fix: wrap generate_agent call in try/except for consistency

Add exception handler for AgentGeneratorNotConfiguredError in generate_agent
call for defensive consistency, even though decompose_goal would typically
catch it first.

Addresses CodeRabbit review suggestion.

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

@@ -0,0 +1,125 @@
+---
+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

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

@@ -0,0 +1,49 @@
+---
+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

@@ -0,0 +1,38 @@
+---
+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

@@ -0,0 +1,80 @@
+---
+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

@@ -0,0 +1,36 @@
+---
+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

@@ -0,0 +1,28 @@
+---
+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

@@ -0,0 +1,99 @@
+---
+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

@@ -0,0 +1,59 @@
+---
+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

@@ -0,0 +1,31 @@
+---
+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

@@ -0,0 +1,49 @@
+---
+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

@@ -0,0 +1,35 @@
+---
+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

@@ -0,0 +1,50 @@
+---
+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

@@ -0,0 +1,74 @@
+---
+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

@@ -0,0 +1,56 @@
+---
+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

@@ -0,0 +1,82 @@
+---
+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

@@ -0,0 +1,80 @@
+---
+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

@@ -0,0 +1,28 @@
+---
+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

@@ -0,0 +1,70 @@
+---
+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

@@ -0,0 +1,32 @@
+---
+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

@@ -0,0 +1,50 @@
+---
+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

@@ -0,0 +1,45 @@
+---
+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

@@ -0,0 +1,37 @@
+---
+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

@@ -0,0 +1,49 @@
+---
+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

@@ -0,0 +1,82 @@
+---
+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

@@ -0,0 +1,24 @@
+---
+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

@@ -0,0 +1,57 @@
+---
+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

@@ -0,0 +1,26 @@
+---
+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

@@ -0,0 +1,47 @@
+---
+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

@@ -0,0 +1,40 @@
+---
+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

@@ -0,0 +1,38 @@
+---
+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

@@ -0,0 +1,46 @@
+---
+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

@@ -0,0 +1,82 @@
+---
+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

@@ -0,0 +1,28 @@
+---
+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

@@ -0,0 +1,39 @@
+---
+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

@@ -0,0 +1,45 @@
+---
+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

@@ -0,0 +1,29 @@
+---
+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

@@ -0,0 +1,74 @@
+---
+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

@@ -0,0 +1,58 @@
+---
+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

@@ -0,0 +1,44 @@
+---
+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

@@ -0,0 +1,40 @@
+---
+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

@@ -0,0 +1,73 @@
+---
+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

@@ -0,0 +1,41 @@
+---
+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

@@ -0,0 +1,26 @@
+---
+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

@@ -0,0 +1,79 @@
+---
+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

@@ -0,0 +1,38 @@
+---
+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>
+}
+```

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

@@ -93,5 +93,5 @@ jobs:
 
             Error logs:
             ${{ toJSON(fromJSON(steps.failure_details.outputs.result).errorLogs) }}
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
           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: ANTHROPIC_API_KEY secret must be configured
+# Requirements: CLAUDE_CODE_OAUTH_TOKEN secret must be configured
 
 name: Claude Dependabot PR Review
 
@@ -308,7 +308,7 @@ jobs:
         id: claude_review
         uses: anthropics/claude-code-action@v1
         with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
           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

@@ -323,7 +323,7 @@ jobs:
         id: claude
         uses: anthropics/claude-code-action@v1
         with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
           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/docs-block-sync.yml

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

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

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

.github/workflows/docs-enhance.yml

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

.gitignore

@@ -178,5 +178,4 @@ autogpt_platform/backend/settings.py
 *.ign.*
 .test-contents
 .claude/settings.local.json
-CLAUDE.local.md
 /autogpt_platform/backend/logs

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
 
@@ -45,12 +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
 
+### 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/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/backend/CLAUDE.md

@@ -1,124 +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
-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 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.
-
-### 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/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

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

@@ -28,6 +28,7 @@ from backend.executor.manager import get_db_async_client
 from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
+settings = Settings()
 
 
 class ExecutionAnalyticsRequest(BaseModel):
@@ -63,6 +64,8 @@ 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):
@@ -224,11 +227,6 @@ 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()
 
@@ -320,6 +318,8 @@ 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,
                 )
             )
 
@@ -349,6 +349,9 @@ 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
@@ -387,6 +390,8 @@ 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
@@ -416,6 +421,8 @@ 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:
@@ -429,6 +436,8 @@ 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/chat/service.py

@@ -4,14 +4,9 @@ from collections.abc import AsyncGenerator
 from typing import Any
 
 import orjson
-from langfuse import Langfuse
-from openai import (
-    APIConnectionError,
-    APIError,
-    APIStatusError,
-    AsyncOpenAI,
-    RateLimitError,
-)
+from langfuse import get_client, propagate_attributes
+from langfuse.openai import openai  # type: ignore
+from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -21,7 +16,6 @@ from backend.data.understanding import (
 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,
@@ -50,10 +44,10 @@ logger = logging.getLogger(__name__)
 
 config = ChatConfig()
 settings = Settings()
-client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+client = openai.AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
-# Langfuse client (lazy initialization)
-_langfuse_client: Langfuse | None = None
+
+langfuse = get_client()
 
 
 class LangfuseNotConfiguredError(Exception):
@@ -69,65 +63,6 @@ def _is_langfuse_configured() -> bool:
     )
 
 
-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.
 
@@ -139,8 +74,6 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         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)
 
@@ -158,7 +91,7 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         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
+    return compiled, understanding
 
 
 async def _generate_session_title(message: str) -> str | None:
@@ -217,6 +150,7 @@ async def assign_user_to_session(
 async def stream_chat_completion(
     session_id: str,
     message: str | None = None,
+    tool_call_response: str | None = None,
     is_user_message: bool = True,
     user_id: str | None = None,
     retry_count: int = 0,
@@ -256,11 +190,6 @@ async def stream_chat_completion(
         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)
@@ -299,9 +228,6 @@ async def stream_chat_completion(
             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)}"
@@ -339,297 +265,259 @@ async def stream_chat_completion(
             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
-    ]
+    system_prompt, understanding = await _build_system_prompt(user_id)
 
     # 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(
+    input = message
+    if not message and tool_call_response:
+        input = tool_call_response
+
+    langfuse = get_client()
+    with langfuse.start_as_current_observation(
+        as_type="span",
+        name="user-copilot-request",
+        input=input,
+    ) as span:
+        with propagate_attributes(
             session_id=session_id,
             user_id=user_id,
-            tags=[env, "copilot"],
-        )
-    except Exception as e:
-        logger.warning(f"Failed to create Langfuse trace: {e}")
+            tags=["copilot"],
+            metadata={
+                "users_information": format_understanding_for_prompt(understanding)[
+                    :200
+                ]  # langfuse only accepts upto to 200 chars
+            },
+        ):
 
-    # 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,
+            # Initialize variables that will be used in finally block (must be defined before try)
+            assistant_response = ChatMessage(
+                role="assistant",
+                content="",
             )
-            if trace
-            else None
-        )
+            accumulated_tool_calls: list[dict[str, Any]] = []
 
-        try:
-            async for chunk in _stream_chat_chunks(
-                session=session,
-                tools=tools,
-                system_prompt=system_prompt,
-                text_block_id=text_block_id,
-            ):
+            # Wrap main logic in try/finally to ensure Langfuse observations are always ended
+            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
 
-                if isinstance(chunk, StreamTextStart):
-                    # Emit text-start before first text delta
-                    if not has_received_text:
+            # 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)
+
+            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, 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
+                    elif isinstance(chunk, StreamTextEnd):
+                        # Emit text-end after text completes
+                        if has_received_text and not text_streaming_ended:
+                            text_streaming_ended = True
+                            if assistant_response.content:
+                                logger.warn(
+                                    f"StreamTextEnd: Attempting to set output {assistant_response.content}"
+                                )
+                                span.update_trace(output=assistant_response.content)
+                                span.update(output=assistant_response.content)
+                            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
-                        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,
+                    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
+                        )
+                if assistant_response.content:
+                    langfuse.update_current_trace(output=assistant_response.content)
+                    langfuse.update_current_span(output=assistant_response.content)
+                elif tool_response_messages:
+                    langfuse.update_current_trace(output=str(tool_response_messages))
+                    langfuse.update_current_span(output=str(tool_response_messages))
+
+            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:
-                    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)
+                    # Non-retryable error or max retries exceeded
+                    # Save any partial progress before reporting error
+                    messages_to_save: list[ChatMessage] = []
 
-            # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
-            is_retryable = isinstance(e, (orjson.JSONDecodeError, KeyError, TypeError))
+                    # 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)
 
-            if is_retryable and retry_count < config.max_retries:
+                    # 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"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
+                    f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
                 )
-                should_retry = True
-            else:
-                # Non-retryable error or max retries exceeded
-                # Save any partial progress before reporting error
-                messages_to_save: list[ChatMessage] = []
+                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
 
-                # 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:
+            # Normal completion path - save session and handle tool call continuation
             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 [])}"
+                f"Normal completion path: session={session.session_id}, "
+                f"current message_count={len(session.messages)}"
             )
 
-        # 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)}"
-        )
+            # Build the messages list in the correct order
+            messages_to_save: list[ChatMessage] = []
 
-        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
-                    ),
+            # 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 [])}"
                 )
-                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}")
+            # 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,
+                    tool_call_response=str(tool_response_messages),
+                ):
+                    yield chunk
 
 
 # Retry configuration for OpenAI API calls
@@ -903,5 +791,4 @@ async def _yield_tool_call(
         session=session,
     )
 
-    logger.info(f"Yielding Tool execution response: {tool_execution_response}")
     yield tool_execution_response

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

@@ -7,9 +7,15 @@ from backend.api.features.chat.model import ChatSession
 from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
 from .base import BaseTool
+from .create_agent import CreateAgentTool
+from .edit_agent import EditAgentTool
 from .find_agent import FindAgentTool
+from .find_block import FindBlockTool
 from .find_library_agent import FindLibraryAgentTool
+from .get_doc_page import GetDocPageTool
 from .run_agent import RunAgentTool
+from .run_block import RunBlockTool
+from .search_docs import SearchDocsTool
 
 if TYPE_CHECKING:
     from backend.api.features.chat.response_model import StreamToolOutputAvailable
@@ -17,10 +23,16 @@ if TYPE_CHECKING:
 # Single source of truth for all tools
 TOOL_REGISTRY: dict[str, BaseTool] = {
     "add_understanding": AddUnderstandingTool(),
+    "create_agent": CreateAgentTool(),
+    "edit_agent": EditAgentTool(),
     "find_agent": FindAgentTool(),
+    "find_block": FindBlockTool(),
     "find_library_agent": FindLibraryAgentTool(),
     "run_agent": RunAgentTool(),
-    "agent_output": AgentOutputTool(),
+    "run_block": RunBlockTool(),
+    "view_agent_output": AgentOutputTool(),
+    "search_docs": SearchDocsTool(),
+    "get_doc_page": GetDocPageTool(),
 }
 
 # Export individual tool instances for backwards compatibility

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

@@ -3,6 +3,8 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 from backend.data.understanding import (
     BusinessUnderstandingInput,
@@ -59,6 +61,7 @@ and automations for the user's specific needs."""
         """Requires authentication to store user-specific data."""
         return True
 
+    @observe(as_type="tool", name="add_understanding")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -0,0 +1,28 @@
+"""Agent generator package - Creates agents from natural language."""
+
+from .core import (
+    AgentGeneratorNotConfiguredError,
+    decompose_goal,
+    generate_agent,
+    generate_agent_patch,
+    get_agent_as_json,
+    json_to_graph,
+    save_agent_to_library,
+)
+from .service import health_check as check_external_service_health
+from .service import is_external_service_configured
+
+__all__ = [
+    # Core functions
+    "decompose_goal",
+    "generate_agent",
+    "generate_agent_patch",
+    "save_agent_to_library",
+    "get_agent_as_json",
+    "json_to_graph",
+    # Exceptions
+    "AgentGeneratorNotConfiguredError",
+    # Service
+    "is_external_service_configured",
+    "check_external_service_health",
+]

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py

@@ -0,0 +1,277 @@
+"""Core agent generation functions."""
+
+import logging
+import uuid
+from typing import Any
+
+from backend.api.features.library import db as library_db
+from backend.data.graph import Graph, Link, Node, create_graph
+
+from .service import (
+    decompose_goal_external,
+    generate_agent_external,
+    generate_agent_patch_external,
+    is_external_service_configured,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class AgentGeneratorNotConfiguredError(Exception):
+    """Raised when the external Agent Generator service is not configured."""
+
+    pass
+
+
+def _check_service_configured() -> None:
+    """Check if the external Agent Generator service is configured.
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the service is not configured.
+    """
+    if not is_external_service_configured():
+        raise AgentGeneratorNotConfiguredError(
+            "Agent Generator service is not configured. "
+            "Set AGENTGENERATOR_HOST environment variable to enable agent generation."
+        )
+
+
+async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
+    """Break down a goal into steps or return clarifying questions.
+
+    Args:
+        description: Natural language goal description
+        context: Additional context (e.g., answers to previous questions)
+
+    Returns:
+        Dict with either:
+        - {"type": "clarifying_questions", "questions": [...]}
+        - {"type": "instructions", "steps": [...]}
+        Or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
+    """
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for decompose_goal")
+    return await decompose_goal_external(description, context)
+
+
+async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
+    """Generate agent JSON from instructions.
+
+    Args:
+        instructions: Structured instructions from decompose_goal
+
+    Returns:
+        Agent JSON dict or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
+    """
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for generate_agent")
+    result = await generate_agent_external(instructions)
+    if result:
+        # Ensure required fields
+        if "id" not in result:
+            result["id"] = str(uuid.uuid4())
+        if "version" not in result:
+            result["version"] = 1
+        if "is_active" not in result:
+            result["is_active"] = True
+    return result
+
+
+def json_to_graph(agent_json: dict[str, Any]) -> Graph:
+    """Convert agent JSON dict to Graph model.
+
+    Args:
+        agent_json: Agent JSON with nodes and links
+
+    Returns:
+        Graph ready for saving
+    """
+    nodes = []
+    for n in agent_json.get("nodes", []):
+        node = Node(
+            id=n.get("id", str(uuid.uuid4())),
+            block_id=n["block_id"],
+            input_default=n.get("input_default", {}),
+            metadata=n.get("metadata", {}),
+        )
+        nodes.append(node)
+
+    links = []
+    for link_data in agent_json.get("links", []):
+        link = Link(
+            id=link_data.get("id", str(uuid.uuid4())),
+            source_id=link_data["source_id"],
+            sink_id=link_data["sink_id"],
+            source_name=link_data["source_name"],
+            sink_name=link_data["sink_name"],
+            is_static=link_data.get("is_static", False),
+        )
+        links.append(link)
+
+    return Graph(
+        id=agent_json.get("id", str(uuid.uuid4())),
+        version=agent_json.get("version", 1),
+        is_active=agent_json.get("is_active", True),
+        name=agent_json.get("name", "Generated Agent"),
+        description=agent_json.get("description", ""),
+        nodes=nodes,
+        links=links,
+    )
+
+
+def _reassign_node_ids(graph: Graph) -> None:
+    """Reassign all node and link IDs to new UUIDs.
+
+    This is needed when creating a new version to avoid unique constraint violations.
+    """
+    # Create mapping from old node IDs to new UUIDs
+    id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
+
+    # Reassign node IDs
+    for node in graph.nodes:
+        node.id = id_map[node.id]
+
+    # Update link references to use new node IDs
+    for link in graph.links:
+        link.id = str(uuid.uuid4())  # Also give links new IDs
+        if link.source_id in id_map:
+            link.source_id = id_map[link.source_id]
+        if link.sink_id in id_map:
+            link.sink_id = id_map[link.sink_id]
+
+
+async def save_agent_to_library(
+    agent_json: dict[str, Any], user_id: str, is_update: bool = False
+) -> tuple[Graph, Any]:
+    """Save agent to database and user's library.
+
+    Args:
+        agent_json: Agent JSON dict
+        user_id: User ID
+        is_update: Whether this is an update to an existing agent
+
+    Returns:
+        Tuple of (created Graph, LibraryAgent)
+    """
+    from backend.data.graph import get_graph_all_versions
+
+    graph = json_to_graph(agent_json)
+
+    if is_update:
+        # For updates, keep the same graph ID but increment version
+        # and reassign node/link IDs to avoid conflicts
+        if graph.id:
+            existing_versions = await get_graph_all_versions(graph.id, user_id)
+            if existing_versions:
+                latest_version = max(v.version for v in existing_versions)
+                graph.version = latest_version + 1
+                # Reassign node IDs (but keep graph ID the same)
+                _reassign_node_ids(graph)
+                logger.info(f"Updating agent {graph.id} to version {graph.version}")
+    else:
+        # For new agents, always generate a fresh UUID to avoid collisions
+        graph.id = str(uuid.uuid4())
+        graph.version = 1
+        # Reassign all node IDs as well
+        _reassign_node_ids(graph)
+        logger.info(f"Creating new agent with ID {graph.id}")
+
+    # Save to database
+    created_graph = await create_graph(graph, user_id)
+
+    # Add to user's library (or update existing library agent)
+    library_agents = await library_db.create_library_agent(
+        graph=created_graph,
+        user_id=user_id,
+        sensitive_action_safe_mode=True,
+        create_library_agents_for_sub_graphs=False,
+    )
+
+    return created_graph, library_agents[0]
+
+
+async def get_agent_as_json(
+    graph_id: str, user_id: str | None
+) -> dict[str, Any] | None:
+    """Fetch an agent and convert to JSON format for editing.
+
+    Args:
+        graph_id: Graph ID or library agent ID
+        user_id: User ID
+
+    Returns:
+        Agent as JSON dict or None if not found
+    """
+    from backend.data.graph import get_graph
+
+    # Try to get the graph (version=None gets the active version)
+    graph = await get_graph(graph_id, version=None, user_id=user_id)
+    if not graph:
+        return None
+
+    # Convert to JSON format
+    nodes = []
+    for node in graph.nodes:
+        nodes.append(
+            {
+                "id": node.id,
+                "block_id": node.block_id,
+                "input_default": node.input_default,
+                "metadata": node.metadata,
+            }
+        )
+
+    links = []
+    for node in graph.nodes:
+        for link in node.output_links:
+            links.append(
+                {
+                    "id": link.id,
+                    "source_id": link.source_id,
+                    "sink_id": link.sink_id,
+                    "source_name": link.source_name,
+                    "sink_name": link.sink_name,
+                    "is_static": link.is_static,
+                }
+            )
+
+    return {
+        "id": graph.id,
+        "name": graph.name,
+        "description": graph.description,
+        "version": graph.version,
+        "is_active": graph.is_active,
+        "nodes": nodes,
+        "links": links,
+    }
+
+
+async def generate_agent_patch(
+    update_request: str, current_agent: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Update an existing agent using natural language.
+
+    The external Agent Generator service handles:
+    - Generating the patch
+    - Applying the patch
+    - Fixing and validating the result
+
+    Args:
+        update_request: Natural language description of changes
+        current_agent: Current agent JSON
+
+    Returns:
+        Updated agent JSON, clarifying questions dict, or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
+    """
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for generate_agent_patch")
+    return await generate_agent_patch_external(update_request, current_agent)

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

@@ -0,0 +1,269 @@
+"""External Agent Generator service client.
+
+This module provides a client for communicating with the external Agent Generator
+microservice. When AGENTGENERATOR_HOST is configured, the agent generation functions
+will delegate to the external service instead of using the built-in LLM-based implementation.
+"""
+
+import logging
+from typing import Any
+
+import httpx
+
+from backend.util.settings import Settings
+
+logger = logging.getLogger(__name__)
+
+_client: httpx.AsyncClient | None = None
+_settings: Settings | None = None
+
+
+def _get_settings() -> Settings:
+    """Get or create settings singleton."""
+    global _settings
+    if _settings is None:
+        _settings = Settings()
+    return _settings
+
+
+def is_external_service_configured() -> bool:
+    """Check if external Agent Generator service is configured."""
+    settings = _get_settings()
+    return bool(settings.config.agentgenerator_host)
+
+
+def _get_base_url() -> str:
+    """Get the base URL for the external service."""
+    settings = _get_settings()
+    host = settings.config.agentgenerator_host
+    port = settings.config.agentgenerator_port
+    return f"http://{host}:{port}"
+
+
+def _get_client() -> httpx.AsyncClient:
+    """Get or create the HTTP client for the external service."""
+    global _client
+    if _client is None:
+        settings = _get_settings()
+        _client = httpx.AsyncClient(
+            base_url=_get_base_url(),
+            timeout=httpx.Timeout(settings.config.agentgenerator_timeout),
+        )
+    return _client
+
+
+async def decompose_goal_external(
+    description: str, context: str = ""
+) -> dict[str, Any] | None:
+    """Call the external service to decompose a goal.
+
+    Args:
+        description: Natural language goal description
+        context: Additional context (e.g., answers to previous questions)
+
+    Returns:
+        Dict with either:
+        - {"type": "clarifying_questions", "questions": [...]}
+        - {"type": "instructions", "steps": [...]}
+        - {"type": "unachievable_goal", ...}
+        - {"type": "vague_goal", ...}
+        Or None on error
+    """
+    client = _get_client()
+
+    # Build the request payload
+    payload: dict[str, Any] = {"description": description}
+    if context:
+        # The external service uses user_instruction for additional context
+        payload["user_instruction"] = context
+
+    try:
+        response = await client.post("/api/decompose-description", json=payload)
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        # Map the response to the expected format
+        response_type = data.get("type")
+        if response_type == "instructions":
+            return {"type": "instructions", "steps": data.get("steps", [])}
+        elif response_type == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+        elif response_type == "unachievable_goal":
+            return {
+                "type": "unachievable_goal",
+                "reason": data.get("reason"),
+                "suggested_goal": data.get("suggested_goal"),
+            }
+        elif response_type == "vague_goal":
+            return {
+                "type": "vague_goal",
+                "suggested_goal": data.get("suggested_goal"),
+            }
+        else:
+            logger.error(
+                f"Unknown response type from external service: {response_type}"
+            )
+            return None
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def generate_agent_external(
+    instructions: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Call the external service to generate an agent from instructions.
+
+    Args:
+        instructions: Structured instructions from decompose_goal
+
+    Returns:
+        Agent JSON dict or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.post(
+            "/api/generate-agent", json={"instructions": instructions}
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def generate_agent_patch_external(
+    update_request: str, current_agent: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Call the external service to generate a patch for an existing agent.
+
+    Args:
+        update_request: Natural language description of changes
+        current_agent: Current agent JSON
+
+    Returns:
+        Updated agent JSON, clarifying questions dict, or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.post(
+            "/api/update-agent",
+            json={
+                "update_request": update_request,
+                "current_agent_json": current_agent,
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        # Check if it's clarifying questions
+        if data.get("type") == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+
+        # Otherwise return the updated agent JSON
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def get_blocks_external() -> list[dict[str, Any]] | None:
+    """Get available blocks from the external service.
+
+    Returns:
+        List of block info dicts or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.get("/api/blocks")
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error("External service returned error getting blocks")
+            return None
+
+        return data.get("blocks", [])
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error getting blocks from external service: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error getting blocks from external service: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error getting blocks from external service: {e}")
+        return None
+
+
+async def health_check() -> bool:
+    """Check if the external service is healthy.
+
+    Returns:
+        True if healthy, False otherwise
+    """
+    if not is_external_service_configured():
+        return False
+
+    client = _get_client()
+
+    try:
+        response = await client.get("/health")
+        response.raise_for_status()
+        data = response.json()
+        return data.get("status") == "healthy" and data.get("blocks_loaded", False)
+    except Exception as e:
+        logger.warning(f"External agent generator health check failed: {e}")
+        return False
+
+
+async def close_client() -> None:
+    """Close the HTTP client."""
+    global _client
+    if _client is not None:
+        await _client.aclose()
+        _client = None

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

@@ -5,6 +5,7 @@ import re
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
+from langfuse import observe
 from pydantic import BaseModel, field_validator
 
 from backend.api.features.chat.model import ChatSession
@@ -103,7 +104,7 @@ class AgentOutputTool(BaseTool):
 
     @property
     def name(self) -> str:
-        return "agent_output"
+        return "view_agent_output"
 
     @property
     def description(self) -> str:
@@ -328,6 +329,7 @@ class AgentOutputTool(BaseTool):
             total_executions=len(available_executions) if available_executions else 1,
         )
 
+    @observe(as_type="tool", name="view_agent_output")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -0,0 +1,238 @@
+"""CreateAgentTool - Creates agents from natural language descriptions."""
+
+import logging
+from typing import Any
+
+from langfuse import observe
+
+from backend.api.features.chat.model import ChatSession
+
+from .agent_generator import (
+    AgentGeneratorNotConfiguredError,
+    decompose_goal,
+    generate_agent,
+    save_agent_to_library,
+)
+from .base import BaseTool
+from .models import (
+    AgentPreviewResponse,
+    AgentSavedResponse,
+    ClarificationNeededResponse,
+    ClarifyingQuestion,
+    ErrorResponse,
+    ToolResponseBase,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class CreateAgentTool(BaseTool):
+    """Tool for creating agents from natural language descriptions."""
+
+    @property
+    def name(self) -> str:
+        return "create_agent"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Create a new agent workflow from a natural language description. "
+            "First generates a preview, then saves to library if save=true."
+        )
+
+    @property
+    def requires_auth(self) -> bool:
+        return True
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "description": {
+                    "type": "string",
+                    "description": (
+                        "Natural language description of what the agent should do. "
+                        "Be specific about inputs, outputs, and the workflow steps."
+                    ),
+                },
+                "context": {
+                    "type": "string",
+                    "description": (
+                        "Additional context or answers to previous clarifying questions. "
+                        "Include any preferences or constraints mentioned by the user."
+                    ),
+                },
+                "save": {
+                    "type": "boolean",
+                    "description": (
+                        "Whether to save the agent to the user's library. "
+                        "Default is true. Set to false for preview only."
+                    ),
+                    "default": True,
+                },
+            },
+            "required": ["description"],
+        }
+
+    @observe(as_type="tool", name="create_agent")
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Execute the create_agent tool.
+
+        Flow:
+        1. Decompose the description into steps (may return clarifying questions)
+        2. Generate agent JSON (external service handles fixing and validation)
+        3. Preview or save based on the save parameter
+        """
+        description = kwargs.get("description", "").strip()
+        context = kwargs.get("context", "")
+        save = kwargs.get("save", True)
+        session_id = session.session_id if session else None
+
+        if not description:
+            return ErrorResponse(
+                message="Please provide a description of what the agent should do.",
+                error="Missing description parameter",
+                session_id=session_id,
+            )
+
+        # Step 1: Decompose goal into steps
+        try:
+            decomposition_result = await decompose_goal(description, context)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent generation is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
+            )
+
+        if decomposition_result is None:
+            return ErrorResponse(
+                message="Failed to analyze the goal. Please try rephrasing.",
+                error="Decomposition failed",
+                session_id=session_id,
+            )
+
+        # Check if LLM returned clarifying questions
+        if decomposition_result.get("type") == "clarifying_questions":
+            questions = decomposition_result.get("questions", [])
+            return ClarificationNeededResponse(
+                message=(
+                    "I need some more information to create this agent. "
+                    "Please answer the following questions:"
+                ),
+                questions=[
+                    ClarifyingQuestion(
+                        question=q.get("question", ""),
+                        keyword=q.get("keyword", ""),
+                        example=q.get("example"),
+                    )
+                    for q in questions
+                ],
+                session_id=session_id,
+            )
+
+        # Check for unachievable/vague goals
+        if decomposition_result.get("type") == "unachievable_goal":
+            suggested = decomposition_result.get("suggested_goal", "")
+            reason = decomposition_result.get("reason", "")
+            return ErrorResponse(
+                message=(
+                    f"This goal cannot be accomplished with the available blocks. "
+                    f"{reason} "
+                    f"Suggestion: {suggested}"
+                ),
+                error="unachievable_goal",
+                details={"suggested_goal": suggested, "reason": reason},
+                session_id=session_id,
+            )
+
+        if decomposition_result.get("type") == "vague_goal":
+            suggested = decomposition_result.get("suggested_goal", "")
+            return ErrorResponse(
+                message=(
+                    f"The goal is too vague to create a specific workflow. "
+                    f"Suggestion: {suggested}"
+                ),
+                error="vague_goal",
+                details={"suggested_goal": suggested},
+                session_id=session_id,
+            )
+
+        # Step 2: Generate agent JSON (external service handles fixing and validation)
+        try:
+            agent_json = await generate_agent(decomposition_result)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent generation is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
+            )
+
+        if agent_json is None:
+            return ErrorResponse(
+                message="Failed to generate the agent. Please try again.",
+                error="Generation failed",
+                session_id=session_id,
+            )
+
+        agent_name = agent_json.get("name", "Generated Agent")
+        agent_description = agent_json.get("description", "")
+        node_count = len(agent_json.get("nodes", []))
+        link_count = len(agent_json.get("links", []))
+
+        # Step 3: Preview or save
+        if not save:
+            return AgentPreviewResponse(
+                message=(
+                    f"I've generated an agent called '{agent_name}' with {node_count} blocks. "
+                    f"Review it and call create_agent with save=true to save it to your library."
+                ),
+                agent_json=agent_json,
+                agent_name=agent_name,
+                description=agent_description,
+                node_count=node_count,
+                link_count=link_count,
+                session_id=session_id,
+            )
+
+        # Save to library
+        if not user_id:
+            return ErrorResponse(
+                message="You must be logged in to save agents.",
+                error="auth_required",
+                session_id=session_id,
+            )
+
+        try:
+            created_graph, library_agent = await save_agent_to_library(
+                agent_json, user_id
+            )
+
+            return AgentSavedResponse(
+                message=f"Agent '{created_graph.name}' has been saved to your library!",
+                agent_id=created_graph.id,
+                agent_name=created_graph.name,
+                library_agent_id=library_agent.id,
+                library_agent_link=f"/library/{library_agent.id}",
+                agent_page_link=f"/build?flowID={created_graph.id}",
+                session_id=session_id,
+            )
+        except Exception as e:
+            return ErrorResponse(
+                message=f"Failed to save the agent: {str(e)}",
+                error="save_failed",
+                details={"exception": str(e)},
+                session_id=session_id,
+            )

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

@@ -0,0 +1,224 @@
+"""EditAgentTool - Edits existing agents using natural language."""
+
+import logging
+from typing import Any
+
+from langfuse import observe
+
+from backend.api.features.chat.model import ChatSession
+
+from .agent_generator import (
+    AgentGeneratorNotConfiguredError,
+    generate_agent_patch,
+    get_agent_as_json,
+    save_agent_to_library,
+)
+from .base import BaseTool
+from .models import (
+    AgentPreviewResponse,
+    AgentSavedResponse,
+    ClarificationNeededResponse,
+    ClarifyingQuestion,
+    ErrorResponse,
+    ToolResponseBase,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class EditAgentTool(BaseTool):
+    """Tool for editing existing agents using natural language."""
+
+    @property
+    def name(self) -> str:
+        return "edit_agent"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Edit an existing agent from the user's library using natural language. "
+            "Generates updates to the agent while preserving unchanged parts."
+        )
+
+    @property
+    def requires_auth(self) -> bool:
+        return True
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "agent_id": {
+                    "type": "string",
+                    "description": (
+                        "The ID of the agent to edit. "
+                        "Can be a graph ID or library agent ID."
+                    ),
+                },
+                "changes": {
+                    "type": "string",
+                    "description": (
+                        "Natural language description of what changes to make. "
+                        "Be specific about what to add, remove, or modify."
+                    ),
+                },
+                "context": {
+                    "type": "string",
+                    "description": (
+                        "Additional context or answers to previous clarifying questions."
+                    ),
+                },
+                "save": {
+                    "type": "boolean",
+                    "description": (
+                        "Whether to save the changes. "
+                        "Default is true. Set to false for preview only."
+                    ),
+                    "default": True,
+                },
+            },
+            "required": ["agent_id", "changes"],
+        }
+
+    @observe(as_type="tool", name="edit_agent")
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Execute the edit_agent tool.
+
+        Flow:
+        1. Fetch the current agent
+        2. Generate updated agent (external service handles fixing and validation)
+        3. Preview or save based on the save parameter
+        """
+        agent_id = kwargs.get("agent_id", "").strip()
+        changes = kwargs.get("changes", "").strip()
+        context = kwargs.get("context", "")
+        save = kwargs.get("save", True)
+        session_id = session.session_id if session else None
+
+        if not agent_id:
+            return ErrorResponse(
+                message="Please provide the agent ID to edit.",
+                error="Missing agent_id parameter",
+                session_id=session_id,
+            )
+
+        if not changes:
+            return ErrorResponse(
+                message="Please describe what changes you want to make.",
+                error="Missing changes parameter",
+                session_id=session_id,
+            )
+
+        # Step 1: Fetch current agent
+        current_agent = await get_agent_as_json(agent_id, user_id)
+
+        if current_agent is None:
+            return ErrorResponse(
+                message=f"Could not find agent with ID '{agent_id}' in your library.",
+                error="agent_not_found",
+                session_id=session_id,
+            )
+
+        # Build the update request with context
+        update_request = changes
+        if context:
+            update_request = f"{changes}\n\nAdditional context:\n{context}"
+
+        # Step 2: Generate updated agent (external service handles fixing and validation)
+        try:
+            result = await generate_agent_patch(update_request, current_agent)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent editing is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
+            )
+
+        if result is None:
+            return ErrorResponse(
+                message="Failed to generate changes. Please try rephrasing.",
+                error="Update generation failed",
+                session_id=session_id,
+            )
+
+        # Check if LLM returned clarifying questions
+        if result.get("type") == "clarifying_questions":
+            questions = result.get("questions", [])
+            return ClarificationNeededResponse(
+                message=(
+                    "I need some more information about the changes. "
+                    "Please answer the following questions:"
+                ),
+                questions=[
+                    ClarifyingQuestion(
+                        question=q.get("question", ""),
+                        keyword=q.get("keyword", ""),
+                        example=q.get("example"),
+                    )
+                    for q in questions
+                ],
+                session_id=session_id,
+            )
+
+        # Result is the updated agent JSON
+        updated_agent = result
+
+        agent_name = updated_agent.get("name", "Updated Agent")
+        agent_description = updated_agent.get("description", "")
+        node_count = len(updated_agent.get("nodes", []))
+        link_count = len(updated_agent.get("links", []))
+
+        # Step 3: Preview or save
+        if not save:
+            return AgentPreviewResponse(
+                message=(
+                    f"I've updated the agent. "
+                    f"The agent now has {node_count} blocks. "
+                    f"Review it and call edit_agent with save=true to save the changes."
+                ),
+                agent_json=updated_agent,
+                agent_name=agent_name,
+                description=agent_description,
+                node_count=node_count,
+                link_count=link_count,
+                session_id=session_id,
+            )
+
+        # Save to library (creates a new version)
+        if not user_id:
+            return ErrorResponse(
+                message="You must be logged in to save agents.",
+                error="auth_required",
+                session_id=session_id,
+            )
+
+        try:
+            created_graph, library_agent = await save_agent_to_library(
+                updated_agent, user_id, is_update=True
+            )
+
+            return AgentSavedResponse(
+                message=f"Updated agent '{created_graph.name}' has been saved to your library!",
+                agent_id=created_graph.id,
+                agent_name=created_graph.name,
+                library_agent_id=library_agent.id,
+                library_agent_link=f"/library/{library_agent.id}",
+                agent_page_link=f"/build?flowID={created_graph.id}",
+                session_id=session_id,
+            )
+        except Exception as e:
+            return ErrorResponse(
+                message=f"Failed to save the updated agent: {str(e)}",
+                error="save_failed",
+                details={"exception": str(e)},
+                session_id=session_id,
+            )

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

@@ -2,6 +2,8 @@
 
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -35,6 +37,7 @@ class FindAgentTool(BaseTool):
             "required": ["query"],
         }
 
+    @observe(as_type="tool", name="find_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

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

@@ -0,0 +1,194 @@
+import logging
+from typing import Any
+
+from langfuse import observe
+from prisma.enums import ContentType
+
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.chat.tools.base import BaseTool, ToolResponseBase
+from backend.api.features.chat.tools.models import (
+    BlockInfoSummary,
+    BlockInputFieldInfo,
+    BlockListResponse,
+    ErrorResponse,
+    NoResultsResponse,
+)
+from backend.api.features.store.hybrid_search import unified_hybrid_search
+from backend.data.block import get_block
+
+logger = logging.getLogger(__name__)
+
+
+class FindBlockTool(BaseTool):
+    """Tool for searching available blocks."""
+
+    @property
+    def name(self) -> str:
+        return "find_block"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Search for available blocks by name or description. "
+            "Blocks are reusable components that perform specific tasks like "
+            "sending emails, making API calls, processing text, etc. "
+            "IMPORTANT: Use this tool FIRST to get the block's 'id' before calling run_block. "
+            "The response includes each block's id, required_inputs, and input_schema."
+        )
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": (
+                        "Search query to find blocks by name or description. "
+                        "Use keywords like 'email', 'http', 'text', 'ai', etc."
+                    ),
+                },
+            },
+            "required": ["query"],
+        }
+
+    @property
+    def requires_auth(self) -> bool:
+        return True
+
+    @observe(as_type="tool", name="find_block")
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Search for blocks matching the query.
+
+        Args:
+            user_id: User ID (required)
+            session: Chat session
+            query: Search query
+
+        Returns:
+            BlockListResponse: List of matching blocks
+            NoResultsResponse: No blocks found
+            ErrorResponse: Error message
+        """
+        query = kwargs.get("query", "").strip()
+        session_id = session.session_id
+
+        if not query:
+            return ErrorResponse(
+                message="Please provide a search query",
+                session_id=session_id,
+            )
+
+        try:
+            # Search for blocks using hybrid search
+            results, total = await unified_hybrid_search(
+                query=query,
+                content_types=[ContentType.BLOCK],
+                page=1,
+                page_size=10,
+            )
+
+            if not results:
+                return NoResultsResponse(
+                    message=f"No blocks found for '{query}'",
+                    suggestions=[
+                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
+                        "Check spelling of technical terms",
+                    ],
+                    session_id=session_id,
+                )
+
+            # Enrich results with full block information
+            blocks: list[BlockInfoSummary] = []
+            for result in results:
+                block_id = result["content_id"]
+                block = get_block(block_id)
+
+                if block:
+                    # Get input/output schemas
+                    input_schema = {}
+                    output_schema = {}
+                    try:
+                        input_schema = block.input_schema.jsonschema()
+                    except Exception:
+                        pass
+                    try:
+                        output_schema = block.output_schema.jsonschema()
+                    except Exception:
+                        pass
+
+                    # Get categories from block instance
+                    categories = []
+                    if hasattr(block, "categories") and block.categories:
+                        categories = [cat.value for cat in block.categories]
+
+                    # Extract required inputs for easier use
+                    required_inputs: list[BlockInputFieldInfo] = []
+                    if input_schema:
+                        properties = input_schema.get("properties", {})
+                        required_fields = set(input_schema.get("required", []))
+                        # Get credential field names to exclude from required inputs
+                        credentials_fields = set(
+                            block.input_schema.get_credentials_fields().keys()
+                        )
+
+                        for field_name, field_schema in properties.items():
+                            # Skip credential fields - they're handled separately
+                            if field_name in credentials_fields:
+                                continue
+
+                            required_inputs.append(
+                                BlockInputFieldInfo(
+                                    name=field_name,
+                                    type=field_schema.get("type", "string"),
+                                    description=field_schema.get("description", ""),
+                                    required=field_name in required_fields,
+                                    default=field_schema.get("default"),
+                                )
+                            )
+
+                    blocks.append(
+                        BlockInfoSummary(
+                            id=block_id,
+                            name=block.name,
+                            description=block.description or "",
+                            categories=categories,
+                            input_schema=input_schema,
+                            output_schema=output_schema,
+                            required_inputs=required_inputs,
+                        )
+                    )
+
+            if not blocks:
+                return NoResultsResponse(
+                    message=f"No blocks found for '{query}'",
+                    suggestions=[
+                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
+                    ],
+                    session_id=session_id,
+                )
+
+            return BlockListResponse(
+                message=(
+                    f"Found {len(blocks)} block(s) matching '{query}'. "
+                    "To execute a block, use run_block with the block's 'id' field "
+                    "and provide 'input_data' matching the block's input_schema."
+                ),
+                blocks=blocks,
+                count=len(blocks),
+                query=query,
+                session_id=session_id,
+            )
+
+        except Exception as e:
+            logger.error(f"Error searching blocks: {e}", exc_info=True)
+            return ErrorResponse(
+                message="Failed to search blocks",
+                error=str(e),
+                session_id=session_id,
+            )

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

@@ -2,6 +2,8 @@
 
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -41,6 +43,7 @@ class FindLibraryAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
+    @observe(as_type="tool", name="find_library_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

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

@@ -0,0 +1,151 @@
+"""GetDocPageTool - Fetch full content of a documentation page."""
+
+import logging
+from pathlib import Path
+from typing import Any
+
+from langfuse import observe
+
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.chat.tools.base import BaseTool
+from backend.api.features.chat.tools.models import (
+    DocPageResponse,
+    ErrorResponse,
+    ToolResponseBase,
+)
+
+logger = logging.getLogger(__name__)
+
+# Base URL for documentation (can be configured)
+DOCS_BASE_URL = "https://docs.agpt.co"
+
+
+class GetDocPageTool(BaseTool):
+    """Tool for fetching full content of a documentation page."""
+
+    @property
+    def name(self) -> str:
+        return "get_doc_page"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Get the full content of a documentation page by its path. "
+            "Use this after search_docs to read the complete content of a relevant page."
+        )
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "path": {
+                    "type": "string",
+                    "description": (
+                        "The path to the documentation file, as returned by search_docs. "
+                        "Example: 'platform/block-sdk-guide.md'"
+                    ),
+                },
+            },
+            "required": ["path"],
+        }
+
+    @property
+    def requires_auth(self) -> bool:
+        return False  # Documentation is public
+
+    def _get_docs_root(self) -> Path:
+        """Get the documentation root directory."""
+        this_file = Path(__file__)
+        project_root = this_file.parent.parent.parent.parent.parent.parent.parent.parent
+        return project_root / "docs"
+
+    def _extract_title(self, content: str, fallback: str) -> str:
+        """Extract title from markdown content."""
+        lines = content.split("\n")
+        for line in lines:
+            if line.startswith("# "):
+                return line[2:].strip()
+        return fallback
+
+    def _make_doc_url(self, path: str) -> str:
+        """Create a URL for a documentation page."""
+        url_path = path.rsplit(".", 1)[0] if "." in path else path
+        return f"{DOCS_BASE_URL}/{url_path}"
+
+    @observe(as_type="tool", name="get_doc_page")
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Fetch full content of a documentation page.
+
+        Args:
+            user_id: User ID (not required for docs)
+            session: Chat session
+            path: Path to the documentation file
+
+        Returns:
+            DocPageResponse: Full document content
+            ErrorResponse: Error message
+        """
+        path = kwargs.get("path", "").strip()
+        session_id = session.session_id if session else None
+
+        if not path:
+            return ErrorResponse(
+                message="Please provide a documentation path.",
+                error="Missing path parameter",
+                session_id=session_id,
+            )
+
+        # Sanitize path to prevent directory traversal
+        if ".." in path or path.startswith("/"):
+            return ErrorResponse(
+                message="Invalid documentation path.",
+                error="invalid_path",
+                session_id=session_id,
+            )
+
+        docs_root = self._get_docs_root()
+        full_path = docs_root / path
+
+        if not full_path.exists():
+            return ErrorResponse(
+                message=f"Documentation page not found: {path}",
+                error="not_found",
+                session_id=session_id,
+            )
+
+        # Ensure the path is within docs root
+        try:
+            full_path.resolve().relative_to(docs_root.resolve())
+        except ValueError:
+            return ErrorResponse(
+                message="Invalid documentation path.",
+                error="invalid_path",
+                session_id=session_id,
+            )
+
+        try:
+            content = full_path.read_text(encoding="utf-8")
+            title = self._extract_title(content, path)
+
+            return DocPageResponse(
+                message=f"Retrieved documentation page: {title}",
+                title=title,
+                path=path,
+                content=content,
+                doc_url=self._make_doc_url(path),
+                session_id=session_id,
+            )
+
+        except Exception as e:
+            logger.error(f"Failed to read documentation page {path}: {e}")
+            return ErrorResponse(
+                message=f"Failed to read documentation page: {str(e)}",
+                error="read_failed",
+                session_id=session_id,
+            )

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

@@ -21,6 +21,13 @@ class ResponseType(str, Enum):
     NO_RESULTS = "no_results"
     AGENT_OUTPUT = "agent_output"
     UNDERSTANDING_UPDATED = "understanding_updated"
+    AGENT_PREVIEW = "agent_preview"
+    AGENT_SAVED = "agent_saved"
+    CLARIFICATION_NEEDED = "clarification_needed"
+    BLOCK_LIST = "block_list"
+    BLOCK_OUTPUT = "block_output"
+    DOC_SEARCH_RESULTS = "doc_search_results"
+    DOC_PAGE = "doc_page"
 
 
 # Base response model
@@ -209,3 +216,121 @@ class UnderstandingUpdatedResponse(ToolResponseBase):
     type: ResponseType = ResponseType.UNDERSTANDING_UPDATED
     updated_fields: list[str] = Field(default_factory=list)
     current_understanding: dict[str, Any] = Field(default_factory=dict)
+
+
+# Agent generation models
+class ClarifyingQuestion(BaseModel):
+    """A question that needs user clarification."""
+
+    question: str
+    keyword: str
+    example: str | None = None
+
+
+class AgentPreviewResponse(ToolResponseBase):
+    """Response for previewing a generated agent before saving."""
+
+    type: ResponseType = ResponseType.AGENT_PREVIEW
+    agent_json: dict[str, Any]
+    agent_name: str
+    description: str
+    node_count: int
+    link_count: int = 0
+
+
+class AgentSavedResponse(ToolResponseBase):
+    """Response when an agent is saved to the library."""
+
+    type: ResponseType = ResponseType.AGENT_SAVED
+    agent_id: str
+    agent_name: str
+    library_agent_id: str
+    library_agent_link: str
+    agent_page_link: str  # Link to the agent builder/editor page
+
+
+class ClarificationNeededResponse(ToolResponseBase):
+    """Response when the LLM needs more information from the user."""
+
+    type: ResponseType = ResponseType.CLARIFICATION_NEEDED
+    questions: list[ClarifyingQuestion] = Field(default_factory=list)
+
+
+# Documentation search models
+class DocSearchResult(BaseModel):
+    """A single documentation search result."""
+
+    title: str
+    path: str
+    section: str
+    snippet: str  # Short excerpt for UI display
+    score: float
+    doc_url: str | None = None
+
+
+class DocSearchResultsResponse(ToolResponseBase):
+    """Response for search_docs tool."""
+
+    type: ResponseType = ResponseType.DOC_SEARCH_RESULTS
+    results: list[DocSearchResult]
+    count: int
+    query: str
+
+
+class DocPageResponse(ToolResponseBase):
+    """Response for get_doc_page tool."""
+
+    type: ResponseType = ResponseType.DOC_PAGE
+    title: str
+    path: str
+    content: str  # Full document content
+    doc_url: str | None = None
+
+
+# Block models
+class BlockInputFieldInfo(BaseModel):
+    """Information about a block input field."""
+
+    name: str
+    type: str
+    description: str = ""
+    required: bool = False
+    default: Any | None = None
+
+
+class BlockInfoSummary(BaseModel):
+    """Summary of a block for search results."""
+
+    id: str
+    name: str
+    description: str
+    categories: list[str]
+    input_schema: dict[str, Any]
+    output_schema: dict[str, Any]
+    required_inputs: list[BlockInputFieldInfo] = Field(
+        default_factory=list,
+        description="List of required input fields for this block",
+    )
+
+
+class BlockListResponse(ToolResponseBase):
+    """Response for find_block tool."""
+
+    type: ResponseType = ResponseType.BLOCK_LIST
+    blocks: list[BlockInfoSummary]
+    count: int
+    query: str
+    usage_hint: str = Field(
+        default="To execute a block, call run_block with block_id set to the block's "
+        "'id' field and input_data containing the required fields from input_schema."
+    )
+
+
+class BlockOutputResponse(ToolResponseBase):
+    """Response for run_block tool."""
+
+    type: ResponseType = ResponseType.BLOCK_OUTPUT
+    block_id: str
+    block_name: str
+    outputs: dict[str, list[Any]]
+    success: bool = True

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

@@ -3,6 +3,7 @@
 import logging
 from typing import Any
 
+from langfuse import observe
 from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
@@ -32,7 +33,7 @@ from .models import (
     UserReadiness,
 )
 from .utils import (
-    check_user_has_required_credentials,
+    build_missing_credentials_from_graph,
     extract_credentials_from_schema,
     fetch_graph_from_store_slug,
     get_or_create_library_agent,
@@ -154,6 +155,7 @@ class RunAgentTool(BaseTool):
         """All operations require authentication."""
         return True
 
+    @observe(as_type="tool", name="run_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -235,15 +237,13 @@ class RunAgentTool(BaseTool):
                 # Return credentials needed response with input data info
                 # The UI handles credential setup automatically, so the message
                 # focuses on asking about input data
-                credentials = extract_credentials_from_schema(
-                    graph.credentials_input_schema
+                requirements_creds_dict = build_missing_credentials_from_graph(
+                    graph, None
                 )
-                missing_creds_check = await check_user_has_required_credentials(
-                    user_id, credentials
+                missing_credentials_dict = build_missing_credentials_from_graph(
+                    graph, graph_credentials
                 )
-                missing_credentials_dict = {
-                    c.id: c.model_dump() for c in missing_creds_check
-                }
+                requirements_creds_list = list(requirements_creds_dict.values())
 
                 return SetupRequirementsResponse(
                     message=self._build_inputs_message(graph, MSG_WHAT_VALUES_TO_USE),
@@ -257,7 +257,7 @@ class RunAgentTool(BaseTool):
                             ready_to_run=False,
                         ),
                         requirements={
-                            "credentials": [c.model_dump() for c in credentials],
+                            "credentials": requirements_creds_list,
                             "inputs": self._get_inputs_list(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },

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

@@ -0,0 +1,305 @@
+"""Tool for executing blocks directly."""
+
+import logging
+from collections import defaultdict
+from typing import Any
+
+from langfuse import observe
+
+from backend.api.features.chat.model import ChatSession
+from backend.data.block import get_block
+from backend.data.execution import ExecutionContext
+from backend.data.model import CredentialsMetaInput
+from backend.integrations.creds_manager import IntegrationCredentialsManager
+from backend.util.exceptions import BlockError
+
+from .base import BaseTool
+from .models import (
+    BlockOutputResponse,
+    ErrorResponse,
+    SetupInfo,
+    SetupRequirementsResponse,
+    ToolResponseBase,
+    UserReadiness,
+)
+from .utils import build_missing_credentials_from_field_info
+
+logger = logging.getLogger(__name__)
+
+
+class RunBlockTool(BaseTool):
+    """Tool for executing a block and returning its outputs."""
+
+    @property
+    def name(self) -> str:
+        return "run_block"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Execute a specific block with the provided input data. "
+            "IMPORTANT: You MUST call find_block first to get the block's 'id' - "
+            "do NOT guess or make up block IDs. "
+            "Use the 'id' from find_block results and provide input_data "
+            "matching the block's required_inputs."
+        )
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "block_id": {
+                    "type": "string",
+                    "description": (
+                        "The block's 'id' field from find_block results. "
+                        "NEVER guess this - always get it from find_block first."
+                    ),
+                },
+                "input_data": {
+                    "type": "object",
+                    "description": (
+                        "Input values for the block. Use the 'required_inputs' field "
+                        "from find_block to see what fields are needed."
+                    ),
+                },
+            },
+            "required": ["block_id", "input_data"],
+        }
+
+    @property
+    def requires_auth(self) -> bool:
+        return True
+
+    async def _check_block_credentials(
+        self,
+        user_id: str,
+        block: Any,
+    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
+        """
+        Check if user has required credentials for a block.
+
+        Returns:
+            tuple[matched_credentials, missing_credentials]
+        """
+        matched_credentials: dict[str, CredentialsMetaInput] = {}
+        missing_credentials: list[CredentialsMetaInput] = []
+
+        # Get credential field info from block's input schema
+        credentials_fields_info = block.input_schema.get_credentials_fields_info()
+
+        if not credentials_fields_info:
+            return matched_credentials, missing_credentials
+
+        # Get user's available credentials
+        creds_manager = IntegrationCredentialsManager()
+        available_creds = await creds_manager.store.get_all_creds(user_id)
+
+        for field_name, field_info in credentials_fields_info.items():
+            # field_info.provider is a frozenset of acceptable providers
+            # field_info.supported_types is a frozenset of acceptable types
+            matching_cred = next(
+                (
+                    cred
+                    for cred in available_creds
+                    if cred.provider in field_info.provider
+                    and cred.type in field_info.supported_types
+                ),
+                None,
+            )
+
+            if matching_cred:
+                matched_credentials[field_name] = CredentialsMetaInput(
+                    id=matching_cred.id,
+                    provider=matching_cred.provider,  # type: ignore
+                    type=matching_cred.type,
+                    title=matching_cred.title,
+                )
+            else:
+                # Create a placeholder for the missing credential
+                provider = next(iter(field_info.provider), "unknown")
+                cred_type = next(iter(field_info.supported_types), "api_key")
+                missing_credentials.append(
+                    CredentialsMetaInput(
+                        id=field_name,
+                        provider=provider,  # type: ignore
+                        type=cred_type,  # type: ignore
+                        title=field_name.replace("_", " ").title(),
+                    )
+                )
+
+        return matched_credentials, missing_credentials
+
+    @observe(as_type="tool", name="run_block")
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Execute a block with the given input data.
+
+        Args:
+            user_id: User ID (required)
+            session: Chat session
+            block_id: Block UUID to execute
+            input_data: Input values for the block
+
+        Returns:
+            BlockOutputResponse: Block execution outputs
+            SetupRequirementsResponse: Missing credentials
+            ErrorResponse: Error message
+        """
+        block_id = kwargs.get("block_id", "").strip()
+        input_data = kwargs.get("input_data", {})
+        session_id = session.session_id
+
+        if not block_id:
+            return ErrorResponse(
+                message="Please provide a block_id",
+                session_id=session_id,
+            )
+
+        if not isinstance(input_data, dict):
+            return ErrorResponse(
+                message="input_data must be an object",
+                session_id=session_id,
+            )
+
+        if not user_id:
+            return ErrorResponse(
+                message="Authentication required",
+                session_id=session_id,
+            )
+
+        # Get the block
+        block = get_block(block_id)
+        if not block:
+            return ErrorResponse(
+                message=f"Block '{block_id}' not found",
+                session_id=session_id,
+            )
+
+        logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
+
+        # Check credentials
+        creds_manager = IntegrationCredentialsManager()
+        matched_credentials, missing_credentials = await self._check_block_credentials(
+            user_id, block
+        )
+
+        if missing_credentials:
+            # Return setup requirements response with missing credentials
+            credentials_fields_info = block.input_schema.get_credentials_fields_info()
+            missing_creds_dict = build_missing_credentials_from_field_info(
+                credentials_fields_info, set(matched_credentials.keys())
+            )
+            missing_creds_list = list(missing_creds_dict.values())
+
+            return SetupRequirementsResponse(
+                message=(
+                    f"Block '{block.name}' requires credentials that are not configured. "
+                    "Please set up the required credentials before running this block."
+                ),
+                session_id=session_id,
+                setup_info=SetupInfo(
+                    agent_id=block_id,
+                    agent_name=block.name,
+                    user_readiness=UserReadiness(
+                        has_all_credentials=False,
+                        missing_credentials=missing_creds_dict,
+                        ready_to_run=False,
+                    ),
+                    requirements={
+                        "credentials": missing_creds_list,
+                        "inputs": self._get_inputs_list(block),
+                        "execution_modes": ["immediate"],
+                    },
+                ),
+                graph_id=None,
+                graph_version=None,
+            )
+
+        try:
+            # Fetch actual credentials and prepare kwargs for block execution
+            # Create execution context with defaults (blocks may require it)
+            exec_kwargs: dict[str, Any] = {
+                "user_id": user_id,
+                "execution_context": ExecutionContext(),
+            }
+
+            for field_name, cred_meta in matched_credentials.items():
+                # Inject metadata into input_data (for validation)
+                if field_name not in input_data:
+                    input_data[field_name] = cred_meta.model_dump()
+
+                # Fetch actual credentials and pass as kwargs (for execution)
+                actual_credentials = await creds_manager.get(
+                    user_id, cred_meta.id, lock=False
+                )
+                if actual_credentials:
+                    exec_kwargs[field_name] = actual_credentials
+                else:
+                    return ErrorResponse(
+                        message=f"Failed to retrieve credentials for {field_name}",
+                        session_id=session_id,
+                    )
+
+            # Execute the block and collect outputs
+            outputs: dict[str, list[Any]] = defaultdict(list)
+            async for output_name, output_data in block.execute(
+                input_data,
+                **exec_kwargs,
+            ):
+                outputs[output_name].append(output_data)
+
+            return BlockOutputResponse(
+                message=f"Block '{block.name}' executed successfully",
+                block_id=block_id,
+                block_name=block.name,
+                outputs=dict(outputs),
+                success=True,
+                session_id=session_id,
+            )
+
+        except BlockError as e:
+            logger.warning(f"Block execution failed: {e}")
+            return ErrorResponse(
+                message=f"Block execution failed: {e}",
+                error=str(e),
+                session_id=session_id,
+            )
+        except Exception as e:
+            logger.error(f"Unexpected error executing block: {e}", exc_info=True)
+            return ErrorResponse(
+                message=f"Failed to execute block: {str(e)}",
+                error=str(e),
+                session_id=session_id,
+            )
+
+    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
+        """Extract non-credential inputs from block schema."""
+        inputs_list = []
+        schema = block.input_schema.jsonschema()
+        properties = schema.get("properties", {})
+        required_fields = set(schema.get("required", []))
+
+        # Get credential field names to exclude
+        credentials_fields = set(block.input_schema.get_credentials_fields().keys())
+
+        for field_name, field_schema in properties.items():
+            # Skip credential fields
+            if field_name in credentials_fields:
+                continue
+
+            inputs_list.append(
+                {
+                    "name": field_name,
+                    "title": field_schema.get("title", field_name),
+                    "type": field_schema.get("type", "string"),
+                    "description": field_schema.get("description", ""),
+                    "required": field_name in required_fields,
+                }
+            )
+
+        return inputs_list

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

@@ -0,0 +1,210 @@
+"""SearchDocsTool - Search documentation using hybrid search."""
+
+import logging
+from typing import Any
+
+from langfuse import observe
+from prisma.enums import ContentType
+
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.chat.tools.base import BaseTool
+from backend.api.features.chat.tools.models import (
+    DocSearchResult,
+    DocSearchResultsResponse,
+    ErrorResponse,
+    NoResultsResponse,
+    ToolResponseBase,
+)
+from backend.api.features.store.hybrid_search import unified_hybrid_search
+
+logger = logging.getLogger(__name__)
+
+# Base URL for documentation (can be configured)
+DOCS_BASE_URL = "https://docs.agpt.co"
+
+# Maximum number of results to return
+MAX_RESULTS = 5
+
+# Snippet length for preview
+SNIPPET_LENGTH = 200
+
+
+class SearchDocsTool(BaseTool):
+    """Tool for searching AutoGPT platform documentation."""
+
+    @property
+    def name(self) -> str:
+        return "search_docs"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Search the AutoGPT platform documentation for information about "
+            "how to use the platform, build agents, configure blocks, and more. "
+            "Returns relevant documentation sections. Use get_doc_page to read full content."
+        )
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": (
+                        "Search query to find relevant documentation. "
+                        "Use natural language to describe what you're looking for."
+                    ),
+                },
+            },
+            "required": ["query"],
+        }
+
+    @property
+    def requires_auth(self) -> bool:
+        return False  # Documentation is public
+
+    def _create_snippet(self, content: str, max_length: int = SNIPPET_LENGTH) -> str:
+        """Create a short snippet from content for preview."""
+        # Remove markdown formatting for cleaner snippet
+        clean_content = content.replace("#", "").replace("*", "").replace("`", "")
+        # Remove extra whitespace
+        clean_content = " ".join(clean_content.split())
+
+        if len(clean_content) <= max_length:
+            return clean_content
+
+        # Truncate at word boundary
+        truncated = clean_content[:max_length]
+        last_space = truncated.rfind(" ")
+        if last_space > max_length // 2:
+            truncated = truncated[:last_space]
+
+        return truncated + "..."
+
+    def _make_doc_url(self, path: str) -> str:
+        """Create a URL for a documentation page."""
+        # Remove file extension for URL
+        url_path = path.rsplit(".", 1)[0] if "." in path else path
+        return f"{DOCS_BASE_URL}/{url_path}"
+
+    @observe(as_type="tool", name="search_docs")
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Search documentation and return relevant sections.
+
+        Args:
+            user_id: User ID (not required for docs)
+            session: Chat session
+            query: Search query
+
+        Returns:
+            DocSearchResultsResponse: List of matching documentation sections
+            NoResultsResponse: No results found
+            ErrorResponse: Error message
+        """
+        query = kwargs.get("query", "").strip()
+        session_id = session.session_id if session else None
+
+        if not query:
+            return ErrorResponse(
+                message="Please provide a search query.",
+                error="Missing query parameter",
+                session_id=session_id,
+            )
+
+        try:
+            # Search using hybrid search for DOCUMENTATION content type only
+            results, total = await unified_hybrid_search(
+                query=query,
+                content_types=[ContentType.DOCUMENTATION],
+                page=1,
+                page_size=MAX_RESULTS * 2,  # Fetch extra for deduplication
+                min_score=0.1,  # Lower threshold for docs
+            )
+
+            if not results:
+                return NoResultsResponse(
+                    message=f"No documentation found for '{query}'.",
+                    suggestions=[
+                        "Try different keywords",
+                        "Use more general terms",
+                        "Check for typos in your query",
+                    ],
+                    session_id=session_id,
+                )
+
+            # Deduplicate by document path (keep highest scoring section per doc)
+            seen_docs: dict[str, dict[str, Any]] = {}
+            for result in results:
+                metadata = result.get("metadata", {})
+                doc_path = metadata.get("path", "")
+
+                if not doc_path:
+                    continue
+
+                # Keep the highest scoring result for each document
+                if doc_path not in seen_docs:
+                    seen_docs[doc_path] = result
+                elif result.get("combined_score", 0) > seen_docs[doc_path].get(
+                    "combined_score", 0
+                ):
+                    seen_docs[doc_path] = result
+
+            # Sort by score and take top MAX_RESULTS
+            deduplicated = sorted(
+                seen_docs.values(),
+                key=lambda x: x.get("combined_score", 0),
+                reverse=True,
+            )[:MAX_RESULTS]
+
+            if not deduplicated:
+                return NoResultsResponse(
+                    message=f"No documentation found for '{query}'.",
+                    suggestions=[
+                        "Try different keywords",
+                        "Use more general terms",
+                    ],
+                    session_id=session_id,
+                )
+
+            # Build response
+            doc_results: list[DocSearchResult] = []
+            for result in deduplicated:
+                metadata = result.get("metadata", {})
+                doc_path = metadata.get("path", "")
+                doc_title = metadata.get("doc_title", "")
+                section_title = metadata.get("section_title", "")
+                searchable_text = result.get("searchable_text", "")
+                score = result.get("combined_score", 0)
+
+                doc_results.append(
+                    DocSearchResult(
+                        title=doc_title or section_title or doc_path,
+                        path=doc_path,
+                        section=section_title,
+                        snippet=self._create_snippet(searchable_text),
+                        score=round(score, 3),
+                        doc_url=self._make_doc_url(doc_path),
+                    )
+                )
+
+            return DocSearchResultsResponse(
+                message=f"Found {len(doc_results)} relevant documentation sections.",
+                results=doc_results,
+                count=len(doc_results),
+                query=query,
+                session_id=session_id,
+            )
+
+        except Exception as e:
+            logger.error(f"Documentation search failed: {e}")
+            return ErrorResponse(
+                message=f"Failed to search documentation: {str(e)}",
+                error="search_failed",
+                session_id=session_id,
+            )

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

@@ -8,7 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import CredentialsMetaInput
+from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -89,6 +89,59 @@ def extract_credentials_from_schema(
     return credentials
 
 
+def _serialize_missing_credential(
+    field_key: str, field_info: CredentialsFieldInfo
+) -> dict[str, Any]:
+    """
+    Convert credential field info into a serializable dict that preserves all supported
+    credential types (e.g., api_key + oauth2) so the UI can offer multiple options.
+    """
+    supported_types = sorted(field_info.supported_types)
+    provider = next(iter(field_info.provider), "unknown")
+    scopes = sorted(field_info.required_scopes or [])
+
+    return {
+        "id": field_key,
+        "title": field_key.replace("_", " ").title(),
+        "provider": provider,
+        "provider_name": provider.replace("_", " ").title(),
+        "type": supported_types[0] if supported_types else "api_key",
+        "types": supported_types,
+        "scopes": scopes,
+    }
+
+
+def build_missing_credentials_from_graph(
+    graph: GraphModel, matched_credentials: dict[str, CredentialsMetaInput] | None
+) -> dict[str, Any]:
+    """
+    Build a missing_credentials mapping from a graph's aggregated credentials inputs,
+    preserving all supported credential types for each field.
+    """
+    matched_keys = set(matched_credentials.keys()) if matched_credentials else set()
+    aggregated_fields = graph.aggregate_credentials_inputs()
+
+    return {
+        field_key: _serialize_missing_credential(field_key, field_info)
+        for field_key, (field_info, _node_fields) in aggregated_fields.items()
+        if field_key not in matched_keys
+    }
+
+
+def build_missing_credentials_from_field_info(
+    credential_fields: dict[str, CredentialsFieldInfo],
+    matched_keys: set[str],
+) -> dict[str, Any]:
+    """
+    Build missing_credentials mapping from a simple credentials field info dictionary.
+    """
+    return {
+        field_key: _serialize_missing_credential(field_key, field_info)
+        for field_key, field_info in credential_fields.items()
+        if field_key not in matched_keys
+    }
+
+
 def extract_credentials_as_dict(
     credentials_input_schema: dict[str, Any] | None,
 ) -> dict[str, CredentialsMetaInput]:

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

@@ -401,27 +401,11 @@ async def add_generated_agent_image(
     )
 
 
-def _initialize_graph_settings(graph: graph_db.GraphModel) -> GraphSettings:
-    """
-    Initialize GraphSettings based on graph content.
-
-    Args:
-        graph: The graph to analyze
-
-    Returns:
-        GraphSettings with appropriate human_in_the_loop_safe_mode value
-    """
-    if graph.has_human_in_the_loop:
-        # Graph has HITL blocks - set safe mode to True by default
-        return GraphSettings(human_in_the_loop_safe_mode=True)
-    else:
-        # Graph has no HITL blocks - keep None
-        return GraphSettings(human_in_the_loop_safe_mode=None)
-
-
 async def create_library_agent(
     graph: graph_db.GraphModel,
     user_id: str,
+    hitl_safe_mode: bool = True,
+    sensitive_action_safe_mode: bool = False,
     create_library_agents_for_sub_graphs: bool = True,
 ) -> list[library_model.LibraryAgent]:
     """
@@ -430,6 +414,8 @@ async def create_library_agent(
     Args:
         agent: The agent/Graph to add to the library.
         user_id: The user to whom the agent will be added.
+        hitl_safe_mode: Whether HITL blocks require manual review (default True).
+        sensitive_action_safe_mode: Whether sensitive action blocks require review.
         create_library_agents_for_sub_graphs: If True, creates LibraryAgent records for sub-graphs as well.
 
     Returns:
@@ -465,7 +451,11 @@ async def create_library_agent(
                             }
                         },
                         settings=SafeJson(
-                            _initialize_graph_settings(graph_entry).model_dump()
+                            GraphSettings.from_graph(
+                                graph_entry,
+                                hitl_safe_mode=hitl_safe_mode,
+                                sensitive_action_safe_mode=sensitive_action_safe_mode,
+                            ).model_dump()
                         ),
                     ),
                     include=library_agent_include(
@@ -627,33 +617,6 @@ async def update_library_agent(
         raise DatabaseError("Failed to update library agent") from e
 
 
-async def update_library_agent_settings(
-    user_id: str,
-    agent_id: str,
-    settings: GraphSettings,
-) -> library_model.LibraryAgent:
-    """
-    Updates the settings for a specific LibraryAgent.
-
-    Args:
-        user_id: The owner of the LibraryAgent.
-        agent_id: The ID of the LibraryAgent to update.
-        settings: New GraphSettings to apply.
-
-    Returns:
-        The updated LibraryAgent.
-
-    Raises:
-        NotFoundError: If the specified LibraryAgent does not exist.
-        DatabaseError: If there's an error in the update operation.
-    """
-    return await update_library_agent(
-        library_agent_id=agent_id,
-        user_id=user_id,
-        settings=settings,
-    )
-
-
 async def delete_library_agent(
     library_agent_id: str, user_id: str, soft_delete: bool = True
 ) -> None:
@@ -838,7 +801,7 @@ async def add_store_agent_to_library(
                 "isCreatedByUser": False,
                 "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
-                    _initialize_graph_settings(graph_model).model_dump()
+                    GraphSettings.from_graph(graph_model).model_dump()
                 ),
             },
             include=library_agent_include(
@@ -1228,8 +1191,15 @@ async def fork_library_agent(
         )
         new_graph = await on_graph_activate(new_graph, user_id=user_id)
 
-        # Create a library agent for the new graph
-        return (await create_library_agent(new_graph, user_id))[0]
+        # Create a library agent for the new graph, preserving safe mode settings
+        return (
+            await create_library_agent(
+                new_graph,
+                user_id,
+                hitl_safe_mode=original_agent.settings.human_in_the_loop_safe_mode,
+                sensitive_action_safe_mode=original_agent.settings.sensitive_action_safe_mode,
+            )
+        )[0]
     except prisma.errors.PrismaError as e:
         logger.error(f"Database error cloning library agent: {e}")
         raise DatabaseError("Failed to fork library agent") from e

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

@@ -73,6 +73,12 @@ class LibraryAgent(pydantic.BaseModel):
     has_external_trigger: bool = pydantic.Field(
         description="Whether the agent has an external trigger (e.g. webhook) node"
     )
+    has_human_in_the_loop: bool = pydantic.Field(
+        description="Whether the agent has human-in-the-loop blocks"
+    )
+    has_sensitive_action: bool = pydantic.Field(
+        description="Whether the agent has sensitive action blocks"
+    )
     trigger_setup_info: Optional[GraphTriggerInfo] = None
 
     # Indicates whether there's a new output (based on recent runs)
@@ -180,6 +186,8 @@ class LibraryAgent(pydantic.BaseModel):
                 graph.credentials_input_schema if sub_graphs is not None else None
             ),
             has_external_trigger=graph.has_external_trigger,
+            has_human_in_the_loop=graph.has_human_in_the_loop,
+            has_sensitive_action=graph.has_sensitive_action,
             trigger_setup_info=graph.trigger_setup_info,
             new_output=new_output,
             can_access_graph=can_access_graph,

autogpt_platform/backend/backend/api/features/library/routes_test.py

@@ -52,6 +52,8 @@ async def test_get_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
+                has_human_in_the_loop=False,
+                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -75,6 +77,8 @@ async def test_get_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
+                has_human_in_the_loop=False,
+                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -150,6 +154,8 @@ async def test_get_favorite_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
+                has_human_in_the_loop=False,
+                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -218,6 +224,8 @@ def test_add_agent_to_library_success(
         output_schema={"type": "object", "properties": {}},
         credentials_input_schema={"type": "object", "properties": {}},
         has_external_trigger=False,
+        has_human_in_the_loop=False,
+        has_sensitive_action=False,
         status=library_model.LibraryAgentStatus.COMPLETED,
         new_output=False,
         can_access_graph=True,

autogpt_platform/backend/backend/api/features/store/content_handlers.py

@@ -275,8 +275,22 @@ class BlockHandler(ContentHandler):
         }
 
 
+@dataclass
+class MarkdownSection:
+    """Represents a section of a markdown document."""
+
+    title: str  # Section heading text
+    content: str  # Section content (including the heading line)
+    level: int  # Heading level (1 for #, 2 for ##, etc.)
+    index: int  # Section index within the document
+
+
 class DocumentationHandler(ContentHandler):
-    """Handler for documentation files (.md/.mdx)."""
+    """Handler for documentation files (.md/.mdx).
+
+    Chunks documents by markdown headings to create multiple embeddings per file.
+    Each section (## heading) becomes a separate embedding for better retrieval.
+    """
 
     @property
     def content_type(self) -> ContentType:
@@ -297,35 +311,162 @@ class DocumentationHandler(ContentHandler):
         docs_root = project_root / "docs"
         return docs_root
 
-    def _extract_title_and_content(self, file_path: Path) -> tuple[str, str]:
-        """Extract title and content from markdown file."""
+    def _extract_doc_title(self, file_path: Path) -> str:
+        """Extract the document title from a markdown file."""
         try:
             content = file_path.read_text(encoding="utf-8")
+            lines = content.split("\n")
 
             # Try to extract title from first # heading
-            lines = content.split("\n")
-            title = ""
-            body_lines = []
-
             for line in lines:
-                if line.startswith("# ") and not title:
-                    title = line[2:].strip()
-                else:
-                    body_lines.append(line)
+                if line.startswith("# "):
+                    return line[2:].strip()
 
             # If no title found, use filename
-            if not title:
-                title = file_path.stem.replace("-", " ").replace("_", " ").title()
+            return file_path.stem.replace("-", " ").replace("_", " ").title()
+        except Exception as e:
+            logger.warning(f"Failed to read title from {file_path}: {e}")
+            return file_path.stem.replace("-", " ").replace("_", " ").title()
 
-            body = "\n".join(body_lines)
+    def _chunk_markdown_by_headings(
+        self, file_path: Path, min_heading_level: int = 2
+    ) -> list[MarkdownSection]:
+        """
+        Split a markdown file into sections based on headings.
 
-            return title, body
+        Args:
+            file_path: Path to the markdown file
+            min_heading_level: Minimum heading level to split on (default: 2 for ##)
+
+        Returns:
+            List of MarkdownSection objects, one per section.
+            If no headings found, returns a single section with all content.
+        """
+        try:
+            content = file_path.read_text(encoding="utf-8")
         except Exception as e:
             logger.warning(f"Failed to read {file_path}: {e}")
-            return file_path.stem, ""
+            return []
+
+        lines = content.split("\n")
+        sections: list[MarkdownSection] = []
+        current_section_lines: list[str] = []
+        current_title = ""
+        current_level = 0
+        section_index = 0
+        doc_title = ""
+
+        for line in lines:
+            # Check if line is a heading
+            if line.startswith("#"):
+                # Count heading level
+                level = 0
+                for char in line:
+                    if char == "#":
+                        level += 1
+                    else:
+                        break
+
+                heading_text = line[level:].strip()
+
+                # Track document title (level 1 heading)
+                if level == 1 and not doc_title:
+                    doc_title = heading_text
+                    # Don't create a section for just the title - add it to first section
+                    current_section_lines.append(line)
+                    continue
+
+                # Check if this heading should start a new section
+                if level >= min_heading_level:
+                    # Save previous section if it has content
+                    if current_section_lines:
+                        section_content = "\n".join(current_section_lines).strip()
+                        if section_content:
+                            # Use doc title for first section if no specific title
+                            title = current_title if current_title else doc_title
+                            if not title:
+                                title = file_path.stem.replace("-", " ").replace(
+                                    "_", " "
+                                )
+                            sections.append(
+                                MarkdownSection(
+                                    title=title,
+                                    content=section_content,
+                                    level=current_level if current_level else 1,
+                                    index=section_index,
+                                )
+                            )
+                            section_index += 1
+
+                    # Start new section
+                    current_section_lines = [line]
+                    current_title = heading_text
+                    current_level = level
+                else:
+                    # Lower level heading (e.g., # when splitting on ##)
+                    current_section_lines.append(line)
+            else:
+                current_section_lines.append(line)
+
+        # Don't forget the last section
+        if current_section_lines:
+            section_content = "\n".join(current_section_lines).strip()
+            if section_content:
+                title = current_title if current_title else doc_title
+                if not title:
+                    title = file_path.stem.replace("-", " ").replace("_", " ")
+                sections.append(
+                    MarkdownSection(
+                        title=title,
+                        content=section_content,
+                        level=current_level if current_level else 1,
+                        index=section_index,
+                    )
+                )
+
+        # If no sections were created (no headings found), create one section with all content
+        if not sections and content.strip():
+            title = (
+                doc_title
+                if doc_title
+                else file_path.stem.replace("-", " ").replace("_", " ")
+            )
+            sections.append(
+                MarkdownSection(
+                    title=title,
+                    content=content.strip(),
+                    level=1,
+                    index=0,
+                )
+            )
+
+        return sections
+
+    def _make_section_content_id(self, doc_path: str, section_index: int) -> str:
+        """Create a unique content ID for a document section.
+
+        Format: doc_path::section_index
+        Example: 'platform/getting-started.md::0'
+        """
+        return f"{doc_path}::{section_index}"
+
+    def _parse_section_content_id(self, content_id: str) -> tuple[str, int]:
+        """Parse a section content ID back into doc_path and section_index.
+
+        Returns: (doc_path, section_index)
+        """
+        if "::" in content_id:
+            parts = content_id.rsplit("::", 1)
+            return parts[0], int(parts[1])
+        # Legacy format (whole document)
+        return content_id, 0
 
     async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """Fetch documentation files without embeddings."""
+        """Fetch documentation sections without embeddings.
+
+        Chunks each document by markdown headings and creates embeddings for each section.
+        Content IDs use the format: 'path/to/doc.md::section_index'
+        """
         docs_root = self._get_docs_root()
 
         if not docs_root.exists():
@@ -335,14 +476,28 @@ class DocumentationHandler(ContentHandler):
         # Find all .md and .mdx files
         all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
 
-        # Get relative paths for content IDs
-        doc_paths = [str(doc.relative_to(docs_root)) for doc in all_docs]
-
-        if not doc_paths:
+        if not all_docs:
             return []
 
+        # Build list of all sections from all documents
+        all_sections: list[tuple[str, Path, MarkdownSection]] = []
+        for doc_file in all_docs:
+            doc_path = str(doc_file.relative_to(docs_root))
+            sections = self._chunk_markdown_by_headings(doc_file)
+            for section in sections:
+                all_sections.append((doc_path, doc_file, section))
+
+        if not all_sections:
+            return []
+
+        # Generate content IDs for all sections
+        section_content_ids = [
+            self._make_section_content_id(doc_path, section.index)
+            for doc_path, _, section in all_sections
+        ]
+
         # Check which ones have embeddings
-        placeholders = ",".join([f"${i+1}" for i in range(len(doc_paths))])
+        placeholders = ",".join([f"${i+1}" for i in range(len(section_content_ids))])
         existing_result = await query_raw_with_schema(
             f"""
             SELECT "contentId"
@@ -350,76 +505,100 @@ class DocumentationHandler(ContentHandler):
             WHERE "contentType" = 'DOCUMENTATION'::{{schema_prefix}}"ContentType"
             AND "contentId" = ANY(ARRAY[{placeholders}])
             """,
-            *doc_paths,
+            *section_content_ids,
         )
 
         existing_ids = {row["contentId"] for row in existing_result}
-        missing_docs = [
-            (doc_path, doc_file)
-            for doc_path, doc_file in zip(doc_paths, all_docs)
-            if doc_path not in existing_ids
+
+        # Filter to missing sections
+        missing_sections = [
+            (doc_path, doc_file, section, content_id)
+            for (doc_path, doc_file, section), content_id in zip(
+                all_sections, section_content_ids
+            )
+            if content_id not in existing_ids
         ]
 
-        # Convert to ContentItem
+        # Convert to ContentItem (up to batch_size)
         items = []
-        for doc_path, doc_file in missing_docs[:batch_size]:
+        for doc_path, doc_file, section, content_id in missing_sections[:batch_size]:
             try:
-                title, content = self._extract_title_and_content(doc_file)
+                # Get document title for context
+                doc_title = self._extract_doc_title(doc_file)
 
-                # Build searchable text
-                searchable_text = f"{title} {content}"
+                # Build searchable text with context
+                # Include doc title and section title for better search relevance
+                searchable_text = f"{doc_title} - {section.title}\n\n{section.content}"
 
                 items.append(
                     ContentItem(
-                        content_id=doc_path,
+                        content_id=content_id,
                         content_type=ContentType.DOCUMENTATION,
                         searchable_text=searchable_text,
                         metadata={
-                            "title": title,
+                            "doc_title": doc_title,
+                            "section_title": section.title,
+                            "section_index": section.index,
+                            "heading_level": section.level,
                             "path": doc_path,
                         },
                         user_id=None,  # Documentation is public
                     )
                 )
             except Exception as e:
-                logger.warning(f"Failed to process doc {doc_path}: {e}")
+                logger.warning(f"Failed to process section {content_id}: {e}")
                 continue
 
         return items
 
+    def _get_all_section_content_ids(self, docs_root: Path) -> set[str]:
+        """Get all current section content IDs from the docs directory.
+
+        Used for stats and cleanup to know what sections should exist.
+        """
+        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
+        content_ids = set()
+
+        for doc_file in all_docs:
+            doc_path = str(doc_file.relative_to(docs_root))
+            sections = self._chunk_markdown_by_headings(doc_file)
+            for section in sections:
+                content_ids.add(self._make_section_content_id(doc_path, section.index))
+
+        return content_ids
+
     async def get_stats(self) -> dict[str, int]:
-        """Get statistics about documentation embedding coverage."""
+        """Get statistics about documentation embedding coverage.
+
+        Counts sections (not documents) since each section gets its own embedding.
+        """
         docs_root = self._get_docs_root()
 
         if not docs_root.exists():
             return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
 
-        # Count all .md and .mdx files
-        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
-        total_docs = len(all_docs)
+        # Get all section content IDs
+        all_section_ids = self._get_all_section_content_ids(docs_root)
+        total_sections = len(all_section_ids)
 
-        if total_docs == 0:
+        if total_sections == 0:
             return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
 
-        doc_paths = [str(doc.relative_to(docs_root)) for doc in all_docs]
-        placeholders = ",".join([f"${i+1}" for i in range(len(doc_paths))])
-
+        # Count embeddings in database for DOCUMENTATION type
         embedded_result = await query_raw_with_schema(
-            f"""
+            """
             SELECT COUNT(*) as count
-            FROM {{schema_prefix}}"UnifiedContentEmbedding"
-            WHERE "contentType" = 'DOCUMENTATION'::{{schema_prefix}}"ContentType"
-            AND "contentId" = ANY(ARRAY[{placeholders}])
-            """,
-            *doc_paths,
+            FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'DOCUMENTATION'::{schema_prefix}"ContentType"
+            """
         )
 
         with_embeddings = embedded_result[0]["count"] if embedded_result else 0
 
         return {
-            "total": total_docs,
+            "total": total_sections,
             "with_embeddings": with_embeddings,
-            "without_embeddings": total_docs - with_embeddings,
+            "without_embeddings": total_sections - with_embeddings,
         }
 
 

autogpt_platform/backend/backend/api/features/store/content_handlers_test.py

@@ -164,20 +164,20 @@ async def test_documentation_handler_get_missing_items(tmp_path, mocker):
 
             assert len(items) == 2
 
-            # Check guide.md
+            # Check guide.md (content_id format: doc_path::section_index)
             guide_item = next(
-                (item for item in items if item.content_id == "guide.md"), None
+                (item for item in items if item.content_id == "guide.md::0"), None
             )
             assert guide_item is not None
             assert guide_item.content_type == ContentType.DOCUMENTATION
             assert "Getting Started" in guide_item.searchable_text
             assert "This is a guide" in guide_item.searchable_text
-            assert guide_item.metadata["title"] == "Getting Started"
+            assert guide_item.metadata["doc_title"] == "Getting Started"
             assert guide_item.user_id is None
 
-            # Check api.mdx
+            # Check api.mdx (content_id format: doc_path::section_index)
             api_item = next(
-                (item for item in items if item.content_id == "api.mdx"), None
+                (item for item in items if item.content_id == "api.mdx::0"), None
             )
             assert api_item is not None
             assert "API Reference" in api_item.searchable_text
@@ -218,17 +218,74 @@ async def test_documentation_handler_title_extraction(tmp_path):
     # Test with heading
     doc_with_heading = tmp_path / "with_heading.md"
     doc_with_heading.write_text("# My Title\n\nContent here")
-    title, content = handler._extract_title_and_content(doc_with_heading)
+    title = handler._extract_doc_title(doc_with_heading)
     assert title == "My Title"
-    assert "# My Title" not in content
-    assert "Content here" in content
 
     # Test without heading
     doc_without_heading = tmp_path / "no-heading.md"
     doc_without_heading.write_text("Just content, no heading")
-    title, content = handler._extract_title_and_content(doc_without_heading)
+    title = handler._extract_doc_title(doc_without_heading)
     assert title == "No Heading"  # Uses filename
-    assert "Just content" in content
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_markdown_chunking(tmp_path):
+    """Test DocumentationHandler chunks markdown by headings."""
+    handler = DocumentationHandler()
+
+    # Test document with multiple sections
+    doc_with_sections = tmp_path / "sections.md"
+    doc_with_sections.write_text(
+        "# Document Title\n\n"
+        "Intro paragraph.\n\n"
+        "## Section One\n\n"
+        "Content for section one.\n\n"
+        "## Section Two\n\n"
+        "Content for section two.\n"
+    )
+    sections = handler._chunk_markdown_by_headings(doc_with_sections)
+
+    # Should have 3 sections: intro (with doc title), section one, section two
+    assert len(sections) == 3
+    assert sections[0].title == "Document Title"
+    assert sections[0].index == 0
+    assert "Intro paragraph" in sections[0].content
+
+    assert sections[1].title == "Section One"
+    assert sections[1].index == 1
+    assert "Content for section one" in sections[1].content
+
+    assert sections[2].title == "Section Two"
+    assert sections[2].index == 2
+    assert "Content for section two" in sections[2].content
+
+    # Test document without headings
+    doc_no_sections = tmp_path / "no-sections.md"
+    doc_no_sections.write_text("Just plain content without any headings.")
+    sections = handler._chunk_markdown_by_headings(doc_no_sections)
+    assert len(sections) == 1
+    assert sections[0].index == 0
+    assert "Just plain content" in sections[0].content
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_section_content_ids():
+    """Test DocumentationHandler creates and parses section content IDs."""
+    handler = DocumentationHandler()
+
+    # Test making content ID
+    content_id = handler._make_section_content_id("docs/guide.md", 2)
+    assert content_id == "docs/guide.md::2"
+
+    # Test parsing content ID
+    doc_path, section_index = handler._parse_section_content_id("docs/guide.md::2")
+    assert doc_path == "docs/guide.md"
+    assert section_index == 2
+
+    # Test parsing legacy format (no section index)
+    doc_path, section_index = handler._parse_section_content_id("docs/old-format.md")
+    assert doc_path == "docs/old-format.md"
+    assert section_index == 0
 
 
 @pytest.mark.asyncio(loop_scope="session")

autogpt_platform/backend/backend/api/features/store/embeddings.py

@@ -154,6 +154,7 @@ async def store_content_embedding(
 
         # Upsert the embedding
         # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
+        # Use unqualified ::vector - pgvector is in search_path on all environments
         await execute_raw_with_schema(
             """
             INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
@@ -177,7 +178,6 @@ async def store_content_embedding(
             searchable_text,
             metadata_json,
             client=client,
-            set_public_search_path=True,
         )
 
         logger.info(f"Stored embedding for {content_type}:{content_id}")
@@ -236,7 +236,6 @@ async def get_content_embedding(
             content_type,
             content_id,
             user_id,
-            set_public_search_path=True,
         )
 
         if result and len(result) > 0:
@@ -683,20 +682,20 @@ async def cleanup_orphaned_embeddings() -> dict[str, Any]:
 
                 current_ids = set(get_blocks().keys())
             elif content_type == ContentType.DOCUMENTATION:
-                from pathlib import Path
-
-                # embeddings.py is at: backend/backend/api/features/store/embeddings.py
-                # Need to go up to project root then into docs/
-                this_file = Path(__file__)
-                project_root = (
-                    this_file.parent.parent.parent.parent.parent.parent.parent
+                # Use DocumentationHandler to get section-based content IDs
+                from backend.api.features.store.content_handlers import (
+                    DocumentationHandler,
                 )
-                docs_root = project_root / "docs"
-                if docs_root.exists():
-                    all_docs = list(docs_root.rglob("*.md")) + list(
-                        docs_root.rglob("*.mdx")
-                    )
-                    current_ids = {str(doc.relative_to(docs_root)) for doc in all_docs}
+
+                doc_handler = CONTENT_HANDLERS.get(ContentType.DOCUMENTATION)
+                if isinstance(doc_handler, DocumentationHandler):
+                    docs_root = doc_handler._get_docs_root()
+                    if docs_root.exists():
+                        current_ids = doc_handler._get_all_section_content_ids(
+                            docs_root
+                        )
+                    else:
+                        current_ids = set()
                 else:
                     current_ids = set()
             else:
@@ -871,31 +870,45 @@ async def semantic_search(
         # Add content type parameters and build placeholders dynamically
         content_type_start_idx = len(params) + 1
         content_type_placeholders = ", ".join(
-            f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
+            "$" + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
             for i in range(len(content_types))
         )
         params.extend([ct.value for ct in content_types])
 
-        sql = f"""
+        # Build min_similarity param index before appending
+        min_similarity_idx = len(params) + 1
+        params.append(min_similarity)
+
+        # Use unqualified ::vector and <=> operator - pgvector is in search_path on all environments
+        sql = (
+            """
             SELECT
                 "contentId" as content_id,
                 "contentType" as content_type,
                 "searchableText" as searchable_text,
                 metadata,
-                1 - (embedding <=> '{embedding_str}'::vector) as similarity
-            FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
-            WHERE "contentType" IN ({content_type_placeholders})
-            {user_filter}
-            AND 1 - (embedding <=> '{embedding_str}'::vector) >= ${len(params) + 1}
+                1 - (embedding <=> '"""
+            + embedding_str
+            + """'::vector) as similarity
+            FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" IN ("""
+            + content_type_placeholders
+            + """)
+            """
+            + user_filter
+            + """
+            AND 1 - (embedding <=> '"""
+            + embedding_str
+            + """'::vector) >= $"""
+            + str(min_similarity_idx)
+            + """
             ORDER BY similarity DESC
             LIMIT $1
         """
-        params.append(min_similarity)
+        )
 
         try:
-            results = await query_raw_with_schema(
-                sql, *params, set_public_search_path=True
-            )
+            results = await query_raw_with_schema(sql, *params)
             return [
                 {
                     "content_id": row["content_id"],
@@ -922,31 +935,41 @@ async def semantic_search(
     # Add content type parameters and build placeholders dynamically
     content_type_start_idx = len(params_lexical) + 1
     content_type_placeholders_lexical = ", ".join(
-        f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
+        "$" + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
         for i in range(len(content_types))
     )
     params_lexical.extend([ct.value for ct in content_types])
 
-    sql_lexical = f"""
+    # Build query param index before appending
+    query_param_idx = len(params_lexical) + 1
+    params_lexical.append(f"%{query}%")
+
+    # Use regular string (not f-string) for template to preserve {schema_prefix} placeholders
+    sql_lexical = (
+        """
         SELECT
             "contentId" as content_id,
             "contentType" as content_type,
             "searchableText" as searchable_text,
             metadata,
             0.0 as similarity
-        FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
-        WHERE "contentType" IN ({content_type_placeholders_lexical})
-        {user_filter}
-        AND "searchableText" ILIKE ${len(params_lexical) + 1}
+        FROM {schema_prefix}"UnifiedContentEmbedding"
+        WHERE "contentType" IN ("""
+        + content_type_placeholders_lexical
+        + """)
+        """
+        + user_filter
+        + """
+        AND "searchableText" ILIKE $"""
+        + str(query_param_idx)
+        + """
         ORDER BY "updatedAt" DESC
         LIMIT $1
     """
-    params_lexical.append(f"%{query}%")
+    )
 
     try:
-        results = await query_raw_with_schema(
-            sql_lexical, *params_lexical, set_public_search_path=True
-        )
+        results = await query_raw_with_schema(sql_lexical, *params_lexical)
         return [
             {
                 "content_id": row["content_id"],

autogpt_platform/backend/backend/api/features/store/embeddings_test.py

@@ -155,18 +155,14 @@ async def test_store_embedding_success(mocker):
     )
 
     assert result is True
-    # execute_raw is called twice: once for SET search_path, once for INSERT
-    assert mock_client.execute_raw.call_count == 2
+    # execute_raw is called once for INSERT (no separate SET search_path needed)
+    assert mock_client.execute_raw.call_count == 1
 
-    # First call: SET search_path
-    first_call_args = mock_client.execute_raw.call_args_list[0][0]
-    assert "SET search_path" in first_call_args[0]
-
-    # Second call: INSERT query with the actual data
-    second_call_args = mock_client.execute_raw.call_args_list[1][0]
-    assert "test-version-id" in second_call_args
-    assert "[0.1,0.2,0.3]" in second_call_args
-    assert None in second_call_args  # userId should be None for store agents
+    # Verify the INSERT query with the actual data
+    call_args = mock_client.execute_raw.call_args_list[0][0]
+    assert "test-version-id" in call_args
+    assert "[0.1,0.2,0.3]" in call_args
+    assert None in call_args  # userId should be None for store agents
 
 
 @pytest.mark.asyncio(loop_scope="session")

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -3,13 +3,16 @@ Unified Hybrid Search
 
 Combines semantic (embedding) search with lexical (tsvector) search
 for improved relevance across all content types (agents, blocks, docs).
+Includes BM25 reranking for improved lexical relevance.
 """
 
 import logging
+import re
 from dataclasses import dataclass
 from typing import Any, Literal
 
 from prisma.enums import ContentType
+from rank_bm25 import BM25Okapi  # type: ignore[import-untyped]
 
 from backend.api.features.store.embeddings import (
     EMBEDDING_DIM,
@@ -21,6 +24,84 @@ from backend.data.db import query_raw_with_schema
 logger = logging.getLogger(__name__)
 
 
+# ============================================================================
+# BM25 Reranking
+# ============================================================================
+
+
+def tokenize(text: str) -> list[str]:
+    """Simple tokenizer for BM25 - lowercase and split on non-alphanumeric."""
+    if not text:
+        return []
+    # Lowercase and split on non-alphanumeric characters
+    tokens = re.findall(r"\b\w+\b", text.lower())
+    return tokens
+
+
+def bm25_rerank(
+    query: str,
+    results: list[dict[str, Any]],
+    text_field: str = "searchable_text",
+    bm25_weight: float = 0.3,
+    original_score_field: str = "combined_score",
+) -> list[dict[str, Any]]:
+    """
+    Rerank search results using BM25.
+
+    Combines the original combined_score with BM25 score for improved
+    lexical relevance, especially for exact term matches.
+
+    Args:
+        query: The search query
+        results: List of result dicts with text_field and original_score_field
+        text_field: Field name containing the text to score
+        bm25_weight: Weight for BM25 score (0-1). Original score gets (1 - bm25_weight)
+        original_score_field: Field name containing the original score
+
+    Returns:
+        Results list sorted by combined score (BM25 + original)
+    """
+    if not results or not query:
+        return results
+
+    # Extract texts and tokenize
+    corpus = [tokenize(r.get(text_field, "") or "") for r in results]
+
+    # Handle edge case where all documents are empty
+    if all(len(doc) == 0 for doc in corpus):
+        return results
+
+    # Build BM25 index
+    bm25 = BM25Okapi(corpus)
+
+    # Score query against corpus
+    query_tokens = tokenize(query)
+    if not query_tokens:
+        return results
+
+    bm25_scores = bm25.get_scores(query_tokens)
+
+    # Normalize BM25 scores to 0-1 range
+    max_bm25 = max(bm25_scores) if max(bm25_scores) > 0 else 1.0
+    normalized_bm25 = [s / max_bm25 for s in bm25_scores]
+
+    # Combine scores
+    original_weight = 1.0 - bm25_weight
+    for i, result in enumerate(results):
+        original_score = result.get(original_score_field, 0) or 0
+        result["bm25_score"] = normalized_bm25[i]
+        final_score = (
+            original_weight * original_score + bm25_weight * normalized_bm25[i]
+        )
+        result["final_score"] = final_score
+        result["relevance"] = final_score
+
+    # Sort by relevance descending
+    results.sort(key=lambda x: x.get("relevance", 0), reverse=True)
+
+    return results
+
+
 @dataclass
 class UnifiedSearchWeights:
     """Weights for unified search (no popularity signal)."""
@@ -273,9 +354,7 @@ async def unified_hybrid_search(
             FROM normalized
         ),
         filtered AS (
-            SELECT
-                *,
-                COUNT(*) OVER () as total_count
+            SELECT *, COUNT(*) OVER () as total_count
             FROM scored
             WHERE combined_score >= {min_score_param}
         )
@@ -284,11 +363,18 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(
-        sql_query, *params, set_public_search_path=True
-    )
+    results = await query_raw_with_schema(sql_query, *params)
 
     total = results[0]["total_count"] if results else 0
+    # Apply BM25 reranking
+    if results:
+        results = bm25_rerank(
+            query=query,
+            results=results,
+            text_field="searchable_text",
+            bm25_weight=0.3,
+            original_score_field="combined_score",
+        )
 
     # Clean up results
     for result in results:
@@ -516,6 +602,8 @@ async def hybrid_search(
                 sa.featured,
                 sa.is_available,
                 sa.updated_at,
+                -- Searchable text for BM25 reranking
+                COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
                 -- Semantic score
                 COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
                 -- Lexical score (raw, will normalize)
@@ -573,6 +661,7 @@ async def hybrid_search(
                 featured,
                 is_available,
                 updated_at,
+                searchable_text,
                 semantic_score,
                 lexical_score,
                 category_score,
@@ -597,14 +686,23 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(
-        sql_query, *params, set_public_search_path=True
-    )
+    results = await query_raw_with_schema(sql_query, *params)
 
     total = results[0]["total_count"] if results else 0
 
+    # Apply BM25 reranking
+    if results:
+        results = bm25_rerank(
+            query=query,
+            results=results,
+            text_field="searchable_text",
+            bm25_weight=0.3,
+            original_score_field="combined_score",
+        )
+
     for result in results:
         result.pop("total_count", None)
+        result.pop("searchable_text", None)
 
     logger.info(f"Hybrid search (store agents): {len(results)} results, {total} total")
 

autogpt_platform/backend/backend/api/features/store/hybrid_search_test.py

@@ -311,11 +311,43 @@ async def test_hybrid_search_min_score_filtering():
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_hybrid_search_pagination():
-    """Test hybrid search pagination."""
+    """Test hybrid search pagination.
+
+    Pagination happens in SQL (LIMIT/OFFSET), then BM25 reranking is applied
+    to the paginated results.
+    """
+    # Create mock results that SQL would return for a page
+    mock_results = [
+        {
+            "slug": f"agent-{i}",
+            "agent_name": f"Agent {i}",
+            "agent_image": "test.png",
+            "creator_username": "test",
+            "creator_avatar": "avatar.png",
+            "sub_heading": "Test",
+            "description": "Test description",
+            "runs": 100 - i,
+            "rating": 4.5,
+            "categories": ["test"],
+            "featured": False,
+            "is_available": True,
+            "updated_at": "2024-01-01T00:00:00Z",
+            "searchable_text": f"Agent {i} test description",
+            "combined_score": 0.9 - (i * 0.01),
+            "semantic_score": 0.7,
+            "lexical_score": 0.6,
+            "category_score": 0.5,
+            "recency_score": 0.4,
+            "popularity_score": 0.3,
+            "total_count": 25,
+        }
+        for i in range(10)  # SQL returns page_size results
+    ]
+
     with patch(
         "backend.api.features.store.hybrid_search.query_raw_with_schema"
     ) as mock_query:
-        mock_query.return_value = []
+        mock_query.return_value = mock_results
 
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
@@ -329,16 +361,18 @@ async def test_hybrid_search_pagination():
                 page_size=10,
             )
 
-            # Verify pagination parameters
+            # Verify results returned
+            assert len(results) == 10
+            assert total == 25  # Total from SQL COUNT(*) OVER()
+
+            # Verify the SQL query uses page_size and offset
             call_args = mock_query.call_args
             params = call_args[0]
-
-            # Last two params should be LIMIT and OFFSET
-            limit = params[-2]
-            offset = params[-1]
-
-            assert limit == 10  # page_size
-            assert offset == 10  # (page - 1) * page_size = (2 - 1) * 10
+            # Last two params are page_size and offset
+            page_size_param = params[-2]
+            offset_param = params[-1]
+            assert page_size_param == 10
+            assert offset_param == 10  # (page 2 - 1) * 10
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -609,14 +643,36 @@ async def test_unified_hybrid_search_empty_query():
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_unified_hybrid_search_pagination():
-    """Test unified search pagination."""
+    """Test unified search pagination with BM25 reranking.
+
+    Pagination happens in SQL (LIMIT/OFFSET), then BM25 reranking is applied
+    to the paginated results.
+    """
+    # Create mock results that SQL would return for a page
+    mock_results = [
+        {
+            "content_type": "STORE_AGENT",
+            "content_id": f"agent-{i}",
+            "searchable_text": f"Agent {i} description",
+            "metadata": {"name": f"Agent {i}"},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.7,
+            "lexical_score": 0.8 - (i * 0.01),
+            "category_score": 0.5,
+            "recency_score": 0.3,
+            "combined_score": 0.6 - (i * 0.01),
+            "total_count": 50,
+        }
+        for i in range(15)  # SQL returns page_size results
+    ]
+
     with patch(
         "backend.api.features.store.hybrid_search.query_raw_with_schema"
     ) as mock_query:
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_query.return_value = []
+            mock_query.return_value = mock_results
             mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             results, total = await unified_hybrid_search(
@@ -625,15 +681,18 @@ async def test_unified_hybrid_search_pagination():
                 page_size=15,
             )
 
-            # Verify pagination parameters (last two params are LIMIT and OFFSET)
+            # Verify results returned
+            assert len(results) == 15
+            assert total == 50  # Total from SQL COUNT(*) OVER()
+
+            # Verify the SQL query uses page_size and offset
             call_args = mock_query.call_args
             params = call_args[0]
-
-            limit = params[-2]
-            offset = params[-1]
-
-            assert limit == 15  # page_size
-            assert offset == 30  # (page - 1) * page_size = (3 - 1) * 15
+            # Last two params are page_size and offset
+            page_size_param = params[-2]
+            offset_param = params[-1]
+            assert page_size_param == 15
+            assert offset_param == 30  # (page 3 - 1) * 15
 
 
 @pytest.mark.asyncio(loop_scope="session")

autogpt_platform/backend/backend/api/features/v1.py

@@ -761,10 +761,8 @@ async def create_new_graph(
     graph.reassign_ids(user_id=user_id, reassign_graph_id=True)
     graph.validate_graph(for_run=False)
 
-    # The return value of the create graph & library function is intentionally not used here,
-    # as the graph already valid and no sub-graphs are returned back.
     await graph_db.create_graph(graph, user_id=user_id)
-    await library_db.create_library_agent(graph, user_id=user_id)
+    await library_db.create_library_agent(graph, user_id)
     activated_graph = await on_graph_activate(graph, user_id=user_id)
 
     if create_graph.source == "builder":
@@ -888,21 +886,19 @@ async def set_graph_active_version(
 async def _update_library_agent_version_and_settings(
     user_id: str, agent_graph: graph_db.GraphModel
 ) -> library_model.LibraryAgent:
-    # Keep the library agent up to date with the new active version
     library = await library_db.update_agent_version_in_library(
         user_id, agent_graph.id, agent_graph.version
     )
-    # If the graph has HITL node, initialize the setting if it's not already set.
-    if (
-        agent_graph.has_human_in_the_loop
-        and library.settings.human_in_the_loop_safe_mode is None
-    ):
-        await library_db.update_library_agent_settings(
+    updated_settings = GraphSettings.from_graph(
+        graph=agent_graph,
+        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
+        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
+    )
+    if updated_settings != library.settings:
+        library = await library_db.update_library_agent(
+            library_agent_id=library.id,
             user_id=user_id,
-            agent_id=library.id,
-            settings=library.settings.model_copy(
-                update={"human_in_the_loop_safe_mode": True}
-            ),
+            settings=updated_settings,
         )
     return library
 
@@ -919,21 +915,18 @@ async def update_graph_settings(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> GraphSettings:
     """Update graph settings for the user's library agent."""
-    # Get the library agent for this graph
     library_agent = await library_db.get_library_agent_by_graph_id(
         graph_id=graph_id, user_id=user_id
     )
     if not library_agent:
         raise HTTPException(404, f"Graph #{graph_id} not found in user's library")
 
-    # Update the library agent settings
-    updated_agent = await library_db.update_library_agent_settings(
+    updated_agent = await library_db.update_library_agent(
+        library_agent_id=library_agent.id,
         user_id=user_id,
-        agent_id=library_agent.id,
         settings=settings,
     )
 
-    # Return the updated settings
     return GraphSettings.model_validate(updated_agent.settings)
 
 

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -174,7 +174,7 @@ class AIShortformVideoCreatorBlock(Block):
         )
         frame_rate: int = SchemaField(description="Frame rate of the video", default=60)
         generation_preset: GenerationPreset = SchemaField(
-            description="Generation preset for visual style - only effects AI generated visuals",
+            description="Generation preset for visual style - only affects AI-generated visuals",
             default=GenerationPreset.LEONARDO,
             placeholder=GenerationPreset.LEONARDO,
         )

autogpt_platform/backend/backend/blocks/apollo/models.py

@@ -381,7 +381,7 @@ Each range you add needs to be a string, with the upper and lower numbers of the
     organization_locations: Optional[list[str]] = SchemaField(
         description="""The location of the company headquarters. You can search across cities, US states, and countries.
 
-If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appearch in your search results, even if they match other parameters.
+If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appear in your search results, even if they match other parameters.
 
 To exclude companies based on location, use the organization_not_locations parameter.
 """,

autogpt_platform/backend/backend/blocks/apollo/organization.py

@@ -34,7 +34,7 @@ Each range you add needs to be a string, with the upper and lower numbers of the
         organization_locations: list[str] = SchemaField(
             description="""The location of the company headquarters. You can search across cities, US states, and countries.
 
-If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appearch in your search results, even if they match other parameters.
+If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appear in your search results, even if they match other parameters.
 
 To exclude companies based on location, use the organization_not_locations parameter.
 """,

autogpt_platform/backend/backend/blocks/basic.py

@@ -81,7 +81,7 @@ class StoreValueBlock(Block):
     def __init__(self):
         super().__init__(
             id="1ff065e9-88e8-4358-9d82-8dc91f622ba9",
-            description="This block forwards an input value as output, allowing reuse without change.",
+            description="A basic block that stores and forwards a value throughout workflows, allowing it to be reused without changes across multiple blocks.",
             categories={BlockCategory.BASIC},
             input_schema=StoreValueBlock.Input,
             output_schema=StoreValueBlock.Output,
@@ -111,7 +111,7 @@ class PrintToConsoleBlock(Block):
     def __init__(self):
         super().__init__(
             id="f3b1c1b2-4c4f-4f0d-8d2f-4c4f0d8d2f4c",
-            description="Print the given text to the console, this is used for a debugging purpose.",
+            description="A debugging block that outputs text to the console for monitoring and troubleshooting workflow execution.",
             categories={BlockCategory.BASIC},
             input_schema=PrintToConsoleBlock.Input,
             output_schema=PrintToConsoleBlock.Output,
@@ -137,7 +137,7 @@ class NoteBlock(Block):
     def __init__(self):
         super().__init__(
             id="cc10ff7b-7753-4ff2-9af6-9399b1a7eddc",
-            description="This block is used to display a sticky note with the given text.",
+            description="A visual annotation block that displays a sticky note in the workflow editor for documentation and organization purposes.",
             categories={BlockCategory.BASIC},
             input_schema=NoteBlock.Input,
             output_schema=NoteBlock.Output,

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -159,7 +159,7 @@ class FindInDictionaryBlock(Block):
     def __init__(self):
         super().__init__(
             id="0e50422c-6dee-4145-83d6-3a5a392f65de",
-            description="Lookup the given key in the input dictionary/object/list and return the value.",
+            description="A block that looks up a value in a dictionary, list, or object by key or index and returns the corresponding value.",
             input_schema=FindInDictionaryBlock.Input,
             output_schema=FindInDictionaryBlock.Output,
             test_input=[
@@ -680,3 +680,58 @@ class ListIsEmptyBlock(Block):
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         yield "is_empty", len(input_data.list) == 0
+
+
+class ConcatenateListsBlock(Block):
+    class Input(BlockSchemaInput):
+        lists: List[List[Any]] = SchemaField(
+            description="A list of lists to concatenate together. All lists will be combined in order into a single list.",
+            placeholder="e.g., [[1, 2], [3, 4], [5, 6]]",
+        )
+
+    class Output(BlockSchemaOutput):
+        concatenated_list: List[Any] = SchemaField(
+            description="The concatenated list containing all elements from all input lists in order."
+        )
+        error: str = SchemaField(
+            description="Error message if concatenation failed due to invalid input types."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3cf9298b-5817-4141-9d80-7c2cc5199c8e",
+            description="Concatenates multiple lists into a single list. All elements from all input lists are combined in order.",
+            categories={BlockCategory.BASIC},
+            input_schema=ConcatenateListsBlock.Input,
+            output_schema=ConcatenateListsBlock.Output,
+            test_input=[
+                {"lists": [[1, 2, 3], [4, 5, 6]]},
+                {"lists": [["a", "b"], ["c"], ["d", "e", "f"]]},
+                {"lists": [[1, 2], []]},
+                {"lists": []},
+            ],
+            test_output=[
+                ("concatenated_list", [1, 2, 3, 4, 5, 6]),
+                ("concatenated_list", ["a", "b", "c", "d", "e", "f"]),
+                ("concatenated_list", [1, 2]),
+                ("concatenated_list", []),
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        concatenated = []
+        for idx, lst in enumerate(input_data.lists):
+            if lst is None:
+                # Skip None values to avoid errors
+                continue
+            if not isinstance(lst, list):
+                # Type validation: each item must be a list
+                # Strings are iterable and would cause extend() to iterate character-by-character
+                # Non-iterable types would raise TypeError
+                yield "error", (
+                    f"Invalid input at index {idx}: expected a list, got {type(lst).__name__}. "
+                    f"All items in 'lists' must be lists (e.g., [[1, 2], [3, 4]])."
+                )
+                return
+            concatenated.extend(lst)
+        yield "concatenated_list", concatenated

autogpt_platform/backend/backend/blocks/github/issues.py

@@ -51,7 +51,7 @@ class GithubCommentBlock(Block):
     def __init__(self):
         super().__init__(
             id="a8db4d8d-db1c-4a25-a1b0-416a8c33602b",
-            description="This block posts a comment on a specified GitHub issue or pull request.",
+            description="A block that posts comments on GitHub issues or pull requests using the GitHub API.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubCommentBlock.Input,
             output_schema=GithubCommentBlock.Output,
@@ -151,7 +151,7 @@ class GithubUpdateCommentBlock(Block):
     def __init__(self):
         super().__init__(
             id="b3f4d747-10e3-4e69-8c51-f2be1d99c9a7",
-            description="This block updates a comment on a specified GitHub issue or pull request.",
+            description="A block that updates an existing comment on a GitHub issue or pull request.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubUpdateCommentBlock.Input,
             output_schema=GithubUpdateCommentBlock.Output,
@@ -249,7 +249,7 @@ class GithubListCommentsBlock(Block):
     def __init__(self):
         super().__init__(
             id="c4b5fb63-0005-4a11-b35a-0c2467bd6b59",
-            description="This block lists all comments for a specified GitHub issue or pull request.",
+            description="A block that retrieves all comments from a GitHub issue or pull request, including comment metadata and content.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubListCommentsBlock.Input,
             output_schema=GithubListCommentsBlock.Output,
@@ -363,7 +363,7 @@ class GithubMakeIssueBlock(Block):
     def __init__(self):
         super().__init__(
             id="691dad47-f494-44c3-a1e8-05b7990f2dab",
-            description="This block creates a new issue on a specified GitHub repository.",
+            description="A block that creates new issues on GitHub repositories with a title and body content.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubMakeIssueBlock.Input,
             output_schema=GithubMakeIssueBlock.Output,
@@ -433,7 +433,7 @@ class GithubReadIssueBlock(Block):
     def __init__(self):
         super().__init__(
             id="6443c75d-032a-4772-9c08-230c707c8acc",
-            description="This block reads the body, title, and user of a specified GitHub issue.",
+            description="A block that retrieves information about a specific GitHub issue, including its title, body content, and creator.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubReadIssueBlock.Input,
             output_schema=GithubReadIssueBlock.Output,
@@ -510,7 +510,7 @@ class GithubListIssuesBlock(Block):
     def __init__(self):
         super().__init__(
             id="c215bfd7-0e57-4573-8f8c-f7d4963dcd74",
-            description="This block lists all issues for a specified GitHub repository.",
+            description="A block that retrieves a list of issues from a GitHub repository with their titles and URLs.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubListIssuesBlock.Input,
             output_schema=GithubListIssuesBlock.Output,
@@ -597,7 +597,7 @@ class GithubAddLabelBlock(Block):
     def __init__(self):
         super().__init__(
             id="98bd6b77-9506-43d5-b669-6b9733c4b1f1",
-            description="This block adds a label to a specified GitHub issue or pull request.",
+            description="A block that adds a label to a GitHub issue or pull request for categorization and organization.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubAddLabelBlock.Input,
             output_schema=GithubAddLabelBlock.Output,
@@ -657,7 +657,7 @@ class GithubRemoveLabelBlock(Block):
     def __init__(self):
         super().__init__(
             id="78f050c5-3e3a-48c0-9e5b-ef1ceca5589c",
-            description="This block removes a label from a specified GitHub issue or pull request.",
+            description="A block that removes a label from a GitHub issue or pull request.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubRemoveLabelBlock.Input,
             output_schema=GithubRemoveLabelBlock.Output,
@@ -720,7 +720,7 @@ class GithubAssignIssueBlock(Block):
     def __init__(self):
         super().__init__(
             id="90507c72-b0ff-413a-886a-23bbbd66f542",
-            description="This block assigns a user to a specified GitHub issue.",
+            description="A block that assigns a GitHub user to an issue for task ownership and tracking.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubAssignIssueBlock.Input,
             output_schema=GithubAssignIssueBlock.Output,
@@ -786,7 +786,7 @@ class GithubUnassignIssueBlock(Block):
     def __init__(self):
         super().__init__(
             id="d154002a-38f4-46c2-962d-2488f2b05ece",
-            description="This block unassigns a user from a specified GitHub issue.",
+            description="A block that removes a user's assignment from a GitHub issue.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubUnassignIssueBlock.Input,
             output_schema=GithubUnassignIssueBlock.Output,

autogpt_platform/backend/backend/blocks/google/gmail.py

@@ -353,7 +353,7 @@ class GmailReadBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="25310c70-b89b-43ba-b25c-4dfa7e2a481c",
-            description="This block reads emails from Gmail.",
+            description="A block that retrieves and reads emails from a Gmail account based on search criteria, returning detailed message information including subject, sender, body, and attachments.",
             categories={BlockCategory.COMMUNICATION},
             disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
             input_schema=GmailReadBlock.Input,
@@ -743,7 +743,7 @@ class GmailListLabelsBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="3e1c2c1c-c689-4520-b956-1f3bf4e02bb7",
-            description="This block lists all labels in Gmail.",
+            description="A block that retrieves all labels (categories) from a Gmail account for organizing and categorizing emails.",
             categories={BlockCategory.COMMUNICATION},
             input_schema=GmailListLabelsBlock.Input,
             output_schema=GmailListLabelsBlock.Output,
@@ -807,7 +807,7 @@ class GmailAddLabelBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="f884b2fb-04f4-4265-9658-14f433926ac9",
-            description="This block adds a label to a Gmail message.",
+            description="A block that adds a label to a specific email message in Gmail, creating the label if it doesn't exist.",
             categories={BlockCategory.COMMUNICATION},
             input_schema=GmailAddLabelBlock.Input,
             output_schema=GmailAddLabelBlock.Output,
@@ -893,7 +893,7 @@ class GmailRemoveLabelBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="0afc0526-aba1-4b2b-888e-a22b7c3f359d",
-            description="This block removes a label from a Gmail message.",
+            description="A block that removes a label from a specific email message in a Gmail account.",
             categories={BlockCategory.COMMUNICATION},
             input_schema=GmailRemoveLabelBlock.Input,
             output_schema=GmailRemoveLabelBlock.Output,
@@ -961,7 +961,7 @@ class GmailGetThreadBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="21a79166-9df7-4b5f-9f36-96f639d86112",
-            description="Get a full Gmail thread by ID",
+            description="A block that retrieves an entire Gmail thread (email conversation) by ID, returning all messages with decoded bodies for reading complete conversations.",
             categories={BlockCategory.COMMUNICATION},
             input_schema=GmailGetThreadBlock.Input,
             output_schema=GmailGetThreadBlock.Output,

autogpt_platform/backend/backend/blocks/google/sheets.py

@@ -282,7 +282,7 @@ class GoogleSheetsReadBlock(Block):
     def __init__(self):
         super().__init__(
             id="5724e902-3635-47e9-a108-aaa0263a4988",
-            description="This block reads data from a Google Sheets spreadsheet.",
+            description="A block that reads data from a Google Sheets spreadsheet using A1 notation range selection.",
             categories={BlockCategory.DATA},
             input_schema=GoogleSheetsReadBlock.Input,
             output_schema=GoogleSheetsReadBlock.Output,
@@ -409,7 +409,7 @@ class GoogleSheetsWriteBlock(Block):
     def __init__(self):
         super().__init__(
             id="d9291e87-301d-47a8-91fe-907fb55460e5",
-            description="This block writes data to a Google Sheets spreadsheet.",
+            description="A block that writes data to a Google Sheets spreadsheet at a specified A1 notation range.",
             categories={BlockCategory.DATA},
             input_schema=GoogleSheetsWriteBlock.Input,
             output_schema=GoogleSheetsWriteBlock.Output,

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -84,7 +84,7 @@ class HITLReviewHelper:
             Exception: If review creation or status update fails
         """
         # Skip review if safe mode is disabled - return auto-approved result
-        if not execution_context.safe_mode:
+        if not execution_context.human_in_the_loop_safe_mode:
             logger.info(
                 f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
             )

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -104,7 +104,7 @@ class HumanInTheLoopBlock(Block):
         execution_context: ExecutionContext,
         **_kwargs,
     ) -> BlockOutput:
-        if not execution_context.safe_mode:
+        if not execution_context.human_in_the_loop_safe_mode:
             logger.info(
                 f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
             )

autogpt_platform/backend/backend/blocks/io.py

@@ -76,7 +76,7 @@ class AgentInputBlock(Block):
         super().__init__(
             **{
                 "id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-                "description": "Base block for user inputs.",
+                "description": "A block that accepts and processes user input values within a workflow, supporting various input types and validation.",
                 "input_schema": AgentInputBlock.Input,
                 "output_schema": AgentInputBlock.Output,
                 "test_input": [
@@ -168,7 +168,7 @@ class AgentOutputBlock(Block):
     def __init__(self):
         super().__init__(
             id="363ae599-353e-4804-937e-b2ee3cef3da4",
-            description="Stores the output of the graph for users to see.",
+            description="A block that records and formats workflow results for display to users, with optional Jinja2 template formatting support.",
             input_schema=AgentOutputBlock.Input,
             output_schema=AgentOutputBlock.Output,
             test_input=[

autogpt_platform/backend/backend/blocks/llm.py

@@ -79,6 +79,10 @@ class ModelMetadata(NamedTuple):
     provider: str
     context_window: int
     max_output_tokens: int | None
+    display_name: str
+    provider_name: str
+    creator_name: str
+    price_tier: Literal[1, 2, 3]
 
 
 class LlmModelMeta(EnumMeta):
@@ -171,6 +175,26 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     V0_1_5_LG = "v0-1.5-lg"
     V0_1_0_MD = "v0-1.0-md"
 
+    @classmethod
+    def __get_pydantic_json_schema__(cls, schema, handler):
+        json_schema = handler(schema)
+        llm_model_metadata = {}
+        for model in cls:
+            model_name = model.value
+            metadata = model.metadata
+            llm_model_metadata[model_name] = {
+                "creator": metadata.creator_name,
+                "creator_name": metadata.creator_name,
+                "title": metadata.display_name,
+                "provider": metadata.provider,
+                "provider_name": metadata.provider_name,
+                "name": model_name,
+                "price_tier": metadata.price_tier,
+            }
+        json_schema["llm_model"] = True
+        json_schema["llm_model_metadata"] = llm_model_metadata
+        return json_schema
+
     @property
     def metadata(self) -> ModelMetadata:
         return MODEL_METADATA[self]
@@ -190,119 +214,291 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
 
 MODEL_METADATA = {
     # https://platform.openai.com/docs/models
-    LlmModel.O3: ModelMetadata("openai", 200000, 100000),
-    LlmModel.O3_MINI: ModelMetadata("openai", 200000, 100000),  # o3-mini-2025-01-31
-    LlmModel.O1: ModelMetadata("openai", 200000, 100000),  # o1-2024-12-17
-    LlmModel.O1_MINI: ModelMetadata("openai", 128000, 65536),  # o1-mini-2024-09-12
+    LlmModel.O3: ModelMetadata("openai", 200000, 100000, "O3", "OpenAI", "OpenAI", 2),
+    LlmModel.O3_MINI: ModelMetadata(
+        "openai", 200000, 100000, "O3 Mini", "OpenAI", "OpenAI", 1
+    ),  # o3-mini-2025-01-31
+    LlmModel.O1: ModelMetadata(
+        "openai", 200000, 100000, "O1", "OpenAI", "OpenAI", 3
+    ),  # o1-2024-12-17
+    LlmModel.O1_MINI: ModelMetadata(
+        "openai", 128000, 65536, "O1 Mini", "OpenAI", "OpenAI", 2
+    ),  # o1-mini-2024-09-12
     # GPT-5 models
-    LlmModel.GPT5_2: ModelMetadata("openai", 400000, 128000),
-    LlmModel.GPT5_1: ModelMetadata("openai", 400000, 128000),
-    LlmModel.GPT5: ModelMetadata("openai", 400000, 128000),
-    LlmModel.GPT5_MINI: ModelMetadata("openai", 400000, 128000),
-    LlmModel.GPT5_NANO: ModelMetadata("openai", 400000, 128000),
-    LlmModel.GPT5_CHAT: ModelMetadata("openai", 400000, 16384),
-    LlmModel.GPT41: ModelMetadata("openai", 1047576, 32768),
-    LlmModel.GPT41_MINI: ModelMetadata("openai", 1047576, 32768),
+    LlmModel.GPT5_2: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5.2", "OpenAI", "OpenAI", 3
+    ),
+    LlmModel.GPT5_1: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5.1", "OpenAI", "OpenAI", 2
+    ),
+    LlmModel.GPT5: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT5_MINI: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5 Mini", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT5_NANO: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5 Nano", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT5_CHAT: ModelMetadata(
+        "openai", 400000, 16384, "GPT-5 Chat Latest", "OpenAI", "OpenAI", 2
+    ),
+    LlmModel.GPT41: ModelMetadata(
+        "openai", 1047576, 32768, "GPT-4.1", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT41_MINI: ModelMetadata(
+        "openai", 1047576, 32768, "GPT-4.1 Mini", "OpenAI", "OpenAI", 1
+    ),
     LlmModel.GPT4O_MINI: ModelMetadata(
-        "openai", 128000, 16384
+        "openai", 128000, 16384, "GPT-4o Mini", "OpenAI", "OpenAI", 1
     ),  # gpt-4o-mini-2024-07-18
-    LlmModel.GPT4O: ModelMetadata("openai", 128000, 16384),  # gpt-4o-2024-08-06
+    LlmModel.GPT4O: ModelMetadata(
+        "openai", 128000, 16384, "GPT-4o", "OpenAI", "OpenAI", 2
+    ),  # gpt-4o-2024-08-06
     LlmModel.GPT4_TURBO: ModelMetadata(
-        "openai", 128000, 4096
+        "openai", 128000, 4096, "GPT-4 Turbo", "OpenAI", "OpenAI", 3
     ),  # gpt-4-turbo-2024-04-09
-    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, 4096),  # gpt-3.5-turbo-0125
+    LlmModel.GPT3_5_TURBO: ModelMetadata(
+        "openai", 16385, 4096, "GPT-3.5 Turbo", "OpenAI", "OpenAI", 1
+    ),  # gpt-3.5-turbo-0125
     # https://docs.anthropic.com/en/docs/about-claude/models
     LlmModel.CLAUDE_4_1_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000
+        "anthropic", 200000, 32000, "Claude Opus 4.1", "Anthropic", "Anthropic", 3
     ),  # claude-opus-4-1-20250805
     LlmModel.CLAUDE_4_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000
+        "anthropic", 200000, 32000, "Claude Opus 4", "Anthropic", "Anthropic", 3
     ),  # claude-4-opus-20250514
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude Sonnet 4", "Anthropic", "Anthropic", 2
     ),  # claude-4-sonnet-20250514
     LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude Opus 4.5", "Anthropic", "Anthropic", 3
     ),  # claude-opus-4-5-20251101
     LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude Sonnet 4.5", "Anthropic", "Anthropic", 3
     ),  # claude-sonnet-4-5-20250929
     LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
     ),  # claude-haiku-4-5-20251001
     LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
     ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
-        "anthropic", 200000, 4096
+        "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
     ),  # claude-3-haiku-20240307
     # https://docs.aimlapi.com/api-overview/model-database/text-models
-    LlmModel.AIML_API_QWEN2_5_72B: ModelMetadata("aiml_api", 32000, 8000),
-    LlmModel.AIML_API_LLAMA3_1_70B: ModelMetadata("aiml_api", 128000, 40000),
-    LlmModel.AIML_API_LLAMA3_3_70B: ModelMetadata("aiml_api", 128000, None),
-    LlmModel.AIML_API_META_LLAMA_3_1_70B: ModelMetadata("aiml_api", 131000, 2000),
-    LlmModel.AIML_API_LLAMA_3_2_3B: ModelMetadata("aiml_api", 128000, None),
-    # https://console.groq.com/docs/models
-    LlmModel.LLAMA3_3_70B: ModelMetadata("groq", 128000, 32768),
-    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 128000, 8192),
-    # https://ollama.com/library
-    LlmModel.OLLAMA_LLAMA3_3: ModelMetadata("ollama", 8192, None),
-    LlmModel.OLLAMA_LLAMA3_2: ModelMetadata("ollama", 8192, None),
-    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192, None),
-    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192, None),
-    LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768, None),
-    # https://openrouter.ai/models
-    LlmModel.GEMINI_2_5_PRO: ModelMetadata("open_router", 1050000, 8192),
-    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata("open_router", 1048576, 65535),
-    LlmModel.GEMINI_2_5_FLASH: ModelMetadata("open_router", 1048576, 65535),
-    LlmModel.GEMINI_2_0_FLASH: ModelMetadata("open_router", 1048576, 8192),
-    LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelMetadata(
-        "open_router", 1048576, 65535
+    LlmModel.AIML_API_QWEN2_5_72B: ModelMetadata(
+        "aiml_api", 32000, 8000, "Qwen 2.5 72B Instruct Turbo", "AI/ML", "Qwen", 1
+    ),
+    LlmModel.AIML_API_LLAMA3_1_70B: ModelMetadata(
+        "aiml_api",
+        128000,
+        40000,
+        "Llama 3.1 Nemotron 70B Instruct",
+        "AI/ML",
+        "Nvidia",
+        1,
+    ),
+    LlmModel.AIML_API_LLAMA3_3_70B: ModelMetadata(
+        "aiml_api", 128000, None, "Llama 3.3 70B Instruct Turbo", "AI/ML", "Meta", 1
+    ),
+    LlmModel.AIML_API_META_LLAMA_3_1_70B: ModelMetadata(
+        "aiml_api", 131000, 2000, "Llama 3.1 70B Instruct Turbo", "AI/ML", "Meta", 1
+    ),
+    LlmModel.AIML_API_LLAMA_3_2_3B: ModelMetadata(
+        "aiml_api", 128000, None, "Llama 3.2 3B Instruct Turbo", "AI/ML", "Meta", 1
+    ),
+    # https://console.groq.com/docs/models
+    LlmModel.LLAMA3_3_70B: ModelMetadata(
+        "groq", 128000, 32768, "Llama 3.3 70B Versatile", "Groq", "Meta", 1
+    ),
+    LlmModel.LLAMA3_1_8B: ModelMetadata(
+        "groq", 128000, 8192, "Llama 3.1 8B Instant", "Groq", "Meta", 1
+    ),
+    # https://ollama.com/library
+    LlmModel.OLLAMA_LLAMA3_3: ModelMetadata(
+        "ollama", 8192, None, "Llama 3.3", "Ollama", "Meta", 1
+    ),
+    LlmModel.OLLAMA_LLAMA3_2: ModelMetadata(
+        "ollama", 8192, None, "Llama 3.2", "Ollama", "Meta", 1
+    ),
+    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata(
+        "ollama", 8192, None, "Llama 3", "Ollama", "Meta", 1
+    ),
+    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata(
+        "ollama", 8192, None, "Llama 3.1 405B", "Ollama", "Meta", 1
+    ),
+    LlmModel.OLLAMA_DOLPHIN: ModelMetadata(
+        "ollama", 32768, None, "Dolphin Mistral Latest", "Ollama", "Mistral AI", 1
+    ),
+    # https://openrouter.ai/models
+    LlmModel.GEMINI_2_5_PRO: ModelMetadata(
+        "open_router",
+        1050000,
+        8192,
+        "Gemini 2.5 Pro Preview 03.25",
+        "OpenRouter",
+        "Google",
+        2,
+    ),
+    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata(
+        "open_router", 1048576, 65535, "Gemini 3 Pro Preview", "OpenRouter", "Google", 2
+    ),
+    LlmModel.GEMINI_2_5_FLASH: ModelMetadata(
+        "open_router", 1048576, 65535, "Gemini 2.5 Flash", "OpenRouter", "Google", 1
+    ),
+    LlmModel.GEMINI_2_0_FLASH: ModelMetadata(
+        "open_router", 1048576, 8192, "Gemini 2.0 Flash 001", "OpenRouter", "Google", 1
+    ),
+    LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelMetadata(
+        "open_router",
+        1048576,
+        65535,
+        "Gemini 2.5 Flash Lite Preview 06.17",
+        "OpenRouter",
+        "Google",
+        1,
+    ),
+    LlmModel.GEMINI_2_0_FLASH_LITE: ModelMetadata(
+        "open_router",
+        1048576,
+        8192,
+        "Gemini 2.0 Flash Lite 001",
+        "OpenRouter",
+        "Google",
+        1,
+    ),
+    LlmModel.MISTRAL_NEMO: ModelMetadata(
+        "open_router", 128000, 4096, "Mistral Nemo", "OpenRouter", "Mistral AI", 1
+    ),
+    LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata(
+        "open_router", 128000, 4096, "Command R 08.2024", "OpenRouter", "Cohere", 1
+    ),
+    LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata(
+        "open_router", 128000, 4096, "Command R Plus 08.2024", "OpenRouter", "Cohere", 2
+    ),
+    LlmModel.DEEPSEEK_CHAT: ModelMetadata(
+        "open_router", 64000, 2048, "DeepSeek Chat", "OpenRouter", "DeepSeek", 1
+    ),
+    LlmModel.DEEPSEEK_R1_0528: ModelMetadata(
+        "open_router", 163840, 163840, "DeepSeek R1 0528", "OpenRouter", "DeepSeek", 1
+    ),
+    LlmModel.PERPLEXITY_SONAR: ModelMetadata(
+        "open_router", 127000, 8000, "Sonar", "OpenRouter", "Perplexity", 1
+    ),
+    LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata(
+        "open_router", 200000, 8000, "Sonar Pro", "OpenRouter", "Perplexity", 2
     ),
-    LlmModel.GEMINI_2_0_FLASH_LITE: ModelMetadata("open_router", 1048576, 8192),
-    LlmModel.MISTRAL_NEMO: ModelMetadata("open_router", 128000, 4096),
-    LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata("open_router", 128000, 4096),
-    LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata("open_router", 128000, 4096),
-    LlmModel.DEEPSEEK_CHAT: ModelMetadata("open_router", 64000, 2048),
-    LlmModel.DEEPSEEK_R1_0528: ModelMetadata("open_router", 163840, 163840),
-    LlmModel.PERPLEXITY_SONAR: ModelMetadata("open_router", 127000, 8000),
-    LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata("open_router", 200000, 8000),
     LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: ModelMetadata(
         "open_router",
         128000,
         16000,
+        "Sonar Deep Research",
+        "OpenRouter",
+        "Perplexity",
+        3,
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B: ModelMetadata(
-        "open_router", 131000, 4096
+        "open_router",
+        131000,
+        4096,
+        "Hermes 3 Llama 3.1 405B",
+        "OpenRouter",
+        "Nous Research",
+        1,
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_70B: ModelMetadata(
-        "open_router", 12288, 12288
+        "open_router",
+        12288,
+        12288,
+        "Hermes 3 Llama 3.1 70B",
+        "OpenRouter",
+        "Nous Research",
+        1,
+    ),
+    LlmModel.OPENAI_GPT_OSS_120B: ModelMetadata(
+        "open_router", 131072, 131072, "GPT-OSS 120B", "OpenRouter", "OpenAI", 1
+    ),
+    LlmModel.OPENAI_GPT_OSS_20B: ModelMetadata(
+        "open_router", 131072, 32768, "GPT-OSS 20B", "OpenRouter", "OpenAI", 1
+    ),
+    LlmModel.AMAZON_NOVA_LITE_V1: ModelMetadata(
+        "open_router", 300000, 5120, "Nova Lite V1", "OpenRouter", "Amazon", 1
+    ),
+    LlmModel.AMAZON_NOVA_MICRO_V1: ModelMetadata(
+        "open_router", 128000, 5120, "Nova Micro V1", "OpenRouter", "Amazon", 1
+    ),
+    LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata(
+        "open_router", 300000, 5120, "Nova Pro V1", "OpenRouter", "Amazon", 1
+    ),
+    LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata(
+        "open_router", 65536, 4096, "WizardLM 2 8x22B", "OpenRouter", "Microsoft", 1
+    ),
+    LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata(
+        "open_router", 4096, 4096, "MythoMax L2 13B", "OpenRouter", "Gryphe", 1
+    ),
+    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata(
+        "open_router", 131072, 131072, "Llama 4 Scout", "OpenRouter", "Meta", 1
+    ),
+    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata(
+        "open_router", 1048576, 1000000, "Llama 4 Maverick", "OpenRouter", "Meta", 1
+    ),
+    LlmModel.GROK_4: ModelMetadata(
+        "open_router", 256000, 256000, "Grok 4", "OpenRouter", "xAI", 3
+    ),
+    LlmModel.GROK_4_FAST: ModelMetadata(
+        "open_router", 2000000, 30000, "Grok 4 Fast", "OpenRouter", "xAI", 1
+    ),
+    LlmModel.GROK_4_1_FAST: ModelMetadata(
+        "open_router", 2000000, 30000, "Grok 4.1 Fast", "OpenRouter", "xAI", 1
+    ),
+    LlmModel.GROK_CODE_FAST_1: ModelMetadata(
+        "open_router", 256000, 10000, "Grok Code Fast 1", "OpenRouter", "xAI", 1
+    ),
+    LlmModel.KIMI_K2: ModelMetadata(
+        "open_router", 131000, 131000, "Kimi K2", "OpenRouter", "Moonshot AI", 1
+    ),
+    LlmModel.QWEN3_235B_A22B_THINKING: ModelMetadata(
+        "open_router",
+        262144,
+        262144,
+        "Qwen 3 235B A22B Thinking 2507",
+        "OpenRouter",
+        "Qwen",
+        1,
+    ),
+    LlmModel.QWEN3_CODER: ModelMetadata(
+        "open_router", 262144, 262144, "Qwen 3 Coder", "OpenRouter", "Qwen", 3
     ),
-    LlmModel.OPENAI_GPT_OSS_120B: ModelMetadata("open_router", 131072, 131072),
-    LlmModel.OPENAI_GPT_OSS_20B: ModelMetadata("open_router", 131072, 32768),
-    LlmModel.AMAZON_NOVA_LITE_V1: ModelMetadata("open_router", 300000, 5120),
-    LlmModel.AMAZON_NOVA_MICRO_V1: ModelMetadata("open_router", 128000, 5120),
-    LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata("open_router", 300000, 5120),
-    LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata("open_router", 65536, 4096),
-    LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata("open_router", 4096, 4096),
-    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata("open_router", 131072, 131072),
-    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata("open_router", 1048576, 1000000),
-    LlmModel.GROK_4: ModelMetadata("open_router", 256000, 256000),
-    LlmModel.GROK_4_FAST: ModelMetadata("open_router", 2000000, 30000),
-    LlmModel.GROK_4_1_FAST: ModelMetadata("open_router", 2000000, 30000),
-    LlmModel.GROK_CODE_FAST_1: ModelMetadata("open_router", 256000, 10000),
-    LlmModel.KIMI_K2: ModelMetadata("open_router", 131000, 131000),
-    LlmModel.QWEN3_235B_A22B_THINKING: ModelMetadata("open_router", 262144, 262144),
-    LlmModel.QWEN3_CODER: ModelMetadata("open_router", 262144, 262144),
     # Llama API models
-    LlmModel.LLAMA_API_LLAMA_4_SCOUT: ModelMetadata("llama_api", 128000, 4028),
-    LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata("llama_api", 128000, 4028),
-    LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata("llama_api", 128000, 4028),
-    LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata("llama_api", 128000, 4028),
+    LlmModel.LLAMA_API_LLAMA_4_SCOUT: ModelMetadata(
+        "llama_api",
+        128000,
+        4028,
+        "Llama 4 Scout 17B 16E Instruct FP8",
+        "Llama API",
+        "Meta",
+        1,
+    ),
+    LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata(
+        "llama_api",
+        128000,
+        4028,
+        "Llama 4 Maverick 17B 128E Instruct FP8",
+        "Llama API",
+        "Meta",
+        1,
+    ),
+    LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata(
+        "llama_api", 128000, 4028, "Llama 3.3 8B Instruct", "Llama API", "Meta", 1
+    ),
+    LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata(
+        "llama_api", 128000, 4028, "Llama 3.3 70B Instruct", "Llama API", "Meta", 1
+    ),
     # v0 by Vercel models
-    LlmModel.V0_1_5_MD: ModelMetadata("v0", 128000, 64000),
-    LlmModel.V0_1_5_LG: ModelMetadata("v0", 512000, 64000),
-    LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000),
+    LlmModel.V0_1_5_MD: ModelMetadata("v0", 128000, 64000, "v0 1.5 MD", "V0", "V0", 1),
+    LlmModel.V0_1_5_LG: ModelMetadata("v0", 512000, 64000, "v0 1.5 LG", "V0", "V0", 1),
+    LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000, "v0 1.0 MD", "V0", "V0", 1),
 }
 
 DEFAULT_LLM_MODEL = LlmModel.GPT5_2
@@ -854,7 +1050,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="ed55ac19-356e-4243-a6cb-bc599e9b716f",
-            description="Call a Large Language Model (LLM) to generate formatted object based on the given prompt.",
+            description="A block that generates structured JSON responses using a Large Language Model (LLM), with schema validation and format enforcement.",
             categories={BlockCategory.AI},
             input_schema=AIStructuredResponseGeneratorBlock.Input,
             output_schema=AIStructuredResponseGeneratorBlock.Output,
@@ -1265,7 +1461,7 @@ class AITextGeneratorBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="1f292d4a-41a4-4977-9684-7c8d560b9f91",
-            description="Call a Large Language Model (LLM) to generate a string based on the given prompt.",
+            description="A block that produces text responses using a Large Language Model (LLM) based on customizable prompts and system instructions.",
             categories={BlockCategory.AI},
             input_schema=AITextGeneratorBlock.Input,
             output_schema=AITextGeneratorBlock.Output,
@@ -1361,7 +1557,7 @@ class AITextSummarizerBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="a0a69be1-4528-491c-a85a-a4ab6873e3f0",
-            description="Utilize a Large Language Model (LLM) to summarize a long text.",
+            description="A block that summarizes long texts using a Large Language Model (LLM), with configurable focus topics and summary styles.",
             categories={BlockCategory.AI, BlockCategory.TEXT},
             input_schema=AITextSummarizerBlock.Input,
             output_schema=AITextSummarizerBlock.Output,
@@ -1562,7 +1758,7 @@ class AIConversationBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="32a87eab-381e-4dd4-bdb8-4c47151be35a",
-            description="Advanced LLM call that takes a list of messages and sends them to the language model.",
+            description="A block that facilitates multi-turn conversations with a Large Language Model (LLM), maintaining context across message exchanges.",
             categories={BlockCategory.AI},
             input_schema=AIConversationBlock.Input,
             output_schema=AIConversationBlock.Output,
@@ -1682,7 +1878,7 @@ class AIListGeneratorBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="9c0b0450-d199-458b-a731-072189dd6593",
-            description="Generate a list of values based on the given prompt using a Large Language Model (LLM).",
+            description="A block that creates lists of items based on prompts using a Large Language Model (LLM), with optional source data for context.",
             categories={BlockCategory.AI, BlockCategory.TEXT},
             input_schema=AIListGeneratorBlock.Input,
             output_schema=AIListGeneratorBlock.Output,

autogpt_platform/backend/backend/blocks/medium.py

@@ -46,7 +46,7 @@ class PublishToMediumBlock(Block):
     class Input(BlockSchemaInput):
         author_id: BlockSecret = SecretField(
             key="medium_author_id",
-            description="""The Medium AuthorID of the user. You can get this by calling the /me endpoint of the Medium API.\n\ncurl -H "Authorization: Bearer YOUR_ACCESS_TOKEN" https://api.medium.com/v1/me" the response will contain the authorId field.""",
+            description="""The Medium AuthorID of the user. You can get this by calling the /me endpoint of the Medium API.\n\ncurl -H "Authorization: Bearer YOUR_ACCESS_TOKEN" https://api.medium.com/v1/me\n\nThe response will contain the authorId field.""",
             placeholder="Enter the author's Medium AuthorID",
         )
         title: str = SchemaField(

autogpt_platform/backend/backend/blocks/talking_head.py

@@ -50,7 +50,7 @@ class CreateTalkingAvatarVideoBlock(Block):
             description="The voice provider to use", default="microsoft"
         )
         voice_id: str = SchemaField(
-            description="The voice ID to use, get list of voices [here](https://docs.agpt.co/server/d_id)",
+            description="The voice ID to use, see [available voice IDs](https://agpt.co/docs/platform/using-ai-services/d_id)",
             default="en-US-JennyNeural",
         )
         presenter_id: str = SchemaField(

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -242,7 +242,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -343,7 +343,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -409,7 +409,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -471,7 +471,7 @@ async def test_smart_decision_maker_parameter_validation():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -535,7 +535,7 @@ async def test_smart_decision_maker_parameter_validation():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -658,7 +658,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -730,7 +730,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -786,7 +786,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -905,7 +905,7 @@ async def test_smart_decision_maker_agent_mode():
         # Create a mock execution context
 
         mock_execution_context = ExecutionContext(
-            safe_mode=False,
+            human_in_the_loop_safe_mode=False,
         )
 
         # Create a mock execution processor for agent mode tests
@@ -1027,7 +1027,7 @@ async def test_smart_decision_maker_traditional_mode_default():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests