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GitHub Profile Scraper: Extract Public Data from Any GitHub Account

GitHub Profile Scraper pulls public profile data from any GitHub user account and returns it as structured JSON. Paste in a username and get back name, bio, location, company, website, follower count, repo count, Twitter handle, email, and more. Or paste in a list of usernames and process them all in one run.

No browser automation, no manual copy-paste, no setup on your end. Just usernames in, data out.

What data does this actor extract?

Each scraped profile returns the following fields:

How to use it

Input

You can provide a single username, a list of usernames, or both. Duplicates are ignored. Usernames with a leading @ are accepted and cleaned automatically.

Example input

{
"username":"torvalds",
"usernames":["gvanrossum","kawsarlog"],
"maxItems":100,
"requestTimeoutSecs":30
}

Example output

{
"username":"torvalds",
"name":"Linus Torvalds",
"bio":"Just a geek",
"location":"Portland, OR",
"company":"Linux Foundation",
"websiteUrl":null,
"twitterUsername":null,
"socialAccounts":[
{"provider":"linkedin","url":"https://www.linkedin.com/in/torvalds"}
],
"avatarUrl":"https://avatars.githubusercontent.com/u/1024025?v=4",
"profileUrl":"https://github.com/torvalds",
"email":null,
"followers":239000,
"following":0,
"publicRepos":7,
"publicGists":0,
"totalStars":218400,
"totalForks":43200,
"hireable":null,
"accountType":"User",
"createdAt":"2011-09-03T15:26:22Z",
"updatedAt":"2024-12-15T08:41:12Z",
"scrapedAt":"2026-04-13T10:00:00.000000+00:00",
"error":null
}

Use cases

• Recruiting: screen developer candidates by checking their public repo count, follower count, and account activity before reaching out
• Sales prospecting: build technical lead lists by collecting company, website, and contact info from GitHub profiles at scale
• Open-source research: track contributor profiles across multiple accounts without visiting each page manually
• HR analytics: compare candidate GitHub presence as part of a structured evaluation process
• Market research: map out the GitHub activity of a competitor's engineering team or open-source community
• Data enrichment: append GitHub profile data to existing contact lists using batch mode

Batch scraping

Supply a list of usernames in the usernames field to scrape many profiles per run. The actor processes them in order, handles errors per profile without stopping the run, and saves each result to the dataset as it goes.

If a username does not exist or returns an error, the output record for that username will have null for all data fields and a message in the error field.

FAQ

How many profiles can I scrape per run? The default limit is 100 profiles per run, adjustable up to 1,000 via the maxItems field.

Does this work for organization accounts? Yes. GitHub organizations have public profiles with similar fields. The accountType field will return "Organization" instead of "User".

What happens if a profile is private or does not exist? The actor logs an error for that username and continues with the rest. The failed profile gets an error record in the output dataset so nothing is silently skipped.

Can I scrape private profile data? No. This actor only collects data that GitHub exposes publicly. Private emails, private repos, and private account settings are not accessible.

How do I input usernames with an @ symbol? You can. The actor strips the leading @ automatically, so @torvalds and torvalds both work.

What format is the output? JSON, saved to the Apify dataset. You can export it as JSON, CSV, or XLSX directly from the Apify console, or use the Apify API to pull it into your pipeline.

Scheduling and monitoring

You can schedule this actor to run on a cron schedule from the Apify console. Useful for monitoring follower growth, tracking profile updates, or refreshing a contact list on a weekly basis.

GitHub profile scraper for recruiters and researchers

Whether you are building a candidate database, enriching a CRM with developer data, or mapping out an open-source community, this GitHub profile scraper gets you structured data without the manual work. Run it on demand or on a schedule and export the results wherever you need them.