@postalsys/bounce-classifier

SMTP bounce message classifier using machine learning. Classifies email bounce/error messages into 16 actionable categories — and maps each to a concrete next step (remove, retry, retry_different_ip, fix_configuration, review, remove_content).

Runs entirely client-side in Node.js or the browser. No API calls, no PII leaves your infrastructure, no runtime dependencies, ~1.3 MB model, ~95% in-distribution accuracy.

Live Demo · Contribute labels → (submitting mislabeled bounces improves the next model)

Note

Built for EmailEngine, a self-hosted email gateway that speaks REST to IMAP/SMTP accounts. See the messageBounce webhook docs for the integration.

When to use

• High-volume bounce pipelines where per-classification API cost or latency matters.
• Privacy-sensitive workloads where bounce bodies must not leave the process.
• Offline / edge / browser contexts where a server call isn't an option.
• Any time you want action (remove vs. retry vs. fix_configuration) rather than just a label.

When not to use

• Bouncing < 100 messages a day, all from a handful of providers — a regex is simpler.
• Non-English bounces outside the trainer's distribution — accuracy will be noticeably lower. Contribute samples at bounces.postalsys.com if this matters.
• You need a single-call "parse this whole MIME bounce" pipeline — this library takes the human-readable diagnostic text; pair it with your DSN/ARF parser of choice.

Labels

Label	Description	Action
user_unknown	Recipient doesn't exist	remove
invalid_address	Bad syntax, domain not found	remove
mailbox_disabled	Account suspended/disabled	remove
mailbox_full	Over quota, storage exceeded	retry
greylisting	Temporary rejection, retry later	retry
rate_limited	Too many connections/messages	retry
server_error	Timeout, connection failed	retry
ip_blacklisted	Sender IP on RBL	retry_different_ip
domain_blacklisted	Sender domain on blocklist	fix_configuration
auth_failure	DMARC/SPF/DKIM failure	fix_configuration
relay_denied	Relaying not permitted	fix_configuration
spam_blocked	Message detected as spam	review
policy_blocked	Local policy rejection	review
virus_detected	Infected content detected	remove_content
geo_blocked	Geographic/country-based rejection	retry_different_ip
unknown	Unclassified — queue for review and submit to the trainer	review

Installation

npm install @postalsys/bounce-classifier

Usage

ES Modules (Browser & Node.js)

import { classify, initialize } from "@postalsys/bounce-classifier";

// Optional: pre-load the model
await initialize();

const result = await classify("550 5.1.1 User Unknown");
console.log(result.label); // 'user_unknown'
console.log(result.confidence); // 0.95
console.log(result.action); // 'remove'

CommonJS (Node.js)

const { classify } = require("@postalsys/bounce-classifier");

async function main() {
  const result = await classify("550 5.1.1 User Unknown");
  console.log(result);
}

main();

Browser Usage

<script type="module">
  import { classify, initialize } from "./src/index.js";

  // Specify model path for browser
  await initialize({ modelPath: "./model" });

  const result = await classify("550 5.1.1 User Unknown");
  console.log(result);
</script>

See the example/ folder for a complete standalone browser demo that works offline.

API

initialize(options?): Promise<void>

Pre-load the model and vocabulary. Called automatically on first classification, but calling it up front lets you report load progress to the user.

// Node.js - uses bundled model automatically
await initialize();

// Browser - specify model path
await initialize({ modelPath: "./path/to/model" });

// With progress reporting (browser streams the weights file)
await initialize({
  modelPath: "./model",
  onProgress: ({ phase, loaded, total }) => {
    console.log(`${phase}: ${loaded}/${total}`);
  },
});

phase is one of "vocab", "labels", "weights", or "config". In the browser the "weights" phase streams and fires multiple events with monotonically increasing loaded; other phases fire once at completion.

classify(message: string): Promise<ClassificationResult>

Classify a single bounce message.

const result = await classify("450 Greylisted, try again in 5 minutes");
// {
//   label: 'greylisting',
//   confidence: 0.947,
//   action: 'retry',
//   retryAfter: 300,  // seconds (only if timing found in message)
//   scores: { ... }
// }

const result2 = await classify("550 blocked using zen.spamhaus.org");
// {
//   label: 'ip_blacklisted',
//   confidence: 0.958,
//   action: 'retry_different_ip',
//   blocklist: { name: 'Spamhaus ZEN', type: 'ip' },
//   scores: { ... }
// }

classifyBatch(messages: string[]): Promise<ClassificationResult[]>

Classify an array of bounce messages. Sequential today; the API is reserved for future vectorization. Errors on any item include .index identifying the failing message.

const results = await classifyBatch([
  "550 5.1.1 User unknown",
  "552 5.2.2 Over quota",
  "421 4.7.0 Try again later",
]);

registerTextFallback({ pattern, label }) / clearTextFallbacks()

Add project-specific text patterns that override the built-in fallback classification. User patterns are scanned before the built-ins. Survives reset() / reload(); clear explicitly with clearTextFallbacks().

import {
  registerTextFallback,
  clearTextFallbacks,
} from "@postalsys/bounce-classifier";

registerTextFallback({
  pattern: /XYZZY-PROVIDER-\d+/,
  label: "spam_blocked",
});

getLabels(): Promise<string[]>

Get list of all possible classification labels.

const labels = await getLabels();
// ['auth_failure', 'domain_blacklisted', 'geo_blocked', ...]

reload(options?): Promise<void>

Reload the model, optionally from a new path. Waits for any in-flight classify() calls to drain before swapping state — safe to call concurrently.

// Reload from the same path (e.g., after retraining)
await reload();

// Switch to a different model directory
await reload({ modelPath: "/path/to/new-model" });

getModelInfo(): ModelInfo

Get metadata about the loaded model. Always returns an object; the initialized flag distinguishes "classifier not yet loaded" from "config.json missing a field."

const info = getModelInfo();
// {
//   modelHash: '6b6a2c75307d59bf',    // truncated SHA-256 of weights
//   trainedAt: '2026-03-16T14:30:00Z', // ISO 8601 UTC
//   trainingSamples: 22630,
//   validationAccuracy: 0.9523,
//   initialized: true
// }

isReady(): boolean

Check if the classifier is initialized.

reset(): void

Reset classifier state for re-initialization. Does not clear user-registered text fallbacks — use clearTextFallbacks() for that.

Low-level helpers

import {
  extractRetryTiming,
  identifyBlocklist,
  getAction,
  extractSmtpCodes,
} from "@postalsys/bounce-classifier";

// Extract retry timing from message
const seconds = extractRetryTiming("try again in 5 minutes");
// 300

// Identify blocklists mentioned
const blocklist = identifyBlocklist("blocked by zen.spamhaus.org");
// { name: 'Spamhaus ZEN', type: 'ip' }

// Get recommended action for a label
const action = getAction("mailbox_full");
// 'retry'

// Extract SMTP codes
const codes = extractSmtpCodes("550 5.1.1 User unknown");
// { mainCode: '550', extendedCode: '5.1.1' }

Custom Model Path

You can point the classifier to a different model directory, for example to use a retrained model:

import {
  initialize,
  classify,
  reload,
  getModelInfo,
} from "@postalsys/bounce-classifier";

// Use a custom model at startup
await initialize({ modelPath: "/path/to/retrained-model" });

// Later, after retraining, reload the model without restarting
await reload();

// Check which model version is loaded
const info = getModelInfo();
console.log(info.modelHash); // '6b6a2c75307d59bf'

The model directory must contain vocab.json, labels.json, and group1-shard1of1.bin. The optional config.json provides metadata exposed through getModelInfo(). (model.json, kept for TensorFlow.js compatibility, is shipped alongside the model but is not loaded by this pure-JS implementation.)

SMTP Code Fallback

When the ML model has low confidence (< 50%), the classifier falls back to SMTP status code–based classification using RFC 3463 enhanced status codes. This ensures reliable classification even for messages the model hasn't seen.

const result = await classify("550 5.2.2 Over quota");
// If ML confidence is low, uses 5.2.2 -> mailbox_full fallback
// result.usedFallback will be true

Running the Demo

The example/ folder contains a browser demo. To run it:

cd example
npx serve ..
# Open http://localhost:3000/example/ in your browser

Model Details

• Architecture: Embedding + GlobalAveragePooling + Dense layers
• Vocabulary size: 5,000 tokens
• Max sequence length: 100 tokens
• Validation accuracy: ~95% (held-out slice of the trainer corpus — this is in-distribution; real-world accuracy on your sender mix will depend on how well it's represented in the training data)
• Model size: ~1.3 MB
• Runtime: Pure JavaScript (no native dependencies)

Help improve accuracy by contributing labeled bounces at bounces.postalsys.com.

License

MIT License - Copyright (c) Postal Systems OU