Most-Used Scraping Actors Right Now (October 2026)
Last updated: November 2, 2026 · Data snapshot: November 2, 2026 (trailing 30-day window) · Weekly refresh
Table 1 — Top 20 by 30-day run volume
Table 2 — Runs-per-user: where scraping looks like an API, not a tool
Actors with ≥50 monthly users, ranked by runs per user. High intensity = few users hammering it in production (API-like); low intensity = many casual users.
| Actor | Runs/30d | Users/30d | Runs/user (intensity) |
|---|---|---|---|
| danek/twitter-scraper | 2,355,889 | 406 | 5,803 |
| web_wanderer/yelp-reviews-scraper | 1,471,298 | 270 | 5,449 |
| vortex_data/instagram-scraper ↗ | 179,150 | 57 | 3,143 |
| scraping_solutions/linkedin-profile-comments-reactions-scraper-no-cookies ↗ | 177,743 | 57 | 3,118 |
| apidojo/instagram-scraper-api ↗ | 1,322,396 | 454 | 2,913 |
Key findings
- apify/instagram-scraper is the most-run scraper on the platform: 18,973,861 runs in 30 days (26,353/hour) for 62,623 monthly active users.
- The market is winner-take-most: the top 10 Actors absorb 41.5% of the 230,144,085 monthly runs we observed; the top 20 absorb 56.7%.
- Social-media scrapers dominate: 15 of the top 20 target social platforms.
- Usage intensity is wildly bimodal. danek/twitter-scraper averages 5,803 runs per user — essentially an API serving a few production pipelines — while popular tools like apify run 303 runs/user.
- List pricing does not track volume: #1 by usage bills PAY_PER_EVENT, and the most expensive top-20 Actor charges $0.50/1k.
How we measure
Cohort: Actors scanned from the public Apify Store API (popularity-sorted top 5,106, 3,898 scraping-related matches) on 2026-11-02. For each Actor we take publicActorRunStats30Days: TOTAL, SUCCEEDED, FAILED, ABORTED, TIMED-OUT over the trailing 30 days. Failure rate = FAILED / TOTAL — a run the user cancelled (ABORTED) or that exceeded its timeout budget (TIMED-OUT) is not counted as a failure; both are small relative to TOTAL in this cohort.
Ranking uses TOTAL runs over the trailing 30 days from the public Store API. "Users" = distinct accounts that started a run in the same window. Runs/user is a rough intensity proxy (one user may run many small jobs, or one huge job may contain many internal runs).
Failures have many causes (bad input, target changes, platform incidents) — a high failure rate is a signal, not a verdict. Display threshold for tables is ~500 runs/30d; ranking threshold is 1,000 runs/30d — entries between the two appear in tables but not in ranked statements. License: CC BY 4.0 — cite TopScraper with the data snapshot date.
Conclusion
If you are choosing infrastructure by popularity, the data says: social media scraping is a solved, hyper-scaled problem on the Apify Store (Instagram/TikTok tooling alone accounts for tens of millions of runs a month), and popularity is a reasonable proxy for maintenance — the top 20 all sit at or below ~2% failure. The inverse signal is intensity: Actors with extreme runs-per-user are de-facto production APIs with fragile single-vendor dependence — check their maintenance recency in our abandonment report before you build on one.