GitHub trending pages show stars gained per day, week and month, for the current window only. Our GitHub scraper keeps trending, star and fork counts as a dated series.
Plans from €169/month · Free project assessment · Reply within 1 business day
ScrapeIt builds the collector, runs it, and watches it. You get a sample extract first, before the full run, so you can check field names and coverage against what you expected. After sign off we run to the schedule you set, daily, weekly or monthly, and we monitor for markup and endpoint changes so the feed does not quietly go stale.
Delivery is CSV, JSON, XLSX or an API endpoint you call, pushed to S3, an SFTP drop or your warehouse. Documentation of every field comes with the dataset, and access to our endpoint is by key.
We extract GitHub data from the public pages a visitor sees, at repository and organization level.
Every row carries its capture date, because most of these figures matter as a series.
GitHub shows what is true now. It does not show you what was true last March. Nothing on the platform backfills a trending list, a star count as of a date, or the size of a search result set over time. If you want those as a series, someone has to capture them on a schedule and keep them. That is the clearest reason to scrape GitHub: a daily capture of the trending pages, held for a year, is a dataset that cannot be reconstructed later at any price.
Series we have been asked for: daily trending position by language, weekly star and fork deltas across a named watchlist of repositories, release cadence per project, and how long open issues stay open across a set of vendor repositories. All of these sit at repository and organization level.
What we do not build is person level. GitHub profiles belong to real people, and their public activity is personal data under the GDPR. Profile identifiers, personal email addresses and contribution histories tied to a named individual are excluded from our work by default. We do not compile developer profiles for recruitment, sourcing or scoring, and we turn those requests down.
GitHub hosts source code and the collaboration around it: repositories, issues, pull requests, commits, releases, discussions and organization accounts. Public repositories and organization accounts are open to any visitor, without logging in.
A public repository page shows its stars, forks, languages, topics, license, open issue count, latest release and the date of the last push, with the rendered README below. An organization page lists its public repositories with the same figures. The trending pages rank repositories by daily, weekly and monthly window, per programming language, with the stars each one gained in the window.
Search is the third surface. The web interface reports a match total for every query, running to the hundreds of thousands for broad ones, and lists the matching repositories in the order it ranks them.
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Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →Teams scrape GitHub to watch the open source ground they compete on: which projects gain stars and forks, which languages and topics rise on the trending pages, how often a vendor ships releases and how long its open issues stay open. Read together, these figures are the closest proxies for adoption and team activity.
Two surfaces show what a single repository view hides. The trending pages publish a stars gained figure per window and language, and search reports how many repositories match a query, which sizes an ecosystem at a glance. Captured on the same schedule for every repository on a watchlist, these figures compare cleanly across vendors and over time.
What a GitHub scraper adds is the operational layer: running collection on a schedule, retaining history that nobody backfills, normalizing GitHub data against other sources, and delivering it in a shape your systems already read.
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ScrapeIt is a managed data collection agency. We write the crawlers, run them on our own infrastructure, repair them when a site changes, and hand over clean files. You do not maintain code and you do not manage proxies.
Every project starts with a free assessment: you name the repositories, organizations or trending windows, and we confirm the fields and the schedule before there is an invoice. Plans start from EUR 169 a month, or EUR 199 for a one-time extract.
Yes. The ScrapeIt GitHub API returns trending positions, stars gained, star and fork counts, languages, topics and release dates as JSON from an endpoint we host, refreshed on the schedule you set. The same data also comes as CSV or XLSX files or straight into your database.
Public pages at repository and organization level: the trending pages by window and language, with stars gained; repositories with stars, forks, languages, topics, licenses, open issue counts, push dates and releases; organization accounts with their public repositories; and search results with the match total the page reports. We never log in, and person level data stays out.
Not from GitHub. The trending pages show a daily, weekly or monthly window computed at the moment you load them, and nothing on the platform returns a past day. The only way to hold a history is to capture the pages on a schedule from now on and store the results. We run that capture and keep the series, which is why the earliest date in such a dataset is the date you start, not the date you ask.
No. We do not build person level datasets from GitHub. Profile identifiers, personal email addresses and contribution histories tied to a named individual are excluded from our GitHub scraper by default. Public activity by a named developer is personal data under the GDPR, and we do not sell it to recruiters, headhunters or job boards. Repository level and organization level work we will take.
GitHub has no review or rating system, so there is no score to collect. Stars, forks, open issue counts, contributor counts and commit frequency are the closest proxies for adoption and team activity, and every one of them can be collected for a repository you name. They read better as a trend than as a number: a project that gained stars steadily over six months looks different from one that gained the same total in a single week. That comparison needs a stored series, and the series is the part we supply.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
GitHub data from €169/month. Free project assessment, reply within 1 business day.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
