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Quickstart

Get up and running with core-ai in just a few minutes. This guide shows you how to create a simple chat completion using OpenAI’s GPT model.

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Prerequisites

Before you begin, make sure you have:

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Create your first chat completion

1

Set up your project

Create a new file called chat.ts in your project:
touch chat.ts
Make sure you have your API key set as an environment variable:
export OPENAI_API_KEY="your-api-key-here"
2

Import dependencies

Add the necessary imports to your chat.ts file:
chat.ts
import { generate } from '@core-ai/core-ai';
import { createOpenAI } from '@core-ai/openai';
The generate function handles chat completions, while createOpenAI initializes the OpenAI provider.
3

Initialize the provider and model

Create an OpenAI provider instance and select a chat model:
chat.ts
const openai = createOpenAI({ apiKey: process.env.OPENAI_API_KEY });
const model = openai.chatModel('gpt-5-mini');
You can use any OpenAI model ID like gpt-5.4, gpt-5-mini, gpt-5, or o3-mini.
4

Generate a response

Call the generate function with your model and messages:
chat.ts
const result = await generate({
  model,
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Explain quantum computing in one sentence.' },
  ],
});

console.log(result.content);
console.log('Usage:', result.usage);
5

Run your code

Execute your script using tsx:
npx tsx chat.ts
You should see the AI’s response printed to the console along with token usage statistics.

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Complete example

Here’s the full working example:
chat.ts
import { generate } from '@core-ai/core-ai';
import { createOpenAI } from '@core-ai/openai';

const openai = createOpenAI({ apiKey: process.env.OPENAI_API_KEY });
const model = openai.chatModel('gpt-5-mini');

const result = await generate({
  model,
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Explain quantum computing in one sentence.' },
  ],
});

console.log('Response:', result.content);
console.log('Usage:', result.usage);
// Output:
// Response: Quantum computing uses quantum mechanical phenomena...
// Usage: { inputTokens: 25, outputTokens: 18, inputTokenDetails: { ... }, outputTokenDetails: { ... } }

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Try streaming

core-ai makes streaming responses just as easy. Here’s how to stream text as it’s generated:
streaming.ts
import { stream } from '@core-ai/core-ai';
import { createOpenAI } from '@core-ai/openai';

const openai = createOpenAI({ apiKey: process.env.OPENAI_API_KEY });
const model = openai.chatModel('gpt-5-mini');

const result = await stream({
  model,
  messages: [
    { role: 'user', content: 'Write a short haiku about TypeScript.' },
  ],
});

// Stream each text chunk as it arrives
for await (const event of result) {
  if (event.type === 'text-delta') {
    process.stdout.write(event.text);
  }
}

// Get the complete response with metadata
const response = await result.result;
console.log('\nFinish reason:', response.finishReason);
console.log('Usage:', response.usage);
The .result property returns a promise that resolves with the aggregated GenerateResult once the stream completes. Streams are replayable — you can iterate multiple times.

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Switch providers

One of core-ai’s key features is provider portability. Switch from OpenAI to Anthropic with just two lines: 
import { generate } from '@core-ai/core-ai';
import { createOpenAI } from '@core-ai/openai';

const openai = createOpenAI({ apiKey: process.env.OPENAI_API_KEY });
const model = openai.chatModel('gpt-5-mini');

const result = await generate({
  model,
  messages: [{ role: 'user', content: 'Hello!' }],
});

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Next steps

Now that you have a working chat completion, explore more advanced features:

Structured outputs

Generate type-safe JSON objects with Zod schemas

Tool calling

Let AI call functions with validated parameters

Embeddings

Generate vector embeddings for semantic search

Image generation

Create images from text prompts