AI Email Automation 2026: GPT-4 + Gmail API for Smart Inbox Management

Use AI to automatically categorize, respond to, and manage your email inbox.

What You Can Automate

Auto-classify emails (support, sales, spam, urgent)

Generate draft replies for common request types

Extract action items from email threads

Route emails to the right team member

Summarize long email threads

Setup: Gmail API

bash
pip install google-auth google-auth-oauthlib google-api-python-client openai

python
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.oauth2.credentials import Credentials
import base64
import os
SCOPES = ['https://www.googleapis.com/auth/gmail.modify']
def get_gmail_service():
    creds = None
    if os.path.exists('token.json'):
        creds = Credentials.from_authorized_user_file('token.json', SCOPES)
    if not creds or not creds.valid:
        flow = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES)
        creds = flow.run_local_server(port=0)
        with open('token.json', 'w') as f:
            f.write(creds.to_json())
    return build('gmail', 'v1', credentials=creds)

Reading Emails

python
from openai import OpenAI
openai = OpenAI()
service = get_gmail_service()
def get_unread_emails(max_results: int = 10):
    results = service.users().messages().list(
        userId='me',
        labelIds=['INBOX', 'UNREAD'],
        maxResults=max_results
    ).execute()
    
    emails = []
    for msg in results.get('messages', []):
        email = service.users().messages().get(userId='me', id=msg['id'], format='full').execute()
        
        headers = {h['name']: h['value'] for h in email['payload']['headers']}
        body = ''
        if 'parts' in email['payload']:
            for part in email['payload']['parts']:
                if part['mimeType'] == 'text/plain' and 'data' in part['body']:
                    body = base64.urlsafe_b64decode(part['body']['data']).decode()
                    break
        
        emails.append({
            'id': msg['id'],
            'from': headers.get('From', ''),
            'subject': headers.get('Subject', ''),
            'body': body[:2000]  # Truncate for token limits
        })
    
    return emails

AI Email Classification

python
import json
def classify_email(email: dict) -> dict:
    r = openai.chat.completions.create(
        model='gpt-4o-mini',
        messages=[{
            'role': 'user',
            'content': f'Classify this email. Return JSON with fields: '
                       f'category (support/sales/spam/internal/urgent/newsletter), '
                       f'priority (high/medium/low), '
                       f'sentiment (positive/neutral/negative), '
                       f'summary (one sentence).\n\n'
                       f'From: {email["from"]}\n'
                       f'Subject: {email["subject"]}\n'
                       f'Body: {email["body"]}'
        }],
        response_format={'type': 'json_object'}
    )
    return json.loads(r.choices[0].message.content)
Process all unread emails
emails = get_unread_emails(20)
for email in emails:
    classification = classify_email(email)
    print(f'{email["subject"]}: {classification["category"]} ({classification["priority"]})')
    print(f'  Summary: {classification["summary"]}')

Auto-Generate Reply Drafts

python
def generate_reply(email: dict, context: str = '') -> str:
    r = openai.chat.completions.create(
        model='gpt-4o',
        messages=[{
            'role': 'system',
            'content': 'You are an professional email assistant. '
                       'Write clear, concise, professional email replies. '
                       'Match the tone of the original email.'
        }, {
            'role': 'user',
            'content': f'Write a reply to this email.\n\n'
                       f'Original email:\n'
                       f'From: {email["from"]}\n'
                       f'Subject: {email["subject"]}\n'
                       f'Body: {email["body"]}\n\n'
                       f'Context/instructions: {context or "Be helpful and professional."}'
        }]
    )
    return r.choices[0].message.content
def save_draft(service, email_id: str, reply_text: str):
    original = service.users().messages().get(userId='me', id=email_id, format='full').execute()
    headers = {h['name']: h['value'] for h in original['payload']['headers']}
    
    import email.mime.text
    msg = email.mime.text.MIMEText(reply_text)
    msg['To'] = headers.get('From', '')
    msg['Subject'] = 'Re: ' + headers.get('Subject', '')
    msg['In-Reply-To'] = headers.get('Message-Id', '')
    
    raw = base64.urlsafe_b64encode(msg.as_bytes()).decode()
    service.users().drafts().create(
        userId='me',
        body={'message': {'raw': raw, 'threadId': original['threadId']}}
    ).execute()

Conclusion

AI email automation saves hours per week for knowledge workers. Start with classification, then add auto-draft generation for high-volume request types. Always review AI-generated drafts before sending in production.