add run block tool

.branchlet.json

@@ -29,7 +29,8 @@
   "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 autogpt_platform/frontend && pnpm install",
+    "cd docs && pip install -r requirements.txt"
   ],
   "terminalCommand": "code .",
   "deleteBranchWithWorktree": false

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

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

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

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

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

@@ -1,49 +0,0 @@
----
-title: useLatest for Stable Callback Refs
-impact: LOW
-impactDescription: prevents effect re-runs
-tags: advanced, hooks, useLatest, refs, optimization
----
-
-## useLatest for Stable Callback Refs
-
-Access latest values in callbacks without adding them to dependency arrays. Prevents effect re-runs while avoiding stale closures.
-
-**Implementation:**
-
-```typescript
-function useLatest<T>(value: T) {
-  const ref = useRef(value)
-  useEffect(() => {
-    ref.current = value
-  }, [value])
-  return ref
-}
-```
-
-**Incorrect (effect re-runs on every callback change):**
-
-```tsx
-function SearchInput({ onSearch }: { onSearch: (q: string) => void }) {
-  const [query, setQuery] = useState('')
-
-  useEffect(() => {
-    const timeout = setTimeout(() => onSearch(query), 300)
-    return () => clearTimeout(timeout)
-  }, [query, onSearch])
-}
-```
-
-**Correct (stable effect, fresh callback):**
-
-```tsx
-function SearchInput({ onSearch }: { onSearch: (q: string) => void }) {
-  const [query, setQuery] = useState('')
-  const onSearchRef = useLatest(onSearch)
-
-  useEffect(() => {
-    const timeout = setTimeout(() => onSearchRef.current(query), 300)
-    return () => clearTimeout(timeout)
-  }, [query])
-}
-```

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,82 +0,0 @@
----
-title: Batch DOM CSS Changes
-impact: MEDIUM
-impactDescription: reduces reflows/repaints
-tags: javascript, dom, css, performance, reflow
----
-
-## Batch DOM CSS Changes
-
-Avoid changing styles one property at a time. Group multiple CSS changes together via classes or `cssText` to minimize browser reflows.
-
-**Incorrect (multiple reflows):**
-
-```typescript
-function updateElementStyles(element: HTMLElement) {
-  // Each line triggers a reflow
-  element.style.width = '100px'
-  element.style.height = '200px'
-  element.style.backgroundColor = 'blue'
-  element.style.border = '1px solid black'
-}
-```
-
-**Correct (add class - single reflow):**
-
-```typescript
-// CSS file
-.highlighted-box {
-  width: 100px;
-  height: 200px;
-  background-color: blue;
-  border: 1px solid black;
-}
-
-// JavaScript
-function updateElementStyles(element: HTMLElement) {
-  element.classList.add('highlighted-box')
-}
-```
-
-**Correct (change cssText - single reflow):**
-
-```typescript
-function updateElementStyles(element: HTMLElement) {
-  element.style.cssText = `
-    width: 100px;
-    height: 200px;
-    background-color: blue;
-    border: 1px solid black;
-  `
-}
-```
-
-**React example:**
-
-```tsx
-// Incorrect: changing styles one by one
-function Box({ isHighlighted }: { isHighlighted: boolean }) {
-  const ref = useRef<HTMLDivElement>(null)
-  
-  useEffect(() => {
-    if (ref.current && isHighlighted) {
-      ref.current.style.width = '100px'
-      ref.current.style.height = '200px'
-      ref.current.style.backgroundColor = 'blue'
-    }
-  }, [isHighlighted])
-  
-  return <div ref={ref}>Content</div>
-}
-
-// Correct: toggle class
-function Box({ isHighlighted }: { isHighlighted: boolean }) {
-  return (
-    <div className={isHighlighted ? 'highlighted-box' : ''}>
-      Content
-    </div>
-  )
-}
-```
-
-Prefer CSS classes over inline styles when possible. Classes are cached by the browser and provide better separation of concerns.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

.dockerignore

@@ -1,17 +1,43 @@
 # Ignore everything by default, selectively add things to context
 *
 
-# Documentation (for embeddings/search)
-!docs/
-
 # Platform - Libs
-!autogpt_platform/autogpt_libs/
+!autogpt_platform/autogpt_libs/autogpt_libs/
+!autogpt_platform/autogpt_libs/pyproject.toml
+!autogpt_platform/autogpt_libs/poetry.lock
+!autogpt_platform/autogpt_libs/README.md
 
 # Platform - Backend
-!autogpt_platform/backend/
+!autogpt_platform/backend/backend/
+!autogpt_platform/backend/test/e2e_test_data.py
+!autogpt_platform/backend/migrations/
+!autogpt_platform/backend/schema.prisma
+!autogpt_platform/backend/pyproject.toml
+!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/
+!autogpt_platform/market/scripts.py
+!autogpt_platform/market/schema.prisma
+!autogpt_platform/market/pyproject.toml
+!autogpt_platform/market/poetry.lock
+!autogpt_platform/market/README.md
 
 # Platform - Frontend
-!autogpt_platform/frontend/
+!autogpt_platform/frontend/src/
+!autogpt_platform/frontend/public/
+!autogpt_platform/frontend/scripts/
+!autogpt_platform/frontend/package.json
+!autogpt_platform/frontend/pnpm-lock.yaml
+!autogpt_platform/frontend/tsconfig.json
+!autogpt_platform/frontend/README.md
+## config
+!autogpt_platform/frontend/*.config.*
+!autogpt_platform/frontend/.env.*
+!autogpt_platform/frontend/.env
 
 # Classic - AutoGPT
 !classic/original_autogpt/autogpt/
@@ -35,38 +61,6 @@
 # Classic - Frontend
 !classic/frontend/build/web/
 
-# Explicitly re-ignore unwanted files from whitelisted directories
-# Note: These patterns MUST come after the whitelist rules to take effect
-
-# Hidden files and directories (but keep frontend .env files needed for build)
-**/.*
-!autogpt_platform/frontend/.env
-!autogpt_platform/frontend/.env.default
-!autogpt_platform/frontend/.env.production
-
-# Python artifacts
-**/__pycache__/
-**/*.pyc
-**/*.pyo
-**/.venv/
-**/.ruff_cache/
-**/.pytest_cache/
-**/.coverage
-**/htmlcov/
-
-# Node artifacts
-**/node_modules/
-**/.next/
-**/storybook-static/
-**/playwright-report/
-**/test-results/
-
-# Build artifacts
-**/dist/
-**/build/
-!autogpt_platform/frontend/src/**/build/
-**/target/
-
-# Logs and temp files
-**/*.log
-**/*.tmp
+# Explicitly re-ignore some folders
+.*
+**/__pycache__

.github/copilot-instructions.md

@@ -160,7 +160,7 @@ pnpm storybook                      # Start component development server
 
 **Backend Entry Points:**
 
-- `backend/backend/api/rest_api.py` - FastAPI application setup
+- `backend/backend/server/server.py` - FastAPI application setup
 - `backend/backend/data/` - Database models and user management
 - `backend/blocks/` - Agent execution blocks and logic
 
@@ -219,7 +219,7 @@ Agents are built using a visual block-based system where each block performs a s
 
 ### API Development
 
-1. Update routes in `/backend/backend/api/features/`
+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
@@ -285,7 +285,7 @@ Agents are built using a visual block-based system where each block performs a s
 
 ### Security Guidelines
 
-**Cache Protection Middleware** (`/backend/backend/api/middleware/security.py`):
+**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)

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

@@ -49,7 +49,7 @@ jobs:
 
       - name: Create PR ${{ env.BUILD_BRANCH }} -> ${{ github.ref_name }}
         if: github.event_name == 'push'
-        uses: peter-evans/create-pull-request@v8
+        uses: peter-evans/create-pull-request@v7
         with:
           add-paths: classic/frontend/build/web
           base: ${{ github.ref_name }}

.github/workflows/claude-ci-failure-auto-fix.yml

@@ -22,7 +22,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.event.workflow_run.head_branch }}
           fetch-depth: 0
@@ -40,51 +40,9 @@ jobs:
           git checkout -b "$BRANCH_NAME"
           echo "branch_name=$BRANCH_NAME" >> $GITHUB_OUTPUT
 
-      # Backend Python/Poetry setup (so Claude can run linting/tests)
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v5
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install 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 (so Claude can run linting/tests)
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: "22"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
-
-      - name: Install JavaScript dependencies
-        working-directory: autogpt_platform/frontend
-        run: pnpm install --frozen-lockfile
-
       - name: Get CI failure details
         id: failure_details
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const run = await github.rest.actions.getWorkflowRun({
@@ -135,5 +93,5 @@ jobs:
 
             Error logs:
             ${{ toJSON(fromJSON(steps.failure_details.outputs.result).errorLogs) }}
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           claude_args: "--allowedTools 'Edit,MultiEdit,Write,Read,Glob,Grep,LS,Bash(git:*),Bash(bun:*),Bash(npm:*),Bash(npx:*),Bash(gh:*)'"

.github/workflows/claude-dependabot.yml

@@ -7,7 +7,7 @@
 # - Provide actionable recommendations for the development team
 #
 # Triggered on: Dependabot PRs (opened, synchronize)
-# Requirements: CLAUDE_CODE_OAUTH_TOKEN secret must be configured
+# Requirements: ANTHROPIC_API_KEY secret must be configured
 
 name: Claude Dependabot PR Review
 
@@ -30,7 +30,7 @@ jobs:
       actions: read # Required for CI access
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 
@@ -41,7 +41,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -77,15 +77,27 @@ jobs:
         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 up Node.js
-        uses: actions/setup-node@v6
+      - 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:
-          node-version: "22"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          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
@@ -112,7 +124,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes
@@ -296,8 +308,7 @@ jobs:
         id: claude_review
         uses: anthropics/claude-code-action@v1
         with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          allowed_bots: "dependabot[bot]"
+          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: |

.github/workflows/claude.yml

@@ -40,7 +40,7 @@ jobs:
       actions: read # Required for CI access
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 
@@ -57,7 +57,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -93,15 +93,27 @@ jobs:
         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 up Node.js
-        uses: actions/setup-node@v6
+      - 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:
-          node-version: "22"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          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
@@ -128,7 +140,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes
@@ -311,7 +323,7 @@ jobs:
         id: claude
         uses: anthropics/claude-code-action@v1
         with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           claude_args: |
             --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*), Bash(gh pr edit:*)"
             --model opus

.github/workflows/codeql.yml

@@ -58,11 +58,11 @@ jobs:
         # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v6
+      uses: actions/checkout@v4
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v4
+      uses: github/codeql-action/init@v3
       with:
         languages: ${{ matrix.language }}
         build-mode: ${{ matrix.build-mode }}
@@ -93,6 +93,6 @@ jobs:
         exit 1
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v4
+      uses: github/codeql-action/analyze@v3
       with:
         category: "/language:${{matrix.language}}"

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

@@ -27,7 +27,7 @@ jobs:
     # If you do not check out your code, Copilot will do this for you.
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
           submodules: true
@@ -39,7 +39,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -76,7 +76,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22"
 
@@ -89,7 +89,7 @@ jobs:
           echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
 
       - name: Cache frontend dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -132,7 +132,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes

.github/workflows/docs-block-sync.yml

@@ -1,78 +0,0 @@
-name: Block Documentation Sync Check
-
-on:
-  push:
-    branches: [master, dev]
-    paths:
-      - "autogpt_platform/backend/backend/blocks/**"
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/scripts/generate_block_docs.py"
-      - ".github/workflows/docs-block-sync.yml"
-  pull_request:
-    branches: [master, dev]
-    paths:
-      - "autogpt_platform/backend/backend/blocks/**"
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/scripts/generate_block_docs.py"
-      - ".github/workflows/docs-block-sync.yml"
-
-jobs:
-  check-docs-sync:
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 1
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v5
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Check block documentation is in sync
-        working-directory: autogpt_platform/backend
-        run: |
-          echo "Checking if block documentation is in sync with code..."
-          poetry run python scripts/generate_block_docs.py --check
-
-      - name: Show diff if out of sync
-        if: failure()
-        working-directory: autogpt_platform/backend
-        run: |
-          echo "::error::Block documentation is out of sync with code!"
-          echo ""
-          echo "To fix this, run the following command locally:"
-          echo "  cd autogpt_platform/backend && poetry run python scripts/generate_block_docs.py"
-          echo ""
-          echo "Then commit the updated documentation files."
-          echo ""
-          echo "Regenerating docs to show diff..."
-          poetry run python scripts/generate_block_docs.py
-          echo ""
-          echo "Changes detected:"
-          git diff ../../docs/integrations/ || true

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

@@ -1,129 +0,0 @@
-name: Claude Block Docs Review
-
-on:
-  pull_request:
-    types: [opened, synchronize]
-    paths:
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/backend/blocks/**"
-
-concurrency:
-  group: claude-docs-review-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
-jobs:
-  claude-review:
-    # Only run for PRs from members/collaborators
-    if: |
-      github.event.pull_request.author_association == 'OWNER' ||
-      github.event.pull_request.author_association == 'MEMBER' ||
-      github.event.pull_request.author_association == 'COLLABORATOR'
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    permissions:
-      contents: read
-      pull-requests: write
-      id-token: write
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v5
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Run Claude Code Review
-        uses: anthropics/claude-code-action@v1
-        with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          claude_args: |
-            --allowedTools "Read,Glob,Grep,Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*)"
-          prompt: |
-            You are reviewing a PR that modifies block documentation or block code for AutoGPT.
-
-            ## Your Task
-            Review the changes in this PR and provide constructive feedback. Focus on:
-
-            1. **Documentation Accuracy**: For any block code changes, verify that:
-               - Input/output tables in docs match the actual block schemas
-               - Description text accurately reflects what the block does
-               - Any new blocks have corresponding documentation
-
-            2. **Manual Content Quality**: Check manual sections (marked with `<!-- MANUAL: -->` markers):
-               - "How it works" sections should have clear technical explanations
-               - "Possible use case" sections should have practical, real-world examples
-               - Content should be helpful for users trying to understand the blocks
-
-            3. **Template Compliance**: Ensure docs follow the standard template:
-               - What it is (brief intro)
-               - What it does (description)
-               - How it works (technical explanation)
-               - Inputs table
-               - Outputs table
-               - Possible use case
-
-            4. **Cross-references**: Check that links and anchors are correct
-
-            ## Review Process
-            1. First, get the PR diff to see what changed: `gh pr diff ${{ github.event.pull_request.number }}`
-            2. Read any modified block files to understand the implementation
-            3. Read corresponding documentation files to verify accuracy
-            4. Provide your feedback as a PR comment
-
-            ## IMPORTANT: Comment Marker
-            Start your PR comment with exactly this HTML comment marker on its own line:
-            <!-- CLAUDE_DOCS_REVIEW -->
-
-            This marker is used to identify and replace your comment on subsequent runs.
-
-            Be constructive and specific. If everything looks good, say so!
-            If there are issues, explain what's wrong and suggest how to fix it.
-
-      - name: Delete old Claude review comments
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          # Get all comment IDs with our marker, sorted by creation date (oldest first)
-          COMMENT_IDS=$(gh api \
-            repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/comments \
-            --jq '[.[] | select(.body | contains("<!-- CLAUDE_DOCS_REVIEW -->"))] | sort_by(.created_at) | .[].id')
-
-          # Count comments
-          COMMENT_COUNT=$(echo "$COMMENT_IDS" | grep -c . || true)
-
-          if [ "$COMMENT_COUNT" -gt 1 ]; then
-            # Delete all but the last (newest) comment
-            echo "$COMMENT_IDS" | head -n -1 | while read -r COMMENT_ID; do
-              if [ -n "$COMMENT_ID" ]; then
-                echo "Deleting old review comment: $COMMENT_ID"
-                gh api -X DELETE repos/${{ github.repository }}/issues/comments/$COMMENT_ID
-              fi
-            done
-          else
-            echo "No old review comments to clean up"
-          fi

.github/workflows/docs-enhance.yml

@@ -1,194 +0,0 @@
-name: Enhance Block Documentation
-
-on:
-  workflow_dispatch:
-    inputs:
-      block_pattern:
-        description: 'Block file pattern to enhance (e.g., "google/*.md" or "*" for all blocks)'
-        required: true
-        default: '*'
-        type: string
-      dry_run:
-        description: 'Dry run mode - show proposed changes without committing'
-        type: boolean
-        default: true
-      max_blocks:
-        description: 'Maximum number of blocks to process (0 for unlimited)'
-        type: number
-        default: 10
-
-jobs:
-  enhance-docs:
-    runs-on: ubuntu-latest
-    timeout-minutes: 45
-    permissions:
-      contents: write
-      pull-requests: write
-      id-token: write
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 1
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v5
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Run Claude Enhancement
-        uses: anthropics/claude-code-action@v1
-        with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          claude_args: |
-            --allowedTools "Read,Edit,Glob,Grep,Write,Bash(git:*),Bash(gh:*),Bash(find:*),Bash(ls:*)"
-          prompt: |
-            You are enhancing block documentation for AutoGPT. Your task is to improve the MANUAL sections
-            of block documentation files by reading the actual block implementations and writing helpful content.
-
-            ## Configuration
-            - Block pattern: ${{ inputs.block_pattern }}
-            - Dry run: ${{ inputs.dry_run }}
-            - Max blocks to process: ${{ inputs.max_blocks }}
-
-            ## Your Task
-
-            1. **Find Documentation Files**
-               Find block documentation files matching the pattern in `docs/integrations/`
-               Pattern: ${{ inputs.block_pattern }}
-
-               Use: `find docs/integrations -name "*.md" -type f`
-
-            2. **For Each Documentation File** (up to ${{ inputs.max_blocks }} files):
-
-               a. Read the documentation file
-
-               b. Identify which block(s) it documents (look for the block class name)
-
-               c. Find and read the corresponding block implementation in `autogpt_platform/backend/backend/blocks/`
-
-               d. Improve the MANUAL sections:
-
-                  **"How it works" section** (within `<!-- MANUAL: how_it_works -->` markers):
-                  - Explain the technical flow of the block
-                  - Describe what APIs or services it connects to
-                  - Note any important configuration or prerequisites
-                  - Keep it concise but informative (2-4 paragraphs)
-
-                  **"Possible use case" section** (within `<!-- MANUAL: use_case -->` markers):
-                  - Provide 2-3 practical, real-world examples
-                  - Make them specific and actionable
-                  - Show how this block could be used in an automation workflow
-
-            3. **Important Rules**
-               - ONLY modify content within `<!-- MANUAL: -->` and `<!-- END MANUAL -->` markers
-               - Do NOT modify auto-generated sections (inputs/outputs tables, descriptions)
-               - Keep content accurate based on the actual block implementation
-               - Write for users who may not be technical experts
-
-            4. **Output**
-               ${{ inputs.dry_run == true && 'DRY RUN MODE: Show proposed changes for each file but do NOT actually edit the files. Describe what you would change.' || 'LIVE MODE: Actually edit the files to improve the documentation.' }}
-
-            ## Example Improvements
-
-            **Before (How it works):**
-            ```
-            _Add technical explanation here._
-            ```
-
-            **After (How it works):**
-            ```
-            This block connects to the GitHub API to retrieve issue information. When executed,
-            it authenticates using your GitHub credentials and fetches issue details including
-            title, body, labels, and assignees.
-
-            The block requires a valid GitHub OAuth connection with repository access permissions.
-            It supports both public and private repositories you have access to.
-            ```
-
-            **Before (Possible use case):**
-            ```
-            _Add practical use case examples here._
-            ```
-
-            **After (Possible use case):**
-            ```
-            **Customer Support Automation**: Monitor a GitHub repository for new issues with
-            the "bug" label, then automatically create a ticket in your support system and
-            notify the on-call engineer via Slack.
-
-            **Release Notes Generation**: When a new release is published, gather all closed
-            issues since the last release and generate a summary for your changelog.
-            ```
-
-            Begin by finding and listing the documentation files to process.
-
-      - name: Create PR with enhanced documentation
-        if: ${{ inputs.dry_run == false }}
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          # Check if there are changes
-          if git diff --quiet docs/integrations/; then
-            echo "No changes to commit"
-            exit 0
-          fi
-
-          # Configure git
-          git config user.name "github-actions[bot]"
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-
-          # Create branch and commit
-          BRANCH_NAME="docs/enhance-blocks-$(date +%Y%m%d-%H%M%S)"
-          git checkout -b "$BRANCH_NAME"
-          git add docs/integrations/
-          git commit -m "docs: enhance block documentation with LLM-generated content
-
-          Pattern: ${{ inputs.block_pattern }}
-          Max blocks: ${{ inputs.max_blocks }}
-
-          🤖 Generated with [Claude Code](https://claude.com/claude-code)
-
-          Co-Authored-By: Claude <noreply@anthropic.com>"
-
-          # Push and create PR
-          git push -u origin "$BRANCH_NAME"
-          gh pr create \
-            --title "docs: LLM-enhanced block documentation" \
-            --body "## Summary
-          This PR contains LLM-enhanced documentation for block files matching pattern: \`${{ inputs.block_pattern }}\`
-
-          The following manual sections were improved:
-          - **How it works**: Technical explanations based on block implementations
-          - **Possible use case**: Practical, real-world examples
-
-          ## Review Checklist
-          - [ ] Content is accurate based on block implementations
-          - [ ] Examples are practical and helpful
-          - [ ] No auto-generated sections were modified
-
-          ---
-          🤖 Generated with [Claude Code](https://claude.com/claude-code)" \
-            --base dev

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

@@ -25,7 +25,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.event.inputs.git_ref || github.ref_name }}
 
@@ -52,7 +52,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

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

@@ -17,7 +17,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.ref_name || 'master' }}
 
@@ -45,7 +45,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

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

@@ -41,18 +41,13 @@ jobs:
         ports:
           - 6379:6379
       rabbitmq:
-        image: rabbitmq:4.1.4
+        image: rabbitmq:3.12-management
         ports:
           - 5672:5672
+          - 15672:15672
         env:
           RABBITMQ_DEFAULT_USER: ${{ env.RABBITMQ_DEFAULT_USER }}
           RABBITMQ_DEFAULT_PASS: ${{ env.RABBITMQ_DEFAULT_PASS }}
-        options: >-
-          --health-cmd "rabbitmq-diagnostics -q ping"
-          --health-interval 30s
-          --health-timeout 10s
-          --health-retries 5
-          --health-start-period 10s
       clamav:
         image: clamav/clamav-debian:latest
         ports:
@@ -73,7 +68,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
           submodules: true
@@ -93,7 +88,7 @@ jobs:
         run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

.github/workflows/platform-dev-deploy-event-dispatcher.yml

@@ -17,7 +17,7 @@ jobs:
       - name: Check comment permissions and deployment status
         id: check_status
         if: github.event_name == 'issue_comment' && github.event.issue.pull_request
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const commentBody = context.payload.comment.body.trim();
@@ -55,7 +55,7 @@ jobs:
 
       - name: Post permission denied comment
         if: steps.check_status.outputs.permission_denied == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             await github.rest.issues.createComment({
@@ -68,7 +68,7 @@ jobs:
       - name: Get PR details for deployment
         id: pr_details
         if: steps.check_status.outputs.should_deploy == 'true' || steps.check_status.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const pr = await github.rest.pulls.get({
@@ -82,7 +82,7 @@ jobs:
           
       - name: Dispatch Deploy Event
         if: steps.check_status.outputs.should_deploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -98,7 +98,7 @@ jobs:
 
       - name: Post deploy success comment
         if: steps.check_status.outputs.should_deploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             await github.rest.issues.createComment({
@@ -110,7 +110,7 @@ jobs:
 
       - name: Dispatch Undeploy Event (from comment)
         if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -126,7 +126,7 @@ jobs:
 
       - name: Post undeploy success comment
         if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             await github.rest.issues.createComment({
@@ -139,7 +139,7 @@ jobs:
       - name: Check deployment status on PR close
         id: check_pr_close
         if: github.event_name == 'pull_request' && github.event.action == 'closed'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const comments = await github.rest.issues.listComments({
@@ -168,7 +168,7 @@ jobs:
           github.event_name == 'pull_request' &&
           github.event.action == 'closed' &&
           steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -187,7 +187,7 @@ jobs:
           github.event_name == 'pull_request' &&
           github.event.action == 'closed' &&
           steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             await github.rest.issues.createComment({

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

@@ -6,16 +6,10 @@ on:
     paths:
       - ".github/workflows/platform-frontend-ci.yml"
       - "autogpt_platform/frontend/**"
-      - "autogpt_platform/backend/Dockerfile"
-      - "autogpt_platform/docker-compose.yml"
-      - "autogpt_platform/docker-compose.platform.yml"
   pull_request:
     paths:
       - ".github/workflows/platform-frontend-ci.yml"
       - "autogpt_platform/frontend/**"
-      - "autogpt_platform/backend/Dockerfile"
-      - "autogpt_platform/docker-compose.yml"
-      - "autogpt_platform/docker-compose.platform.yml"
   merge_group:
   workflow_dispatch:
 
@@ -32,31 +26,34 @@ jobs:
   setup:
     runs-on: ubuntu-latest
     outputs:
-      components-changed: ${{ steps.filter.outputs.components }}
+      cache-key: ${{ steps.cache-key.outputs.key }}
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
 
-      - name: Check for component changes
-        uses: dorny/paths-filter@v3
-        id: filter
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
         with:
-          filters: |
-            components:
-              - 'autogpt_platform/frontend/src/components/**'
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable
 
-      - name: Set up Node
-        uses: actions/setup-node@v6
-        with:
-          node-version: "22.18.0"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+      - name: Generate cache key
+        id: cache-key
+        run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
 
-      - name: Install dependencies to populate cache
+      - name: Cache dependencies
+        uses: actions/cache@v4
+        with:
+          path: ~/.pnpm-store
+          key: ${{ steps.cache-key.outputs.key }}
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
+
+      - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
   lint:
@@ -65,17 +62,24 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable
 
-      - name: Set up Node
-        uses: actions/setup-node@v6
+      - name: Restore dependencies cache
+        uses: actions/cache@v4
         with:
-          node-version: "22.18.0"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          path: ~/.pnpm-store
+          key: ${{ needs.setup.outputs.cache-key }}
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
@@ -86,27 +90,31 @@ jobs:
   chromatic:
     runs-on: ubuntu-latest
     needs: setup
-    # Disabled: to re-enable, remove 'false &&' from the condition below
-    if: >-
-      false
-      && (github.ref == 'refs/heads/dev' || github.base_ref == 'dev')
-      && needs.setup.outputs.components-changed == 'true'
+    # Only run on dev branch pushes or PRs targeting dev
+    if: github.ref == 'refs/heads/dev' || github.base_ref == 'dev'
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22.18.0"
+
       - name: Enable corepack
         run: corepack enable
 
-      - name: Set up Node
-        uses: actions/setup-node@v6
+      - name: Restore dependencies cache
+        uses: actions/cache@v4
         with:
-          node-version: "22.18.0"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          path: ~/.pnpm-store
+          key: ${{ needs.setup.outputs.cache-key }}
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
@@ -120,21 +128,31 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           exitOnceUploaded: true
 
-  e2e_test:
-    name: end-to-end tests
+  test:
     runs-on: big-boi
+    needs: setup
+    strategy:
+      fail-fast: false
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           submodules: recursive
 
-      - name: Set up Platform - Copy default supabase .env
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22.18.0"
+
+      - name: Enable corepack
+        run: corepack enable
+
+      - name: Copy default supabase .env
         run: |
           cp ../.env.default ../.env
 
-      - name: Set up Platform - Copy backend .env and set OpenAI API key
+      - 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
@@ -142,125 +160,77 @@ jobs:
           # Used by E2E test data script to generate embeddings for approved store agents
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
 
-      - name: Set up Platform - Set up Docker Buildx
+      - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
+
+      - name: Cache Docker layers
+        uses: actions/cache@v4
         with:
-          driver: docker-container
-          driver-opts: network=host
+          path: /tmp/.buildx-cache
+          key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
+          restore-keys: |
+            ${{ runner.os }}-buildx-frontend-test-
 
-      - name: Set up Platform - Expose GHA cache to docker buildx CLI
-        uses: crazy-max/ghaction-github-runtime@v3
-
-      - name: Set up Platform - Build Docker images (with cache)
-        working-directory: autogpt_platform
+      - name: Run docker compose
         run: |
-          pip install pyyaml
-
-          # Resolve extends and generate a flat compose file that bake can understand
-          docker compose -f docker-compose.yml config > docker-compose.resolved.yml
-
-          # Add cache configuration to the resolved compose file
-          python ../.github/workflows/scripts/docker-ci-fix-compose-build-cache.py \
-            --source docker-compose.resolved.yml \
-            --cache-from "type=gha" \
-            --cache-to "type=gha,mode=max" \
-            --backend-hash "${{ hashFiles('autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/poetry.lock', 'autogpt_platform/backend/backend') }}" \
-            --frontend-hash "${{ hashFiles('autogpt_platform/frontend/Dockerfile', 'autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/src') }}" \
-            --git-ref "${{ github.ref }}"
-
-          # Build with bake using the resolved compose file (now includes cache config)
-          docker buildx bake --allow=fs.read=.. -f docker-compose.resolved.yml --load
+          NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
         env:
-          NEXT_PUBLIC_PW_TEST: true
+          DOCKER_BUILDKIT: 1
+          BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
+          BUILDX_CACHE_TO: type=local,dest=/tmp/.buildx-cache-new,mode=max
 
-      - name: Set up tests - Cache E2E test data
-        id: e2e-data-cache
-        uses: actions/cache@v5
-        with:
-          path: /tmp/e2e_test_data.sql
-          key: e2e-test-data-${{ hashFiles('autogpt_platform/backend/test/e2e_test_data.py', 'autogpt_platform/backend/migrations/**', '.github/workflows/platform-frontend-ci.yml') }}
-
-      - name: Set up Platform - Start Supabase DB + Auth
+      - name: Move cache
         run: |
-          docker compose -f ../docker-compose.resolved.yml up -d db auth --no-build
-          echo "Waiting for database to be ready..."
-          timeout 60 sh -c 'until docker compose -f ../docker-compose.resolved.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done'
-          echo "Waiting for auth service to be ready..."
-          timeout 60 sh -c 'until docker compose -f ../docker-compose.resolved.yml exec -T db psql -U postgres -d postgres -c "SELECT 1 FROM auth.users LIMIT 1" 2>/dev/null; do sleep 2; done' || echo "Auth schema check timeout, continuing..."
+          rm -rf /tmp/.buildx-cache
+          if [ -d "/tmp/.buildx-cache-new" ]; then
+            mv /tmp/.buildx-cache-new /tmp/.buildx-cache
+          fi
 
-      - name: Set up Platform - Run migrations
+      - name: Wait for services to be ready
         run: |
-          echo "Running migrations..."
-          docker compose -f ../docker-compose.resolved.yml run --rm migrate
-          echo "✅ Migrations completed"
-        env:
-          NEXT_PUBLIC_PW_TEST: true
-
-      - name: Set up tests - Load cached E2E test data
-        if: steps.e2e-data-cache.outputs.cache-hit == 'true'
-        run: |
-          echo "✅ Found cached E2E test data, restoring..."
-          {
-            echo "SET session_replication_role = 'replica';"
-            cat /tmp/e2e_test_data.sql
-            echo "SET session_replication_role = 'origin';"
-          } | docker compose -f ../docker-compose.resolved.yml exec -T db psql -U postgres -d postgres -b
-          # Refresh materialized views after restore
-          docker compose -f ../docker-compose.resolved.yml exec -T db \
-            psql -U postgres -d postgres -b -c "SET search_path TO platform; SELECT refresh_store_materialized_views();" || true
-
-          echo "✅ E2E test data restored from cache"
-
-      - name: Set up Platform - Start (all other services)
-        run: |
-          docker compose -f ../docker-compose.resolved.yml up -d --no-build
           echo "Waiting for rest_server to be ready..."
           timeout 60 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
-        env:
-          NEXT_PUBLIC_PW_TEST: true
+          echo "Waiting for database to be ready..."
+          timeout 60 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
 
-      - name: Set up tests - Create E2E test data
-        if: steps.e2e-data-cache.outputs.cache-hit != 'true'
+      - name: Create E2E test data
         run: |
           echo "Creating E2E test data..."
-          docker cp ../backend/test/e2e_test_data.py $(docker compose -f ../docker-compose.resolved.yml ps -q rest_server):/tmp/e2e_test_data.py
-          docker compose -f ../docker-compose.resolved.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python /tmp/e2e_test_data.py" || {
-            echo "❌ E2E test data creation failed!"
-            docker compose -f ../docker-compose.resolved.yml logs --tail=50 rest_server
-            exit 1
-          }
+          # First try to run the script from inside the container
+          if docker compose -f ../docker-compose.yml exec -T rest_server test -f /app/autogpt_platform/backend/test/e2e_test_data.py; then
+            echo "✅ Found e2e_test_data.py in container, running it..."
+            docker compose -f ../docker-compose.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python backend/test/e2e_test_data.py" || {
+              echo "❌ E2E test data creation failed!"
+              docker compose -f ../docker-compose.yml logs --tail=50 rest_server
+              exit 1
+            }
+          else
+            echo "⚠️ e2e_test_data.py not found in container, copying and running..."
+            # Copy the script into the container and run it
+            docker cp ../backend/test/e2e_test_data.py $(docker compose -f ../docker-compose.yml ps -q rest_server):/tmp/e2e_test_data.py || {
+              echo "❌ Failed to copy script to container"
+              exit 1
+            }
+            docker compose -f ../docker-compose.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python /tmp/e2e_test_data.py" || {
+              echo "❌ E2E test data creation failed!"
+              docker compose -f ../docker-compose.yml logs --tail=50 rest_server
+              exit 1
+            }
+          fi
 
-          # Dump auth.users + platform schema for cache (two separate dumps)
-          echo "Dumping database for cache..."
-          {
-            docker compose -f ../docker-compose.resolved.yml exec -T db \
-              pg_dump -U postgres --data-only --column-inserts \
-              --table='auth.users' postgres
-            docker compose -f ../docker-compose.resolved.yml exec -T db \
-              pg_dump -U postgres --data-only --column-inserts \
-              --schema=platform \
-              --exclude-table='platform._prisma_migrations' \
-              --exclude-table='platform.apscheduler_jobs' \
-              --exclude-table='platform.apscheduler_jobs_batched_notifications' \
-              postgres
-          } > /tmp/e2e_test_data.sql
-
-          echo "✅ Database dump created for caching ($(wc -l < /tmp/e2e_test_data.sql) lines)"
-
-      - name: Set up tests - Enable corepack
-        run: corepack enable
-
-      - name: Set up tests - Set up Node
-        uses: actions/setup-node@v6
+      - name: Restore dependencies cache
+        uses: actions/cache@v4
         with:
-          node-version: "22.18.0"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          path: ~/.pnpm-store
+          key: ${{ needs.setup.outputs.cache-key }}
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
 
-      - name: Set up tests - Install dependencies
+      - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
-      - name: Set up tests - Install browser 'chromium'
+      - name: Install Browser 'chromium'
         run: pnpm playwright install --with-deps chromium
 
       - name: Run Playwright tests
@@ -287,33 +257,4 @@ jobs:
 
       - name: Print Final Docker Compose logs
         if: always()
-        run: docker compose -f ../docker-compose.resolved.yml logs
-
-  integration_test:
-    runs-on: ubuntu-latest
-    needs: setup
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v6
-        with:
-          submodules: recursive
-
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Set up Node
-        uses: actions/setup-node@v6
-        with:
-          node-version: "22.18.0"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-      - name: Generate API client
-        run: pnpm generate:api
-
-      - name: Run Integration Tests
-        run: pnpm test:unit
+        run: docker compose -f ../docker-compose.yml logs

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

@@ -29,10 +29,10 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -44,7 +44,7 @@ jobs:
         run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
 
       - name: Cache dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ steps.cache-key.outputs.key }}
@@ -56,19 +56,19 @@ jobs:
         run: pnpm install --frozen-lockfile
 
   types:
-    runs-on: big-boi
+    runs-on: ubuntu-latest
     needs: setup
     strategy:
       fail-fast: false
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -85,10 +85,10 @@ jobs:
 
       - name: Run docker compose
         run: |
-          docker compose -f ../docker-compose.yml --profile local up -d deps_backend
+          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
 
       - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}

.github/workflows/pr-overlap-check.yml

@@ -1,39 +0,0 @@
-name: PR Overlap Detection
-
-on:
-  pull_request:
-    types: [opened, synchronize, reopened]
-    branches:
-      - dev
-      - master
-
-permissions:
-  contents: read
-  pull-requests: write
-
-jobs:
-  check-overlaps:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0  # Need full history for merge testing
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Configure git
-        run: |
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-          git config user.name "github-actions[bot]"
-
-      - name: Run overlap detection
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        # Always succeed - this check informs contributors, it shouldn't block merging
-        continue-on-error: true
-        run: |
-          python .github/scripts/detect_overlaps.py ${{ github.event.pull_request.number }}

.github/workflows/repo-workflow-checker.yml

@@ -11,7 +11,7 @@ jobs:
     steps:
       # - name: Wait some time for all actions to start
       #   run: sleep 30
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
         # with:
           # fetch-depth: 0
       - name: Set up Python

.github/workflows/scripts/docker-ci-fix-compose-build-cache.py

@@ -1,195 +0,0 @@
-#!/usr/bin/env python3
-"""
-Add cache configuration to a resolved docker-compose file for all services
-that have a build key, and ensure image names match what docker compose expects.
-"""
-
-import argparse
-
-import yaml
-
-
-DEFAULT_BRANCH = "dev"
-CACHE_BUILDS_FOR_COMPONENTS = ["backend", "frontend"]
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Add cache config to a resolved compose file"
-    )
-    parser.add_argument(
-        "--source",
-        required=True,
-        help="Source compose file to read (should be output of `docker compose config`)",
-    )
-    parser.add_argument(
-        "--cache-from",
-        default="type=gha",
-        help="Cache source configuration",
-    )
-    parser.add_argument(
-        "--cache-to",
-        default="type=gha,mode=max",
-        help="Cache destination configuration",
-    )
-    for component in CACHE_BUILDS_FOR_COMPONENTS:
-        parser.add_argument(
-            f"--{component}-hash",
-            default="",
-            help=f"Hash for {component} cache scope (e.g., from hashFiles())",
-        )
-    parser.add_argument(
-        "--git-ref",
-        default="",
-        help="Git ref for branch-based cache scope (e.g., refs/heads/master)",
-    )
-    args = parser.parse_args()
-
-    # Normalize git ref to a safe scope name (e.g., refs/heads/master -> master)
-    git_ref_scope = ""
-    if args.git_ref:
-        git_ref_scope = args.git_ref.replace("refs/heads/", "").replace("/", "-")
-
-    with open(args.source, "r") as f:
-        compose = yaml.safe_load(f)
-
-    # Get project name from compose file or default
-    project_name = compose.get("name", "autogpt_platform")
-
-    def get_image_name(dockerfile: str, target: str) -> str:
-        """Generate image name based on Dockerfile folder and build target."""
-        dockerfile_parts = dockerfile.replace("\\", "/").split("/")
-        if len(dockerfile_parts) >= 2:
-            folder_name = dockerfile_parts[-2]  # e.g., "backend" or "frontend"
-        else:
-            folder_name = "app"
-        return f"{project_name}-{folder_name}:{target}"
-
-    def get_build_key(dockerfile: str, target: str) -> str:
-        """Generate a unique key for a Dockerfile+target combination."""
-        return f"{dockerfile}:{target}"
-
-    def get_component(dockerfile: str) -> str | None:
-        """Get component name (frontend/backend) from dockerfile path."""
-        for component in CACHE_BUILDS_FOR_COMPONENTS:
-            if component in dockerfile:
-                return component
-        return None
-
-    # First pass: collect all services with build configs and identify duplicates
-    # Track which (dockerfile, target) combinations we've seen
-    build_key_to_first_service: dict[str, str] = {}
-    services_to_build: list[str] = []
-    services_to_dedupe: list[str] = []
-
-    for service_name, service_config in compose.get("services", {}).items():
-        if "build" not in service_config:
-            continue
-
-        build_config = service_config["build"]
-        dockerfile = build_config.get("dockerfile", "Dockerfile")
-        target = build_config.get("target", "default")
-        build_key = get_build_key(dockerfile, target)
-
-        if build_key not in build_key_to_first_service:
-            # First service with this build config - it will do the actual build
-            build_key_to_first_service[build_key] = service_name
-            services_to_build.append(service_name)
-        else:
-            # Duplicate - will just use the image from the first service
-            services_to_dedupe.append(service_name)
-
-    # Second pass: configure builds and deduplicate
-    modified_services = []
-    for service_name, service_config in compose.get("services", {}).items():
-        if "build" not in service_config:
-            continue
-
-        build_config = service_config["build"]
-        dockerfile = build_config.get("dockerfile", "Dockerfile")
-        target = build_config.get("target", "latest")
-        image_name = get_image_name(dockerfile, target)
-
-        # Set image name for all services (needed for both builders and deduped)
-        service_config["image"] = image_name
-
-        if service_name in services_to_dedupe:
-            # Remove build config - this service will use the pre-built image
-            del service_config["build"]
-            continue
-
-        # This service will do the actual build - add cache config
-        cache_from_list = []
-        cache_to_list = []
-
-        component = get_component(dockerfile)
-        if not component:
-            # Skip services that don't clearly match frontend/backend
-            continue
-
-        # Get the hash for this component
-        component_hash = getattr(args, f"{component}_hash")
-
-        # Scope format: platform-{component}-{target}-{hash|ref}
-        # Example: platform-backend-server-abc123
-
-        if "type=gha" in args.cache_from:
-            # 1. Primary: exact hash match (most specific)
-            if component_hash:
-                hash_scope = f"platform-{component}-{target}-{component_hash}"
-                cache_from_list.append(f"{args.cache_from},scope={hash_scope}")
-
-            # 2. Fallback: branch-based cache
-            if git_ref_scope:
-                ref_scope = f"platform-{component}-{target}-{git_ref_scope}"
-                cache_from_list.append(f"{args.cache_from},scope={ref_scope}")
-
-            # 3. Fallback: dev branch cache (for PRs/feature branches)
-            if git_ref_scope and git_ref_scope != DEFAULT_BRANCH:
-                master_scope = f"platform-{component}-{target}-{DEFAULT_BRANCH}"
-                cache_from_list.append(f"{args.cache_from},scope={master_scope}")
-
-        if "type=gha" in args.cache_to:
-            # Write to both hash-based and branch-based scopes
-            if component_hash:
-                hash_scope = f"platform-{component}-{target}-{component_hash}"
-                cache_to_list.append(f"{args.cache_to},scope={hash_scope}")
-
-            if git_ref_scope:
-                ref_scope = f"platform-{component}-{target}-{git_ref_scope}"
-                cache_to_list.append(f"{args.cache_to},scope={ref_scope}")
-
-        # Ensure we have at least one cache source/target
-        if not cache_from_list:
-            cache_from_list.append(args.cache_from)
-        if not cache_to_list:
-            cache_to_list.append(args.cache_to)
-
-        build_config["cache_from"] = cache_from_list
-        build_config["cache_to"] = cache_to_list
-        modified_services.append(service_name)
-
-    # Write back to the same file
-    with open(args.source, "w") as f:
-        yaml.dump(compose, f, default_flow_style=False, sort_keys=False)
-
-    print(f"Added cache config to {len(modified_services)} services in {args.source}:")
-    for svc in modified_services:
-        svc_config = compose["services"][svc]
-        build_cfg = svc_config.get("build", {})
-        cache_from_list = build_cfg.get("cache_from", ["none"])
-        cache_to_list = build_cfg.get("cache_to", ["none"])
-        print(f"  - {svc}")
-        print(f"      image: {svc_config.get('image', 'N/A')}")
-        print(f"      cache_from: {cache_from_list}")
-        print(f"      cache_to: {cache_to_list}")
-    if services_to_dedupe:
-        print(
-            f"Deduplicated {len(services_to_dedupe)} services (will use pre-built images):"
-        )
-        for svc in services_to_dedupe:
-            print(f"  - {svc} -> {compose['services'][svc].get('image', 'N/A')}")
-
-
-if __name__ == "__main__":
-    main()

.gitignore

@@ -178,6 +178,4 @@ autogpt_platform/backend/settings.py
 *.ign.*
 .test-contents
 .claude/settings.local.json
-CLAUDE.local.md
 /autogpt_platform/backend/logs
-.next

AGENTS.md

@@ -16,34 +16,6 @@ See `docs/content/platform/getting-started.md` for setup instructions.
 - Format Python code with `poetry run format`.
 - Format frontend code using `pnpm format`.
 
-## Frontend guidelines:
-
-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
-
-- Component props should be `interface Props { ... }` (not exported) unless the interface needs to be used outside the component
-- Separate render logic from business logic (component.tsx + useComponent.ts + helpers.ts)
-- Colocate state when possible and avoid creating large components, use sub-components ( local `/components` folder next to the parent component ) when sensible
-- Avoid large hooks, abstract logic into `helpers.ts` files when sensible
-- Use function declarations for components, arrow functions only for callbacks
-- No barrel files or `index.ts` re-exports
-- Avoid comments at all times unless the code is very complex
-- Do not use `useCallback` or `useMemo` unless asked to optimise a given function
-- Do not type hook returns, let Typescript infer as much as possible
-- Never type with `any`, if not types available use `unknown`
-
 ## Testing
 
 - Backend: `poetry run test` (runs pytest with a docker based postgres + prisma).
@@ -51,8 +23,22 @@ See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
 
 Always run the relevant linters and tests before committing.
 Use conventional commit messages for all commits (e.g. `feat(backend): add API`).
-Types: - feat - fix - refactor - ci - dx (developer experience)
-Scopes: - platform - platform/library - platform/marketplace - backend - backend/executor - frontend - frontend/library - frontend/marketplace - blocks
+  Types:
+    - feat
+    - fix
+    - refactor
+    - ci
+    - dx (developer experience)
+  Scopes:
+    - platform
+      - platform/library
+      - platform/marketplace
+      - backend
+        - backend/executor
+      - frontend
+        - frontend/library
+        - frontend/marketplace
+      - blocks
 
 ## Pull requests
 

README.md

@@ -54,7 +54,7 @@ Before proceeding with the installation, ensure your system meets the following
 ### Updated Setup Instructions:
 We've moved to a fully maintained and regularly updated documentation site.
 
-👉 [Follow the official self-hosting guide here](https://agpt.co/docs/platform/getting-started/getting-started)
+👉 [Follow the official self-hosting guide here](https://docs.agpt.co/platform/getting-started/)
 
 
 This tutorial assumes you have Docker, VSCode, git and npm installed.

autogpt_platform/CLAUDE.md

@@ -6,30 +6,152 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 AutoGPT Platform is a monorepo containing:
 
-- **Backend** (`backend`): Python FastAPI server with async support
-- **Frontend** (`frontend`): Next.js React application
-- **Shared Libraries** (`autogpt_libs`): Common Python utilities
+- **Backend** (`/backend`): Python FastAPI server with async support
+- **Frontend** (`/frontend`): Next.js React application
+- **Shared Libraries** (`/autogpt_libs`): Common Python utilities
 
-## Component Documentation
+## Essential Commands
 
-- **Backend**: See @backend/CLAUDE.md for backend-specific commands, architecture, and development tasks
-- **Frontend**: See @frontend/CLAUDE.md for frontend-specific commands, architecture, and development patterns
+### Backend Development
 
-## Key Concepts
+```bash
+# Install dependencies
+cd backend && poetry install
+
+# Run database migrations
+poetry run prisma migrate dev
+
+# Start all services (database, redis, rabbitmq, clamav)
+docker compose up -d
+
+# Run the backend server
+poetry run serve
+
+# Run tests
+poetry run test
+
+# Run specific test
+poetry run pytest path/to/test_file.py::test_function_name
+
+# Run block tests (tests that validate all blocks work correctly)
+poetry run pytest backend/blocks/test/test_block.py -xvs
+
+# Run tests for a specific block (e.g., GetCurrentTimeBlock)
+poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[GetCurrentTimeBlock]' -xvs
+
+# Lint and format
+# prefer format if you want to just "fix" it and only get the errors that can't be autofixed
+poetry run format  # Black + isort
+poetry run lint    # ruff
+```
+
+More details can be found in TESTING.md
+
+#### Creating/Updating Snapshots
+
+When you first write a test or when the expected output changes:
+
+```bash
+poetry run pytest path/to/test.py --snapshot-update
+```
+
+⚠️ **Important**: Always review snapshot changes before committing! Use `git diff` to verify the changes are expected.
+
+### Frontend Development
+
+```bash
+# Install dependencies
+cd frontend && pnpm i
+
+# Generate API client from OpenAPI spec
+pnpm generate:api
+
+# Start development server
+pnpm dev
+
+# Run E2E tests
+pnpm test
+
+# Run Storybook for component development
+pnpm storybook
+
+# Build production
+pnpm build
+
+# Format and lint
+pnpm format
+
+# Type checking
+pnpm types
+```
+
+**📖 Complete Guide**: See `/frontend/CONTRIBUTING.md` and `/frontend/.cursorrules` for comprehensive frontend patterns.
+
+**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
+
+- **API Layer**: FastAPI with REST and WebSocket endpoints
+- **Database**: PostgreSQL with Prisma ORM, includes pgvector for embeddings
+- **Queue System**: RabbitMQ for async task processing
+- **Execution Engine**: Separate executor service processes agent workflows
+- **Authentication**: JWT-based with Supabase integration
+- **Security**: Cache protection middleware prevents sensitive data caching in browsers/proxies
+
+### 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)
+- **Workflow Builder**: Visual graph editor using @xyflow/react
+- **UI Components**: shadcn/ui (Radix UI primitives) with Tailwind CSS styling
+- **Icons**: Phosphor Icons only
+- **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
 
 1. **Agent Graphs**: Workflow definitions stored as JSON, executed by the backend
-2. **Blocks**: Reusable components in `backend/backend/blocks/` that perform specific tasks
+2. **Blocks**: Reusable components in `/backend/blocks/` that perform specific tasks
 3. **Integrations**: OAuth and API connections stored per user
 4. **Store**: Marketplace for sharing agent templates
 5. **Virus Scanning**: ClamAV integration for file upload security
 
+### Testing Approach
+
+- Backend uses pytest with snapshot testing for API responses
+- Test files are colocated with source files (`*_test.py`)
+- Frontend uses Playwright for E2E tests
+- Component testing via Storybook
+
+### Database Schema
+
+Key models (defined in `/backend/schema.prisma`):
+
+- `User`: Authentication and profile data
+- `AgentGraph`: Workflow definitions with version control
+- `AgentGraphExecution`: Execution history and results
+- `AgentNode`: Individual nodes in a workflow
+- `StoreListing`: Marketplace listings for sharing agents
+
 ### Environment Configuration
 
 #### Configuration Files
 
-- **Backend**: `backend/.env.default` (defaults) → `backend/.env` (user overrides)
-- **Frontend**: `frontend/.env.default` (defaults) → `frontend/.env` (user overrides)
-- **Platform**: `.env.default` (Supabase/shared defaults) → `.env` (user overrides)
+- **Backend**: `/backend/.env.default` (defaults) → `/backend/.env` (user overrides)
+- **Frontend**: `/frontend/.env.default` (defaults) → `/frontend/.env` (user overrides)
+- **Platform**: `/.env.default` (Supabase/shared defaults) → `/.env` (user overrides)
 
 #### Docker Environment Loading Order
 
@@ -45,17 +167,75 @@ AutoGPT Platform is a monorepo containing:
 - Backend/Frontend services use YAML anchors for consistent configuration
 - Supabase services (`db/docker/docker-compose.yml`) follow the same pattern
 
-### Branching Strategy
+### Common Development Tasks
 
-- **`dev`** is the main development branch. All PRs should target `dev`.
-- **`master`** is the production branch. Only used for production releases.
+**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`
+
+Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
+ex: do the inputs and outputs tie well together?
+
+If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
+
+**Modifying the API:**
+
+1. Update route in `/backend/backend/server/routers/`
+2. Add/update Pydantic models in same directory
+3. Write tests alongside the route file
+4. Run `poetry run test` to verify
+
+**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
+
+### Security Implementation
+
+**Cache Protection Middleware:**
+
+- Located in `/backend/backend/server/middleware/security.py`
+- Default behavior: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
+- Uses an allow list approach - only explicitly permitted paths can be cached
+- Cacheable paths include: static assets (`/static/*`, `/_next/static/*`), health checks, public store pages, documentation
+- Prevents sensitive data (auth tokens, API keys, user data) from being cached by browsers/proxies
+- To allow caching for a new endpoint, add it to `CACHEABLE_PATHS` in the middleware
+- Applied to both main API server and external API applications
 
 ### Creating Pull Requests
 
-- Create the PR against the `dev` branch of the repository.
-- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)
-- Use conventional commit messages (see below)
-- Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description
+- Create the PR aginst the `dev` branch of the repository.
+- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)/
+- Use conventional commit messages (see below)/
+- Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description/
 - Run the github pre-commit hooks to ensure code quality.
 
 ### Reviewing/Revising Pull Requests

autogpt_platform/autogpt_libs/pyproject.toml

@@ -9,25 +9,25 @@ packages = [{ include = "autogpt_libs" }]
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 colorama = "^0.4.6"
-cryptography = "^46.0"
+cryptography = "^45.0"
 expiringdict = "^1.2.2"
-fastapi = "^0.128.7"
-google-cloud-logging = "^3.13.0"
-launchdarkly-server-sdk = "^9.15.0"
-pydantic = "^2.12.5"
-pydantic-settings = "^2.12.0"
-pyjwt = { version = "^2.11.0", extras = ["crypto"] }
+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"] }
 redis = "^6.2.0"
-supabase = "^2.28.0"
-uvicorn = "^0.40.0"
+supabase = "^2.16.0"
+uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]
-pyright = "^1.1.408"
+pyright = "^1.1.404"
 pytest = "^8.4.1"
-pytest-asyncio = "^1.3.0"
-pytest-mock = "^3.15.1"
-pytest-cov = "^7.0.0"
-ruff = "^0.15.0"
+pytest-asyncio = "^1.1.0"
+pytest-mock = "^3.14.1"
+pytest-cov = "^6.2.1"
+ruff = "^0.12.11"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.env.default

@@ -104,12 +104,6 @@ TWITTER_CLIENT_SECRET=
 # Make a new workspace for your OAuth APP -- trust me
 # https://linear.app/settings/api/applications/new
 # Callback URL: http://localhost:3000/auth/integrations/oauth_callback
-LINEAR_API_KEY=
-# Linear project and team IDs for the feature request tracker.
-# Find these in your Linear workspace URL: linear.app/<workspace>/project/<project-id>
-# and in team settings. Used by the chat copilot to file and search feature requests.
-LINEAR_FEATURE_REQUEST_PROJECT_ID=
-LINEAR_FEATURE_REQUEST_TEAM_ID=
 LINEAR_CLIENT_ID=
 LINEAR_CLIENT_SECRET=
 
@@ -158,7 +152,6 @@ REPLICATE_API_KEY=
 REVID_API_KEY=
 SCREENSHOTONE_API_KEY=
 UNREAL_SPEECH_API_KEY=
-ELEVENLABS_API_KEY=
 
 # Data & Search Services
 E2B_API_KEY=
@@ -185,10 +178,5 @@ AYRSHARE_JWT_KEY=
 SMARTLEAD_API_KEY=
 ZEROBOUNCE_API_KEY=
 
-# PostHog Analytics
-# Get API key from https://posthog.com - Project Settings > Project API Key
-POSTHOG_API_KEY=
-POSTHOG_HOST=https://eu.i.posthog.com
-
 # Other Services
 AUTOMOD_API_KEY=

autogpt_platform/backend/.gitignore

@@ -19,6 +19,3 @@ load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
 migrations/*/rollback*.sql
-
-# Workspace files
-workspaces/

autogpt_platform/backend/CLAUDE.md

@@ -1,170 +0,0 @@
-# CLAUDE.md - Backend
-
-This file provides guidance to Claude Code when working with the backend.
-
-## Essential Commands
-
-To run something with Python package dependencies you MUST use `poetry run ...`.
-
-```bash
-# Install dependencies
-poetry install
-
-# Run database migrations
-poetry run prisma migrate dev
-
-# Start all services (database, redis, rabbitmq, clamav)
-docker compose up -d
-
-# Run the backend as a whole
-poetry run app
-
-# Run tests
-poetry run test
-
-# Run specific test
-poetry run pytest path/to/test_file.py::test_function_name
-
-# Run block tests (tests that validate all blocks work correctly)
-poetry run pytest backend/blocks/test/test_block.py -xvs
-
-# Run tests for a specific block (e.g., GetCurrentTimeBlock)
-poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[GetCurrentTimeBlock]' -xvs
-
-# Lint and format
-# prefer format if you want to just "fix" it and only get the errors that can't be autofixed
-poetry run format  # Black + isort
-poetry run lint    # ruff
-```
-
-More details can be found in @TESTING.md
-
-### Creating/Updating Snapshots
-
-When you first write a test or when the expected output changes:
-
-```bash
-poetry run pytest path/to/test.py --snapshot-update
-```
-
-⚠️ **Important**: Always review snapshot changes before committing! Use `git diff` to verify the changes are expected.
-
-## Architecture
-
-- **API Layer**: FastAPI with REST and WebSocket endpoints
-- **Database**: PostgreSQL with Prisma ORM, includes pgvector for embeddings
-- **Queue System**: RabbitMQ for async task processing
-- **Execution Engine**: Separate executor service processes agent workflows
-- **Authentication**: JWT-based with Supabase integration
-- **Security**: Cache protection middleware prevents sensitive data caching in browsers/proxies
-
-## Testing Approach
-
-- Uses pytest with snapshot testing for API responses
-- Test files are colocated with source files (`*_test.py`)
-
-## Database Schema
-
-Key models (defined in `schema.prisma`):
-
-- `User`: Authentication and profile data
-- `AgentGraph`: Workflow definitions with version control
-- `AgentGraphExecution`: Execution history and results
-- `AgentNode`: Individual nodes in a workflow
-- `StoreListing`: Marketplace listings for sharing agents
-
-## Environment Configuration
-
-- **Backend**: `.env.default` (defaults) → `.env` (user overrides)
-
-## Common Development Tasks
-
-### 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/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`
-
-Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph-based editor or would they struggle to connect productively?
-ex: do the inputs and outputs tie well together?
-
-If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
-
-#### Handling files in blocks with `store_media_file()`
-
-When blocks need to work with files (images, videos, documents), use `store_media_file()` from `backend.util.file`. The `return_format` parameter determines what you get back:
-
-| Format | Use When | Returns |
-|--------|----------|---------|
-| `"for_local_processing"` | Processing with local tools (ffmpeg, MoviePy, PIL) | Local file path (e.g., `"image.png"`) |
-| `"for_external_api"` | Sending content to external APIs (Replicate, OpenAI) | Data URI (e.g., `"data:image/png;base64,..."`) |
-| `"for_block_output"` | Returning output from your block | Smart: `workspace://` in CoPilot, data URI in graphs |
-
-**Examples:**
-
-```python
-# INPUT: Need to process file locally with ffmpeg
-local_path = await store_media_file(
-    file=input_data.video,
-    execution_context=execution_context,
-    return_format="for_local_processing",
-)
-# local_path = "video.mp4" - use with Path/ffmpeg/etc
-
-# INPUT: Need to send to external API like Replicate
-image_b64 = await store_media_file(
-    file=input_data.image,
-    execution_context=execution_context,
-    return_format="for_external_api",
-)
-# image_b64 = "data:image/png;base64,iVBORw0..." - send to API
-
-# OUTPUT: Returning result from block
-result_url = await store_media_file(
-    file=generated_image_url,
-    execution_context=execution_context,
-    return_format="for_block_output",
-)
-yield "image_url", result_url
-# In CoPilot: result_url = "workspace://abc123"
-# In graphs:  result_url = "data:image/png;base64,..."
-```
-
-**Key points:**
-
-- `for_block_output` is the ONLY format that auto-adapts to execution context
-- Always use `for_block_output` for block outputs unless you have a specific reason not to
-- Never hardcode workspace checks - let `for_block_output` handle it
-
-### Modifying the API
-
-1. Update route in `backend/api/features/`
-2. Add/update Pydantic models in same directory
-3. Write tests alongside the route file
-4. Run `poetry run test` to verify
-
-## Security Implementation
-
-### Cache Protection Middleware
-
-- Located in `backend/api/middleware/security.py`
-- Default behavior: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
-- Uses an allow list approach - only explicitly permitted paths can be cached
-- Cacheable paths include: static assets (`static/*`, `_next/static/*`), health checks, public store pages, documentation
-- Prevents sensitive data (auth tokens, API keys, user data) from being cached by browsers/proxies
-- To allow caching for a new endpoint, add it to `CACHEABLE_PATHS` in the middleware
-- Applied to both main API server and external API applications

autogpt_platform/backend/Dockerfile

@@ -1,5 +1,3 @@
-# ============================ DEPENDENCY BUILDER ============================ #
-
 FROM debian:13-slim AS builder
 
 # Set environment variables
@@ -53,62 +51,25 @@ COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/parti
 COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
 RUN poetry run prisma generate && poetry run gen-prisma-stub
 
-# =============================== DB MIGRATOR =============================== #
-
-# Lightweight migrate stage - only needs Prisma CLI, not full Python environment
-FROM debian:13-slim AS migrate
-
-WORKDIR /app/autogpt_platform/backend
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-# Install only what's needed for prisma migrate: Node.js and minimal Python for prisma-python
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3.13 \
-    python3-pip \
-    ca-certificates \
-    && rm -rf /var/lib/apt/lists/*
-
-# Copy Node.js from builder (needed for Prisma CLI)
-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 Prisma binaries
-COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
-
-# Install prisma-client-py directly (much smaller than copying full venv)
-RUN pip3 install prisma>=0.15.0 --break-system-packages
-
-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 ./
-COPY autogpt_platform/backend/migrations ./migrations
-
-# ============================== BACKEND SERVER ============================== #
-
-FROM debian:13-slim AS server
+FROM debian:13-slim AS server_dependencies
 
 WORKDIR /app
 
-ENV DEBIAN_FRONTEND=noninteractive
+ENV POETRY_HOME=/opt/poetry \
+    POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_CREATE=true \
+    POETRY_VIRTUALENVS_IN_PROJECT=true \
+    DEBIAN_FRONTEND=noninteractive
+ENV PATH=/opt/poetry/bin:$PATH
 
-# Install Python, FFmpeg, ImageMagick, and CLI tools for agent use.
-# bubblewrap provides OS-level sandbox (whitelist-only FS + no network)
-# for the bash_exec MCP tool.
-# Using --no-install-recommends saves ~650MB by skipping unnecessary deps like llvm, mesa, etc.
-RUN apt-get update && apt-get install -y --no-install-recommends \
+# Install Python without upgrading system-managed packages
+RUN apt-get update && apt-get install -y \
     python3.13 \
     python3-pip \
-    ffmpeg \
-    imagemagick \
-    jq \
-    ripgrep \
-    tree \
-    bubblewrap \
     && rm -rf /var/lib/apt/lists/*
 
-# Copy poetry (build-time only, for `poetry install --only-root` to create entry points)
+# 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
@@ -118,25 +79,29 @@ COPY --from=builder /usr/bin/npm /usr/bin/npm
 COPY --from=builder /usr/bin/npx /usr/bin/npx
 COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
 
-WORKDIR /app/autogpt_platform/backend
-
-# Copy only the .venv from builder (not the entire /app directory)
-# The .venv includes the generated Prisma client
-COPY --from=builder /app/autogpt_platform/backend/.venv ./.venv
 ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
 
-# Copy dependency files + autogpt_libs (path dependency)
-COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
-COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml ./
+RUN mkdir -p /app/autogpt_platform/autogpt_libs
+RUN mkdir -p /app/autogpt_platform/backend
 
-# Copy backend code + docs (for Copilot docs search)
-COPY autogpt_platform/backend ./
-COPY docs /app/docs
-# Install the project package to create entry point scripts in .venv/bin/
-# (e.g., rest, executor, ws, db, scheduler, notification - see [tool.poetry.scripts])
-RUN POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true \
-    poetry install --no-ansi --only-root
+COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
+
+COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml /app/autogpt_platform/backend/
+
+WORKDIR /app/autogpt_platform/backend
+
+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
+RUN poetry install --no-ansi --only-root
 
 ENV PORT=8000
 
-CMD ["rest"]
+CMD ["poetry", "run", "rest"]

autogpt_platform/backend/TESTING.md

@@ -138,7 +138,7 @@ If the test doesn't need the `user_id` specifically, mocking is not necessary as
 
 #### Using Global Auth Fixtures
 
-Two global auth fixtures are provided by `backend/api/conftest.py`:
+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")

autogpt_platform/backend/backend/api/conftest.py

@@ -1,9 +1,4 @@
-"""Common test fixtures for server tests.
-
-Note: Common fixtures like test_user_id, admin_user_id, target_user_id,
-setup_test_user, and setup_admin_user are defined in the parent conftest.py
-(backend/conftest.py) and are available here automatically.
-"""
+"""Common test fixtures for server tests."""
 
 import pytest
 from pytest_snapshot.plugin import Snapshot
@@ -16,6 +11,54 @@ def configured_snapshot(snapshot: Snapshot) -> Snapshot:
     return snapshot
 
 
+@pytest.fixture
+def test_user_id() -> str:
+    """Test user ID fixture."""
+    return "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+
+
+@pytest.fixture
+def admin_user_id() -> str:
+    """Admin user ID fixture."""
+    return "4e53486c-cf57-477e-ba2a-cb02dc828e1b"
+
+
+@pytest.fixture
+def target_user_id() -> str:
+    """Target user ID fixture."""
+    return "5e53486c-cf57-477e-ba2a-cb02dc828e1c"
+
+
+@pytest.fixture
+async def setup_test_user(test_user_id):
+    """Create test user in database before tests."""
+    from backend.data.user import get_or_create_user
+
+    # Create the test user in the database using JWT token format
+    user_data = {
+        "sub": test_user_id,
+        "email": "test@example.com",
+        "user_metadata": {"name": "Test User"},
+    }
+    await get_or_create_user(user_data)
+    return test_user_id
+
+
+@pytest.fixture
+async def setup_admin_user(admin_user_id):
+    """Create admin user in database before tests."""
+    from backend.data.user import get_or_create_user
+
+    # Create the admin user in the database using JWT token format
+    user_data = {
+        "sub": admin_user_id,
+        "email": "test-admin@example.com",
+        "user_metadata": {"name": "Test Admin"},
+    }
+    await get_or_create_user(user_data)
+    return admin_user_id
+
+
 @pytest.fixture
 def mock_jwt_user(test_user_id):
     """Provide mock JWT payload for regular user testing."""

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

@@ -10,7 +10,7 @@ from typing_extensions import TypedDict
 
 import backend.api.features.store.cache as store_cache
 import backend.api.features.store.model as store_model
-import backend.blocks
+import backend.data.block
 from backend.api.external.middleware import require_permission
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
@@ -67,7 +67,7 @@ async def get_user_info(
     dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
 )
 async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
-    blocks = [block() for block in backend.blocks.get_blocks().values()]
+    blocks = [block() for block in backend.data.block.get_blocks().values()]
     return [b.to_dict() for b in blocks if not b.disabled]
 
 
@@ -83,11 +83,9 @@ async def execute_graph_block(
         require_permission(APIKeyPermission.EXECUTE_BLOCK)
     ),
 ) -> CompletedBlockOutput:
-    obj = backend.blocks.get_block(block_id)
+    obj = backend.data.block.get_block(block_id)
     if not obj:
         raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
-    if obj.disabled:
-        raise HTTPException(status_code=403, detail=f"Block #{block_id} is disabled.")
 
     output = defaultdict(list)
     async for name, data in obj.execute(data):

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

@@ -15,9 +15,9 @@ from prisma.enums import APIKeyPermission
 from pydantic import BaseModel, Field
 
 from backend.api.external.middleware import require_permission
-from backend.copilot.model import ChatSession
-from backend.copilot.tools import find_agent_tool, run_agent_tool
-from backend.copilot.tools.models import ToolResponseBase
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.chat.tools import find_agent_tool, run_agent_tool
+from backend.api.features.chat.tools.models import ToolResponseBase
 from backend.data.auth.base import APIAuthorizationInfo
 
 logger = logging.getLogger(__name__)

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

@@ -28,7 +28,6 @@ from backend.executor.manager import get_db_async_client
 from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
-settings = Settings()
 
 
 class ExecutionAnalyticsRequest(BaseModel):
@@ -64,8 +63,6 @@ class ExecutionAnalyticsResult(BaseModel):
     score: Optional[float]
     status: str  # "success", "failed", "skipped"
     error_message: Optional[str] = None
-    started_at: Optional[datetime] = None
-    ended_at: Optional[datetime] = None
 
 
 class ExecutionAnalyticsResponse(BaseModel):
@@ -227,6 +224,11 @@ async def generate_execution_analytics(
     )
 
     try:
+        # Validate model configuration
+        settings = Settings()
+        if not settings.secrets.openai_internal_api_key:
+            raise HTTPException(status_code=500, detail="OpenAI API key not configured")
+
         # Get database client
         db_client = get_db_async_client()
 
@@ -318,8 +320,6 @@ async def generate_execution_analytics(
                     ),
                     status="skipped",
                     error_message=None,  # Not an error - just already processed
-                    started_at=execution.started_at,
-                    ended_at=execution.ended_at,
                 )
             )
 
@@ -349,9 +349,6 @@ async def _process_batch(
 ) -> list[ExecutionAnalyticsResult]:
     """Process a batch of executions concurrently."""
 
-    if not settings.secrets.openai_internal_api_key:
-        raise HTTPException(status_code=500, detail="OpenAI API key not configured")
-
     async def process_single_execution(execution) -> ExecutionAnalyticsResult:
         try:
             # Generate activity status and score using the specified model
@@ -390,8 +387,6 @@ async def _process_batch(
                     score=None,
                     status="skipped",
                     error_message="Activity generation returned None",
-                    started_at=execution.started_at,
-                    ended_at=execution.ended_at,
                 )
 
             # Update the execution stats
@@ -421,8 +416,6 @@ async def _process_batch(
                 summary_text=activity_response["activity_status"],
                 score=activity_response["correctness_score"],
                 status="success",
-                started_at=execution.started_at,
-                ended_at=execution.ended_at,
             )
 
         except Exception as e:
@@ -436,8 +429,6 @@ async def _process_batch(
                 score=None,
                 status="failed",
                 error_message=str(e),
-                started_at=execution.started_at,
-                ended_at=execution.ended_at,
             )
 
     # Process all executions in the batch concurrently

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

@@ -10,15 +10,10 @@ import backend.api.features.library.db as library_db
 import backend.api.features.library.model as library_model
 import backend.api.features.store.db as store_db
 import backend.api.features.store.model as store_model
+import backend.data.block
 from backend.blocks import load_all_blocks
-from backend.blocks._base import (
-    AnyBlockSchema,
-    BlockCategory,
-    BlockInfo,
-    BlockSchema,
-    BlockType,
-)
 from backend.blocks.llm import LlmModel
+from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
 from backend.data.db import query_raw_with_schema
 from backend.integrations.providers import ProviderName
 from backend.util.cache import cached
@@ -27,7 +22,7 @@ from backend.util.models import Pagination
 from .model import (
     BlockCategoryResponse,
     BlockResponse,
-    BlockTypeFilter,
+    BlockType,
     CountResponse,
     FilterType,
     Provider,
@@ -93,7 +88,7 @@ def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse
 def get_blocks(
     *,
     category: str | None = None,
-    type: BlockTypeFilter | None = None,
+    type: BlockType | None = None,
     provider: ProviderName | None = None,
     page: int = 1,
     page_size: int = 50,
@@ -674,9 +669,9 @@ async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
     for block_type in load_all_blocks().values():
         block: AnyBlockSchema = block_type()
         if block.disabled or block.block_type in (
-            BlockType.INPUT,
-            BlockType.OUTPUT,
-            BlockType.AGENT,
+            backend.data.block.BlockType.INPUT,
+            backend.data.block.BlockType.OUTPUT,
+            backend.data.block.BlockType.AGENT,
         ):
             continue
         # Find the execution count for this block

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

@@ -4,7 +4,7 @@ from pydantic import BaseModel
 
 import backend.api.features.library.model as library_model
 import backend.api.features.store.model as store_model
-from backend.blocks._base import BlockInfo
+from backend.data.block import BlockInfo
 from backend.integrations.providers import ProviderName
 from backend.util.models import Pagination
 
@@ -15,7 +15,7 @@ FilterType = Literal[
     "my_agents",
 ]
 
-BlockTypeFilter = Literal["all", "input", "action", "output"]
+BlockType = Literal["all", "input", "action", "output"]
 
 
 class SearchEntry(BaseModel):

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

@@ -17,7 +17,7 @@ router = fastapi.APIRouter(
 )
 
 
-# Taken from backend/api/features/store/db.py
+# Taken from backend/server/v2/store/db.py
 def sanitize_query(query: str | None) -> str | None:
     if query is None:
         return query
@@ -88,7 +88,7 @@ async def get_block_categories(
 )
 async def get_blocks(
     category: Annotated[str | None, fastapi.Query()] = None,
-    type: Annotated[builder_model.BlockTypeFilter | None, fastapi.Query()] = None,
+    type: Annotated[builder_model.BlockType | None, fastapi.Query()] = None,
     provider: Annotated[ProviderName | None, fastapi.Query()] = None,
     page: Annotated[int, fastapi.Query()] = 1,
     page_size: Annotated[int, fastapi.Query()] = 50,

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

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

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

@@ -14,27 +14,29 @@ from prisma.types import (
     ChatSessionWhereInput,
 )
 
-from backend.data import db
+from backend.data.db import transaction
 from backend.util.json import SafeJson
 
-from .model import ChatMessage, ChatSession, ChatSessionInfo
-
 logger = logging.getLogger(__name__)
 
 
-async def get_chat_session(session_id: str) -> ChatSession | None:
+async def get_chat_session(session_id: str) -> PrismaChatSession | None:
     """Get a chat session by ID from the database."""
     session = await PrismaChatSession.prisma().find_unique(
         where={"id": session_id},
-        include={"Messages": {"order_by": {"sequence": "asc"}}},
+        include={"Messages": True},
     )
-    return ChatSession.from_db(session) if session else None
+    if session and session.Messages:
+        # Sort messages by sequence in Python - Prisma Python client doesn't support
+        # order_by in include clauses (unlike Prisma JS), so we sort after fetching
+        session.Messages.sort(key=lambda m: m.sequence)
+    return session
 
 
 async def create_chat_session(
     session_id: str,
     user_id: str,
-) -> ChatSessionInfo:
+) -> PrismaChatSession:
     """Create a new chat session in the database."""
     data = ChatSessionCreateInput(
         id=session_id,
@@ -43,8 +45,10 @@ async def create_chat_session(
         successfulAgentRuns=SafeJson({}),
         successfulAgentSchedules=SafeJson({}),
     )
-    prisma_session = await PrismaChatSession.prisma().create(data=data)
-    return ChatSessionInfo.from_db(prisma_session)
+    return await PrismaChatSession.prisma().create(
+        data=data,
+        include={"Messages": True},
+    )
 
 
 async def update_chat_session(
@@ -55,7 +59,7 @@ async def update_chat_session(
     total_prompt_tokens: int | None = None,
     total_completion_tokens: int | None = None,
     title: str | None = None,
-) -> ChatSession | None:
+) -> PrismaChatSession | None:
     """Update a chat session's metadata."""
     data: ChatSessionUpdateInput = {"updatedAt": datetime.now(UTC)}
 
@@ -75,9 +79,12 @@ async def update_chat_session(
     session = await PrismaChatSession.prisma().update(
         where={"id": session_id},
         data=data,
-        include={"Messages": {"order_by": {"sequence": "asc"}}},
+        include={"Messages": True},
     )
-    return ChatSession.from_db(session) if session else None
+    if session and session.Messages:
+        # Sort in Python - Prisma Python doesn't support order_by in include clauses
+        session.Messages.sort(key=lambda m: m.sequence)
+    return session
 
 
 async def add_chat_message(
@@ -90,7 +97,7 @@ async def add_chat_message(
     refusal: str | None = None,
     tool_calls: list[dict[str, Any]] | None = None,
     function_call: dict[str, Any] | None = None,
-) -> ChatMessage:
+) -> PrismaChatMessage:
     """Add a message to a chat session."""
     # Build input dict dynamically rather than using ChatMessageCreateInput directly
     # because Prisma's TypedDict validation rejects optional fields set to None.
@@ -125,14 +132,14 @@ async def add_chat_message(
         ),
         PrismaChatMessage.prisma().create(data=cast(ChatMessageCreateInput, data)),
     )
-    return ChatMessage.from_db(message)
+    return message
 
 
 async def add_chat_messages_batch(
     session_id: str,
     messages: list[dict[str, Any]],
     start_sequence: int,
-) -> list[ChatMessage]:
+) -> list[PrismaChatMessage]:
     """Add multiple messages to a chat session in a batch.
 
     Uses a transaction for atomicity - if any message creation fails,
@@ -143,7 +150,7 @@ async def add_chat_messages_batch(
 
     created_messages = []
 
-    async with db.transaction() as tx:
+    async with transaction() as tx:
         for i, msg in enumerate(messages):
             # Build input dict dynamically rather than using ChatMessageCreateInput
             # directly because Prisma's TypedDict validation rejects optional fields
@@ -183,22 +190,21 @@ async def add_chat_messages_batch(
             data={"updatedAt": datetime.now(UTC)},
         )
 
-    return [ChatMessage.from_db(m) for m in created_messages]
+    return created_messages
 
 
 async def get_user_chat_sessions(
     user_id: str,
     limit: int = 50,
     offset: int = 0,
-) -> list[ChatSessionInfo]:
+) -> list[PrismaChatSession]:
     """Get chat sessions for a user, ordered by most recent."""
-    prisma_sessions = await PrismaChatSession.prisma().find_many(
+    return await PrismaChatSession.prisma().find_many(
         where={"userId": user_id},
         order={"updatedAt": "desc"},
         take=limit,
         skip=offset,
     )
-    return [ChatSessionInfo.from_db(s) for s in prisma_sessions]
 
 
 async def get_user_session_count(user_id: str) -> int:
@@ -241,45 +247,3 @@ async def get_chat_session_message_count(session_id: str) -> int:
     """Get the number of messages in a chat session."""
     count = await PrismaChatMessage.prisma().count(where={"sessionId": session_id})
     return count
-
-
-async def update_tool_message_content(
-    session_id: str,
-    tool_call_id: str,
-    new_content: str,
-) -> bool:
-    """Update the content of a tool message in chat history.
-
-    Used by background tasks to update pending operation messages with final results.
-
-    Args:
-        session_id: The chat session ID.
-        tool_call_id: The tool call ID to find the message.
-        new_content: The new content to set.
-
-    Returns:
-        True if a message was updated, False otherwise.
-    """
-    try:
-        result = await PrismaChatMessage.prisma().update_many(
-            where={
-                "sessionId": session_id,
-                "toolCallId": tool_call_id,
-            },
-            data={
-                "content": new_content,
-            },
-        )
-        if result == 0:
-            logger.warning(
-                f"No message found to update for session {session_id}, "
-                f"tool_call_id {tool_call_id}"
-            )
-            return False
-        return True
-    except Exception as e:
-        logger.error(
-            f"Failed to update tool message for session {session_id}, "
-            f"tool_call_id {tool_call_id}: {e}"
-        )
-        return False

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

@@ -2,7 +2,7 @@ import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
-from typing import Any, Self, cast
+from typing import Any
 from weakref import WeakValueDictionary
 
 from openai.types.chat import (
@@ -23,17 +23,26 @@ from prisma.models import ChatMessage as PrismaChatMessage
 from prisma.models import ChatSession as PrismaChatSession
 from pydantic import BaseModel
 
-from backend.data.db_accessors import chat_db
 from backend.data.redis_client import get_redis_async
 from backend.util import json
 from backend.util.exceptions import DatabaseError, RedisError
 
+from . import db as chat_db
 from .config import ChatConfig
 
 logger = logging.getLogger(__name__)
 config = ChatConfig()
 
 
+def _parse_json_field(value: str | dict | list | None, default: Any = None) -> Any:
+    """Parse a JSON field that may be stored as string or already parsed."""
+    if value is None:
+        return default
+    if isinstance(value, str):
+        return json.loads(value)
+    return value
+
+
 # Redis cache key prefix for chat sessions
 CHAT_SESSION_CACHE_PREFIX = "chat:session:"
 
@@ -43,7 +52,28 @@ def _get_session_cache_key(session_id: str) -> str:
     return f"{CHAT_SESSION_CACHE_PREFIX}{session_id}"
 
 
-# ===================== Chat data models ===================== #
+# Session-level locks to prevent race conditions during concurrent upserts.
+# Uses WeakValueDictionary to automatically garbage collect locks when no longer referenced,
+# preventing unbounded memory growth while maintaining lock semantics for active sessions.
+# Invalidation: Locks are auto-removed by GC when no coroutine holds a reference (after
+# async with lock: completes). Explicit cleanup also occurs in delete_chat_session().
+_session_locks: WeakValueDictionary[str, asyncio.Lock] = WeakValueDictionary()
+_session_locks_mutex = asyncio.Lock()
+
+
+async def _get_session_lock(session_id: str) -> asyncio.Lock:
+    """Get or create a lock for a specific session to prevent concurrent upserts.
+
+    Uses WeakValueDictionary for automatic cleanup: locks are garbage collected
+    when no coroutine holds a reference to them, preventing memory leaks from
+    unbounded growth of session locks.
+    """
+    async with _session_locks_mutex:
+        lock = _session_locks.get(session_id)
+        if lock is None:
+            lock = asyncio.Lock()
+            _session_locks[session_id] = lock
+        return lock
 
 
 class ChatMessage(BaseModel):
@@ -55,19 +85,6 @@ class ChatMessage(BaseModel):
     tool_calls: list[dict] | None = None
     function_call: dict | None = None
 
-    @staticmethod
-    def from_db(prisma_message: PrismaChatMessage) -> "ChatMessage":
-        """Convert a Prisma ChatMessage to a Pydantic ChatMessage."""
-        return ChatMessage(
-            role=prisma_message.role,
-            content=prisma_message.content,
-            name=prisma_message.name,
-            tool_call_id=prisma_message.toolCallId,
-            refusal=prisma_message.refusal,
-            tool_calls=_parse_json_field(prisma_message.toolCalls),
-            function_call=_parse_json_field(prisma_message.functionCall),
-        )
-
 
 class Usage(BaseModel):
     prompt_tokens: int
@@ -75,10 +92,11 @@ class Usage(BaseModel):
     total_tokens: int
 
 
-class ChatSessionInfo(BaseModel):
+class ChatSession(BaseModel):
     session_id: str
     user_id: str
     title: str | None = None
+    messages: list[ChatMessage]
     usage: list[Usage]
     credentials: dict[str, dict] = {}  # Map of provider -> credential metadata
     started_at: datetime
@@ -86,9 +104,40 @@ class ChatSessionInfo(BaseModel):
     successful_agent_runs: dict[str, int] = {}
     successful_agent_schedules: dict[str, int] = {}
 
-    @classmethod
-    def from_db(cls, prisma_session: PrismaChatSession) -> Self:
-        """Convert Prisma ChatSession to Pydantic ChatSession."""
+    @staticmethod
+    def new(user_id: str) -> "ChatSession":
+        return ChatSession(
+            session_id=str(uuid.uuid4()),
+            user_id=user_id,
+            title=None,
+            messages=[],
+            usage=[],
+            credentials={},
+            started_at=datetime.now(UTC),
+            updated_at=datetime.now(UTC),
+        )
+
+    @staticmethod
+    def from_db(
+        prisma_session: PrismaChatSession,
+        prisma_messages: list[PrismaChatMessage] | None = None,
+    ) -> "ChatSession":
+        """Convert Prisma models to Pydantic ChatSession."""
+        messages = []
+        if prisma_messages:
+            for msg in prisma_messages:
+                messages.append(
+                    ChatMessage(
+                        role=msg.role,
+                        content=msg.content,
+                        name=msg.name,
+                        tool_call_id=msg.toolCallId,
+                        refusal=msg.refusal,
+                        tool_calls=_parse_json_field(msg.toolCalls),
+                        function_call=_parse_json_field(msg.functionCall),
+                    )
+                )
+
         # Parse JSON fields from Prisma
         credentials = _parse_json_field(prisma_session.credentials, default={})
         successful_agent_runs = _parse_json_field(
@@ -110,10 +159,11 @@ class ChatSessionInfo(BaseModel):
                 )
             )
 
-        return cls(
+        return ChatSession(
             session_id=prisma_session.id,
             user_id=prisma_session.userId,
             title=prisma_session.title,
+            messages=messages,
             usage=usage,
             credentials=credentials,
             started_at=prisma_session.createdAt,
@@ -122,56 +172,6 @@ class ChatSessionInfo(BaseModel):
             successful_agent_schedules=successful_agent_schedules,
         )
 
-
-class ChatSession(ChatSessionInfo):
-    messages: list[ChatMessage]
-
-    @classmethod
-    def new(cls, user_id: str) -> Self:
-        return cls(
-            session_id=str(uuid.uuid4()),
-            user_id=user_id,
-            title=None,
-            messages=[],
-            usage=[],
-            credentials={},
-            started_at=datetime.now(UTC),
-            updated_at=datetime.now(UTC),
-        )
-
-    @classmethod
-    def from_db(cls, prisma_session: PrismaChatSession) -> Self:
-        """Convert Prisma ChatSession to Pydantic ChatSession."""
-        if prisma_session.Messages is None:
-            raise ValueError(
-                f"Prisma session {prisma_session.id} is missing Messages relation"
-            )
-
-        return cls(
-            **ChatSessionInfo.from_db(prisma_session).model_dump(),
-            messages=[ChatMessage.from_db(m) for m in prisma_session.Messages],
-        )
-
-    def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
-        """Attach a tool_call to the current turn's assistant message.
-
-        Searches backwards for the most recent assistant message (stopping at
-        any user message boundary). If found, appends the tool_call to it.
-        Otherwise creates a new assistant message with the tool_call.
-        """
-        for msg in reversed(self.messages):
-            if msg.role == "user":
-                break
-            if msg.role == "assistant":
-                if not msg.tool_calls:
-                    msg.tool_calls = []
-                msg.tool_calls.append(tool_call)
-                return
-
-        self.messages.append(
-            ChatMessage(role="assistant", content="", tool_calls=[tool_call])
-        )
-
     def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
         messages = []
         for message in self.messages:
@@ -258,85 +258,110 @@ class ChatSession(ChatSessionInfo):
                         name=message.name or "",
                     )
                 )
-        return self._merge_consecutive_assistant_messages(messages)
-
-    @staticmethod
-    def _merge_consecutive_assistant_messages(
-        messages: list[ChatCompletionMessageParam],
-    ) -> list[ChatCompletionMessageParam]:
-        """Merge consecutive assistant messages into single messages.
-
-        Long-running tool flows can create split assistant messages: one with
-        text content and another with tool_calls. Anthropic's API requires
-        tool_result blocks to reference a tool_use in the immediately preceding
-        assistant message, so these splits cause 400 errors via OpenRouter.
-        """
-        if len(messages) < 2:
-            return messages
-
-        result: list[ChatCompletionMessageParam] = [messages[0]]
-        for msg in messages[1:]:
-            prev = result[-1]
-            if prev.get("role") != "assistant" or msg.get("role") != "assistant":
-                result.append(msg)
-                continue
-
-            prev = cast(ChatCompletionAssistantMessageParam, prev)
-            curr = cast(ChatCompletionAssistantMessageParam, msg)
-
-            curr_content = curr.get("content") or ""
-            if curr_content:
-                prev_content = prev.get("content") or ""
-                prev["content"] = (
-                    f"{prev_content}\n{curr_content}" if prev_content else curr_content
-                )
-
-            curr_tool_calls = curr.get("tool_calls")
-            if curr_tool_calls:
-                prev_tool_calls = prev.get("tool_calls")
-                prev["tool_calls"] = (
-                    list(prev_tool_calls) + list(curr_tool_calls)
-                    if prev_tool_calls
-                    else list(curr_tool_calls)
-                )
-        return result
+        return messages
 
 
-def _parse_json_field(value: str | dict | list | None, default: Any = None) -> Any:
-    """Parse a JSON field that may be stored as string or already parsed."""
-    if value is None:
-        return default
-    if isinstance(value, str):
-        return json.loads(value)
-    return value
+async def _get_session_from_cache(session_id: str) -> ChatSession | None:
+    """Get a chat session from Redis cache."""
+    redis_key = _get_session_cache_key(session_id)
+    async_redis = await get_redis_async()
+    raw_session: bytes | None = await async_redis.get(redis_key)
+
+    if raw_session is None:
+        return None
+
+    try:
+        session = ChatSession.model_validate_json(raw_session)
+        logger.info(
+            f"Loading session {session_id} from cache: "
+            f"message_count={len(session.messages)}, "
+            f"roles={[m.role for m in session.messages]}"
+        )
+        return session
+    except Exception as e:
+        logger.error(f"Failed to deserialize session {session_id}: {e}", exc_info=True)
+        raise RedisError(f"Corrupted session data for {session_id}") from e
 
 
-# ================ Chat cache + DB operations ================ #
-
-# NOTE: Database calls are automatically routed through DatabaseManager if Prisma is not
-#       connected directly.
-
-
-async def cache_chat_session(session: ChatSession) -> None:
-    """Cache a chat session in Redis (without persisting to the database)."""
+async def _cache_session(session: ChatSession) -> None:
+    """Cache a chat session in Redis."""
     redis_key = _get_session_cache_key(session.session_id)
     async_redis = await get_redis_async()
     await async_redis.setex(redis_key, config.session_ttl, session.model_dump_json())
 
 
-async def invalidate_session_cache(session_id: str) -> None:
-    """Invalidate a chat session from Redis cache.
+async def _get_session_from_db(session_id: str) -> ChatSession | None:
+    """Get a chat session from the database."""
+    prisma_session = await chat_db.get_chat_session(session_id)
+    if not prisma_session:
+        return None
 
-    Used by background tasks to ensure fresh data is loaded on next access.
-    This is best-effort - Redis failures are logged but don't fail the operation.
-    """
-    try:
-        redis_key = _get_session_cache_key(session_id)
-        async_redis = await get_redis_async()
-        await async_redis.delete(redis_key)
-    except Exception as e:
-        # Best-effort: log but don't fail - cache will expire naturally
-        logger.warning(f"Failed to invalidate session cache for {session_id}: {e}")
+    messages = prisma_session.Messages
+    logger.info(
+        f"Loading session {session_id} from DB: "
+        f"has_messages={messages is not None}, "
+        f"message_count={len(messages) if messages else 0}, "
+        f"roles={[m.role for m in messages] if messages else []}"
+    )
+
+    return ChatSession.from_db(prisma_session, messages)
+
+
+async def _save_session_to_db(
+    session: ChatSession, existing_message_count: int
+) -> None:
+    """Save or update a chat session in the database."""
+    # Check if session exists in DB
+    existing = await chat_db.get_chat_session(session.session_id)
+
+    if not existing:
+        # Create new session
+        await chat_db.create_chat_session(
+            session_id=session.session_id,
+            user_id=session.user_id,
+        )
+        existing_message_count = 0
+
+    # Calculate total tokens from usage
+    total_prompt = sum(u.prompt_tokens for u in session.usage)
+    total_completion = sum(u.completion_tokens for u in session.usage)
+
+    # Update session metadata
+    await chat_db.update_chat_session(
+        session_id=session.session_id,
+        credentials=session.credentials,
+        successful_agent_runs=session.successful_agent_runs,
+        successful_agent_schedules=session.successful_agent_schedules,
+        total_prompt_tokens=total_prompt,
+        total_completion_tokens=total_completion,
+    )
+
+    # Add new messages (only those after existing count)
+    new_messages = session.messages[existing_message_count:]
+    if new_messages:
+        messages_data = []
+        for msg in new_messages:
+            messages_data.append(
+                {
+                    "role": msg.role,
+                    "content": msg.content,
+                    "name": msg.name,
+                    "tool_call_id": msg.tool_call_id,
+                    "refusal": msg.refusal,
+                    "tool_calls": msg.tool_calls,
+                    "function_call": msg.function_call,
+                }
+            )
+        logger.info(
+            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
+            f"roles={[m['role'] for m in messages_data]}, "
+            f"start_sequence={existing_message_count}"
+        )
+        await chat_db.add_chat_messages_batch(
+            session_id=session.session_id,
+            messages=messages_data,
+            start_sequence=existing_message_count,
+        )
 
 
 async def get_chat_session(
@@ -370,7 +395,7 @@ async def get_chat_session(
         logger.warning(f"Unexpected cache error for session {session_id}: {e}")
 
     # Fall back to database
-    logger.debug(f"Session {session_id} not in cache, checking database")
+    logger.info(f"Session {session_id} not in cache, checking database")
     session = await _get_session_from_db(session_id)
 
     if session is None:
@@ -386,7 +411,7 @@ async def get_chat_session(
 
     # Cache the session from DB
     try:
-        await cache_chat_session(session)
+        await _cache_session(session)
         logger.info(f"Cached session {session_id} from database")
     except Exception as e:
         logger.warning(f"Failed to cache session {session_id}: {e}")
@@ -394,45 +419,9 @@ async def get_chat_session(
     return session
 
 
-async def _get_session_from_cache(session_id: str) -> ChatSession | None:
-    """Get a chat session from Redis cache."""
-    redis_key = _get_session_cache_key(session_id)
-    async_redis = await get_redis_async()
-    raw_session: bytes | None = await async_redis.get(redis_key)
-
-    if raw_session is None:
-        return None
-
-    try:
-        session = ChatSession.model_validate_json(raw_session)
-        logger.info(
-            f"Loading session {session_id} from cache: "
-            f"message_count={len(session.messages)}, "
-            f"roles={[m.role for m in session.messages]}"
-        )
-        return session
-    except Exception as e:
-        logger.error(f"Failed to deserialize session {session_id}: {e}", exc_info=True)
-        raise RedisError(f"Corrupted session data for {session_id}") from e
-
-
-async def _get_session_from_db(session_id: str) -> ChatSession | None:
-    """Get a chat session from the database."""
-    session = await chat_db().get_chat_session(session_id)
-    if not session:
-        return None
-
-    logger.info(
-        f"Loaded session {session_id} from DB: "
-        f"has_messages={bool(session.messages)}, "
-        f"message_count={len(session.messages)}, "
-        f"roles={[m.role for m in session.messages]}"
-    )
-
-    return session
-
-
-async def upsert_chat_session(session: ChatSession) -> ChatSession:
+async def upsert_chat_session(
+    session: ChatSession,
+) -> ChatSession:
     """Update a chat session in both cache and database.
 
     Uses session-level locking to prevent race conditions when concurrent
@@ -450,7 +439,7 @@ async def upsert_chat_session(session: ChatSession) -> ChatSession:
 
     async with lock:
         # Get existing message count from DB for incremental saves
-        existing_message_count = await chat_db().get_chat_session_message_count(
+        existing_message_count = await chat_db.get_chat_session_message_count(
             session.session_id
         )
 
@@ -467,7 +456,7 @@ async def upsert_chat_session(session: ChatSession) -> ChatSession:
 
         # Save to cache (best-effort, even if DB failed)
         try:
-            await cache_chat_session(session)
+            await _cache_session(session)
         except Exception as e:
             # If DB succeeded but cache failed, raise cache error
             if db_error is None:
@@ -488,99 +477,6 @@ async def upsert_chat_session(session: ChatSession) -> ChatSession:
         return session
 
 
-async def _save_session_to_db(
-    session: ChatSession, existing_message_count: int
-) -> None:
-    """Save or update a chat session in the database."""
-    db = chat_db()
-
-    # Check if session exists in DB
-    existing = await db.get_chat_session(session.session_id)
-
-    if not existing:
-        # Create new session
-        await db.create_chat_session(
-            session_id=session.session_id,
-            user_id=session.user_id,
-        )
-        existing_message_count = 0
-
-    # Calculate total tokens from usage
-    total_prompt = sum(u.prompt_tokens for u in session.usage)
-    total_completion = sum(u.completion_tokens for u in session.usage)
-
-    # Update session metadata
-    await db.update_chat_session(
-        session_id=session.session_id,
-        credentials=session.credentials,
-        successful_agent_runs=session.successful_agent_runs,
-        successful_agent_schedules=session.successful_agent_schedules,
-        total_prompt_tokens=total_prompt,
-        total_completion_tokens=total_completion,
-    )
-
-    # Add new messages (only those after existing count)
-    new_messages = session.messages[existing_message_count:]
-    if new_messages:
-        messages_data = []
-        for msg in new_messages:
-            messages_data.append(
-                {
-                    "role": msg.role,
-                    "content": msg.content,
-                    "name": msg.name,
-                    "tool_call_id": msg.tool_call_id,
-                    "refusal": msg.refusal,
-                    "tool_calls": msg.tool_calls,
-                    "function_call": msg.function_call,
-                }
-            )
-        logger.info(
-            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
-            f"roles={[m['role'] for m in messages_data]}, "
-            f"start_sequence={existing_message_count}"
-        )
-        await db.add_chat_messages_batch(
-            session_id=session.session_id,
-            messages=messages_data,
-            start_sequence=existing_message_count,
-        )
-
-
-async def append_and_save_message(session_id: str, message: ChatMessage) -> ChatSession:
-    """Atomically append a message to a session and persist it.
-
-    Acquires the session lock, re-fetches the latest session state,
-    appends the message, and saves — preventing message loss when
-    concurrent requests modify the same session.
-    """
-    lock = await _get_session_lock(session_id)
-
-    async with lock:
-        session = await get_chat_session(session_id)
-        if session is None:
-            raise ValueError(f"Session {session_id} not found")
-
-        session.messages.append(message)
-        existing_message_count = await chat_db().get_chat_session_message_count(
-            session_id
-        )
-
-        try:
-            await _save_session_to_db(session, existing_message_count)
-        except Exception as e:
-            raise DatabaseError(
-                f"Failed to persist message to session {session_id}"
-            ) from e
-
-        try:
-            await cache_chat_session(session)
-        except Exception as e:
-            logger.warning(f"Cache write failed for session {session_id}: {e}")
-
-        return session
-
-
 async def create_chat_session(user_id: str) -> ChatSession:
     """Create a new chat session and persist it.
 
@@ -593,7 +489,7 @@ async def create_chat_session(user_id: str) -> ChatSession:
 
     # Create in database first - fail fast if this fails
     try:
-        await chat_db().create_chat_session(
+        await chat_db.create_chat_session(
             session_id=session.session_id,
             user_id=user_id,
         )
@@ -605,7 +501,7 @@ async def create_chat_session(user_id: str) -> ChatSession:
 
     # Cache the session (best-effort optimization, DB is source of truth)
     try:
-        await cache_chat_session(session)
+        await _cache_session(session)
     except Exception as e:
         logger.warning(f"Failed to cache new session {session.session_id}: {e}")
 
@@ -616,16 +512,20 @@ async def get_user_sessions(
     user_id: str,
     limit: int = 50,
     offset: int = 0,
-) -> tuple[list[ChatSessionInfo], int]:
+) -> tuple[list[ChatSession], int]:
     """Get chat sessions for a user from the database with total count.
 
     Returns:
         A tuple of (sessions, total_count) where total_count is the overall
         number of sessions for the user (not just the current page).
     """
-    db = chat_db()
-    sessions = await db.get_user_chat_sessions(user_id, limit, offset)
-    total_count = await db.get_user_session_count(user_id)
+    prisma_sessions = await chat_db.get_user_chat_sessions(user_id, limit, offset)
+    total_count = await chat_db.get_user_session_count(user_id)
+
+    sessions = []
+    for prisma_session in prisma_sessions:
+        # Convert without messages for listing (lighter weight)
+        sessions.append(ChatSession.from_db(prisma_session, None))
 
     return sessions, total_count
 
@@ -643,7 +543,7 @@ async def delete_chat_session(session_id: str, user_id: str | None = None) -> bo
     """
     # Delete from database first (with optional user_id validation)
     # This confirms ownership before invalidating cache
-    deleted = await chat_db().delete_chat_session(session_id, user_id)
+    deleted = await chat_db.delete_chat_session(session_id, user_id)
 
     if not deleted:
         return False
@@ -678,52 +578,20 @@ async def update_session_title(session_id: str, title: str) -> bool:
         True if updated successfully, False otherwise.
     """
     try:
-        result = await chat_db().update_chat_session(session_id=session_id, title=title)
+        result = await chat_db.update_chat_session(session_id=session_id, title=title)
         if result is None:
             logger.warning(f"Session {session_id} not found for title update")
             return False
 
-        # Update title in cache if it exists (instead of invalidating).
-        # This prevents race conditions where cache invalidation causes
-        # the frontend to see stale DB data while streaming is still in progress.
+        # Invalidate cache so next fetch gets updated title
         try:
-            cached = await _get_session_from_cache(session_id)
-            if cached:
-                cached.title = title
-                await cache_chat_session(cached)
+            redis_key = _get_session_cache_key(session_id)
+            async_redis = await get_redis_async()
+            await async_redis.delete(redis_key)
         except Exception as e:
-            # Not critical - title will be correct on next full cache refresh
-            logger.warning(
-                f"Failed to update title in cache for session {session_id}: {e}"
-            )
+            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
 
         return True
     except Exception as e:
         logger.error(f"Failed to update title for session {session_id}: {e}")
         return False
-
-
-# ==================== Chat session locks ==================== #
-
-_session_locks: WeakValueDictionary[str, asyncio.Lock] = WeakValueDictionary()
-_session_locks_mutex = asyncio.Lock()
-
-
-async def _get_session_lock(session_id: str) -> asyncio.Lock:
-    """Get or create a lock for a specific session to prevent concurrent upserts.
-
-    This was originally added to solve the specific problem of race conditions between
-    the session title thread and the conversation thread, which always occurs on the
-    same instance as we prevent rapid request sends on the frontend.
-
-    Uses WeakValueDictionary for automatic cleanup: locks are garbage collected
-    when no coroutine holds a reference to them, preventing memory leaks from
-    unbounded growth of session locks. Explicit cleanup also occurs
-    in `delete_chat_session()`.
-    """
-    async with _session_locks_mutex:
-        lock = _session_locks.get(session_id)
-        if lock is None:
-            lock = asyncio.Lock()
-            _session_locks[session_id] = lock
-        return lock

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

@@ -0,0 +1,119 @@
+import pytest
+
+from .model import (
+    ChatMessage,
+    ChatSession,
+    Usage,
+    get_chat_session,
+    upsert_chat_session,
+)
+
+messages = [
+    ChatMessage(content="Hello, how are you?", role="user"),
+    ChatMessage(
+        content="I'm fine, thank you!",
+        role="assistant",
+        tool_calls=[
+            {
+                "id": "t123",
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "arguments": '{"city": "New York"}',
+                },
+            }
+        ],
+    ),
+    ChatMessage(
+        content="I'm using the tool to get the weather",
+        role="tool",
+        tool_call_id="t123",
+    ),
+]
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_chatsession_serialization_deserialization():
+    s = ChatSession.new(user_id="abc123")
+    s.messages = messages
+    s.usage = [Usage(prompt_tokens=100, completion_tokens=200, total_tokens=300)]
+    serialized = s.model_dump_json()
+    s2 = ChatSession.model_validate_json(serialized)
+    assert s2.model_dump() == s.model_dump()
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_chatsession_redis_storage(setup_test_user, test_user_id):
+
+    s = ChatSession.new(user_id=test_user_id)
+    s.messages = messages
+
+    s = await upsert_chat_session(s)
+
+    s2 = await get_chat_session(
+        session_id=s.session_id,
+        user_id=s.user_id,
+    )
+
+    assert s2 == s
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_chatsession_redis_storage_user_id_mismatch(
+    setup_test_user, test_user_id
+):
+
+    s = ChatSession.new(user_id=test_user_id)
+    s.messages = messages
+    s = await upsert_chat_session(s)
+
+    s2 = await get_chat_session(s.session_id, "different_user_id")
+
+    assert s2 is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_chatsession_db_storage(setup_test_user, test_user_id):
+    """Test that messages are correctly saved to and loaded from DB (not cache)."""
+    from backend.data.redis_client import get_redis_async
+
+    # Create session with messages including assistant message
+    s = ChatSession.new(user_id=test_user_id)
+    s.messages = messages  # Contains user, assistant, and tool messages
+    assert s.session_id is not None, "Session id is not set"
+    # Upsert to save to both cache and DB
+    s = await upsert_chat_session(s)
+
+    # Clear the Redis cache to force DB load
+    redis_key = f"chat:session:{s.session_id}"
+    async_redis = await get_redis_async()
+    await async_redis.delete(redis_key)
+
+    # Load from DB (cache was cleared)
+    s2 = await get_chat_session(
+        session_id=s.session_id,
+        user_id=s.user_id,
+    )
+
+    assert s2 is not None, "Session not found after loading from DB"
+    assert len(s2.messages) == len(
+        s.messages
+    ), f"Message count mismatch: expected {len(s.messages)}, got {len(s2.messages)}"
+
+    # Verify all roles are present
+    roles = [m.role for m in s2.messages]
+    assert "user" in roles, f"User message missing. Roles found: {roles}"
+    assert "assistant" in roles, f"Assistant message missing. Roles found: {roles}"
+    assert "tool" in roles, f"Tool message missing. Roles found: {roles}"
+
+    # Verify message content
+    for orig, loaded in zip(s.messages, s2.messages):
+        assert orig.role == loaded.role, f"Role mismatch: {orig.role} != {loaded.role}"
+        assert (
+            orig.content == loaded.content
+        ), f"Content mismatch for {orig.role}: {orig.content} != {loaded.content}"
+        if orig.tool_calls:
+            assert (
+                loaded.tool_calls is not None
+            ), f"Tool calls missing for {orig.role} message"
+            assert len(orig.tool_calls) == len(loaded.tool_calls)

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

@@ -10,8 +10,6 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from backend.util.json import dumps as json_dumps
-
 
 class ResponseType(str, Enum):
     """Types of streaming responses following AI SDK protocol."""
@@ -20,10 +18,6 @@ class ResponseType(str, Enum):
     START = "start"
     FINISH = "finish"
 
-    # Step lifecycle (one LLM API call within a message)
-    START_STEP = "start-step"
-    FINISH_STEP = "finish-step"
-
     # Text streaming
     TEXT_START = "text-start"
     TEXT_DELTA = "text-delta"
@@ -37,7 +31,6 @@ class ResponseType(str, Enum):
     # Other
     ERROR = "error"
     USAGE = "usage"
-    HEARTBEAT = "heartbeat"
 
 
 class StreamBaseResponse(BaseModel):
@@ -58,20 +51,6 @@ class StreamStart(StreamBaseResponse):
 
     type: ResponseType = ResponseType.START
     messageId: str = Field(..., description="Unique message ID")
-    taskId: str | None = Field(
-        default=None,
-        description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
-    )
-
-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-protocol fields like taskId."""
-        import json
-
-        data: dict[str, Any] = {
-            "type": self.type.value,
-            "messageId": self.messageId,
-        }
-        return f"data: {json.dumps(data)}\n\n"
 
 
 class StreamFinish(StreamBaseResponse):
@@ -80,26 +59,6 @@ class StreamFinish(StreamBaseResponse):
     type: ResponseType = ResponseType.FINISH
 
 
-class StreamStartStep(StreamBaseResponse):
-    """Start of a step (one LLM API call within a message).
-
-    The AI SDK uses this to add a step-start boundary to message.parts,
-    enabling visual separation between multiple LLM calls in a single message.
-    """
-
-    type: ResponseType = ResponseType.START_STEP
-
-
-class StreamFinishStep(StreamBaseResponse):
-    """End of a step (one LLM API call within a message).
-
-    The AI SDK uses this to reset activeTextParts and activeReasoningParts,
-    so the next LLM call in a tool-call continuation starts with clean state.
-    """
-
-    type: ResponseType = ResponseType.FINISH_STEP
-
-
 # ========== Text Streaming ==========
 
 
@@ -153,7 +112,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
     type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
     toolCallId: str = Field(..., description="Tool call ID this responds to")
     output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Keep these for internal backend use
+    # Additional fields for internal use (not part of AI SDK spec but useful)
     toolName: str | None = Field(
         default=None, description="Name of the tool that was executed"
     )
@@ -161,17 +120,6 @@ class StreamToolOutputAvailable(StreamBaseResponse):
         default=True, description="Whether the tool execution succeeded"
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-spec fields."""
-        import json
-
-        data = {
-            "type": self.type.value,
-            "toolCallId": self.toolCallId,
-            "output": self.output,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-
 
 # ========== Other ==========
 
@@ -194,32 +142,3 @@ class StreamError(StreamBaseResponse):
     details: dict[str, Any] | None = Field(
         default=None, description="Additional error details"
     )
-
-    def to_sse(self) -> str:
-        """Convert to SSE format, only emitting fields required by AI SDK protocol.
-
-        The AI SDK uses z.strictObject({type, errorText}) which rejects
-        any extra fields like `code` or `details`.
-        """
-        data = {
-            "type": self.type.value,
-            "errorText": self.errorText,
-        }
-        return f"data: {json_dumps(data)}\n\n"
-
-
-class StreamHeartbeat(StreamBaseResponse):
-    """Heartbeat to keep SSE connection alive during long-running operations.
-
-    Uses SSE comment format (: comment) which is ignored by clients but keeps
-    the connection alive through proxies and load balancers.
-    """
-
-    type: ResponseType = ResponseType.HEARTBEAT
-    toolCallId: str | None = Field(
-        default=None, description="Tool call ID if heartbeat is for a specific tool"
-    )
-
-    def to_sse(self) -> str:
-        """Convert to SSE comment format to keep connection alive."""
-        return ": heartbeat\n\n"

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

@@ -1,60 +1,20 @@
 """Chat API routes for chat session management and streaming via SSE."""
 
-import asyncio
 import logging
-import uuid as uuid_module
 from collections.abc import AsyncGenerator
 from typing import Annotated
 
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
+from fastapi import APIRouter, Depends, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
-from backend.copilot import service as chat_service
-from backend.copilot import stream_registry
-from backend.copilot.completion_handler import (
-    process_operation_failure,
-    process_operation_success,
-)
-from backend.copilot.config import ChatConfig
-from backend.copilot.executor.utils import enqueue_copilot_task
-from backend.copilot.model import (
-    ChatMessage,
-    ChatSession,
-    append_and_save_message,
-    create_chat_session,
-    delete_chat_session,
-    get_chat_session,
-    get_user_sessions,
-)
-from backend.copilot.response_model import StreamError, StreamFinish, StreamHeartbeat
-from backend.copilot.tools.models import (
-    AgentDetailsResponse,
-    AgentOutputResponse,
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    AgentsFoundResponse,
-    BlockDetailsResponse,
-    BlockListResponse,
-    BlockOutputResponse,
-    ClarificationNeededResponse,
-    DocPageResponse,
-    DocSearchResultsResponse,
-    ErrorResponse,
-    ExecutionStartedResponse,
-    InputValidationErrorResponse,
-    NeedLoginResponse,
-    NoResultsResponse,
-    OperationInProgressResponse,
-    OperationPendingResponse,
-    OperationStartedResponse,
-    SetupRequirementsResponse,
-    UnderstandingUpdatedResponse,
-)
-from backend.copilot.tracking import track_user_message
 from backend.util.exceptions import NotFoundError
 
+from . import service as chat_service
+from .config import ChatConfig
+from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
+
 config = ChatConfig()
 
 
@@ -95,15 +55,6 @@ class CreateSessionResponse(BaseModel):
     user_id: str | None
 
 
-class ActiveStreamInfo(BaseModel):
-    """Information about an active stream for reconnection."""
-
-    task_id: str
-    last_message_id: str  # Redis Stream message ID for resumption
-    operation_id: str  # Operation ID for completion tracking
-    tool_name: str  # Name of the tool being executed
-
-
 class SessionDetailResponse(BaseModel):
     """Response model providing complete details for a chat session, including messages."""
 
@@ -112,7 +63,6 @@ class SessionDetailResponse(BaseModel):
     updated_at: str
     user_id: str | None
     messages: list[dict]
-    active_stream: ActiveStreamInfo | None = None  # Present if stream is still active
 
 
 class SessionSummaryResponse(BaseModel):
@@ -131,14 +81,6 @@ class ListSessionsResponse(BaseModel):
     total: int
 
 
-class OperationCompleteRequest(BaseModel):
-    """Request model for external completion webhook."""
-
-    success: bool
-    result: dict | str | None = None
-    error: str | None = None
-
-
 # ========== Routes ==========
 
 
@@ -213,43 +155,6 @@ async def create_session(
     )
 
 
-@router.delete(
-    "/sessions/{session_id}",
-    dependencies=[Security(auth.requires_user)],
-    status_code=204,
-    responses={404: {"description": "Session not found or access denied"}},
-)
-async def delete_session(
-    session_id: str,
-    user_id: Annotated[str, Security(auth.get_user_id)],
-) -> Response:
-    """
-    Delete a chat session.
-
-    Permanently removes a chat session and all its messages.
-    Only the owner can delete their sessions.
-
-    Args:
-        session_id: The session ID to delete.
-        user_id: The authenticated user's ID.
-
-    Returns:
-        204 No Content on success.
-
-    Raises:
-        HTTPException: 404 if session not found or not owned by user.
-    """
-    deleted = await delete_chat_session(session_id, user_id)
-
-    if not deleted:
-        raise HTTPException(
-            status_code=404,
-            detail=f"Session {session_id} not found or access denied",
-        )
-
-    return Response(status_code=204)
-
-
 @router.get(
     "/sessions/{session_id}",
 )
@@ -261,47 +166,25 @@ async def get_session(
     Retrieve the details of a specific chat session.
 
     Looks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.
-    If there's an active stream for this session, returns the task_id for reconnection.
 
     Args:
         session_id: The unique identifier for the desired chat session.
         user_id: The optional authenticated user ID, or None for anonymous access.
 
     Returns:
-        SessionDetailResponse: Details for the requested session, including active_stream info if applicable.
+        SessionDetailResponse: Details for the requested session; raises NotFoundError if not found.
 
     """
     session = await get_chat_session(session_id, user_id)
     if not session:
-        raise NotFoundError(f"Session {session_id} not found.")
+        raise NotFoundError(f"Session {session_id} not found")
 
     messages = [message.model_dump() for message in session.messages]
-
-    # Check if there's an active stream for this session
-    active_stream_info = None
-    active_task, last_message_id = await stream_registry.get_active_task_for_session(
-        session_id, user_id
-    )
     logger.info(
-        f"[GET_SESSION] session={session_id}, active_task={active_task is not None}, "
-        f"msg_count={len(messages)}, last_role={messages[-1].get('role') if messages else 'none'}"
+        f"Returning session {session_id}: "
+        f"message_count={len(messages)}, "
+        f"roles={[m.get('role') for m in messages]}"
     )
-    if active_task:
-        # Filter out the in-progress assistant message from the session response.
-        # The client will receive the complete assistant response through the SSE
-        # stream replay instead, preventing duplicate content.
-        if messages and messages[-1].get("role") == "assistant":
-            messages = messages[:-1]
-
-        # Use "0-0" as last_message_id to replay the stream from the beginning.
-        # Since we filtered out the cached assistant message, the client needs
-        # the full stream to reconstruct the response.
-        active_stream_info = ActiveStreamInfo(
-            task_id=active_task.task_id,
-            last_message_id="0-0",
-            operation_id=active_task.operation_id,
-            tool_name=active_task.tool_name,
-        )
 
     return SessionDetailResponse(
         id=session.session_id,
@@ -309,7 +192,6 @@ async def get_session(
         updated_at=session.updated_at.isoformat(),
         user_id=session.user_id or None,
         messages=messages,
-        active_stream=active_stream_info,
     )
 
 
@@ -329,225 +211,28 @@ async def stream_chat_post(
       - Tool call UI elements (if invoked)
       - Tool execution results
 
-    The AI generation runs in a background task that continues even if the client disconnects.
-    All chunks are written to Redis for reconnection support. If the client disconnects,
-    they can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.
-
     Args:
         session_id: The chat session identifier to associate with the streamed messages.
         request: Request body containing message, is_user_message, and optional context.
         user_id: Optional authenticated user ID.
     Returns:
-        StreamingResponse: SSE-formatted response chunks. First chunk is a "start" event
-        containing the task_id for reconnection.
+        StreamingResponse: SSE-formatted response chunks.
 
     """
-    import asyncio
-    import time
+    session = await _validate_and_get_session(session_id, user_id)
 
-    stream_start_time = time.perf_counter()
-    log_meta = {"component": "ChatStream", "session_id": session_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] stream_chat_post STARTED, session={session_id}, "
-        f"user={user_id}, message_len={len(request.message)}",
-        extra={"json_fields": log_meta},
-    )
-    await _validate_and_get_session(session_id, user_id)
-    logger.info(
-        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time) * 1000:.1f}ms",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": (time.perf_counter() - stream_start_time) * 1000,
-            }
-        },
-    )
-
-    # Atomically append user message to session BEFORE creating task to avoid
-    # race condition where GET_SESSION sees task as "running" but message isn't
-    # saved yet.  append_and_save_message re-fetches inside a lock to prevent
-    # message loss from concurrent requests.
-    if request.message:
-        message = ChatMessage(
-            role="user" if request.is_user_message else "assistant",
-            content=request.message,
-        )
-        if request.is_user_message:
-            track_user_message(
-                user_id=user_id,
-                session_id=session_id,
-                message_length=len(request.message),
-            )
-        logger.info(f"[STREAM] Saving user message to session {session_id}")
-        await append_and_save_message(session_id, message)
-        logger.info(f"[STREAM] User message saved for session {session_id}")
-
-    # Create a task in the stream registry for reconnection support
-    task_id = str(uuid_module.uuid4())
-    operation_id = str(uuid_module.uuid4())
-    log_meta["task_id"] = task_id
-
-    task_create_start = time.perf_counter()
-    await stream_registry.create_task(
-        task_id=task_id,
-        session_id=session_id,
-        user_id=user_id,
-        tool_call_id="chat_stream",  # Not a tool call, but needed for the model
-        tool_name="chat",
-        operation_id=operation_id,
-    )
-    logger.info(
-        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start) * 1000:.1f}ms",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": (time.perf_counter() - task_create_start) * 1000,
-            }
-        },
-    )
-
-    await enqueue_copilot_task(
-        task_id=task_id,
-        session_id=session_id,
-        user_id=user_id,
-        operation_id=operation_id,
-        message=request.message,
-        is_user_message=request.is_user_message,
-        context=request.context,
-    )
-
-    setup_time = (time.perf_counter() - stream_start_time) * 1000
-    logger.info(
-        f"[TIMING] Task enqueued to RabbitMQ, setup={setup_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
-    )
-
-    # SSE endpoint that subscribes to the task's stream
     async def event_generator() -> AsyncGenerator[str, None]:
-        import time as time_module
-
-        event_gen_start = time_module.perf_counter()
-        logger.info(
-            f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
-            f"user={user_id}",
-            extra={"json_fields": log_meta},
-        )
-        subscriber_queue = None
-        first_chunk_yielded = False
-        chunks_yielded = 0
-        try:
-            # Subscribe to the task stream (this replays existing messages + live updates)
-            subscriber_queue = await stream_registry.subscribe_to_task(
-                task_id=task_id,
-                user_id=user_id,
-                last_message_id="0-0",  # Get all messages from the beginning
-            )
-
-            if subscriber_queue is None:
-                yield StreamFinish().to_sse()
-                yield "data: [DONE]\n\n"
-                return
-
-            # Read from the subscriber queue and yield to SSE
-            logger.info(
-                "[TIMING] Starting to read from subscriber_queue",
-                extra={"json_fields": log_meta},
-            )
-            while True:
-                try:
-                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-                    chunks_yielded += 1
-
-                    if not first_chunk_yielded:
-                        first_chunk_yielded = True
-                        elapsed = time_module.perf_counter() - event_gen_start
-                        logger.info(
-                            f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
-                            f"type={type(chunk).__name__}",
-                            extra={
-                                "json_fields": {
-                                    **log_meta,
-                                    "chunk_type": type(chunk).__name__,
-                                    "elapsed_ms": elapsed * 1000,
-                                }
-                            },
-                        )
-
-                    yield chunk.to_sse()
-
-                    # Check for finish signal
-                    if isinstance(chunk, StreamFinish):
-                        total_time = time_module.perf_counter() - event_gen_start
-                        logger.info(
-                            f"[TIMING] StreamFinish received in {total_time:.2f}s; "
-                            f"n_chunks={chunks_yielded}",
-                            extra={
-                                "json_fields": {
-                                    **log_meta,
-                                    "chunks_yielded": chunks_yielded,
-                                    "total_time_ms": total_time * 1000,
-                                }
-                            },
-                        )
-                        break
-                except asyncio.TimeoutError:
-                    yield StreamHeartbeat().to_sse()
-
-        except GeneratorExit:
-            logger.info(
-                f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "chunks_yielded": chunks_yielded,
-                        "reason": "client_disconnect",
-                    }
-                },
-            )
-            pass  # Client disconnected - background task continues
-        except Exception as e:
-            elapsed = (time_module.perf_counter() - event_gen_start) * 1000
-            logger.error(
-                f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
-                extra={
-                    "json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
-                },
-            )
-            # Surface error to frontend so it doesn't appear stuck
-            yield StreamError(
-                errorText="An error occurred. Please try again.",
-                code="stream_error",
-            ).to_sse()
-            yield StreamFinish().to_sse()
-        finally:
-            # Unsubscribe when client disconnects or stream ends
-            if subscriber_queue is not None:
-                try:
-                    await stream_registry.unsubscribe_from_task(
-                        task_id, subscriber_queue
-                    )
-                except Exception as unsub_err:
-                    logger.error(
-                        f"Error unsubscribing from task {task_id}: {unsub_err}",
-                        exc_info=True,
-                    )
-            # AI SDK protocol termination - always yield even if unsubscribe fails
-            total_time = time_module.perf_counter() - event_gen_start
-            logger.info(
-                f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
-                f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time * 1000,
-                        "chunks_yielded": chunks_yielded,
-                    }
-                },
-            )
-            yield "data: [DONE]\n\n"
+        async for chunk in chat_service.stream_chat_completion(
+            session_id,
+            request.message,
+            is_user_message=request.is_user_message,
+            user_id=user_id,
+            session=session,  # Pass pre-fetched session to avoid double-fetch
+            context=request.context,
+        ):
+            yield chunk.to_sse()
+        # AI SDK protocol termination
+        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -564,90 +249,42 @@ async def stream_chat_post(
 @router.get(
     "/sessions/{session_id}/stream",
 )
-async def resume_session_stream(
+async def stream_chat_get(
     session_id: str,
+    message: Annotated[str, Query(min_length=1, max_length=10000)],
     user_id: str | None = Depends(auth.get_user_id),
+    is_user_message: bool = Query(default=True),
 ):
     """
-    Resume an active stream for a session.
+    Stream chat responses for a session (GET - legacy endpoint).
 
-    Called by the AI SDK's ``useChat(resume: true)`` on page load.
-    Checks for an active (in-progress) task on the session and either replays
-    the full SSE stream or returns 204 No Content if nothing is running.
+    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
+      - Text fragments as they are generated
+      - Tool call UI elements (if invoked)
+      - Tool execution results
 
     Args:
-        session_id: The chat session identifier.
+        session_id: The chat session identifier to associate with the streamed messages.
+        message: The user's new message to process.
         user_id: Optional authenticated user ID.
-
+        is_user_message: Whether the message is a user message.
     Returns:
-        StreamingResponse (SSE) when an active stream exists,
-        or 204 No Content when there is nothing to resume.
+        StreamingResponse: SSE-formatted response chunks.
+
     """
-    import asyncio
-
-    active_task, _last_id = await stream_registry.get_active_task_for_session(
-        session_id, user_id
-    )
-
-    if not active_task:
-        return Response(status_code=204)
-
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=active_task.task_id,
-        user_id=user_id,
-        last_message_id="0-0",  # Full replay so useChat rebuilds the message
-    )
-
-    if subscriber_queue is None:
-        return Response(status_code=204)
+    session = await _validate_and_get_session(session_id, user_id)
 
     async def event_generator() -> AsyncGenerator[str, None]:
-        chunk_count = 0
-        first_chunk_type: str | None = None
-        try:
-            while True:
-                try:
-                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-                    if chunk_count < 3:
-                        logger.info(
-                            "Resume stream chunk",
-                            extra={
-                                "session_id": session_id,
-                                "chunk_type": str(chunk.type),
-                            },
-                        )
-                    if not first_chunk_type:
-                        first_chunk_type = str(chunk.type)
-                    chunk_count += 1
-                    yield chunk.to_sse()
-
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    yield StreamHeartbeat().to_sse()
-        except GeneratorExit:
-            pass
-        except Exception as e:
-            logger.error(f"Error in resume stream for session {session_id}: {e}")
-        finally:
-            try:
-                await stream_registry.unsubscribe_from_task(
-                    active_task.task_id, subscriber_queue
-                )
-            except Exception as unsub_err:
-                logger.error(
-                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
-                    exc_info=True,
-                )
-            logger.info(
-                "Resume stream completed",
-                extra={
-                    "session_id": session_id,
-                    "n_chunks": chunk_count,
-                    "first_chunk_type": first_chunk_type,
-                },
-            )
-            yield "data: [DONE]\n\n"
+        async for chunk in chat_service.stream_chat_completion(
+            session_id,
+            message,
+            is_user_message=is_user_message,
+            user_id=user_id,
+            session=session,  # Pass pre-fetched session to avoid double-fetch
+        ):
+            yield chunk.to_sse()
+        # AI SDK protocol termination
+        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -655,8 +292,8 @@ async def resume_session_stream(
         headers={
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",
+            "X-Accel-Buffering": "no",  # Disable nginx buffering
+            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
         },
     )
 
@@ -687,249 +324,6 @@ async def session_assign_user(
     return {"status": "ok"}
 
 
-# ========== Task Streaming (SSE Reconnection) ==========
-
-
-@router.get(
-    "/tasks/{task_id}/stream",
-)
-async def stream_task(
-    task_id: str,
-    user_id: str | None = Depends(auth.get_user_id),
-    last_message_id: str = Query(
-        default="0-0",
-        description="Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
-    ),
-):
-    """
-    Reconnect to a long-running task's SSE stream.
-
-    When a long-running operation (like agent generation) starts, the client
-    receives a task_id. If the connection drops, the client can reconnect
-    using this endpoint to resume receiving updates.
-
-    Args:
-        task_id: The task ID from the operation_started response.
-        user_id: Authenticated user ID for ownership validation.
-        last_message_id: Last Redis Stream message ID received ("0-0" for full replay).
-
-    Returns:
-        StreamingResponse: SSE-formatted response chunks starting after last_message_id.
-
-    Raises:
-        HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.
-    """
-    # Check task existence and expiry before subscribing
-    task, error_code = await stream_registry.get_task_with_expiry_info(task_id)
-
-    if error_code == "TASK_EXPIRED":
-        raise HTTPException(
-            status_code=410,
-            detail={
-                "code": "TASK_EXPIRED",
-                "message": "This operation has expired. Please try again.",
-            },
-        )
-
-    if error_code == "TASK_NOT_FOUND":
-        raise HTTPException(
-            status_code=404,
-            detail={
-                "code": "TASK_NOT_FOUND",
-                "message": f"Task {task_id} not found.",
-            },
-        )
-
-    # Validate ownership if task has an owner
-    if task and task.user_id and user_id != task.user_id:
-        raise HTTPException(
-            status_code=403,
-            detail={
-                "code": "ACCESS_DENIED",
-                "message": "You do not have access to this task.",
-            },
-        )
-
-    # Get subscriber queue from stream registry
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=task_id,
-        user_id=user_id,
-        last_message_id=last_message_id,
-    )
-
-    if subscriber_queue is None:
-        raise HTTPException(
-            status_code=404,
-            detail={
-                "code": "TASK_NOT_FOUND",
-                "message": f"Task {task_id} not found or access denied.",
-            },
-        )
-
-    async def event_generator() -> AsyncGenerator[str, None]:
-        heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
-        try:
-            while True:
-                try:
-                    # Wait for next chunk with timeout for heartbeats
-                    chunk = await asyncio.wait_for(
-                        subscriber_queue.get(), timeout=heartbeat_interval
-                    )
-                    yield chunk.to_sse()
-
-                    # Check for finish signal
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    # Send heartbeat to keep connection alive
-                    yield StreamHeartbeat().to_sse()
-        except Exception as e:
-            logger.error(f"Error in task stream {task_id}: {e}", exc_info=True)
-        finally:
-            # Unsubscribe when client disconnects or stream ends
-            try:
-                await stream_registry.unsubscribe_from_task(task_id, subscriber_queue)
-            except Exception as unsub_err:
-                logger.error(
-                    f"Error unsubscribing from task {task_id}: {unsub_err}",
-                    exc_info=True,
-                )
-            # AI SDK protocol termination - always yield even if unsubscribe fails
-            yield "data: [DONE]\n\n"
-
-    return StreamingResponse(
-        event_generator(),
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",
-        },
-    )
-
-
-@router.get(
-    "/tasks/{task_id}",
-)
-async def get_task_status(
-    task_id: str,
-    user_id: str | None = Depends(auth.get_user_id),
-) -> dict:
-    """
-    Get the status of a long-running task.
-
-    Args:
-        task_id: The task ID to check.
-        user_id: Authenticated user ID for ownership validation.
-
-    Returns:
-        dict: Task status including task_id, status, tool_name, and operation_id.
-
-    Raises:
-        NotFoundError: If task_id is not found or user doesn't have access.
-    """
-    task = await stream_registry.get_task(task_id)
-
-    if task is None:
-        raise NotFoundError(f"Task {task_id} not found.")
-
-    # Validate ownership - if task has an owner, requester must match
-    if task.user_id and user_id != task.user_id:
-        raise NotFoundError(f"Task {task_id} not found.")
-
-    return {
-        "task_id": task.task_id,
-        "session_id": task.session_id,
-        "status": task.status,
-        "tool_name": task.tool_name,
-        "operation_id": task.operation_id,
-        "created_at": task.created_at.isoformat(),
-    }
-
-
-# ========== External Completion Webhook ==========
-
-
-@router.post(
-    "/operations/{operation_id}/complete",
-    status_code=200,
-)
-async def complete_operation(
-    operation_id: str,
-    request: OperationCompleteRequest,
-    x_api_key: str | None = Header(default=None),
-) -> dict:
-    """
-    External completion webhook for long-running operations.
-
-    Called by Agent Generator (or other services) when an operation completes.
-    This triggers the stream registry to publish completion and continue LLM generation.
-
-    Args:
-        operation_id: The operation ID to complete.
-        request: Completion payload with success status and result/error.
-        x_api_key: Internal API key for authentication.
-
-    Returns:
-        dict: Status of the completion.
-
-    Raises:
-        HTTPException: If API key is invalid or operation not found.
-    """
-    # Validate internal API key - reject if not configured or invalid
-    if not config.internal_api_key:
-        logger.error(
-            "Operation complete webhook rejected: CHAT_INTERNAL_API_KEY not configured"
-        )
-        raise HTTPException(
-            status_code=503,
-            detail="Webhook not available: internal API key not configured",
-        )
-    if x_api_key != config.internal_api_key:
-        raise HTTPException(status_code=401, detail="Invalid API key")
-
-    # Find task by operation_id
-    task = await stream_registry.find_task_by_operation_id(operation_id)
-    if task is None:
-        raise HTTPException(
-            status_code=404,
-            detail=f"Operation {operation_id} not found",
-        )
-
-    logger.info(
-        f"Received completion webhook for operation {operation_id} "
-        f"(task_id={task.task_id}, success={request.success})"
-    )
-
-    if request.success:
-        await process_operation_success(task, request.result)
-    else:
-        await process_operation_failure(task, request.error)
-
-    return {"status": "ok", "task_id": task.task_id}
-
-
-# ========== Configuration ==========
-
-
-@router.get("/config/ttl", status_code=200)
-async def get_ttl_config() -> dict:
-    """
-    Get the stream TTL configuration.
-
-    Returns the Time-To-Live settings for chat streams, which determines
-    how long clients can reconnect to an active stream.
-
-    Returns:
-        dict: TTL configuration with seconds and milliseconds values.
-    """
-    return {
-        "stream_ttl_seconds": config.stream_ttl,
-        "stream_ttl_ms": config.stream_ttl * 1000,
-    }
-
-
 # ========== Health Check ==========
 
 
@@ -966,43 +360,3 @@ async def health_check() -> dict:
         "service": "chat",
         "version": "0.1.0",
     }
-
-
-# ========== Schema Export (for OpenAPI / Orval codegen) ==========
-
-ToolResponseUnion = (
-    AgentsFoundResponse
-    | NoResultsResponse
-    | AgentDetailsResponse
-    | SetupRequirementsResponse
-    | ExecutionStartedResponse
-    | NeedLoginResponse
-    | ErrorResponse
-    | InputValidationErrorResponse
-    | AgentOutputResponse
-    | UnderstandingUpdatedResponse
-    | AgentPreviewResponse
-    | AgentSavedResponse
-    | ClarificationNeededResponse
-    | BlockListResponse
-    | BlockDetailsResponse
-    | BlockOutputResponse
-    | DocSearchResultsResponse
-    | DocPageResponse
-    | OperationStartedResponse
-    | OperationPendingResponse
-    | OperationInProgressResponse
-)
-
-
-@router.get(
-    "/schema/tool-responses",
-    response_model=ToolResponseUnion,
-    include_in_schema=True,
-    summary="[Dummy] Tool response type export for codegen",
-    description="This endpoint is not meant to be called. It exists solely to "
-    "expose tool response models in the OpenAPI schema for frontend codegen.",
-)
-async def _tool_response_schema() -> ToolResponseUnion:  # type: ignore[return]
-    """Never called at runtime. Exists only so Orval generates TS types."""
-    raise HTTPException(status_code=501, detail="Schema-only endpoint")

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

@@ -0,0 +1,907 @@
+import asyncio
+import logging
+from collections.abc import AsyncGenerator
+from typing import Any
+
+import orjson
+from langfuse import Langfuse
+from openai import (
+    APIConnectionError,
+    APIError,
+    APIStatusError,
+    AsyncOpenAI,
+    RateLimitError,
+)
+from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
+
+from backend.data.understanding import (
+    format_understanding_for_prompt,
+    get_business_understanding,
+)
+from backend.util.exceptions import NotFoundError
+from backend.util.settings import Settings
+
+from . import db as chat_db
+from .config import ChatConfig
+from .model import (
+    ChatMessage,
+    ChatSession,
+    Usage,
+    get_chat_session,
+    update_session_title,
+    upsert_chat_session,
+)
+from .response_model import (
+    StreamBaseResponse,
+    StreamError,
+    StreamFinish,
+    StreamStart,
+    StreamTextDelta,
+    StreamTextEnd,
+    StreamTextStart,
+    StreamToolInputAvailable,
+    StreamToolInputStart,
+    StreamToolOutputAvailable,
+    StreamUsage,
+)
+from .tools import execute_tool, tools
+
+logger = logging.getLogger(__name__)
+
+config = ChatConfig()
+settings = Settings()
+client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+
+# Langfuse client (lazy initialization)
+_langfuse_client: Langfuse | None = None
+
+
+class LangfuseNotConfiguredError(Exception):
+    """Raised when Langfuse is required but not configured."""
+
+    pass
+
+
+def _is_langfuse_configured() -> bool:
+    """Check if Langfuse credentials are configured."""
+    return bool(
+        settings.secrets.langfuse_public_key and settings.secrets.langfuse_secret_key
+    )
+
+
+def _get_langfuse_client() -> Langfuse:
+    """Get or create the Langfuse client for prompt management and tracing."""
+    global _langfuse_client
+    if _langfuse_client is None:
+        if not _is_langfuse_configured():
+            raise LangfuseNotConfiguredError(
+                "Langfuse is not configured. The chat feature requires Langfuse for prompt management. "
+                "Please set the LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY environment variables."
+            )
+        _langfuse_client = Langfuse(
+            public_key=settings.secrets.langfuse_public_key,
+            secret_key=settings.secrets.langfuse_secret_key,
+            host=settings.secrets.langfuse_host or "https://cloud.langfuse.com",
+        )
+    return _langfuse_client
+
+
+def _get_environment() -> str:
+    """Get the current environment name for Langfuse tagging."""
+    return settings.config.app_env.value
+
+
+def _get_langfuse_prompt() -> str:
+    """Fetch the latest production prompt from Langfuse.
+
+    Returns:
+        The compiled prompt text from Langfuse.
+
+    Raises:
+        Exception: If Langfuse is unavailable or prompt fetch fails.
+    """
+    try:
+        langfuse = _get_langfuse_client()
+        # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
+        prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
+        compiled = prompt.compile()
+        logger.info(
+            f"Fetched prompt '{config.langfuse_prompt_name}' from Langfuse "
+            f"(version: {prompt.version})"
+        )
+        return compiled
+    except Exception as e:
+        logger.error(f"Failed to fetch prompt from Langfuse: {e}")
+        raise
+
+
+async def _is_first_session(user_id: str) -> bool:
+    """Check if this is the user's first chat session.
+
+    Returns True if the user has 1 or fewer sessions (meaning this is their first).
+    """
+    try:
+        session_count = await chat_db.get_user_session_count(user_id)
+        return session_count <= 1
+    except Exception as e:
+        logger.warning(f"Failed to check session count for user {user_id}: {e}")
+        return False  # Default to non-onboarding if we can't check
+
+
+async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
+    """Build the full system prompt including business understanding if available.
+
+    Args:
+        user_id: The user ID for fetching business understanding
+                     If "default" and this is the user's first session, will use "onboarding" instead.
+
+    Returns:
+        Tuple of (compiled prompt string, Langfuse prompt object for tracing)
+    """
+
+    langfuse = _get_langfuse_client()
+
+    # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
+    prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
+
+    # If user is authenticated, try to fetch their business understanding
+    understanding = None
+    if user_id:
+        try:
+            understanding = await get_business_understanding(user_id)
+        except Exception as e:
+            logger.warning(f"Failed to fetch business understanding: {e}")
+            understanding = None
+    if understanding:
+        context = format_understanding_for_prompt(understanding)
+    else:
+        context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
+
+    compiled = prompt.compile(users_information=context)
+    return compiled, prompt
+
+
+async def _generate_session_title(message: str) -> str | None:
+    """Generate a concise title for a chat session based on the first message.
+
+    Args:
+        message: The first user message in the session
+
+    Returns:
+        A short title (3-6 words) or None if generation fails
+    """
+    try:
+        response = await client.chat.completions.create(
+            model=config.title_model,
+            messages=[
+                {
+                    "role": "system",
+                    "content": (
+                        "Generate a very short title (3-6 words) for a chat conversation "
+                        "based on the user's first message. The title should capture the "
+                        "main topic or intent. Return ONLY the title, no quotes or punctuation."
+                    ),
+                },
+                {"role": "user", "content": message[:500]},  # Limit input length
+            ],
+            max_tokens=20,
+        )
+        title = response.choices[0].message.content
+        if title:
+            # Clean up the title
+            title = title.strip().strip("\"'")
+            # Limit length
+            if len(title) > 50:
+                title = title[:47] + "..."
+            return title
+        return None
+    except Exception as e:
+        logger.warning(f"Failed to generate session title: {e}")
+        return None
+
+
+async def assign_user_to_session(
+    session_id: str,
+    user_id: str,
+) -> ChatSession:
+    """
+    Assign a user to a chat session.
+    """
+    session = await get_chat_session(session_id, None)
+    if not session:
+        raise NotFoundError(f"Session {session_id} not found")
+    session.user_id = user_id
+    return await upsert_chat_session(session)
+
+
+async def stream_chat_completion(
+    session_id: str,
+    message: str | None = None,
+    is_user_message: bool = True,
+    user_id: str | None = None,
+    retry_count: int = 0,
+    session: ChatSession | None = None,
+    context: dict[str, str] | None = None,  # {url: str, content: str}
+) -> AsyncGenerator[StreamBaseResponse, None]:
+    """Main entry point for streaming chat completions with database handling.
+
+    This function handles all database operations and delegates streaming
+    to the internal _stream_chat_chunks function.
+
+    Args:
+        session_id: Chat session ID
+        user_message: User's input message
+        user_id: User ID for authentication (None for anonymous)
+        session: Optional pre-loaded session object (for recursive calls to avoid Redis refetch)
+
+    Yields:
+        StreamBaseResponse objects formatted as SSE
+
+    Raises:
+        NotFoundError: If session_id is invalid
+        ValueError: If max_context_messages is exceeded
+
+    """
+    logger.info(
+        f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
+    )
+
+    # Check if Langfuse is configured - required for chat functionality
+    if not _is_langfuse_configured():
+        logger.error("Chat request failed: Langfuse is not configured")
+        yield StreamError(
+            errorText="Chat service is not available. Langfuse must be configured "
+            "with LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY environment variables."
+        )
+        yield StreamFinish()
+        return
+
+    # Langfuse observations will be created after session is loaded (need messages for input)
+    # Initialize to None so finally block can safely check and end them
+    trace = None
+    generation = None
+
+    # Only fetch from Redis if session not provided (initial call)
+    if session is None:
+        session = await get_chat_session(session_id, user_id)
+        logger.info(
+            f"Fetched session from Redis: {session.session_id if session else 'None'}, "
+            f"message_count={len(session.messages) if session else 0}"
+        )
+    else:
+        logger.info(
+            f"Using provided session object: {session.session_id}, "
+            f"message_count={len(session.messages)}"
+        )
+
+    if not session:
+        raise NotFoundError(
+            f"Session {session_id} not found. Please create a new session first."
+        )
+
+    if message:
+        # Build message content with context if provided
+        message_content = message
+        if context and context.get("url") and context.get("content"):
+            context_text = f"Page URL: {context['url']}\n\nPage Content:\n{context['content']}\n\n---\n\nUser Message: {message}"
+            message_content = context_text
+            logger.info(
+                f"Including page context: URL={context['url']}, content_length={len(context['content'])}"
+            )
+
+        session.messages.append(
+            ChatMessage(
+                role="user" if is_user_message else "assistant", content=message_content
+            )
+        )
+        logger.info(
+            f"Appended message (role={'user' if is_user_message else 'assistant'}), "
+            f"new message_count={len(session.messages)}"
+        )
+
+    if len(session.messages) > config.max_context_messages:
+        raise ValueError(f"Max messages exceeded: {config.max_context_messages}")
+
+    logger.info(
+        f"Upserting session: {session.session_id} with user id {session.user_id}, "
+        f"message_count={len(session.messages)}"
+    )
+    session = await upsert_chat_session(session)
+    assert session, "Session not found"
+
+    # Generate title for new sessions on first user message (non-blocking)
+    # Check: is_user_message, no title yet, and this is the first user message
+    if is_user_message and message and not session.title:
+        user_messages = [m for m in session.messages if m.role == "user"]
+        if len(user_messages) == 1:
+            # First user message - generate title in background
+            import asyncio
+
+            # Capture only the values we need (not the session object) to avoid
+            # stale data issues when the main flow modifies the session
+            captured_session_id = session_id
+            captured_message = message
+
+            async def _update_title():
+                try:
+                    title = await _generate_session_title(captured_message)
+                    if title:
+                        # Use dedicated title update function that doesn't
+                        # touch messages, avoiding race conditions
+                        await update_session_title(captured_session_id, title)
+                        logger.info(
+                            f"Generated title for session {captured_session_id}: {title}"
+                        )
+                except Exception as e:
+                    logger.warning(f"Failed to update session title: {e}")
+
+            # Fire and forget - don't block the chat response
+            asyncio.create_task(_update_title())
+
+    # Build system prompt with business understanding
+    system_prompt, langfuse_prompt = await _build_system_prompt(user_id)
+
+    # Build input messages including system prompt for complete Langfuse logging
+    trace_input_messages = [{"role": "system", "content": system_prompt}] + [
+        m.model_dump() for m in session.messages
+    ]
+
+    # Create Langfuse trace for this LLM call (each call gets its own trace, grouped by session_id)
+    # Using v3 SDK: start_observation creates a root span, update_trace sets trace-level attributes
+    try:
+        langfuse = _get_langfuse_client()
+        env = _get_environment()
+        trace = langfuse.start_observation(
+            name="chat_completion",
+            input={"messages": trace_input_messages},
+            metadata={
+                "environment": env,
+                "model": config.model,
+                "message_count": len(session.messages),
+                "prompt_name": langfuse_prompt.name if langfuse_prompt else None,
+                "prompt_version": langfuse_prompt.version if langfuse_prompt else None,
+            },
+        )
+        # Set trace-level attributes (session_id, user_id, tags)
+        trace.update_trace(
+            session_id=session_id,
+            user_id=user_id,
+            tags=[env, "copilot"],
+        )
+    except Exception as e:
+        logger.warning(f"Failed to create Langfuse trace: {e}")
+
+    # Initialize variables that will be used in finally block (must be defined before try)
+    assistant_response = ChatMessage(
+        role="assistant",
+        content="",
+    )
+    accumulated_tool_calls: list[dict[str, Any]] = []
+
+    # Wrap main logic in try/finally to ensure Langfuse observations are always ended
+    try:
+        has_yielded_end = False
+        has_yielded_error = False
+        has_done_tool_call = False
+        has_received_text = False
+        text_streaming_ended = False
+        tool_response_messages: list[ChatMessage] = []
+        should_retry = False
+
+        # Generate unique IDs for AI SDK protocol
+        import uuid as uuid_module
+
+        message_id = str(uuid_module.uuid4())
+        text_block_id = str(uuid_module.uuid4())
+
+        # Yield message start
+        yield StreamStart(messageId=message_id)
+
+        # Create Langfuse generation for each LLM call, linked to the prompt
+        # Using v3 SDK: start_observation with as_type="generation"
+        generation = (
+            trace.start_observation(
+                as_type="generation",
+                name="llm_call",
+                model=config.model,
+                input={"messages": trace_input_messages},
+                prompt=langfuse_prompt,
+            )
+            if trace
+            else None
+        )
+
+        try:
+            async for chunk in _stream_chat_chunks(
+                session=session,
+                tools=tools,
+                system_prompt=system_prompt,
+                text_block_id=text_block_id,
+            ):
+
+                if isinstance(chunk, StreamTextStart):
+                    # Emit text-start before first text delta
+                    if not has_received_text:
+                        yield chunk
+                elif isinstance(chunk, StreamTextDelta):
+                    delta = chunk.delta or ""
+                    assert assistant_response.content is not None
+                    assistant_response.content += delta
+                    has_received_text = True
+                    yield chunk
+                elif isinstance(chunk, StreamTextEnd):
+                    # Emit text-end after text completes
+                    if has_received_text and not text_streaming_ended:
+                        text_streaming_ended = True
+                        yield chunk
+                elif isinstance(chunk, StreamToolInputStart):
+                    # Emit text-end before first tool call, but only if we've received text
+                    if has_received_text and not text_streaming_ended:
+                        yield StreamTextEnd(id=text_block_id)
+                        text_streaming_ended = True
+                    yield chunk
+                elif isinstance(chunk, StreamToolInputAvailable):
+                    # Accumulate tool calls in OpenAI format
+                    accumulated_tool_calls.append(
+                        {
+                            "id": chunk.toolCallId,
+                            "type": "function",
+                            "function": {
+                                "name": chunk.toolName,
+                                "arguments": orjson.dumps(chunk.input).decode("utf-8"),
+                            },
+                        }
+                    )
+                elif isinstance(chunk, StreamToolOutputAvailable):
+                    result_content = (
+                        chunk.output
+                        if isinstance(chunk.output, str)
+                        else orjson.dumps(chunk.output).decode("utf-8")
+                    )
+                    tool_response_messages.append(
+                        ChatMessage(
+                            role="tool",
+                            content=result_content,
+                            tool_call_id=chunk.toolCallId,
+                        )
+                    )
+                    has_done_tool_call = True
+                    # Track if any tool execution failed
+                    if not chunk.success:
+                        logger.warning(
+                            f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
+                        )
+                    yield chunk
+                elif isinstance(chunk, StreamFinish):
+                    if not has_done_tool_call:
+                        # Emit text-end before finish if we received text but haven't closed it
+                        if has_received_text and not text_streaming_ended:
+                            yield StreamTextEnd(id=text_block_id)
+                            text_streaming_ended = True
+                        has_yielded_end = True
+                        yield chunk
+                elif isinstance(chunk, StreamError):
+                    has_yielded_error = True
+                elif isinstance(chunk, StreamUsage):
+                    session.usage.append(
+                        Usage(
+                            prompt_tokens=chunk.promptTokens,
+                            completion_tokens=chunk.completionTokens,
+                            total_tokens=chunk.totalTokens,
+                        )
+                    )
+                else:
+                    logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
+        except Exception as e:
+            logger.error(f"Error during stream: {e!s}", exc_info=True)
+
+            # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
+            is_retryable = isinstance(e, (orjson.JSONDecodeError, KeyError, TypeError))
+
+            if is_retryable and retry_count < config.max_retries:
+                logger.info(
+                    f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
+                )
+                should_retry = True
+            else:
+                # Non-retryable error or max retries exceeded
+                # Save any partial progress before reporting error
+                messages_to_save: list[ChatMessage] = []
+
+                # Add assistant message if it has content or tool calls
+                if accumulated_tool_calls:
+                    assistant_response.tool_calls = accumulated_tool_calls
+                if assistant_response.content or assistant_response.tool_calls:
+                    messages_to_save.append(assistant_response)
+
+                # Add tool response messages after assistant message
+                messages_to_save.extend(tool_response_messages)
+
+                session.messages.extend(messages_to_save)
+                await upsert_chat_session(session)
+
+                if not has_yielded_error:
+                    error_message = str(e)
+                    if not is_retryable:
+                        error_message = f"Non-retryable error: {error_message}"
+                    elif retry_count >= config.max_retries:
+                        error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
+
+                    error_response = StreamError(errorText=error_message)
+                    yield error_response
+                if not has_yielded_end:
+                    yield StreamFinish()
+                return
+
+        # Handle retry outside of exception handler to avoid nesting
+        if should_retry and retry_count < config.max_retries:
+            logger.info(
+                f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
+            )
+            async for chunk in stream_chat_completion(
+                session_id=session.session_id,
+                user_id=user_id,
+                retry_count=retry_count + 1,
+                session=session,
+                context=context,
+            ):
+                yield chunk
+            return  # Exit after retry to avoid double-saving in finally block
+
+        # Normal completion path - save session and handle tool call continuation
+        logger.info(
+            f"Normal completion path: session={session.session_id}, "
+            f"current message_count={len(session.messages)}"
+        )
+
+        # Build the messages list in the correct order
+        messages_to_save: list[ChatMessage] = []
+
+        # Add assistant message with tool_calls if any
+        if accumulated_tool_calls:
+            assistant_response.tool_calls = accumulated_tool_calls
+            logger.info(
+                f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
+            )
+        if assistant_response.content or assistant_response.tool_calls:
+            messages_to_save.append(assistant_response)
+            logger.info(
+                f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
+            )
+
+        # Add tool response messages after assistant message
+        messages_to_save.extend(tool_response_messages)
+        logger.info(
+            f"Saving {len(tool_response_messages)} tool response messages, "
+            f"total_to_save={len(messages_to_save)}"
+        )
+
+        session.messages.extend(messages_to_save)
+        logger.info(
+            f"Extended session messages, new message_count={len(session.messages)}"
+        )
+        await upsert_chat_session(session)
+
+        # If we did a tool call, stream the chat completion again to get the next response
+        if has_done_tool_call:
+            logger.info(
+                "Tool call executed, streaming chat completion again to get assistant response"
+            )
+            async for chunk in stream_chat_completion(
+                session_id=session.session_id,
+                user_id=user_id,
+                session=session,  # Pass session object to avoid Redis refetch
+                context=context,
+            ):
+                yield chunk
+
+    finally:
+        # Always end Langfuse observations to prevent resource leaks
+        # Guard against None and catch errors to avoid masking original exceptions
+        if generation is not None:
+            try:
+                latest_usage = session.usage[-1] if session.usage else None
+                generation.update(
+                    model=config.model,
+                    output={
+                        "content": assistant_response.content,
+                        "tool_calls": accumulated_tool_calls or None,
+                    },
+                    usage_details=(
+                        {
+                            "input": latest_usage.prompt_tokens,
+                            "output": latest_usage.completion_tokens,
+                            "total": latest_usage.total_tokens,
+                        }
+                        if latest_usage
+                        else None
+                    ),
+                )
+                generation.end()
+            except Exception as e:
+                logger.warning(f"Failed to end Langfuse generation: {e}")
+
+        if trace is not None:
+            try:
+                if accumulated_tool_calls:
+                    trace.update_trace(output={"tool_calls": accumulated_tool_calls})
+                else:
+                    trace.update_trace(output={"response": assistant_response.content})
+                trace.end()
+            except Exception as e:
+                logger.warning(f"Failed to end Langfuse trace: {e}")
+
+
+# Retry configuration for OpenAI API calls
+MAX_RETRIES = 3
+BASE_DELAY_SECONDS = 1.0
+MAX_DELAY_SECONDS = 30.0
+
+
+def _is_retryable_error(error: Exception) -> bool:
+    """Determine if an error is retryable."""
+    if isinstance(error, RateLimitError):
+        return True
+    if isinstance(error, APIConnectionError):
+        return True
+    if isinstance(error, APIStatusError):
+        # APIStatusError has a response with status_code
+        # Retry on 5xx status codes (server errors)
+        if error.response.status_code >= 500:
+            return True
+    if isinstance(error, APIError):
+        # Retry on overloaded errors or 500 errors (may not have status code)
+        error_message = str(error).lower()
+        if "overloaded" in error_message or "internal server error" in error_message:
+            return True
+    return False
+
+
+async def _stream_chat_chunks(
+    session: ChatSession,
+    tools: list[ChatCompletionToolParam],
+    system_prompt: str | None = None,
+    text_block_id: str | None = None,
+) -> AsyncGenerator[StreamBaseResponse, None]:
+    """
+    Pure streaming function for OpenAI chat completions with tool calling.
+
+    This function is database-agnostic and focuses only on streaming logic.
+    Implements exponential backoff retry for transient API errors.
+
+    Args:
+        session: Chat session with conversation history
+        tools: Available tools for the model
+        system_prompt: System prompt to prepend to messages
+
+    Yields:
+        SSE formatted JSON response objects
+
+    """
+    model = config.model
+
+    logger.info("Starting pure chat stream")
+
+    # Build messages with system prompt prepended
+    messages = session.to_openai_messages()
+    if system_prompt:
+        from openai.types.chat import ChatCompletionSystemMessageParam
+
+        system_message = ChatCompletionSystemMessageParam(
+            role="system",
+            content=system_prompt,
+        )
+        messages = [system_message] + messages
+
+    # Loop to handle tool calls and continue conversation
+    while True:
+        retry_count = 0
+        last_error: Exception | None = None
+
+        while retry_count <= MAX_RETRIES:
+            try:
+                logger.info(
+                    f"Creating OpenAI chat completion stream..."
+                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
+                )
+
+                # Create the stream with proper types
+                stream = await client.chat.completions.create(
+                    model=model,
+                    messages=messages,
+                    tools=tools,
+                    tool_choice="auto",
+                    stream=True,
+                    stream_options={"include_usage": True},
+                )
+
+                # Variables to accumulate tool calls
+                tool_calls: list[dict[str, Any]] = []
+                active_tool_call_idx: int | None = None
+                finish_reason: str | None = None
+                # Track which tool call indices have had their start event emitted
+                emitted_start_for_idx: set[int] = set()
+
+                # Track if we've started the text block
+                text_started = False
+
+                # Process the stream
+                chunk: ChatCompletionChunk
+                async for chunk in stream:
+                    if chunk.usage:
+                        yield StreamUsage(
+                            promptTokens=chunk.usage.prompt_tokens,
+                            completionTokens=chunk.usage.completion_tokens,
+                            totalTokens=chunk.usage.total_tokens,
+                        )
+
+                    if chunk.choices:
+                        choice = chunk.choices[0]
+                        delta = choice.delta
+
+                        # Capture finish reason
+                        if choice.finish_reason:
+                            finish_reason = choice.finish_reason
+                            logger.info(f"Finish reason: {finish_reason}")
+
+                        # Handle content streaming
+                        if delta.content:
+                            # Emit text-start on first text content
+                            if not text_started and text_block_id:
+                                yield StreamTextStart(id=text_block_id)
+                                text_started = True
+                            # Stream the text delta
+                            text_response = StreamTextDelta(
+                                id=text_block_id or "",
+                                delta=delta.content,
+                            )
+                            yield text_response
+
+                        # Handle tool calls
+                        if delta.tool_calls:
+                            for tc_chunk in delta.tool_calls:
+                                idx = tc_chunk.index
+
+                                # Update active tool call index if needed
+                                if (
+                                    active_tool_call_idx is None
+                                    or active_tool_call_idx != idx
+                                ):
+                                    active_tool_call_idx = idx
+
+                                # Ensure we have a tool call object at this index
+                                while len(tool_calls) <= idx:
+                                    tool_calls.append(
+                                        {
+                                            "id": "",
+                                            "type": "function",
+                                            "function": {
+                                                "name": "",
+                                                "arguments": "",
+                                            },
+                                        },
+                                    )
+
+                                # Accumulate the tool call data
+                                if tc_chunk.id:
+                                    tool_calls[idx]["id"] = tc_chunk.id
+                                if tc_chunk.function:
+                                    if tc_chunk.function.name:
+                                        tool_calls[idx]["function"][
+                                            "name"
+                                        ] = tc_chunk.function.name
+                                    if tc_chunk.function.arguments:
+                                        tool_calls[idx]["function"][
+                                            "arguments"
+                                        ] += tc_chunk.function.arguments
+
+                                # Emit StreamToolInputStart only after we have the tool call ID
+                                if (
+                                    idx not in emitted_start_for_idx
+                                    and tool_calls[idx]["id"]
+                                    and tool_calls[idx]["function"]["name"]
+                                ):
+                                    yield StreamToolInputStart(
+                                        toolCallId=tool_calls[idx]["id"],
+                                        toolName=tool_calls[idx]["function"]["name"],
+                                    )
+                                    emitted_start_for_idx.add(idx)
+                logger.info(f"Stream complete. Finish reason: {finish_reason}")
+
+                # Yield all accumulated tool calls after the stream is complete
+                # This ensures all tool call arguments have been fully received
+                for idx, tool_call in enumerate(tool_calls):
+                    try:
+                        async for tc in _yield_tool_call(tool_calls, idx, session):
+                            yield tc
+                    except (orjson.JSONDecodeError, KeyError, TypeError) as e:
+                        logger.error(
+                            f"Failed to parse tool call {idx}: {e}",
+                            exc_info=True,
+                            extra={"tool_call": tool_call},
+                        )
+                        yield StreamError(
+                            errorText=f"Invalid tool call arguments for tool {tool_call.get('function', {}).get('name', 'unknown')}: {e}",
+                        )
+                        # Re-raise to trigger retry logic in the parent function
+                        raise
+
+                yield StreamFinish()
+                return
+            except Exception as e:
+                last_error = e
+                if _is_retryable_error(e) and retry_count < MAX_RETRIES:
+                    retry_count += 1
+                    # Calculate delay with exponential backoff
+                    delay = min(
+                        BASE_DELAY_SECONDS * (2 ** (retry_count - 1)),
+                        MAX_DELAY_SECONDS,
+                    )
+                    logger.warning(
+                        f"Retryable error in stream: {e!s}. "
+                        f"Retrying in {delay:.1f}s (attempt {retry_count}/{MAX_RETRIES})"
+                    )
+                    await asyncio.sleep(delay)
+                    continue  # Retry the stream
+                else:
+                    # Non-retryable error or max retries exceeded
+                    logger.error(
+                        f"Error in stream (not retrying): {e!s}",
+                        exc_info=True,
+                    )
+                    error_response = StreamError(errorText=str(e))
+                    yield error_response
+                    yield StreamFinish()
+                    return
+
+        # If we exit the retry loop without returning, it means we exhausted retries
+        if last_error:
+            logger.error(
+                f"Max retries ({MAX_RETRIES}) exceeded. Last error: {last_error!s}",
+                exc_info=True,
+            )
+            yield StreamError(errorText=f"Max retries exceeded: {last_error!s}")
+            yield StreamFinish()
+            return
+
+
+async def _yield_tool_call(
+    tool_calls: list[dict[str, Any]],
+    yield_idx: int,
+    session: ChatSession,
+) -> AsyncGenerator[StreamBaseResponse, None]:
+    """
+    Yield a tool call and its execution result.
+
+    Raises:
+        orjson.JSONDecodeError: If tool call arguments cannot be parsed as JSON
+        KeyError: If expected tool call fields are missing
+        TypeError: If tool call structure is invalid
+    """
+    tool_name = tool_calls[yield_idx]["function"]["name"]
+    tool_call_id = tool_calls[yield_idx]["id"]
+    logger.info(f"Yielding tool call: {tool_calls[yield_idx]}")
+
+    # Parse tool call arguments - handle empty arguments gracefully
+    raw_arguments = tool_calls[yield_idx]["function"]["arguments"]
+    if raw_arguments:
+        arguments = orjson.loads(raw_arguments)
+    else:
+        arguments = {}
+
+    yield StreamToolInputAvailable(
+        toolCallId=tool_call_id,
+        toolName=tool_name,
+        input=arguments,
+    )
+
+    tool_execution_response: StreamToolOutputAvailable = await execute_tool(
+        tool_name=tool_name,
+        parameters=arguments,
+        tool_call_id=tool_call_id,
+        user_id=session.user_id,
+        session=session,
+    )
+
+    logger.info(f"Yielding Tool execution response: {tool_execution_response}")
+    yield tool_execution_response

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

@@ -0,0 +1,82 @@
+import logging
+from os import getenv
+
+import pytest
+
+from . import service as chat_service
+from .model import create_chat_session, get_chat_session, upsert_chat_session
+from .response_model import (
+    StreamError,
+    StreamFinish,
+    StreamTextDelta,
+    StreamToolOutputAvailable,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_stream_chat_completion(setup_test_user, test_user_id):
+    """
+    Test the stream_chat_completion function.
+    """
+    api_key: str | None = getenv("OPEN_ROUTER_API_KEY")
+    if not api_key:
+        return pytest.skip("OPEN_ROUTER_API_KEY is not set, skipping test")
+
+    session = await create_chat_session(test_user_id)
+
+    has_errors = False
+    has_ended = False
+    assistant_message = ""
+    async for chunk in chat_service.stream_chat_completion(
+        session.session_id, "Hello, how are you?", user_id=session.user_id
+    ):
+        logger.info(chunk)
+        if isinstance(chunk, StreamError):
+            has_errors = True
+        if isinstance(chunk, StreamTextDelta):
+            assistant_message += chunk.delta
+        if isinstance(chunk, StreamFinish):
+            has_ended = True
+
+    assert has_ended, "Chat completion did not end"
+    assert not has_errors, "Error occurred while streaming chat completion"
+    assert assistant_message, "Assistant message is empty"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_stream_chat_completion_with_tool_calls(setup_test_user, test_user_id):
+    """
+    Test the stream_chat_completion function.
+    """
+    api_key: str | None = getenv("OPEN_ROUTER_API_KEY")
+    if not api_key:
+        return pytest.skip("OPEN_ROUTER_API_KEY is not set, skipping test")
+
+    session = await create_chat_session(test_user_id)
+    session = await upsert_chat_session(session)
+
+    has_errors = False
+    has_ended = False
+    had_tool_calls = False
+    async for chunk in chat_service.stream_chat_completion(
+        session.session_id,
+        "Please find me an agent that can help me with my business. Use the query 'moneny printing agent'",
+        user_id=session.user_id,
+    ):
+        logger.info(chunk)
+        if isinstance(chunk, StreamError):
+            has_errors = True
+
+        if isinstance(chunk, StreamFinish):
+            has_ended = True
+        if isinstance(chunk, StreamToolOutputAvailable):
+            had_tool_calls = True
+
+    assert has_ended, "Chat completion did not end"
+    assert not has_errors, "Error occurred while streaming chat completion"
+    assert had_tool_calls, "Tool calls did not occur"
+    session = await get_chat_session(session.session_id)
+    assert session, "Session not found"
+    assert session.usage, "Usage is empty"

autogpt_platform/backend/backend/api/features/chat/tools/__init__.py

@@ -0,0 +1,49 @@
+from typing import TYPE_CHECKING, Any
+
+from openai.types.chat import ChatCompletionToolParam
+
+from backend.api.features.chat.model import ChatSession
+
+from .add_understanding import AddUnderstandingTool
+from .agent_output import AgentOutputTool
+from .base import BaseTool
+from .find_agent import FindAgentTool
+from .find_library_agent import FindLibraryAgentTool
+from .run_block import RunBlockTool
+from .run_agent import RunAgentTool
+
+if TYPE_CHECKING:
+    from backend.api.features.chat.response_model import StreamToolOutputAvailable
+
+# Single source of truth for all tools
+TOOL_REGISTRY: dict[str, BaseTool] = {
+    "add_understanding": AddUnderstandingTool(),
+    "find_agent": FindAgentTool(),
+    "find_library_agent": FindLibraryAgentTool(),
+    "run_agent": RunAgentTool(),
+    "agent_output": AgentOutputTool(),
+    "run_block": RunBlockTool(),
+}
+
+# Export individual tool instances for backwards compatibility
+find_agent_tool = TOOL_REGISTRY["find_agent"]
+run_agent_tool = TOOL_REGISTRY["run_agent"]
+
+# Generated from registry for OpenAI API
+tools: list[ChatCompletionToolParam] = [
+    tool.as_openai_tool() for tool in TOOL_REGISTRY.values()
+]
+
+
+async def execute_tool(
+    tool_name: str,
+    parameters: dict[str, Any],
+    user_id: str | None,
+    session: ChatSession,
+    tool_call_id: str,
+) -> "StreamToolOutputAvailable":
+    """Execute a tool by name."""
+    tool = TOOL_REGISTRY.get(tool_name)
+    if not tool:
+        raise ValueError(f"Tool {tool_name} not found")
+    return await tool.execute(user_id, session, tool_call_id, **parameters)

autogpt_platform/backend/backend/api/features/chat/tools/_test_data.py

@@ -6,11 +6,11 @@ import pytest
 from prisma.types import ProfileCreateInput
 from pydantic import SecretStr
 
+from backend.api.features.chat.model import ChatSession
 from backend.api.features.store import db as store_db
 from backend.blocks.firecrawl.scrape import FirecrawlScrapeBlock
 from backend.blocks.io import AgentInputBlock, AgentOutputBlock
 from backend.blocks.llm import AITextGeneratorBlock
-from backend.copilot.model import ChatSession
 from backend.data.db import prisma
 from backend.data.graph import Graph, Link, Node, create_graph
 from backend.data.model import APIKeyCredentials

autogpt_platform/backend/backend/api/features/chat/tools/add_understanding.py

@@ -3,9 +3,11 @@
 import logging
 from typing import Any
 
-from backend.copilot.model import ChatSession
-from backend.data.db_accessors import understanding_db
-from backend.data.understanding import BusinessUnderstandingInput
+from backend.api.features.chat.model import ChatSession
+from backend.data.understanding import (
+    BusinessUnderstandingInput,
+    upsert_business_understanding,
+)
 
 from .base import BaseTool
 from .models import ErrorResponse, ToolResponseBase, UnderstandingUpdatedResponse
@@ -97,9 +99,7 @@ and automations for the user's specific needs."""
         ]
 
         # Upsert with merge
-        understanding = await understanding_db().upsert_business_understanding(
-            user_id, input_data
-        )
+        understanding = await upsert_business_understanding(user_id, input_data)
 
         # Build current understanding summary (filter out empty values)
         current_understanding = {

autogpt_platform/backend/backend/api/features/chat/tools/agent_output.py

@@ -7,9 +7,10 @@ from typing import Any
 
 from pydantic import BaseModel, field_validator
 
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.library import db as library_db
 from backend.api.features.library.model import LibraryAgent
-from backend.copilot.model import ChatSession
-from backend.data.db_accessors import execution_db, library_db
+from backend.data import execution as execution_db
 from backend.data.execution import ExecutionStatus, GraphExecution, GraphExecutionMeta
 
 from .base import BaseTool
@@ -102,7 +103,7 @@ class AgentOutputTool(BaseTool):
 
     @property
     def name(self) -> str:
-        return "view_agent_output"
+        return "agent_output"
 
     @property
     def description(self) -> str:
@@ -164,12 +165,10 @@ class AgentOutputTool(BaseTool):
         Resolve agent from provided identifiers.
         Returns (library_agent, error_message).
         """
-        lib_db = library_db()
-
         # Priority 1: Exact library agent ID
         if library_agent_id:
             try:
-                agent = await lib_db.get_library_agent(library_agent_id, user_id)
+                agent = await library_db.get_library_agent(library_agent_id, user_id)
                 return agent, None
             except Exception as e:
                 logger.warning(f"Failed to get library agent by ID: {e}")
@@ -183,7 +182,7 @@ class AgentOutputTool(BaseTool):
                 return None, f"Agent '{store_slug}' not found in marketplace"
 
             # Find in user's library by graph_id
-            agent = await lib_db.get_library_agent_by_graph_id(user_id, graph.id)
+            agent = await library_db.get_library_agent_by_graph_id(user_id, graph.id)
             if not agent:
                 return (
                     None,
@@ -195,7 +194,7 @@ class AgentOutputTool(BaseTool):
         # Priority 3: Fuzzy name search in library
         if agent_name:
             try:
-                response = await lib_db.list_library_agents(
+                response = await library_db.list_library_agents(
                     user_id=user_id,
                     search_term=agent_name,
                     page_size=5,
@@ -229,11 +228,9 @@ class AgentOutputTool(BaseTool):
         Fetch execution(s) based on filters.
         Returns (single_execution, available_executions_meta, error_message).
         """
-        exec_db = execution_db()
-
         # If specific execution_id provided, fetch it directly
         if execution_id:
-            execution = await exec_db.get_graph_execution(
+            execution = await execution_db.get_graph_execution(
                 user_id=user_id,
                 execution_id=execution_id,
                 include_node_executions=False,
@@ -243,7 +240,7 @@ class AgentOutputTool(BaseTool):
             return execution, [], None
 
         # Get completed executions with time filters
-        executions = await exec_db.get_graph_executions(
+        executions = await execution_db.get_graph_executions(
             graph_id=graph_id,
             user_id=user_id,
             statuses=[ExecutionStatus.COMPLETED],
@@ -257,7 +254,7 @@ class AgentOutputTool(BaseTool):
 
         # If only one execution, fetch full details
         if len(executions) == 1:
-            full_execution = await exec_db.get_graph_execution(
+            full_execution = await execution_db.get_graph_execution(
                 user_id=user_id,
                 execution_id=executions[0].id,
                 include_node_executions=False,
@@ -265,7 +262,7 @@ class AgentOutputTool(BaseTool):
             return full_execution, [], None
 
         # Multiple executions - return latest with full details, plus list of available
-        full_execution = await exec_db.get_graph_execution(
+        full_execution = await execution_db.get_graph_execution(
             user_id=user_id,
             execution_id=executions[0].id,
             include_node_executions=False,
@@ -383,7 +380,7 @@ class AgentOutputTool(BaseTool):
             and not input_data.store_slug
         ):
             # Fetch execution directly to get graph_id
-            execution = await execution_db().get_graph_execution(
+            execution = await execution_db.get_graph_execution(
                 user_id=user_id,
                 execution_id=input_data.execution_id,
                 include_node_executions=False,
@@ -395,7 +392,7 @@ class AgentOutputTool(BaseTool):
                 )
 
             # Find library agent by graph_id
-            agent = await library_db().get_library_agent_by_graph_id(
+            agent = await library_db.get_library_agent_by_graph_id(
                 user_id, execution.graph_id
             )
             if not agent:

autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py

@@ -0,0 +1,151 @@
+"""Shared agent search functionality for find_agent and find_library_agent tools."""
+
+import logging
+from typing import Literal
+
+from backend.api.features.library import db as library_db
+from backend.api.features.store import db as store_db
+from backend.util.exceptions import DatabaseError, NotFoundError
+
+from .models import (
+    AgentInfo,
+    AgentsFoundResponse,
+    ErrorResponse,
+    NoResultsResponse,
+    ToolResponseBase,
+)
+
+logger = logging.getLogger(__name__)
+
+SearchSource = Literal["marketplace", "library"]
+
+
+async def search_agents(
+    query: str,
+    source: SearchSource,
+    session_id: str | None,
+    user_id: str | None = None,
+) -> ToolResponseBase:
+    """
+    Search for agents in marketplace or user library.
+
+    Args:
+        query: Search query string
+        source: "marketplace" or "library"
+        session_id: Chat session ID
+        user_id: User ID (required for library search)
+
+    Returns:
+        AgentsFoundResponse, NoResultsResponse, or ErrorResponse
+    """
+    if not query:
+        return ErrorResponse(
+            message="Please provide a search query", session_id=session_id
+        )
+
+    if source == "library" and not user_id:
+        return ErrorResponse(
+            message="User authentication required to search library",
+            session_id=session_id,
+        )
+
+    agents: list[AgentInfo] = []
+    try:
+        if source == "marketplace":
+            logger.info(f"Searching marketplace for: {query}")
+            results = await store_db.get_store_agents(search_query=query, page_size=5)
+            for agent in results.agents:
+                agents.append(
+                    AgentInfo(
+                        id=f"{agent.creator}/{agent.slug}",
+                        name=agent.agent_name,
+                        description=agent.description or "",
+                        source="marketplace",
+                        in_library=False,
+                        creator=agent.creator,
+                        category="general",
+                        rating=agent.rating,
+                        runs=agent.runs,
+                        is_featured=False,
+                    )
+                )
+        else:  # library
+            logger.info(f"Searching user library for: {query}")
+            results = await library_db.list_library_agents(
+                user_id=user_id,  # type: ignore[arg-type]
+                search_term=query,
+                page_size=10,
+            )
+            for agent in results.agents:
+                agents.append(
+                    AgentInfo(
+                        id=agent.id,
+                        name=agent.name,
+                        description=agent.description or "",
+                        source="library",
+                        in_library=True,
+                        creator=agent.creator_name,
+                        status=agent.status.value,
+                        can_access_graph=agent.can_access_graph,
+                        has_external_trigger=agent.has_external_trigger,
+                        new_output=agent.new_output,
+                        graph_id=agent.graph_id,
+                    )
+                )
+        logger.info(f"Found {len(agents)} agents in {source}")
+    except NotFoundError:
+        pass
+    except DatabaseError as e:
+        logger.error(f"Error searching {source}: {e}", exc_info=True)
+        return ErrorResponse(
+            message=f"Failed to search {source}. Please try again.",
+            error=str(e),
+            session_id=session_id,
+        )
+
+    if not agents:
+        suggestions = (
+            [
+                "Try more general terms",
+                "Browse categories in the marketplace",
+                "Check spelling",
+            ]
+            if source == "marketplace"
+            else [
+                "Try different keywords",
+                "Use find_agent to search the marketplace",
+                "Check your library at /library",
+            ]
+        )
+        no_results_msg = (
+            f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
+            if source == "marketplace"
+            else f"No agents matching '{query}' found in your library."
+        )
+        return NoResultsResponse(
+            message=no_results_msg, session_id=session_id, suggestions=suggestions
+        )
+
+    title = f"Found {len(agents)} agent{'s' if len(agents) != 1 else ''} "
+    title += (
+        f"for '{query}'"
+        if source == "marketplace"
+        else f"in your library for '{query}'"
+    )
+
+    message = (
+        "Now you have found some options for the user to choose from. "
+        "You can add a link to a recommended agent at: /marketplace/agent/agent_id "
+        "Please ask the user if they would like to use any of these agents."
+        if source == "marketplace"
+        else "Found agents in the user's library. You can provide a link to view an agent at: "
+        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute."
+    )
+
+    return AgentsFoundResponse(
+        message=message,
+        title=title,
+        agents=agents,
+        count=len(agents),
+        session_id=session_id,
+    )