AI Integration

The 2328.io documentation is built to be LLM-friendly. You can hand the entire API reference to any modern AI assistant and have it generate a working integration in the language of your choice — PHP, Node.js, Python, Go, Rust — in minutes instead of hours.

This page explains how to do it efficiently.

Why use AI to integrate

• Faster onboarding — skip boilerplate, jump straight to business logic
• Correct signing — AI reliably reproduces HMAC-SHA256 signing in any language
• Webhook handlers — generate signature verification and idempotent handlers out of the box
• Up-to-date — our llms-full.txt is regenerated on every docs update, so you always get current schemas

Machine-readable docs

We publish three endpoints following the llmstxt.org standard:

Every HTML page also exposes <link rel="alternate" type="text/markdown"> pointing to its Markdown version, so AI crawlers discover it automatically.

Quick start with Claude or ChatGPT

Step 1 — Provide the docs

Open a fresh chat and paste the contents of llms-full.txt as your first message, or just share the link if the model can fetch it.

Step 2 — Describe your stack

Tell the assistant what you are building:

I'm building a Laravel 11 application. I need to:
1. Create a payment for an order (amount in USD, user pays in USDT TRC20)
2. Handle the webhook and credit the user's balance
3. Store payment records in a `payments` table

Use the 2328.io API above. Include HMAC signing, webhook signature
verification, and idempotency.

Step 3 — Review and test

The assistant will produce a controller, a service class, and a webhook handler. Before shipping:

• Verify that apiSign() encodes the body as Base64 before HMAC-SHA256
• Check that webhook handlers call hash_equals() (not ===) to compare signatures
• Make sure the handler is idempotent — check order_id / txid before crediting
• Test with a small real payment on a dev environment first

IDE integrations

Cursor

Add the docs as a custom docs source in Cursor settings:

Settings → Features → Docs → Add new doc
URL: https://doc.2328.io

Then in chat, prefix your question with @2328.io:

@2328.io generate a webhook handler in Next.js App Router
with signature verification and idempotent credit logic

GitHub Copilot

Copilot Chat can read llms-full.txt directly:

#fetch https://doc.2328.io/llms-full.txt

Using the 2328.io API docs above, implement a payout endpoint
in Express that withdraws USDT BEP20 to a user-supplied address.

Windsurf / Continue / other assistants

Any assistant that supports a URL context or file attachment works the same way — attach llms-full.txt and describe your goal.

Claude API (Agent SDK)

If you're building your own agent or chatbot that needs to interact with 2328.io, inject the docs once into the system prompt:

from anthropic import Anthropic
import urllib.request

docs = urllib.request.urlopen(
    "https://doc.2328.io/llms-full.txt"
).read().decode()

client = Anthropic()

response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=4096,
    system=f"""You are an integration assistant for 2328.io.
Use the API reference below to answer questions and generate code.

<docs>
{docs}
</docs>""",
    messages=[
        {"role": "user", "content": "Write a Python function that creates a USDT payment"}
    ],
)

print(response.content[0].text)

Example prompts that work well

Copy these into Claude, ChatGPT, or your AI IDE after sharing llms-full.txt:

Full backend integration:

Build a Node.js + Express service that exposes two routes:
- POST /checkout → creates a 2328.io payment and returns the payment URL
- POST /webhook/2328 → verifies the signature and marks the order as paid
Use TypeScript, Zod for validation, and a simple in-memory store.

Payout tool:

Write a CLI in Go that takes a currency, network, amount, and address
and creates a payout via the 2328.io Payout API. Use a separate payout
API key from env. Poll the status endpoint until the payout is completed.

Static wallet for user deposits:

I have a Django app where users deposit USDT TRC20 to top up their balance.
Each user should have a permanent deposit address. Implement this using
2328.io static wallets, including the webhook handler that credits their
balance when a deposit arrives.

Best practices for AI-assisted integration

• Start from llms-full.txt — it's designed for LLM context, no boilerplate
• Be specific about your stack — framework, language version, ORM
• Ask for tests — AI is good at generating unit tests for signing logic
• Double-check error handling — AI sometimes skips failure paths
• Review signature code manually — this is the only part that must be exactly right
• Refresh periodically — if our API changes, refetch llms-full.txt and re-prompt