TypeScript SDK

npm install @alveare-ai/sdk

import Alveare from '@alveare-ai/sdk';

const client = new Alveare({ apiKey: 'alv_live_abc123...' });

const response = await client.infer({
  specialist: 'summarise',
  prompt: 'Quarterly revenue increased 23% year-over-year...',
  maxTokens: 256,
});

console.log(response.result);
console.log(`Tokens: ${response.tokensUsed}`);
console.log(`Latency: ${response.latencyMs}ms`);

import Alveare from '@alveare-ai/sdk';

// Reads ALVEARE_API_KEY from process.env automatically
const client = new Alveare();

// Or configure everything explicitly
const client = new Alveare({
  apiKey: process.env.MY_ALVEARE_KEY,
  baseUrl: 'https://api.alveare.ai',
  timeout: 60000,
  maxRetries: 5,
});

// Classification
const result = await client.infer({
  specialist: 'classify',
  prompt: 'I need to cancel my subscription immediately',
  temperature: 0.3,
});
console.log(result.result); // "cancellation_request"

// Extraction
const extracted = await client.infer({
  specialist: 'extract',
  prompt: `Extract contact info: Hi, I'm Jane Smith
from Acme Corp. Reach me at jane@acme.com or 555-0123.`,
  maxTokens: 256,
});
console.log(extracted.result);
// {"name":"Jane Smith","company":"Acme Corp","email":"jane@acme.com","phone":"555-0123"}

const response = await client.chat.completions.create({
  model: 'alveare-chat',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is a cognitive hive?' },
  ],
  maxTokens: 512,
  temperature: 0.7,
});

console.log(response.choices[0].message.content);
console.log(`Usage: ${response.usage.totalTokens} tokens`);

const models = await client.models.list();

for (const model of models.data) {
  console.log(`${model.id} — ${model.ownedBy}`);
}

const usage = await client.usage();

console.log(`Requests: ${usage.requestsUsed} / ${usage.requestsLimit}`);
console.log(`Tokens:   ${usage.tokensUsed}`);

for (const [name, count] of Object.entries(usage.bySpecialist)) {
  console.log(`  ${name}: ${count}`);
}

import Alveare, {
  AlveareError,
  AuthenticationError,
  RateLimitError,
} from '@alveare-ai/sdk';

const client = new Alveare();

try {
  const result = await client.infer({
    specialist: 'summarise',
    prompt: 'Some long text...',
  });
} catch (err) {
  if (err instanceof AuthenticationError) {
    console.error('Check your API key:', err.message);
  } else if (err instanceof RateLimitError) {
    console.error(`Rate limited. Retry after ${err.retryAfter}s`);
  } else if (err instanceof AlveareError) {
    console.error(`API error: ${err.message} (${err.code})`);
  }
}

import type {
  InferRequest,         // { specialist, prompt, maxTokens?, temperature? }
  InferResponse,        // { id, specialist, result, tokensUsed, latencyMs }
  ChatCompletionRequest,  // { model, messages, maxTokens?, temperature?, stream? }
  ChatCompletionResponse, // { id, object, created, model, choices[], usage }
  ChatMessage,          // { role: 'system'|'user'|'assistant', content: string }
  Model,                // { id, object, ownedBy }
  Usage,                // { requestsUsed, requestsLimit, tokensUsed, ... }
  Specialist,           // 'classify'|'summarise'|'extract'|'qa'|'chat'|'code'
} from '@alveare-ai/sdk';

// All methods are fully typed — your editor provides
// autocomplete and type checking out of the box.
const req: InferRequest = {
  specialist: 'classify',  // autocomplete suggests valid specialists
  prompt: 'Hello world',
  maxTokens: 64,
};