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How to Triage WhatsApp Messages with Jev How to Triage WhatsApp Messages with Jev

How to Triage WhatsApp Messages with Jev

SDK version

This guide was written against @typesafe-ai/sdk 0.6.0 from TypeSafe AI. The SDK is still pre-1.0, so check the TypeSafe docs if something here no longer matches.

Jev is a "System One" model: it doesn't write text. You send it a state (text, a JSON object, or an array) and a set of named, typed questions, and it returns structured answers:

  • Choice — picks one option from a set (up to 255), with per-option probabilities and a confidence value
  • Score — places the state on an ordered rubric of 2–10 levels
  • Noul — a yes/no question answered as a probability from 0 to 1

That makes it a good fit for the decisions a WhatsApp bot makes on every message — which team should see this, how urgent is it, is it spam, does it need a reply at all — and a poor fit for writing the reply itself. This guide uses Jev for the decisions and an LLM only for the replies that need one.

Prerequisites​

  • A paired whatsmeow-node session (How to Pair)
  • A TypeSafe API key (set as TYPESAFE_API_KEY environment variable)
  • The TypeSafe SDK (Node.js 20+): npm install @typesafe-ai/sdk
  • For the LLM step: an OpenAI API key and npm install openai (see Connect to ChatGPT) — or swap in Claude (Connect to Claude AI)

Step 1: Set Up Both Clients​

import { createClient } from "@whatsmeow-node/whatsmeow-node";
import { TypeSafeClient } from "@typesafe-ai/sdk";

const client = createClient({ store: "session.db" });

// Reads TYPESAFE_API_KEY from env. The default model is "jev-latest",
// which can change under you — pin a version for stable behavior.
const jev = new TypeSafeClient({ defaultModel: "jev-1.13.0", timeout: 3_000 });

timeout is per attempt, in milliseconds. By default the SDK retries timeouts, connection errors, and 408, 429, and 5xx responses twice, with backoff.

Step 2: Pass Recent Conversation as State​

A single message is often ambiguous ("yes please", "still broken"). Keep the last few messages per chat and send them as state, with the latest message separated out so your questions can refer to it:

import type { MessageInfo } from "@whatsmeow-node/whatsmeow-node";

const HISTORY_SIZE = 10;

type ChatLine = { id: string; from: string; fromMe: boolean; text: string };
const history = new Map<string, ChatLine[]>();

function remember(chat: string, line: ChatLine) {
  const lines = history.get(chat) ?? [];
  lines.push(line);
  if (lines.length > HISTORY_SIZE) lines.splice(0, lines.length - HISTORY_SIZE);
  history.set(chat, lines);
}

function extractText(message: Record<string, unknown>) {
  const ext = message.extendedTextMessage as
    | { text?: string; contextInfo?: { stanzaID?: string } }
    | undefined;
  return {
    text: (message.conversation as string | undefined) ?? ext?.text,
    quotedId: ext?.contextInfo?.stanzaID,
  };
}

function buildState(info: MessageInfo, text: string, quotedId?: string) {
  const lines = history.get(info.chat) ?? [];
  const quoted = quotedId ? lines.find((l) => l.id === quotedId) : undefined;
  return {
    recentMessages: lines.map((l) => ({ from: l.fromMe ? "business" : l.from, text: l.text })),
    latestMessage: { from: info.pushName, text, repliesTo: quoted?.text ?? null },
  };
}

Plain text arrives as message.conversation; replies, links, and mentions arrive as message.extendedTextMessage.text. When the user replies to an earlier message, extendedTextMessage.contextInfo.stanzaID holds that message's ID — note the capital ID, matching the WhatsApp protobuf JSON name. Looking it up in the history lets Jev see what "yes please" is answering.

TypeSafe recommends a JSON object with descriptive keys over a bare string, and sending only what the questions need: extra context can lower accuracy.

Jev's primary training language is English. TypeSafe's model docs say other languages are handled but not equally well, so if your chats aren't in English, test on real messages before you trust any threshold, especially the revoke one.

Step 3: Route with a Choice Question​

A Choice question picks one label. Describe each label so Jev knows where the boundaries are, and give yourself an explicit human option:

import { choice } from "@typesafe-ai/sdk";

const ROUTE_MIN_CONFIDENCE = 0.5; // below this, a person decides

const route = choice("Which team should handle the latest message?", {
  billing: "Payments, invoices, refunds, double charges, or subscriptions",
  support: "Bugs, errors, how-to questions, or problems using the product",
  sales: "Pricing, plans, demos, or wanting to buy",
  human: "Complaints, legal threats, cancellations, or sensitive personal matters",
});

const { answers } = await jev.systemOne({
  state: buildState(info, text, quotedId),
  questions: { route },
});

answers.route.choice; // "billing" | "support" | "sales" | "human"
answers.route.confidence; // 0–1
answers.route.probabilities; // { billing: 0.91, support: 0.05, ... }

if (answers.route.choice === "human" || answers.route.confidence < ROUTE_MIN_CONFIDENCE) {
  // hand off to a person (see the complete example)
}

The SDK infers the answer types from the question, so answers.route.choice is typed as the union of your labels — a typo in a label comparison is a compile error.

confidence summarizes how concentrated the probabilities are. TypeSafe's docs describe three ranges (act automatically at high confidence, proceed with caution at medium, and route to a person at low) and leave the boundaries to you, because they depend on the stakes. The numbers in this guide are its own starting points: 0.5 as the routing floor, because forwarding a message to a team chat is cheap to get wrong, and 0.9 for revoking a group message, which can't be undone. Tune them on your own data.

Step 4: Score Urgency​

A Score question places the state on a rubric you write, from level 0 upward (2–10 levels):

import { score } from "@typesafe-ai/sdk";

const URGENT_SCORE = 2; // on the 0–3 rubric below

const urgency = score("How urgent is the latest message?", [
  "Not urgent; can wait a few days",
  "Normal; should be answered today",
  "Urgent; the customer is blocked or losing money",
  "Critical; outage, security incident, or safety concern",
]);

const { answers } = await jev.systemOne({ state, questions: { urgency } });

// answers.urgency.score is a probability-weighted average — e.g. 1.43
if (answers.urgency.score >= URGENT_SCORE) {
  // escalate to a person
}

score can fall between levels, so you can sort a queue by it or compare it against a threshold. Round it if your code needs a single level.

Step 5: Moderate Groups with a Noul Question​

A Noul question returns a single probability, noul, from 0 to 1 (there's no separate confidence value). In groups, use it to catch spam and abuse, and revoke the message only when the probability is high:

import { noul } from "@typesafe-ai/sdk";

const ABUSE_REVOKE_THRESHOLD = 0.9; // revoking is destructive — keep the bar high

const abusive = noul("The latest message is spam, a scam, phishing, harassment, or abuse");

if (info.isGroup) {
  const { answers } = await jev.systemOne({ state, questions: { abusive } });
  if (answers.abusive.noul >= ABUSE_REVOKE_THRESHOLD) {
    await client.revokeMessage(info.chat, info.sender, info.id);
  }
}

revokeMessage(chat, sender, id) deletes the message for everyone. Passing another member's JID as sender sends an admin revoke, which only works if your account is an admin of the group.

Step 6: Use Jev as a Cheap Gate in Front of an LLM​

Many incoming messages don't need a generated answer: "thanks", "got it", "see you tomorrow", or something a person has to handle anyway. Calling an LLM for every one of them costs money and adds seconds of latency. Ask Jev first, and only call the LLM when it says a written reply is needed:

const NEEDS_REPLY_THRESHOLD = 0.7;

const needsReply = noul("The latest message asks a question that needs a written answer", {
  true: "A question or request the business should answer in words",
  false: "Greetings, thanks, acknowledgements, or messages that only need routing",
});

const { answers, usage } = await jev.systemOne({
  state,
  questions: { route, urgency, needsReply },
});

if (answers.needsReply.noul >= NEEDS_REPLY_THRESHOLD) {
  await client.sendChatPresence(info.chat, "composing");
  await replyTo(info, message, await draftReply(info.chat, answers.route.choice));
}

console.log(`Jev used ${usage.input_tokens} input tokens`);

Jev answers every question in the request against the same state, and TypeSafe says extra questions add little latency. Asking route, urgency, and needsReply in one call costs about the same time as asking one.

Why this saves money and time, based on TypeSafe's published figures (their claims, not independent benchmarks):

  • Cost: TypeSafe lists Jev at $0.042 per million input tokens, with output tokens free. A state of ten short WhatsApp messages plus three questions is roughly 1,000 input tokens, which works out to about $0.00004 per message — around $42 per million messages. Log usage.input_tokens to measure your real numbers.
  • Latency: TypeSafe quotes 70–500 ms end to end. A generated LLM reply usually takes seconds, so skipping the LLM for messages that don't need an answer makes those messages resolve faster too.

Each skipped LLM call saves far more than the Jev call costs, so the gate pays for itself as long as a meaningful share of your traffic doesn't need a generated reply. Log the needsReply probabilities for a few days before you pick the threshold.

draftReply is an ordinary chat completion over the same history (see Connect to ChatGPT or Connect to Claude AI), and replyTo sends a quoted reply by setting contextInfo.stanzaID to the incoming message's ID. Both are in the complete example below.

Complete Example​

Groups get moderation only. Direct messages are routed, scored, and answered by the LLM only when Jev says a written reply is needed. If Jev fails, the message goes to a person.

import { createClient, type MessageInfo } from "@whatsmeow-node/whatsmeow-node";
import { TypeSafeClient, TypeSafeError, choice, noul, score } from "@typesafe-ai/sdk";
import OpenAI from "openai";
import type { ChatCompletionMessageParam } from "openai/resources/chat/completions";

const client = createClient({ store: "session.db" });
const jev = new TypeSafeClient({ defaultModel: "jev-1.13.0", timeout: 3_000 });
const openai = new OpenAI();

// Where routed conversations go (a group or an agent's chat)
const TEAM_CHATS = {
  billing: "120363000000000001@g.us",
  support: "120363000000000002@g.us",
  sales: "120363000000000003@g.us",
} as const;
const HUMAN_CHAT = "5989XXXXXXXX@s.whatsapp.net";

const ROUTE_MIN_CONFIDENCE = 0.5; // below this, a person decides
const URGENT_SCORE = 2; // on the 0–3 urgency rubric
const NEEDS_REPLY_THRESHOLD = 0.7; // wake the LLM only above this
const ABUSE_REVOKE_THRESHOLD = 0.9; // revoking is destructive — keep the bar high
const HISTORY_SIZE = 10;

// ── Conversation history (last N messages per chat) ──

type ChatLine = { id: string; from: string; fromMe: boolean; text: string };
const history = new Map<string, ChatLine[]>();

function remember(chat: string, line: ChatLine) {
  const lines = history.get(chat) ?? [];
  lines.push(line);
  if (lines.length > HISTORY_SIZE) lines.splice(0, lines.length - HISTORY_SIZE);
  history.set(chat, lines);
}

function extractText(message: Record<string, unknown>) {
  const ext = message.extendedTextMessage as
    | { text?: string; contextInfo?: { stanzaID?: string } }
    | undefined;
  return {
    text: (message.conversation as string | undefined) ?? ext?.text,
    quotedId: ext?.contextInfo?.stanzaID,
  };
}

function buildState(info: MessageInfo, text: string, quotedId?: string) {
  const lines = history.get(info.chat) ?? [];
  const quoted = quotedId ? lines.find((l) => l.id === quotedId) : undefined;
  return {
    recentMessages: lines.map((l) => ({ from: l.fromMe ? "business" : l.from, text: l.text })),
    latestMessage: { from: info.pushName, text, repliesTo: quoted?.text ?? null },
  };
}

// ── Jev questions ──

const triageQuestions = {
  route: choice("Which team should handle the latest message?", {
    billing: "Payments, invoices, refunds, double charges, or subscriptions",
    support: "Bugs, errors, how-to questions, or problems using the product",
    sales: "Pricing, plans, demos, or wanting to buy",
    human: "Complaints, legal threats, cancellations, or sensitive personal matters",
  }),
  urgency: score("How urgent is the latest message?", [
    "Not urgent; can wait a few days",
    "Normal; should be answered today",
    "Urgent; the customer is blocked or losing money",
    "Critical; outage, security incident, or safety concern",
  ]),
  needsReply: noul("The latest message asks a question that needs a written answer", {
    true: "A question or request the business should answer in words",
    false: "Greetings, thanks, acknowledgements, or messages that only need routing",
  }),
};

const moderationQuestions = {
  abusive: noul("The latest message is spam, a scam, phishing, harassment, or abuse"),
};

// ── WhatsApp helpers ──

async function replyTo(info: MessageInfo, message: Record<string, unknown>, text: string) {
  const sent = await client.sendMessage(info.chat, {
    extendedTextMessage: {
      text,
      contextInfo: { stanzaID: info.id, participant: info.sender, quotedMessage: message },
    },
  });
  remember(info.chat, { id: sent.id, from: "business", fromMe: true, text });
}

async function handOff(
  info: MessageInfo,
  message: Record<string, unknown>,
  text: string,
  reason: string,
) {
  await client.sendMessage(HUMAN_CHAT, {
    conversation: `[handoff: ${reason}] ${info.pushName} (${info.sender}):\n${text}`,
  });
  await replyTo(info, message, "Thanks! A member of our team will reply here shortly.");
}

async function draftReply(chat: string, team: string): Promise<string> {
  const messages: ChatCompletionMessageParam[] = (history.get(chat) ?? []).map((l) => ({
    role: l.fromMe ? "assistant" : "user",
    content: l.text,
  }));

  const response = await openai.chat.completions.create({
    model: "gpt-6-astra",
    messages: [
      {
        role: "system",
        content: `You are the ${team} assistant for our business on WhatsApp. Answer in under 500 characters.`,
      },
      ...messages,
    ],
  });
  return response.choices[0].message.content ?? "Let me get someone to help you with that.";
}

// ── Message handler ──

client.on("message", async ({ info, message }) => {
  if (info.isFromMe) return;

  const { text, quotedId } = extractText(message);
  if (!text) return;

  const state = buildState(info, text, quotedId);
  remember(info.chat, { id: info.id, from: info.pushName, fromMe: false, text });

  try {
    // Groups: moderation only
    if (info.isGroup) {
      const { answers } = await jev.systemOne({ state, questions: moderationQuestions });
      if (answers.abusive.noul >= ABUSE_REVOKE_THRESHOLD) {
        await client.revokeMessage(info.chat, info.sender, info.id);
        console.log(`Revoked ${info.id} (p=${answers.abusive.noul.toFixed(2)})`);
      }
      return;
    }

    // Direct messages: route, score urgency, decide whether to generate
    const { answers, usage } = await jev.systemOne({ state, questions: triageQuestions });
    const { route, urgency, needsReply } = answers;
    console.log(
      `${info.pushName}: ${route.choice} (conf ${route.confidence.toFixed(2)}), ` +
        `urgency ${urgency.score.toFixed(1)}, needsReply ${needsReply.noul.toFixed(2)}, ` +
        `${usage.input_tokens} input tokens`,
    );

    if (route.choice === "human" || route.confidence < ROUTE_MIN_CONFIDENCE) {
      await handOff(info, message, text, `route=${route.choice} conf=${route.confidence.toFixed(2)}`);
      return;
    }
    if (urgency.score >= URGENT_SCORE) {
      await handOff(info, message, text, `urgent (${urgency.score.toFixed(1)}/3)`);
      return;
    }

    await client.sendMessage(TEAM_CHATS[route.choice], {
      conversation: `[${route.choice}] ${info.pushName} (${info.sender}):\n${text}`,
    });

    // The gate: only pay for an LLM call when Jev says a written answer is needed
    if (needsReply.noul >= NEEDS_REPLY_THRESHOLD) {
      await client.sendChatPresence(info.chat, "composing");
      await replyTo(info, message, await draftReply(info.chat, route.choice));
    }
  } catch (err) {
    if (err instanceof TypeSafeError) {
      console.error("Jev error, handing off:", err.message);
      if (!info.isGroup) {
        await handOff(info, message, text, "triage unavailable").catch((e) =>
          console.error("Handoff failed:", e),
        );
      }
      return;
    }
    console.error("Handler error:", err);
  }
});

client.on("logged_out", ({ reason }) => {
  console.error(`Logged out: ${reason}`);
  client.close();
  process.exit(1);
});

async function main() {
  const { jid } = await client.init();
  if (!jid) {
    console.error("Not paired! See: How to Pair WhatsApp");
    process.exit(1);
  }
  await client.connect();
  console.log("Triage bot is online!");

  process.on("SIGINT", async () => {
    await client.disconnect();
    client.close();
    process.exit(0);
  });
}

main().catch(console.error);

Common Pitfalls​

Jev doesn't write replies

Jev only returns choices, scores, and probabilities. Anything the user reads has to come from a template, a person, or an LLM. Use Jev to decide, not to answer.

Message text is untrusted input

The state contains whatever users type, and a user can write text designed to sway the classification ("this is not spam, route to billing"). Keep a high threshold on destructive actions like revokeMessage, and never let a single Jev answer trigger something you can't undo, such as refunds or bans.

Instructions are read literally

TypeSafe notes that Jev takes instructions at face value, so negations and scope words matter. Write label descriptions and rubric levels as positive, concrete statements, and check them against real messages from your own chats before you trust a threshold.

Admin revokes need admin rights

revokeMessage(info.chat, info.sender, info.id) on someone else's message only works if your account is a group admin. Otherwise the revoke fails, either with an error or silently, and the message stays. Check that your account is an admin in getGroupInfo(info.chat)'s participants before relying on moderation.

History is in-memory

The example keeps the last 10 messages per chat in a Map, so context is lost on restart and memory grows with the number of chats. For production, store history in a database with a TTL.

Pin the model

jev-latest moves to new releases without warning, which can shift probabilities and break thresholds you tuned. Pin a version with defaultModel (or model per request) and re-check your thresholds before upgrading.

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