Abandoned But Still Running: The Maintenance Gap

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.

"Abandoned" = the Actor's code has not been modified for at least 182 days (modifiedAt in the Store metadata). This is a code-freshness measure, not a quality verdict: a stable scraper for a static site may need no updates. Run counts come from the same public 30-day stats as our other reports.

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.

The maintenance gap is the scraping ecosystem's quiet risk: demand does not disappear when a maintainer leaves — runs keep hitting code that no longer tracks its targets, and failure rates drift upward (our worst abandoned cases fail 5-15x the median). Two practical uses of this list: (1) if your pipeline depends on one of these Actors, plan a migration now; (2) if you build Actors, every row in Table 1 is validated demand for a maintained alternative — an open niche a competent maintainer can capture with a fresher fork.

This page cross-references two independent scans: the abandonment scan (code modifiedAt across 3,135 Actors, 2026-10-21) and the weekly usage scan (2026-11-02).