Use cases

Ten end-to-end Python recipes, from bulk exports and review automation to analytics reports, listings audits, and bulk posting.

Set your API key once before running any example:

bash
export LISTINGSAPI_KEY="your-api-key"

1. List your locations

Connect and print your first page of locations.

01_quickstart.py
import listingsapi client = listingsapi.ListingsAPI() page = client.locations.list(first=5)for loc in page:  print(loc.name, "--", getattr(loc, "city", "N/A"), getattr(loc, "stateIso", "N/A")) print(f"Locations on page: {len(page)}, has_more: {page.has_more}")

2. Bulk export to CSV

Download every location to a CSV using auto_paging_iter().

02_bulk_export_locations.py
import csvimport listingsapi client = listingsapi.ListingsAPI() locations = list(client.locations.list(first=100).auto_paging_iter())print(f"Fetched {len(locations)} locations") fields = ["id", "name", "storeId", "street", "city", "stateIso", "postalCode", "countryIso", "phone"] with open("locations_export.csv", "w", newline="") as f:  writer = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore")  writer.writeheader()  for loc in locations:      writer.writerow(loc.to_dict()) print("Exported to locations_export.csv")

auto_paging_iter() handles all cursor pages automatically. See Locations for the full method reference.


3. Create a location

locations.add() takes every mandatory field as a keyword argument and validates the payload client-side, so a bad request fails fast with a clear message before any network call. Look up a valid sub_category_id with client.subcategories() (use the databaseId).

03_create_location.py
import listingsapifrom listingsapi import ValidationError client = listingsapi.ListingsAPI() subcategories = client.subcategories()dental = [s for s in subcategories if "dentist" in (s.get("name") or "").lower()]sub_category_id = dental[0].databaseId if dental else 1432 try:  result = client.locations.add(      name="Acme Dental",      description=(          "Acme Dental is a family-owned dental practice serving downtown "          "New York for over fifteen years. Our team offers preventive care, "          "cosmetic dentistry, orthodontics, and emergency appointments, with "          "weekend hours and a patient-first approach that keeps every visit "          "comfortable and affordable for the whole family."      ),      sub_category_id=sub_category_id,      country_iso="US",      city="New York",      street="123 Jump Street",      state_iso="NY",      postal_code="10013",      phone="6443859313",      store_id="NYC-001",  )except ValidationError as e:  print(f"Fix the payload: {e}")else:  print(f"Created location: {result.location.id}")

The description must be at least 200 characters; publishers use it as the primary listing copy. client.countries() lists supported country and state ISO codes, and client.plan_sites() shows which directories your plan publishes to.


4. Review monitoring

Scan recent reviews and flag negative ones that need a reply.

04_review_monitoring.py
import listingsapi client = listingsapi.ListingsAPI() page = client.locations.list(first=5) for loc in page:  print(f"\n--- {loc.name} ---")  reviews = client.reviews.list(loc.id, first=10)  if not reviews:      print("  No recent reviews")      continue   for review in reviews:      rating = getattr(review, "rating", "N/A")      author = getattr(review, "authorName", "Anonymous")      site = getattr(review, "siteName", "Unknown")      responded = bool(getattr(review, "responses", None))      flag = " ** NEEDS ATTENTION **" if isinstance(rating, (int, float)) and rating <= 2 else ""      status = "Responded" if responded else "No reply"      print(f"  [{rating}] {author} on {site} ({status}){flag}")

See Reviews for filter options including rating_filters, site_urls, and date ranges.


5. Auto-reply to reviews

workflows.auto_reply_to_reviews() fetches recent reviews, filters by rating and response status, and posts replies from your template in one call. Preview with dry_run=True first. The {rating} placeholder is replaced with each review's star rating.

05_auto_reply_reviews.py
import listingsapi client = listingsapi.ListingsAPI() LOCATION_ID = 16808  # Replace with your location IDTEMPLATE = "Thanks for the {rating}-star review! We appreciate you taking the time." # Preview what would be sentpreview = client.workflows.auto_reply_to_reviews(  LOCATION_ID,  template=TEMPLATE,  min_rating=4,  dry_run=True,)for entry in preview:  print(f"  Would reply to {entry['id']} ({entry['rating']} stars)") # Post the replies for realresults = client.workflows.auto_reply_to_reviews(  LOCATION_ID,  template=TEMPLATE,  min_rating=4,)sent = [r for r in results if r["status"] == "sent"]print(f"Replied to {len(sent)} of {len(results)} matching reviews")

The default min_rating=4 skips negative reviews on purpose: those deserve a personal reply. Handle them individually with reviews.respond():

05b_respond_to_negative.py
import listingsapifrom listingsapi import APIError client = listingsapi.ListingsAPI() negative = client.reviews.list(16808, rating_filters=[1, 2], first=20)for review in negative:  if getattr(review, "responses", None):      continue  try:      client.reviews.respond(          review.interactionId,          "We are sorry to hear about your experience. Please reach out "          "directly so we can make it right.",      )      print(f"Replied to {getattr(review, 'authorName', 'Anonymous')}")  except APIError as e:      print(f"Failed: {e}")

6. Analytics report

Pull Google profile analytics and review stats for every location.

06_analytics_report.py
import listingsapi client = listingsapi.ListingsAPI() FROM_DATE = "2026-01-01"TO_DATE = "2026-06-30" all_locations = list(client.locations.list(first=100).auto_paging_iter())print(f"Generating report for {len(all_locations)} locations ({FROM_DATE} to {TO_DATE})\n") for loc in all_locations[:10]:  print(f"--- {loc.name} ---")   google = client.analytics.google(loc.id, from_date=FROM_DATE, to_date=TO_DATE)  if google:      print(f"  Google: {google.to_dict()}")   review_stats = client.reviews.analytics.overview(loc.id, start_date=FROM_DATE, end_date=TO_DATE)  if review_stats:      print(f"  Reviews: {review_stats.to_dict()}")   sites = client.reviews.analytics.sites_stats(loc.id, start_date=FROM_DATE, end_date=TO_DATE)  if sites:      print(f"  Sites: {sites.to_dict()}")   print()

See Analytics for bing() and facebook() profile metrics.


7. Weekly reputation report

workflows.weekly_reputation_report() combines reviews, review analytics, Google and Bing profile analytics, and listings sync status into a single report object.

07_reputation_report.py
import listingsapi client = listingsapi.ListingsAPI() report = client.workflows.weekly_reputation_report(  16808,  start_date="2026-06-29",  end_date="2026-07-05",) summary = report.review_summaryprint(f"Average rating: {summary.get('averageRating')}")print(f"Recent reviews: {len(report.recent_reviews)}") analytics = report.analyticsprint(f"Google: {analytics.get('google')}")print(f"Bing: {analytics.get('bing')}") health = report.listings_healthprint(f"Listings synced: {health.get('synced')}/{health.get('total')} ({health.get('sync_rate')})")

8. Listings health audit

workflows.listings_health_audit() checks premium listings, voice listings, and duplicates for a location and computes a 0-100 health score.

08_listings_audit.py
import listingsapi client = listingsapi.ListingsAPI() page = client.locations.list(first=10)print(f"Auditing listings for {len(page)} locations\n") for loc in page:  audit = client.workflows.listings_health_audit(loc.id)  print(f"--- {loc.name} ---")  print(f"  Health score: {audit.health_score}%")  print(f"  Synced: {audit.synced_count}, issues: {audit.issue_count}")  print(f"  Voice listings: {len(audit.voice)}, duplicates: {len(audit.duplicates)}")  for issue in audit.issues:      print(f"    [{issue.get('syncStatus')}] {issue.get('site')}")  print()

See Listings for the underlying premium, voice, duplicates, and mark-as-duplicate methods.


9. Bulk publish a post

posts.bulk_publish() publishes one post across many locations, defaulting to both Google and Facebook. Location IDs are encoded automatically and the payload is validated client-side before any network call.

09_bulk_publish_post.py
import listingsapi client = listingsapi.ListingsAPI() result = client.posts.bulk_publish(  name="Summer hours 2026",  location_ids=[16808, 16809, 16810],  message="We are open late all summer! Come see us until 9pm, Monday through Saturday.",  media_url="https://cdn.example.com/summer-hours.jpg",  cta_type="LEARN_MORE",  cta_url="https://www.acmedental.com/hours",) post = result.get("socialPost") or {}print(f"Created bulk post: {post.get('id')}")

Pass a dict as message to customize the copy per site:

09b_per_site_message.py
result = client.posts.bulk_publish(  name="July whitening offer",  location_ids=[16808],  message={      "GOOGLE": "20% off teeth whitening this July. Book online today!",      "FACEBOOK": "July special: 20% off teeth whitening. Tap to book your visit!",  },  sites=["GOOGLE", "FACEBOOK"],)

For single-site typed posts, use posts.create_announcement(), posts.create_event(), or posts.create_offer().


10. Google connect flow

Generate an OAuth URL, list connected accounts, and review match suggestions.

10_google_connect_flow.py
import listingsapi client = listingsapi.ListingsAPI() # Step 1: Generate an OAuth URL (valid 24 hours)result = client.connected_accounts.connect_google(  success_url="https://yourapp.com/connect/success",  error_url="https://yourapp.com/connect/error",)print(f"Redirect user to: {result.get('url', 'N/A')}") # Step 2: List connected Google accountsaccounts = client.connected_accounts.list(publisher="google")connected = getattr(accounts, "connectedAccounts", None) or []print(f"\nConnected Google accounts:")for acc in connected:  print(f"  {acc.email} -- status: {acc.status}")   # Step 3: Check match suggestions  suggestions = client.connected_accounts.suggestions(acc.id, page=1, per_page=10)  records = getattr(suggestions, "matchedRecords", None) or []  print(f"  Suggestions: {len(records)} matches found")

Confirm matches with connected_accounts.confirm_matches() and link a specific listing to a location with listings.connect().


Where to go next