Crypto Events Scraper — crypto conference data & events API for lu.ma

Structured crypto conference data: conferences, hackathons, meetups, and summits, pulled from lu.ma and classified with crypto-specific tags. Use it as a crypto events API — run it on a schedule and pull fresh results with the Apify API or client libraries, or just export a dataset from the Console. Start from the crypto hub, a single organizer's calendar, or any city page, and get back one dataset row per event: name, dates, venue, price, organizer — plus the crypto-specific fields below that you won't get from a generic event scraper.

What makes this different from a generic lu.ma scraper

This actor is purpose-built for crypto/web3 event research. On top of the standard event fields, every item is enriched with:

Field	Description
eventType	One of conference, hackathon, meetup, summit, party (incl. side-events), or other — classified from the event's own name/description/tags.
chainMentions	Array of chains/ecosystems the event text mentions — Bitcoin, Ethereum, Solana, Base, Arbitrum, Polygon, Avalanche, Cosmos, Polkadot, TON, Sui, Aptos, NEAR, Cardano, and more.
isFree	true/false — collapses lu.ma's ticket data into a clean boolean.
isVirtual	true for online/hybrid events.
multiDay	true when the event spans more than one calendar day (conferences, summits, hacker houses).

All enrichment is computed with deterministic, testable text heuristics — no LLM calls, no black box.

Default seed: the crypto hub

Leave startUrls empty (or use the default) and the actor starts from lu.ma's own crypto topic hub, currently listing ~500 events across dozens of organizer calendars — conferences, hackathons, side-events, and community meetups worldwide. You can just as easily point it at a single calendar or city instead.

Full field list

Field	Description
url	Canonical event page URL
eventId	Luma's internal event ID
name	Event title
description	Full event description as plain text
startAt / endAt	ISO 8601 start/end datetimes
timezone	IANA timezone of the event
isVirtual	Whether the event is online/hybrid
venueName, address, city, region, country	Location details, when public
coverImageUrl	Event cover image
isFree, priceDisplay, spotsRemaining, isSoldOut	Ticket/pricing info as shown on the page
organizerName, organizerSlug, organizerWebsite	The hosting calendar/organizer
organizerSocials	Public organizer social handles (Twitter/X, Instagram, LinkedIn, TikTok, YouTube)
tags	Category tags lu.ma itself assigns (e.g. Crypto, AI)
scrapedAt	Timestamp of extraction
eventType	Crypto-specific classification — see above
chainMentions	Crypto-specific chain/ecosystem tags — see above
multiDay	Crypto-specific span flag — see above

Sample output

{
  "url": "https://lu.ma/37hv55q0",
  "eventId": "evt-M7fzXQdEUctQNYV",
  "name": "OffChainers @ Malaysia Blockchain Week 2026",
  "description": "Heading to Malaysia Blockchain Week 2026? Let's connect! Join fellow OffChainers for an informal meetup right at the venue...",
  "startAt": "2026-07-29T02:00:00.000Z",
  "endAt": "2026-07-29T21:00:00.000Z",
  "timezone": "Asia/Kuala_Lumpur",
  "venueName": "World Trade Centre Kuala Lumpur",
  "address": "World Trade Centre Kuala Lumpur, 41, Jalan Tun Ismail, Chow Kit, 50480 Kuala Lumpur, Malaysia",
  "city": "Kuala Lumpur",
  "region": "Wilayah Persekutuan Kuala Lumpur",
  "country": "Malaysia",
  "coverImageUrl": "https://images.lumacdn.com/uploads/bb/6b6df1d7-ec82-45f1-abab-107380ed9dcc.png",
  "priceDisplay": "Free",
  "spotsRemaining": null,
  "isSoldOut": false,
  "organizerName": "OffChain Global Events",
  "organizerSlug": "offchainglobalevents",
  "organizerWebsite": "https://offchain.social",
  "organizerSocials": {
    "twitter": "offchainglobal",
    "instagram": "offchain_global",
    "linkedin": "/company/offchain",
    "youtube": "offchaing"
  },
  "tags": ["Crypto"],
  "scrapedAt": "2026-07-29T09:09:36.847Z",
  "eventType": "meetup",
  "chainMentions": [],
  "isFree": true,
  "isVirtual": false,
  "multiDay": false
}

This is real output from a live run against luma.com/crypto.

Input

Field	Type	Description
startUrls	array	lu.ma URLs to scrape. Defaults to the crypto hub (https://luma.com/crypto) if left empty. See below for other supported URL kinds.
maxEvents	integer	Stop after this many unique events have been scraped. Leave empty for no limit.
dateFrom / dateTo	string	Optional ISO date bounds — only events starting in this window are kept.
eventTypeFilter	array	Optional — keep only conference, hackathon, meetup, summit, party, and/or other. Leave empty to keep every type.
maxConcurrency	integer	Parallel page fetches (default 5). Kept modest to be respectful of lu.ma.
maxRequestRetries	integer	Retries per page before it's skipped with a warning (default 3).

Supported URL types

1. The crypto hub — https://luma.com/crypto (the default) — the actor discovers the calendars featured there, then crawls each calendar for its events.
2. Calendar / organizer pages — https://lu.ma/token2049 — the actor discovers that organizer's listed events and scrapes each one.
3. City or category discovery pages — https://lu.ma/sf — the actor discovers the events shown on that page and scrapes each one.
4. Individual event pages — https://lu.ma/on-mercury-sf — scraped directly.

Mix and match any of these in one startUrls list.

Example input

{
  "startUrls": [{ "url": "https://luma.com/crypto" }],
  "maxEvents": 200,
  "eventTypeFilter": ["conference", "hackathon", "summit"]
}

Use cases

• Sponsor / BD prospecting — find every hackathon or conference in a chain's ecosystem to pitch sponsorship, a booth, or a talk.
• Conference calendars — build a running calendar of major crypto conferences and summits for your team or community.
• Community research — track what a specific ecosystem foundation, DAO, or accelerator is running, and where the crypto event calendar is thinning or thickening by region.
• Chain-specific outreach — filter chainMentions to find every event touching a given ecosystem (e.g. every Solana- or Base-tagged event this quarter).

How it works (and why it's reliable)

Lu.ma is built on Next.js and server-renders each public page with a full JSON snapshot of the page's data embedded in a __NEXT_DATA__ script tag. This actor reads that JSON directly instead of scraping visible HTML/CSS — no headless browser required. That means:

• It survives lu.ma redesigning its page layout or CSS classes.
• Field extraction (dates, prices, organizer info) is exact — it comes from the same data lu.ma's own frontend renders from, not fragile text parsing.
• Crawling the crypto hub, a calendar, or a city page all follow the same pattern: read the embedded JSON, pull out the linked calendars/events, and queue them directly (bypassing same-hostname link-following, since lu.ma redirects its bare domain to luma.com).

Every page fetch is retried on failure; a page that can't be parsed is skipped with a logged warning rather than crashing the run. Events are deduplicated by their lu.ma event ID, so the same event linked from multiple discovery pages is only saved once. The run ends with a summary log of events scraped vs. pages that failed or were skipped, and only exits with a failure status if every input page failed.

Limitations (honest, by design)

• No attendee/guest-list data. This actor only reads public event/calendar/discovery pages. It does not, and will not, extract guest lists, RSVPs, or any data behind a login.
• No login, no CAPTCHA bypassing. Private or invite-only events are out of scope.
• Enrichment is heuristic, not perfect. eventType and chainMentions are derived from text pattern matching, not a model — a poorly-described event may land in other, and chain detection is intentionally conservative on ambiguous brand names (e.g. "Base", "NEAR", "TON") to avoid false positives, which means it can occasionally under-count a genuine mention buried in unusual phrasing.
• The crypto hub surfaces active/featured calendars, not every historical listing. Very large calendars that lu.ma paginates client-side may not surface every event in one page load; re-run periodically to pick up new listings, or supply direct calendar/event URLs for anything missed.
• Fields depend on what the organizer made public. If an organizer hides the exact address or ticket price, this actor won't have it either — it reads only what lu.ma itself shows to a visitor.

FAQ

Is it legal to scrape crypto events from lu.ma? This actor only reads public pages — the same event, calendar, and hub pages anyone can open in a browser without logging in. It doesn't access private events, guest lists, or anything behind authentication. You're responsible for how you use the data, but the extraction itself is limited to public information by design.

What does it cost to run? Pay per result — you're charged per event returned, not per page crawled or per run. See the Pricing section below for the current rate.

How do I run this on a schedule? In the Apify Console, open this actor, save an input as a Task, then add a Schedule to that task (e.g. daily or weekly). Each scheduled run appends new events to a fresh dataset, so you can track which conferences/hackathons are newly announced.

Can I get a field that isn't listed here? Only if lu.ma itself shows it on the public page, or if it's derivable with a deterministic text rule (like eventType and chainMentions are). This actor doesn't invent data or call an LLM. If you need something specific, open an Issue on the actor's Issues tab.

Does chainMentions tell me who's sponsoring or attending? No. It only flags which chains/ecosystems are mentioned in the event's own name, description, or tags — useful for finding events to target for sponsorship or BD outreach, not a guest list or sponsor list.

Can I pull just conferences and hackathons, skip meetups? Yes — set eventTypeFilter to ["conference", "hackathon"] (or any subset) and everything else is dropped before it's saved.

Pricing

Pay per result — you're charged per event returned, not per page crawled or per run. One dataset item = one event.

Output

One dataset item per event. Use the Crypto events dataset view in the Apify Console for a quick table (name, type, chains, dates, city, price), or export the full dataset as JSON/CSV/Excel for further analysis.