[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-tanstack-ai-coremedia-generation":3,"mdc--bdyse5-key":59,"related-org-tanstack-ai-coremedia-generation":15083,"related-repo-tanstack-ai-coremedia-generation":15227},{"slug":4,"name":5,"fn":6,"description":7,"org":8,"tags":12,"stars":30,"repoUrl":31,"updatedAt":32,"license":33,"forks":34,"topics":35,"repo":54,"sourceUrl":57,"mdContent":58},"ai-coremedia-generation","ai-core\u002Fmedia-generation","generate media and speech with AI","Image, audio, video, speech (TTS), and transcription generation using activity-specific adapters: generateImage() with openaiImage\u002FgeminiImage, generateAudio() with geminiAudio\u002FfalAudio, generateVideo() with async polling (openaiVideo\u002FgeminiVideo\u002FgrokVideo\u002FfalVideo, per-model typed durations), generateSpeech() with openaiSpeech, generateTranscription() with openaiTranscription. React hooks: useGenerateImage, useGenerateAudio, useGenerateSpeech, useTranscription, useGenerateVideo. TanStack Start server function integration with toServerSentEventsResponse.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},"tanstack","TanStack","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Ftanstack.png",[13,17,20,23,26,27],{"name":14,"slug":15,"type":16},"Images","images","tag",{"name":18,"slug":19,"type":16},"AI SDK","ai-sdk",{"name":21,"slug":22,"type":16},"Audio","audio",{"name":24,"slug":25,"type":16},"Video","video",{"name":10,"slug":9,"type":16},{"name":28,"slug":29,"type":16},"Speech","speech",2884,"https:\u002F\u002Fgithub.com\u002FTanStack\u002Fai","2026-07-16T06:04:17.479185",null,269,[36,37,19,38,39,40,41,42,43,44,45,46,47,48,49,9,50,51,52,53],"ai","ai-agents","anthropic","chatbot","function-calling","gemini","generative-ai","llm","multimodal","openai","react","solidjs","streaming","svelte","tool-calling","typescript","typescript-sdk","vue",{"repoUrl":31,"stars":30,"forks":34,"topics":55,"description":56},[36,37,19,38,39,40,41,42,43,44,45,46,47,48,49,9,50,51,52,53],"🤖 Type-safe, provider-agnostic TypeScript AI SDK for streaming chat, tool calling, agents, and multimodal apps across OpenAI, Anthropic, Gemini, React, Vue, Svelte, and Solid.","https:\u002F\u002Fgithub.com\u002FTanStack\u002Fai\u002Ftree\u002FHEAD\u002Fpackages\u002Fai\u002Fskills\u002Fai-core\u002Fmedia-generation","---\nname: ai-core\u002Fmedia-generation\ndescription: >\n Image, audio, video, speech (TTS), and transcription generation using\n activity-specific adapters: generateImage() with openaiImage\u002FgeminiImage,\n generateAudio() with geminiAudio\u002FfalAudio, generateVideo() with async\n polling (openaiVideo\u002FgeminiVideo\u002FgrokVideo\u002FfalVideo, per-model typed\n durations), generateSpeech() with openaiSpeech, generateTranscription()\n with openaiTranscription. React hooks: useGenerateImage, useGenerateAudio,\n useGenerateSpeech, useTranscription, useGenerateVideo.\n TanStack Start server function integration with toServerSentEventsResponse.\ntype: sub-skill\nlibrary: tanstack-ai\nlibrary_version: '0.10.0'\nsources:\n - 'TanStack\u002Fai:docs\u002Fmedia\u002Fgenerations.md'\n - 'TanStack\u002Fai:docs\u002Fmedia\u002Fgeneration-hooks.md'\n - 'TanStack\u002Fai:docs\u002Fmedia\u002Fimage-generation.md'\n - 'TanStack\u002Fai:docs\u002Fmedia\u002Faudio-generation.md'\n - 'TanStack\u002Fai:docs\u002Fmedia\u002Fvideo-generation.md'\n - 'TanStack\u002Fai:docs\u002Fmedia\u002Ftext-to-speech.md'\n - 'TanStack\u002Fai:docs\u002Fmedia\u002Ftranscription.md'\n - 'TanStack\u002Fai:docs\u002Fadvanced\u002Fdebug-logging.md'\n---\n\n# Media Generation\n\n> **Dependency note:** This skill builds on ai-core. Read it first for critical rules.\n\nAll media activities (image, speech, transcription, video) follow the same\nserver\u002Fclient architecture: a `generate*()` function on the server, an SSE\ntransport via `toServerSentEventsResponse()`, and a framework hook on the\nclient.\n\n## Setup -- Image Generation End-to-End\n\n### Server (API route or TanStack Start server function)\n\n```typescript\n\u002F\u002F routes\u002Fapi\u002Fgenerate\u002Fimage.ts\nimport { generateImage, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { openaiImage } from '@tanstack\u002Fai-openai'\n\nexport async function POST(req: Request) {\n const { prompt, size, numberOfImages } = await req.json()\n\n const stream = generateImage({\n adapter: openaiImage('gpt-image-1'),\n prompt,\n size,\n numberOfImages,\n stream: true,\n })\n\n return toServerSentEventsResponse(stream)\n}\n```\n\n### Client (React)\n\n```tsx\nimport { useGenerateImage, fetchServerSentEvents } from '@tanstack\u002Fai-react'\nimport { useState } from 'react'\n\nfunction ImageGenerator() {\n const [prompt, setPrompt] = useState('')\n const { generate, result, isLoading, error, reset } = useGenerateImage({\n connection: fetchServerSentEvents('\u002Fapi\u002Fgenerate\u002Fimage'),\n })\n\n return (\n \u003Cdiv>\n \u003Cinput\n value={prompt}\n onChange={(e) => setPrompt(e.target.value)}\n placeholder=\"Describe an image...\"\n \u002F>\n \u003Cbutton\n onClick={() => generate({ prompt })}\n disabled={isLoading || !prompt.trim()}\n >\n {isLoading ? 'Generating...' : 'Generate'}\n \u003C\u002Fbutton>\n\n {error && \u003Cp>Error: {error.message}\u003C\u002Fp>}\n\n {result?.images.map((img, i) => (\n \u003Cimg\n key={i}\n src={img.url || `data:image\u002Fpng;base64,${img.b64Json}`}\n alt={img.revisedPrompt || 'Generated image'}\n \u002F>\n ))}\n\n {result && \u003Cbutton onClick={reset}>Clear\u003C\u002Fbutton>}\n \u003C\u002Fdiv>\n )\n}\n```\n\n### TanStack Start: Server Function Streaming (recommended)\n\nWhen using TanStack Start, return `toServerSentEventsResponse()` from a\nserver function. The client fetcher receives a `Response` and the hook\nparses it as SSE automatically:\n\n```typescript\n\u002F\u002F lib\u002Fserver-functions.ts\nimport { createServerFn } from '@tanstack\u002Freact-start'\nimport { generateImage, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { openaiImage } from '@tanstack\u002Fai-openai'\n\nexport const generateImageStreamFn = createServerFn({ method: 'POST' })\n .inputValidator((data: { prompt: string; model?: string }) => data)\n .handler(({ data }) => {\n return toServerSentEventsResponse(\n generateImage({\n adapter: openaiImage(data.model ?? 'gpt-image-1'),\n prompt: data.prompt,\n stream: true,\n }),\n )\n })\n```\n\n```tsx\nimport { useGenerateImage } from '@tanstack\u002Fai-react'\nimport { generateImageStreamFn } from '..\u002Flib\u002Fserver-functions'\n\nfunction ImageGenerator() {\n const { generate, result, isLoading } = useGenerateImage({\n fetcher: (input) => generateImageStreamFn({ data: input }),\n })\n\n return (\n \u003Cbutton\n onClick={() => generate({ prompt: 'A sunset over mountains' })}\n disabled={isLoading}\n >\n {isLoading ? 'Generating...' : 'Generate'}\n \u003C\u002Fbutton>\n )\n}\n```\n\n---\n\n## Core Patterns\n\n### 1. Image Generation\n\nSupported adapters: `openaiImage` (dall-e-2, dall-e-3, gpt-image-1,\ngpt-image-1-mini, gpt-image-2) and `geminiImage` (gemini-3.1-flash-image-preview,\ngemini-3.1-flash-lite-image, imagen-4.0-generate-001, etc.).\n\n```typescript\nimport { generateImage } from '@tanstack\u002Fai'\nimport { openaiImage } from '@tanstack\u002Fai-openai'\nimport { geminiImage } from '@tanstack\u002Fai-gemini'\n\n\u002F\u002F OpenAI with quality\u002Fbackground options\nconst openaiResult = await generateImage({\n adapter: openaiImage('gpt-image-1'),\n prompt: 'A cat wearing a hat',\n size: '1024x1024',\n numberOfImages: 2,\n modelOptions: {\n quality: 'high',\n background: 'transparent',\n outputFormat: 'png',\n },\n})\n\n\u002F\u002F Gemini native model with aspect-ratio sizes\nconst geminiResult = await generateImage({\n adapter: geminiImage('gemini-3.1-flash-image-preview'),\n prompt: 'A futuristic cityscape at night',\n size: '16:9_4K',\n})\n\n\u002F\u002F Gemini Imagen model\nconst imagenResult = await generateImage({\n adapter: geminiImage('imagen-4.0-generate-001'),\n prompt: 'A landscape photo',\n modelOptions: { aspectRatio: '16:9' },\n})\n```\n\nResult shape: `ImageGenerationResult` with `images` array where each entry\nhas `b64Json?`, `url?`, and `revisedPrompt?`. OpenAI image URLs expire\nafter 1 hour -- download or display immediately.\n\n#### Image-conditioned generation: multimodal `prompt` parts\n\nBoth `generateImage()` and `generateVideo()` accept the `prompt` either as\na plain string or as an ordered array of content parts (`TextPart` \u002F\n`ImagePart` \u002F `VideoPart` \u002F `AudioPart` — the same shapes used elsewhere in\nTanStack AI). Part order is meaningful: natively multimodal providers\n(Gemini, OpenRouter) receive parts in order; named-field providers (OpenAI,\nfal, xAI) extract media parts and flatten the text. Prompt text is always\nsent verbatim — to reference inputs from the prompt, write the provider's\nown syntax (fal `@Image1`, OpenAI \"image 1\" prose); the SDK never injects\nor rewrites markers. Each media part may carry an optional\n`metadata.role` hint that adapters use to route the part to the\nprovider-specific field. The accepted part types are narrowed per model at\ncompile time via the adapter's input-modality map.\n\n```typescript\nimport { generateImage } from '@tanstack\u002Fai'\nimport { openaiImage } from '@tanstack\u002Fai-openai'\n\n\u002F\u002F Image-to-image (OpenAI gpt-image-2 \u002F gpt-image-1, dall-e-2)\nawait generateImage({\n adapter: openaiImage('gpt-image-2'),\n prompt: [\n { type: 'text', content: 'Turn this into a cinematic product photo' },\n { type: 'image', source: { type: 'url', value: 'https:\u002F\u002F…\u002Fproduct.png' } },\n ],\n})\n\n\u002F\u002F Multi-reference (up to 16 for gpt-image models; up to ~14 for Gemini native\n\u002F\u002F — a provider limit, not enforced by the SDK)\nawait generateImage({\n adapter: openaiImage('gpt-image-2'),\n prompt: [\n { type: 'text', content: 'Apply the second image as style to the first' },\n { type: 'image', source: { type: 'url', value: 'https:\u002F\u002F…\u002Fproduct.png' } },\n { type: 'image', source: { type: 'url', value: 'https:\u002F\u002F…\u002Fstyle.png' } },\n ],\n})\n\n\u002F\u002F Inpaint via metadata.role === 'mask' (OpenAI gpt-image models, dall-e-2; fal mask_url)\nawait generateImage({\n adapter: openaiImage('gpt-image-2'),\n prompt: [\n { type: 'text', content: 'Replace the masked region with a tree' },\n { type: 'image', source: { type: 'url', value: photoUrl } },\n {\n type: 'image',\n source: { type: 'url', value: maskUrl },\n metadata: { role: 'mask' },\n },\n ],\n})\n\n\u002F\u002F Image-to-video (OpenAI Sora: single input_reference; fal: image_url + optional end_image_url)\nimport { generateVideo } from '@tanstack\u002Fai'\nimport { falVideo } from '@tanstack\u002Fai-fal'\n\nawait generateVideo({\n adapter: falVideo('fal-ai\u002Fkling-video\u002Fv3\u002Fpro\u002Fimage-to-video'),\n prompt: [\n { type: 'image', source: { type: 'url', value: firstFrameUrl } },\n { type: 'text', content: 'Slow cinematic push-in' },\n {\n type: 'image',\n source: { type: 'url', value: lastFrameUrl },\n metadata: { role: 'end_frame' },\n },\n ],\n})\n```\n\n**URL inputs that require an upload throw by default.** Most adapters pass a\n`type: 'url'` source straight through to the provider. Three paths can't —\nOpenAI `images.edit()`, OpenAI Sora `input_reference`, and Gemini **Veo** —\nbecause the provider only accepts uploaded bytes (Veo also takes a `gs:\u002F\u002F`\nreference). For those, an HTTP(S) URL would have to be downloaded and buffered\nin memory, which can OOM constrained runtimes, so they **throw** on an HTTP(S)\nURL image input by default. Pass a `data:` URI (or `gs:\u002F\u002F` for Veo), or opt in\nwith `allowUrlFetch: true` on the adapter config\n(`createOpenaiImage(model, apiKey, { allowUrlFetch: true })`, and likewise on\n`createOpenaiVideo` \u002F `createGeminiVideo`). `data:` URIs never need the flag.\n\n**Role hints** (`metadata.role`):\n\n| Role | Maps to |\n| --------------- | ----------------------------------------------------------------------------------------------------- |\n| `'reference'` | fal `reference_image_urls`; Gemini multimodal part; positional otherwise |\n| `'character'` | Same as `'reference'`; Veo `referenceImages` slot (planned — no Veo adapter yet) |\n| `'mask'` | OpenAI `mask` (gpt-image-2, gpt-image-1, dall-e-2); fal `mask_url` |\n| `'control'` | fal `control_image_url` (ControlNet \u002F depth \u002F pose) |\n| `'start_frame'` | fal `start_image_url` (or the endpoint's field, e.g. `image_url` on Kling i2v); Veo `image` (planned) |\n| `'end_frame'` | fal `end_image_url` (or e.g. `tail_image_url` \u002F `last_frame_url`); Veo `lastFrame` (planned) |\n\n**Provider support matrix:**\n\n| Provider | `generateImage` image parts | `generateVideo` image parts |\n| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| OpenAI | gpt-image-2 \u002F gpt-image-1 \u002F -mini → `images.edit()` (up to 16). dall-e-2 → edit (1). dall-e-3 throws. | Sora-2 \u002F -pro → `input_reference` (single). Throws if >1. |\n| Gemini | Native (gemini-\\*-flash-image, \"nano-banana\") → multimodal `contents`. Imagen throws. | No native Veo adapter yet — deferred to a follow-up. |\n| fal | Per-endpoint field names from a generated map (`pnpm generate:fal-image-fields`). Defaults: 1 input → `image_url`; >1 → `image_urls`; roles → `mask_url` \u002F `control_image_url` \u002F `reference_image_urls`. | Per-endpoint map (e.g. Kling i2v start frame → `image_url`). Defaults: 1 input → `image_url`; `start_frame`\u002F`end_frame` → `start_image_url`\u002F`end_image_url`; `reference` → `reference_image_urls`. |\n| Grok | grok-imagine models → `\u002Fv1\u002Fimages\u002Fedits` JSON endpoint (≤3 sources, addressed by xAI in request order; prompt sent verbatim; mask\u002Fcontrol throw). grok-2-image-1212 throws. | n\u002Fa |\n| OpenRouter | Prompt parts map 1:1 onto multimodal `text` \u002F `image_url` content parts, preserving interleaved order. | n\u002Fa |\n| Anthropic | n\u002Fa (no image generation API). | n\u002Fa |\n\nVideo and audio prompt parts follow the same `metadata.role` convention\nfor video-to-video and lipsync flows on fal; other providers throw when\nthey're passed.\n\n### 2. Audio Generation (Music, Sound Effects)\n\nDistinct from TTS — `generateAudio()` produces non-speech audio content.\nSupported adapters: `geminiAudio` (Lyria 3 Pro \u002F Lyria 3 Clip) and\n`falAudio` (MiniMax Music, DiffRhythm, Stable Audio, ElevenLabs SFX, etc.).\n\n```typescript\nimport { generateAudio } from '@tanstack\u002Fai'\nimport { falAudio } from '@tanstack\u002Fai-fal'\n\nconst result = await generateAudio({\n adapter: falAudio('fal-ai\u002Fdiffrhythm'),\n prompt: 'An upbeat electronic track with synths',\n duration: 10,\n})\n\n\u002F\u002F result.audio.url or result.audio.b64Json (provider-dependent)\n\u002F\u002F result.audio.contentType e.g. \"audio\u002Fmpeg\"\n```\n\nClient hook:\n\n```tsx\nimport { useGenerateAudio, fetchServerSentEvents } from '@tanstack\u002Fai-react'\n\nconst { generate, result, isLoading } = useGenerateAudio({\n connection: fetchServerSentEvents('\u002Fapi\u002Fgenerate\u002Faudio'),\n})\n\n\u002F\u002F Trigger: generate({ prompt: 'Upbeat synths', duration: 10 })\n\u002F\u002F Play: \u003Caudio src={result.audio.url} controls \u002F>\n```\n\n### 3. Text-to-Speech\n\nAdapter: `openaiSpeech` (tts-1, tts-1-hd, gpt-4o-audio-preview).\n\n```typescript\nimport { generateSpeech } from '@tanstack\u002Fai'\nimport { openaiSpeech } from '@tanstack\u002Fai-openai'\n\nconst result = await generateSpeech({\n adapter: openaiSpeech('tts-1-hd'),\n text: 'Hello, welcome to TanStack AI!',\n voice: 'alloy', \u002F\u002F alloy | echo | fable | onyx | nova | shimmer | ash | ballad | coral | sage | verse\n format: 'mp3', \u002F\u002F mp3 | opus | aac | flac | wav | pcm\n speed: 1.0, \u002F\u002F 0.25 to 4.0\n})\n\n\u002F\u002F result.audio is base64-encoded audio\n\u002F\u002F result.format is the output format string\n\u002F\u002F result.contentType is the MIME type (e.g. \"audio\u002Fmpeg\")\n```\n\nClient hook:\n\n```tsx\nimport { useGenerateSpeech, fetchServerSentEvents } from '@tanstack\u002Fai-react'\n\nconst { generate, result, isLoading } = useGenerateSpeech({\n connection: fetchServerSentEvents('\u002Fapi\u002Fgenerate\u002Fspeech'),\n})\n\n\u002F\u002F Trigger: generate({ text: 'Hello!', voice: 'alloy' })\n\u002F\u002F Play: \u003Caudio src={`data:audio\u002F${result.format};base64,${result.audio}`} controls \u002F>\n```\n\n### 4. Audio Transcription\n\nAdapter: `openaiTranscription` (whisper-1, gpt-4o-transcribe,\ngpt-4o-mini-transcribe, gpt-4o-transcribe-diarize).\n\n> **Capturing audio in the browser:** Use `useAudioRecorder` from `@tanstack\u002Fai-react` to record directly in the browser, then pass the recording as the `audio` input to `generate()`, or use `recording.part` as a prompt part in chat\u002Fgeneration calls. No transcoding or extra dependencies required — the recorder returns the native browser format (`audio\u002Fwebm` or `audio\u002Fmp4`). For transcription, wrap it as a `data:` URL so the provider gets the real content type; passing raw `recording.base64` makes the adapter assume `audio\u002Fmpeg` and mislabel the webm\u002Fmp4 bytes.\n>\n> ```typescript\n> const { isRecording, start, stop } = useAudioRecorder()\n> const { generate } = useTranscription({\n> connection: fetchServerSentEvents('\u002Fapi\u002Ftranscribe'),\n> })\n> \u002F\u002F ...\n> const recording = await stop()\n> const mimeType = recording.mimeType.split(';')[0] \u002F\u002F strip ;codecs=...\n> await generate({ audio: `data:${mimeType};base64,${recording.base64}` })\n> ```\n\n```typescript\nimport { generateTranscription } from '@tanstack\u002Fai'\nimport { openaiTranscription } from '@tanstack\u002Fai-openai'\n\nconst result = await generateTranscription({\n adapter: openaiTranscription('whisper-1'),\n audio: audioFile, \u002F\u002F File, Blob, base64 string, or data URL\n language: 'en',\n responseFormat: 'verbose_json',\n modelOptions: {\n timestamp_granularities: ['word', 'segment'],\n },\n})\n\n\u002F\u002F result.text -- full transcribed text\n\u002F\u002F result.language -- detected\u002Fspecified language\n\u002F\u002F result.duration -- audio duration in seconds\n\u002F\u002F result.segments -- timestamped segments (word-level timestamps are in result.words)\n```\n\nFor speaker diarization, use `openaiTranscription('gpt-4o-transcribe-diarize')`.\nWhen no response format is given it defaults the request to `response_format: 'diarized_json'`\nand `chunking_strategy: 'auto'` (a top-level `responseFormat` of `'json'`\u002F`'text'` opts out of\nspeaker segments); do not pass `prompt`, `include`, or `timestamp_granularities` with this model.\n\nClient hook:\n\n```tsx\nimport { useTranscription, fetchServerSentEvents } from '@tanstack\u002Fai-react'\n\nconst { generate, result, isLoading } = useTranscription({\n connection: fetchServerSentEvents('\u002Fapi\u002Ftranscribe'),\n})\n\n\u002F\u002F Trigger: generate({ audio: dataUrl, language: 'en' })\n```\n\n### 5. Video Generation (Experimental -- async polling)\n\nVideo generation uses a jobs\u002Fpolling architecture. The server creates a job,\npolls for status, and streams updates to the client.\n\n```typescript\nimport {\n generateVideo,\n getVideoJobStatus,\n toServerSentEventsResponse,\n} from '@tanstack\u002Fai'\nimport { openaiVideo } from '@tanstack\u002Fai-openai'\n\n\u002F\u002F Non-streaming: manual polling loop\nconst { jobId } = await generateVideo({\n adapter: openaiVideo('sora-2'),\n prompt: 'A golden retriever playing in sunflowers',\n size: '1280x720',\n duration: 8,\n})\n\nlet status = await getVideoJobStatus({ adapter: openaiVideo('sora-2'), jobId })\nwhile (status.status !== 'completed' && status.status !== 'failed') {\n await new Promise((r) => setTimeout(r, 5000))\n status = await getVideoJobStatus({ adapter: openaiVideo('sora-2'), jobId })\n}\n\n\u002F\u002F Streaming: server handles polling, client gets real-time updates\nconst stream = generateVideo({\n adapter: openaiVideo('sora-2'),\n prompt: 'A flying car over a city',\n stream: true,\n pollingInterval: 3000,\n maxDuration: 600_000,\n})\nreturn toServerSentEventsResponse(stream)\n```\n\nGoogle Veo (`@tanstack\u002Fai-gemini`) uses the same jobs\u002Fpolling flow. Its\n`duration` option is typed per model (`4 | 6 | 8` for the Veo 3.1 models);\nuse `adapter.snapDuration(seconds)` to coerce raw\nseconds and `adapter.availableDurations()` to enumerate the valid set.\nImage prompt parts route by `metadata.role`: first un-roled \u002F\n`'start_frame'` image → input image, `'end_frame'` → `lastFrame`,\n`'reference'` \u002F `'character'` → `referenceImages`:\n\n```typescript\nimport { geminiVideo } from '@tanstack\u002Fai-gemini'\n\nconst adapter = geminiVideo('veo-3.1-generate-preview')\nadapter.availableDurations() \u002F\u002F { kind: 'discrete', values: [4, 6, 8] }\n\nconst { jobId } = await generateVideo({\n adapter,\n prompt: 'A golden retriever playing in sunflowers',\n size: '16:9', \u002F\u002F Veo sizes are aspect ratios: '16:9' | '9:16'\n duration: adapter.snapDuration(7), \u002F\u002F 6\n modelOptions: { resolution: '1080p', generateAudio: true },\n})\n\u002F\u002F Note: Veo result URLs require the Google API key to download\n\u002F\u002F (x-goog-api-key header or ?key= query parameter).\n```\n\nGemini Omni Flash (`geminiVideo('gemini-omni-flash-preview')`) is served by\nthe Interactions API instead of Veo's operations flow — same adapter, routed\nby model. Clips are 720p; `duration` is any number of seconds in the 3–10\nrange (fractional ok, default 10 — availableDurations() reports the range),\n`size` is the aspect ratio (`'16:9' | '9:16'`), and the finished video arrives\n**inline** as a `data:video\u002Fmp4;base64,…` URL (no key needed to use it).\nImage\u002Fvideo prompt parts are sent as interaction content blocks, grouped\nas images, then videos, then text (no\n`metadata.role` routing); `data` sources go inline, `url` sources pass\nthrough as-is (never downloaded — use Gemini Files API URIs for remote\nmedia). For conversational editing, pass a prior generation's `jobId` as\n`modelOptions.previous_interaction_id` with a prompt describing the change:\n\n```typescript\nimport { geminiVideo } from '@tanstack\u002Fai-gemini'\n\nconst omni = geminiVideo('gemini-omni-flash-preview')\nconst first = await generateVideo({\n adapter: omni,\n prompt: 'A violinist outdoors',\n})\n\u002F\u002F …poll first.jobId to completion, then edit it:\nconst edited = await generateVideo({\n adapter: omni,\n prompt: 'Make the violin invisible',\n modelOptions: { previous_interaction_id: first.jobId },\n})\n```\n\nOther video adapters: `openaiVideo('sora-2')` (pixel sizes like `'1280x720'`,\ndurations 4\u002F8\u002F12s, single `input_reference` image prompt part), `grokVideo(...)`\n(`grok-imagine-video` does text-to-video + image-to-video; `grok-imagine-video-1.5` is\nimage-to-video only — needs an `image` prompt part as the starting frame, text-only throws;\naspect-ratio size template like `'16:9_720p'`, integer durations 1-15s, reports\n`usage.unitsBilled` seconds and exact `usage.cost`), and `falVideo(...)` (hosted models, see cost tracking below).\n\nClient hook with job tracking:\n\n```tsx\nimport { useGenerateVideo, fetchServerSentEvents } from '@tanstack\u002Fai-react'\n\nconst { generate, result, jobId, videoStatus, isLoading } = useGenerateVideo({\n connection: fetchServerSentEvents('\u002Fapi\u002Fgenerate\u002Fvideo'),\n onJobCreated: (id) => console.log('Job created:', id),\n onStatusUpdate: (status) =>\n console.log(`${status.status} (${status.progress}%)`),\n})\n\n\u002F\u002F videoStatus: { jobId, status, progress?, url?, error?, usage? }\n\u002F\u002F result (on completion): { url }\n```\n\n### 6. Cost tracking (fal billable units)\n\nfal bills media generation by usage-based units, not tokens. Every fal media\nadapter (`falImage`, `falAudio`, `falSpeech`, `falTranscription`, `falVideo`)\nsurfaces the real billed quantity on the result as `usage.unitsBilled`, read\nfrom fal's `x-fal-billable-units` response header — no `fetch` interceptor\nneeded. It rides on the canonical `TokenUsage` shape (token fields are `0` for\nmedia), mirroring how duration-billed transcription surfaces `durationSeconds`.\n\n```typescript\nimport { generateImage } from '@tanstack\u002Fai'\nimport { falImage } from '@tanstack\u002Fai-fal'\n\nconst result = await generateImage({\n adapter: falImage('fal-ai\u002Fflux\u002Fdev'),\n prompt: 'a serene mountain lake',\n})\n\n\u002F\u002F usage.unitsBilled is the priced quantity. Multiply by the endpoint unit\n\u002F\u002F price (GET https:\u002F\u002Fapi.fal.ai\u002Fv1\u002Fmodels\u002Fpricing?endpoint_id=…) for exact cost.\nif (result.usage?.unitsBilled != null) {\n const cost = result.usage.unitsBilled * unitPrice\n}\n```\n\nFor video, the units arrive with the completed result: `getVideoJobStatus()`\nreturns `usage` and emits a `video:usage` devtools event when fal reports it.\n\n---\n\n## Common Hook API\n\nAll generation hooks return the same shape:\n\n| Property | Type | Description |\n| ----------- | -------------------------- | ------------------------------------------------ |\n| `generate` | `(input) => Promise\u003Cvoid>` | Trigger generation |\n| `result` | `T \\| null` | Result (optionally transformed via `onResult`) |\n| `isLoading` | `boolean` | Whether generation is in progress |\n| `error` | `Error \\| undefined` | Current error |\n| `status` | `GenerationClientState` | `'idle' \\| 'generating' \\| 'success' \\| 'error'` |\n| `stop` | `() => void` | Abort current generation |\n| `reset` | `() => void` | Clear state, return to idle |\n\nProvide either `connection` (streaming SSE transport) or `fetcher`\n(direct async call \u002F server function returning `Response`). Use `onResult`\nto transform what is stored:\n\n```tsx\nconst { result } = useGenerateSpeech({\n connection: fetchServerSentEvents('\u002Fapi\u002Fgenerate\u002Fspeech'),\n onResult: (raw) => ({\n audioUrl: `data:${raw.contentType};base64,${raw.audio}`,\n duration: raw.duration,\n }),\n})\n\u002F\u002F result is typed as { audioUrl: string; duration?: number } | null\n```\n\n---\n\n## Common Mistakes\n\n### a. HIGH: Using the removed `embedding()` function\n\nThe `embedding()` function and `openaiEmbed` adapter were removed in v0.5.0.\nAgents trained on older code may still generate this pattern.\n\n**Wrong:**\n\n```typescript\nimport { embedding } from '@tanstack\u002Fai'\nimport { openaiEmbed } from '@tanstack\u002Fai-openai'\n\nconst result = await embedding({\n adapter: openaiEmbed(),\n model: 'text-embedding-3-small',\n input: 'Hello, world!',\n})\n```\n\n**Correct -- use the provider SDK directly:**\n\n```typescript\nimport OpenAI from 'openai'\n\nconst openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY })\n\nconst result = await openai.embeddings.create({\n model: 'text-embedding-3-small',\n input: 'Hello, world!',\n})\n```\n\n> Source: docs\u002Fmigration\u002Fmigration.md. Note: Fixed in v0.5.0 but agents\n> trained on older code may still generate this pattern.\n\n### b. HIGH: Forgetting `toServerSentEventsResponse` with TanStack Start server functions\n\nWhen using TanStack Start server functions with `stream: true`, you MUST\nwrap the stream with `toServerSentEventsResponse()`. Returning the raw\nstream from a server function will not work.\n\n**Wrong:**\n\n```typescript\nexport const generateImageStreamFn = createServerFn({ method: 'POST' }).handler(\n ({ data }) => {\n \u002F\u002F BUG: returning raw stream -- client cannot parse this\n return generateImage({\n adapter: openaiImage('gpt-image-1'),\n prompt: data.prompt,\n stream: true,\n })\n },\n)\n```\n\n**Correct:**\n\n```typescript\nimport { generateImage, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { openaiImage } from '@tanstack\u002Fai-openai'\n\nexport const generateImageStreamFn = createServerFn({ method: 'POST' }).handler(\n ({ data }) => {\n return toServerSentEventsResponse(\n generateImage({\n adapter: openaiImage('gpt-image-1'),\n prompt: data.prompt,\n stream: true,\n }),\n )\n },\n)\n```\n\n> Source: maintainer interview.\n\n### c. MEDIUM: Not downloading OpenAI image URLs before they expire\n\nOpenAI image URLs expire after 1 hour. If you store the URL and display it\nlater, the image will silently break. Always download or display the image\nimmediately, or convert to base64 for persistence.\n\n```typescript\nconst result = await generateImage({\n adapter: openaiImage('dall-e-3'),\n prompt: 'A mountain landscape',\n})\n\n\u002F\u002F GOOD: download immediately\nfor (const img of result.images) {\n if (img.url) {\n const response = await fetch(img.url)\n const blob = await response.blob()\n \u002F\u002F Save blob to storage...\n }\n}\n\n\u002F\u002F GOOD: use b64Json when available (no expiration)\n\u002F\u002F gpt-image-1 returns b64Json by default\n```\n\n> Source: docs\u002Fmedia\u002Fimage-generation.md.\n\n### d. MEDIUM: Using `stream: true` for activities that do not support streaming\n\nNot all generation activities support streaming. Passing `stream: true` to\nan activity that does not support it may hang or produce unexpected results.\nCheck the activity documentation before enabling streaming. All built-in\nactivities (`generateImage`, `generateAudio`, `generateSpeech`,\n`generateTranscription`, `generateVideo`, `summarize`) support `stream: true`,\nbut custom `useGeneration` setups may not.\n\n> Source: docs\u002Fmedia\u002Fgenerations.md.\n\n### e. HIGH: Passing `responseMimeType` or `negativePrompt` to Gemini Lyria\n\nGemini's `GenerateContentConfig` (used by Lyria 3 Pro \u002F Lyria 3 Clip) does\n**not** support `responseMimeType` or `negativePrompt`. Lyria 3 Clip always\nreturns 30-second `audio\u002Fmp3`; Lyria 3 Pro returns `audio\u002Fmp3`. These fields\nare not in `GeminiAudioProviderOptions` — don't reach for them via `as any`.\n\n```typescript\n\u002F\u002F WRONG — both fields are silently ignored or rejected by the SDK\ngenerateAudio({\n adapter: geminiAudio('lyria-3-pro-preview'),\n prompt: 'ambient piano',\n modelOptions: {\n responseMimeType: 'audio\u002Fwav', \u002F\u002F unsupported\n negativePrompt: 'vocals', \u002F\u002F unsupported\n } as any,\n})\n\n\u002F\u002F CORRECT — shape the prompt itself for what you want\ngenerateAudio({\n adapter: geminiAudio('lyria-3-pro-preview'),\n prompt: 'ambient piano, no vocals',\n})\n```\n\n> Source: Gemini API `GenerateContentConfig` type; docs\u002Fmedia\u002Faudio-generation.md.\n\n### f. MEDIUM: Passing `duration` to Lyria expecting it to control length\n\nLyria 3 Clip is fixed at 30 seconds — the `duration` option is ignored on\nthat model. Lyria 3 Pro accepts duration via natural-language in the\n**prompt** (\"2-minute ambient track with a 30-second build\"), not via the\n`duration` field. `duration` works for fal audio models (mapped to each\nmodel's native field like `music_length_ms` or `seconds_total`), but not\nfor Lyria.\n\n```typescript\n\u002F\u002F For Lyria: put length guidance in the prompt\ngenerateAudio({\n adapter: geminiAudio('lyria-3-pro-preview'),\n prompt: 'A 2-minute ambient piano piece with gentle strings',\n \u002F\u002F duration: 120 \u002F\u002F ← does nothing; rely on the prompt\n})\n\n\u002F\u002F For fal: duration works and is translated per-model\ngenerateAudio({\n adapter: falAudio('fal-ai\u002Fminimax-music\u002Fv2'),\n prompt: 'upbeat synth melody',\n duration: 60, \u002F\u002F → music_length_ms: 60_000\n})\n```\n\n> Source: Google Lyria 3 docs; docs\u002Fmedia\u002Faudio-generation.md.\n\n### g. MEDIUM: Gemini TTS multi-speaker with 0 or 3+ speakers\n\n`multiSpeakerVoiceConfig.speakerVoiceConfigs` is validated to be length 1 or 2. Passing an empty array or three+ entries throws at the adapter boundary\n(not at Gemini's API) with a clear error. Don't try to work around it with\n`as any`.\n\n```typescript\ngenerateSpeech({\n adapter: geminiSpeech('gemini-2.5-pro-preview-tts'),\n text: '[Alice] Hi. [Bob] Hello!',\n modelOptions: {\n multiSpeakerVoiceConfig: {\n speakerVoiceConfigs: [\n {\n speaker: 'Alice',\n voiceConfig: { prebuiltVoiceConfig: { voiceName: 'Kore' } },\n },\n {\n speaker: 'Bob',\n voiceConfig: { prebuiltVoiceConfig: { voiceName: 'Puck' } },\n },\n ],\n },\n },\n})\n```\n\n> Source: Gemini TTS adapter validation; CodeRabbit review of PR #463.\n\n### h. HIGH: Passing image prompt parts to a model that doesn't support image-conditioned generation\n\nNot every model accepts image-conditioned prompts. The `prompt` type is\nnarrowed per model, so passing an image part to a text-only model\n(dall-e-3, Imagen, grok-2-image) is a **compile-time error**; adapters\nalso throw a clear runtime error as a backstop, so users learn at call\ntime rather than getting silently wrong output.\n\n```typescript\n\u002F\u002F WRONG — dall-e-3 has no edit\u002Finputs API; image parts are a type error\ngenerateImage({\n adapter: openaiImage('dall-e-3'),\n prompt: [\n { type: 'text', content: 'Edit this' },\n { type: 'image', source: { type: 'url', value: url } }, \u002F\u002F ❌ type error\n ],\n})\n\n\u002F\u002F WRONG — Imagen is text-to-image only; same compile-time rejection\ngenerateImage({\n adapter: geminiImage('imagen-4.0-generate-001'),\n prompt: [\n { type: 'text', content: 'Edit this' },\n { type: 'image', source: { type: 'url', value: url } }, \u002F\u002F ❌ type error\n ],\n})\n\n\u002F\u002F CORRECT — use a model that supports image-conditioned generation\ngenerateImage({\n adapter: openaiImage('gpt-image-2'), \u002F\u002F edits up to 16 images\n prompt: [\n { type: 'text', content: 'Edit this' },\n { type: 'image', source: { type: 'url', value: url } },\n ],\n})\n\ngenerateImage({\n adapter: geminiImage('gemini-3.1-flash-image-preview'), \u002F\u002F native multimodal\n prompt: [\n { type: 'text', content: 'Edit this' },\n { type: 'image', source: { type: 'url', value: url } },\n ],\n})\n```\n\n> Source: docs\u002Fmedia\u002Fimage-generation.md, docs\u002Fmedia\u002Fvideo-generation.md.\n\n### i. LOW: Writing a logging middleware to see media chunks flow through\n\nEvery media activity — `generateAudio`, `generateSpeech`,\n`generateTranscription`, `generateImage`, `generateVideo` — accepts the\nsame `debug?: DebugOption` option that `chat()` does. Reach for `debug`\ninstead of wiring up logging middleware.\n\n```typescript\n\u002F\u002F When a speech generation sounds wrong or a transcription returns garbage\ngenerateSpeech({\n adapter: openaiSpeech('tts-1'),\n text: 'Hello',\n debug: { provider: true, output: true }, \u002F\u002F raw SDK chunks + yielded chunks\n})\n```\n\nSee the `ai-core\u002Fdebug-logging` sub-skill for full details on categories\nand piping into a custom logger.\n\n> Source: docs\u002Fadvanced\u002Fdebug-logging.md.\n\n---\n\n## Cross-References\n\n- See also: **ai-core\u002Fadapter-configuration\u002FSKILL.md** -- Each media\n activity requires a specific activity adapter (e.g., `openaiImage` for\n images, `openaiSpeech` for speech, `openaiTranscription` for transcription,\n `openaiVideo` for video). 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Covers per-category toggling and piping into pino\u002Fwinston.",{"type":77,"tag":15078,"props":15079,"children":15080},"style",{},[15081],{"type":83,"value":15082},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":15084,"total":15226},[15085,15101,15113,15125,15140,15152,15162,15172,15185,15195,15206,15216],{"slug":15086,"name":15086,"fn":15087,"description":15088,"org":15089,"tags":15090,"stars":15098,"repoUrl":15099,"updatedAt":15100},"aggregation","perform data aggregation in TanStack Table","Aggregate TanStack Table columns independently of grouping, including grand totals, caller-selected row totals, multiple keyed aggregations, custom context-based definitions, grouped merges, manual values, and worker constraints.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15091,15094,15097],{"name":15092,"slug":15093,"type":16},"Data Analysis","data-analysis",{"name":15095,"slug":15096,"type":16},"Frontend","frontend",{"name":10,"slug":9,"type":16},28175,"https:\u002F\u002Fgithub.com\u002FTanStack\u002Ftable","2026-07-30T05:25:59.429787",{"slug":15102,"name":15102,"fn":15103,"description":15104,"org":15105,"tags":15106,"stars":15098,"repoUrl":15099,"updatedAt":15112},"api-not-found","diagnose TanStack Table API errors","Diagnose missing TanStack Table v9 exports, options, state slices, and instance methods. Load before inventing an API when code sees a type error, undefined feature method, absent object key, adapter mismatch, or v8-shaped example.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15107,15110,15111],{"name":15108,"slug":15109,"type":16},"Debugging","debugging",{"name":15095,"slug":15096,"type":16},{"name":10,"slug":9,"type":16},"2026-07-30T05:26:05.418735",{"slug":15114,"name":15114,"fn":15115,"description":15116,"org":15117,"tags":15118,"stars":15098,"repoUrl":15099,"updatedAt":15124},"cell-selection","select rectangular cell ranges in tables","Select rectangular cell ranges with cellSelectionFeature: two-corner range state keyed by row and column id, mousedown\u002Fmouseenter handlers, selection edges, render-order resolution under pinning, and autoResetCellSelection. Load when ranges widen unexpectedly after sorting or column reordering, when a drag re-renders the whole table, or when building copy-to-clipboard from a selection.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15119,15120,15121],{"name":15092,"slug":15093,"type":16},{"name":10,"slug":9,"type":16},{"name":15122,"slug":15123,"type":16},"UI Components","ui-components","2026-07-30T05:25:38.403427",{"slug":15126,"name":15126,"fn":15127,"description":15128,"org":15129,"tags":15130,"stars":15098,"repoUrl":15099,"updatedAt":15139},"client-vs-server","manage TanStack Table data pipelines","Choose client or server ownership for filtering, grouping, sorting, expanding, and pagination in TanStack Table v9. Load for manual* flags, mixed pipelines, server counts, or deciding which dataset each row-model stage receives.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15131,15134,15135,15138],{"name":15132,"slug":15133,"type":16},"Data Pipeline","data-pipeline",{"name":15095,"slug":15096,"type":16},{"name":15136,"slug":15137,"type":16},"Performance","performance",{"name":10,"slug":9,"type":16},"2026-07-30T05:25:45.400104",{"slug":15141,"name":15141,"fn":15142,"description":15143,"org":15144,"tags":15145,"stars":15098,"repoUrl":15099,"updatedAt":15151},"column-faceting","build faceted filter UIs","Build faceted filter UIs with columnFacetingFeature, facetedRowModel, facetedUniqueValues, and facetedMinMaxValues. Load for facet counts, numeric ranges, own-filter exclusion, or server-page facet completeness.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15146,15149,15150],{"name":15147,"slug":15148,"type":16},"Data Visualization","data-visualization",{"name":15095,"slug":15096,"type":16},{"name":10,"slug":9,"type":16},"2026-07-30T05:25:41.397257",{"slug":15153,"name":15153,"fn":15154,"description":15155,"org":15156,"tags":15157,"stars":15098,"repoUrl":15099,"updatedAt":15161},"column-filtering","implement column filtering in TanStack Table","Filter columns with columnFilteringFeature, filteredRowModel, filterFns, filterMeta, nested-row direction, and manualFiltering. Load for accessor compatibility, controlled filter updaters, fuzzy metadata, or client\u002Fserver ownership.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15158,15159,15160],{"name":15092,"slug":15093,"type":16},{"name":15095,"slug":15096,"type":16},{"name":10,"slug":9,"type":16},"2026-07-30T05:25:53.391632",{"slug":15163,"name":15163,"fn":15164,"description":15165,"org":15166,"tags":15167,"stars":15098,"repoUrl":15099,"updatedAt":15171},"column-ordering","manage TanStack Table column ordering","Control TanStack Table v9 leaf columnOrder with stable IDs while accounting for pinning regions, visibility, and groupedColumnMode precedence. Load for drag-and-drop columns or rendered order that differs from state.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15168,15169,15170],{"name":15095,"slug":15096,"type":16},{"name":10,"slug":9,"type":16},{"name":15122,"slug":15123,"type":16},"2026-07-30T05:26:03.37801",{"slug":15173,"name":15173,"fn":15174,"description":15175,"org":15176,"tags":15177,"stars":15098,"repoUrl":15099,"updatedAt":15184},"column-pinning","configure column pinning in TanStack Table","Pin columns into logical start, center, and end regions with columnPinningFeature and renderer-owned sticky CSS. Load for RTL offsets, z-index, backgrounds, overflow, widths, gaps, or overlaps.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15178,15181,15182,15183],{"name":15179,"slug":15180,"type":16},"CSS","css",{"name":15095,"slug":15096,"type":16},{"name":10,"slug":9,"type":16},{"name":15122,"slug":15123,"type":16},"2026-07-30T05:25:55.377366",{"slug":15186,"name":15186,"fn":15187,"description":15188,"org":15189,"tags":15190,"stars":15098,"repoUrl":15099,"updatedAt":15194},"column-resizing","implement column resizing in TanStack Table","Wire columnResizingFeature, header.getResizeHandler, resize mode and direction, pointer or touch events, and performant CSS-variable updates. Load when resize state changes but widths do not, or large tables resize slowly.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15191,15192,15193],{"name":15095,"slug":15096,"type":16},{"name":10,"slug":9,"type":16},{"name":15122,"slug":15123,"type":16},"2026-07-30T05:25:51.400011",{"slug":15196,"name":15196,"fn":15197,"description":15198,"org":15199,"tags":15200,"stars":15098,"repoUrl":15099,"updatedAt":15205},"column-sizing","configure column sizing in TanStack Table","Use columnSizingFeature numeric size, minSize, maxSize, getSize, getStart, getAfter, and total-size APIs in table, grid, or flex CSS. Load for auto or percentage misconceptions and sizing\u002Fpinning layout mismatch.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15201,15202,15203,15204],{"name":15179,"slug":15180,"type":16},{"name":15095,"slug":15096,"type":16},{"name":10,"slug":9,"type":16},{"name":15122,"slug":15123,"type":16},"2026-07-30T05:25:48.703799",{"slug":15207,"name":15207,"fn":15208,"description":15209,"org":15210,"tags":15211,"stars":15098,"repoUrl":15099,"updatedAt":15215},"column-visibility","manage column visibility in TanStack Table","Hide columns with columnVisibilityFeature while rendering visibility-aware header, column, and cell collections. Load when hidden columns remain in the DOM, false-versus-absent state is confused, or enableHiding is misunderstood.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15212,15213,15214],{"name":15095,"slug":15096,"type":16},{"name":10,"slug":9,"type":16},{"name":15122,"slug":15123,"type":16},"2026-07-30T05:25:47.367943",{"slug":15217,"name":15217,"fn":15218,"description":15219,"org":15220,"tags":15221,"stars":15098,"repoUrl":15099,"updatedAt":15225},"core","build data grids with TanStack Table","Use TanStack Table v9 as a headless data-grid state and row-processing engine. Load for first-table architecture, stable data and columns, row numbering with getDisplayIndex, semantic rendering, framework adapter choice, or deciding what Table owns versus the renderer.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15222,15223,15224],{"name":15092,"slug":15093,"type":16},{"name":15095,"slug":15096,"type":16},{"name":15122,"slug":15123,"type":16},"2026-07-30T05:25:52.366295",125,{"items":15228,"total":1405},[15229,15246,15261,15276,15289,15301,15315],{"slug":15230,"name":15230,"fn":15231,"description":15232,"org":15233,"tags":15234,"stars":30,"repoUrl":31,"updatedAt":15245},"ai-code-mode","execute sandboxed TypeScript code with LLMs","LLM-generated TypeScript execution in sandboxed environments: createCodeModeTool() with isolate drivers (createNodeIsolateDriver, createQuickJSIsolateDriver, createCloudflareIsolateDriver), codeModeWithSkills() for persistent skill libraries, trust strategies, skill storage (FileSystem, LocalStorage, InMemory, Mongo), client-side execution progress via code_mode:* custom events in useChat.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15235,15236,15239,15242,15243],{"name":18,"slug":19,"type":16},{"name":15237,"slug":15238,"type":16},"Code Execution","code-execution",{"name":15240,"slug":15241,"type":16},"Sandboxing","sandboxing",{"name":10,"slug":9,"type":16},{"name":15244,"slug":51,"type":16},"TypeScript","2026-07-16T06:04:13.597905",{"slug":15247,"name":15247,"fn":15248,"description":15249,"org":15250,"tags":15251,"stars":30,"repoUrl":31,"updatedAt":15260},"ai-core","configure TanStack AI agent features","Entry point for TanStack AI skills. Routes to chat-experience, tool-calling, media-generation, structured-outputs, adapter-configuration, ag-ui-protocol, middleware, locks, custom-backend-integration, and debug-logging, plus the skills shipped by companion packages (@tanstack\u002Fai-persistence, @tanstack\u002Fai-code-mode). Use chat() not streamText(), openaiText() not createOpenAI(), toServerSentEventsResponse() not manual SSE, middleware hooks not onEnd callbacks.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15252,15255,15257],{"name":15253,"slug":15254,"type":16},"Agents","agents",{"name":15256,"slug":36,"type":16},"AI",{"name":15258,"slug":15259,"type":16},"Middleware","middleware","2026-07-30T05:26:10.404565",{"slug":15262,"name":15263,"fn":15264,"description":15265,"org":15266,"tags":15267,"stars":30,"repoUrl":31,"updatedAt":15275},"ai-coreadapter-configuration","ai-core\u002Fadapter-configuration","configure AI provider adapters","Provider adapter selection and configuration: openaiText, anthropicText, geminiText, ollamaText, grokText, groqText, openRouterText, bedrockText, openaiCompatible. Per-model type safety with modelOptions, reasoning\u002Fthinking configuration, runtime adapter switching, extendAdapter() for custom models, createModel(). Generic OpenAI-compatible providers (DeepSeek, Together, Fireworks, etc.) via openaiCompatible({ baseURL, apiKey, models }) from @tanstack\u002Fai-openai\u002Fcompatible. API key env vars: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY\u002FGEMINI_API_KEY, XAI_API_KEY, GROQ_API_KEY, OPENROUTER_API_KEY, OLLAMA_HOST, BEDROCK_API_KEY (or AWS_BEARER_TOKEN_BEDROCK).\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15268,15269,15272,15274],{"name":18,"slug":19,"type":16},{"name":15270,"slug":15271,"type":16},"Configuration","configuration",{"name":15273,"slug":43,"type":16},"LLM",{"name":10,"slug":9,"type":16},"2026-07-16T06:04:17.82075",{"slug":15277,"name":15278,"fn":15279,"description":15280,"org":15281,"tags":15282,"stars":30,"repoUrl":31,"updatedAt":15288},"ai-coreag-ui-protocol","ai-core\u002Fag-ui-protocol","implement TanStack AI streaming protocol","Server-side AG-UI streaming protocol implementation: StreamChunk event types (RUN_STARTED, TEXT_MESSAGE_START\u002FCONTENT\u002FEND, TOOL_CALL_START\u002FARGS\u002FEND, RUN_FINISHED, RUN_ERROR, STEP_STARTED\u002FSTEP_FINISHED, STATE_SNAPSHOT\u002FDELTA, CUSTOM), toServerSentEventsStream() for SSE format, toHttpStream() for NDJSON format. For backends serving AG-UI events without client packages.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15283,15286,15287],{"name":15284,"slug":15285,"type":16},"API Development","api-development",{"name":15273,"slug":43,"type":16},{"name":10,"slug":9,"type":16},"2026-07-16T06:04:10.093367",{"slug":15290,"name":15291,"fn":15292,"description":15293,"org":15294,"tags":15295,"stars":30,"repoUrl":31,"updatedAt":15300},"ai-corechat-experience","ai-core\u002Fchat-experience","implement chat experiences with TanStack AI","End-to-end chat implementation: server endpoint with chat() and toServerSentEventsResponse(), client-side useChat hook with fetchServerSentEvents(), message rendering with UIMessage parts, multimodal content, thinking\u002Freasoning display. Covers streaming states, connection adapters, and message format conversions. NOT Vercel AI SDK — uses chat() not streamText().\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15296,15297,15298,15299],{"name":15284,"slug":15285,"type":16},{"name":15095,"slug":15096,"type":16},{"name":15273,"slug":43,"type":16},{"name":10,"slug":9,"type":16},"2026-07-30T05:26:11.505241",{"slug":15302,"name":15303,"fn":15304,"description":15305,"org":15306,"tags":15307,"stars":30,"repoUrl":31,"updatedAt":15314},"ai-coreclient-persistence","ai-core\u002Fclient-persistence","implement browser chat persistence for TanStack AI","Browser chat persistence on useChat \u002F ChatClient: localStoragePersistence, sessionStoragePersistence, indexedDBPersistence. Client-authoritative (adapter, full transcript) vs server-authoritative (persistence: true, no client cache). Reload restore, pending interrupts, mid-stream rejoin with delivery durability. Use for SPA reload durability — NOT server history alone. No extra package: the adapters ship in the framework packages.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15308,15309,15310,15313],{"name":15256,"slug":36,"type":16},{"name":15095,"slug":15096,"type":16},{"name":15311,"slug":15312,"type":16},"Persistence","persistence",{"name":10,"slug":9,"type":16},"2026-07-30T05:53:35.503176",{"slug":15316,"name":15317,"fn":15318,"description":15319,"org":15320,"tags":15321,"stars":30,"repoUrl":31,"updatedAt":15328},"ai-corecustom-backend-integration","ai-core\u002Fcustom-backend-integration","integrate custom backends with TanStack AI","Connect useChat to a non-TanStack-AI backend through custom connection adapters. ConnectConnectionAdapter (single async iterable) vs SubscribeConnectionAdapter (separate subscribe\u002Fsend). Customize fetchServerSentEvents() and fetchHttpStream() with auth headers, custom URLs, and request options. Import from framework package, not @tanstack\u002Fai-client.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[15322,15323,15324,15327],{"name":15256,"slug":36,"type":16},{"name":15284,"slug":15285,"type":16},{"name":15325,"slug":15326,"type":16},"Backend","backend",{"name":10,"slug":9,"type":16},"2026-07-17T06:06:39.855351"]