AI Agent for Google Reviews: Auto-Reply Script with Real Examples

How the System Works

Google Business Profile API
        ↓
  Fetch new reviews (unanswered)
        ↓
  Classify: positive / negative / neutral
        ↓
  Generate personalized reply via Groq AI
        ↓
  Post reply via Google Business Profile API
        ↓
  Log to JSON (avoid double-replying)

What You'll Need

• Google Cloud project with My Business Business Information API and My Business Reviews API enabled
• OAuth2 credentials (service account or user OAuth flow)
• Groq API key (free tier: 14,400 requests/day on LLaMA 3.3)
• Python 3.10+

The Complete Script

#!/usr/bin/env python3
"""
Google Reviews AI Reply Agent
Fetches unanswered reviews and posts AI-generated personalized replies.
"""

import os
import json
import urllib.request
import urllib.parse
from datetime import datetime
from pathlib import Path

# ── Config ────────────────────────────────────────────────────────────────────
GROQ_API_KEY      = os.environ.get("GROQ_API_KEY", "")
GROQ_MODEL        = "llama-3.3-70b-versatile"

GOOGLE_TOKEN_FILE = os.environ.get("GOOGLE_TOKEN_FILE", "google_token.json")
LOCATION_NAME     = os.environ.get("LOCATION_NAME", "")  # accounts/{id}/locations/{id}
BUSINESS_NAME     = os.environ.get("BUSINESS_NAME", "our business")
OWNER_NAME        = os.environ.get("OWNER_NAME", "")

REPLIED_LOG_FILE  = "replied_reviews.json"

# ── Token management ──────────────────────────────────────────────────────────
def get_access_token() -> str:
    """Refresh OAuth2 token using stored credentials."""
    token_data = json.loads(Path(GOOGLE_TOKEN_FILE).read_text())
    
    # If token is still valid, return it
    if token_data.get("access_token") and token_data.get("expiry"):
        expiry = datetime.fromisoformat(token_data["expiry"].replace("Z", "+00:00"))
        if expiry > datetime.now(expiry.tzinfo):
            return token_data["access_token"]
    
    # Refresh
    payload = urllib.parse.urlencode({
        "client_id":     token_data["client_id"],
        "client_secret": token_data["client_secret"],
        "refresh_token": token_data["refresh_token"],
        "grant_type":    "refresh_token",
    }).encode()

    req = urllib.request.Request(
        "https://oauth2.googleapis.com/token",
        data=payload,
        headers={"Content-Type": "application/x-www-form-urlencoded"},
        method="POST",
    )

    with urllib.request.urlopen(req) as resp:
        new_token = json.loads(resp.read())
    
    token_data["access_token"] = new_token["access_token"]
    Path(GOOGLE_TOKEN_FILE).write_text(json.dumps(token_data, indent=2))
    return new_token["access_token"]

# ── Fetch reviews ─────────────────────────────────────────────────────────────
def fetch_reviews(access_token: str) -> list[dict]:
    """Get reviews for the location that haven't been replied to yet."""
    url = f"https://mybusinessreviews.googleapis.com/v1/{LOCATION_NAME}/reviews"
    
    req = urllib.request.Request(
        url,
        headers={"Authorization": f"Bearer {access_token}"},
    )

    with urllib.request.urlopen(req, timeout=15) as resp:
        data = json.loads(resp.read())
    
    reviews = data.get("reviews", [])
    # Filter to reviews without a reply
    unanswered = [r for r in reviews if "reviewReply" not in r]
    
    print(f"Found {len(reviews)} total reviews, {len(unanswered)} unanswered")
    return unanswered

# ── Classify review sentiment ─────────────────────────────────────────────────
def classify_review(review: dict) -> str:
    rating = review.get("starRating", "FIVE")
    rating_map = {"ONE": 1, "TWO": 2, "THREE": 3, "FOUR": 4, "FIVE": 5}
    stars = rating_map.get(rating, 5)
    
    if stars >= 4:
        return "positive"
    elif stars == 3:
        return "neutral"
    else:
        return "negative"

# ── Generate AI reply ─────────────────────────────────────────────────────────
def generate_reply(review: dict, sentiment: str) -> str:
    reviewer_name = review.get("reviewer", {}).get("displayName", "")
    first_name = reviewer_name.split()[0] if reviewer_name else ""
    review_text = review.get("comment", "(no text provided)")
    rating = review.get("starRating", "FIVE")
    
    if sentiment == "positive":
        instructions = """
Write a warm, genuine thank-you reply to this positive Google review.
- Start with their first name if available
- Reference 1–2 specific things they mentioned (don't be vague)
- Express that we look forward to seeing them again
- Keep it under 100 words — longer replies feel like templates
- Sound like a real person, not a corporation
- Do NOT use: "We appreciate your feedback", "Thank you for taking the time", 
  "Your satisfaction is our priority", or any other generic phrases
"""
    elif sentiment == "negative":
        instructions = """
Write a professional, empathetic reply to this negative Google review.
- Acknowledge their experience without being defensive
- Apologize for falling short of expectations (even if you disagree with their account)
- Offer a specific next step: "Please call us at [number] or email us directly so we can make this right"
- Keep it under 120 words
- Do NOT make excuses, argue, or ask for the review to be changed
- The goal is to show OTHER readers that you handle problems professionally
"""
    else:  # neutral
        instructions = """
Write a friendly, conversational reply to this neutral Google review.
- Thank them genuinely (not generically)
- Address anything specific they mentioned — especially any constructive feedback
- Invite them to return and suggest what they might enjoy next time
- Keep it under 80 words
"""

    prompt = f"""You're the owner of {BUSINESS_NAME}. Reply to this Google review.

Reviewer: {first_name or 'Anonymous'}
Rating: {rating} stars
Review text: "{review_text}"

Owner name for signing off: {OWNER_NAME or 'The team'}

{instructions}

Write only the reply text. No quotes around it. No "Reply:" prefix."""

    payload = json.dumps({
        "model": GROQ_MODEL,
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": 200,
        "temperature": 0.6,
    }).encode()

    req = urllib.request.Request(
        "https://api.groq.com/openai/v1/chat/completions",
        data=payload,
        headers={
            "Authorization": f"Bearer {GROQ_API_KEY}",
            "Content-Type": "application/json",
        },
        method="POST",
    )

    try:
        with urllib.request.urlopen(req, timeout=20) as resp:
            result = json.loads(resp.read())
            return result["choices"][0]["message"]["content"].strip()
    except Exception as e:
        print(f"  ✗ AI generation failed: {e}")
        # Fallback replies
        if sentiment == "positive":
            return f"Thank you so much{', ' + first_name if first_name else ''}! We're thrilled you had a great experience and look forward to seeing you again soon. — {OWNER_NAME or 'The Team'}"
        elif sentiment == "negative":
            return f"We're sorry to hear about your experience. Please reach out to us directly so we can make this right. Your feedback is important to us. — {OWNER_NAME or 'The Team'}"
        else:
            return f"Thank you for sharing your experience{', ' + first_name if first_name else ''}! We appreciate your feedback and hope to see you again. — {OWNER_NAME or 'The Team'}"

# ── Post reply to Google ──────────────────────────────────────────────────────
def post_reply(review_name: str, reply_text: str, access_token: str) -> bool:
    """Post reply via Google Business Profile API."""
    url = f"https://mybusinessreviews.googleapis.com/v1/{review_name}/reply"
    
    payload = json.dumps({"comment": reply_text}).encode()
    
    req = urllib.request.Request(
        url,
        data=payload,
        headers={
            "Authorization": f"Bearer {access_token}",
            "Content-Type": "application/json",
        },
        method="PUT",
    )

    try:
        with urllib.request.urlopen(req, timeout=15) as resp:
            resp.read()
            print(f"  ✓ Reply posted to {review_name}")
            return True
    except Exception as e:
        print(f"  ✗ Failed to post reply: {e}")
        return False

# ── Main ──────────────────────────────────────────────────────────────────────
def main():
    replied_log = json.loads(Path(REPLIED_LOG_FILE).read_text()) if Path(REPLIED_LOG_FILE).exists() else {}
    
    print("Getting access token...")
    access_token = get_access_token()
    
    print("Fetching reviews...")
    reviews = fetch_reviews(access_token)
    
    replied_count = 0
    for review in reviews:
        review_name = review.get("name", "")
        
        if review_name in replied_log:
            continue  # Already handled this review
        
        reviewer = review.get("reviewer", {}).get("displayName", "Anonymous")
        rating   = review.get("starRating", "?")
        sentiment = classify_review(review)
        
        print(f"\nProcessing: {reviewer} ({rating} stars / {sentiment})")
        
        reply = generate_reply(review, sentiment)
        print(f"  Draft reply: {reply[:100]}...")
        
        success = post_reply(review_name, reply, access_token)
        
        if success:
            replied_log[review_name] = {
                "replied_at":  datetime.now().isoformat(),
                "reviewer":    reviewer,
                "rating":      rating,
                "sentiment":   sentiment,
                "reply_preview": reply[:100],
            }
            replied_count += 1
    
    Path(REPLIED_LOG_FILE).write_text(json.dumps(replied_log, indent=2))
    print(f"\n✓ Done. Replied to {replied_count} new review(s).")

if __name__ == "__main__":
    main()

Example AI-Generated Replies

Setting Up the Google Business Profile API

1. Go to Google Cloud Console
2. Create a project → Enable "My Business Reviews API" and "My Business Business Information API"
3. Create OAuth2 credentials (Desktop app type)
4. Run the OAuth flow once to generate google_token.json with a refresh token
5. Get your Location Name: accounts/{account_id}/locations/{location_id} — visible in the GBP API Explorer

Frequently Asked Questions

Automate Your Google Review Responses

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