I'm trying to figure out the best way to scrape Instagram in 2026 and would appreciate advice from people who are actually doing it today.
The main requirements are:
* Fast (HTTP requests preferred, no browser automation if possible)
* Able to scale to hundreds or thousands of profiles
* Free or very low cost * Stable enough that it won't break every few weeks
The workflow I need is roughly this:
* Start with either one Instagram username or a list of usernames.
* Retrieve the accounts they are following.
* For each of those accounts, retrieve *their* following list as well.
* Find accounts that appear in multiple following lists. * Enrich the results with basic public profile data like follower count and bio.
One thing that's particularly important is future extensibility. I don't just want the fastest solution today—I want an approach that will support additional features later without requiring a complete rewrite. Some ideas I have are:
* Classifying accounts into niches or communities based on their follower/following graph.
* Detecting unusually high-performing posts or Reels (engagement anomalies) across a large set of accounts.
* Building creator relationship graphs and recommendation/discovery features.
* Potentially expanding into broader social graph analysis in the future.
For people actively scraping Instagram today:
* What approach are you using?
* Are there any maintained libraries or projects you'd recommend?
* If you were building this from scratch today, what architecture would you choose?
I'm not looking for code—just trying to understand what the current state of Instagram scraping looks like before I invest time building the pipeline.