State of Apify Multi-Board Job Aggregators — Q1 2026

The multi-board-aggregator segment on Apify is the first category in this series where the defining feature is behavioral, not a named target. Aggregators are actors that pull jobs from two or more distinct boards or ATS platforms in a single run. In Q1 2026 there are 46 such actors on the Apify Store, together carrying 1,433 aggregated 30-day active users — a real but focused slice of the broader job-data ecosystem.

The headline is how that demand is advertised. 82.6% of aggregator demand goes to actors whose title doesn’t use “aggregator”, “multi-board”, or “multi-source” — and doesn’t list the boards it covers. Buyers are not searching for aggregators. They are searching for specific jobs, specific boards, or specific industries, and discovering multi-board actors through the description body — or through word of mouth.

Three structural facts shape the rest.

First, one publisher owns the segment. fantastic-jobs holds 667 of 1,433 measured 30-day users — 46.5% of the entire category, from just two actors: career-site-job-listing-api and career-site-job-listing-feed. The same publisher that took 58% of the Q1 ATS segment takes nearly half the aggregator segment. That is not two separate wins. It is one product, positioned differently, carrying two categories.

Second, silent aggregators dominate demand; named aggregators dominate count. Of the 11 actors that explicitly use aggregator language (“aggregator”, “multi-source”, “all jobs”), together they move 83 users — 5.8% of segment demand. Of the 26 actors that quietly aggregate without saying so, together they move 1,184 users — 82.6%. Buyers are not converting on the aggregator label. They are converting on specific job-search use cases, and aggregation is a feature they discover, not a purchase criterion.

Third, the dominant aggregation surface is career-site APIs, not job-board scraping. The top actor combines LinkedIn job URLs with direct career-site data from Greenhouse, Workday, and Ashby. The pattern that wins is span the ATS layer, not span the job-board layer. Every aggregator above 100 users hits at least one ATS; no job-board-only aggregator (Indeed + LinkedIn + Glassdoor) clears 90 users.

Top actors

In plain prose, the top twelve multi-board aggregator actors are: fantastic-jobs / career-site-job-listing-api (522 users, 36.4%, silent, covers LinkedIn + Greenhouse + Workday + Ashby); agentx / all-jobs-scraper (149, 10.4%, silent, 4 boards); fantastic-jobs / career-site-job-listing-feed (145, 10.1%, silent, same 4 as above); openclawai / job-board-scraper (85, 5.9%, multi-named, 4 boards); spartany / new-job-posts-finder (71, 5.0%, silent, Indeed + LinkedIn + Naukri); jobo.world / ats-jobs-search (65, 4.5%, silent, Greenhouse + Ashby + Workable); cheapget / best-job-search (46, 3.2%, silent, 3 boards); codexdhanush / scrape-entry-level-it-jobs-in-india (43, 3.0%, silent, 4 platforms); khadinakbar / jobs-scraper (39, 2.7%, multi-named, 3 boards); doggo / uk-jobs-board-scraper (38, 2.7%, silent, 4 UK-focused boards); piotrv1001 / company-career-page-scraper (34, 2.4%, silent, 3 ATS); lenient_grove / Daily-Job-Pulse (24, 1.7%, both signals, 5 boards). The top three take 57% of segment demand; the top ten take 84%.

Signal: how aggregators advertise themselves

In plain prose: of the 46 multi-board aggregators on the Store, 26 are silent (1,184 users, 82.6% of segment demand) — their titles describe a general job-scraping function with no aggregation claim, even when the description lists several platforms. 8 are multi-named (165 users, 11.5%) — their titles explicitly list the boards they cover, e.g., “LinkedIn Indeed Glassdoor Job Scraper”. 11 use aggregator language (83 users, 5.8%) — titles like “Jobs Aggregator”, “Multi-Source Scraper”, “All Jobs”. Only 1 actor uses both signals (1 user, 0.1%). The most explicitly-marketed aggregators are the smallest.

That inversion is the category’s signature. In ATS and freelance marketplaces, naming the platform in the title is a positive ranking signal — buyers search for “greenhouse scraper” or “upwork scraper”. In aggregator land, naming the aggregation isn’t what buyers search for. They search for “job scraper” or for a specific board, and convert on actors that happen to span more than the board they came in for.

Publisher concentration

In plain prose, the top ten publishers in Q1 2026 multi-board aggregator demand run: fantastic-jobs (667 users, 46.5% of segment, 2 actors); agentx (149, 10.4%, 1 actor); openclawai (85, 5.9%, 1 actor); spartany (71, 5.0%, 1 actor); jobo.world (65, 4.5%, 1 actor); cheapget (46, 3.2%, 1 actor); codexdhanush (43, 3.0%, 1 actor); khadinakbar (39, 2.7%, 1 actor); doggo (38, 2.7%, 1 actor); piotrv1001 (34, 2.4%, 1 actor). Top-3 publishers take 63%, top-10 take 87%.

The shape is concentrated at the top and flat below. fantastic-jobs alone is roughly four times the size of the #2 publisher and larger than publishers #3 through #10 combined. Between publishers #2 and #10, demand falls off smoothly from 149 to 34 users — no second peak, no category of mid-tier specialists.

Cross-category pattern: fantastic-jobs

The fantastic-jobs story is the most interesting cross-category finding this quarter. In the Q1 2026 ATS report, fantastic-jobs~career-site-job-listing-api was the #1 actor on Greenhouse (520 of 1,068 users, 49% of that market), and fantastic-jobs held top-3 positions on Workday, Ashby, and Lever as well — 58% of total ATS demand from a single publisher. That result read as “portfolio publishers win ATS”.

In the aggregator view, the same actor appears at #1 with 522 users — essentially the same number. It is not a separate ATS product and a separate aggregator product. It is one product, counted two different ways. The ATS report saw it as “a scraper that covers each major ATS”. This report sees it as “a scraper that spans multiple ATS in a single run”. Both framings are true; they describe the same API.

The implication for publisher strategy is concrete. In a category where 82.6% of demand doesn’t search for “aggregator”, the winning position is to build one good aggregated API and market it per-platform. fantastic-jobs runs that playbook twice: once in the ATS category (where individual per-platform pages convert), once here (where the aggregation itself is the product). The per-platform naming is the marketing surface; the aggregation is the product.

What was excluded

The discovery pass surfaced several close calls that failed the inclusion rules.

Single-platform actors with “feed” naming conventions — e.g., <platform>-jobs-feed slugs — were excluded. The “feed” suffix is a delta/incremental-refresh pattern for single-target scrapers, not an aggregation claim. Six actors matched this pattern and sat out.

Actors that name-drop one competitor in their description — e.g., a Workday-only scraper whose description mentions “also works with LinkedIn shares” — were excluded. Two platforms mentioned in a description is not enough evidence of aggregation; three or more is required if the title itself does not advertise multi-board coverage.

Google Jobs scrapers were kept on a case-by-case basis. Google for Jobs is itself an aggregator; a scraper hitting it is arguably indirect aggregation. Only actors that explicitly also hit another board beyond Google counted; pure Google-for-Jobs scrapers were classified as single-platform Google actors.

Non-job “multi-platform” scrapers — reviews aggregators, social-media scrapers, price-comparison tools — were filtered by requiring the name to contain job, career, vacancy, or recruitment vocabulary. Fifteen actors with aggregator language but no job context were filtered this way.

Methodology

This report covers every Apify Store actor whose behavior spans two or more distinct job boards or ATS platforms in a single run. The Q1 2026 census began with a full snapshot of 24,333 Store actors. Each was reviewed for two signals in its title or slug: aggregator language (words like “aggregator”, “multi-source”, “multi-platform”, “all jobs”) and explicit listing of multiple platform names. Actors whose titles carry neither signal but whose descriptions name three or more distinct boards or ATS platforms were also included — strong description evidence is enough, but weaker mentions are not.

Only actors whose names are plausibly job-related were counted. Multi-platform scrapers for reviews, social media, e-commerce, or real estate were excluded even when they use aggregator language, to keep the category coherent. Actors that mention an aggregation phrase but scrape a single platform were also excluded — the word “aggregator” in isolation is not behavior.

The resulting 46-actor set carries 1,433 aggregated 30-day active users. No per-target concentration band is reported because the category itself is the target; instead, actor-level and publisher-level concentrations are shown directly. A publisher’s segment share is its actors’ combined 30-day users divided by the category total.

Raw per-actor classification is at discovery.csv. The included set is at included.csv. Per-publisher aggregation is at publishers.csv.

Next quarter’s edition rolls the census forward to the Q2 2026 snapshot. The signal/platform rules will be held constant; if the category changes shape, the report will show it.