TypeScript SDK

📦 View on npm

Installation

Install the AI Inbx TypeScript SDK using your preferred package manager:

npm install aiinbx

yarn add aiinbx

pnpm add aiinbx

bun add aiinbx

Quick Start

Initialize the client with your API key:

import AIInbx from 'aiinbx';

// If using the default environment variable AI_INBX_API_KEY
const aiInbx = new AIInbx();

// Or explicitly pass your API key
// const aiInbx = new AIInbx({
//   apiKey: 'your-api-key-here',
// });

// Search threads
const response = await aiInbx.threads.search({});
console.log(response.threads);

const newestThread = response.threads[0];
const newestEmail = newestThread.emails[0];

// Reply to an email
await aiInbx.emails.reply(newestEmail.id, {
  from: "your-domain@example.com",
  html: "<p>This is a test reply email</p>"
});

Built-in Helpers

1. Next.js webhook handler

createNextRouteHandler() generates a ready-to-use POST handler that:

Validates the request signature using the AI_INBX_SECRET env variable.
Parses the webhook payload and routes it to the correct callback.

Handler parameters

2. LLM-friendly formatting

Use emailToLLMString() and threadToLLMString() to transform rich email objects into compact, XML-ish strings optimised for Large Language Models:

import {
  emailToLLMString,
  threadToLLMString,
} from 'aiinbx/helpers';

const emailPrompt = emailToLLMString(email);
const threadPrompt = threadToLLMString(thread);

These helpers:

Keep only fields relevant for reasoning (addresses, subject, text, etc.).
Preserve structure with lightweight XML-like tags.
Return a single string ready to drop into your prompts.

Full example: AI-powered support bot

app/api/aiinbx-webhook/route.ts

import { openai } from '@ai-sdk/openai';
import { generateObject } from 'ai';
import { z } from 'zod';
import AIInbx from 'aiinbx';
import {
  createNextRouteHandler,
  emailToLLMString,
  threadToLLMString,
} from 'aiinbx/helpers';

const aiInbx = new AIInbx();

export const POST = createNextRouteHandler({
  onInboundEmail: async ({ payload }) => {
    const { email } = payload.data;
    const thread = await aiInbx.threads.retrieve(email.threadId);

    const { object } = await generateObject({
      model: openai('gpt-4.1-mini'),
      schema: z.object({
        responseHtml: z.string(),
      }),
      system: `You are a helpful assistant that can gets incoming emails and should answer them as a human would.`,
      prompt: `Here are the emails so far: ${threadToLLMString(thread)}

Here is the new email: ${emailToLLMString(email)}

Please answer the email as a human would.`,
    });

    await aiInbx.emails.reply(email.id, {
      from: 'support@your-domain.com',
      html: object.responseHtml,
    });

    return Response.json({ sent: true });
  },
});

ℹ️ The example above is completely serverless and needs no additional infrastructure.

Complete Documentation

For comprehensive documentation including:

Request & response types
Error handling
Advanced configuration
All available methods
TypeScript examples

Visit the complete TypeScript SDK documentation on npm.

Getting started

SDKs

Webhooks

Entities

Installation

Quick Start

Built-in Helpers

1. Next.js webhook handler

Handler parameters

2. LLM-friendly formatting

Full example: AI-powered support bot

Complete Documentation

​Installation

​Quick Start

​Built-in Helpers

​1. Next.js webhook handler

​Handler parameters

​2. LLM-friendly formatting

​Full example: AI-powered support bot

​Complete Documentation

Installation

Quick Start

Built-in Helpers

1. Next.js webhook handler

Handler parameters

2. LLM-friendly formatting

Full example: AI-powered support bot

Complete Documentation