Profile Comments Scraper for LinkedIn: the Fetchin drop-in

Fetchin vs Profile Comments Scraper for LinkedIn, feature by feature

One row deserves reading twice. The success rate an actor advertises counts a run as successful once it finishes, whether or not the dataset came back complete, so it is not the same measurement as a published availability figure. You are also buying from the individual publisher rather than from Apify: there is no SLA for this actor, no status page for it, and nothing stopping it being repriced, deprecated or abandoned.

Change the base URL, keep everything else

The official Apify client works unchanged. The actor ID, the input shape and the asynchronous run semantics all stay the same. Only the base URL and the token differ.

import { ApifyClient } from 'apify-client'

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
})

const run = await client
  .actor('7TNcROe1C2CQDO3wl')
  .call({ usernames: ['williamhgates'], limit: 100 })

const { items } = await client
  .dataset(run.defaultDatasetId)
  .listItems()

import { ApifyClient } from 'apify-client'

const client = new ApifyClient({
  token: process.env.FETCHIN_API_KEY,
  baseUrl: 'https://api.fetchin.io/v1/apify',
})

const run = await client
  .actor('7TNcROe1C2CQDO3wl')
  .call({ usernames: ['williamhgates'], limit: 100 })

const { items } = await client
  .dataset(run.defaultDatasetId)
  .listItems()

The actor can also be referenced by its username/actor-name form, apimaestro/linkedin-profile-comments, if that is what your integration already passes.

Questions

How does paging work?

By page number, matching the actor. Items do not carry a root pagination token, which is the same behaviour as apimaestro/linkedin-profile-comments. Integrations that page by incrementing the page number keep working unchanged.

Does it accept both username inputs?

Yes. Both the plural usernames array and the singular username string are accepted, so either integration style works.

Is the field mapping validated?

Yes. The comments emulation is validated field-by-field against real output from the actor and reaches zero field errors on matched items.

How reliable is this actor?

You are buying from the individual publisher, not from Apify, so there is no SLA for the actor and no status page for it. It can be repriced, deprecated or abandoned without notice, and a pipeline built on it finds out when results stop arriving. Read the advertised success rate carefully as well: it counts a run as successful once it finishes, whether or not the dataset came back complete. Fetchin publishes one uptime figure for the whole API and exposes it at status.fetchin.io.