (.sh) setup scripts to improve error handling, logging, and user prompts. The scripts now check for prerequisites, handle Sentry enablement more clearly, ensure environment files are copied with error checks, and consolidate service startup into a single docker compose command with log output. Unused or redundant code was removed for maintainability.
2025-08-07 10:35:48 +01:00
Bentlybro
e79b7a95dc Remove auto-start of frontend dev server in setup scripts
The setup-autogpt.bat and setup-autogpt.sh scripts no longer automatically start the frontend development server after setup. Users are now instructed to manually stop services with 'docker compose down', and the scripts prompt for exit while keeping services running.
2025-08-07 10:03:45 +01:00
1771 changed files with 29318 additions and 215432 deletions

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@@ -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
}

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@@ -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`

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@@ -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.

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@@ -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])
}
```

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---
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).

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---
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.

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---
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)

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---
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()
])
```

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@@ -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.

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---
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)

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---
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.

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@@ -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>
)
}
```

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@@ -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} />
}
```

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---
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.

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@@ -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', () => { /* ... */ })
// ...
}
```

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@@ -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)

View File

@@ -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.

View File

@@ -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)

View File

@@ -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)
}
```

View File

@@ -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()
}
})
```

View File

@@ -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)
}
```

View File

@@ -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 }
}
```

View File

@@ -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
```

View File

@@ -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.

View File

@@ -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

View File

@@ -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.

View File

@@ -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))
```

View File

@@ -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

View File

@@ -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.

View File

@@ -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.

View File

@@ -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>
```

View File

@@ -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).

View File

@@ -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.

View File

@@ -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.

View File

@@ -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
```

View File

@@ -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>
}
```

View File

@@ -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])
```

View File

@@ -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'}>
}
```

View File

@@ -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.

View File

@@ -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.

View File

@@ -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.

View File

@@ -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)
}, [])
}
```

View File

@@ -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)

View File

@@ -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)

View File

@@ -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.

View File

@@ -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>
)
}
```

View File

@@ -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>
}
```

View File

@@ -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/

View File

@@ -1,322 +0,0 @@
# GitHub Copilot Instructions for AutoGPT
This file provides comprehensive onboarding information for GitHub Copilot coding agent to work efficiently with the AutoGPT repository.
## Repository Overview
**AutoGPT** is a powerful platform for creating, deploying, and managing continuous AI agents that automate complex workflows. This is a large monorepo (~150MB) containing multiple components:
- **AutoGPT Platform** (`autogpt_platform/`) - Main focus: Modern AI agent platform (Polyform Shield License)
- **Classic AutoGPT** (`classic/`) - Legacy agent system (MIT License)
- **Documentation** (`docs/`) - MkDocs-based documentation site
- **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
## Build and Validation Instructions
### Essential Setup Commands
**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
poetry run prisma migrate dev # Run database migrations
poetry run prisma generate # Generate Prisma client
```
3. **Frontend Setup** (always run before frontend development):
```bash
cd autogpt_platform/frontend
pnpm install # Install dependencies
```
### Runtime Requirements
**Critical:** Always ensure Docker services are running before starting development:
```bash
cd autogpt_platform && docker compose --profile local up deps --build --detach
```
**Python Version:** Use Python 3.11 (required; managed by Poetry via pyproject.toml)
**Node.js Version:** Use Node.js 21+ with pnpm package manager
### Development Commands
**Backend Development:**
```bash
cd autogpt_platform/backend
poetry run serve # Start development server (port 8000)
poetry run test # Run all tests (requires ~5 minutes)
poetry run pytest path/to/test.py # Run specific test
poetry run format # Format code (Black + isort) - always run first
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
pnpm build # Build for production (only needed for E2E tests or deployment)
pnpm test # Run Playwright E2E tests (requires build first)
pnpm test-ui # Run tests with UI
pnpm format # Format and lint code
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:
- **Test timeouts**: Backend tests can take 5+ minutes, use `-x` flag to stop on first failure
## Project Layout & Architecture
### Core Architecture
**AutoGPT Platform** (`autogpt_platform/`):
- `backend/` - FastAPI server with async support
- `backend/backend/` - Core API logic
- `backend/blocks/` - Agent execution blocks
- `backend/data/` - Database models and schemas
- `schema.prisma` - Database schema definition
- `frontend/` - Next.js application
- `src/app/` - App Router pages and layouts
- `src/components/` - Reusable React components
- `src/lib/` - Utilities and configurations
- `autogpt_libs/` - Shared Python utilities
- `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
- `next.config.mjs` - Next.js configuration
- `tailwind.config.ts` - Styling configuration
### Security & Middleware
**Cache Protection**: Backend includes middleware preventing sensitive data caching in browsers/proxies
**Authentication**: JWT-based with Supabase integration
**User ID Validation**: All data access requires user ID checks - verify this for any `data/*.py` changes
### 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
**Pre-commit Hooks**: Run linting and formatting checks
**Conventional Commits**: Use format `type(scope): description` (e.g., `feat(backend): add API`)
### 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
**Protected Routes**: Update `frontend/lib/supabase/middleware.ts` when adding protected routes
### 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
### 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
## Environment Configuration
### Configuration Files Priority Order
1. **Backend**: `/backend/.env.default` → `/backend/.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
- Copy `.env.default` files to `.env` for local development customization
## 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
4. Generate block UUID using `uuid.uuid4()`
5. Register in block registry
6. Write tests alongside block implementation
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
4. For `data/*.py` changes, validate user ID checks
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
### 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
Match these patterns when developing locally - the copilot setup environment mirrors these CI configurations.
## 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
- Consider that changes may be reviewed and extended by both human developers and AI assistants
## 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.

View File

@@ -1,97 +0,0 @@
name: Auto Fix CI Failures
on:
workflow_run:
workflows: ["CI"]
types:
- completed
permissions:
contents: write
pull-requests: write
actions: read
issues: write
id-token: write # Required for OIDC token exchange
jobs:
auto-fix:
if: |
github.event.workflow_run.conclusion == 'failure' &&
github.event.workflow_run.pull_requests[0] &&
!startsWith(github.event.workflow_run.head_branch, 'claude-auto-fix-ci-')
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
ref: ${{ github.event.workflow_run.head_branch }}
fetch-depth: 0
token: ${{ secrets.GITHUB_TOKEN }}
- name: Setup git identity
run: |
git config --global user.email "claude[bot]@users.noreply.github.com"
git config --global user.name "claude[bot]"
- name: Create fix branch
id: branch
run: |
BRANCH_NAME="claude-auto-fix-ci-${{ github.event.workflow_run.head_branch }}-${{ github.run_id }}"
git checkout -b "$BRANCH_NAME"
echo "branch_name=$BRANCH_NAME" >> $GITHUB_OUTPUT
- name: Get CI failure details
id: failure_details
uses: actions/github-script@v7
with:
script: |
const run = await github.rest.actions.getWorkflowRun({
owner: context.repo.owner,
repo: context.repo.repo,
run_id: ${{ github.event.workflow_run.id }}
});
const jobs = await github.rest.actions.listJobsForWorkflowRun({
owner: context.repo.owner,
repo: context.repo.repo,
run_id: ${{ github.event.workflow_run.id }}
});
const failedJobs = jobs.data.jobs.filter(job => job.conclusion === 'failure');
let errorLogs = [];
for (const job of failedJobs) {
const logs = await github.rest.actions.downloadJobLogsForWorkflowRun({
owner: context.repo.owner,
repo: context.repo.repo,
job_id: job.id
});
errorLogs.push({
jobName: job.name,
logs: logs.data
});
}
return {
runUrl: run.data.html_url,
failedJobs: failedJobs.map(j => j.name),
errorLogs: errorLogs
};
- name: Fix CI failures with Claude
id: claude
uses: anthropics/claude-code-action@v1
with:
prompt: |
/fix-ci
Failed CI Run: ${{ fromJSON(steps.failure_details.outputs.result).runUrl }}
Failed Jobs: ${{ join(fromJSON(steps.failure_details.outputs.result).failedJobs, ', ') }}
PR Number: ${{ github.event.workflow_run.pull_requests[0].number }}
Branch Name: ${{ steps.branch.outputs.branch_name }}
Base Branch: ${{ github.event.workflow_run.head_branch }}
Repository: ${{ github.repository }}
Error logs:
${{ toJSON(fromJSON(steps.failure_details.outputs.result).errorLogs) }}
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:*)'"

View File

@@ -1,379 +0,0 @@
# Claude Dependabot PR Review Workflow
#
# This workflow automatically runs Claude analysis on Dependabot PRs to:
# - Identify dependency changes and their versions
# - Look up changelogs for updated packages
# - Assess breaking changes and security impacts
# - Provide actionable recommendations for the development team
#
# Triggered on: Dependabot PRs (opened, synchronize)
# Requirements: ANTHROPIC_API_KEY secret must be configured
name: Claude Dependabot PR Review
on:
pull_request:
types: [opened, synchronize]
jobs:
dependabot-review:
# Only run on Dependabot PRs
if: github.actor == 'dependabot[bot]'
runs-on: ubuntu-latest
timeout-minutes: 30
permissions:
contents: write
pull-requests: read
issues: read
id-token: write
actions: read # Required for CI access
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 1
# Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
- name: Install Poetry
run: |
# Extract Poetry version from backend/poetry.lock (matches CI)
cd autogpt_platform/backend
HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
# Install Poetry
curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
# Add Poetry to PATH
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Check poetry.lock
working-directory: autogpt_platform/backend
run: |
poetry lock
if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
echo "Warning: poetry.lock not up to date, but continuing for setup"
git checkout poetry.lock # Reset for clean setup
fi
- name: Install Python dependencies
working-directory: autogpt_platform/backend
run: poetry install
- name: Generate Prisma Client
working-directory: autogpt_platform/backend
run: poetry run prisma generate && poetry run gen-prisma-stub
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22"
- name: Enable corepack
run: corepack enable
- name: Set pnpm store directory
run: |
pnpm config set store-dir ~/.pnpm-store
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
- name: Install JavaScript dependencies
working-directory: autogpt_platform/frontend
run: pnpm install --frozen-lockfile
# Install Playwright browsers for frontend testing
# NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
# - name: Install Playwright browsers
# working-directory: autogpt_platform/frontend
# run: pnpm playwright install --with-deps chromium
# Docker setup for development environment
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Copy default environment files
working-directory: autogpt_platform
run: |
# Copy default environment files for development
cp .env.default .env
cp backend/.env.default backend/.env
cp frontend/.env.default frontend/.env
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes
key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
restore-keys: |
docker-images-v2-${{ runner.os }}-
docker-images-v1-${{ runner.os }}-
- name: Load or pull Docker images
working-directory: autogpt_platform
run: |
mkdir -p ~/docker-cache
# Define image list for easy maintenance
IMAGES=(
"redis:latest"
"rabbitmq:management"
"clamav/clamav-debian:latest"
"busybox:latest"
"kong:2.8.1"
"supabase/gotrue:v2.170.0"
"supabase/postgres:15.8.1.049"
"supabase/postgres-meta:v0.86.1"
"supabase/studio:20250224-d10db0f"
)
# Check if any cached tar files exist (more reliable than cache-hit)
if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
echo "Docker cache found, loading images in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
if [ -f ~/docker-cache/${filename}.tar ]; then
echo "Loading $image..."
docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
fi
done
wait
echo "All cached images loaded"
else
echo "No Docker cache found, pulling images in parallel..."
# Pull all images in parallel
for image in "${IMAGES[@]}"; do
docker pull "$image" &
done
wait
# Only save cache on main branches (not PRs) to avoid cache pollution
if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
echo "Saving Docker images to cache in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
echo "Saving $image..."
docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
done
wait
echo "Docker image cache saved"
else
echo "Skipping cache save for PR/feature branch"
fi
fi
echo "Docker images ready for use"
# Phase 2: Build migrate service with GitHub Actions cache
- name: Build migrate Docker image with cache
working-directory: autogpt_platform
run: |
# Build the migrate image with buildx for GHA caching
docker buildx build \
--cache-from type=gha \
--cache-to type=gha,mode=max \
--target migrate \
--tag autogpt_platform-migrate:latest \
--load \
-f backend/Dockerfile \
..
# Start services using pre-built images
- name: Start Docker services for development
working-directory: autogpt_platform
run: |
# Start essential services (migrate image already built with correct tag)
docker compose --profile local up deps --no-build --detach
echo "Waiting for services to be ready..."
# Wait for database to be ready
echo "Checking database readiness..."
timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do
echo " Waiting for database..."
sleep 2
done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
# Check migrate service status
echo "Checking migration status..."
docker compose ps migrate || echo " Migrate service not visible in ps output"
# Wait for migrate service to complete
echo "Waiting for migrations to complete..."
timeout 30 bash -c '
ATTEMPTS=0
while [ $ATTEMPTS -lt 15 ]; do
ATTEMPTS=$((ATTEMPTS + 1))
# Check using docker directly (more reliable than docker compose ps)
CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
if [ -z "$CONTAINER_STATUS" ]; then
echo " Attempt $ATTEMPTS: Migrate container not found yet..."
elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
echo "✅ Migrations completed successfully"
docker compose logs migrate --tail=5 2>/dev/null || true
exit 0
elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
echo "❌ Migrations failed with exit code: $EXIT_CODE"
echo "Migration logs:"
docker compose logs migrate --tail=20 2>/dev/null || true
exit 1
elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
echo " Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
else
echo " Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
fi
sleep 2
done
echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
echo "Final container check:"
docker ps -a --filter "label=com.docker.compose.service=migrate" || true
echo "Migration logs (if available):"
docker compose logs migrate --tail=10 2>/dev/null || echo " No logs available"
' || echo "⚠️ Migration check completed with warnings, continuing..."
# Brief wait for other services to stabilize
echo "Waiting 5 seconds for other services to stabilize..."
sleep 5
# Verify installations and provide environment info
- name: Verify setup and show environment info
run: |
echo "=== Python Setup ==="
python --version
poetry --version
echo "=== Node.js Setup ==="
node --version
pnpm --version
echo "=== Additional Tools ==="
docker --version
docker compose version
gh --version || true
echo "=== Services Status ==="
cd autogpt_platform
docker compose ps || true
echo "=== Backend Dependencies ==="
cd backend
poetry show | head -10 || true
echo "=== Frontend Dependencies ==="
cd ../frontend
pnpm list --depth=0 | head -10 || true
echo "=== Environment Files ==="
ls -la ../.env* || true
ls -la .env* || true
ls -la ../backend/.env* || true
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)"
- name: Run Claude Dependabot Analysis
id: claude_review
uses: anthropics/claude-code-action@v1
with:
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: |
You are Claude, an AI assistant specialized in reviewing Dependabot dependency update PRs.
Your primary tasks are:
1. **Analyze the dependency changes** in this Dependabot PR
2. **Look up changelogs** for all updated dependencies to understand what changed
3. **Identify breaking changes** and assess potential impact on the AutoGPT codebase
4. **Provide actionable recommendations** for the development team
## Analysis Process:
1. **Identify Changed Dependencies**:
- Use git diff to see what dependencies were updated
- Parse package.json, poetry.lock, requirements files, etc.
- List all package versions: old → new
2. **Changelog Research**:
- For each updated dependency, look up its changelog/release notes
- Use WebFetch to access GitHub releases, NPM package pages, PyPI project pages. The pr should also have some details
- Focus on versions between the old and new versions
- Identify: breaking changes, deprecations, security fixes, new features
3. **Breaking Change Assessment**:
- Categorize changes: BREAKING, MAJOR, MINOR, PATCH, SECURITY
- Assess impact on AutoGPT's usage patterns
- Check if AutoGPT uses affected APIs/features
- Look for migration guides or upgrade instructions
4. **Codebase Impact Analysis**:
- Search the AutoGPT codebase for usage of changed APIs
- Identify files that might be affected by breaking changes
- Check test files for deprecated usage patterns
- Look for configuration changes needed
## Output Format:
Provide a comprehensive review comment with:
### 🔍 Dependency Analysis Summary
- List of updated packages with version changes
- Overall risk assessment (LOW/MEDIUM/HIGH)
### 📋 Detailed Changelog Review
For each updated dependency:
- **Package**: name (old_version → new_version)
- **Changes**: Summary of key changes
- **Breaking Changes**: List any breaking changes
- **Security Fixes**: Note security improvements
- **Migration Notes**: Any upgrade steps needed
### ⚠️ Impact Assessment
- **Breaking Changes Found**: Yes/No with details
- **Affected Files**: List AutoGPT files that may need updates
- **Test Impact**: Any tests that may need updating
- **Configuration Changes**: Required config updates
### 🛠️ Recommendations
- **Action Required**: What the team should do
- **Testing Focus**: Areas to test thoroughly
- **Follow-up Tasks**: Any additional work needed
- **Merge Recommendation**: APPROVE/REVIEW_NEEDED/HOLD
### 📚 Useful Links
- Links to relevant changelogs, migration guides, documentation
Be thorough but concise. Focus on actionable insights that help the development team make informed decisions about the dependency updates.

View File

@@ -30,302 +30,18 @@ jobs:
github.event.issue.author_association == 'COLLABORATOR'
)
runs-on: ubuntu-latest
timeout-minutes: 45
permissions:
contents: write
contents: read
pull-requests: read
issues: read
id-token: write
actions: read # Required for CI access
steps:
- name: Checkout code
- name: Checkout repository
uses: actions/checkout@v4
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
with:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
- name: Install Poetry
run: |
# Extract Poetry version from backend/poetry.lock (matches CI)
cd autogpt_platform/backend
HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
# Install Poetry
curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
# Add Poetry to PATH
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Check poetry.lock
working-directory: autogpt_platform/backend
run: |
poetry lock
if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
echo "Warning: poetry.lock not up to date, but continuing for setup"
git checkout poetry.lock # Reset for clean setup
fi
- name: Install Python dependencies
working-directory: autogpt_platform/backend
run: poetry install
- name: Generate Prisma Client
working-directory: autogpt_platform/backend
run: poetry run prisma generate && poetry run gen-prisma-stub
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22"
- name: Enable corepack
run: corepack enable
- name: Set pnpm store directory
run: |
pnpm config set store-dir ~/.pnpm-store
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
- name: Install JavaScript dependencies
working-directory: autogpt_platform/frontend
run: pnpm install --frozen-lockfile
# Install Playwright browsers for frontend testing
# NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
# - name: Install Playwright browsers
# working-directory: autogpt_platform/frontend
# run: pnpm playwright install --with-deps chromium
# Docker setup for development environment
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Copy default environment files
working-directory: autogpt_platform
run: |
# Copy default environment files for development
cp .env.default .env
cp backend/.env.default backend/.env
cp frontend/.env.default frontend/.env
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes
key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
restore-keys: |
docker-images-v2-${{ runner.os }}-
docker-images-v1-${{ runner.os }}-
- name: Load or pull Docker images
working-directory: autogpt_platform
run: |
mkdir -p ~/docker-cache
# Define image list for easy maintenance
IMAGES=(
"redis:latest"
"rabbitmq:management"
"clamav/clamav-debian:latest"
"busybox:latest"
"kong:2.8.1"
"supabase/gotrue:v2.170.0"
"supabase/postgres:15.8.1.049"
"supabase/postgres-meta:v0.86.1"
"supabase/studio:20250224-d10db0f"
)
# Check if any cached tar files exist (more reliable than cache-hit)
if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
echo "Docker cache found, loading images in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
if [ -f ~/docker-cache/${filename}.tar ]; then
echo "Loading $image..."
docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
fi
done
wait
echo "All cached images loaded"
else
echo "No Docker cache found, pulling images in parallel..."
# Pull all images in parallel
for image in "${IMAGES[@]}"; do
docker pull "$image" &
done
wait
# Only save cache on main branches (not PRs) to avoid cache pollution
if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
echo "Saving Docker images to cache in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
echo "Saving $image..."
docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
done
wait
echo "Docker image cache saved"
else
echo "Skipping cache save for PR/feature branch"
fi
fi
echo "Docker images ready for use"
# Phase 2: Build migrate service with GitHub Actions cache
- name: Build migrate Docker image with cache
working-directory: autogpt_platform
run: |
# Build the migrate image with buildx for GHA caching
docker buildx build \
--cache-from type=gha \
--cache-to type=gha,mode=max \
--target migrate \
--tag autogpt_platform-migrate:latest \
--load \
-f backend/Dockerfile \
..
# Start services using pre-built images
- name: Start Docker services for development
working-directory: autogpt_platform
run: |
# Start essential services (migrate image already built with correct tag)
docker compose --profile local up deps --no-build --detach
echo "Waiting for services to be ready..."
# Wait for database to be ready
echo "Checking database readiness..."
timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do
echo " Waiting for database..."
sleep 2
done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
# Check migrate service status
echo "Checking migration status..."
docker compose ps migrate || echo " Migrate service not visible in ps output"
# Wait for migrate service to complete
echo "Waiting for migrations to complete..."
timeout 30 bash -c '
ATTEMPTS=0
while [ $ATTEMPTS -lt 15 ]; do
ATTEMPTS=$((ATTEMPTS + 1))
# Check using docker directly (more reliable than docker compose ps)
CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
if [ -z "$CONTAINER_STATUS" ]; then
echo " Attempt $ATTEMPTS: Migrate container not found yet..."
elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
echo "✅ Migrations completed successfully"
docker compose logs migrate --tail=5 2>/dev/null || true
exit 0
elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
echo "❌ Migrations failed with exit code: $EXIT_CODE"
echo "Migration logs:"
docker compose logs migrate --tail=20 2>/dev/null || true
exit 1
elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
echo " Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
else
echo " Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
fi
sleep 2
done
echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
echo "Final container check:"
docker ps -a --filter "label=com.docker.compose.service=migrate" || true
echo "Migration logs (if available):"
docker compose logs migrate --tail=10 2>/dev/null || echo " No logs available"
' || echo "⚠️ Migration check completed with warnings, continuing..."
# Brief wait for other services to stabilize
echo "Waiting 5 seconds for other services to stabilize..."
sleep 5
# Verify installations and provide environment info
- name: Verify setup and show environment info
run: |
echo "=== Python Setup ==="
python --version
poetry --version
echo "=== Node.js Setup ==="
node --version
pnpm --version
echo "=== Additional Tools ==="
docker --version
docker compose version
gh --version || true
echo "=== Services Status ==="
cd autogpt_platform
docker compose ps || true
echo "=== Backend Dependencies ==="
cd backend
poetry show | head -10 || true
echo "=== Frontend Dependencies ==="
cd ../frontend
pnpm list --depth=0 | head -10 || true
echo "=== Environment Files ==="
ls -la ../.env* || true
ls -la .env* || true
ls -la ../backend/.env* || true
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)"
- name: Run Claude Code
id: claude
uses: anthropics/claude-code-action@v1
uses: anthropics/claude-code-action@beta
with:
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
additional_permissions: |
actions: read

View File

@@ -1,312 +0,0 @@
name: "Copilot Setup Steps"
# Automatically run the setup steps when they are changed to allow for easy validation, and
# allow manual testing through the repository's "Actions" tab
on:
workflow_dispatch:
push:
paths:
- .github/workflows/copilot-setup-steps.yml
pull_request:
paths:
- .github/workflows/copilot-setup-steps.yml
jobs:
# The job MUST be called `copilot-setup-steps` or it will not be picked up by Copilot.
copilot-setup-steps:
runs-on: ubuntu-latest
timeout-minutes: 45
# Set the permissions to the lowest permissions possible needed for your steps.
# Copilot will be given its own token for its operations.
permissions:
# If you want to clone the repository as part of your setup steps, for example to install dependencies, you'll need the `contents: read` permission. If you don't clone the repository in your setup steps, Copilot will do this for you automatically after the steps complete.
contents: read
# You can define any steps you want, and they will run before the agent starts.
# If you do not check out your code, Copilot will do this for you.
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
submodules: true
# Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
- name: Install Poetry
run: |
# Extract Poetry version from backend/poetry.lock (matches CI)
cd autogpt_platform/backend
HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
# Install Poetry
curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
# Add Poetry to PATH
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Check poetry.lock
working-directory: autogpt_platform/backend
run: |
poetry lock
if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
echo "Warning: poetry.lock not up to date, but continuing for setup"
git checkout poetry.lock # Reset for clean setup
fi
- name: Install Python dependencies
working-directory: autogpt_platform/backend
run: poetry install
- name: Generate Prisma Client
working-directory: autogpt_platform/backend
run: poetry run prisma generate && poetry run gen-prisma-stub
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22"
- name: Enable corepack
run: corepack enable
- name: Set pnpm store directory
run: |
pnpm config set store-dir ~/.pnpm-store
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
- name: Install JavaScript dependencies
working-directory: autogpt_platform/frontend
run: pnpm install --frozen-lockfile
# Install Playwright browsers for frontend testing
# NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
# - name: Install Playwright browsers
# working-directory: autogpt_platform/frontend
# run: pnpm playwright install --with-deps chromium
# Docker setup for development environment
- name: Free up disk space
run: |
# Remove large unused tools to free disk space for Docker builds
sudo rm -rf /usr/share/dotnet
sudo rm -rf /usr/local/lib/android
sudo rm -rf /opt/ghc
sudo rm -rf /opt/hostedtoolcache/CodeQL
sudo docker system prune -af
df -h
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Copy default environment files
working-directory: autogpt_platform
run: |
# Copy default environment files for development
cp .env.default .env
cp backend/.env.default backend/.env
cp frontend/.env.default frontend/.env
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes
key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
restore-keys: |
docker-images-v2-${{ runner.os }}-
docker-images-v1-${{ runner.os }}-
- name: Load or pull Docker images
working-directory: autogpt_platform
run: |
mkdir -p ~/docker-cache
# Define image list for easy maintenance
IMAGES=(
"redis:latest"
"rabbitmq:management"
"clamav/clamav-debian:latest"
"busybox:latest"
"kong:2.8.1"
"supabase/gotrue:v2.170.0"
"supabase/postgres:15.8.1.049"
"supabase/postgres-meta:v0.86.1"
"supabase/studio:20250224-d10db0f"
)
# Check if any cached tar files exist (more reliable than cache-hit)
if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
echo "Docker cache found, loading images in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
if [ -f ~/docker-cache/${filename}.tar ]; then
echo "Loading $image..."
docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
fi
done
wait
echo "All cached images loaded"
else
echo "No Docker cache found, pulling images in parallel..."
# Pull all images in parallel
for image in "${IMAGES[@]}"; do
docker pull "$image" &
done
wait
# Only save cache on main branches (not PRs) to avoid cache pollution
if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
echo "Saving Docker images to cache in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
echo "Saving $image..."
docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
done
wait
echo "Docker image cache saved"
else
echo "Skipping cache save for PR/feature branch"
fi
fi
echo "Docker images ready for use"
# Phase 2: Build migrate service with GitHub Actions cache
- name: Build migrate Docker image with cache
working-directory: autogpt_platform
run: |
# Build the migrate image with buildx for GHA caching
docker buildx build \
--cache-from type=gha \
--cache-to type=gha,mode=max \
--target migrate \
--tag autogpt_platform-migrate:latest \
--load \
-f backend/Dockerfile \
..
# Start services using pre-built images
- name: Start Docker services for development
working-directory: autogpt_platform
run: |
# Start essential services (migrate image already built with correct tag)
docker compose --profile local up deps --no-build --detach
echo "Waiting for services to be ready..."
# Wait for database to be ready
echo "Checking database readiness..."
timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do
echo " Waiting for database..."
sleep 2
done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
# Check migrate service status
echo "Checking migration status..."
docker compose ps migrate || echo " Migrate service not visible in ps output"
# Wait for migrate service to complete
echo "Waiting for migrations to complete..."
timeout 30 bash -c '
ATTEMPTS=0
while [ $ATTEMPTS -lt 15 ]; do
ATTEMPTS=$((ATTEMPTS + 1))
# Check using docker directly (more reliable than docker compose ps)
CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
if [ -z "$CONTAINER_STATUS" ]; then
echo " Attempt $ATTEMPTS: Migrate container not found yet..."
elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
echo "✅ Migrations completed successfully"
docker compose logs migrate --tail=5 2>/dev/null || true
exit 0
elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
echo "❌ Migrations failed with exit code: $EXIT_CODE"
echo "Migration logs:"
docker compose logs migrate --tail=20 2>/dev/null || true
exit 1
elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
echo " Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
else
echo " Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
fi
sleep 2
done
echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
echo "Final container check:"
docker ps -a --filter "label=com.docker.compose.service=migrate" || true
echo "Migration logs (if available):"
docker compose logs migrate --tail=10 2>/dev/null || echo " No logs available"
' || echo "⚠️ Migration check completed with warnings, continuing..."
# Brief wait for other services to stabilize
echo "Waiting 5 seconds for other services to stabilize..."
sleep 5
# Verify installations and provide environment info
- name: Verify setup and show environment info
run: |
echo "=== Python Setup ==="
python --version
poetry --version
echo "=== Node.js Setup ==="
node --version
pnpm --version
echo "=== Additional Tools ==="
docker --version
docker compose version
gh --version || true
echo "=== Services Status ==="
cd autogpt_platform
docker compose ps || true
echo "=== Backend Dependencies ==="
cd backend
poetry show | head -10 || true
echo "=== Frontend Dependencies ==="
cd ../frontend
pnpm list --depth=0 | head -10 || true
echo "=== Environment Files ==="
ls -la ../.env* || true
ls -la .env* || true
ls -la ../backend/.env* || true
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)"

View File

@@ -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 }}"}'

View File

@@ -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 }}"}'

View File

@@ -32,12 +32,14 @@ jobs:
strategy:
fail-fast: false
matrix:
python-version: ["3.11", "3.12", "3.13"]
python-version: ["3.11"]
runs-on: ubuntu-latest
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 }}
@@ -199,9 +201,10 @@ jobs:
DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
SUPABASE_URL: ${{ steps.supabase.outputs.API_URL }}
SUPABASE_SERVICE_ROLE_KEY: ${{ steps.supabase.outputs.SERVICE_ROLE_KEY }}
JWT_VERIFY_KEY: ${{ steps.supabase.outputs.JWT_SECRET }}
SUPABASE_JWT_SECRET: ${{ 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:

View File

@@ -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
@@ -173,7 +160,7 @@ jobs:
- name: Run docker compose
run: |
NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
docker compose -f ../docker-compose.yml up -d
env:
DOCKER_BUILDKIT: 1
BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
@@ -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()

View File

@@ -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

View File

@@ -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: >

View File

@@ -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

1
.gitignore vendored
View File

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

View File

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

View File

@@ -61,41 +61,24 @@ poetry run pytest path/to/test.py --snapshot-update
```bash
# Install dependencies
cd frontend && pnpm i
# Generate API client from OpenAPI spec
pnpm generate:api
cd frontend && npm install
# Start development server
pnpm dev
npm run dev
# Run E2E tests
pnpm test
npm run test
# Run Storybook for component development
pnpm storybook
npm run storybook
# Build production
pnpm build
# Format and lint
pnpm format
npm run build
# Type checking
pnpm types
npm run types
```
**📖 Complete Guide**: See `/frontend/CONTRIBUTING.md` and `/frontend/.cursorrules` for comprehensive frontend patterns.
**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
### Backend Architecture
@@ -109,16 +92,11 @@ 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
@@ -171,29 +149,16 @@ 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)
- Testing and validation
- 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
4. Define input/output schemas using `BlockSchema`
5. Implement async `run` method
6. Generate unique block ID using `uuid.uuid4()`
7. Test with `poetry run pytest backend/blocks/test/test_block.py`
2. Inherit from `Block` base class
3. Define input/output schemas
4. Implement `run` method
5. Register in block registry
6. Generate the block uuid using `uuid.uuid4()`
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?
Note: when making many new blocks analyze the interfaces for each of these blcoks 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 +168,10 @@ 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

View File

@@ -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"

View File

@@ -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:

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@@ -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)

View File

@@ -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()

View File

@@ -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

View File

@@ -1,19 +1,13 @@
from .config import verify_settings
from .dependencies import (
get_optional_user_id,
get_user_id,
requires_admin_user,
requires_user,
)
from .helpers import add_auth_responses_to_openapi
from .depends import requires_admin_user, requires_user
from .jwt_utils import parse_jwt_token
from .middleware import APIKeyValidator, auth_middleware
from .models import User
__all__ = [
"verify_settings",
"get_user_id",
"requires_admin_user",
"parse_jwt_token",
"requires_user",
"get_optional_user_id",
"add_auth_responses_to_openapi",
"requires_admin_user",
"APIKeyValidator",
"auth_middleware",
"User",
]

View File

@@ -1,90 +1,11 @@
import logging
import os
from jwt.algorithms import get_default_algorithms, has_crypto
logger = logging.getLogger(__name__)
class AuthConfigError(ValueError):
"""Raised when authentication configuration is invalid."""
pass
ALGO_RECOMMENDATION = (
"We highly recommend using an asymmetric algorithm such as ES256, "
"because when leaked, a shared secret would allow anyone to "
"forge valid tokens and impersonate users. "
"More info: https://supabase.com/docs/guides/auth/signing-keys#choosing-the-right-signing-algorithm" # noqa
)
class Settings:
def __init__(self):
self.JWT_VERIFY_KEY: str = os.getenv(
"JWT_VERIFY_KEY", os.getenv("SUPABASE_JWT_SECRET", "")
).strip()
self.JWT_ALGORITHM: str = os.getenv("JWT_SIGN_ALGORITHM", "HS256").strip()
self.validate()
def validate(self):
if not self.JWT_VERIFY_KEY:
raise AuthConfigError(
"JWT_VERIFY_KEY must be set. "
"An empty JWT secret would allow anyone to forge valid tokens."
)
if len(self.JWT_VERIFY_KEY) < 32:
logger.warning(
"⚠️ JWT_VERIFY_KEY appears weak (less than 32 characters). "
"Consider using a longer, cryptographically secure secret."
)
supported_algorithms = get_default_algorithms().keys()
if not has_crypto:
logger.warning(
"⚠️ Asymmetric JWT verification is not available "
"because the 'cryptography' package is not installed. "
+ ALGO_RECOMMENDATION
)
if (
self.JWT_ALGORITHM not in supported_algorithms
or self.JWT_ALGORITHM == "none"
):
raise AuthConfigError(
f"Invalid JWT_SIGN_ALGORITHM: '{self.JWT_ALGORITHM}'. "
"Supported algorithms are listed on "
"https://pyjwt.readthedocs.io/en/stable/algorithms.html"
)
if self.JWT_ALGORITHM.startswith("HS"):
logger.warning(
f"⚠️ JWT_SIGN_ALGORITHM is set to '{self.JWT_ALGORITHM}', "
"a symmetric shared-key signature algorithm. " + ALGO_RECOMMENDATION
)
self.JWT_SECRET_KEY: str = os.getenv("SUPABASE_JWT_SECRET", "")
self.ENABLE_AUTH: bool = os.getenv("ENABLE_AUTH", "false").lower() == "true"
self.JWT_ALGORITHM: str = "HS256"
_settings: Settings = None # type: ignore
def get_settings() -> Settings:
global _settings
if not _settings:
_settings = Settings()
return _settings
def verify_settings() -> None:
global _settings
if not _settings:
_settings = Settings() # calls validation indirectly
return
_settings.validate()
settings = Settings()

View File

@@ -1,306 +0,0 @@
"""
Comprehensive tests for auth configuration to ensure 100% line and branch coverage.
These tests verify critical security checks preventing JWT token forgery.
"""
import logging
import os
import pytest
from pytest_mock import MockerFixture
from autogpt_libs.auth.config import AuthConfigError, Settings
def test_environment_variable_precedence(mocker: MockerFixture):
"""Test that environment variables take precedence over defaults."""
secret = "environment-secret-key-with-proper-length-123456"
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == secret
def test_environment_variable_backwards_compatible(mocker: MockerFixture):
"""Test that SUPABASE_JWT_SECRET is read if JWT_VERIFY_KEY is not set."""
secret = "environment-secret-key-with-proper-length-123456"
mocker.patch.dict(os.environ, {"SUPABASE_JWT_SECRET": secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == secret
def test_auth_config_error_inheritance():
"""Test that AuthConfigError is properly defined as an Exception."""
assert issubclass(AuthConfigError, Exception)
error = AuthConfigError("test message")
assert str(error) == "test message"
def test_settings_static_after_creation(mocker: MockerFixture):
"""Test that settings maintain their values after creation."""
secret = "immutable-secret-key-with-proper-length-12345"
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
settings = Settings()
original_secret = settings.JWT_VERIFY_KEY
# Changing environment after creation shouldn't affect settings
os.environ["JWT_VERIFY_KEY"] = "different-secret"
assert settings.JWT_VERIFY_KEY == original_secret
def test_settings_load_with_valid_secret(mocker: MockerFixture):
"""Test auth enabled with a valid JWT secret."""
valid_secret = "a" * 32 # 32 character secret
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": valid_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == valid_secret
def test_settings_load_with_strong_secret(mocker: MockerFixture):
"""Test auth enabled with a cryptographically strong secret."""
strong_secret = "super-secret-jwt-token-with-at-least-32-characters-long"
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": strong_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == strong_secret
assert len(settings.JWT_VERIFY_KEY) >= 32
def test_secret_empty_raises_error(mocker: MockerFixture):
"""Test that auth enabled with empty secret raises AuthConfigError."""
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": ""}, clear=True)
with pytest.raises(Exception) as exc_info:
Settings()
assert "JWT_VERIFY_KEY" in str(exc_info.value)
def test_secret_missing_raises_error(mocker: MockerFixture):
"""Test that auth enabled without secret env var raises AuthConfigError."""
mocker.patch.dict(os.environ, {}, clear=True)
with pytest.raises(Exception) as exc_info:
Settings()
assert "JWT_VERIFY_KEY" in str(exc_info.value)
@pytest.mark.parametrize("secret", [" ", " ", "\t", "\n", " \t\n "])
def test_secret_only_whitespace_raises_error(mocker: MockerFixture, secret: str):
"""Test that auth enabled with whitespace-only secret raises error."""
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
with pytest.raises(ValueError):
Settings()
def test_secret_weak_logs_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture
):
"""Test that weak JWT secret triggers warning log."""
weak_secret = "short" # Less than 32 characters
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": weak_secret}, clear=True)
with caplog.at_level(logging.WARNING):
settings = Settings()
assert settings.JWT_VERIFY_KEY == weak_secret
assert "key appears weak" in caplog.text.lower()
assert "less than 32 characters" in caplog.text
def test_secret_31_char_logs_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture
):
"""Test that 31-character secret triggers warning (boundary test)."""
secret_31 = "a" * 31 # Exactly 31 characters
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret_31}, clear=True)
with caplog.at_level(logging.WARNING):
settings = Settings()
assert len(settings.JWT_VERIFY_KEY) == 31
assert "key appears weak" in caplog.text.lower()
def test_secret_32_char_no_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture
):
"""Test that 32-character secret does not trigger warning (boundary test)."""
secret_32 = "a" * 32 # Exactly 32 characters
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret_32}, clear=True)
with caplog.at_level(logging.WARNING):
settings = Settings()
assert len(settings.JWT_VERIFY_KEY) == 32
assert "JWT secret appears weak" not in caplog.text
def test_secret_whitespace_stripped(mocker: MockerFixture):
"""Test that JWT secret whitespace is stripped."""
secret = "a" * 32
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": f" {secret} "}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == secret
def test_secret_with_special_characters(mocker: MockerFixture):
"""Test JWT secret with special characters."""
special_secret = "!@#$%^&*()_+-=[]{}|;:,.<>?`~" + "a" * 10 # 40 chars total
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": special_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == special_secret
def test_secret_with_unicode(mocker: MockerFixture):
"""Test JWT secret with unicode characters."""
unicode_secret = "秘密🔐キー" + "a" * 25 # Ensure >32 bytes
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": unicode_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == unicode_secret
def test_secret_very_long(mocker: MockerFixture):
"""Test JWT secret with excessive length."""
long_secret = "a" * 1000 # 1000 character secret
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": long_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == long_secret
assert len(settings.JWT_VERIFY_KEY) == 1000
def test_secret_with_newline(mocker: MockerFixture):
"""Test JWT secret containing newlines."""
multiline_secret = "secret\nwith\nnewlines" + "a" * 20
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": multiline_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == multiline_secret
def test_secret_base64_encoded(mocker: MockerFixture):
"""Test JWT secret that looks like base64."""
base64_secret = "dGhpc19pc19hX3NlY3JldF9rZXlfd2l0aF9wcm9wZXJfbGVuZ3Ro"
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": base64_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == base64_secret
def test_secret_numeric_only(mocker: MockerFixture):
"""Test JWT secret with only numbers."""
numeric_secret = "1234567890" * 4 # 40 character numeric secret
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": numeric_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == numeric_secret
def test_algorithm_default_hs256(mocker: MockerFixture):
"""Test that JWT algorithm defaults to HS256."""
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": "a" * 32}, clear=True)
settings = Settings()
assert settings.JWT_ALGORITHM == "HS256"
def test_algorithm_whitespace_stripped(mocker: MockerFixture):
"""Test that JWT algorithm whitespace is stripped."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": " HS256 "},
clear=True,
)
settings = Settings()
assert settings.JWT_ALGORITHM == "HS256"
def test_no_crypto_warning(mocker: MockerFixture, caplog: pytest.LogCaptureFixture):
"""Test warning when crypto package is not available."""
secret = "a" * 32
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
# Mock has_crypto to return False
mocker.patch("autogpt_libs.auth.config.has_crypto", False)
with caplog.at_level(logging.WARNING):
Settings()
assert "Asymmetric JWT verification is not available" in caplog.text
assert "cryptography" in caplog.text
def test_algorithm_invalid_raises_error(mocker: MockerFixture):
"""Test that invalid JWT algorithm raises AuthConfigError."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": "INVALID_ALG"},
clear=True,
)
with pytest.raises(AuthConfigError) as exc_info:
Settings()
assert "Invalid JWT_SIGN_ALGORITHM" in str(exc_info.value)
assert "INVALID_ALG" in str(exc_info.value)
def test_algorithm_none_raises_error(mocker: MockerFixture):
"""Test that 'none' algorithm raises AuthConfigError."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": "none"},
clear=True,
)
with pytest.raises(AuthConfigError) as exc_info:
Settings()
assert "Invalid JWT_SIGN_ALGORITHM" in str(exc_info.value)
@pytest.mark.parametrize("algorithm", ["HS256", "HS384", "HS512"])
def test_algorithm_symmetric_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture, algorithm: str
):
"""Test warning for symmetric algorithms (HS256, HS384, HS512)."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": algorithm},
clear=True,
)
with caplog.at_level(logging.WARNING):
settings = Settings()
assert algorithm in caplog.text
assert "symmetric shared-key signature algorithm" in caplog.text
assert settings.JWT_ALGORITHM == algorithm
@pytest.mark.parametrize(
"algorithm",
["ES256", "ES384", "ES512", "RS256", "RS384", "RS512", "PS256", "PS384", "PS512"],
)
def test_algorithm_asymmetric_no_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture, algorithm: str
):
"""Test that asymmetric algorithms do not trigger warning."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": algorithm},
clear=True,
)
with caplog.at_level(logging.WARNING):
settings = Settings()
# Should not contain the symmetric algorithm warning
assert "symmetric shared-key signature algorithm" not in caplog.text
assert settings.JWT_ALGORITHM == algorithm

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@@ -1,117 +0,0 @@
"""
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:
"""
FastAPI dependency that requires a valid authenticated user.
Raises:
HTTPException: 401 for authentication failures
"""
return verify_user(jwt_payload, admin_only=False)
async def requires_admin_user(
jwt_payload: dict = fastapi.Security(get_jwt_payload),
) -> User:
"""
FastAPI dependency that requires a valid admin user.
Raises:
HTTPException: 401 for authentication failures, 403 for insufficient permissions
"""
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:
"""
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

View File

@@ -1,554 +0,0 @@
"""
Comprehensive integration tests for authentication dependencies.
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.testclient import TestClient
from pytest_mock import MockerFixture
from autogpt_libs.auth.dependencies import (
get_user_id,
requires_admin_user,
requires_user,
)
from autogpt_libs.auth.models import User
class TestAuthDependencies:
"""Test suite for authentication dependency functions."""
@pytest.fixture
def app(self):
"""Create a test FastAPI application."""
app = FastAPI()
@app.get("/user")
def get_user_endpoint(user: User = Security(requires_user)):
return {"user_id": user.user_id, "role": user.role}
@app.get("/admin")
def get_admin_endpoint(user: User = Security(requires_admin_user)):
return {"user_id": user.user_id, "role": user.role}
@app.get("/user-id")
def get_user_id_endpoint(user_id: str = Security(get_user_id)):
return {"user_id": user_id}
return app
@pytest.fixture
def client(self, app):
"""Create a test client."""
return TestClient(app)
@pytest.mark.asyncio
async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
"""Test requires_user with valid JWT payload."""
jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
# Mock get_jwt_payload to return our test payload
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user = await 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):
"""Test requires_user accepts admin users."""
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 = await 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):
"""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)
assert exc_info.value.status_code == 401
assert "User ID not found" in exc_info.value.detail
@pytest.mark.asyncio
async def test_requires_user_empty_sub(self):
"""Test requires_user with empty user ID."""
jwt_payload = {"sub": "", "role": "user"}
with pytest.raises(HTTPException) as exc_info:
await requires_user(jwt_payload)
assert exc_info.value.status_code == 401
@pytest.mark.asyncio
async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
"""Test requires_admin_user with admin role."""
jwt_payload = {
"sub": "admin-789",
"role": "admin",
"email": "admin@example.com",
}
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user = await 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):
"""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)
assert exc_info.value.status_code == 403
assert "Admin access required" in exc_info.value.detail
@pytest.mark.asyncio
async def test_requires_admin_user_missing_role(self):
"""Test requires_admin_user with missing role."""
jwt_payload = {"sub": "user-123", "email": "user@example.com"}
with pytest.raises(KeyError):
await requires_admin_user(jwt_payload)
@pytest.mark.asyncio
async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
"""Test get_user_id extracts user ID correctly."""
request = Mock(spec=Request)
request.headers = {}
jwt_payload = {"sub": "user-id-xyz", "role": "user"}
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user_id = await get_user_id(request, jwt_payload)
assert user_id == "user-id-xyz"
@pytest.mark.asyncio
async def test_get_user_id_missing_sub(self):
"""Test get_user_id with missing user ID."""
request = Mock(spec=Request)
request.headers = {}
jwt_payload = {"role": "user"}
with pytest.raises(HTTPException) as exc_info:
await get_user_id(request, jwt_payload)
assert exc_info.value.status_code == 401
assert "User ID not found" in exc_info.value.detail
@pytest.mark.asyncio
async def test_get_user_id_none_sub(self):
"""Test get_user_id with None user ID."""
request = Mock(spec=Request)
request.headers = {}
jwt_payload = {"sub": None, "role": "user"}
with pytest.raises(HTTPException) as exc_info:
await get_user_id(request, jwt_payload)
assert exc_info.value.status_code == 401
class TestAuthDependenciesIntegration:
"""Integration tests for auth dependencies with FastAPI."""
acceptable_jwt_secret = "test-secret-with-proper-length-123456"
@pytest.fixture
def create_token(self, mocker: MockerFixture):
"""Helper to create JWT tokens."""
import jwt
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": self.acceptable_jwt_secret},
clear=True,
)
def _create_token(payload, secret=self.acceptable_jwt_secret):
return jwt.encode(payload, secret, algorithm="HS256")
return _create_token
@pytest.mark.asyncio
async def test_endpoint_auth_enabled_no_token(self):
"""Test endpoints require token when auth is enabled."""
app = FastAPI()
@app.get("/test")
def test_endpoint(user: User = Security(requires_user)):
return {"user_id": user.user_id}
client = TestClient(app)
# Should fail without auth header
response = client.get("/test")
assert response.status_code == 401
@pytest.mark.asyncio
async def test_endpoint_with_valid_token(self, create_token):
"""Test endpoint with valid JWT token."""
app = FastAPI()
@app.get("/test")
def test_endpoint(user: User = Security(requires_user)):
return {"user_id": user.user_id, "role": user.role}
client = TestClient(app)
token = create_token(
{"sub": "test-user", "role": "user", "aud": "authenticated"},
secret=self.acceptable_jwt_secret,
)
response = client.get("/test", headers={"Authorization": f"Bearer {token}"})
assert response.status_code == 200
assert response.json()["user_id"] == "test-user"
@pytest.mark.asyncio
async def test_admin_endpoint_requires_admin_role(self, create_token):
"""Test admin endpoint rejects non-admin users."""
app = FastAPI()
@app.get("/admin")
def admin_endpoint(user: User = Security(requires_admin_user)):
return {"user_id": user.user_id}
client = TestClient(app)
# Regular user token
user_token = create_token(
{"sub": "regular-user", "role": "user", "aud": "authenticated"},
secret=self.acceptable_jwt_secret,
)
response = client.get(
"/admin", headers={"Authorization": f"Bearer {user_token}"}
)
assert response.status_code == 403
# Admin token
admin_token = create_token(
{"sub": "admin-user", "role": "admin", "aud": "authenticated"},
secret=self.acceptable_jwt_secret,
)
response = client.get(
"/admin", headers={"Authorization": f"Bearer {admin_token}"}
)
assert response.status_code == 200
assert response.json()["user_id"] == "admin-user"
class TestAuthDependenciesEdgeCases:
"""Edge case tests for authentication dependencies."""
@pytest.mark.asyncio
async def test_dependency_with_complex_payload(self):
"""Test dependencies handle complex JWT payloads."""
complex_payload = {
"sub": "user-123",
"role": "admin",
"email": "test@example.com",
"app_metadata": {"provider": "email", "providers": ["email"]},
"user_metadata": {
"full_name": "Test User",
"avatar_url": "https://example.com/avatar.jpg",
},
"aud": "authenticated",
"iat": 1234567890,
"exp": 9999999999,
}
user = await requires_user(complex_payload)
assert user.user_id == "user-123"
assert user.email == "test@example.com"
admin = await requires_admin_user(complex_payload)
assert admin.role == "admin"
@pytest.mark.asyncio
async def test_dependency_with_unicode_in_payload(self):
"""Test dependencies handle unicode in JWT payloads."""
unicode_payload = {
"sub": "user-😀-123",
"role": "user",
"email": "测试@example.com",
"name": "日本語",
}
user = await 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):
"""Test dependencies handle null values in payload."""
null_payload = {
"sub": "user-123",
"role": "user",
"email": None,
"phone": None,
"metadata": None,
}
user = await 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):
"""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)
assert user1.user_id == "user-1"
assert user2.user_id == "user-2"
assert user1.role == "user"
assert user2.role == "admin"
@pytest.mark.parametrize(
"payload,expected_error,admin_only",
[
(None, "Authorization header is missing", False),
({}, "User ID not found", False),
({"sub": ""}, "User ID not found", False),
({"role": "user"}, "User ID not found", False),
({"sub": "user", "role": "user"}, "Admin access required", True),
],
)
@pytest.mark.asyncio
async def test_dependency_error_cases(
self, payload, expected_error: str, admin_only: bool
):
"""Test that errors propagate correctly through dependencies."""
# Import verify_user to test it directly since dependencies use FastAPI Security
from autogpt_libs.auth.jwt_utils import verify_user
with pytest.raises(HTTPException) as exc_info:
verify_user(payload, admin_only=admin_only)
assert expected_error in exc_info.value.detail
@pytest.mark.asyncio
async def test_dependency_valid_user(self):
"""Test valid user case for dependency."""
# Import verify_user to test it directly since dependencies use FastAPI Security
from autogpt_libs.auth.jwt_utils import verify_user
# 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()

View File

@@ -0,0 +1,46 @@
import fastapi
from .config import settings
from .middleware import auth_middleware
from .models import DEFAULT_USER_ID, User
def requires_user(payload: dict = fastapi.Depends(auth_middleware)) -> User:
return verify_user(payload, admin_only=False)
def requires_admin_user(
payload: dict = fastapi.Depends(auth_middleware),
) -> User:
return verify_user(payload, admin_only=True)
def verify_user(payload: dict | None, admin_only: bool) -> User:
if not payload:
if settings.ENABLE_AUTH:
raise fastapi.HTTPException(
status_code=401, detail="Authorization header is missing"
)
# This handles the case when authentication is disabled
payload = {"sub": DEFAULT_USER_ID, "role": "admin"}
user_id = payload.get("sub")
if not user_id:
raise fastapi.HTTPException(
status_code=401, detail="User ID not found in token"
)
if admin_only and payload["role"] != "admin":
raise fastapi.HTTPException(status_code=403, detail="Admin access required")
return User.from_payload(payload)
def get_user_id(payload: dict = fastapi.Depends(auth_middleware)) -> str:
user_id = payload.get("sub")
if not user_id:
raise fastapi.HTTPException(
status_code=401, detail="User ID not found in token"
)
return user_id

View File

@@ -0,0 +1,68 @@
import pytest
from .depends import requires_admin_user, requires_user, verify_user
def test_verify_user_no_payload():
user = verify_user(None, admin_only=False)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "admin"
def test_verify_user_no_user_id():
with pytest.raises(Exception):
verify_user({"role": "admin"}, admin_only=False)
def test_verify_user_not_admin():
with pytest.raises(Exception):
verify_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"},
admin_only=True,
)
def test_verify_user_with_admin_role():
user = verify_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"},
admin_only=True,
)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "admin"
def test_verify_user_with_user_role():
user = verify_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"},
admin_only=False,
)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "user"
def test_requires_user():
user = requires_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"}
)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "user"
def test_requires_user_no_user_id():
with pytest.raises(Exception):
requires_user({"role": "user"})
def test_requires_admin_user():
user = requires_admin_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"}
)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "admin"
def test_requires_admin_user_not_admin():
with pytest.raises(Exception):
requires_admin_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"}
)

View File

@@ -1,64 +0,0 @@
from fastapi import FastAPI
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
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()
# Add 401 response to all endpoints that have security requirements
for path, methods in openapi_schema["paths"].items():
for method, details in methods.items():
security_schemas = [
schema
for auth_option in details.get("security", [])
for schema in auth_option.keys()
]
if bearer_jwt_auth.scheme_name not in security_schemas:
continue
if "responses" not in details:
details["responses"] = {}
details["responses"]["401"] = {
"$ref": "#/components/responses/HTTP401NotAuthenticatedError"
}
# Ensure #/components/responses exists
if "components" not in openapi_schema:
openapi_schema["components"] = {}
if "responses" not in openapi_schema["components"]:
openapi_schema["components"]["responses"] = {}
# Define 401 response
openapi_schema["components"]["responses"]["HTTP401NotAuthenticatedError"] = {
"description": "Authentication required",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {"detail": {"type": "string"}},
}
}
},
}
app.openapi_schema = openapi_schema
return app.openapi_schema
app.openapi = custom_openapi

View File

@@ -1,435 +0,0 @@
"""
Comprehensive tests for auth helpers module to achieve 100% coverage.
Tests OpenAPI schema generation and authentication response handling.
"""
from unittest import mock
from fastapi import FastAPI
from fastapi.openapi.utils import get_openapi
from autogpt_libs.auth.helpers import add_auth_responses_to_openapi
from autogpt_libs.auth.jwt_utils import bearer_jwt_auth
def test_add_auth_responses_to_openapi_basic():
"""Test adding 401 responses to OpenAPI schema."""
app = FastAPI(title="Test App", version="1.0.0")
# Add some test endpoints with authentication
from fastapi import Depends
from autogpt_libs.auth.dependencies import requires_user
@app.get("/protected", dependencies=[Depends(requires_user)])
def protected_endpoint():
return {"message": "Protected"}
@app.get("/public")
def public_endpoint():
return {"message": "Public"}
# Apply the OpenAPI customization
add_auth_responses_to_openapi(app)
# Get the OpenAPI schema
schema = app.openapi()
# Verify basic schema properties
assert schema["info"]["title"] == "Test App"
assert schema["info"]["version"] == "1.0.0"
# Verify 401 response component is added
assert "components" in schema
assert "responses" in schema["components"]
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
# Verify 401 response structure
error_response = schema["components"]["responses"]["HTTP401NotAuthenticatedError"]
assert error_response["description"] == "Authentication required"
assert "application/json" in error_response["content"]
assert "schema" in error_response["content"]["application/json"]
# Verify schema properties
response_schema = error_response["content"]["application/json"]["schema"]
assert response_schema["type"] == "object"
assert "detail" in response_schema["properties"]
assert response_schema["properties"]["detail"]["type"] == "string"
def test_add_auth_responses_to_openapi_with_security():
"""Test that 401 responses are added only to secured endpoints."""
app = FastAPI()
# Mock endpoint with security
from fastapi import Security
from autogpt_libs.auth.dependencies import get_user_id
@app.get("/secured")
def secured_endpoint(user_id: str = Security(get_user_id)):
return {"user_id": user_id}
@app.post("/also-secured")
def another_secured(user_id: str = Security(get_user_id)):
return {"status": "ok"}
@app.get("/unsecured")
def unsecured_endpoint():
return {"public": True}
# Apply OpenAPI customization
add_auth_responses_to_openapi(app)
# Get schema
schema = app.openapi()
# Check that secured endpoints have 401 responses
if "/secured" in schema["paths"]:
if "get" in schema["paths"]["/secured"]:
secured_get = schema["paths"]["/secured"]["get"]
if "responses" in secured_get:
assert "401" in secured_get["responses"]
assert (
secured_get["responses"]["401"]["$ref"]
== "#/components/responses/HTTP401NotAuthenticatedError"
)
if "/also-secured" in schema["paths"]:
if "post" in schema["paths"]["/also-secured"]:
secured_post = schema["paths"]["/also-secured"]["post"]
if "responses" in secured_post:
assert "401" in secured_post["responses"]
# Check that unsecured endpoint does not have 401 response
if "/unsecured" in schema["paths"]:
if "get" in schema["paths"]["/unsecured"]:
unsecured_get = schema["paths"]["/unsecured"]["get"]
if "responses" in unsecured_get:
assert "401" not in unsecured_get.get("responses", {})
def test_add_auth_responses_to_openapi_cached_schema():
"""Test that OpenAPI schema is cached after first generation."""
app = FastAPI()
# Apply customization
add_auth_responses_to_openapi(app)
# Get schema twice
schema1 = app.openapi()
schema2 = app.openapi()
# Should return the same cached object
assert schema1 is schema2
def test_add_auth_responses_to_openapi_existing_responses():
"""Test handling endpoints that already have responses defined."""
app = FastAPI()
from fastapi import Security
from autogpt_libs.auth.jwt_utils import get_jwt_payload
@app.get(
"/with-responses",
responses={
200: {"description": "Success"},
404: {"description": "Not found"},
},
)
def endpoint_with_responses(jwt: dict = Security(get_jwt_payload)):
return {"data": "test"}
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Check that existing responses are preserved and 401 is added
if "/with-responses" in schema["paths"]:
if "get" in schema["paths"]["/with-responses"]:
responses = schema["paths"]["/with-responses"]["get"].get("responses", {})
# Original responses should be preserved
if "200" in responses:
assert responses["200"]["description"] == "Success"
if "404" in responses:
assert responses["404"]["description"] == "Not found"
# 401 should be added
if "401" in responses:
assert (
responses["401"]["$ref"]
== "#/components/responses/HTTP401NotAuthenticatedError"
)
def test_add_auth_responses_to_openapi_no_security_endpoints():
"""Test with app that has no secured endpoints."""
app = FastAPI()
@app.get("/public1")
def public1():
return {"message": "public1"}
@app.post("/public2")
def public2():
return {"message": "public2"}
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Component should still be added for consistency
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
# But no endpoints should have 401 responses
for path in schema["paths"].values():
for method in path.values():
if isinstance(method, dict) and "responses" in method:
assert "401" not in method["responses"]
def test_add_auth_responses_to_openapi_multiple_security_schemes():
"""Test endpoints with multiple security requirements."""
app = FastAPI()
from fastapi import Security
from autogpt_libs.auth.dependencies import requires_admin_user, requires_user
from autogpt_libs.auth.models import User
@app.get("/multi-auth")
def multi_auth(
user: User = Security(requires_user),
admin: User = Security(requires_admin_user),
):
return {"status": "super secure"}
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Should have 401 response
if "/multi-auth" in schema["paths"]:
if "get" in schema["paths"]["/multi-auth"]:
responses = schema["paths"]["/multi-auth"]["get"].get("responses", {})
if "401" in responses:
assert (
responses["401"]["$ref"]
== "#/components/responses/HTTP401NotAuthenticatedError"
)
def test_add_auth_responses_to_openapi_empty_components():
"""Test when OpenAPI schema has no components section initially."""
app = FastAPI()
# Mock get_openapi to return schema without components
original_get_openapi = get_openapi
def mock_get_openapi(*args, **kwargs):
schema = original_get_openapi(*args, **kwargs)
# Remove components if it exists
if "components" in schema:
del schema["components"]
return schema
with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Components should be created
assert "components" in schema
assert "responses" in schema["components"]
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
def test_add_auth_responses_to_openapi_all_http_methods():
"""Test that all HTTP methods are handled correctly."""
app = FastAPI()
from fastapi import Security
from autogpt_libs.auth.jwt_utils import get_jwt_payload
@app.get("/resource")
def get_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "GET"}
@app.post("/resource")
def post_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "POST"}
@app.put("/resource")
def put_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "PUT"}
@app.patch("/resource")
def patch_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "PATCH"}
@app.delete("/resource")
def delete_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "DELETE"}
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# All methods should have 401 response
if "/resource" in schema["paths"]:
for method in ["get", "post", "put", "patch", "delete"]:
if method in schema["paths"]["/resource"]:
method_spec = schema["paths"]["/resource"][method]
if "responses" in method_spec:
assert "401" in method_spec["responses"]
def test_bearer_jwt_auth_scheme_config():
"""Test that bearer_jwt_auth is configured correctly."""
assert bearer_jwt_auth.scheme_name == "HTTPBearerJWT"
assert bearer_jwt_auth.auto_error is False
def test_add_auth_responses_with_no_routes():
"""Test OpenAPI generation with app that has no routes."""
app = FastAPI(title="Empty App")
# Apply customization to empty app
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Should still have basic structure
assert schema["info"]["title"] == "Empty App"
assert "components" in schema
assert "responses" in schema["components"]
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
def test_custom_openapi_function_replacement():
"""Test that the custom openapi function properly replaces the default."""
app = FastAPI()
# Store original function
original_openapi = app.openapi
# Apply customization
add_auth_responses_to_openapi(app)
# Function should be replaced
assert app.openapi != original_openapi
assert callable(app.openapi)
def test_endpoint_without_responses_section():
"""Test endpoint that has security but no responses section initially."""
app = FastAPI()
from fastapi import Security
from fastapi.openapi.utils import get_openapi as original_get_openapi
from autogpt_libs.auth.jwt_utils import get_jwt_payload
# Create endpoint
@app.get("/no-responses")
def endpoint_without_responses(jwt: dict = Security(get_jwt_payload)):
return {"data": "test"}
# Mock get_openapi to remove responses from the endpoint
def mock_get_openapi(*args, **kwargs):
schema = original_get_openapi(*args, **kwargs)
# Remove responses from our endpoint to trigger line 40
if "/no-responses" in schema.get("paths", {}):
if "get" in schema["paths"]["/no-responses"]:
# Delete responses to force the code to create it
if "responses" in schema["paths"]["/no-responses"]["get"]:
del schema["paths"]["/no-responses"]["get"]["responses"]
return schema
with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
# Apply customization
add_auth_responses_to_openapi(app)
# Get schema and verify 401 was added
schema = app.openapi()
# The endpoint should now have 401 response
if "/no-responses" in schema["paths"]:
if "get" in schema["paths"]["/no-responses"]:
responses = schema["paths"]["/no-responses"]["get"].get("responses", {})
assert "401" in responses
assert (
responses["401"]["$ref"]
== "#/components/responses/HTTP401NotAuthenticatedError"
)
def test_components_with_existing_responses():
"""Test when components already has a responses section."""
app = FastAPI()
# Mock get_openapi to return schema with existing components/responses
from fastapi.openapi.utils import get_openapi as original_get_openapi
def mock_get_openapi(*args, **kwargs):
schema = original_get_openapi(*args, **kwargs)
# Add existing components/responses
if "components" not in schema:
schema["components"] = {}
schema["components"]["responses"] = {
"ExistingResponse": {"description": "An existing response"}
}
return schema
with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Both responses should exist
assert "ExistingResponse" in schema["components"]["responses"]
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
# Verify our 401 response structure
error_response = schema["components"]["responses"][
"HTTP401NotAuthenticatedError"
]
assert error_response["description"] == "Authentication required"
def test_openapi_schema_persistence():
"""Test that modifications to OpenAPI schema persist correctly."""
app = FastAPI()
from fastapi import Security
from autogpt_libs.auth.jwt_utils import get_jwt_payload
@app.get("/test")
def test_endpoint(jwt: dict = Security(get_jwt_payload)):
return {"test": True}
# Apply customization
add_auth_responses_to_openapi(app)
# Get schema multiple times
schema1 = app.openapi()
# Modify the cached schema (shouldn't affect future calls)
schema1["info"]["title"] = "Modified Title"
# Clear cache and get again
app.openapi_schema = None
schema2 = app.openapi()
# Should regenerate with original title
assert schema2["info"]["title"] == app.title
assert schema2["info"]["title"] != "Modified Title"

View File

@@ -1,48 +1,11 @@
import logging
from typing import Any
from typing import Any, Dict
import jwt
from fastapi import HTTPException, Security
from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
from .config import get_settings
from .models import User
logger = logging.getLogger(__name__)
# Bearer token authentication scheme
bearer_jwt_auth = HTTPBearer(
bearerFormat="jwt", scheme_name="HTTPBearerJWT", auto_error=False
)
from .config import settings
async def get_jwt_payload(
credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
) -> dict[str, Any]:
"""
Extract and validate JWT payload from HTTP Authorization header.
This is the core authentication function that handles:
- Reading the `Authorization` header to obtain the JWT token
- Verifying the JWT token's signature
- Decoding the JWT token's payload
:param credentials: HTTP Authorization credentials from bearer token
:return: JWT payload dictionary
:raises HTTPException: 401 if authentication fails
"""
if not credentials:
raise HTTPException(status_code=401, detail="Authorization header is missing")
try:
payload = parse_jwt_token(credentials.credentials)
logger.debug("Token decoded successfully")
return payload
except ValueError as e:
raise HTTPException(status_code=401, detail=str(e))
def parse_jwt_token(token: str) -> dict[str, Any]:
def parse_jwt_token(token: str) -> Dict[str, Any]:
"""
Parse and validate a JWT token.
@@ -50,11 +13,10 @@ def parse_jwt_token(token: str) -> dict[str, Any]:
:return: The decoded payload
:raises ValueError: If the token is invalid or expired
"""
settings = get_settings()
try:
payload = jwt.decode(
token,
settings.JWT_VERIFY_KEY,
settings.JWT_SECRET_KEY,
algorithms=[settings.JWT_ALGORITHM],
audience="authenticated",
)
@@ -63,18 +25,3 @@ def parse_jwt_token(token: str) -> dict[str, Any]:
raise ValueError("Token has expired")
except jwt.InvalidTokenError as e:
raise ValueError(f"Invalid token: {str(e)}")
def verify_user(jwt_payload: dict | None, admin_only: bool) -> User:
if jwt_payload is None:
raise HTTPException(status_code=401, detail="Authorization header is missing")
user_id = jwt_payload.get("sub")
if not user_id:
raise HTTPException(status_code=401, detail="User ID not found in token")
if admin_only and jwt_payload["role"] != "admin":
raise HTTPException(status_code=403, detail="Admin access required")
return User.from_payload(jwt_payload)

View File

@@ -1,308 +0,0 @@
"""
Comprehensive tests for JWT token parsing and validation.
Ensures 100% line and branch coverage for JWT security functions.
"""
import os
from datetime import datetime, timedelta, timezone
import jwt
import pytest
from fastapi import HTTPException
from fastapi.security import HTTPAuthorizationCredentials
from pytest_mock import MockerFixture
from autogpt_libs.auth import config, jwt_utils
from autogpt_libs.auth.config import Settings
from autogpt_libs.auth.models import User
MOCK_JWT_SECRET = "test-secret-key-with-at-least-32-characters"
TEST_USER_PAYLOAD = {
"sub": "test-user-id",
"role": "user",
"aud": "authenticated",
"email": "test@example.com",
}
TEST_ADMIN_PAYLOAD = {
"sub": "admin-user-id",
"role": "admin",
"aud": "authenticated",
"email": "admin@example.com",
}
@pytest.fixture(autouse=True)
def mock_config(mocker: MockerFixture):
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": MOCK_JWT_SECRET}, clear=True)
mocker.patch.object(config, "_settings", Settings())
yield
def create_token(payload, secret=None, algorithm="HS256"):
"""Helper to create JWT tokens."""
if secret is None:
secret = MOCK_JWT_SECRET
return jwt.encode(payload, secret, algorithm=algorithm)
def test_parse_jwt_token_valid():
"""Test parsing a valid JWT token."""
token = create_token(TEST_USER_PAYLOAD)
result = jwt_utils.parse_jwt_token(token)
assert result["sub"] == "test-user-id"
assert result["role"] == "user"
assert result["aud"] == "authenticated"
def test_parse_jwt_token_expired():
"""Test parsing an expired JWT token."""
expired_payload = {
**TEST_USER_PAYLOAD,
"exp": datetime.now(timezone.utc) - timedelta(hours=1),
}
token = create_token(expired_payload)
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Token has expired" in str(exc_info.value)
def test_parse_jwt_token_invalid_signature():
"""Test parsing a token with invalid signature."""
# Create token with different secret
token = create_token(TEST_USER_PAYLOAD, secret="wrong-secret")
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)
def test_parse_jwt_token_malformed():
"""Test parsing a malformed token."""
malformed_tokens = [
"not.a.token",
"invalid",
"",
# Header only
"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9",
# No signature
"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ0ZXN0In0",
]
for token in malformed_tokens:
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)
def test_parse_jwt_token_wrong_audience():
"""Test parsing a token with wrong audience."""
wrong_aud_payload = {**TEST_USER_PAYLOAD, "aud": "wrong-audience"}
token = create_token(wrong_aud_payload)
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)
def test_parse_jwt_token_missing_audience():
"""Test parsing a token without audience claim."""
no_aud_payload = {k: v for k, v in TEST_USER_PAYLOAD.items() if k != "aud"}
token = create_token(no_aud_payload)
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)
async 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)
assert result["sub"] == "test-user-id"
assert result["role"] == "user"
async 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)
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():
"""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)
assert exc_info.value.status_code == 401
assert "Invalid token" in exc_info.value.detail
def test_verify_user_with_valid_user():
"""Test verifying a valid user."""
user = jwt_utils.verify_user(TEST_USER_PAYLOAD, admin_only=False)
assert isinstance(user, User)
assert user.user_id == "test-user-id"
assert user.role == "user"
assert user.email == "test@example.com"
def test_verify_user_with_admin():
"""Test verifying an admin user."""
user = jwt_utils.verify_user(TEST_ADMIN_PAYLOAD, admin_only=True)
assert isinstance(user, User)
assert user.user_id == "admin-user-id"
assert user.role == "admin"
def test_verify_user_admin_only_with_regular_user():
"""Test verifying regular user when admin is required."""
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(TEST_USER_PAYLOAD, admin_only=True)
assert exc_info.value.status_code == 403
assert "Admin access required" in exc_info.value.detail
def test_verify_user_no_payload():
"""Test verifying user with no payload."""
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(None, admin_only=False)
assert exc_info.value.status_code == 401
assert "Authorization header is missing" in exc_info.value.detail
def test_verify_user_missing_sub():
"""Test verifying user with payload missing 'sub' field."""
invalid_payload = {"role": "user", "email": "test@example.com"}
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(invalid_payload, admin_only=False)
assert exc_info.value.status_code == 401
assert "User ID not found in token" in exc_info.value.detail
def test_verify_user_empty_sub():
"""Test verifying user with empty 'sub' field."""
invalid_payload = {"sub": "", "role": "user"}
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(invalid_payload, admin_only=False)
assert exc_info.value.status_code == 401
assert "User ID not found in token" in exc_info.value.detail
def test_verify_user_none_sub():
"""Test verifying user with None 'sub' field."""
invalid_payload = {"sub": None, "role": "user"}
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(invalid_payload, admin_only=False)
assert exc_info.value.status_code == 401
assert "User ID not found in token" in exc_info.value.detail
def test_verify_user_missing_role_admin_check():
"""Test verifying admin when role field is missing."""
no_role_payload = {"sub": "user-id"}
with pytest.raises(KeyError):
# This will raise KeyError when checking payload["role"]
jwt_utils.verify_user(no_role_payload, admin_only=True)
# ======================== EDGE CASES ======================== #
def test_jwt_with_additional_claims():
"""Test JWT token with additional custom claims."""
extra_claims_payload = {
"sub": "user-id",
"role": "user",
"aud": "authenticated",
"custom_claim": "custom_value",
"permissions": ["read", "write"],
"metadata": {"key": "value"},
}
token = create_token(extra_claims_payload)
result = jwt_utils.parse_jwt_token(token)
assert result["sub"] == "user-id"
assert result["custom_claim"] == "custom_value"
assert result["permissions"] == ["read", "write"]
def test_jwt_with_numeric_sub():
"""Test JWT token with numeric user ID."""
payload = {
"sub": 12345, # Numeric ID
"role": "user",
"aud": "authenticated",
}
# Should convert to string internally
user = jwt_utils.verify_user(payload, admin_only=False)
assert user.user_id == 12345
def test_jwt_with_very_long_sub():
"""Test JWT token with very long user ID."""
long_id = "a" * 1000
payload = {
"sub": long_id,
"role": "user",
"aud": "authenticated",
}
user = jwt_utils.verify_user(payload, admin_only=False)
assert user.user_id == long_id
def test_jwt_with_special_characters_in_claims():
"""Test JWT token with special characters in claims."""
payload = {
"sub": "user@example.com/special-chars!@#$%",
"role": "admin",
"aud": "authenticated",
"email": "test+special@example.com",
}
user = jwt_utils.verify_user(payload, admin_only=True)
assert "special-chars!@#$%" in user.user_id
def test_jwt_with_future_iat():
"""Test JWT token with issued-at time in future."""
future_payload = {
"sub": "user-id",
"role": "user",
"aud": "authenticated",
"iat": datetime.now(timezone.utc) + timedelta(hours=1),
}
token = create_token(future_payload)
# PyJWT validates iat claim and should reject future tokens
with pytest.raises(ValueError, match="not yet valid"):
jwt_utils.parse_jwt_token(token)
def test_jwt_with_different_algorithms():
"""Test that only HS256 algorithm is accepted."""
payload = {
"sub": "user-id",
"role": "user",
"aud": "authenticated",
}
# Try different algorithms
algorithms = ["HS384", "HS512", "none"]
for algo in algorithms:
if algo == "none":
# Special case for 'none' algorithm (security vulnerability if accepted)
token = create_token(payload, "", algorithm="none")
else:
token = create_token(payload, algorithm=algo)
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)

View File

@@ -0,0 +1,140 @@
import inspect
import logging
import secrets
from typing import Any, Callable, Optional
from fastapi import HTTPException, Request, Security
from fastapi.security import APIKeyHeader, HTTPBearer
from starlette.status import HTTP_401_UNAUTHORIZED
from .config import settings
from .jwt_utils import parse_jwt_token
security = HTTPBearer()
logger = logging.getLogger(__name__)
async def auth_middleware(request: Request):
if not settings.ENABLE_AUTH:
# If authentication is disabled, allow the request to proceed
logger.warning("Auth disabled")
return {}
security = HTTPBearer()
credentials = await security(request)
if not credentials:
raise HTTPException(status_code=401, detail="Authorization header is missing")
try:
payload = parse_jwt_token(credentials.credentials)
request.state.user = payload
logger.debug("Token decoded successfully")
except ValueError as e:
raise HTTPException(status_code=401, detail=str(e))
return payload
class APIKeyValidator:
"""
Configurable API key validator that supports custom validation functions
for FastAPI applications.
This class provides a flexible way to implement API key authentication with optional
custom validation logic. It can be used for simple token matching
or more complex validation scenarios like database lookups.
Examples:
Simple token validation:
```python
validator = APIKeyValidator(
header_name="X-API-Key",
expected_token="your-secret-token"
)
@app.get("/protected", dependencies=[Depends(validator.get_dependency())])
def protected_endpoint():
return {"message": "Access granted"}
```
Custom validation with database lookup:
```python
async def validate_with_db(api_key: str):
api_key_obj = await db.get_api_key(api_key)
return api_key_obj if api_key_obj and api_key_obj.is_active else None
validator = APIKeyValidator(
header_name="X-API-Key",
validate_fn=validate_with_db
)
```
Args:
header_name (str): The name of the header containing the API key
expected_token (Optional[str]): The expected API key value for simple token matching
validate_fn (Optional[Callable]): Custom validation function that takes an API key
string and returns a boolean or object. Can be async.
error_status (int): HTTP status code to use for validation errors
error_message (str): Error message to return when validation fails
"""
def __init__(
self,
header_name: str,
expected_token: Optional[str] = None,
validate_fn: Optional[Callable[[str], bool]] = None,
error_status: int = HTTP_401_UNAUTHORIZED,
error_message: str = "Invalid API key",
):
# Create the APIKeyHeader as a class property
self.security_scheme = APIKeyHeader(name=header_name)
self.expected_token = expected_token
self.custom_validate_fn = validate_fn
self.error_status = error_status
self.error_message = error_message
async def default_validator(self, api_key: str) -> bool:
if not self.expected_token:
raise ValueError(
"Expected Token Required to be set when uisng API Key Validator default validation"
)
return secrets.compare_digest(api_key, self.expected_token)
async def __call__(
self, request: Request, api_key: str = Security(APIKeyHeader)
) -> Any:
if api_key is None:
raise HTTPException(status_code=self.error_status, detail="Missing API key")
# Use custom validation if provided, otherwise use default equality check
validator = self.custom_validate_fn or self.default_validator
result = (
await validator(api_key)
if inspect.iscoroutinefunction(validator)
else validator(api_key)
)
if not result:
raise HTTPException(
status_code=self.error_status, detail=self.error_message
)
# Store validation result in request state if it's not just a boolean
if result is not True:
request.state.api_key = result
return result
def get_dependency(self):
"""
Returns a callable dependency that FastAPI will recognize as a security scheme
"""
async def validate_api_key(
request: Request, api_key: str = Security(self.security_scheme)
) -> Any:
return await self(request, api_key)
# This helps FastAPI recognize it as a security dependency
validate_api_key.__name__ = f"validate_{self.security_scheme.model.name}"
return validate_api_key

View File

@@ -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,
)

View File

@@ -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",
)

View File

@@ -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(

View File

@@ -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)

View File

@@ -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

View File

@@ -54,7 +54,7 @@ version = "1.2.0"
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python-versions = "<3.11,>=3.8"
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@@ -480,7 +235,7 @@ version = "1.3.0"
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python-versions = ">=3.7"
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@@ -1036,7 +779,7 @@ version = "25.0"
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]
[[package]]
@@ -1725,7 +1410,7 @@ version = "2.2.1"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.8"
groups = ["dev"]
groups = ["main"]
markers = "python_version < \"3.11\""
files = [
{file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
@@ -1768,7 +1453,7 @@ version = "4.14.1"
description = "Backported and Experimental Type Hints for Python 3.9+"
optional = false
python-versions = ">=3.9"
groups = ["main", "dev"]
groups = ["main"]
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"},
@@ -1929,4 +1614,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.10,<4.0"
content-hash = "0c40b63c3c921846cf05ccfb4e685d4959854b29c2c302245f9832e20aac6954"
content-hash = "f67db13e6f68b1d67a55eee908c1c560bfa44da8509f98f842889a7570a9830f"

View File

@@ -9,25 +9,21 @@ 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"
launchdarkly-server-sdk = "^9.12.0"
pydantic = "^2.11.7"
pydantic-settings = "^2.10.1"
pyjwt = { version = "^2.10.1", extras = ["crypto"] }
pyjwt = "^2.10.1"
pytest-asyncio = "^1.1.0"
pytest-mock = "^3.14.1"
redis = "^6.2.0"
supabase = "^2.16.0"
uvicorn = "^0.35.0"
[tool.poetry.group.dev.dependencies]
pyright = "^1.1.404"
pytest = "^8.4.1"
pytest-asyncio = "^1.1.0"
pytest-mock = "^3.14.1"
pytest-cov = "^6.2.1"
ruff = "^0.12.11"
ruff = "^0.12.3"
[build-system]
requires = ["poetry-core"]

View File

@@ -16,12 +16,13 @@ DB_SCHEMA=platform
DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
DIRECT_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
PRISMA_SCHEMA="postgres/schema.prisma"
ENABLE_AUTH=true
## ===== REQUIRED SERVICE CREDENTIALS ===== ##
# Redis Configuration
REDIS_HOST=localhost
REDIS_PORT=6379
# REDIS_PASSWORD=
REDIS_PASSWORD=password
# RabbitMQ Credentials
RABBITMQ_DEFAULT_USER=rabbitmq_user_default
@@ -30,7 +31,7 @@ RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7
# Supabase Authentication
SUPABASE_URL=http://localhost:8000
SUPABASE_SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
JWT_VERIFY_KEY=your-super-secret-jwt-token-with-at-least-32-characters-long
SUPABASE_JWT_SECRET=your-super-secret-jwt-token-with-at-least-32-characters-long
## ===== REQUIRED SECURITY KEYS ===== ##
# Generate using: from cryptography.fernet import Fernet;Fernet.generate_key().decode()
@@ -58,13 +59,6 @@ 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 +67,6 @@ 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):
@@ -117,15 +106,6 @@ TODOIST_CLIENT_SECRET=
NOTION_CLIENT_ID=
NOTION_CLIENT_SECRET=
# Discord OAuth App credentials
# 1. Go to https://discord.com/developers/applications
# 2. Create a new application
# 3. Go to OAuth2 section and add redirect URI: http://localhost:3000/auth/integrations/oauth_callback
# 4. Copy Client ID and Client Secret below
DISCORD_CLIENT_ID=
DISCORD_CLIENT_SECRET=
REDDIT_CLIENT_ID=
REDDIT_CLIENT_SECRET=
@@ -141,6 +121,13 @@ 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=
@@ -179,4 +166,4 @@ SMARTLEAD_API_KEY=
ZEROBOUNCE_API_KEY=
# Other Services
AUTOMOD_API_KEY=
AUTOMOD_API_KEY=

View File

@@ -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.*

View File

@@ -1,43 +1,31 @@
FROM debian:13-slim AS builder
FROM python:3.11.10-slim-bookworm AS builder
# Set environment variables
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
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 build dependencies in a single layer
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 \
python3.13-venv \
python3-pip \
build-essential \
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
ENV POETRY_VIRTUALENVS_CREATE=true
ENV POETRY_VIRTUALENVS_IN_PROJECT=true
ENV POETRY_VIRTUALENVS_CREATE=false
ENV PATH=/opt/poetry/bin:$PATH
RUN pip3 install poetry --break-system-packages
# Upgrade pip and setuptools to fix security vulnerabilities
RUN pip3 install --upgrade pip setuptools
RUN pip3 install poetry
# Copy and install dependencies
COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
@@ -47,39 +35,29 @@ RUN poetry install --no-ansi --no-root
# Generate Prisma client
COPY autogpt_platform/backend/schema.prisma ./
COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
RUN poetry run prisma generate && poetry run gen-prisma-stub
RUN poetry run prisma generate
FROM debian:13-slim AS server_dependencies
FROM python:3.11.10-slim-bookworm AS server_dependencies
WORKDIR /app
ENV POETRY_HOME=/opt/poetry \
POETRY_NO_INTERACTION=1 \
POETRY_VIRTUALENVS_CREATE=true \
POETRY_VIRTUALENVS_IN_PROJECT=true \
DEBIAN_FRONTEND=noninteractive
POETRY_VIRTUALENVS_CREATE=false
ENV PATH=/opt/poetry/bin:$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/*
# Upgrade pip and setuptools to fix security vulnerabilities
RUN pip3 install --upgrade pip setuptools
# 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 --from=builder /usr/local/lib/python3.11 /usr/local/lib/python3.11
COPY --from=builder /usr/local/bin /usr/local/bin
# Copy Prisma binaries
COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
ENV PATH="/app/.venv/bin:$PATH"
RUN mkdir -p /app/autogpt_platform/autogpt_libs
RUN mkdir -p /app/autogpt_platform/backend
@@ -94,13 +72,11 @@ 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

View File

@@ -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)
@@ -132,58 +132,17 @@ def test_endpoint_success(snapshot: Snapshot):
### Testing with Authentication
For the main API routes that use JWT authentication, auth is provided by the `autogpt_libs.auth` module. If the test actually uses the `user_id`, the recommended approach for testing is to mock the `get_jwt_payload` function, which underpins all higher-level auth functions used in the API (`requires_user`, `requires_admin_user`, `get_user_id`).
If the test doesn't need the `user_id` specifically, mocking is not necessary as during tests auth is disabled anyway (see `conftest.py`).
#### Using Global Auth Fixtures
Two global auth fixtures are provided by `backend/server/conftest.py`:
- `mock_jwt_user` - Regular user with `test_user_id` ("test-user-id")
- `mock_jwt_admin` - Admin user with `admin_user_id` ("admin-user-id")
These provide the easiest way to set up authentication mocking in test modules:
```python
import fastapi
import fastapi.testclient
import pytest
from backend.api.features.myroute import router
def override_auth_middleware():
return {"sub": "test-user-id"}
app = fastapi.FastAPI()
app.include_router(router)
client = fastapi.testclient.TestClient(app)
def override_get_user_id():
return "test-user-id"
@pytest.fixture(autouse=True)
def setup_app_auth(mock_jwt_user):
"""Setup auth overrides for all tests in this module"""
from autogpt_libs.auth.jwt_utils import get_jwt_payload
app.dependency_overrides[get_jwt_payload] = mock_jwt_user['get_jwt_payload']
yield
app.dependency_overrides.clear()
app.dependency_overrides[auth_middleware] = override_auth_middleware
app.dependency_overrides[get_user_id] = override_get_user_id
```
For admin-only endpoints, use `mock_jwt_admin` instead:
```python
@pytest.fixture(autouse=True)
def setup_app_auth(mock_jwt_admin):
"""Setup auth overrides for admin tests"""
from autogpt_libs.auth.jwt_utils import get_jwt_payload
app.dependency_overrides[get_jwt_payload] = mock_jwt_admin['get_jwt_payload']
yield
app.dependency_overrides.clear()
```
The IDs are also available separately as fixtures:
- `test_user_id`
- `admin_user_id`
- `target_user_id` (for admin <-> user operations)
### Mocking External Services
```python
@@ -194,10 +153,10 @@ def test_external_api_call(mocker, snapshot):
"backend.services.external_api.call",
return_value=mock_response
)
response = client.post("/api/process")
assert response.status_code == 200
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(response.json(), indent=2, sort_keys=True),
@@ -228,17 +187,6 @@ def test_external_api_call(mocker, snapshot):
- Use `async def` with `@pytest.mark.asyncio` for testing async functions directly
### 5. Fixtures
#### Global Fixtures (conftest.py)
Authentication fixtures are available globally from `conftest.py`:
- `mock_jwt_user` - Standard user authentication
- `mock_jwt_admin` - Admin user authentication
- `configured_snapshot` - Pre-configured snapshot fixture
#### Custom Fixtures
Create reusable fixtures for common test data:
```python
@@ -254,18 +202,9 @@ def test_create_user(sample_user, snapshot):
# ... test implementation
```
#### Test Isolation
All tests must use fixtures that ensure proper isolation:
- Authentication overrides are automatically cleaned up after each test
- Database connections are properly managed with cleanup
- Mock objects are reset between tests
## CI/CD Integration
The GitHub Actions workflow automatically runs tests on:
- Pull requests
- Pushes to main branch
@@ -277,19 +216,16 @@ Snapshot tests work in CI by:
## Troubleshooting
### Snapshot Mismatches
- Review the diff carefully
- If changes are expected: `poetry run pytest --snapshot-update`
- If changes are unexpected: Fix the code causing the difference
### Async Test Issues
- Ensure async functions use `@pytest.mark.asyncio`
- Use `AsyncMock` for mocking async functions
- FastAPI TestClient handles async automatically
### Import Errors
- Check that all dependencies are in `pyproject.toml`
- Run `poetry install` to ensure dependencies are installed
- Verify import paths are correct
@@ -298,4 +234,4 @@ Snapshot tests work in CI by:
Snapshot testing provides a powerful way to ensure API responses remain consistent. Combined with traditional assertions, it creates a robust test suite that catches regressions while remaining maintainable.
Remember: Good tests are as important as good code!
Remember: Good tests are as important as good code!

View File

@@ -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 youre 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 arent 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 3035 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 Marketplaces 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 YOULL 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,Autocraft LinkedIn gold,"Create researchdriven, highimpact 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 topranked videos so you dont have to
• AIcurated synthesis combines multiple transcripts into one authoritative narrative
• Full creative control adjust style, tone, objective, opinion, clarity, target word count and number of videos
• LinkedInoptimised output hook, 23 key points, CTA, strategic line breaks, 35 hashtags, no markdown
• Oneclick publish returns a readytopost 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
• Thoughtleadership 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 YOULL LOVE IT
Save hours of manual research, avoid surfacelevel hottakes, 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
- Youll 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 35 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 shouldnt 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 AIpowered assistant for turning plainEnglish 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 naturallanguage 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"": ""19921103"", ""job_title"": ""Data Analyst"", ""age"": 32 },
{ ""name"": ""Liam OConnor"", ""date_of_birth"": ""19850719"", ""job_title"": ""Marketing Manager"", ""age"": 39 },
…18 more entries…
]","[""development""]",false,true
1 listing_id storeListingVersionId slug agent_name agent_video agent_image featured sub_heading description categories useForOnboarding is_available
2 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
3 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
4 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
5 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
6 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
7 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
8 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
9 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
10 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
11 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
12 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
13 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
14 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
15 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
16 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
17 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
18 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

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View File

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