TypeScript SDK Integration Guide

@acedatacloud/sdk is the official TypeScript / JavaScript SDK for Ace Data Cloud, encapsulating all services on api.acedata.cloud into typed methods such as client.openai.chat.completions.create(...), client.images.generate(...), client.search.google(...), etc., with built-in SSE streaming, retry backoff, and typed exceptions.

It can be used in Node.js, Deno, Bun, and modern browsers (with bundler).

Source code and package address:

Installation

npm install @acedatacloud/sdk
# or pnpm add / yarn add / bun add

If you need to pay on the X402 chain (without API Token path), install another one:

npm install @acedatacloud/x402-client ethers

Clean npm project version check output:

$ npm ls @acedatacloud/sdk
└── @acedatacloud/sdk@2026.504.2

$ node -e "console.log(require('@acedatacloud/sdk').AceDataCloud?.name)"
AceDataCloud

Result explanation:

  • The package version is 2026.504.2 (CalVer, the 2nd revision of the 504th ISO week of 2026).
  • AceDataCloud is the main class used to construct the client, accessible from the default export.

Prepare API Token

Refer to SDK Overview - Apply for API Token to obtain the token, then export it in the shell:

export ACEDATACLOUD_API_TOKEN={token}

When constructing the client, if apiToken is not passed, the SDK will automatically read the ACEDATACLOUD_API_TOKEN environment variable. If you already have ACEDATACLOUD_API_KEY stored in your environment (as per project repository convention), you can explicitly pass it: new AceDataCloud({ apiToken: process.env.ACEDATACLOUD_API_KEY }).

Example 1: chat.completions (non-streaming)

import { AceDataCloud } from '@acedatacloud/sdk';

const client = new AceDataCloud();

const t0 = Date.now();
const res = await client.openai.chat.completions.create({
  model: 'gpt-4o-mini',
  messages: [
    { role: 'user', content: 'Reply with exactly: ADC_TS_SDK_OK' }
  ],
  max_tokens: 20,
  temperature: 0
});
console.log('elapsed_ms', Date.now() - t0);
console.log('id', res.id);
console.log('model', res.model);
console.log('content', res.choices[0].message.content);
console.log('usage', JSON.stringify(res.usage));

Program output:

elapsed_ms 2543
id chatcmpl-DldCcLvkTFaioST8e6SjOl0wJScQA
model gpt-4o-mini
content ADC_TS_SDK_OK
usage {"prompt_tokens":16,"completion_tokens":6,"total_tokens":22}

Result explanation:

  • id chatcmpl-DldCcLvkTFaioST8e6SjOl0wJScQA is the OpenAI compatible response ID, which can be found in the console usage history.
  • content ADC_TS_SDK_OK is the fixed identifier returned by the model, proving that the response has not been tampered with by the SDK.
  • One chat completion consumes about 22 tokens, billed at the rate of gpt-4o-mini.
  • The SDK declares the response as Record<string, unknown>, which is a JSON object at runtime; dot access like .id / .choices[0].message.content can run under .mjs, Node REPL, and Bun; strict TypeScript projects may require (res as any).id or turning off noImplicitAny in tsconfig.

Example 2: chat.completions (SSE streaming)

By setting stream: true, create returns an asynchronous iterator, with each frame being a ChatCompletionChunk.

import { AceDataCloud } from '@acedatacloud/sdk';

const client = new AceDataCloud();

const t0 = Date.now();
let firstChunkMs: number | null = null;
let chunks = 0;
const collected: string[] = [];

const stream = await client.openai.chat.completions.create({
  model: 'gpt-4o-mini',
  messages: [
    { role: 'user', content: 'Count from 1 to 5, separated by single spaces, no extra text.' }
  ],
  max_tokens: 20,
  stream: true
});

for await (const chunk of stream) {
  if (firstChunkMs === null) firstChunkMs = Date.now() - t0;
  chunks++;
  const delta = chunk.choices[0]?.delta?.content;
  if (delta) collected.push(delta);
}

console.log('total_elapsed_ms', Date.now() - t0);
console.log('first_chunk_ms', firstChunkMs);
console.log('chunks', chunks);
console.log('collected', collected.join('').trim());

Program output:

total_elapsed_ms 2616
first_chunk_ms 2481
chunks 13
collected 1 2 3 4 5

Result explanation:

  • The first frame delay of 2481 ms is the time taken by the model to generate the first token; the subsequent 12 frames arrived within 135 ms.
  • The 13 frames together form "1 2 3 4 5", with each token forming a separate frame + the last frame containing finish_reason.
  • Streaming does not save more tokens than non-streaming, but the first token delay is significantly reduced, making it suitable for real-time UI.

Example 3: images.generate (NanoBanana)

client.images.generate({ provider: 'nano-banana', ... }) returns directly synchronously, no need to pass the wait parameter—NanoBanana itself generates synchronously.

import { AceDataCloud } from '@acedatacloud/sdk';

const client = new AceDataCloud();

const t0 = Date.now();
const img = await client.images.generate({
  provider: 'nano-banana',
  prompt: 'A minimalist logo of a yellow banana on a white background, flat design'
});
console.log('elapsed_ms', Date.now() - t0);
console.log('task_id', img.task_id);
console.log('trace_id', img.trace_id);
console.log('image_url', img.data[0].image_url);

Program output:

elapsed_ms 16634
task_id 8e4b44a6-5ece-46a4-9013-9e0c8aca2217
trace_id 9529e241-54fe-40da-98a2-871e14989fb5
image_url https://platform.cdn.acedata.cloud/nanobanana/331be1d3-3330-4196-bd1c-aa75717c549c.png

Result explanation:

  • image_url is a stable address on the CDN, which can be directly used in <img src> or downloaded.
  • Most of the 16.6 seconds is spent on upstream model inference, with local SDK overhead being negligible.
  • trace_id is the request ID assigned by the platform; if there are issues, providing this ID to customer service can help locate the problem quickly.
  • For asynchronous services (Midjourney, Sora, Veo, etc.), TaskHandle polling is required; see SDK Task Polling and Streaming Response.

Example 4: Typed Error Handling

The SDK will throw errors as specific subclasses (e.g., AuthenticationError, BadRequestError, RateLimitError, InternalServerError, APIConnectionError, etc.) based on HTTP status, allowing for precise branching using instanceof.

import { AceDataCloud, AuthenticationError } from '@acedatacloud/sdk';

const bad = new AceDataCloud({ apiToken: 'definitely-not-a-real-token' });

try {
  await bad.openai.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: 'hi' }],
    max_tokens: 5
  });
} catch (err: any) {
  console.log('err_class', err.constructor.name);
  console.log('status', err.statusCode);
  console.log('code', err.code);
  console.log('instanceof AuthenticationError =', err instanceof AuthenticationError);
}

Program output:

A. err_class AuthenticationError
A. status 401
A. code invalid_token
A. instanceof AuthenticationError = true

Result explanation:

  • 401 automatically maps to AuthenticationError, business code can use instanceof for precise branching.
  • code: invalid_token comes from PlatformGateway, facilitating comparison with backend logs.
  • Similarly, 429 → RateLimitError, 400 → BadRequestError, 5xx → InternalServerError.

Example 5: Multi-model Routing

The same client can switch freely between multiple providers, as long as the model names are consistent.

import { AceDataCloud } from '@acedatacloud/sdk';

const client = new AceDataCloud();

const MODELS = ['gpt-4o-mini', 'gemini-2.5-flash', 'deepseek-v3', 'grok-3-fast'];

for (const model of MODELS) {
  const t0 = Date.now();
  try {
    const r = await client.openai.chat.completions.create({
      model,
      messages: [{ role: 'user', content: 'Reply with exactly: ADC_OK' }],
      max_tokens: 5
    });
    console.log(model.padEnd(28), `${Date.now() - t0}ms`, `content="${r.choices[0].message.content}"`);
  } catch (err: any) {
    console.log(model.padEnd(28), `${Date.now() - t0}ms`, 'ERR', err.statusCode, err.code);
  }
}

Program output:

gpt-4o-mini                  2189ms   content="ADC_OK"
gemini-2.5-flash             2569ms   content=""
deepseek-v3                  2047ms   content="ADC_OK"
grok-3-fast                  3598ms   content="ADC_OK"

Result explanation:

  • One code, one token, covering OpenAI / Google / DeepSeek / xAI four upstreams.
  • gemini-2.5-flash did not return ADC_OK this time, due to the inherent output style differences of the upstream model—the SDK did not silently swallow anything, faithfully passing the model's original words to the business.
  • Pricing is based on each respective real token unit price, with the path passing through PlatformGateway only once.
import { AceDataCloud } from '@acedatacloud/sdk';

const client = new AceDataCloud();

const t0 = Date.now();
const r = await client.search.google({
  query: 'Ace Data Cloud',
  resource: 'web'
});
const items = (r as any).organic ?? [];
console.log('elapsed_ms', Date.now() - t0);
console.log('organic_count', items.length);
items.slice(0, 2).forEach((it: any, i: number) => {
  console.log(`#${i + 1}`, it.title, '->', it.link);
});

Program output:

elapsed_ms 2382
organic_count 10
#1 Ace Data Cloud -> https://platform.acedata.cloud/
#2 Ace Data Cloud - GitHub -> https://github.com/acedatacloud

Result explanation:

  • A single request retrieves 10 organic results, with the field name organic (not organic_results).
  • The search goes through the Serp service, billed per request.
  • The same client instance can both chat and search, one token is sufficient.

Configuration Options

const client = new AceDataCloud({
  // One of the required: explicit token or environment variable ACEDATACLOUD_API_TOKEN
  apiToken: process.env.MY_TOKEN,

  // Platform API root address, default https://api.acedata.cloud
  baseURL: 'https://api.acedata.cloud',

  // Some services (like dashboard metadata) go through the platform domain
  platformBaseURL: 'https://platform.acedata.cloud',

  // Single request timeout, in milliseconds; default 300_000 (5 minutes)
  timeout: 300_000,

  // Automatic retry count, default 2; retry conditions: 408 / 409 / 429 / 5xx / network errors
  maxRetries: 2,

  // Custom request headers
  defaultHeaders: { 'x-app': 'my-service/1.0' }
});

Browser Usage

@acedatacloud/sdk is an ESM + ISO (universal) package that can be directly imported in modern browsers with bundlers. Note: Do not hard-code the API Token in frontend code. Recommended for frontend:

  1. Use X402 paymentHandler — user wallets pay per use in USDC, no token required.
  2. Or use the SDK on your own server, with the browser only calling your own backend.

Advanced: Task Polling and Streaming Responses

  • Task-based services (Midjourney, Sora, Veo, Suno): use TaskHandle for polling, unit, timeout, and retry details see SDK Task Polling and Streaming.
  • Streaming chat: already demonstrated in Example 2; streaming audio/video is also supported.

Advanced: X402 Payment Hooks

If you do not want to apply for an API Token and wish to pay per use on-chain, you can use paymentHandler:

import { AceDataCloud } from '@acedatacloud/sdk';
import { createX402PaymentHandler } from '@acedatacloud/x402-client';

const client = new AceDataCloud({
  paymentHandler: createX402PaymentHandler({
    network: 'base',
    evmProvider: (window as any).ethereum,  // EIP-1193 provider, or viem walletClient
    evmAddress: userAddress
  })
});

createX402PaymentHandler accepts { network, evmProvider, evmAddress, preferScheme? } (EVM chain) or { network: 'solana', solanaWallet } (Solana) on the TypeScript side. When the Node server does not have window.ethereum, please use viem's createWalletClient (based on private key) to wrap an EIP-1193 compatible provider and pass it in; detailed methods and real on-chain results see SDK + X402 Payment Hooks.

How to Check Remaining Quota

You can check the current account's remaining quota through the Ace Data Cloud Console - Application List.

You can view all usage history and billing details through the Ace Data Cloud Console - Usage History.

Learn More