BytePlus

The BytePlus adapter connects TanStack AI to BytePlus, ByteDance's international model platform, across four generation kinds:

• Chat — the Seed model family (plus GLM, DeepSeek and gpt-oss) on ModelArk's OpenAI-compatible /chat/completions endpoint, with reasoning on by default.
• Video — Seedance, an asynchronous task API.
• Image — Seedream.
• Speech — Seed Speech text-to-speech and transcription, which live on a different host with a different API key (see Two products, two keys).

Installation#

npm install @tanstack/ai-byteplus
# or
pnpm add @tanstack/ai-byteplus
# or
yarn add @tanstack/ai-byteplus

Two products, two keys#

BytePlus splits these models across two products, and they do not share credentials:

# ModelArk: chat, Seedance video, Seedream image
ARK_API_KEY=...

# Seed Speech: TTS and transcription — a separate product key
BYTEPLUS_VOICE_API_KEY=...

Passing an Ark key to the speech adapters fails with 45000010 Invalid X-Api-Key.

Ark keys are also region-isolated: the default base URL is the Asia-Pacific south-east endpoint (https://ark.ap-southeast.bytepluses.com/api/v3), and a key issued for one region will not authenticate against another. Point the adapter elsewhere with baseURL:

import { createBytePlusText } from '@tanstack/ai-byteplus'
import { arkApiKey } from './config'

const adapter = createBytePlusText('dola-seed-2-1-turbo-260628', arkApiKey, {
  baseURL: 'https://ark.eu-west.bytepluses.com/api/v3',
})

Per the BytePlus docs the EU endpoint serves chat and image only — Seedance video is Asia-Pacific only.

Chat#

The adapter carries the model, so there is no separate model option.

Server — an endpoint that streams the reply back over SSE:

import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { byteplusText } from '@tanstack/ai-byteplus'

export async function POST(request: Request) {
  const { messages } = await request.json()

  const stream = chat({
    adapter: byteplusText('dola-seed-2-1-turbo-260628'),
    messages,
  })

  return toServerSentEventsResponse(stream)
}

Client — the same useChat hook as every other provider:

import { useState } from 'react'
import { fetchServerSentEvents, useChat } from '@tanstack/ai-react'

export function Chat() {
  const [input, setInput] = useState('')

  const { messages, sendMessage, isLoading } = useChat({
    connection: fetchServerSentEvents('/api/chat'),
  })

  return (
    <div>
      {messages.map((message) => (
        <div key={message.id}>
          <strong>{message.role}</strong>
          {message.parts.map((part, index) =>
            part.type === 'text' ? <p key={index}>{part.content}</p> : null,
          )}
        </div>
      ))}

      <form
        onSubmit={(event) => {
          event.preventDefault()
          if (!input.trim() || isLoading) return
          sendMessage(input)
          setInput('')
        }}
      >
        <input value={input} onChange={(e) => setInput(e.target.value)} />
        <button type="submit" disabled={isLoading}>
          Send
        </button>
      </form>
    </div>
  )
}

Model options#

Ark's chat endpoint is OpenAI-compatible, so sampling parameters keep their OpenAI snake_case names and live in modelOptions. thinking, reasoning_effort, repetition_penalty and service_tier are the Ark-only additions:

import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { byteplusText } from '@tanstack/ai-byteplus'

export async function POST(request: Request) {
  const { messages } = await request.json()

  const stream = chat({
    adapter: byteplusText('dola-seed-2-1-turbo-260628'),
    messages,
    modelOptions: {
      temperature: 0.7,
      top_p: 0.9,
      max_tokens: 2048,
      thinking: { type: 'enabled' },
      reasoning_effort: 'medium',
    },
  })

  return toServerSentEventsResponse(stream)
}

Two constraints the type system can't express, both live-verified as 400s:

• max_tokens and max_completion_tokens are mutually exclusive.
• reasoning_effort cannot be combined with thinking: { type: 'disabled' }.

service_tier: 'flex' routes the request to the cheaper offline batch queue with no latency guarantee.

Reasoning and encrypted_content#

Seed models reason by default. Reasoning arrives as its own stream of reasoning_content deltas and is surfaced as reasoning content rather than answer text, so useChat renders it separately from the reply. Turn it off per request:

import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { byteplusText } from '@tanstack/ai-byteplus'

export async function POST(request: Request) {
  const { messages } = await request.json()

  const stream = chat({
    adapter: byteplusText('dola-seed-2-1-turbo-260628'),
    messages,
    modelOptions: { thinking: { type: 'disabled' } },
  })

  return toServerSentEventsResponse(stream)
}

disabled works everywhere; auto is accepted only by gpt-oss-120b-250805. deepseek-v3-2-251201 is the one model that defaults to reasoning off.

The four "thinking summary" models — dola-seed-2-1-turbo-260628, seed-2-0-lite-260428, seed-2-0-mini-260428 and seed-2-0-pro-260328 — also emit an opaque encrypted_content blob alongside the reasoning trace. It is a signature over that trace, and BytePlus's docs ask for it back verbatim on the assistant message in the next turn.

The adapter round-trips it for you, over the same seam Anthropic's thinking signatures use: the blob is captured off the stream and attached to the reasoning step as its signature, which lands on the thinking part of the assistant message and is echoed back on the next request. Two consequences worth knowing:

• If you persist and replay conversation history yourself, keep the thinking parts' signature — dropping it costs you the reasoning-cache hit. It is not fatal: a live probe confirmed Ark accepts a turn whose assistant message omits encrypted_content.
• A structured-output turn doesn't capture the blob, for the same reason — again a lost cache hit, not a failed request.

Structured output#

Ten of the eighteen chat models accept response_format: { type: 'json_schema' }:

dola-seed-2-1-turbo-260628, seed-2-0-pro-260328, seed-2-0-lite-260228, seed-2-0-mini-260215, seed-1-8-251228, seed-1-6-250915, seed-1-6-250615, seed-1-6-flash-250715, seed-1-6-flash-250615, glm-5-2-260617.

import { chat } from '@tanstack/ai'
import { byteplusText } from '@tanstack/ai-byteplus'
import { z } from 'zod'

const RecipeSchema = z.object({
  name: z.string().meta({ description: 'Name of the dish' }),
  minutes: z.number().meta({ description: 'Total cooking time in minutes' }),
  ingredients: z.array(z.string()),
})

const recipe = await chat({
  adapter: byteplusText('dola-seed-2-1-turbo-260628'),
  messages: [{ role: 'user', content: 'Give me a recipe for carbonara' }],
  outputSchema: RecipeSchema,
})

console.log(recipe.name, recipe.minutes)

On the other eight models the adapter fails loudly rather than degrading: chat({ outputSchema }) throws, and the streaming form emits a RUN_ERROR naming the models that do work. There is no JSON-mode fallback to fall back to — Ark rejects response_format: { type: 'json_object' } on every one of them too.

Two traps worth knowing, both found by probing the live API rather than reading the tables:

• The published capability tables are wrong in both directions. seed-2-0-lite-260428 — the obvious default model — is one of the eight that reject a JSON schema. Reach for seed-2-0-lite-260228 or dola-seed-2-1-turbo-260628 when you need typed output.
• glm-4-7-251222 accepts a schema and then ignores it, answering in prose with a 200. It is deliberately excluded from the supported list, so the adapter rejects it up front instead of handing you unparseable output.

BYTEPLUS_STRUCTURED_OUTPUT_CHAT_MODELS is exported if you want to gate a model picker on it.

Video generation (Seedance)#

Video generation is an experimental feature.

Seedance is an asynchronous task API: generateVideo() opens the job and returns a jobId, and the video URL arrives with the terminal status.

import { generateVideo, getVideoJobStatus } from '@tanstack/ai'
import { byteplusVideo } from '@tanstack/ai-byteplus'

const adapter = byteplusVideo('dreamina-seedance-2-0-260128')

const { jobId } = await generateVideo({
  adapter,
  prompt: 'a guitar being played in a store',
  size: '16:9_720p',
  duration: 5,
})

let status = await getVideoJobStatus({ adapter, jobId })
while (status.status === 'pending' || status.status === 'processing') {
  await new Promise((resolve) => setTimeout(resolve, 5000))
  status = await getVideoJobStatus({ adapter, jobId })
}

console.log(status.status === 'completed' ? status.url : status.error)

Generated video URLs expire 24 hours after the task completes (the task record itself is kept for seven days), so download anything you intend to keep — see Keeping generated files.

size is a ratio or ratio_resolution template ('16:9', '16:9_720p'). Ratios are 16:9, 9:16, 4:3, 3:4, 1:1, 21:9 and adaptive.

Driving the whole lifecycle from the client#

Server — hand the polling to the core with stream: true and pipe the chunks out:

import { generateVideo, toServerSentEventsResponse } from '@tanstack/ai'
import { byteplusVideo } from '@tanstack/ai-byteplus'

export async function POST(request: Request) {
  const { prompt } = await request.json()

  const stream = generateVideo({
    adapter: byteplusVideo('dreamina-seedance-2-0-260128'),
    prompt,
    size: '16:9_720p',
    duration: 5,
    stream: true,
    pollingInterval: 5000,
  })

  return toServerSentEventsResponse(stream)
}

Client — useGenerateVideo tracks the job for you:

import { fetchServerSentEvents, useGenerateVideo } from '@tanstack/ai-react'

export function SeedanceGenerator() {
  const { generate, result, videoStatus, isLoading, error } = useGenerateVideo({
    connection: fetchServerSentEvents('/api/generate/video'),
  })

  return (
    <div>
      <button
        onClick={() => generate({ prompt: 'a guitar being played in a store' })}
        disabled={isLoading}
      >
        {isLoading ? 'Generating…' : 'Generate video'}
      </button>

      {isLoading && <p>Status: {videoStatus?.status ?? 'starting…'}</p>}
      {error && <p>Error: {error.message}</p>}
      {result && <video src={result.url} controls width={640} />}
    </div>
  )
}

Per-model options#

Seedance options are model-specific, and Ark rejects an inapplicable field with a 400 rather than ignoring it — "the specified parameter draft is not supported for model seedance-1-0-pro in t2v, must be empty". The applicable set:

watermark defaults to false for video (the opposite of Seedream images). generate_audio is accepted everywhere but only Seedance 2.5, 2.0 and 1.5-pro actually produce an audio track.

Resolutions are per model too: there is no 2K tier on any Seedance model, 4k exists only on dreamina-seedance-2-0-260128, Seedance 2.5 is 480p/720p only, and seedance-1-0-pro-fast-251015 does accept 1080p despite older prose listing it as 480p/720p only. An unsupported combination is caught locally with a clear error before the request goes out.

Reference media follows the shared role hints — start_frame, end_frame and reference. Seedance 2.5 and the 2.0 family take full multimodal references (reference images, video and audio); Seedance 2.5 additionally accepts audio-only reference input and up to 30 reference images / 10 videos / 10 audio clips (2.0 is 9 / 3 / 3). The 1.x models take start/end frames only, and seedance-1-0-pro-fast-251015 takes a start frame only.

Seedance 2.5#

Seedance 2.5 (dreamina-seedance-2-5-260628) is a first-class model in this package. It is the current multimodal flagship for longer clips (up to 30 seconds), audio-only reference input, and output_format: 'mov'. Like the 2.0 series it may still require model activation / a resource pack in the Ark Console before the account can call it (404 ModelNotOpen until then).

import { generateVideo } from '@tanstack/ai'
import { byteplusVideo } from '@tanstack/ai-byteplus'

const { jobId } = await generateVideo({
  adapter: byteplusVideo('dreamina-seedance-2-5-260628'),
  prompt: 'a guitar being played in a store',
  size: '16:9_720p',
  duration: 10,
  modelOptions: {
    generate_audio: true,
    priority: 5,
    output_format: 'mp4',
  },
})

byteplusVideo() still accepts any string for ids BytePlus ships between package releases. An unknown id relaxes local guards so Ark validates the request; known ids (including 2.5) keep their documented narrowing.

Seedance here vs. Seedance via fal#

Seedance is also reachable through @tanstack/ai-fal, which proxies it alongside hundreds of other hosted models. This adapter talks to BytePlus directly, which means BytePlus billing and rate limits, model ids in BytePlus's own naming, and the first-class Seedance request fields above (camera_fixed, draft, priority, reference-media roles) rather than fal's normalized subset. Pick whichever matches the account you already have; there is no reason to install both just for Seedance.

Image generation (Seedream)#

import { generateImage } from '@tanstack/ai'
import { byteplusImage } from '@tanstack/ai-byteplus'

const result = await generateImage({
  adapter: byteplusImage('dola-seedream-5-0-pro-260628'),
  prompt: 'a guitar being played in a store',
  size: '2K',
  modelOptions: { watermark: false },
})

console.log(result.images[0]?.url)

size takes either a token (1K, 2K, 4K) or explicit pixels (2048x2048) — never a mix of the two. Pass image parts in the prompt array to edit or condition on existing images (up to 14 references, 10 on dola-seedream-5-0-pro-260628):

import { generateImage } from '@tanstack/ai'
import { byteplusImage } from '@tanstack/ai-byteplus'

const result = await generateImage({
  adapter: byteplusImage('seedream-5-0-260128'),
  prompt: [
    { type: 'text', content: 'Put this guitar on a concert stage' },
    {
      type: 'image',
      source: { type: 'url', value: 'https://example.com/guitar.png' },
    },
  ],
})

console.log(result.images.length)

Two behaviours surprise people:

• watermark defaults to true. BytePlus stamps "AI generated" into the bottom-right corner unless you pass watermark: false. The adapter never sets the field implicitly, so the provider default applies.
• numberOfImages is an upper bound, not a count. Seedream has no n parameter; asking for more than one image maps onto its group-image mode (sequential_image_generation: 'auto' with max_images), where the model decides how many images the prompt actually warrants. A request for four can come back with two, and the adapter logs a warning when it does.

Result URLs expire after 24 hours. Pass response_format: 'b64_json' in modelOptions to get the bytes inline instead.

Text-to-speech (Seed Speech)#

Seed Speech uses BYTEPLUS_VOICE_API_KEY, not the Ark key.

import { generateSpeech } from '@tanstack/ai'
import { byteplusSpeech } from '@tanstack/ai-byteplus'

const result = await generateSpeech({
  adapter: byteplusSpeech('seed-audio-1.0'),
  text: 'welcome to the guitar store',
  voice: 'en_female_stokie_uranus_bigtts',
  format: 'mp3',
})

console.log(result.contentType, result.audio.length)

Seed Speech has no top-level speaker field — the voice travels inside a references entry, so the adapter turns voice into references: [{ speaker: 'en_female_stokie_uranus_bigtts' }] for you. The consequence: modelOptions.references replaces that entry rather than merging with it, so a request that passes references for voice cloning silently drops voice. Include a speaker member yourself if you still want a stock voice alongside your clips.

The suffix on a voice id tells you which generation you are asking for:

• _uranus_bigtts — TTS 2.0 voices, the current generation.
• _mars_bigtts / _moon_bigtts — TTS 1.0 voices.
• *_emo_v2_* — TTS 1.0 voices that additionally accept emotion tags.

The full roster lives in the BytePlus voice list and changes far more often than this package ships, so any string is accepted.

Output formats are wav, mp3, pcm and ogg_opus. Reach for modelOptions for ogg_opus, for an explicit sample_rate, for references (voice cloning — up to three 30-second audio clips, addressed from the text as @Audio1–@Audio3), for watermark, or for word-level timings:

import { generateSpeech } from '@tanstack/ai'
import { byteplusSpeech, type BytePlusTTSResult } from '@tanstack/ai-byteplus'

const result: BytePlusTTSResult = await generateSpeech({
  adapter: byteplusSpeech('seed-audio-1.0'),
  text: 'welcome to the guitar store',
  modelOptions: {
    format: 'ogg_opus',
    sample_rate: 48000,
    speech_rate: 20,
    enable_subtitle: true,
  },
})

for (const sentence of result.subtitle?.sentences ?? []) {
  console.log(sentence.text, sentence.start_time)
}

Three things to plan around:

• Synthesis is capped at 120 seconds of output. The cap applies to originalDuration (the length before speech_rate is applied), which is also what BytePlus bills on.
• Subtitle timings are milliseconds while duration and originalDuration are seconds. The units genuinely differ.
• The url on the result expires roughly two hours after generation — persist the base64 audio, not the link.

The client half is the standard useGenerateSpeech hook; nothing about it is BytePlus-specific.

Transcription (Seed Speech ASR)#

Also on the voice key. The endpoint is synchronous — audio in, transcript out, no polling:

import { generateTranscription } from '@tanstack/ai'
import { byteplusTranscription } from '@tanstack/ai-byteplus'
import { audioFile } from './audio'

const result = await generateTranscription({
  adapter: byteplusTranscription('seed-asr'),
  audio: audioFile,
  modelOptions: { enable_punc: true, enable_speaker_info: true },
})

console.log(result.text)
for (const segment of result.segments ?? []) {
  console.log(segment.speaker, segment.text)
}

Audio can be a File, base64, a data URL or a public URL, up to two hours and 100 MB. enable_itn renders spoken numbers and dates as digits, enable_punc inserts punctuation, enable_ddc strips fillers, and enable_speaker_info attaches speaker labels that surface as segment.speaker.

Supported models#

• Chat — dola-seed-2-1-turbo-260628, seed-2-0-lite-260428, seed-2-0-mini-260428, seed-2-0-pro-260328, seed-2-0-lite-260228, seed-2-0-mini-260215, seed-2-0-code-preview-260328, seed-1-8-251228, seed-1-6-250915, seed-1-6-250615, seed-1-6-flash-250715, seed-1-6-flash-250615, glm-5-2-260617, glm-4-7-251222, deepseek-v4-pro-260425, deepseek-v4-flash-260425, deepseek-v3-2-251201, gpt-oss-120b-250805.
• Video — dreamina-seedance-2-5-260628, dreamina-seedance-2-0-260128, dreamina-seedance-2-0-fast-260128, dreamina-seedance-2-0-mini-260615, seedance-1-5-pro-251215, seedance-1-0-pro-250528, seedance-1-0-pro-fast-251015.
• Image — dola-seedream-5-0-pro-260628, seedream-5-0-260128, seedream-5-0-lite-260128, seedream-4-5-251128, seedream-4-0-250828.
• Speech — seed-audio-1.0 (TTS) and seed-asr (transcription).

BytePlus retires model ids aggressively and its published lists include ids that no longer resolve, so this package ships only dated ids that answered a live request. BYTEPLUS_CHAT_MODELS, BYTEPLUS_VIDEO_MODELS, BYTEPLUS_IMAGE_MODELS, BYTEPLUS_TTS_MODELS and BYTEPLUS_TRANSCRIPTION_MODELS are the authoritative lists and are exported for building model pickers.

API Reference#

Every factory has an environment-variable form and an explicit-key form.

byteplusText(model, config?) / createBytePlusText(model, apiKey, config?)#

Chat adapter for the Seed, GLM, DeepSeek and gpt-oss models. Reads ARK_API_KEY (or BYTEPLUS_API_KEY). config.baseURL overrides the region endpoint.

byteplusVideo(model, config?) / createBytePlusVideo(model, apiKey, config?)#

Seedance video adapter (experimental). Reads ARK_API_KEY.

byteplusImage(model, config?) / createBytePlusImage(model, apiKey, config?)#

Seedream image adapter. Reads ARK_API_KEY.

byteplusSpeech(model, config?) / createBytePlusSpeech(model, apiKey, config?)#

Seed Speech TTS adapter. Reads BYTEPLUS_VOICE_API_KEY.

byteplusTranscription(model, config?) / createBytePlusTranscription(model, apiKey, config?)#

Seed Speech transcription adapter. Reads BYTEPLUS_VOICE_API_KEY.

Provider Tools#

BytePlus does not expose provider-specific tool factories. Define your own with toolDefinition() from @tanstack/ai — Ark's tool calling is the standard OpenAI shape and works with the usual tools flow.

Next Steps#

• Video Generation — the full jobs/polling flow and the useGenerateVideo hook
• Image Generation — image-conditioned generation and role hints
• Structured Outputs — schemas, validation and streaming
• Other Adapters — explore other providers