If you landed here wondering how to have OpenClaw read an inbox and fire back polite, on-brand answers while you get real work done — this is the guide. I’ll walk through wiring Gmail (or any IMAP box) into OpenClaw, teaching it what “routine” means, drafting response templates, adding a human approval step, then tapering supervision as confidence climbs. I’ll also highlight the dark corners: tone drift, hallucinated facts, infinite reply loops. Everything below is from a real setup that currently handles ~140 customer-support emails per day on our side project.

Prerequisites: What You Need Before Automating Email Replies

OpenClaw moves fast but it’s still code. Save yourself context switching later by ticking the boxes up front.

  • OpenClaw ≥ v3.4.0 (requires Node 22+). npm i -g openclaw@latest
  • A ClawCloud account or a server with public HTTPS (ngrok works for testing).
  • Mailbox credentials. I’ll use Gmail OAuth via Composio, but any IMAP/SMTP pair is fine.
  • Labeling discipline. You’ll need a dedicated label/folder for “routine” mail. Trust me.
  • At least one hour for initial triage; two coffees for fine-tuning.

Step 1 — Wire Your Mailbox into OpenClaw

1.1 Install and bootstrap an agent

# fresh terminal npm install -g openclaw@latest # 3.4.0 as of writing openclaw init --name "inbox-bot" --cloud # pushes skeleton to ClawCloud

This spins up the gateway UI in ClawCloud; the daemon keeps it alive. You’ll get a URL like https://inbox-bot.claw.cloud.

1.2 Grant read/write email scopes via Composio

Inside the gateway, go to Tools → Add integration → Gmail. Composio requests the following scopes:

  • https://www.googleapis.com/auth/gmail.modify (read + label mail)
  • https://www.googleapis.com/auth/gmail.send (send replies)

Other providers look similar: IMAP credentials plus an SMTP host/port.

1.3 Minimal task definition

// tasks/inbox-routine.ts import { defineTask } from 'openclaw'; import { gmail } from 'composio'; defineTask('inbox.routine', async (ctx) => { const messages = await gmail.list({ labelIds: ['ROUTINE_PENDING'], maxResults: 10 }); return messages.map(m => ({ id: m.id, subject: m.snippet })); // hand off to next tool });

At this point the agent can read mail and expose candidate messages to other skills. It can’t reply yet; that’s next.

Step 2 — Teach OpenClaw What “Routine” Looks Like

The engine isn’t psychic. You must feed it examples. Two approaches:

  1. Manual labeling. Create a Gmail label ROUTINE_PENDING. Anytime you think “the bot could handle this”, apply the label. After ~100 samples results get usable.
  2. Rule-based pre-filter. Gmail filters like subject:(reset password) → label ROUTINE_PENDING cover low-hanging fruit.

Don’t over-optimize too early. You’ll refine after seeing false positives.

2.1 Optional: Vector memory for semantic matching

If topics vary slightly (“I forgot my password” vs “Can’t sign in”), enable the built-in vector store.

openclaw shell > memory.enable("routine-intent") > memory.train("routine-intent", { examples: [ "how do i reset my password", "lost login details", "password link expired" ] })

Later we’ll query this memory to decide if a new email is routine or escalates.

Step 3 — Define Response Templates

OpenClaw supports plain strings, Handlebars, or full LLM.generate() calls. Start conservative: canned text with interpolation tokens. Less risk than freestyling GPT-4 on day one.

Hi {{firstName}}, No worries—passwords slip everyone’s mind. Click the link below to set a new one: {{resetLink}} If the link gives you trouble just reply here. Happy to help. — Support Bot

Register the template:

// scripts/register-templates.ts import { templates } from 'openclaw'; import fs from 'fs'; templates.register('reset_password', fs.readFileSync('templates/reset-password.hbs', 'utf8'));

For 90% of routine tickets we have three templates: reset_password, invoice_request, and hours_info.

3.1 Mapping intents to templates

// config/intents.yml reset_password: match: "routine-intent:password" template: "reset_password" invoice_request: match: "subject:invoice" template: "invoice_request"

The match syntax supports memory queries (memory:), subject/body regexes, or a JS function.

Step 4 — Create the Human-in-the-Loop Approval Flow

Blindly sending emails after one weekend of training is a good way to annoy customers and maybe legal. Keep a human veto button for at least the first thousand messages.

4.1 Pipeline skeleton

// tasks/reply-with-approval.ts import { gmail, slack } from 'composio'; import { templates } from 'openclaw'; defineTask('inbox.replyWithApproval', async (ctx, { id }) => { const msg = await gmail.get({ id }); const intent = await ctx.classifyIntent(msg); if (!intent) return; const draft = templates.render(intent.template, extractVars(msg)); const permalink = await gmail.createDraft({ threadId: msg.threadId, body: draft }); // ping Slack channel #support-approvals await slack.postMessage({ channel: '#support-approvals', text: `Draft ready for <${msg.sender}> → <${permalink}|review>` }); });

The Slack message has two buttons: Approve or Edit. Approve triggers gmail.sendDraft(). Edit opens Gmail’s native composer for tweaks.

4.2 Scheduling the job

openclaw cron add "*/5 * * * *" inbox.routine → inbox.replyWithApproval

Every five minutes the daemon pulls up to ten labeled messages, drafts replies, and waits for sign-off.

Step 5 — Gradually Removing Training Wheels

After two weeks we’d approved 321 drafts; only 12 needed manual rewrites. Good but not perfect. Here’s the staged rollout we used:

  1. Shadow mode (week 1). Bot drafts only. Humans reply manually.
  2. Approval required (week 2). Slack buttons send.
  3. Confidence threshold (week 3-4). If intent.score > 0.9 and sender domain is in our allowlist, auto-send; others still require approval.
  4. Full auto except corner cases. Today only unrecognized intents hit the queue.

We track metrics in Postgres: false positives per intent, average approval lag. Anything worse than 98% accuracy reverts to step 2 automatically via a simple watchdog.

Common Failure Modes and How to Mitigate Them

Wrong tone / cultural mismatch

A German customer once got “Howdy!” because the template hard-coded it. Fix: add locale detection and variant templates.

Hallucinated data

LLM responses can invent refund policies. Stay template-first; use the LLM only for minor phrasing tweaks (rewrite this politely). Pass the canonical answer as structured vars.

Infinite threads

If the agent sends “Does that solve your issue?” and the customer replies “Yes, thanks” the intent matcher may still classify it as routine and reply again. Add a last_human_timestamp check: don’t respond if the prior agent message is <48h old unless asked a new question.

Reply-all disasters

Always set to = original sender when using gmail.send. Strip CCs unless the thread is explicitly a group support ticket.

Security / PII

Run the daemon with NO_LOG_BODY=1 to avoid storing raw email bodies in logs. Encrypt ~/.openclaw/credentials.json with gpg if self-hosting.

Putting It All Together: End-to-End Flow Diagram

(ASCII because you’re reading this in a terminal anyway.)

┌──────────┐ label ROUTINE_PENDING ┌────────────────┐ Slack approve ┌────────────┐ │ Gmail │──────────────────────────▶│ inbox.routine │───────────────────▶│ Humans │ └──────────┘ └────────────────┘ └────┬───────┘ │ approval=yes │ ▼ │ ┌────────────────┐ send draft │ │ replyWithAppr. │──────────────────────▶┘ └────────────────┘

Next Step: Start Labeling 20 Emails Today

You won’t know if automation is worth it until you see drafts in your outbox. Pick the lowest-risk category — password resets, FAQ links, anything non-financial — and label the next 20 mails. By tomorrow morning OpenClaw will have usable suggestions. Iterate, measure accuracy, tighten guardrails, widen scope. That’s it. No further ceremony required.