From 121be74ee9fdcf763705a29caebdb11f685eaca0 Mon Sep 17 00:00:00 2001
From: Waleed Latif <walif6@gmail.com>
Date: Tue, 14 Jul 2026 17:48:57 -0700
Subject: [PATCH 1/4] fix(docs): remove Ask AI widget

---
 apps/docs/app/[lang]/layout.tsx          |   2 -
 apps/docs/app/api/chat/route.ts          | 344 -----------------------
 apps/docs/components/ai/ask-ai-panel.tsx | 234 ---------------
 apps/docs/components/ai/ask-ai.tsx       |  57 ----
 apps/docs/package.json                   |   4 -
 bun.lock                                 |  10 -
 6 files changed, 651 deletions(-)
 delete mode 100644 apps/docs/app/api/chat/route.ts
 delete mode 100644 apps/docs/components/ai/ask-ai-panel.tsx
 delete mode 100644 apps/docs/components/ai/ask-ai.tsx

diff --git a/apps/docs/app/[lang]/layout.tsx b/apps/docs/app/[lang]/layout.tsx
index 4fb73ecee32..f27d3187e69 100644
--- a/apps/docs/app/[lang]/layout.tsx
+++ b/apps/docs/app/[lang]/layout.tsx
@@ -3,7 +3,6 @@ import { defineI18nUI } from 'fumadocs-ui/i18n'
 import { DocsLayout } from 'fumadocs-ui/layouts/docs'
 import { RootProvider } from 'fumadocs-ui/provider/next'
 import { Geist_Mono, Inter } from 'next/font/google'
-import { AskAI } from '@/components/ai/ask-ai'
 import {
   SidebarFolder,
   SidebarItem,
@@ -124,7 +123,6 @@ export default async function Layout({ children, params }: LayoutProps) {
             {children}
           </DocsLayout>
           <Footer />
-          <AskAI locale={lang} />
         </RootProvider>
       </body>
     </html>
diff --git a/apps/docs/app/api/chat/route.ts b/apps/docs/app/api/chat/route.ts
deleted file mode 100644
index 2150e044980..00000000000
--- a/apps/docs/app/api/chat/route.ts
+++ /dev/null
@@ -1,344 +0,0 @@
-import { openai } from '@ai-sdk/openai'
-import { convertToModelMessages, stepCountIs, streamText, tool, type UIMessage } from 'ai'
-import { sql } from 'drizzle-orm'
-import { z } from 'zod'
-import { db, docsEmbeddings } from '@/lib/db'
-import { generateSearchEmbedding } from '@/lib/embeddings'
-
-export const runtime = 'nodejs'
-export const maxDuration = 30
-
-/** Model used for the Ask AI chat. Override with OPENAI_CHAT_MODEL in the environment. */
-const CHAT_MODEL = process.env.OPENAI_CHAT_MODEL || 'gpt-5.4-mini'
-
-/** Max documentation chunks returned per search to ground an answer. */
-const SEARCH_LIMIT = 6
-
-/** Candidates pulled before locale filtering, so a locale still yields SEARCH_LIMIT results. */
-const SEARCH_CANDIDATES = SEARCH_LIMIT * 4
-
-/** Minimum cosine similarity for an English vector match (mirrors the site search route). */
-const SIMILARITY_THRESHOLD = 0.6
-
-/** Locales the docs are published in (mirrors the site search route). */
-const KNOWN_LOCALES = ['en', 'es', 'fr', 'de', 'ja', 'zh']
-const DEFAULT_LOCALE = 'en'
-
-/** Postgres full-text config per locale (mirrors the site search route). */
-const TS_CONFIG: Record<string, string> = {
-  en: 'english',
-  es: 'spanish',
-  fr: 'french',
-  de: 'german',
-  ja: 'simple',
-  zh: 'simple',
-}
-
-/**
- * Abuse guards. This endpoint proxies a paid LLM, so an unauthenticated public
- * route is a target for scripted "free inference". These bounds cap the cost of
- * any single request; an in-memory per-IP rate limit (below) caps volume on the
- * hot path. A shared-store rate limit, a provider spend cap, and edge bot
- * protection remain the durable controls (see the PR checklist).
- *
- * The size cap counts only user-authored text — NOT the conversation history,
- * assistant turns, or retrieved doc chunks we add via the searchDocs tool, which
- * legitimately grow large over a multi-turn chat.
- */
-const MAX_MESSAGES = 200
-const MAX_USER_INPUT_CHARS = 400_000
-const MAX_OUTPUT_TOKENS = 4000
-const MAX_STEPS = 6
-/** Backstop on the sanitized model payload — bounds total LLM input (e.g. stuffed assistant text). */
-const MAX_TOTAL_CHARS = 1_000_000
-
-/**
- * Per-IP rate limit. Fixed window, in-memory: this bounds volume from a single
- * source on a warm instance without external infra. It is best-effort on
- * serverless (state is per-instance, not shared across regions/cold starts);
- * a shared store (e.g. Vercel KV) and an edge WAF remain the durable controls,
- * but this closes the "no volume limit at all" gap on the hot path.
- */
-const RATE_LIMIT_MAX = 20
-const RATE_LIMIT_WINDOW_MS = 60_000
-const rateLimitHits = new Map<string, { count: number; resetAt: number }>()
-
-/** Resolve the client IP from forwarding headers, falling back to a shared bucket. */
-function getClientIp(req: Request): string {
-  const forwarded = req.headers.get('x-forwarded-for')
-  if (forwarded) return forwarded.split(',')[0].trim()
-  return req.headers.get('x-real-ip') ?? 'unknown'
-}
-
-/** Fixed-window check. Returns retry-after seconds when the caller is over the limit, else null. */
-function rateLimit(ip: string, now: number): number | null {
-  const entry = rateLimitHits.get(ip)
-  if (!entry || now >= entry.resetAt) {
-    rateLimitHits.set(ip, { count: 1, resetAt: now + RATE_LIMIT_WINDOW_MS })
-    return null
-  }
-  if (entry.count >= RATE_LIMIT_MAX) {
-    return Math.ceil((entry.resetAt - now) / 1000)
-  }
-  entry.count += 1
-  return null
-}
-
-/** Drop expired buckets so the Map doesn't grow unbounded on a long-lived instance. */
-function sweepRateLimit(now: number): void {
-  if (rateLimitHits.size < 10_000) return
-  for (const [ip, entry] of rateLimitHits) {
-    if (now >= entry.resetAt) rateLimitHits.delete(ip)
-  }
-}
-
-/** A structurally valid UI message: has a role and a parts array. */
-function isValidMessage(message: unknown): message is UIMessage {
-  return (
-    typeof message === 'object' &&
-    message !== null &&
-    typeof (message as { role?: unknown }).role === 'string' &&
-    Array.isArray((message as { parts?: unknown }).parts)
-  )
-}
-
-/** Total length of user-authored text across the conversation. */
-function userInputChars(messages: UIMessage[]): number {
-  let total = 0
-  for (const message of messages) {
-    if (message.role !== 'user') continue
-    for (const part of message.parts) {
-      if (part.type === 'text' && typeof part.text === 'string') total += part.text.length
-    }
-  }
-  return total
-}
-
-/**
- * Strip everything the model shouldn't trust from client-supplied history:
- * drop `system` messages (client-injected instructions) and every non-text part
- * (e.g. crafted tool results faking searchDocs output). Only user/assistant text
- * survives, so grounding comes from the server-run searchDocs tool — not the
- * client's payload.
- */
-function sanitizeMessages(messages: UIMessage[]): UIMessage[] {
-  return messages
-    .filter((message) => message.role === 'user' || message.role === 'assistant')
-    .map((message) => ({
-      ...message,
-      parts: message.parts.filter((part) => part.type === 'text' && typeof part.text === 'string'),
-    }))
-    .filter((message) => message.parts.length > 0)
-}
-
-/**
- * Reject obvious cross-origin calls. Same-origin browser requests send an
- * `Origin` header matching the host; we allow those, plus any host in
- * DOCS_ALLOWED_ORIGINS (comma-separated). Requests with no Origin (e.g. curl)
- * are allowed through to the cost caps rather than blocked, since Origin is
- * trivially spoofable and is a filter, not a security boundary.
- */
-function isAllowedOrigin(req: Request): boolean {
-  const origin = req.headers.get('origin')
-  if (!origin) return true
-
-  let originHost: string
-  try {
-    originHost = new URL(origin).host.toLowerCase()
-  } catch {
-    return false
-  }
-
-  const forwardedHost = req.headers.get('x-forwarded-host') ?? req.headers.get('host')
-  const requestHost = forwardedHost?.split(',')[0].trim().toLowerCase()
-  if (requestHost && originHost === requestHost) return true
-
-  const allowlist = (process.env.DOCS_ALLOWED_ORIGINS ?? '')
-    .split(',')
-    .map((value) => value.trim().toLowerCase())
-    .filter(Boolean)
-  return allowlist.includes(originHost)
-}
-
-const SYSTEM_PROMPT = `You are the documentation assistant for Sim — the open-source AI workspace where teams build, deploy, and manage AI agents.
-
-Answer questions about Sim using the documentation. Always call the searchDocs tool before answering anything specific about Sim's features, configuration, or usage — do not answer from memory. Base your answer only on the returned documentation; if the docs do not cover the question, say so plainly rather than guessing.
-
-Guidelines:
-- Be direct and concrete. Lead with the answer, then the detail.
-- Reference the relevant pages by their titles so the user knows where to read more.
-- When you show configuration or code, keep it minimal and correct.
-- The agent is called "Sim" and the chat surface is "Chat" — never say "Mothership" or "copilot".
-- If a question is unrelated to Sim, briefly say it's outside the docs' scope.`
-
-const SEARCH_COLUMNS = {
-  chunkId: docsEmbeddings.chunkId,
-  title: docsEmbeddings.headerText,
-  url: docsEmbeddings.sourceLink,
-  content: docsEmbeddings.chunkText,
-  sourceDocument: docsEmbeddings.sourceDocument,
-}
-
-/** Reciprocal-rank-fusion constant, matching the site search route. */
-const RRF_K = 60
-
-/**
- * SQL predicate selecting only the locale's documents, so the row limit applies
- * to matching rows: non-English docs are prefixed with their locale segment;
- * English is everything not prefixed with another locale.
- */
-function localeFilter(locale: string) {
-  const firstSegment = sql`split_part(${docsEmbeddings.sourceDocument}, '/', 1)`
-  if (locale === DEFAULT_LOCALE) {
-    const others = KNOWN_LOCALES.filter((l) => l !== DEFAULT_LOCALE)
-    return sql`${firstSegment} not in (${sql.join(
-      others.map((l) => sql`${l}`),
-      sql`, `
-    )})`
-  }
-  return sql`${firstSegment} = ${locale}`
-}
-
-type SearchRow = {
-  chunkId: string
-  title: string
-  url: string
-  content: string
-  sourceDocument: string
-}
-
-/**
- * Retrieve candidate chunks for grounding, mirroring the site search route's
- * hybrid strategy: Postgres full-text keyword search for every locale, plus
- * vector similarity (thresholded) for English — fused by reciprocal rank so a
- * page found by either signal can ground the answer.
- */
-async function searchDocs(query: string, locale: string) {
-  const tsConfig = TS_CONFIG[locale] ?? 'simple'
-
-  // Each retrieval path is best-effort and independent: a failure in one still
-  // lets the other ground the answer (both empty just yields no grounding).
-  let keywordRows: SearchRow[] = []
-  try {
-    keywordRows = await db
-      .select(SEARCH_COLUMNS)
-      .from(docsEmbeddings)
-      .where(
-        sql`${docsEmbeddings.chunkTextTsv} @@ plainto_tsquery(${tsConfig}, ${query}) and ${localeFilter(locale)}`
-      )
-      .orderBy(
-        sql`ts_rank(${docsEmbeddings.chunkTextTsv}, plainto_tsquery(${tsConfig}, ${query})) DESC`
-      )
-      .limit(SEARCH_CANDIDATES)
-  } catch (error) {
-    console.error('Ask AI keyword search failed:', error)
-  }
-
-  let vectorRows: SearchRow[] = []
-  if (locale === DEFAULT_LOCALE) {
-    // Vector retrieval (embedding call + pgvector query) is best-effort: if it
-    // fails, fall back to the keyword rows already fetched rather than losing all
-    // grounding for the turn.
-    try {
-      const embedding = await generateSearchEmbedding(query)
-      const vectorLiteral = JSON.stringify(embedding)
-      vectorRows = await db
-        .select(SEARCH_COLUMNS)
-        .from(docsEmbeddings)
-        .where(
-          sql`1 - (${docsEmbeddings.embedding} <=> ${vectorLiteral}::vector) >= ${SIMILARITY_THRESHOLD} and ${localeFilter(locale)}`
-        )
-        .orderBy(sql`${docsEmbeddings.embedding} <=> ${vectorLiteral}::vector`)
-        .limit(SEARCH_CANDIDATES)
-    } catch (error) {
-      console.error('Ask AI vector search failed; using keyword results only:', error)
-    }
-  }
-
-  // Reciprocal rank fusion across the two rankings, deduped by chunk.
-  const scores = new Map<string, number>()
-  const rowById = new Map<string, SearchRow>()
-  for (const list of [vectorRows, keywordRows]) {
-    list.forEach((row, index) => {
-      scores.set(row.chunkId, (scores.get(row.chunkId) ?? 0) + 1 / (RRF_K + index + 1))
-      if (!rowById.has(row.chunkId)) rowById.set(row.chunkId, row)
-    })
-  }
-
-  return [...rowById.values()]
-    .sort((a, b) => (scores.get(b.chunkId) ?? 0) - (scores.get(a.chunkId) ?? 0))
-    .slice(0, SEARCH_LIMIT)
-    .map((row) => ({
-      title: row.title,
-      url: row.url,
-      content: row.content,
-    }))
-}
-
-export async function POST(req: Request) {
-  if (!isAllowedOrigin(req)) {
-    return new Response('Forbidden', { status: 403 })
-  }
-
-  const now = Date.now()
-  sweepRateLimit(now)
-  const retryAfter = rateLimit(getClientIp(req), now)
-  if (retryAfter !== null) {
-    return new Response('Too many requests', {
-      status: 429,
-      headers: { 'Retry-After': String(retryAfter) },
-    })
-  }
-
-  let body: { messages: UIMessage[]; locale?: string }
-  try {
-    body = await req.json()
-  } catch {
-    return new Response('Invalid JSON', { status: 400 })
-  }
-  const { messages } = body
-  const locale = KNOWN_LOCALES.includes(body.locale ?? '')
-    ? (body.locale as string)
-    : DEFAULT_LOCALE
-
-  if (!Array.isArray(messages) || messages.length === 0 || messages.length > MAX_MESSAGES) {
-    return new Response('Invalid request', { status: 400 })
-  }
-  if (!messages.every(isValidMessage)) {
-    return new Response('Invalid request', { status: 400 })
-  }
-  if (userInputChars(messages) > MAX_USER_INPUT_CHARS) {
-    return new Response('Request too large', { status: 413 })
-  }
-
-  const modelMessages = sanitizeMessages(messages)
-  if (modelMessages.length === 0) {
-    return new Response('Invalid request', { status: 400 })
-  }
-  // Bound what actually reaches the model. Measured AFTER sanitization, so the
-  // prior searchDocs tool outputs that accumulate in client history (and are
-  // stripped here) don't count — only user/assistant text the model will see.
-  if (JSON.stringify(modelMessages).length > MAX_TOTAL_CHARS) {
-    return new Response('Request too large', { status: 413 })
-  }
-
-  const result = streamText({
-    model: openai(CHAT_MODEL),
-    system: SYSTEM_PROMPT,
-    messages: convertToModelMessages(modelMessages),
-    stopWhen: stepCountIs(MAX_STEPS),
-    maxOutputTokens: MAX_OUTPUT_TOKENS,
-    tools: {
-      searchDocs: tool({
-        description:
-          'Search the Sim documentation for relevant content. Use this before answering any question about Sim.',
-        inputSchema: z.object({
-          query: z.string().describe('A focused natural-language search query.'),
-        }),
-        execute: async ({ query }) => searchDocs(query, locale),
-      }),
-    },
-  })
-
-  return result.toUIMessageStreamResponse()
-}
diff --git a/apps/docs/components/ai/ask-ai-panel.tsx b/apps/docs/components/ai/ask-ai-panel.tsx
deleted file mode 100644
index 9d41e68d1b8..00000000000
--- a/apps/docs/components/ai/ask-ai-panel.tsx
+++ /dev/null
@@ -1,234 +0,0 @@
-'use client'
-
-import { type FormEvent, useEffect, useMemo, useRef, useState } from 'react'
-import { useChat } from '@ai-sdk/react'
-import { DefaultChatTransport } from 'ai'
-import { ArrowUp, MessageCircle, Square, X } from 'lucide-react'
-import { Streamdown } from 'streamdown'
-import { cn } from '@/lib/utils'
-import 'streamdown/styles.css'
-
-interface DocSource {
-  title: string
-  url: string
-}
-
-/** Pull the deduped doc sources surfaced by the searchDocs tool out of a message's parts. */
-function getSources(parts: ReadonlyArray<{ type: string; [key: string]: unknown }>): DocSource[] {
-  const seen = new Set<string>()
-  const sources: DocSource[] = []
-
-  for (const part of parts) {
-    if (part.type !== 'tool-searchDocs') continue
-    const output = (part as { output?: unknown }).output
-    if (!Array.isArray(output)) continue
-    for (const item of output as DocSource[]) {
-      if (!item?.url || seen.has(item.url)) continue
-      seen.add(item.url)
-      sources.push({ title: item.title, url: item.url })
-    }
-  }
-
-  return sources
-}
-
-/** Concatenate the streamed text parts of a message. */
-function getText(parts: ReadonlyArray<{ type: string; [key: string]: unknown }>): string {
-  return parts
-    .filter((part) => part.type === 'text')
-    .map((part) => (part as unknown as { text: string }).text)
-    .join('')
-}
-
-interface AskAIPanelProps {
-  /** Active docs locale, forwarded so retrieval is scoped to the reader's language. */
-  locale: string
-  open: boolean
-  onClose: () => void
-}
-
-export function AskAIPanel({ locale, open, onClose }: AskAIPanelProps) {
-  const [input, setInput] = useState('')
-  const scrollRef = useRef<HTMLDivElement>(null)
-  const textareaRef = useRef<HTMLTextAreaElement>(null)
-
-  const transport = useMemo(() => new DefaultChatTransport({ api: '/api/chat' }), [])
-
-  const { messages, sendMessage, status, stop, error } = useChat({ transport })
-
-  const isBusy = status === 'submitted' || status === 'streaming'
-
-  const handleClose = () => {
-    stop()
-    onClose()
-  }
-
-  useEffect(() => {
-    if (!open) return
-    textareaRef.current?.focus()
-    const handleKeyDown = (event: KeyboardEvent) => {
-      if (event.key === 'Escape') {
-        handleClose()
-      }
-    }
-    document.addEventListener('keydown', handleKeyDown)
-    return () => document.removeEventListener('keydown', handleKeyDown)
-  }, [open])
-
-  useEffect(() => {
-    if (!open) return
-    scrollRef.current?.scrollTo({ top: scrollRef.current.scrollHeight })
-  }, [open])
-
-  useEffect(() => {
-    scrollRef.current?.scrollTo({ top: scrollRef.current.scrollHeight, behavior: 'smooth' })
-  }, [messages])
-
-  const handleSubmit = (event: FormEvent) => {
-    event.preventDefault()
-    const text = input.trim()
-    if (!text || isBusy) return
-    sendMessage({ text }, { body: { locale } })
-    setInput('')
-  }
-
-  if (!open) return null
-
-  return (
-    <div
-      role='dialog'
-      aria-label='Ask Sim'
-      className='fixed right-4 bottom-4 z-50 flex h-[600px] max-h-[calc(100vh-2rem)] w-[400px] max-w-[calc(100vw-2rem)] flex-col overflow-hidden rounded-xl border border-[var(--border-1)] bg-[var(--surface-5)] shadow-[var(--shadow-medium)] dark:bg-[var(--surface-4)]'
-    >
-      <div className='flex items-center justify-between border-[var(--border-1)] border-b px-4 py-3'>
-        <span className='flex items-center gap-1.5 font-season text-[var(--text-body)] text-sm'>
-          <MessageCircle className='size-[16px] text-[var(--text-icon)]' />
-          Ask Sim
-        </span>
-        <button
-          type='button'
-          aria-label='Close'
-          onClick={handleClose}
-          className='flex size-7 items-center justify-center rounded-lg text-[var(--text-icon)] transition-colors hover:bg-[var(--surface-active)]'
-        >
-          <X className='size-[16px]' />
-        </button>
-      </div>
-
-      <div ref={scrollRef} className='flex-1 space-y-4 overflow-y-auto px-4 py-4'>
-        {messages.length === 0 && (
-          <p className='text-[var(--text-muted)] text-sm'>
-            Ask anything about building, deploying, and managing AI agents in Sim.
-          </p>
-        )}
-
-        {messages.map((message, index) => {
-          const text = getText(message.parts)
-          const isStreaming = isBusy && index === messages.length - 1
-          const sources = message.role === 'assistant' ? getSources(message.parts) : []
-          return (
-            <div
-              key={message.id}
-              className={cn(
-                'flex flex-col gap-1.5',
-                message.role === 'user' ? 'items-end' : 'items-start'
-              )}
-            >
-              {message.role === 'user' ? (
-                <div className='max-w-[85%] whitespace-pre-wrap rounded-[16px] bg-[var(--surface-5)] px-3 py-2 text-[var(--text-primary)] text-base leading-[23px]'>
-                  {text}
-                </div>
-              ) : (
-                <div className='max-w-full text-[var(--text-primary)] text-base'>
-                  {text ? (
-                    <Streamdown
-                      className={cn(
-                        'space-y-3 text-[var(--text-primary)] text-base leading-relaxed',
-                        '[&_a]:text-[var(--text-primary)] [&_a]:underline [&_a]:decoration-dashed [&_a]:underline-offset-4',
-                        '[&_strong]:font-[600]',
-                        '[&_h1]:font-[600] [&_h2]:font-[600] [&_h3]:font-[600] [&_h4]:font-[600]',
-                        '[&_li]:my-1 [&_ol]:my-3 [&_ol]:list-decimal [&_ol]:pl-5 [&_ul]:my-3 [&_ul]:list-disc [&_ul]:pl-5',
-                        '[&_code]:font-mono [&_pre]:my-3 [&_pre]:overflow-x-auto [&_pre]:rounded-lg [&_pre]:bg-[var(--surface-5)] [&_pre]:p-3 [&_pre]:text-small'
-                      )}
-                    >
-                      {text}
-                    </Streamdown>
-                  ) : isStreaming ? (
-                    '…'
-                  ) : sources.length === 0 ? (
-                    <span className='text-[var(--text-muted)]'>No answer returned.</span>
-                  ) : null}
-                </div>
-              )}
-              {sources.length > 0 && (
-                <div className='flex max-w-[90%] flex-wrap gap-1.5'>
-                  {sources.map((source) => (
-                    <a
-                      key={source.url}
-                      href={source.url}
-                      target='_blank'
-                      rel='noopener noreferrer'
-                      className='rounded-lg border border-[var(--border-1)] px-2 py-0.5 text-[var(--text-muted)] text-xs transition-colors hover:bg-[var(--surface-active)]'
-                    >
-                      {source.title || source.url}
-                    </a>
-                  ))}
-                </div>
-              )}
-            </div>
-          )
-        })}
-
-        {error && (
-          <p className='text-[var(--text-muted)] text-sm'>
-            Something went wrong. Please try again.
-          </p>
-        )}
-      </div>
-
-      <form onSubmit={handleSubmit} className='px-3 pb-3'>
-        <div className='flex items-end gap-2 rounded-2xl border border-[var(--border-1)] bg-white px-2.5 py-1.5 dark:bg-[var(--surface-5)]'>
-          <textarea
-            ref={textareaRef}
-            aria-label='Ask Sim about the docs'
-            value={input}
-            onChange={(event) => setInput(event.target.value)}
-            onKeyDown={(event) => {
-              if (event.key === 'Enter' && !event.shiftKey) {
-                event.preventDefault()
-                handleSubmit(event)
-              }
-            }}
-            rows={1}
-            placeholder='Ask Sim about the docs…'
-            className='max-h-32 flex-1 resize-none bg-transparent py-1 font-season text-[var(--text-body)] text-sm outline-none placeholder:text-[var(--text-muted)]'
-          />
-          {isBusy ? (
-            <button
-              type='button'
-              aria-label='Stop'
-              onClick={() => stop()}
-              className='flex size-[28px] shrink-0 items-center justify-center rounded-full bg-[#383838] transition-colors hover:bg-[#575757] dark:bg-[#e0e0e0] dark:hover:bg-[#cfcfcf]'
-            >
-              <Square className='size-[12px] fill-white text-white dark:fill-black dark:text-black' />
-            </button>
-          ) : (
-            <button
-              type='submit'
-              aria-label='Send'
-              disabled={!input.trim()}
-              className={cn(
-                'flex size-[28px] shrink-0 items-center justify-center rounded-full transition-colors',
-                input.trim()
-                  ? 'bg-[#383838] hover:bg-[#575757] dark:bg-[#e0e0e0] dark:hover:bg-[#cfcfcf]'
-                  : 'bg-[#808080]'
-              )}
-            >
-              <ArrowUp className='size-[16px] text-white dark:text-black' />
-            </button>
-          )}
-        </div>
-      </form>
-    </div>
-  )
-}
diff --git a/apps/docs/components/ai/ask-ai.tsx b/apps/docs/components/ai/ask-ai.tsx
deleted file mode 100644
index ba3699da21f..00000000000
--- a/apps/docs/components/ai/ask-ai.tsx
+++ /dev/null
@@ -1,57 +0,0 @@
-'use client'
-
-import { useRef, useState } from 'react'
-import { MessageCircle } from 'lucide-react'
-import dynamic from 'next/dynamic'
-
-const AskAIPanel = dynamic(() => import('./ask-ai-panel').then((m) => m.AskAIPanel), {
-  ssr: false,
-  loading: () => (
-    <div
-      aria-hidden
-      className='fixed right-4 bottom-4 z-50 flex size-11 items-center justify-center rounded-full border border-[var(--border-1)] bg-[var(--surface-5)] shadow-[var(--shadow-medium)] dark:bg-[var(--surface-4)]'
-    >
-      <MessageCircle className='size-[16px] animate-pulse text-[var(--text-icon)]' />
-    </div>
-  ),
-})
-
-interface AskAIProps {
-  /** Active docs locale, forwarded so retrieval is scoped to the reader's language. */
-  locale: string
-}
-
-export function AskAI({ locale }: AskAIProps) {
-  const [open, setOpen] = useState(false)
-  const [hasOpened, setHasOpened] = useState(false)
-  const openButtonRef = useRef<HTMLButtonElement>(null)
-
-  const handleOpen = () => {
-    setHasOpened(true)
-    setOpen(true)
-  }
-
-  const handleClose = () => {
-    setOpen(false)
-    openButtonRef.current?.focus()
-  }
-
-  return (
-    <>
-      {!open && (
-        <button
-          ref={openButtonRef}
-          type='button'
-          aria-label='Ask Sim'
-          onClick={handleOpen}
-          className='fixed right-4 bottom-4 z-50 flex h-11 items-center gap-1.5 rounded-full border border-[var(--border-1)] bg-[var(--surface-5)] px-4 font-season text-[var(--text-body)] text-sm shadow-[var(--shadow-medium)] transition-colors hover:bg-[var(--surface-active)] dark:bg-[var(--surface-4)]'
-        >
-          <MessageCircle className='size-[16px] text-[var(--text-icon)]' />
-          Ask Sim
-        </button>
-      )}
-
-      {hasOpened && <AskAIPanel locale={locale} open={open} onClose={handleClose} />}
-    </>
-  )
-}
diff --git a/apps/docs/package.json b/apps/docs/package.json
index 41a7490421c..4f768739e77 100644
--- a/apps/docs/package.json
+++ b/apps/docs/package.json
@@ -15,13 +15,10 @@
     "format:check": "biome format ."
   },
   "dependencies": {
-    "@ai-sdk/openai": "2.0.107",
-    "@ai-sdk/react": "2.0.205",
     "@sim/db": "workspace:*",
     "@sim/emcn": "workspace:*",
     "@sim/workflow-renderer": "workspace:*",
     "@vercel/og": "^0.6.5",
-    "ai": "5.0.203",
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",
     "drizzle-orm": "^0.45.2",
@@ -37,7 +34,6 @@
     "react-dom": "19.2.4",
     "remark-breaks": "^4.0.0",
     "shiki": "4.3.1",
-    "streamdown": "2.5.0",
     "tailwind-merge": "^3.0.2",
     "reactflow": "^11.11.4",
     "framer-motion": "^12.5.0",
diff --git a/bun.lock b/bun.lock
index 76d9a07de1f..39054de4297 100644
--- a/bun.lock
+++ b/bun.lock
@@ -22,13 +22,10 @@
       "name": "docs",
       "version": "0.0.0",
       "dependencies": {
-        "@ai-sdk/openai": "2.0.107",
-        "@ai-sdk/react": "2.0.205",
         "@sim/db": "workspace:*",
         "@sim/emcn": "workspace:*",
         "@sim/workflow-renderer": "workspace:*",
         "@vercel/og": "^0.6.5",
-        "ai": "5.0.203",
         "class-variance-authority": "^0.7.1",
         "clsx": "^2.1.1",
         "drizzle-orm": "^0.45.2",
@@ -46,7 +43,6 @@
         "reactflow": "^11.11.4",
         "remark-breaks": "^4.0.0",
         "shiki": "4.3.1",
-        "streamdown": "2.5.0",
         "tailwind-merge": "^3.0.2",
         "zod": "^4.3.6",
       },
@@ -635,8 +631,6 @@
 
     "@ai-sdk/provider-utils": ["@ai-sdk/provider-utils@3.0.26", "", { "dependencies": { "@ai-sdk/provider": "2.0.3", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-dNciNI4knep6z3cqDNng7yORCcBnEDBHZYj8rJLcLn9pLzEtNVkf6WLg9HR6AnVDDRxHsUeGAEqyF8M+FAtRgg=="],
 
-    "@ai-sdk/react": ["@ai-sdk/react@2.0.205", "", { "dependencies": { "@ai-sdk/provider-utils": "3.0.26", "ai": "5.0.203", "swr": "^2.2.5", "throttleit": "2.1.0" }, "peerDependencies": { "react": "^18 || ~19.0.1 || ~19.1.2 || ^19.2.1", "zod": "^3.25.76 || ^4.1.8" }, "optionalPeers": ["zod"] }, "sha512-9EmBl2wa2lqbYoSImdAwmp1Wa9r7suJdEOWdfnRPlDSBLNYo2BfwD0MGSULYLlX9T7nz8Vfo1+EwvwctdN1HNQ=="],
-
     "@ai-sdk/togetherai": ["@ai-sdk/togetherai@1.0.43", "", { "dependencies": { "@ai-sdk/openai-compatible": "1.0.40", "@ai-sdk/provider": "2.0.3", "@ai-sdk/provider-utils": "3.0.26" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-FwcKwNKSjkCc80yD7JFtHOXxroTpFD/FF84jfiGlRGTfv2rQpv3Pto9h/NcKjmDXXr/H01Rzo/TodrsLXq0B8g=="],
 
     "@ai-sdk/xai": ["@ai-sdk/xai@2.0.74", "", { "dependencies": { "@ai-sdk/openai-compatible": "1.0.40", "@ai-sdk/provider": "2.0.3", "@ai-sdk/provider-utils": "3.0.26" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-Ff24f/G13EjKKw411XQh08/owBorFvaLf3LEuDvDnDz4XePpkYN6oXdyHpZCDrvUD0/+7hbwaGwTXfm5nmqPGQ=="],
@@ -3861,8 +3855,6 @@
 
     "svix": ["svix@1.88.0", "", { "dependencies": { "standardwebhooks": "1.0.0", "uuid": "^10.0.0" } }, "sha512-vm/JrrUd3bVyBE+3L33TIyVSs8gS5fYx7lrISvKlDJXTYX1ACH4REX8P1tHxsSKoZi/rvifM1t0XRc5Vc45THw=="],
 
-    "swr": ["swr@2.4.1", "", { "dependencies": { "dequal": "^2.0.3", "use-sync-external-store": "^1.6.0" }, "peerDependencies": { "react": "^16.11.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" } }, "sha512-2CC6CiKQtEwaEeNiqWTAw9PGykW8SR5zZX8MZk6TeAvEAnVS7Visz8WzphqgtQ8v2xz/4Q5K+j+SeMaKXeeQIA=="],
-
     "symbol-tree": ["symbol-tree@3.2.4", "", {}, "sha512-9QNk5KwDF+Bvz+PyObkmSYjI5ksVUYtjW7AU22r2NKcfLJcXp96hkDWU3+XndOsUb+AQ9QhfzfCT2O+CNWT5Tw=="],
 
     "systeminformation": ["systeminformation@5.23.8", "", { "os": "!aix", "bin": { "systeminformation": "lib/cli.js" } }, "sha512-Osd24mNKe6jr/YoXLLK3k8TMdzaxDffhpCxgkfgBHcapykIkd50HXThM3TCEuHO2pPuCsSx2ms/SunqhU5MmsQ=="],
@@ -3891,8 +3883,6 @@
 
     "three": ["three@0.177.0", "", {}, "sha512-EiXv5/qWAaGI+Vz2A+JfavwYCMdGjxVsrn3oBwllUoqYeaBO75J63ZfyaQKoiLrqNHoTlUc6PFgMXnS0kI45zg=="],
 
-    "throttleit": ["throttleit@2.1.0", "", {}, "sha512-nt6AMGKW1p/70DF/hGBdJB57B8Tspmbp5gfJ8ilhLnt7kkr2ye7hzD6NVG8GGErk2HWF34igrL2CXmNIkzKqKw=="],
-
     "through": ["through@2.3.8", "", {}, "sha512-w89qg7PI8wAdvX60bMDP+bFoD5Dvhm9oLheFp5O4a2QF0cSBGsBX4qZmadPMvVqlLJBBci+WqGGOAPvcDeNSVg=="],
 
     "tiny-inflate": ["tiny-inflate@1.0.3", "", {}, "sha512-pkY1fj1cKHb2seWDy0B16HeWyczlJA9/WW3u3c4z/NiWDsO3DOU5D7nhTLE9CF0yXv/QZFY7sEJmj24dK+Rrqw=="],

From 51ba71052c5c9f650343bd4809ec3f8e200557f0 Mon Sep 17 00:00:00 2001
From: Waleed Latif <walif6@gmail.com>
Date: Tue, 14 Jul 2026 19:00:27 -0700
Subject: [PATCH 2/4] improvement(landing): add visible FAQ schema, fix SEO
 redirects and Organization schema

- Move existing plain-markdown FAQ sections in 8 library posts + emcn blog
  into the faq frontmatter array so they emit FAQPage JSON-LD (previously
  visible text with zero structured data)
- Add new, fact-checked FAQ content to the 8 posts with none: openai-vs-n8n-vs-sim,
  v0-5, enterprise, copilot, executor, multiplayer, mothership, series-a
- Add sales ContactPoint to the site-wide Organization schema
- Add 301 redirects for legitimate crawler/dead-link 404s (/$, /&, /Sim,
  /homepage, /logo, /en-US) surfaced by an external SEO audit; left out
  bot-noise paths, already-fixed OG image 404s (#5636), and *.sim.ai
  customer chat subdomains that need manual DB verification, not a
  blanket redirect
---
 .../site-structured-data.tsx                  | 20 ++++++---
 apps/sim/content/blog/copilot/index.mdx       | 13 ++++++
 apps/sim/content/blog/emcn/index.mdx          | 21 ++++++----
 apps/sim/content/blog/enterprise/index.mdx    | 13 ++++++
 apps/sim/content/blog/executor/index.mdx      | 13 ++++++
 apps/sim/content/blog/mothership/index.mdx    | 13 ++++++
 apps/sim/content/blog/multiplayer/index.mdx   | 13 ++++++
 apps/sim/content/blog/series-a/index.mdx      | 13 ++++++
 apps/sim/content/blog/v0-5/index.mdx          | 13 ++++++
 .../content/library/ai-agent-ideas/index.mdx  | 33 +++++----------
 .../library/ai-agent-vs-chatbot/index.mdx     | 33 +++++----------
 .../library/ai-agents-vs-rpa/index.mdx        | 33 +++++----------
 .../best-zapier-alternatives/index.mdx        | 37 +++++++---------
 .../how-to-create-an-ai-agent/index.mdx       | 27 ++++--------
 .../library/langgraph-alternatives/index.mdx  | 37 +++++-----------
 .../library/n8n-alternatives/index.mdx        | 33 +++++----------
 .../open-source-ai-agent-platforms/index.mdx  | 42 +++++--------------
 .../library/openai-vs-n8n-vs-sim/index.mdx    | 13 ++++++
 apps/sim/next.config.ts                       | 38 +++++++++++++++++
 19 files changed, 259 insertions(+), 199 deletions(-)

diff --git a/apps/sim/app/(landing)/components/site-structured-data/site-structured-data.tsx b/apps/sim/app/(landing)/components/site-structured-data/site-structured-data.tsx
index 920f8975858..c0bd3372cb7 100644
--- a/apps/sim/app/(landing)/components/site-structured-data/site-structured-data.tsx
+++ b/apps/sim/app/(landing)/components/site-structured-data/site-structured-data.tsx
@@ -39,12 +39,20 @@ const SITE_JSON_LD = {
         'https://www.linkedin.com/company/simstudioai/',
         'https://join.slack.com/t/sim-ott9864/shared_invite/zt-43lp8tc5v-0qrrqHGBKUsvQlpoouH~TA',
       ],
-      contactPoint: {
-        '@type': 'ContactPoint',
-        contactType: 'customer support',
-        url: `${SITE_URL}/contact`,
-        availableLanguage: ['en'],
-      },
+      contactPoint: [
+        {
+          '@type': 'ContactPoint',
+          contactType: 'customer support',
+          url: `${SITE_URL}/contact`,
+          availableLanguage: ['en'],
+        },
+        {
+          '@type': 'ContactPoint',
+          contactType: 'sales',
+          url: `${SITE_URL}/contact`,
+          availableLanguage: ['en'],
+        },
+      ],
     },
     {
       '@type': 'WebSite',
diff --git a/apps/sim/content/blog/copilot/index.mdx b/apps/sim/content/blog/copilot/index.mdx
index 5e65575549d..7ea345209ed 100644
--- a/apps/sim/content/blog/copilot/index.mdx
+++ b/apps/sim/content/blog/copilot/index.mdx
@@ -15,6 +15,19 @@ timeRequired: PT7M
 canonical: https://www.sim.ai/blog/copilot
 featured: false
 draft: true
+faq:
+  - q: "What is Sim Copilot?"
+    a: "Copilot is a context-aware assistant embedded directly in the Sim editor. It has first-class access, with user approval, to your workflows, blocks, logs, and docs, and the system is retrieval-centric with strong guardrails and deterministic execution paths."
+  - q: "How does Copilot retrieve context from my workflows?"
+    a: "Copilot indexes workspace workflows, block metadata, execution logs, and product docs using hybrid scoring that combines BM25 lexical search with embeddings, plus recency decay and per-source caps. Content is chunked with stable anchors so proposed diffs stay line-referential."
+  - q: "Can Copilot make changes to my workflows automatically?"
+    a: "No. All writes are gated behind explicit user approval in a human-in-the-loop model: diffs are reviewed and applied only on approval. Actions are also scope-limited to the open workspace and require explicit user triggers."
+  - q: "What kinds of actions can Copilot take beyond answering questions?"
+    a: "Copilot's tooling spans read-only tools like explain, compare, and search; propose-changes tools that generate diffs; and execution tools for approved actions. It can also help set up test runs and compare outputs side by side."
+  - q: "How accurate is Copilot at proposing fixes or generating block configurations?"
+    a: "The post is explicit that this section is a benchmark scaffold: the listed numbers, including 92% top-1 retrieval and 88% edit accuracy for explaining a workflow block, versus 74% retrieval and 65% edit accuracy for generating a new block configuration, are described as placeholders for the table's structure rather than results from completed benchmark runs. Sim plans to publish a full benchmark harness with anonymized evaluation sets and a detailed methodology post."
+  - q: "Does Copilot use the same permissions as the rest of Sim?"
+    a: "Yes. Copilot works with the same permissions model already used in Sim, and it enforces scope so actions stay limited to the open workspace, with sensitive data policies and redaction applied in logs."
 ---
 
 > This is a technical deep‑dive scaffold for Sim Copilot. We’ll keep updating it as we publish more results and open up additional capabilities.
diff --git a/apps/sim/content/blog/emcn/index.mdx b/apps/sim/content/blog/emcn/index.mdx
index 8b427baf27d..bb0366d605d 100644
--- a/apps/sim/content/blog/emcn/index.mdx
+++ b/apps/sim/content/blog/emcn/index.mdx
@@ -15,6 +15,19 @@ timeRequired: PT6M
 canonical: https://www.sim.ai/blog/emcn
 featured: false
 draft: true
+faq:
+  - q: "What is Emcn?"
+    a: "Emcn is the design system that powers Sim's product and brand. It combines strongly-typed, themeable tokens with a composable component library to keep UI consistent and accessible across web surfaces."
+  - q: "Is Emcn open source?"
+    a: "Sim plans to share Emcn's foundations and many of its components as part of its broader open-source commitment. A public docs and examples site is planned for a later release stage."
+  - q: "What accessibility standard does Emcn target?"
+    a: "Emcn is built to be accessible by default at WCAG AA and above, with color contrast tracked at the token level and keyboard and screen reader interactions defined per component."
+  - q: "Does Emcn support dark mode?"
+    a: "Yes. Emcn ships light and dark themes with brand accent tokens, plus an SSR-safe color mode designed to avoid a flash on hydration."
+  - q: "How does Emcn connect Figma and code?"
+    a: "Emcn's Figma library is mapped one-to-one to code components, and its tokens are exported as both TypeScript and CSS variables, giving a direct path from design files to implementation."
+  - q: "What components does Emcn include so far?"
+    a: "The initial set spans primitives like Button, Input, and Select, navigation elements like NavBar and Tabs, feedback patterns like Toast and Dialog, layout building blocks like Grid and Card, and content components like CodeBlock and Table."
 ---
 
 > This post is the scaffolding for Emcn, our new design system. We’ll fill it in as we publish the full documentation and component gallery.
@@ -80,14 +93,6 @@ Emcn is the design system that powers Sim’s product and brand. It aims to give
 - v1: Public docs and examples site  
 - v1.x: Data display, advanced forms, charts bridge  
 
-## FAQ
-
-- What does “Emcn” mean?  
-  A short, crisp name we liked—easy to type and remember.  
-
-- Will Emcn be open‑sourced?  
-  We plan to share the foundations and many components as part of our commitment to open source.  
-
 ## We’re hiring
 
 We’re hiring designers and engineers who care deeply about craft and DX. If you want to help shape Emcn and Sim’s product, we’d love to talk.
diff --git a/apps/sim/content/blog/enterprise/index.mdx b/apps/sim/content/blog/enterprise/index.mdx
index 59fe8b32040..42123bacc5b 100644
--- a/apps/sim/content/blog/enterprise/index.mdx
+++ b/apps/sim/content/blog/enterprise/index.mdx
@@ -15,6 +15,19 @@ timeRequired: PT10M
 canonical: https://www.sim.ai/blog/enterprise
 featured: true
 draft: false
+faq:
+  - q: "Can Sim be deployed fully on-premises or in an air-gapped environment?"
+    a: "Yes. Sim can run entirely on your infrastructure via Docker Compose or Helm charts for Kubernetes, with the application, WebSocket server, and PostgreSQL database staying inside your network. Air-gapped setups are supported by pairing self-hosting with Ollama or vLLM for local model inference, so no external network access is required."
+  - q: "Does using our own LLM API keys (BYOK) keep our data from passing through Sim's servers?"
+    a: "Yes. When you configure your own API keys for providers like OpenAI, Anthropic, Google, Azure OpenAI, or AWS Bedrock, prompts and completions route directly between Sim and that provider without passing through Sim's infrastructure. BYOK is available to everyone, not just enterprise plans, and is the default with no Sim-managed keys involved in self-hosted deployments."
+  - q: "Can Copilot be used without sending workflow data to an external AI service?"
+    a: "Yes. Copilot can run entirely within a self-hosted deployment using your own LLM keys, so prompts containing context from your workflows, execution logs, and workspace configuration route directly to your chosen provider and never leave your network."
+  - q: "What identity providers does Sim support for SSO, and what happens when an employee is deprovisioned?"
+    a: "Sim integrates with Okta, Azure AD (Entra ID), Google Workspace, OneLogin, Auth0, JumpCloud, Ping Identity, ADFS, and any SAML 2.0 or OIDC compliant identity provider. Session management ties to your IdP, so logging out there terminates Sim sessions, and account deprovisioning immediately revokes access."
+  - q: "Is Sim SOC 2 certified, and can we get a copy of the report for vendor review?"
+    a: "Sim maintains SOC 2 Type II certification with annual audits covering security, availability, and confidentiality controls, and shares the report directly with prospective customers under NDA. Sim also provides penetration test reports, architecture documentation, and completed security questionnaires (SIG, CAIQ, and custom formats) for vendor review."
+  - q: "Can we restrict which model providers or integrations are available to certain teams?"
+    a: "Yes, through permission groups that are enforced at the execution layer, not just hidden in the UI. Administrators can allowlist approved model providers so unapproved ones fail to execute, disable specific workflow blocks like HTTP calls, and block integrations that haven't cleared security review — while users outside any permission group keep full access by default."
 ---
 
 We've been working with security teams at larger organizations to bring Sim into environments with strict compliance and data handling requirements. This post covers the enterprise capabilities we've built: granular access control, bring-your-own-keys, self-hosted deployments, on-prem Copilot, SSO & SAML, whitelabeling, compliance, and programmatic management via the Admin API.
diff --git a/apps/sim/content/blog/executor/index.mdx b/apps/sim/content/blog/executor/index.mdx
index 634bb334dca..5541c73f2a4 100644
--- a/apps/sim/content/blog/executor/index.mdx
+++ b/apps/sim/content/blog/executor/index.mdx
@@ -15,6 +15,19 @@ timeRequired: PT12M
 canonical: https://www.sim.ai/blog/executor
 featured: false
 draft: false
+faq:
+  - q: "How does Sim's executor handle true parallel execution instead of just interleaving branches?"
+    a: "The executor uses an event-driven ready queue: when a parallel block expands into branch-indexed nodes, all entry nodes across every branch enter the queue simultaneously and launch concurrently rather than one after another. Sim doesn't distinguish parallel branches from any other independent nodes that happen to be ready at the same time — concurrency simply emerges from the DAG structure."
+  - q: "How does Sim implement loops in a DAG, which is acyclic by definition?"
+    a: "Sim expands each loop into a sentinel start and sentinel end node during compilation. The backward edge from end to start doesn't count as a dependency initially — it only activates if the loop condition says to continue — which preserves the DAG's acyclic property while still allowing iteration."
+  - q: "Can a Sim workflow pause for human approval in the middle of a loop or across parallel branches?"
+    a: "Yes. When a block returns pause metadata, the executor stops processing its outgoing edges and captures the full execution state, including loop iteration and parallel branch progress. Each pause point gets a unique context ID based on its position, so multiple simultaneous pauses (e.g. three parallel branches each hitting an approval block) can each resume independently."
+  - q: "What gets saved when a Sim workflow pauses, and is it enough to resume later without losing state?"
+    a: "The snapshot serializes block states, execution logs, loop and parallel scopes, routing decisions, and workflow variables to JSON. The critical piece is the DAG's incoming edge state — which dependencies each node still has outstanding — so partially completed loops and in-flight parallel branches can be resumed exactly where they left off."
+  - q: "How does AI-driven routing (a router block choosing a path) stay deterministic and debuggable in Sim?"
+    a: "When a router block returns its chosen block ID, the edge manager activates only the matching outgoing edge and deactivates all others, with deactivation cascading recursively to unreachable downstream blocks. This means you can inspect the execution log afterward and see exactly which path the model chose and which alternatives were pruned."
+  - q: "How does the Sim executor know when a block is ready to run without re-scanning all connections?"
+    a: "Each node tracks its own incoming edges in a set; when a dependency completes, one element is removed from that set, and the node becomes ready the moment the set hits zero. This replaced an earlier approach that rescanned the full connection array after every block execution, which degraded as the graph grew larger."
 ---
 
 Modern workflows aren't just linear automations anymore. They involve a variety of APIs and services, loop over a model's output, pause for human decisions, and resume hours or days later exactly where they left off.
diff --git a/apps/sim/content/blog/mothership/index.mdx b/apps/sim/content/blog/mothership/index.mdx
index 1e978e5db87..937f2846e95 100644
--- a/apps/sim/content/blog/mothership/index.mdx
+++ b/apps/sim/content/blog/mothership/index.mdx
@@ -15,6 +15,19 @@ timeRequired: PT10M
 canonical: https://www.sim.ai/blog/mothership
 featured: true
 draft: false
+faq:
+  - q: "What is Mothership in Sim?"
+    a: "Mothership is the control plane for a Sim workspace—an orchestrator with full context over your workflows, tables, knowledge bases, files, and connected integrations. It can build, test, and deploy workflows from a plain-English description and call any of your 100+ integration tools directly."
+  - q: "Can I trigger Mothership by email?"
+    a: "Yes. Enabling a Sim inbox lets Mothership read incoming emails, execute tasks using your integrations and data, and reply—turning your workspace into an email-driven automation layer."
+  - q: "What is Tables in Sim and how does it work with agents?"
+    a: "Tables gives your workspace a built-in database with typed columns (string, number, boolean, date, JSON) that agents can query, insert into, and update over time. It acts as persistent memory, with 10+ operations available as workflow blocks and direct management by Mothership."
+  - q: "How do Knowledge Base Connectors ground agent responses?"
+    a: "Connectors sync documents from sources like Google Docs, Notion, Confluence, Slack, GitHub, Jira, Linear, HubSpot, Salesforce, Zendesk, Dropbox, OneDrive, Gmail, Discord, and 15+ more, automatically chunking, embedding, and indexing them with incremental sync to stay current. This produces a vector-searchable knowledge base that any workflow block or Mothership can search directly, grounding responses in real business context instead of generic answers."
+  - q: "What's the difference between persistent and until_complete Scheduled Tasks?"
+    a: "Persistent tasks run indefinitely on a set schedule, while until_complete tasks poll repeatedly until a defined success condition is met and then stop automatically. Both support configurable max runs, 40+ timezones, and automatic disabling after 3 consecutive failures."
+  - q: "Can I upload a CSV file and turn it into a queryable table?"
+    a: "Yes. Files uploaded as CSV can be imported directly into Tables—Sim auto-detects delimiters, infers column types, and batch-inserts up to 1,000 rows, turning a raw export into a workflow-ready table in one click."
 ---
 
 I often wonder why AI agents don't already do everything for me today. Why don't they take my meetings for me? Why can't they send follow-ups to customers? Why can't they track the progress of our product launches and just start solving problems for us?
diff --git a/apps/sim/content/blog/multiplayer/index.mdx b/apps/sim/content/blog/multiplayer/index.mdx
index e8259aef14f..f45096bc7a5 100644
--- a/apps/sim/content/blog/multiplayer/index.mdx
+++ b/apps/sim/content/blog/multiplayer/index.mdx
@@ -11,6 +11,19 @@ tags: [Multiplayer, Realtime, Collaboration, WebSockets, Architecture]
 ogImage: /blog/multiplayer/cover.jpg
 canonical: https://www.sim.ai/blog/multiplayer
 draft: false
+faq:
+  - q: "Does Sim use CRDTs or operational transforms for multiplayer editing?"
+    a: "No. Sim uses a simpler last-writer-wins strategy with client-generated timestamps rather than Figma-style operational transforms. Workflow building has lower conflict density than text editing, since users typically work on different blocks or parts of the canvas, so the simpler approach is sufficient."
+  - q: "What happens if two users edit the same block at the same time in Sim?"
+    a: "Both clients render their local changes optimistically, then the server persists both updates based on client timestamps and broadcasts each in sequence. Clients receive the conflicting positions or values and converge to whichever carries the latest timestamp, with value conflicts on the same text field resolved by last-to-arrive within a 25ms server-side coalescing window."
+  - q: "What happens if a collaborator deletes a block I'm currently editing?"
+    a: "When the server broadcasts the deletion, your client immediately cancels all pending operations targeting that block, including subblock updates, and removes it from local state without sending those stale operations to the server or showing an error. This client-side cancellation avoids wasting network requests on operations that would fail anyway."
+  - q: "How does undo/redo work when multiple people are editing the same Sim workflow?"
+    a: "Undo/redo is a local, per-user stack: undoing an action generates an inverse operation (e.g., undo an add becomes a remove) that flows through the same operation queue as a normal edit, so collaborators see the change in real time. Undo stacks are per-user and only cover your own actions, and entries referencing entities another collaborator deleted are automatically pruned as invalid."
+  - q: "What happens if a client loses connection while editing a Sim workflow?"
+    a: "Failed operations retry with exponential backoff — structural changes get 3 attempts and text edits get 5. If all retries fail, the queue is cleared and the UI becomes read-only until the user manually refreshes."
+  - q: "Why does Sim separate blocks, edges, and subflows into different database tables?"
+    a: "Blocks, edges, and subflows have different update patterns and conflict characteristics, so storing them in separate normalized tables (workflow_blocks, workflow_edges, workflow_subflows) lets Sim make targeted updates, such as moving a block only touching its position fields, without loading or locking the entire workflow."
 ---
 
 When we started building Sim, we noticed that AI workflow development looked a lot like the design process [Figma](https://www.figma.com/blog/how-figmas-multiplayer-technology-works/) had already solved for. Product managers need to sketch out user-facing flows, engineers need to configure integrations and APIs, and domain experts need to validate business logic—often all at the same time. Traditional workflow builders force serial collaboration: one person edits, saves, exports, and notifies the next person. This creates unnecessary friction.
diff --git a/apps/sim/content/blog/series-a/index.mdx b/apps/sim/content/blog/series-a/index.mdx
index 4d75ed6408e..85e7d6d272e 100644
--- a/apps/sim/content/blog/series-a/index.mdx
+++ b/apps/sim/content/blog/series-a/index.mdx
@@ -17,6 +17,19 @@ canonical: https://www.sim.ai/blog/series-a
 featured: true
 draft: false
 technical: false
+faq:
+  - q: "How much did Sim raise in its Series A?"
+    a: "Sim raised a $7M Series A. The round was led by Standard Capital, with participation from Perplexity Fund, SV Angel, YCombinator, and angels including Paul Graham, Paul Bucheit, Ali Rowghani, and Kaz Nejatian."
+  - q: "Who led Sim's Series A funding round?"
+    a: "Standard Capital led the round, joined by Perplexity Fund, SV Angel, YCombinator, and several notable angel investors."
+  - q: "What will Sim do with the new funding?"
+    a: "Sim is using the funding to double down on its mission: making it simple for teams to build, ship, and scale agentic workflows in production. Separately, Sim plans to keep investing in building the community around the platform."
+  - q: "How many developers and companies use Sim?"
+    a: "Sim has grown to 60,000+ developers on the platform and over 100 companies, ranging from startups to large enterprises, using it in production. The project has also processed more than 10M workflows and reached 18,000 GitHub stars."
+  - q: "Is Sim an open-source project?"
+    a: "Yes. Sim was built to be open source from day one, and that commitment has continued as the project has grown."
+  - q: "What is Sim's long-term vision for agentic workflows?"
+    a: "Sim aims to provide the infrastructure and UX that make agentic software practical at scale, from prototyping to production and from single-agent flows to complex multi-agent systems. Its goal is to be both easy to use and powerful enough to build complex agentic workflows, unlike frameworks that require heavy coding or platforms that limit what can be built."
 ---
 
 ![Sim team photo](/blog/series-a/team.jpg)
diff --git a/apps/sim/content/blog/v0-5/index.mdx b/apps/sim/content/blog/v0-5/index.mdx
index 13604697eb3..e2297417528 100644
--- a/apps/sim/content/blog/v0-5/index.mdx
+++ b/apps/sim/content/blog/v0-5/index.mdx
@@ -15,6 +15,19 @@ timeRequired: PT8M
 canonical: https://www.sim.ai/blog/v0-5
 featured: true
 draft: false
+faq:
+  - q: "What is new in Sim v0.5?"
+    a: "Sim v0.5 adds a context-aware Copilot, MCP server deployment for any workflow, full execution logs with cost attribution, realtime multi-user collaboration, workflow versioning with rollback, and 100+ integrations spanning 300+ actions."
+  - q: "How does Sim's Copilot differ from a general-purpose AI assistant?"
+    a: "Copilot is embedded in the Sim editor and has direct access to your workspace: workflows, block configurations, execution logs, connected credentials, and documentation. It uses hybrid retrieval over an index of your workspace to ground answers in your actual workflow state, and supports slash commands like /fast, /research, and /actions for specialized tasks."
+  - q: "Can I turn a Sim workflow into an MCP server?"
+    a: "Yes. Any workflow can be deployed as an MCP server, and it becomes a callable tool for MCP-compatible agents like Claude Desktop or Cursor. Sim auto-generates the tool definition and JSON schema from your Start block, uses Streamable HTTP transport, and supports authentication via API key headers or public access."
+  - q: "What kind of observability does Sim provide for workflow executions?"
+    a: "Every execution generates a full trace capturing each block's start/end time, inputs, outputs, and errors, plus token usage and cost per LLM call. The dashboard surfaces this as trace spans, cost attribution, error context with stack traces, filterable queries, and execution snapshots you can restore to see the exact workflow state at run time."
+  - q: "Does Sim support real-time collaboration on workflows?"
+    a: "Yes. Multiple users can edit the same workflow at the same time, with cursors, block additions, and configuration changes syncing live across all connected clients. The editor also adds full undo/redo history and copy-paste for individual blocks, groups, or entire subflows."
+  - q: "How many integrations does Sim v0.5 support, and how is versioning handled?"
+    a: "v0.5 adds 100+ integrations with 300+ actions covering CRMs, communication tools, databases, developer tools, and more. Every deployment also creates a new version, and you can roll back to any previous version with one click, with a full audit trail of who deployed what and when."
 ---
 
 **Sim v0.5** is the next evolution of our agent workflow platform—built for teams shipping AI agents to production.
diff --git a/apps/sim/content/library/ai-agent-ideas/index.mdx b/apps/sim/content/library/ai-agent-ideas/index.mdx
index 575a830fc22..beb40e84f50 100644
--- a/apps/sim/content/library/ai-agent-ideas/index.mdx
+++ b/apps/sim/content/library/ai-agent-ideas/index.mdx
@@ -11,6 +11,17 @@ tags: [AI Agents, Use Cases, Automation, Sim]
 ogImage: /library/ai-agent-ideas/cover.jpg
 canonical: https://www.sim.ai/library/ai-agent-ideas
 draft: false
+faq:
+  - q: "What are the best AI agent ideas for beginners?"
+    a: "Start with email triage, meeting follow-ups, or report generation. These three ideas have clear inputs (an email, a transcript, a data source) and clear outputs (a classified message, a list of action items, a formatted report), which makes them easy to validate. If the agent gets something wrong, you'll notice immediately and can adjust."
+  - q: "How long does it take to build an AI agent from scratch?"
+    a: "With a visual builder like Sim, a working prototype can be ready in under an hour. You describe the workflow to Chat or start from a blank canvas, connect your integrations, configure the AI model, and test. Custom multi-step agents with conditional logic and multiple data sources take longer, but you're still measuring build time in days, not months."
+  - q: "Do I need coding skills to build AI agents?"
+    a: "Not to get started. Sim's drag-and-drop canvas and Chat let you build and deploy agents without writing infrastructure code. You configure blocks visually, connect integrations, and define logic through the interface. For teams that want deeper customization, custom functions and API access are available, but they're optional."
+  - q: "What is the difference between an AI agent and a workflow automation like Zapier?"
+    a: "Zapier-style tools follow fixed if-then rules: when this trigger fires, do this action, every time, the same way. An AI agent reasons over context, evaluates information, and chooses between paths based on what it finds. It can handle ambiguous inputs, make decisions, and act across multiple systems in sequence. The core distinction is reasoning versus rule-following."
+  - q: "Which AI agent idea has the highest ROI for a small team?"
+    a: "Lead enrichment, email triage, and meeting follow-ups tend to deliver the highest time-reclaim per hour of build effort. Sales-related agents often show the fastest, most attributable returns because you can directly tie agent output, like qualified leads routed to reps, to pipeline and revenue metrics. For most small teams, starting with email triage or meeting follow-ups is the fastest path to visible results."
 ---
 
 AI agents have moved from "we should explore this" to "this is running in production" faster than most teams anticipated. Customer service, eCommerce, and operations are already seeing real returns, and the pace of adoption across the enterprise isn't slowing down. The shift is real, and it's happening fast.
@@ -201,25 +212,3 @@ The gap between "AI agents sound useful" and "we have an agent running in produc
 The teams seeing the best results in 2026 aren't building grand autonomous systems. They're prioritizing task-specific, governed AI agents that integrate with real business systems rather than broad autonomous experimentation. They're starting narrow, proving value, and expanding.
 
 Pick the idea that matches your team's biggest pain point. Open Sim, build the workflow, and deploy your first agent today. You'll learn more in that first hour of building than in another month of reading about what's possible.
-
-## FAQ
-
-### What are the best AI agent ideas for beginners?
-
-Start with email triage, meeting follow-ups, or report generation. These three ideas have clear inputs (an email, a transcript, a data source) and clear outputs (a classified message, a list of action items, a formatted report), which makes them easy to validate. If the agent gets something wrong, you'll notice immediately and can adjust.
-
-### How long does it take to build an AI agent from scratch?
-
-With a visual builder like Sim, a working prototype can be ready in under an hour. You describe the workflow to Chat or start from a blank canvas, connect your integrations, configure the AI model, and test. Custom multi-step agents with conditional logic and multiple data sources take longer, but you're still measuring build time in days, not months.
-
-### Do I need coding skills to build AI agents?
-
-Not to get started. Sim's drag-and-drop canvas and Chat let you build and deploy agents without writing infrastructure code. You configure blocks visually, connect integrations, and define logic through the interface. For teams that want deeper customization, custom functions, and API access are available, but they're optional.
-
-### What is the difference between an AI agent and a workflow automation like Zapier?
-
-Zapier-style tools follow fixed if-then rules: when this trigger fires, do this action. Every time, the same way. An AI agent reasons over context, evaluates information, and chooses between paths based on what it finds. It can handle ambiguous inputs, make decisions, and act across multiple systems in sequence. The core distinction is reasoning versus rule-following.
-
-### Which AI agent idea has the highest ROI for a small team?
-
-Lead enrichment, email triage, and meeting follow-ups tend to deliver the highest time-reclaim per hour of build effort. Sales-related agents often show the fastest, most attributable returns because you can directly tie agent output (qualified leads routed to reps) to pipeline and revenue metrics. For most small teams, starting with email triage or meeting follow-ups is the fastest path to visible results.
diff --git a/apps/sim/content/library/ai-agent-vs-chatbot/index.mdx b/apps/sim/content/library/ai-agent-vs-chatbot/index.mdx
index 3467e946268..963e71ffd55 100644
--- a/apps/sim/content/library/ai-agent-vs-chatbot/index.mdx
+++ b/apps/sim/content/library/ai-agent-vs-chatbot/index.mdx
@@ -11,6 +11,17 @@ tags: [AI Agents, Chatbots, AI Workspace, Sim]
 ogImage: /library/ai-agent-vs-chatbot/cover.jpg
 canonical: https://www.sim.ai/library/ai-agent-vs-chatbot
 draft: false
+faq:
+  - q: "What is the difference between an AI agent and a chatbot?"
+    a: "A chatbot is a reactive system that matches user input to predefined responses using rules or basic NLP; it can only answer within its scripted boundaries. An AI agent is an autonomous system that reasons toward a goal, accesses external tools and data, retains memory across interactions, and executes multi-step actions with minimal human direction."
+  - q: "Can a chatbot become an AI agent?"
+    a: "Adding an LLM like GPT to a chatbot doesn't make it an agent — it makes it a chatbot with better language generation. True AI agents require a fundamentally different architecture: persistent memory (short-term and long-term), tool access for taking real actions across systems, and goal-directed planning that lets the system break down objectives into steps and execute them."
+  - q: "Are AI agents more expensive than chatbots?"
+    a: "AI agents can require more setup: integration with APIs and databases, memory configuration, LLM costs, and orchestration logic. But new tools like Sim make it easy to deploy a first agent in minutes. Once live, agents reduce manual overhead at scale — less time spent on repetitive multi-step processes, and fewer errors in cross-system workflows. For teams drowning in manual tasks or coordination, the ROI on an agent proves itself within months."
+  - q: "What are the best use cases for AI agents in 2026?"
+    a: "Common enterprise use cases include end-to-end support automation (resolving tickets without human escalation), data enrichment (pulling and combining data across CRMs, third-party sources, and internal databases), compliance workflows (monitoring regulatory requirements and flagging issues across documents), multi-system reporting (aggregating data from analytics, billing, and project platforms into actionable summaries), and employee or customer onboarding (coordinating account provisioning, training schedules, and documentation across multiple tools)."
+  - q: "How do I know if my team is ready to build AI agents?"
+    a: "Check for four things: clear workflows you want to automate with defined steps rather than a vague AI goal, the right access credentials to the systems involved (CRM, payment gateway, databases, communication tools), a chosen platform or framework such as a code-first tool like LangChain or a visual builder like Sim, and someone who will own the agent rollout and keep monitoring performance and expanding scope over time."
 ---
 
 You've deployed a chatbot. It answers the easy stuff fine: store hours, password resets, order status checks. Then a customer shows up with something slightly more complex: a refund tied to a promotional code, a shipping update across two orders, a billing question that requires pulling data from your CRM. The chatbot stalls. It loops. It punts to a human agent. Nearly one in five consumers who have used AI for customer service saw no benefit from the experience, according to the Qualtrics 2026 Customer Experience Trends Report.
@@ -201,25 +212,3 @@ If your workflow requires multi-step reasoning, cross-system coordination, or ac
 AI agents are moving from experimental to expected across enterprise teams, and the adoption curve is steep. For your team, the question isn't whether to bring agents in; it's where to start.
 
 Pick one workflow that's currently breaking down: reports that take hours to compile manually, and employee onboarding sequences that require five people to coordinate. Build an agent for that, prove ROI, then expand from there.
-
-## FAQ
-
-### What is the difference between an AI agent and a chatbot?
-
-A chatbot is a reactive system that matches user input to predefined responses using rules or basic NLP; it can only answer within its scripted boundaries. An AI agent is an autonomous system that reasons toward a goal, accesses external tools and data, retains memory across interactions, and executes multi-step actions with minimal human direction.
-
-### Can a chatbot become an AI agent?
-
-Adding an LLM like GPT to a chatbot doesn't make it an agent. It makes it a chatbot with better language generation. True AI agents require a fundamentally different architecture: persistent memory (short-term and long-term), tool access for taking real actions across systems, and goal-directed planning that lets the system break down objectives into steps and execute them.
-
-### Are AI agents more expensive than chatbots?
-
-AI agents can require more setup: integration with APIs and databases, memory configuration, LLM costs, and orchestration logic. But new tools like Sim make it easy to deploy your first agent in minutes. Once live, agents reduce manual overhead at scale: less time spent on repetitive multi-step processes, and fewer errors in cross-system workflows. For teams drowning in manual tasks or coordination, the ROI on an agent proves itself within months.
-
-### What are the best use cases for AI agents in 2026?
-
-Some common enterprise use cases right now include end-to-end support automation (resolving tickets without human escalation), data enrichment (pulling and combining data across CRMs, third-party sources, and internal databases), compliance workflows (monitoring regulatory requirements and flagging issues across documents), multi-system reporting (aggregating data from analytics, billing, and project platforms into actionable summaries), and employee or customer onboarding (coordinating account provisioning, training schedules, and documentation across multiple tools).
-
-### How do I know if my team is ready to build AI agents?
-
-Run through this checklist. Do you have clear workflows you want to automate: specific processes with defined steps, not vague "we want AI" goals? Do you have the right access credentials to the systems involved: CRM, payment gateway, databases, and communication tools? Have you chosen a platform or framework, whether that's a code-first tool like LangChain or a visual builder like Sim? And do you have someone who will own agent rollout, a person or team responsible for monitoring performance, adjusting workflows, and expanding scope over time? If so, you're ready to start building.
diff --git a/apps/sim/content/library/ai-agents-vs-rpa/index.mdx b/apps/sim/content/library/ai-agents-vs-rpa/index.mdx
index 9c5122f30fb..2c28017abce 100644
--- a/apps/sim/content/library/ai-agents-vs-rpa/index.mdx
+++ b/apps/sim/content/library/ai-agents-vs-rpa/index.mdx
@@ -11,6 +11,17 @@ tags: [AI Agents, RPA, Enterprise Automation, Sim]
 ogImage: /library/ai-agents-vs-rpa/cover.jpg
 canonical: https://www.sim.ai/library/ai-agents-vs-rpa
 draft: false
+faq:
+  - q: "What is the main difference between AI agents and RPA?"
+    a: "RPA bots follow pre-programmed scripts to execute specific, rule-based tasks exactly the same way every time. AI agents use large language models to perceive inputs, reason about what needs to happen, and pursue goals by planning and adapting their steps."
+  - q: "Can AI agents replace enterprise RPA entirely?"
+    a: "RPA remains strong for deterministic, compliance-critical, high-volume structured tasks where you need identical execution every time with a fully auditable trail. AI agents add the most value on top of RPA, handling the exceptions, unstructured inputs, and judgment calls that RPA was never built for. Think of it as complementary, not competitive."
+  - q: "What kinds of processes should use RPA vs AI agents?"
+    a: "Look at three process characteristics: data structure, variability, and exception rate. If your inputs are structured and consistent, steps never change, and exceptions are rare, use RPA. If inputs vary in format, the process requires interpretation or multi-step reasoning, and exceptions are frequent, use AI agents."
+  - q: "How do you combine AI agents and RPA in the same workflow?"
+    a: "Use the brain-and-hands model: the AI agent orchestrates the workflow by reading inputs, making decisions, and routing actions, while RPA bots execute the deterministic steps inside legacy systems. For example, in a finance workflow, an AI agent interprets and validates a customer's service request, then triggers an RPA bot to execute the approved transaction in the ERP system."
+  - q: "How long does it take to implement AI agents compared to RPA?"
+    a: "RPA bots typically take one to four months to deploy for a well-scoped use case. AI agents used to take months because of the additional work required to define goals, set confidence thresholds, and test edge cases. Visual agent builder platforms like Sim can cut that timeline significantly by removing the need to write orchestration code from scratch, making hybrid deployments accessible to teams without deep ML engineering resources."
 ---
 
 Picture this: your accounts payable team deployed an RPA bot two years ago to process vendor invoices. It ran flawlessly, pulling data from the same portal, matching line items, and pushing entries into the ERP system with zero human intervention. Then the vendor updated their web portal. New layout, new field names, new login flow. The bot broke overnight. Your team spent the next week re-scripting instead of building anything new.
@@ -211,25 +222,3 @@ This is also when governance frameworks need to mature:
 The AI agents vs RPA question isn't really a versus at all. RPA gives you consistent, auditable execution on structured tasks and legacy systems. AI agents give you reasoning, adaptability, and the ability to handle the messy, variable work that RPA was never designed for. Trying to solve every automation problem with just one of these tools means you're either over-engineering simple tasks or leaving complex processes stuck in manual mode.
 
 The practical path forward: audit where your current RPA bots hand off to humans, deploy AI agents at those specific seams, and build the integration layer that lets both technologies work as a single system. Start small, prove the hybrid model on one or two high-value workflows, and scale from there.
-
-## FAQ
-
-### What is the main difference between AI agents and RPA?
-
-RPA bots follow pre-programmed scripts to execute specific, rule-based tasks exactly the same way every time. AI agents use large language models to perceive inputs, reason about what needs to happen, and pursue goals by planning and adapting their steps.
-
-### Can AI agents replace enterprise RPA entirely?
-
-RPA remains strong for deterministic, compliance-critical, high-volume structured tasks where you need identical execution every time with a fully auditable trail. AI agents add the most value on top of RPA, handling the exceptions, unstructured inputs, and judgment calls that RPA was never built for. Think of it as complementary, not competitive.
-
-### What kinds of processes should use RPA vs AI agents?
-
-Look at three process characteristics: data structure, variability, and exception rate. If your inputs are structured and consistent, steps never change, and exceptions are rare, use RPA. If inputs vary in format, the process requires interpretation or multi-step reasoning, and exceptions are frequent, use AI agents.
-
-### How do you combine AI agents and RPA in the same workflow?
-
-Use the brain-and-hands model: the AI agent orchestrates the workflow by reading inputs, making decisions, and routing actions, while RPA bots execute the deterministic steps inside legacy systems. For example, in a finance workflow, an AI agent interprets and validates a customer's service request, then triggers an RPA bot to execute the approved transaction in the ERP system.
-
-### How long does it take to implement AI agents compared to RPA?
-
-RPA bots typically take one to four months to deploy for a well-scoped use case. AI agents used to take months because of the additional work required to define goals, set confidence thresholds, and test edge cases. Visual agent builder platforms like Sim can cut that timeline significantly by removing the need to write orchestration code from scratch, making hybrid deployments accessible to teams without deep ML engineering resources.
diff --git a/apps/sim/content/library/best-zapier-alternatives/index.mdx b/apps/sim/content/library/best-zapier-alternatives/index.mdx
index 357095ce1a7..e0003932e94 100644
--- a/apps/sim/content/library/best-zapier-alternatives/index.mdx
+++ b/apps/sim/content/library/best-zapier-alternatives/index.mdx
@@ -11,6 +11,21 @@ tags: [Zapier Alternatives, Workflow Automation, AI Agents, Sim]
 ogImage: /library/best-zapier-alternatives/cover.jpg
 canonical: https://www.sim.ai/library/best-zapier-alternatives
 draft: false
+faq:
+  - q: "What is the best free alternative to Zapier?"
+    a: "It depends on whether you mean cloud-hosted or self-hosted. For cloud free tiers, Sim offers a free plan with no credit card required, and Make provides 1,000 operations per month on its free plan. For genuinely unlimited free automation, n8n (self-hosted) and Activepieces (self-hosted) both offer unrestricted execution at no cost, though they require you to provision and maintain your own server. The cloud free tiers are best for testing; the self-hosted options are viable for production if your team has the technical resources."
+  - q: "Is n8n better than Zapier?"
+    a: "For teams with developer resources, n8n wins on cost (free self-hosted with no execution limits) and data control (nothing leaves your infrastructure). Zapier wins on ease of use, integration breadth (7,000+ apps vs. n8n's smaller catalog), and polished onboarding. The best answer depends on whether your team has the expertise to manage a self-hosted instance. If you do, n8n can save thousands annually. If you don't, the time cost of maintaining n8n may exceed the subscription savings."
+  - q: "What is the cheapest Zapier alternative?"
+    a: "For zero-cost automation, n8n and Activepieces self-hosted options are free with no execution limits. Among paid hosted platforms, Pabbly Connect starts at $16/month with annual billing and includes 12,000 tasks with no charges for internal steps. Pabbly also offers lifetime pricing from $249 one-time, making it the cheapest long-term option for teams that want hosted automation without managing their own servers."
+  - q: "Can I migrate my Zaps to another platform?"
+    a: "There is no automated migration tool between platforms. You need to rebuild workflows manually. Simple two- to three-step Zaps typically take 10 - 15 minutes to recreate on any alternative platform. Complex multi-branch workflows with conditional logic, custom formatting, and multiple API connections can take 30 - 60 minutes each. Most Zapier Zaps can be recreated in Make in 15 - 30 minutes because the concepts are similar, and a full migration typically takes one to two days for a moderate-sized automation library."
+  - q: "What is the difference between Zapier and an AI agent workflow platform?"
+    a: "Zapier follows a trigger-action model: an event happens in App A, and data moves to App B. The logic is predefined, deterministic, and sequential. An AI agent workflow platform like Sim adds a fundamentally different capability: blocks in your workflow where an AI model reasons about the data, makes a decision, and chooses the next step. Instead of 'when a new email arrives, add a row to a spreadsheet,' you build workflows like 'when a new email arrives, an AI agent reads the content, classifies the intent, decides whether to respond, escalate, or archive, and then executes the appropriate path.' The distinction is between routing data and making decisions about data."
+  - q: "Does Sim charge per task like Zapier does?"
+    a: "No. Sim uses a credit-based model instead of per-task billing, and it supports bring-your-own API keys so you can avoid markup on model usage entirely. A free plan is available with no credit card required."
+  - q: "Is there a Zapier alternative that supports human approval steps in an automation?"
+    a: "Relay.app is purpose-built for this — it pauses a workflow, notifies the right person, collects their input, and continues, rather than treating every step as fully automated. It's best suited to RevOps, HR, legal, and finance workflows where a judgment call needs a human in the loop before the automation proceeds."
 ---
 
 You're here because the invoice surprised you.
@@ -212,25 +227,3 @@ Key insights from this table:
 The best Zapier alternatives in 2026 are split into three categories. If your primary goal is cost reduction, Make and Pabbly Connect deliver the most dramatic savings with different trade-offs: Make gives you visual complexity at lower per-operation pricing, while Pabbly's flat rate and lifetime deal make costs fully predictable. If your goal is data control and compliance, n8n (self-hosted), Activepieces, and Sim all offer self-hosting options that keep your data on your own infrastructure. And if your workflows have outgrown trigger-action logic entirely and you need AI agents that reason, decide, and act across your stack, Sim is the platform built from the ground up for that shift.
 
 No single tool replaces Zapier for every team. Start by identifying the specific constraint that brought you here, whether it's pricing, complexity, data control, or AI capabilities, and match it against the routing table above. Most teams that switch successfully do so because they understand what they actually need rather than chasing the tool with the longest feature list.
-
-## FAQ
-
-### What is the best free alternative to Zapier?
-
-It depends on whether you mean cloud-hosted or self-hosted. For cloud free tiers, Sim offers a free plan with no credit card required, and Make provides 1,000 operations per month on its free plan. For genuinely unlimited free automation, n8n (self-hosted) and Activepieces (self-hosted) both offer unrestricted execution at no cost, though they require you to provision and maintain your own server. The cloud free tiers are best for testing; the self-hosted options are viable for production if your team has the technical resources.
-
-### Is n8n better than Zapier?
-
-For teams with developer resources, n8n wins on cost (free self-hosted with no execution limits) and data control (nothing leaves your infrastructure). Zapier wins on ease of use, integration breadth (7,000+ apps vs. n8n's smaller catalog), and polished onboarding. The best answer depends on whether your team has the expertise to manage a self-hosted instance. If you do, n8n can save thousands annually. If you don't, the time cost of maintaining n8n may exceed the subscription savings.
-
-### What is the cheapest Zapier alternative?
-
-For zero-cost automation, n8n and Activepieces self-hosted options are free with no execution limits. Among paid hosted platforms, Pabbly Connect starts at $16/month with annual billing and includes 12,000 tasks with no charges for internal steps. Pabbly also offers lifetime pricing from $249 one-time, making it the cheapest long-term option for teams that want hosted automation without managing their own servers.
-
-### Can I migrate my Zaps to another platform?
-
-There is no automated migration tool between platforms. You need to rebuild workflows manually. Simple two- to three-step Zaps typically take 10 - 15 minutes to recreate on any alternative platform. Complex multi-branch workflows with conditional logic, custom formatting, and multiple API connections can take 30 - 60 minutes each. Most Zapier Zaps can be recreated in Make in 15 - 30 minutes because the concepts are similar, and a full migration typically takes one to two days for a moderate-sized automation library.
-
-### What is the difference between Zapier and an AI agent workflow platform?
-
-Zapier follows a trigger-action model: an event happens in App A, and data moves to App B. The logic is predefined, deterministic, and sequential. An AI agent workflow platform like Sim adds a fundamentally different capability: blocks in your workflow where an AI model reasons about the data, makes a decision, and chooses the next step. Instead of "when a new email arrives, add a row to a spreadsheet," you build workflows like "when a new email arrives, an AI agent reads the content, classifies the intent, decides whether to respond, escalate, or archive, and then executes the appropriate path." The distinction is between routing data and making decisions about data.
diff --git a/apps/sim/content/library/how-to-create-an-ai-agent/index.mdx b/apps/sim/content/library/how-to-create-an-ai-agent/index.mdx
index 5bf70e1c60a..dad63c030d6 100644
--- a/apps/sim/content/library/how-to-create-an-ai-agent/index.mdx
+++ b/apps/sim/content/library/how-to-create-an-ai-agent/index.mdx
@@ -11,6 +11,15 @@ tags: [AI Agents, Tutorial, No-Code, Sim, Workflow Automation]
 ogImage: /library/how-to-create-an-ai-agent/cover.jpg
 canonical: https://www.sim.ai/library/how-to-create-an-ai-agent
 draft: false
+faq:
+  - q: "What is an AI agent, and how is it different from a chatbot?"
+    a: "An AI agent perceives input, reasons over it, uses tools to take real-world actions (sending emails, updating databases, calling APIs), and evaluates results; often looping through multiple steps autonomously. A chatbot only generates text responses to direct prompts. The core distinction is that agents act; chatbots reply."
+  - q: "Can I build an AI agent for free?"
+    a: "Yes. Sim's free plan requires no credit card and includes execution credits for testing and development, access to the full visual workflow builder, and 1,000+ integrations. You can build, test, and deploy a working agent without spending anything."
+  - q: "How long does it take to build an AI agent with Sim?"
+    a: "A simple agent, like the people research example in Sim's getting-started tutorial, takes about 10 minutes from account creation to a working deployment. Production-grade agents with multiple tools, conditional logic, and error handling take longer, but the visual builder's feedback loop (edit, run, read the trace, refine) is significantly faster than iterating on a code framework where every change requires redeployment."
+  - q: "Do I need to know how to code to build an AI agent?"
+    a: "No. Sim's visual builder and Chat let you create, configure, and deploy agents entirely without code. You drag blocks, write system prompts in plain English, and connect integrations through the UI. That said, Sim also supports custom functions, a Python SDK, and full API access for developers who want deeper control or need to embed agents into existing codebases."
 ---
 
 Every tutorial on how to build AI agents seems to start in the same place: pick a framework, install dependencies, configure your environment, write boilerplate, debug cryptic errors, and if you're lucky, get something running a few hours later. For teams that don't live in Python every day, that first hour can feel like the entire project.
@@ -191,21 +200,3 @@ Learning how to build AI agents doesn't require a computer science degree, a com
 The pattern is straightforward: define a narrow task, open a workflow, add an Agent block with the right LLM and a tight system prompt, connect two or three tools, set a trigger, and iterate using execution logs. Start with the narrowest possible version of the idea, get it working reliably, then expand from there.
 
 Trusted by over 100,000 builders at startups and Fortune 500 companies, Sim offers a free plan with no credit card required. Open it, build something, and see what an agent can do for your workflow before the week is out.
-
-## FAQ
-
-### What is an AI agent, and how is it different from a chatbot?
-
-An AI agent perceives input, reasons over it, uses tools to take real-world actions (sending emails, updating databases, calling APIs), and evaluates results; often looping through multiple steps autonomously. A chatbot only generates text responses to direct prompts. The core distinction is that agents act; chatbots reply.
-
-### Can I build an AI agent for free?
-
-Yes. Sim's free plan requires no credit card and includes execution credits for testing and development, access to the full visual workflow builder, and 1,000+ integrations. You can build, test, and deploy a working agent without spending anything.
-
-### How long does it take to build an AI agent with Sim?
-
-A simple agent, like the people research example in Sim's getting-started tutorial, takes about 10 minutes from account creation to a working deployment. Production-grade agents with multiple tools, conditional logic, and error handling take longer, but the visual builder's feedback loop (edit, run, read the trace, refine) is significantly faster than iterating on a code framework where every change requires redeployment.
-
-### Do I need to know how to code to build an AI agent?
-
-No. Sim's visual builder and Chat let you create, configure, and deploy agents entirely without code. You drag blocks, write system prompts in plain English, and connect integrations through the UI. That said, Sim also supports custom functions, a Python SDK, and full API access for developers who want deeper control or need to embed agents into existing codebases.
diff --git a/apps/sim/content/library/langgraph-alternatives/index.mdx b/apps/sim/content/library/langgraph-alternatives/index.mdx
index c2fd7cba659..de93b905b3e 100644
--- a/apps/sim/content/library/langgraph-alternatives/index.mdx
+++ b/apps/sim/content/library/langgraph-alternatives/index.mdx
@@ -11,6 +11,17 @@ tags: [LangGraph Alternatives, AI Agents, Agent Frameworks, Sim]
 ogImage: /library/langgraph-alternatives/cover.jpg
 canonical: https://www.sim.ai/library/langgraph-alternatives
 draft: false
+faq:
+  - q: "What is the main difference between LangGraph and its alternatives?"
+    a: "LangGraph uses an explicit graph paradigm where you define nodes, edges, and state reducers to control every transition in an agent workflow. Most code-first alternatives replace this with different models: CrewAI uses role-based task delegation, AutoGen uses multi-turn conversation, and Google ADK uses hierarchical agent trees. Workspace platforms like Sim and Dify take a fundamentally different approach by handling deployment, integrations, and collaboration as built-in features rather than asking you to build them yourself. The split is really two decisions: which architectural model fits your agent logic, and whether you want to own the production infrastructure or have it handled for you."
+  - q: "Can I migrate an existing LangGraph workflow to one of these alternatives?"
+    a: "The agent logic (what each step does and the business rules governing flow) can transfer to any alternative. The graph definitions themselves don't port directly because each framework uses a different abstraction model. For code-first frameworks, expect to rewrite the orchestration layer while preserving your prompt engineering and tool implementations. For workspace platforms like Sim or Dify, you'll rebuild the workflow in a visual or conversational interface, which is often faster than the original code-based build but requires rethinking how you express branching and state management."
+  - q: "Which LangGraph alternative is best for non-technical team members?"
+    a: "Workspace platforms with visual builders are the clear answer. Sim's drag-and-drop visual builder and Chat let non-engineers describe what they want in natural language and see it built visually. Dify's visual workflow builder offers a similar low-code experience for RAG-heavy applications. Code-first frameworks like CrewAI, AutoGen, or Google ADK are not the right fit for non-engineers since they require Python or TypeScript proficiency and comfort with command-line tooling."
+  - q: "Is LangGraph still worth learning in 2026?"
+    a: "Yes, if you genuinely require stateful graph control with human-in-the-loop approvals and your industry demands auditable, deterministic execution paths. Financial services, healthcare, and legal tech teams building compliance-sensitive agent workflows will find real value in LangGraph's explicit state management model. It is not worth learning if your goal is fast production deployment for business workflow automation, since the engineering overhead of building deployment, collaboration, and integration infrastructure on top of LangGraph is significant, and workspace platforms now handle that layer natively."
+  - q: "How does Sim compare to LangGraph for enterprise use?"
+    a: "Sim and LangGraph serve enterprise teams from opposite directions. Sim ships deployment infrastructure (cloud-hosted with automatic scaling or self-hosted via Docker/Kubernetes), SOC2 and HIPAA compliance, real-time team collaboration with permission controls, 1,000+ pre-built integrations, and per-model cost tracking as built-in features. LangGraph offers deep audit trails and deterministic control over individual state transitions, which matter for regulated industry workflows requiring explicit approval chains. If your compliance team needs to trace every state transition, LangGraph wins. If your team needs agents running in production next month with 20 integrations and five people collaborating, Sim closes that gap faster."
 ---
 
 Your LangGraph prototype works. Agents handle branching logic, state persists across turns, and the demo goes well. Then you try to ship it: deployment means building your own serving layer, debugging parallel execution traces turns into a guessing game, and wiring 50 integrations means 50 custom connectors. Getting it to run in production for your team, rather than just on your machine, is easier said than done.
@@ -179,29 +190,3 @@ LangGraph is a strong tool for a specific set of problems. If your team requires
 For Python teams that want a different architectural model but still want to own their stack, CrewAI, Google ADK, and the OpenAI Agents SDK each offer compelling trade-offs depending on your cloud provider and use case. TypeScript teams have Mastra. Teams invested in Azure should watch the Microsoft Agent Framework closely.
 
 For teams where the real bottleneck is getting agents into production, connected to business tools, and maintained by more than one person, a workspace platform like Sim removes the infrastructure burden so you can focus on the agent logic itself. You can [start building in Sim](https://sim.ai) and see how visual, conversational, and API-driven agent building compares to graph definitions in code.
-
-## FAQ
-
-### What is the main difference between LangGraph and its alternatives?
-
-LangGraph uses an explicit graph paradigm where you define nodes, edges, and state reducers to control every transition in an agent workflow. Most code-first alternatives replace this with different models: CrewAI uses role-based task delegation, AutoGen uses multi-turn conversation, and Google ADK uses hierarchical agent trees. Workspace platforms like Sim and Dify take a fundamentally different approach by handling deployment, integrations, and collaboration as built-in features rather than asking you to build them yourself. The split is really two decisions: which architectural model fits your agent logic, and whether you want to own the production infrastructure or have it handled for you.
-
-### Can I migrate an existing LangGraph workflow to one of these alternatives?
-
-The agent logic (what each step does and the business rules governing flow) can transfer to any alternative. The graph definitions themselves don't port directly because each framework uses a different abstraction model. For code-first frameworks, expect to rewrite the orchestration layer while preserving your prompt engineering and tool implementations. For workspace platforms like Sim or Dify, you'll rebuild the workflow in a visual or conversational interface, which is often faster than the original code-based build but requires rethinking how you express branching and state management.
-
-### Which LangGraph alternative is best for non-technical team members?
-
-Workspace platforms with visual builders are the clear answer. Sim's drag-and-drop visual builder and Chat let non-engineers describe what they want in natural language and see it built visually. Dify's visual workflow builder offers a similar low-code experience for RAG-heavy applications. Code-first frameworks like CrewAI, AutoGen, or Google ADK are not the right fit for non-engineers since they require Python or TypeScript proficiency and comfort with command-line tooling.
-
-### Is LangGraph still worth learning in 2026?
-
-Yes, if you genuinely require stateful graph control with human-in-the-loop approvals and your industry demands auditable, deterministic execution paths. Financial services, healthcare, and legal tech teams building compliance-sensitive agent workflows will find real value in LangGraph's explicit state management model.
-
-It is not worth learning if your goal is fast production deployment for business workflow automation. The engineering overhead of building deployment, collaboration, and integration infrastructure on top of LangGraph is significant, and workspace platforms now handle that layer natively.
-
-### How does Sim compare to LangGraph for enterprise use?
-
-Sim and LangGraph serve enterprise teams from opposite directions. Sim ships deployment infrastructure (cloud-hosted with automatic scaling or self-hosted via Docker/Kubernetes), SOC2 and HIPAA compliance, real-time team collaboration with permission controls, 1,000+ pre-built integrations, and per-model cost tracking as built-in features. LangGraph offers deep audit trails and deterministic control over individual state transitions, which matter for regulated industry workflows requiring explicit approval chains.
-
-The practical question is whether your enterprise bottleneck is graph-level control or production infrastructure. If your compliance team needs to trace every state transition, LangGraph wins. If your team needs agents running in production next month with 20 integrations and five people collaborating, Sim closes that gap faster.
diff --git a/apps/sim/content/library/n8n-alternatives/index.mdx b/apps/sim/content/library/n8n-alternatives/index.mdx
index 32090f395fa..2f3c07e3653 100644
--- a/apps/sim/content/library/n8n-alternatives/index.mdx
+++ b/apps/sim/content/library/n8n-alternatives/index.mdx
@@ -11,6 +11,17 @@ tags: [n8n Alternatives, Workflow Automation, AI Agents, Sim]
 ogImage: /library/n8n-alternatives/cover.jpg
 canonical: https://www.sim.ai/library/n8n-alternatives
 draft: false
+faq:
+  - q: "What is the best free alternative to n8n?"
+    a: "It depends on your use case. For open-source self-hosting with no execution limits and no commercial restrictions, Activepieces under the MIT license is the strongest free option. For AI agent workflows, Sim's free plan gives you access to multi-model orchestration and visual workflow building without a credit card. For visual SaaS automation, Make's free tier provides 1,000 credits per month to test basic scenarios."
+  - q: "Which n8n alternative is best for AI agent workflows?"
+    a: "Sim is the best general-purpose choice for visual, multi-model agent building with native memory, MCP support, and collaboration features. Gumloop is strong for non-developer teams that want an AI-native canvas for LLM-powered workflows. Botpress is the best pick specifically for conversational agents with built-in RAG, goal tracking, and multi-turn context. Choose based on whether your agents are general-purpose, LLM-workflow-focused, or conversation-focused."
+  - q: "Is Make better than n8n?"
+    a: "Neither is universally better. Make wins on debugging experience, accessibility for non-developers, and managed infrastructure that eliminates self-hosting overhead. n8n wins on extensibility, developer control, self-hosting flexibility, and community ecosystem depth. If your team is primarily non-technical and values visual debugging, Make is the better fit. If your team is developer-heavy and values code-level customization, n8n still has the edge."
+  - q: "What is the best open-source n8n alternative?"
+    a: "Activepieces offers the cleanest MIT license with a growing integration library of 280+ pieces and active community contribution. Sim provides open-source AI agent workflows with Docker and Kubernetes deployment for teams that need agentic capabilities. Node-RED remains the standard for IoT and hardware automation contexts where n8n's web-focused architecture is overkill. Match the tool to your use case rather than defaulting to the one with the most GitHub stars."
+  - q: "How hard is it to migrate from n8n to another tool?"
+    a: "It depends on which tool you are migrating to. Moving to a platform with a similar trigger-action paradigm (like Activepieces) is the closest format match and typically takes a few weeks of focused engineering for a mid-size portfolio. Before committing, calculate the full cost of migration, including potential productivity dips during transition. For most teams, the investment pays off within two to three quarters if the new platform eliminates major pain points and bottlenecks."
 ---
 
 This guide covers 10 n8n alternatives for two distinct groups: teams that want simpler, managed SaaS automation without the self-hosting overhead, and teams building genuine AI agent workflows that need native multi-model orchestration, memory, and agentic reasoning.
@@ -260,25 +271,3 @@ If you're leaving because self-hosting is consuming too much engineering time, M
 If licensing restrictions concern you, Activepieces offers the cleanest MIT-licensed alternative with a growing integration ecosystem.
 
 Don't try to evaluate all 10 tools on this list. Identify which of the four archetypes from the framework section fits your team, narrow the list to two or three candidates, and build a real workflow in each. The free tiers across Sim, Make, Zapier, Activepieces, and Pipedream allow you to do this without any upfront spend.
-
-## FAQ
-
-### What is the best free alternative to n8n?
-
-It depends on your use case. For open-source self-hosting with no execution limits and no commercial restrictions, Activepieces under the MIT license is the strongest free option. For AI agent workflows, Sim's free plan gives you access to multi-model orchestration and visual workflow building without a credit card. For visual SaaS automation, Make's free tier provides 1,000 credits per month to test basic scenarios.
-
-### Which n8n alternative is best for AI agent workflows?
-
-Sim is the best general-purpose choice for visual, multi-model agent building with native memory, MCP support, and collaboration features. Gumloop is strong for non-developer teams that want an AI-native canvas for LLM-powered workflows. Botpress is the best pick specifically for conversational agents with built-in RAG, goal tracking, and multi-turn context. Choose based on whether your agents are general-purpose, LLM-workflow-focused, or conversation-focused.
-
-### Is Make better than n8n?
-
-Neither is universally better. Make wins on debugging experience, accessibility for non-developers, and managed infrastructure that eliminates self-hosting overhead. n8n wins on extensibility, developer control, self-hosting flexibility, and community ecosystem depth. If your team is primarily non-technical and values visual debugging, Make is the better fit. If your team is developer-heavy and values code-level customization, n8n still has the edge.
-
-### What is the best open-source n8n alternative?
-
-Activepieces offers the cleanest MIT license with a growing integration library of 280+ pieces and active community contribution. Sim provides open-source AI agent workflows with Docker and Kubernetes deployment for teams that need agentic capabilities. Node-RED remains the standard for IoT and hardware automation contexts where n8n's web-focused architecture is overkill. Match the tool to your use case rather than defaulting to the one with the most GitHub stars.
-
-### How hard is it to migrate from n8n to another tool?
-
-It depends on which tool you are migrating to. Moving to a platform with a similar trigger-action paradigm (like Activepieces) is the closest format match and typically takes a few weeks of focused engineering for a mid-size portfolio. Before committing, calculate the full cost of migration, including potential productivity dips during transition. For most teams, the investment pays off within two to three quarters if the new platform eliminates major pain points and bottlenecks.
diff --git a/apps/sim/content/library/open-source-ai-agent-platforms/index.mdx b/apps/sim/content/library/open-source-ai-agent-platforms/index.mdx
index cb69ca1d9e3..80196709e36 100644
--- a/apps/sim/content/library/open-source-ai-agent-platforms/index.mdx
+++ b/apps/sim/content/library/open-source-ai-agent-platforms/index.mdx
@@ -11,6 +11,17 @@ tags: [Open Source, AI Agents, LangGraph, CrewAI, Dify, Sim]
 ogImage: /library/open-source-ai-agent-platforms/cover.jpg
 canonical: https://www.sim.ai/library/open-source-ai-agent-platforms
 draft: false
+faq:
+  - q: "What is the difference between an AI agent framework and an AI agent platform?"
+    a: "An AI agent framework is a code library that provides primitives for building agents: tool use, multi-step reasoning, memory, and orchestration. You write code and own everything else. An AI agent platform bundles those primitives with deployment infrastructure, observability, collaboration features, and often a visual interface. The practical difference is how much your team builds versus how much comes out of the box."
+  - q: "Can I self-host all of these open-source AI agent platforms?"
+    a: "Most, but not all, support full self-hosting. LangGraph, CrewAI, Dify, and Sim can all be self-hosted via Docker or Kubernetes. AutoGen is now in maintenance mode and will not receive new features, but remains self-hostable. n8n supports self-hosting under its Sustainable Use License, which has specific commercial-use restrictions worth reviewing. Always check the license terms, since some platforms label enterprise features like RBAC, SSO, and advanced observability as paid add-ons even when the core is open source."
+  - q: "Which open-source AI agent platform is best for non-developers?"
+    a: "Visual builders like Dify and workspace platforms like Sim are the best starting points for non-developers. Both offer drag-and-drop interfaces that don't require writing code. Code-first frameworks like LangGraph, CrewAI, and AutoGen are a poor fit without engineering support since they require Python proficiency and comfort with infrastructure management. If your team is mixed (some developers, some not), a workspace like Sim lets both groups contribute in the same environment."
+  - q: "How does LangGraph compare to CrewAI for production use?"
+    a: "LangGraph gives you explicit, stateful control over every decision branch in your agent's workflow, making it the stronger choice for complex conditional logic. Crews provide autonomous agent collaboration ideal for tasks requiring flexible decision-making, while Flows offer precise, event-driven control ideal for managing detailed execution paths and secure state management. The real differentiator in production is the deployment layer: both frameworks leave production infrastructure, monitoring, and team collaboration as exercises for the builder, so your choice may hinge on which ecosystem your team prefers to invest in."
+  - q: "What should I look for in an open-source AI agent platform before committing?"
+    a: "Evaluate six things: license type (MIT, Apache 2.0, or a custom license with restrictions), self-hosting support (Docker/Kubernetes readiness and local model compatibility via Ollama), observability (built-in logging and tracing versus requiring a paid add-on like LangSmith), LLM flexibility (multi-provider support so you're not locked into one model vendor), community activity (commit frequency, issue response time, contributor count), and enterprise feature gating (whether RBAC, SSO, and audit logs require a paid tier). That last point matters most: an open-source label doesn't guarantee the features you need in production are in the free tier."
 ---
 
 This guide splits open-source AI agent platforms into three clear categories, compares the leading options within each, and offers a decision framework based on your team's situation rather than by feature count.
@@ -179,34 +190,3 @@ Visual builders like Dify and n8n lower the barrier to entry but trade away arch
 Start with the decision paths above. Identify which category fits your team, then evaluate within that category. Trying to compare a Python framework against a visual workspace on the same checklist is how teams end up six months into a tool that doesn't fit.
 
 If your team needs collaboration, multi-model support, and a visual builder with production-grade deployment, [explore Sim](https://sim.ai) and see whether the workspace model matches how your team actually works.
-
-## FAQ
-
-### What is the difference between an AI agent framework and an AI agent platform?
-
-An AI agent framework is a code library that provides primitives for building agents: tool use, multi-step reasoning, memory, and orchestration. You write code and own everything else. An AI agent platform bundles those primitives with deployment infrastructure, observability, collaboration features, and often a visual interface. The practical difference is how much your team builds versus how much comes out of the box.
-
-### Can I self-host all of these open-source AI agent platforms?
-
-Most, but not all, support full self-hosting. LangGraph, CrewAI, Dify, and Sim can all be self-hosted via Docker or Kubernetes. AutoGen is now in maintenance mode and will not receive new features, but remains self-hostable. n8n supports self-hosting under its Sustainable Use License, which has specific commercial-use restrictions worth reviewing. Always check the license terms. Some platforms label enterprise features (RBAC, SSO, advanced observability) as paid add-ons even when the core is open source.
-
-### Which open-source AI agent platform is best for non-developers?
-
-Visual builders like Dify and workspace platforms like Sim are the best starting points for non-developers. Both offer drag-and-drop interfaces that don't require writing code. Code-first frameworks like LangGraph, CrewAI, and AutoGen are a poor fit without engineering support since they require Python proficiency and comfort with infrastructure management. If your team is mixed (some developers, some not), a workspace like Sim lets both groups contribute in the same environment.
-
-### How does LangGraph compare to CrewAI for production use?
-
-LangGraph gives you explicit, stateful control over every decision branch in your agent's workflow, making it the stronger choice for complex conditional logic. Crews provide autonomous agent collaboration ideal for tasks requiring flexible decision-making, while Flows offer precise, event-driven control ideal for managing detailed execution paths and secure state management. The real differentiator in production is the deployment layer: both frameworks leave production infrastructure, monitoring, and team collaboration as exercises for the builder, so your choice may hinge on which ecosystem (LangChain vs. CrewAI Enterprise) your team prefers to invest in.
-
-### What should I look for in an open-source AI agent platform before committing?
-
-Six things to evaluate before committing:
-
-1. **License type** (MIT, Apache 2.0, or a custom license with restrictions)
-2. **Self-hosting support** (Docker/Kubernetes readiness and local model compatibility via Ollama)
-3. **Observability** (built-in logging and tracing vs. requiring a paid add-on like LangSmith)
-4. **LLM flexibility** (multi-provider support so you're not locked into one model vendor)
-5. **Community activity** (commit frequency, issue response time, contributor count)
-6. **Enterprise feature gating** (whether RBAC, SSO, and audit logs require a paid tier)
-
-The last point is especially important: an open-source label doesn't guarantee that the features you need in production are in the free tier.
diff --git a/apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx b/apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx
index 49f47b4daae..514f8c9047f 100644
--- a/apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx
+++ b/apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx
@@ -11,6 +11,19 @@ tags: [AI Agents, Workflow Automation, OpenAI AgentKit, n8n, Sim, MCP]
 ogImage: /library/openai-vs-n8n-vs-sim/cover.jpg
 canonical: https://www.sim.ai/library/openai-vs-n8n-vs-sim
 draft: false
+faq:
+  - q: "Can OpenAI AgentKit run models from providers other than OpenAI?"
+    a: "No. AgentKit only runs OpenAI models, while Sim connects to multiple AI providers including OpenAI, Anthropic, Google, Groq, Cerebras, and local Ollama models."
+  - q: "Is n8n fully open source?"
+    a: "No. n8n is fair-use licensed with some restrictions, not fully open source. Sim, by contrast, is Apache 2.0 licensed and fully open source."
+  - q: "Does OpenAI AgentKit provide execution logs for debugging agents in production?"
+    a: "No, AgentKit has no execution logs or detailed monitoring for debugging and observability. Sim provides enterprise-grade logging with execution IDs, block-level timing, token/cost tracking, and error traces."
+  - q: "Can n8n workflows be built programmatically instead of through the visual builder?"
+    a: "No, n8n has no SDK for building workflows programmatically and is limited to its visual builder only."
+  - q: "How many ways can you trigger a workflow in each platform?"
+    a: "AgentKit offers no way to trigger workflows via external integrations. n8n supports webhooks, scheduled executions, and manual triggers. Sim supports chat interfaces, REST APIs, webhooks, scheduled jobs, and external events from integrated services like Slack and GitHub."
+  - q: "Which platform has more built-in integrations, n8n or Sim?"
+    a: "n8n has a larger integration library overall, but Sim ships 80+ built-in integrations spanning AI providers, communication tools, productivity apps, and developer tools, plus MCP protocol support for custom integrations to fill any gaps."
 ---
 
 When building AI agent workflows, developers often evaluate multiple platforms to find the right fit for their needs. Three platforms frequently come up in these discussions: OpenAI's new AgentKit, the established workflow automation tool n8n, and Sim, a purpose-built AI agent workflow builder. While all three enable AI agent development, they take fundamentally different approaches, each with distinct strengths and ideal use cases.
diff --git a/apps/sim/next.config.ts b/apps/sim/next.config.ts
index 720a0830a3a..4d1ce2b9104 100644
--- a/apps/sim/next.config.ts
+++ b/apps/sim/next.config.ts
@@ -485,6 +485,44 @@ const nextConfig: NextConfig = {
       }
     )
 
+    /**
+     * Stray crawler/artifact URLs picked up in an external SEO audit — no
+     * page ever existed at these paths, but they were indexed or linked
+     * somewhere with junk characters/casing. Send them home instead of 404.
+     */
+    redirects.push(
+      {
+        source: '/$',
+        destination: '/',
+        permanent: true,
+      },
+      {
+        source: '/&',
+        destination: '/',
+        permanent: true,
+      },
+      {
+        source: '/Sim',
+        destination: '/',
+        permanent: true,
+      },
+      {
+        source: '/homepage',
+        destination: '/',
+        permanent: true,
+      },
+      {
+        source: '/logo',
+        destination: '/',
+        permanent: true,
+      },
+      {
+        source: '/en-US',
+        destination: '/',
+        permanent: true,
+      }
+    )
+
     return redirects
   },
   async rewrites() {

From 2f13b1de7ee098b0fc31d9fad1f5933d4f1bf402 Mon Sep 17 00:00:00 2001
From: Waleed Latif <walif6@gmail.com>
Date: Tue, 14 Jul 2026 19:11:52 -0700
Subject: [PATCH 3/4] revert(docs): restore Ask AI widget
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Ask AI removal is a separate change, out of scope for this PR — restoring
the component, chat panel, api/chat route, and its deps to their
pre-existing state.
---
 apps/docs/app/[lang]/layout.tsx          |   2 +
 apps/docs/app/api/chat/route.ts          | 344 +++++++++++++++++++++++
 apps/docs/components/ai/ask-ai-panel.tsx | 234 +++++++++++++++
 apps/docs/components/ai/ask-ai.tsx       |  57 ++++
 apps/docs/package.json                   |   4 +
 bun.lock                                 |  10 +
 6 files changed, 651 insertions(+)
 create mode 100644 apps/docs/app/api/chat/route.ts
 create mode 100644 apps/docs/components/ai/ask-ai-panel.tsx
 create mode 100644 apps/docs/components/ai/ask-ai.tsx

diff --git a/apps/docs/app/[lang]/layout.tsx b/apps/docs/app/[lang]/layout.tsx
index f27d3187e69..4fb73ecee32 100644
--- a/apps/docs/app/[lang]/layout.tsx
+++ b/apps/docs/app/[lang]/layout.tsx
@@ -3,6 +3,7 @@ import { defineI18nUI } from 'fumadocs-ui/i18n'
 import { DocsLayout } from 'fumadocs-ui/layouts/docs'
 import { RootProvider } from 'fumadocs-ui/provider/next'
 import { Geist_Mono, Inter } from 'next/font/google'
+import { AskAI } from '@/components/ai/ask-ai'
 import {
   SidebarFolder,
   SidebarItem,
@@ -123,6 +124,7 @@ export default async function Layout({ children, params }: LayoutProps) {
             {children}
           </DocsLayout>
           <Footer />
+          <AskAI locale={lang} />
         </RootProvider>
       </body>
     </html>
diff --git a/apps/docs/app/api/chat/route.ts b/apps/docs/app/api/chat/route.ts
new file mode 100644
index 00000000000..2150e044980
--- /dev/null
+++ b/apps/docs/app/api/chat/route.ts
@@ -0,0 +1,344 @@
+import { openai } from '@ai-sdk/openai'
+import { convertToModelMessages, stepCountIs, streamText, tool, type UIMessage } from 'ai'
+import { sql } from 'drizzle-orm'
+import { z } from 'zod'
+import { db, docsEmbeddings } from '@/lib/db'
+import { generateSearchEmbedding } from '@/lib/embeddings'
+
+export const runtime = 'nodejs'
+export const maxDuration = 30
+
+/** Model used for the Ask AI chat. Override with OPENAI_CHAT_MODEL in the environment. */
+const CHAT_MODEL = process.env.OPENAI_CHAT_MODEL || 'gpt-5.4-mini'
+
+/** Max documentation chunks returned per search to ground an answer. */
+const SEARCH_LIMIT = 6
+
+/** Candidates pulled before locale filtering, so a locale still yields SEARCH_LIMIT results. */
+const SEARCH_CANDIDATES = SEARCH_LIMIT * 4
+
+/** Minimum cosine similarity for an English vector match (mirrors the site search route). */
+const SIMILARITY_THRESHOLD = 0.6
+
+/** Locales the docs are published in (mirrors the site search route). */
+const KNOWN_LOCALES = ['en', 'es', 'fr', 'de', 'ja', 'zh']
+const DEFAULT_LOCALE = 'en'
+
+/** Postgres full-text config per locale (mirrors the site search route). */
+const TS_CONFIG: Record<string, string> = {
+  en: 'english',
+  es: 'spanish',
+  fr: 'french',
+  de: 'german',
+  ja: 'simple',
+  zh: 'simple',
+}
+
+/**
+ * Abuse guards. This endpoint proxies a paid LLM, so an unauthenticated public
+ * route is a target for scripted "free inference". These bounds cap the cost of
+ * any single request; an in-memory per-IP rate limit (below) caps volume on the
+ * hot path. A shared-store rate limit, a provider spend cap, and edge bot
+ * protection remain the durable controls (see the PR checklist).
+ *
+ * The size cap counts only user-authored text — NOT the conversation history,
+ * assistant turns, or retrieved doc chunks we add via the searchDocs tool, which
+ * legitimately grow large over a multi-turn chat.
+ */
+const MAX_MESSAGES = 200
+const MAX_USER_INPUT_CHARS = 400_000
+const MAX_OUTPUT_TOKENS = 4000
+const MAX_STEPS = 6
+/** Backstop on the sanitized model payload — bounds total LLM input (e.g. stuffed assistant text). */
+const MAX_TOTAL_CHARS = 1_000_000
+
+/**
+ * Per-IP rate limit. Fixed window, in-memory: this bounds volume from a single
+ * source on a warm instance without external infra. It is best-effort on
+ * serverless (state is per-instance, not shared across regions/cold starts);
+ * a shared store (e.g. Vercel KV) and an edge WAF remain the durable controls,
+ * but this closes the "no volume limit at all" gap on the hot path.
+ */
+const RATE_LIMIT_MAX = 20
+const RATE_LIMIT_WINDOW_MS = 60_000
+const rateLimitHits = new Map<string, { count: number; resetAt: number }>()
+
+/** Resolve the client IP from forwarding headers, falling back to a shared bucket. */
+function getClientIp(req: Request): string {
+  const forwarded = req.headers.get('x-forwarded-for')
+  if (forwarded) return forwarded.split(',')[0].trim()
+  return req.headers.get('x-real-ip') ?? 'unknown'
+}
+
+/** Fixed-window check. Returns retry-after seconds when the caller is over the limit, else null. */
+function rateLimit(ip: string, now: number): number | null {
+  const entry = rateLimitHits.get(ip)
+  if (!entry || now >= entry.resetAt) {
+    rateLimitHits.set(ip, { count: 1, resetAt: now + RATE_LIMIT_WINDOW_MS })
+    return null
+  }
+  if (entry.count >= RATE_LIMIT_MAX) {
+    return Math.ceil((entry.resetAt - now) / 1000)
+  }
+  entry.count += 1
+  return null
+}
+
+/** Drop expired buckets so the Map doesn't grow unbounded on a long-lived instance. */
+function sweepRateLimit(now: number): void {
+  if (rateLimitHits.size < 10_000) return
+  for (const [ip, entry] of rateLimitHits) {
+    if (now >= entry.resetAt) rateLimitHits.delete(ip)
+  }
+}
+
+/** A structurally valid UI message: has a role and a parts array. */
+function isValidMessage(message: unknown): message is UIMessage {
+  return (
+    typeof message === 'object' &&
+    message !== null &&
+    typeof (message as { role?: unknown }).role === 'string' &&
+    Array.isArray((message as { parts?: unknown }).parts)
+  )
+}
+
+/** Total length of user-authored text across the conversation. */
+function userInputChars(messages: UIMessage[]): number {
+  let total = 0
+  for (const message of messages) {
+    if (message.role !== 'user') continue
+    for (const part of message.parts) {
+      if (part.type === 'text' && typeof part.text === 'string') total += part.text.length
+    }
+  }
+  return total
+}
+
+/**
+ * Strip everything the model shouldn't trust from client-supplied history:
+ * drop `system` messages (client-injected instructions) and every non-text part
+ * (e.g. crafted tool results faking searchDocs output). Only user/assistant text
+ * survives, so grounding comes from the server-run searchDocs tool — not the
+ * client's payload.
+ */
+function sanitizeMessages(messages: UIMessage[]): UIMessage[] {
+  return messages
+    .filter((message) => message.role === 'user' || message.role === 'assistant')
+    .map((message) => ({
+      ...message,
+      parts: message.parts.filter((part) => part.type === 'text' && typeof part.text === 'string'),
+    }))
+    .filter((message) => message.parts.length > 0)
+}
+
+/**
+ * Reject obvious cross-origin calls. Same-origin browser requests send an
+ * `Origin` header matching the host; we allow those, plus any host in
+ * DOCS_ALLOWED_ORIGINS (comma-separated). Requests with no Origin (e.g. curl)
+ * are allowed through to the cost caps rather than blocked, since Origin is
+ * trivially spoofable and is a filter, not a security boundary.
+ */
+function isAllowedOrigin(req: Request): boolean {
+  const origin = req.headers.get('origin')
+  if (!origin) return true
+
+  let originHost: string
+  try {
+    originHost = new URL(origin).host.toLowerCase()
+  } catch {
+    return false
+  }
+
+  const forwardedHost = req.headers.get('x-forwarded-host') ?? req.headers.get('host')
+  const requestHost = forwardedHost?.split(',')[0].trim().toLowerCase()
+  if (requestHost && originHost === requestHost) return true
+
+  const allowlist = (process.env.DOCS_ALLOWED_ORIGINS ?? '')
+    .split(',')
+    .map((value) => value.trim().toLowerCase())
+    .filter(Boolean)
+  return allowlist.includes(originHost)
+}
+
+const SYSTEM_PROMPT = `You are the documentation assistant for Sim — the open-source AI workspace where teams build, deploy, and manage AI agents.
+
+Answer questions about Sim using the documentation. Always call the searchDocs tool before answering anything specific about Sim's features, configuration, or usage — do not answer from memory. Base your answer only on the returned documentation; if the docs do not cover the question, say so plainly rather than guessing.
+
+Guidelines:
+- Be direct and concrete. Lead with the answer, then the detail.
+- Reference the relevant pages by their titles so the user knows where to read more.
+- When you show configuration or code, keep it minimal and correct.
+- The agent is called "Sim" and the chat surface is "Chat" — never say "Mothership" or "copilot".
+- If a question is unrelated to Sim, briefly say it's outside the docs' scope.`
+
+const SEARCH_COLUMNS = {
+  chunkId: docsEmbeddings.chunkId,
+  title: docsEmbeddings.headerText,
+  url: docsEmbeddings.sourceLink,
+  content: docsEmbeddings.chunkText,
+  sourceDocument: docsEmbeddings.sourceDocument,
+}
+
+/** Reciprocal-rank-fusion constant, matching the site search route. */
+const RRF_K = 60
+
+/**
+ * SQL predicate selecting only the locale's documents, so the row limit applies
+ * to matching rows: non-English docs are prefixed with their locale segment;
+ * English is everything not prefixed with another locale.
+ */
+function localeFilter(locale: string) {
+  const firstSegment = sql`split_part(${docsEmbeddings.sourceDocument}, '/', 1)`
+  if (locale === DEFAULT_LOCALE) {
+    const others = KNOWN_LOCALES.filter((l) => l !== DEFAULT_LOCALE)
+    return sql`${firstSegment} not in (${sql.join(
+      others.map((l) => sql`${l}`),
+      sql`, `
+    )})`
+  }
+  return sql`${firstSegment} = ${locale}`
+}
+
+type SearchRow = {
+  chunkId: string
+  title: string
+  url: string
+  content: string
+  sourceDocument: string
+}
+
+/**
+ * Retrieve candidate chunks for grounding, mirroring the site search route's
+ * hybrid strategy: Postgres full-text keyword search for every locale, plus
+ * vector similarity (thresholded) for English — fused by reciprocal rank so a
+ * page found by either signal can ground the answer.
+ */
+async function searchDocs(query: string, locale: string) {
+  const tsConfig = TS_CONFIG[locale] ?? 'simple'
+
+  // Each retrieval path is best-effort and independent: a failure in one still
+  // lets the other ground the answer (both empty just yields no grounding).
+  let keywordRows: SearchRow[] = []
+  try {
+    keywordRows = await db
+      .select(SEARCH_COLUMNS)
+      .from(docsEmbeddings)
+      .where(
+        sql`${docsEmbeddings.chunkTextTsv} @@ plainto_tsquery(${tsConfig}, ${query}) and ${localeFilter(locale)}`
+      )
+      .orderBy(
+        sql`ts_rank(${docsEmbeddings.chunkTextTsv}, plainto_tsquery(${tsConfig}, ${query})) DESC`
+      )
+      .limit(SEARCH_CANDIDATES)
+  } catch (error) {
+    console.error('Ask AI keyword search failed:', error)
+  }
+
+  let vectorRows: SearchRow[] = []
+  if (locale === DEFAULT_LOCALE) {
+    // Vector retrieval (embedding call + pgvector query) is best-effort: if it
+    // fails, fall back to the keyword rows already fetched rather than losing all
+    // grounding for the turn.
+    try {
+      const embedding = await generateSearchEmbedding(query)
+      const vectorLiteral = JSON.stringify(embedding)
+      vectorRows = await db
+        .select(SEARCH_COLUMNS)
+        .from(docsEmbeddings)
+        .where(
+          sql`1 - (${docsEmbeddings.embedding} <=> ${vectorLiteral}::vector) >= ${SIMILARITY_THRESHOLD} and ${localeFilter(locale)}`
+        )
+        .orderBy(sql`${docsEmbeddings.embedding} <=> ${vectorLiteral}::vector`)
+        .limit(SEARCH_CANDIDATES)
+    } catch (error) {
+      console.error('Ask AI vector search failed; using keyword results only:', error)
+    }
+  }
+
+  // Reciprocal rank fusion across the two rankings, deduped by chunk.
+  const scores = new Map<string, number>()
+  const rowById = new Map<string, SearchRow>()
+  for (const list of [vectorRows, keywordRows]) {
+    list.forEach((row, index) => {
+      scores.set(row.chunkId, (scores.get(row.chunkId) ?? 0) + 1 / (RRF_K + index + 1))
+      if (!rowById.has(row.chunkId)) rowById.set(row.chunkId, row)
+    })
+  }
+
+  return [...rowById.values()]
+    .sort((a, b) => (scores.get(b.chunkId) ?? 0) - (scores.get(a.chunkId) ?? 0))
+    .slice(0, SEARCH_LIMIT)
+    .map((row) => ({
+      title: row.title,
+      url: row.url,
+      content: row.content,
+    }))
+}
+
+export async function POST(req: Request) {
+  if (!isAllowedOrigin(req)) {
+    return new Response('Forbidden', { status: 403 })
+  }
+
+  const now = Date.now()
+  sweepRateLimit(now)
+  const retryAfter = rateLimit(getClientIp(req), now)
+  if (retryAfter !== null) {
+    return new Response('Too many requests', {
+      status: 429,
+      headers: { 'Retry-After': String(retryAfter) },
+    })
+  }
+
+  let body: { messages: UIMessage[]; locale?: string }
+  try {
+    body = await req.json()
+  } catch {
+    return new Response('Invalid JSON', { status: 400 })
+  }
+  const { messages } = body
+  const locale = KNOWN_LOCALES.includes(body.locale ?? '')
+    ? (body.locale as string)
+    : DEFAULT_LOCALE
+
+  if (!Array.isArray(messages) || messages.length === 0 || messages.length > MAX_MESSAGES) {
+    return new Response('Invalid request', { status: 400 })
+  }
+  if (!messages.every(isValidMessage)) {
+    return new Response('Invalid request', { status: 400 })
+  }
+  if (userInputChars(messages) > MAX_USER_INPUT_CHARS) {
+    return new Response('Request too large', { status: 413 })
+  }
+
+  const modelMessages = sanitizeMessages(messages)
+  if (modelMessages.length === 0) {
+    return new Response('Invalid request', { status: 400 })
+  }
+  // Bound what actually reaches the model. Measured AFTER sanitization, so the
+  // prior searchDocs tool outputs that accumulate in client history (and are
+  // stripped here) don't count — only user/assistant text the model will see.
+  if (JSON.stringify(modelMessages).length > MAX_TOTAL_CHARS) {
+    return new Response('Request too large', { status: 413 })
+  }
+
+  const result = streamText({
+    model: openai(CHAT_MODEL),
+    system: SYSTEM_PROMPT,
+    messages: convertToModelMessages(modelMessages),
+    stopWhen: stepCountIs(MAX_STEPS),
+    maxOutputTokens: MAX_OUTPUT_TOKENS,
+    tools: {
+      searchDocs: tool({
+        description:
+          'Search the Sim documentation for relevant content. Use this before answering any question about Sim.',
+        inputSchema: z.object({
+          query: z.string().describe('A focused natural-language search query.'),
+        }),
+        execute: async ({ query }) => searchDocs(query, locale),
+      }),
+    },
+  })
+
+  return result.toUIMessageStreamResponse()
+}
diff --git a/apps/docs/components/ai/ask-ai-panel.tsx b/apps/docs/components/ai/ask-ai-panel.tsx
new file mode 100644
index 00000000000..9d41e68d1b8
--- /dev/null
+++ b/apps/docs/components/ai/ask-ai-panel.tsx
@@ -0,0 +1,234 @@
+'use client'
+
+import { type FormEvent, useEffect, useMemo, useRef, useState } from 'react'
+import { useChat } from '@ai-sdk/react'
+import { DefaultChatTransport } from 'ai'
+import { ArrowUp, MessageCircle, Square, X } from 'lucide-react'
+import { Streamdown } from 'streamdown'
+import { cn } from '@/lib/utils'
+import 'streamdown/styles.css'
+
+interface DocSource {
+  title: string
+  url: string
+}
+
+/** Pull the deduped doc sources surfaced by the searchDocs tool out of a message's parts. */
+function getSources(parts: ReadonlyArray<{ type: string; [key: string]: unknown }>): DocSource[] {
+  const seen = new Set<string>()
+  const sources: DocSource[] = []
+
+  for (const part of parts) {
+    if (part.type !== 'tool-searchDocs') continue
+    const output = (part as { output?: unknown }).output
+    if (!Array.isArray(output)) continue
+    for (const item of output as DocSource[]) {
+      if (!item?.url || seen.has(item.url)) continue
+      seen.add(item.url)
+      sources.push({ title: item.title, url: item.url })
+    }
+  }
+
+  return sources
+}
+
+/** Concatenate the streamed text parts of a message. */
+function getText(parts: ReadonlyArray<{ type: string; [key: string]: unknown }>): string {
+  return parts
+    .filter((part) => part.type === 'text')
+    .map((part) => (part as unknown as { text: string }).text)
+    .join('')
+}
+
+interface AskAIPanelProps {
+  /** Active docs locale, forwarded so retrieval is scoped to the reader's language. */
+  locale: string
+  open: boolean
+  onClose: () => void
+}
+
+export function AskAIPanel({ locale, open, onClose }: AskAIPanelProps) {
+  const [input, setInput] = useState('')
+  const scrollRef = useRef<HTMLDivElement>(null)
+  const textareaRef = useRef<HTMLTextAreaElement>(null)
+
+  const transport = useMemo(() => new DefaultChatTransport({ api: '/api/chat' }), [])
+
+  const { messages, sendMessage, status, stop, error } = useChat({ transport })
+
+  const isBusy = status === 'submitted' || status === 'streaming'
+
+  const handleClose = () => {
+    stop()
+    onClose()
+  }
+
+  useEffect(() => {
+    if (!open) return
+    textareaRef.current?.focus()
+    const handleKeyDown = (event: KeyboardEvent) => {
+      if (event.key === 'Escape') {
+        handleClose()
+      }
+    }
+    document.addEventListener('keydown', handleKeyDown)
+    return () => document.removeEventListener('keydown', handleKeyDown)
+  }, [open])
+
+  useEffect(() => {
+    if (!open) return
+    scrollRef.current?.scrollTo({ top: scrollRef.current.scrollHeight })
+  }, [open])
+
+  useEffect(() => {
+    scrollRef.current?.scrollTo({ top: scrollRef.current.scrollHeight, behavior: 'smooth' })
+  }, [messages])
+
+  const handleSubmit = (event: FormEvent) => {
+    event.preventDefault()
+    const text = input.trim()
+    if (!text || isBusy) return
+    sendMessage({ text }, { body: { locale } })
+    setInput('')
+  }
+
+  if (!open) return null
+
+  return (
+    <div
+      role='dialog'
+      aria-label='Ask Sim'
+      className='fixed right-4 bottom-4 z-50 flex h-[600px] max-h-[calc(100vh-2rem)] w-[400px] max-w-[calc(100vw-2rem)] flex-col overflow-hidden rounded-xl border border-[var(--border-1)] bg-[var(--surface-5)] shadow-[var(--shadow-medium)] dark:bg-[var(--surface-4)]'
+    >
+      <div className='flex items-center justify-between border-[var(--border-1)] border-b px-4 py-3'>
+        <span className='flex items-center gap-1.5 font-season text-[var(--text-body)] text-sm'>
+          <MessageCircle className='size-[16px] text-[var(--text-icon)]' />
+          Ask Sim
+        </span>
+        <button
+          type='button'
+          aria-label='Close'
+          onClick={handleClose}
+          className='flex size-7 items-center justify-center rounded-lg text-[var(--text-icon)] transition-colors hover:bg-[var(--surface-active)]'
+        >
+          <X className='size-[16px]' />
+        </button>
+      </div>
+
+      <div ref={scrollRef} className='flex-1 space-y-4 overflow-y-auto px-4 py-4'>
+        {messages.length === 0 && (
+          <p className='text-[var(--text-muted)] text-sm'>
+            Ask anything about building, deploying, and managing AI agents in Sim.
+          </p>
+        )}
+
+        {messages.map((message, index) => {
+          const text = getText(message.parts)
+          const isStreaming = isBusy && index === messages.length - 1
+          const sources = message.role === 'assistant' ? getSources(message.parts) : []
+          return (
+            <div
+              key={message.id}
+              className={cn(
+                'flex flex-col gap-1.5',
+                message.role === 'user' ? 'items-end' : 'items-start'
+              )}
+            >
+              {message.role === 'user' ? (
+                <div className='max-w-[85%] whitespace-pre-wrap rounded-[16px] bg-[var(--surface-5)] px-3 py-2 text-[var(--text-primary)] text-base leading-[23px]'>
+                  {text}
+                </div>
+              ) : (
+                <div className='max-w-full text-[var(--text-primary)] text-base'>
+                  {text ? (
+                    <Streamdown
+                      className={cn(
+                        'space-y-3 text-[var(--text-primary)] text-base leading-relaxed',
+                        '[&_a]:text-[var(--text-primary)] [&_a]:underline [&_a]:decoration-dashed [&_a]:underline-offset-4',
+                        '[&_strong]:font-[600]',
+                        '[&_h1]:font-[600] [&_h2]:font-[600] [&_h3]:font-[600] [&_h4]:font-[600]',
+                        '[&_li]:my-1 [&_ol]:my-3 [&_ol]:list-decimal [&_ol]:pl-5 [&_ul]:my-3 [&_ul]:list-disc [&_ul]:pl-5',
+                        '[&_code]:font-mono [&_pre]:my-3 [&_pre]:overflow-x-auto [&_pre]:rounded-lg [&_pre]:bg-[var(--surface-5)] [&_pre]:p-3 [&_pre]:text-small'
+                      )}
+                    >
+                      {text}
+                    </Streamdown>
+                  ) : isStreaming ? (
+                    '…'
+                  ) : sources.length === 0 ? (
+                    <span className='text-[var(--text-muted)]'>No answer returned.</span>
+                  ) : null}
+                </div>
+              )}
+              {sources.length > 0 && (
+                <div className='flex max-w-[90%] flex-wrap gap-1.5'>
+                  {sources.map((source) => (
+                    <a
+                      key={source.url}
+                      href={source.url}
+                      target='_blank'
+                      rel='noopener noreferrer'
+                      className='rounded-lg border border-[var(--border-1)] px-2 py-0.5 text-[var(--text-muted)] text-xs transition-colors hover:bg-[var(--surface-active)]'
+                    >
+                      {source.title || source.url}
+                    </a>
+                  ))}
+                </div>
+              )}
+            </div>
+          )
+        })}
+
+        {error && (
+          <p className='text-[var(--text-muted)] text-sm'>
+            Something went wrong. Please try again.
+          </p>
+        )}
+      </div>
+
+      <form onSubmit={handleSubmit} className='px-3 pb-3'>
+        <div className='flex items-end gap-2 rounded-2xl border border-[var(--border-1)] bg-white px-2.5 py-1.5 dark:bg-[var(--surface-5)]'>
+          <textarea
+            ref={textareaRef}
+            aria-label='Ask Sim about the docs'
+            value={input}
+            onChange={(event) => setInput(event.target.value)}
+            onKeyDown={(event) => {
+              if (event.key === 'Enter' && !event.shiftKey) {
+                event.preventDefault()
+                handleSubmit(event)
+              }
+            }}
+            rows={1}
+            placeholder='Ask Sim about the docs…'
+            className='max-h-32 flex-1 resize-none bg-transparent py-1 font-season text-[var(--text-body)] text-sm outline-none placeholder:text-[var(--text-muted)]'
+          />
+          {isBusy ? (
+            <button
+              type='button'
+              aria-label='Stop'
+              onClick={() => stop()}
+              className='flex size-[28px] shrink-0 items-center justify-center rounded-full bg-[#383838] transition-colors hover:bg-[#575757] dark:bg-[#e0e0e0] dark:hover:bg-[#cfcfcf]'
+            >
+              <Square className='size-[12px] fill-white text-white dark:fill-black dark:text-black' />
+            </button>
+          ) : (
+            <button
+              type='submit'
+              aria-label='Send'
+              disabled={!input.trim()}
+              className={cn(
+                'flex size-[28px] shrink-0 items-center justify-center rounded-full transition-colors',
+                input.trim()
+                  ? 'bg-[#383838] hover:bg-[#575757] dark:bg-[#e0e0e0] dark:hover:bg-[#cfcfcf]'
+                  : 'bg-[#808080]'
+              )}
+            >
+              <ArrowUp className='size-[16px] text-white dark:text-black' />
+            </button>
+          )}
+        </div>
+      </form>
+    </div>
+  )
+}
diff --git a/apps/docs/components/ai/ask-ai.tsx b/apps/docs/components/ai/ask-ai.tsx
new file mode 100644
index 00000000000..ba3699da21f
--- /dev/null
+++ b/apps/docs/components/ai/ask-ai.tsx
@@ -0,0 +1,57 @@
+'use client'
+
+import { useRef, useState } from 'react'
+import { MessageCircle } from 'lucide-react'
+import dynamic from 'next/dynamic'
+
+const AskAIPanel = dynamic(() => import('./ask-ai-panel').then((m) => m.AskAIPanel), {
+  ssr: false,
+  loading: () => (
+    <div
+      aria-hidden
+      className='fixed right-4 bottom-4 z-50 flex size-11 items-center justify-center rounded-full border border-[var(--border-1)] bg-[var(--surface-5)] shadow-[var(--shadow-medium)] dark:bg-[var(--surface-4)]'
+    >
+      <MessageCircle className='size-[16px] animate-pulse text-[var(--text-icon)]' />
+    </div>
+  ),
+})
+
+interface AskAIProps {
+  /** Active docs locale, forwarded so retrieval is scoped to the reader's language. */
+  locale: string
+}
+
+export function AskAI({ locale }: AskAIProps) {
+  const [open, setOpen] = useState(false)
+  const [hasOpened, setHasOpened] = useState(false)
+  const openButtonRef = useRef<HTMLButtonElement>(null)
+
+  const handleOpen = () => {
+    setHasOpened(true)
+    setOpen(true)
+  }
+
+  const handleClose = () => {
+    setOpen(false)
+    openButtonRef.current?.focus()
+  }
+
+  return (
+    <>
+      {!open && (
+        <button
+          ref={openButtonRef}
+          type='button'
+          aria-label='Ask Sim'
+          onClick={handleOpen}
+          className='fixed right-4 bottom-4 z-50 flex h-11 items-center gap-1.5 rounded-full border border-[var(--border-1)] bg-[var(--surface-5)] px-4 font-season text-[var(--text-body)] text-sm shadow-[var(--shadow-medium)] transition-colors hover:bg-[var(--surface-active)] dark:bg-[var(--surface-4)]'
+        >
+          <MessageCircle className='size-[16px] text-[var(--text-icon)]' />
+          Ask Sim
+        </button>
+      )}
+
+      {hasOpened && <AskAIPanel locale={locale} open={open} onClose={handleClose} />}
+    </>
+  )
+}
diff --git a/apps/docs/package.json b/apps/docs/package.json
index 4f768739e77..41a7490421c 100644
--- a/apps/docs/package.json
+++ b/apps/docs/package.json
@@ -15,10 +15,13 @@
     "format:check": "biome format ."
   },
   "dependencies": {
+    "@ai-sdk/openai": "2.0.107",
+    "@ai-sdk/react": "2.0.205",
     "@sim/db": "workspace:*",
     "@sim/emcn": "workspace:*",
     "@sim/workflow-renderer": "workspace:*",
     "@vercel/og": "^0.6.5",
+    "ai": "5.0.203",
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",
     "drizzle-orm": "^0.45.2",
@@ -34,6 +37,7 @@
     "react-dom": "19.2.4",
     "remark-breaks": "^4.0.0",
     "shiki": "4.3.1",
+    "streamdown": "2.5.0",
     "tailwind-merge": "^3.0.2",
     "reactflow": "^11.11.4",
     "framer-motion": "^12.5.0",
diff --git a/bun.lock b/bun.lock
index 39054de4297..454f3857417 100644
--- a/bun.lock
+++ b/bun.lock
@@ -22,10 +22,13 @@
       "name": "docs",
       "version": "0.0.0",
       "dependencies": {
+        "@ai-sdk/openai": "2.0.107",
+        "@ai-sdk/react": "2.0.205",
         "@sim/db": "workspace:*",
         "@sim/emcn": "workspace:*",
         "@sim/workflow-renderer": "workspace:*",
         "@vercel/og": "^0.6.5",
+        "ai": "5.0.203",
         "class-variance-authority": "^0.7.1",
         "clsx": "^2.1.1",
         "drizzle-orm": "^0.45.2",
@@ -43,6 +46,7 @@
         "reactflow": "^11.11.4",
         "remark-breaks": "^4.0.0",
         "shiki": "4.3.1",
+        "streamdown": "2.5.0",
         "tailwind-merge": "^3.0.2",
         "zod": "^4.3.6",
       },
@@ -631,6 +635,8 @@
 
     "@ai-sdk/provider-utils": ["@ai-sdk/provider-utils@3.0.26", "", { "dependencies": { "@ai-sdk/provider": "2.0.3", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-dNciNI4knep6z3cqDNng7yORCcBnEDBHZYj8rJLcLn9pLzEtNVkf6WLg9HR6AnVDDRxHsUeGAEqyF8M+FAtRgg=="],
 
+    "@ai-sdk/react": ["@ai-sdk/react@2.0.205", "", { "dependencies": { "@ai-sdk/provider-utils": "3.0.26", "ai": "5.0.203", "swr": "^2.2.5", "throttleit": "2.1.0" }, "peerDependencies": { "react": "^18 || ~19.0.1 || ~19.1.2 || ^19.2.1", "zod": "^3.25.76 || ^4.1.8" }, "optionalPeers": ["zod"] }, "sha512-9EmBl2wa2lqbYoSImdAwmp1Wa9r7suJdEOWdfnRPlDSBLNYo2BfwD0MGSULYLlX9T7nz8Vfo1+EwvwctdN1HNQ=="],
+
     "@ai-sdk/togetherai": ["@ai-sdk/togetherai@1.0.43", "", { "dependencies": { "@ai-sdk/openai-compatible": "1.0.40", "@ai-sdk/provider": "2.0.3", "@ai-sdk/provider-utils": "3.0.26" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-FwcKwNKSjkCc80yD7JFtHOXxroTpFD/FF84jfiGlRGTfv2rQpv3Pto9h/NcKjmDXXr/H01Rzo/TodrsLXq0B8g=="],
 
     "@ai-sdk/xai": ["@ai-sdk/xai@2.0.74", "", { "dependencies": { "@ai-sdk/openai-compatible": "1.0.40", "@ai-sdk/provider": "2.0.3", "@ai-sdk/provider-utils": "3.0.26" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-Ff24f/G13EjKKw411XQh08/owBorFvaLf3LEuDvDnDz4XePpkYN6oXdyHpZCDrvUD0/+7hbwaGwTXfm5nmqPGQ=="],
@@ -3855,6 +3861,8 @@
 
     "svix": ["svix@1.88.0", "", { "dependencies": { "standardwebhooks": "1.0.0", "uuid": "^10.0.0" } }, "sha512-vm/JrrUd3bVyBE+3L33TIyVSs8gS5fYx7lrISvKlDJXTYX1ACH4REX8P1tHxsSKoZi/rvifM1t0XRc5Vc45THw=="],
 
+    "swr": ["swr@2.4.2", "", { "dependencies": { "dequal": "^2.0.3", "use-sync-external-store": "^1.6.0" }, "peerDependencies": { "react": "^16.11.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" } }, "sha512-ej644Y2bvkIajfR32KGeSSdBXQW+ScjGjkybZgSE7kFpk9eGnV44XY9FJylXi+W75pavSX1PVNB57W5EbhGIYw=="],
+
     "symbol-tree": ["symbol-tree@3.2.4", "", {}, "sha512-9QNk5KwDF+Bvz+PyObkmSYjI5ksVUYtjW7AU22r2NKcfLJcXp96hkDWU3+XndOsUb+AQ9QhfzfCT2O+CNWT5Tw=="],
 
     "systeminformation": ["systeminformation@5.23.8", "", { "os": "!aix", "bin": { "systeminformation": "lib/cli.js" } }, "sha512-Osd24mNKe6jr/YoXLLK3k8TMdzaxDffhpCxgkfgBHcapykIkd50HXThM3TCEuHO2pPuCsSx2ms/SunqhU5MmsQ=="],
@@ -3883,6 +3891,8 @@
 
     "three": ["three@0.177.0", "", {}, "sha512-EiXv5/qWAaGI+Vz2A+JfavwYCMdGjxVsrn3oBwllUoqYeaBO75J63ZfyaQKoiLrqNHoTlUc6PFgMXnS0kI45zg=="],
 
+    "throttleit": ["throttleit@2.1.0", "", {}, "sha512-nt6AMGKW1p/70DF/hGBdJB57B8Tspmbp5gfJ8ilhLnt7kkr2ye7hzD6NVG8GGErk2HWF34igrL2CXmNIkzKqKw=="],
+
     "through": ["through@2.3.8", "", {}, "sha512-w89qg7PI8wAdvX60bMDP+bFoD5Dvhm9oLheFp5O4a2QF0cSBGsBX4qZmadPMvVqlLJBBci+WqGGOAPvcDeNSVg=="],
 
     "tiny-inflate": ["tiny-inflate@1.0.3", "", {}, "sha512-pkY1fj1cKHb2seWDy0B16HeWyczlJA9/WW3u3c4z/NiWDsO3DOU5D7nhTLE9CF0yXv/QZFY7sEJmj24dK+Rrqw=="],

From cceea10de11313e764293fe7cc285884054d9b7c Mon Sep 17 00:00:00 2001
From: Waleed Latif <walif6@gmail.com>
Date: Tue, 14 Jul 2026 19:30:52 -0700
Subject: [PATCH 4/4] fix(content): correct stale/mislabeled competitor facts
 surfaced in FAQ audit
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- n8n's license is "fair-code" (n8n's own coined term), not "fair-use" —
  a different legal concept entirely; fixed 4 occurrences
- Activepieces' integration count is 750+ per their live catalog, not the
  280+ figure carried over from an older snapshot; fixed 4 occurrences
  across best-zapier-alternatives and n8n-alternatives
---
 apps/sim/content/library/best-zapier-alternatives/index.mdx | 4 ++--
 apps/sim/content/library/n8n-alternatives/index.mdx         | 4 ++--
 apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx     | 6 +++---
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/apps/sim/content/library/best-zapier-alternatives/index.mdx b/apps/sim/content/library/best-zapier-alternatives/index.mdx
index e0003932e94..da1bb30a4d5 100644
--- a/apps/sim/content/library/best-zapier-alternatives/index.mdx
+++ b/apps/sim/content/library/best-zapier-alternatives/index.mdx
@@ -46,7 +46,7 @@ Whether you're hitting the complexity ceiling, need self-hosting for compliance,
 
 - **Workato:** Enterprise-grade integration with strong governance, audit logging, and support SLAs. Priced accordingly, starting around $1,000/month.
 
-- **Activepieces:** Open-source with an MIT license and a cleaner, more approachable UI than n8n. Growing integration library (280+) with a free self-hosted tier.
+- **Activepieces:** Open-source with an MIT license and a cleaner, more approachable UI than n8n. Growing integration library (750+) with a free self-hosted tier.
 
 - **Microsoft Power Automate:** Deep native integration with Microsoft 365, Azure, and Dynamics 365, often already included in your existing license. Best for organizations standardized on the Microsoft stack.
 
@@ -144,7 +144,7 @@ Activepieces occupies a useful niche: open-source automation that doesn't requir
 
 The interface is cleaner and more approachable than n8n's, which makes Activepieces the better open-source pick for teams that want the benefits of open source (data control, no vendor lock-in, community-driven development) without the technical overhead of managing a self-hosted n8n instance.
 
-The integration library is growing but still smaller than the established players, with roughly 280+ pieces as of 2026. That's a trade-off worth acknowledging upfront: if your workflow depends on a niche app, check compatibility before committing. For common SaaS tools (Slack, Google Workspace, Notion, GitHub, CRMs), coverage is solid and expanding steadily.
+The integration library is growing but still smaller than the established players, with roughly 750+ pieces as of 2026. That's a trade-off worth acknowledging upfront: if your workflow depends on a niche app, check compatibility before committing. For common SaaS tools (Slack, Google Workspace, Notion, GitHub, CRMs), coverage is solid and expanding steadily.
 
 **Best for:** Teams that want open-source flexibility with a friendlier interface than n8n, especially non-technical users who value data control but don't want to manage complex server infrastructure.
 
diff --git a/apps/sim/content/library/n8n-alternatives/index.mdx b/apps/sim/content/library/n8n-alternatives/index.mdx
index 2f3c07e3653..26067b4f59f 100644
--- a/apps/sim/content/library/n8n-alternatives/index.mdx
+++ b/apps/sim/content/library/n8n-alternatives/index.mdx
@@ -19,7 +19,7 @@ faq:
   - q: "Is Make better than n8n?"
     a: "Neither is universally better. Make wins on debugging experience, accessibility for non-developers, and managed infrastructure that eliminates self-hosting overhead. n8n wins on extensibility, developer control, self-hosting flexibility, and community ecosystem depth. If your team is primarily non-technical and values visual debugging, Make is the better fit. If your team is developer-heavy and values code-level customization, n8n still has the edge."
   - q: "What is the best open-source n8n alternative?"
-    a: "Activepieces offers the cleanest MIT license with a growing integration library of 280+ pieces and active community contribution. Sim provides open-source AI agent workflows with Docker and Kubernetes deployment for teams that need agentic capabilities. Node-RED remains the standard for IoT and hardware automation contexts where n8n's web-focused architecture is overkill. Match the tool to your use case rather than defaulting to the one with the most GitHub stars."
+    a: "Activepieces offers the cleanest MIT license with a growing integration library of 750+ pieces and active community contribution. Sim provides open-source AI agent workflows with Docker and Kubernetes deployment for teams that need agentic capabilities. Node-RED remains the standard for IoT and hardware automation contexts where n8n's web-focused architecture is overkill. Match the tool to your use case rather than defaulting to the one with the most GitHub stars."
   - q: "How hard is it to migrate from n8n to another tool?"
     a: "It depends on which tool you are migrating to. Moving to a platform with a similar trigger-action paradigm (like Activepieces) is the closest format match and typically takes a few weeks of focused engineering for a mid-size portfolio. Before committing, calculate the full cost of migration, including potential productivity dips during transition. For most teams, the investment pays off within two to three quarters if the new platform eliminates major pain points and bottlenecks."
 ---
@@ -142,7 +142,7 @@ Activepieces is the cleanest open-source alternative to n8n if licensing terms m
 **Strengths:**
 
 - **MIT license with no strings attached:** The codebase lives on GitHub under the MIT license, the most permissive of open-source licenses, giving organizations complete freedom to self-host, modify, fork, and extend the platform without any licensing fees or usage restrictions.
-- **Growing integration and MCP ecosystem:** All 280+ pieces are available as MCP servers that you can use with LLMs on Claude Desktop, Cursor, or Windsurf. The community contributes actively, with 60% of the pieces contributed by the community.
+- **Growing integration and MCP ecosystem:** All 750+ pieces are available as MCP servers that you can use with LLMs on Claude Desktop, Cursor, or Windsurf. The community contributes actively, with 60% of the pieces contributed by the community.
 - **Clean, accessible UI:** Activepieces has the most polished UI of any open-source automation platform, making it accessible to non-technical users while still supporting code steps for custom logic.
 - **Flat-rate cloud pricing:** Cloud pricing starts free with 10 free active flows, then $5 per active flow per month with unlimited runs. No per-execution billing surprises.
 
diff --git a/apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx b/apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx
index 514f8c9047f..d25bbaaf1de 100644
--- a/apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx
+++ b/apps/sim/content/library/openai-vs-n8n-vs-sim/index.mdx
@@ -15,7 +15,7 @@ faq:
   - q: "Can OpenAI AgentKit run models from providers other than OpenAI?"
     a: "No. AgentKit only runs OpenAI models, while Sim connects to multiple AI providers including OpenAI, Anthropic, Google, Groq, Cerebras, and local Ollama models."
   - q: "Is n8n fully open source?"
-    a: "No. n8n is fair-use licensed with some restrictions, not fully open source. Sim, by contrast, is Apache 2.0 licensed and fully open source."
+    a: "No. n8n is fair-code licensed with some restrictions, not fully open source. Sim, by contrast, is Apache 2.0 licensed and fully open source."
   - q: "Does OpenAI AgentKit provide execution logs for debugging agents in production?"
     a: "No, AgentKit has no execution logs or detailed monitoring for debugging and observability. Sim provides enterprise-grade logging with execution IDs, block-level timing, token/cost tracking, and error traces."
   - q: "Can n8n workflows be built programmatically instead of through the visual builder?"
@@ -116,7 +116,7 @@ n8n is best suited for:
 While n8n is excellent for traditional automation, it has some limitations for AI agent development:
 
 - No SDK to build workflows programmatically—limited to visual builder only
-- Not fully open source but fair-use licensed, with some restrictions
+- Not fully open source but fair-code licensed, with some restrictions
 - Free trial limited to 14 days, after which paid plans are required
 - Limited/complex parallel execution handling
 
@@ -204,7 +204,7 @@ While all three platforms enable AI agent development, they excel in different a
 
 **Best for:** Traditional workflow automation with some AI enhancement. Excellent when your primary need is connecting business tools and services, with AI as a complementary feature rather than the core focus.
 
-**Limitations:** No SDK for programmatic workflow building, fair-use licensing (not fully open source), 14-day trial limitation, and AI agent capabilities are newer and less mature compared to its traditional automation features.
+**Limitations:** No SDK for programmatic workflow building, fair-code licensing (not fully open source), 14-day trial limitation, and AI agent capabilities are newer and less mature compared to its traditional automation features.
 
 ### Sim