Timeleft Schedules Scraper: City Event Calendars with Booking Headcounts

Timeleft Schedules Scraper turns timeleft.com into structured schedule data. Get every upcoming dinner, drinks, coffee, run, walk and brunch session across 200 cities with dates, zones, group sizes, booking cutoffs and booking headcounts. Search by city, filter by format, then export to JSON, CSV or Excel, or pull the results straight into your app through the API.

Why This Scraper?

• Every city, one run. Sweep all 197 Timeleft cities or name the ones you care about (Paris, New York, London) and get roughly three weeks of scheduled sessions per city.
• Booking momentum included. Each session carries its live booking headcount, so you can track which formats and cities fill fastest.
• No credentials needed. Pick cities and run; access is handled automatically.
• Built for schedules. Incremental mode returns only new, changed and expired sessions on recurring runs, and a resume option continues one interrupted sweep without re-collecting rows.
• Honest limits. Venues, attendees and booking stay behind Timeleft's paid subscription, so this actor returns schedules only, and says so.

Use Cases

• Demand researchers: track event volume and booking momentum across cities to spot where social-dining demand runs hottest.
• Travel and relocation planners: see which formats run in a destination city before a trip.
• Competitive analysts: monitor how a subscription-based experiences marketplace paces its supply.
• Data teams: feed a recurring schedule snapshot into dashboards via the API or scheduled runs.
• Marketers: time outreach around high-momentum formats and cities.

Data You Get

Sample shape: values are illustrative placeholders, not from a live record.

Field	Example
id	e1df5a79-5cdc-47a5-a474-d4d59abbda1b
city	Paris
country	France
cityLatitude	48.8666929
cityLongitude	2.3333353
cityUserCount	121637
cityImageUrl	https://v2.timeleft.com/images/paris.jpg
date	2026-10-08
eventType	drinks
status	available
zoneId	"351"
dayUsersBooked	176
groupSize	7
maxParticipants	80
bookingCutoffISO	2026-10-08T06:00:00.000+02:00
changeType	NEW
dayEventId	377a8308-b5f6-4e64-afb8-a643da1653a0
ageRestrictions	0
coverUrl	https://cdn.timeleft.com/events/covers/timeleft_drink_cover.png
introTitle	Timeleft Drinks
introDescription	Spark new connections with a small group over drinks in a local bar.
scrapedAt	2026-10-07T10:20:00+00:00
waitlistOnlyGenders	man
hasBookingGate	false
partnerImageUrl	https://storage.googleapis.com/timeleft-cdn-assets/events/drinks_for_singles.png

Each row is one bookable zone-session; a day-event with two zones yields two rows. dayUsersBooked is the day-level booking total shared by all sessions of that day-event (a 7-table does not seat 17 alone), populated when Fetch booking headcounts is on. changeType, changedFields, firstSeenAt and lastSeenAt appear in Incremental mode.

How to Use

Sweep three cities with headcounts:

{
  "cities": ["Paris", "New York", "London"],
  "maxItems": 200,
  "fetchHeadcounts": true
}

Dinners in Paris only, schedules without headcounts:

{
  "cities": ["Paris"],
  "eventTypes": ["dinner"],
  "maxItems": 50,
  "fetchHeadcounts": false
}

Weekly incremental monitor for London:

{
  "cities": ["London"],
  "maxItems": 0,
  "incrementalMode": true,
  "stateKey": "london-watch",
  "emitExpired": true
}

Run it from your code

Python:

from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("abotapi/timeleft-schedules-scraper").call(run_input={"cities": ["Paris"], "maxItems": 50})

JavaScript:

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });
const run = await client.actor('abotapi/timeleft-schedules-scraper').call({ cities: ['Paris'], maxItems: 50 });

Input Parameters

Sample shape: values are illustrative placeholders, not from a live record.

Parameter	Type	Default	Description
cities	string[]	[] (all 197)	City names or numeric ids; unknown names are skipped with a warning
eventTypes	string[]	[] (all)	dinner, drinks, coffee, run, walk, brunch
maxItems	integer	20	Stop after this many sessions; 0 = unlimited full sweep
fetchHeadcounts	boolean	true	Read per-event booking totals (one extra read per city)
accountKey	string	default	Gives each key its own saved state for multi-monitor fleets
proxy	object	residential	Connection used for the schedule reads
resumeFromRunId	string	empty	Continue one interrupted sweep from a run or dataset id
incrementalMode	boolean	false	Track NEW/UPDATED/UNCHANGED/REAPPEARED/EXPIRED across scheduled runs
stateKey	string	empty (auto)	Name for this monitor's saved state; empty keys the baseline to the exact scope
emitUnchanged	boolean	false	Also push UNCHANGED rows in incremental mode
emitExpired	boolean	false	Also push EXPIRED rows after a complete scan
ignoreFieldsForChanges	string[]	[]	Fields that must not mark a row UPDATED
mcpConnectors	string[]	[]	Pipe result summaries into Notion, Linear, Airtable or Apify
notionParentPageUrl	string	empty	Parent page for the Notion export
maxNotifyListings	integer	50	Cap on items written per connector

Output Example

Sample shape: values are illustrative placeholders, not from a live record.

One session in Paris:

{
  "id": "e1df5a79-5cdc-47a5-a474-d4d59abbda1b",
  "city": "Paris",
  "date": "2026-10-08",
  "eventType": "drinks",
  "zoneId": "351",
  "dayUsersBooked": 176,
  "groupSize": 7
}

Plan Requirement

Paid plan recommended: the actor reads through the platform proxy, and free plans have limited proxy access. The scraped schedules themselves need no Timeleft subscription.

Send results into your apps (MCP connectors)

Select mcpConnectors in the input to pipe a condensed, human-readable summary per session (title plus key fields, not the full JSON) into Notion, Linear, Airtable or Apify. Authorize a connector once under Apify, Settings, Integrations, then reference it here. Set notionParentPageUrl to choose the Notion parent page, and cap the per-connector volume with maxNotifyListings (default 50, maximum 1000). The complete record always stays in the dataset; connector export never affects it and never fails a run.

FAQ

Is it legal to scrape Timeleft?

Scraping publicly visible schedule data for lawful use is generally permitted, but you are responsible for complying with applicable laws and Timeleft's terms. Keep usage respectful of the source.

Do I need an account or API key?

No. Just pick cities and run; no credentials needed.

Can it book dinners or show venues and attendees?

No. Booking, venues and attendee lists require Timeleft's paid subscription, which an actor cannot purchase. This actor returns schedules and booking headcounts only.

Why did my run return zero sessions?

Either every requested city name missed the catalog (check the run log for the unknown-cities warning), the scope filters excluded everything, or the source was temporarily unreachable, so rerun later.

How do scheduled runs work?

Turn on Incremental mode with a state key and schedule the actor: the first run tags everything NEW, later runs return only NEW, UPDATED and REAPPEARED sessions (plus EXPIRED when enabled) instead of the full snapshot.

Does it work with AI and MCP tools?

Yes. Select MCP connectors in the input to pipe per-session summaries into Notion, Linear, Airtable or Apify while the full JSON stays in the dataset.

🔗 Pair with more event data. Combine Timeleft schedules with these related scrapers from the same team:

• 🎟️ Ticketmaster Events Scraper : concerts, festivals and sports with dates, venues and ticket tiers
• 🎫 Eventim Scraper : German live events, dates and tickets
• 🎸 Concert Archives Scraper : concert history, tours, venues and setlists

💬 Support & custom scrapers

• 🐞 Found a bug or a missing field? Open a ticket on the Issues tab. We usually reply within hours.
• 🛠️ Need another site, extra fields or a private build? Email contact@abotapi.com or message Telegram @abotapi.
• ⭐ Enjoying it? A quick review on the actor page helps other users find it.