One line. Every request protected.

npm install @squadem/sdk

import { squadem } from "@squadem/sdk";

// One line — patches fetch & axios globally
squadem.init({ prompt: true, humanize: true });

// Use OpenAI normally — the SDK handles the rest
const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    {
      role: "user",
      content: "Summarize the contract for John Smith, SSN 412-55-7890"
    }
  ]
});

// What actually left your machine:
// "Summarize the contract for {{NAME_1}}, SSN {{SSN_1}}"
//
// What the LLM responded with:
// "The contract for {{NAME_1}} (SSN: {{SSN_1}}) outlines the following..."
//
// What you see in your code (humanized + restored):
// "The contract for John Smith (SSN: 412-55-7890) covers these terms..."

import Anthropic from "@anthropic-ai/sdk";
import { squadem } from "@squadem/sdk";

squadem.init();

const anthropic = new Anthropic();
const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-20250514",
  max_tokens: 1024,
  messages: [
    {
      role: "user",
      content: "Draft an NDA between Alice Johnson (alice@acme.com) and Bob Lee"
    }
  ]
});

// Sent to Anthropic:
// "Draft an NDA between {{NAME_1}} ({{EMAIL_1}}) and {{NAME_2}}"
//
// Returned to your code:
// Full NDA with real names and emails restored

import express from "express";
import { squadem } from "@squadem/sdk";

const app = express();

// Protect every route that talks to an LLM
app.use(squadem.middleware());

app.post("/api/chat", async (req, res) => {
  // req.body.message is already masked
  const reply = await callAnyLLM(req.body.message);
  // res.json sends the restored version to the client
  res.json({ reply });
});

import squadem
from openai import OpenAI

squadem.init()  # patches requests & httpx

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Send the invoice to david@company.io, amount $42,500"}
    ]
)

# Sent to OpenAI:
# "Send the invoice to {{EMAIL_1}}, amount {{CURRENCY_1}}"
#
# Returned to your code:
# Real email and amount restored seamlessly

import { squadem } from "@squadem/sdk";

squadem.init({
  anonymize: true,   // mask PII before it leaves
  prompt: true,      // compress & restructure to save tokens
  humanize: true,    // rewrite AI responses to sound human
});

// Step 1 — You write a messy prompt with real data
const prompt = `Hey can you please write a follow-up email to Sarah Parker
  her email is sarah@delta.io and its about the $85,000 deal
  we discussed last week, make it professional`;

// Step 2 — Anonymized + compressed (what the LLM actually receives)
// "Write professional follow-up email to {{NAME_1}} ({{EMAIL_1}})
//  re: {{CURRENCY_1}} deal discussed last week"
//
// 52 tokens → 24 tokens (54% saved)

const reply = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: prompt }]
});

// Step 3 — Raw LLM output (robotic, with tokens)
// "Dear {{NAME_1}}, I hope this message finds you well. I am writing to
//  follow up regarding the {{CURRENCY_1}} deal we discussed previously..."
//
// Step 4 — What your code gets back (humanized + PII restored)
// "Hi Sarah, Just circling back on the $85,000 deal we talked about..."