We extract repositories, developer profiles, commit histories, and issue trackers from Github. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Repositories objects from github.com. All fields typed and schema-versioned.
"repo_name": "react", "owner": "facebook", "stars": 203491, "forks": 42194, "language": "TypeScript", "topics": "['javascript', 'react', 'ui']", "license": "MIT"
| # | repo_name | owner | description | stars | forks | watchers |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Developer Profiles objects from github.com. All fields typed and schema-versioned.
"username": "gaearon", "name": "Dan Abramov", "company": "Meta", "followers": 82419, "public_repos": 243, "location": "London", "blog": "overreacted.io"
| # | username | name | bio | company | location | blog |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Issues and PRs objects from github.com. All fields typed and schema-versioned.
"number": 2145, "title": "Fix hydration mismatch", "state": "closed", "author": "acdlite", "comments_count": 14, "reaction_count": 42, "labels": "['bug', 'priority: high']"
| # | number | title | state | author | assignee | labels |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Commits objects from github.com. All fields typed and schema-versioned.
"sha": "8a4f9d2b", "message": "Update README.md", "author_name": "John Doe", "additions": 45, "deletions": 12, "changed_files": 1, "repo_name": "react"
| # | sha | message | author_name | author_email | date | additions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Organisations objects from github.com. All fields typed and schema-versioned.
"org_name": "vercel", "display_name": "Vercel", "location": "San Francisco", "verified_domain": true, "members_count": 142, "repos_count": 312, "website": "https://vercel.com"
| # | org_name | display_name | description | location | website | verified_domain |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Github scraper handles every layer of the platform: repositories, developer profiles, issue trackers, and commit histories, with rate limit circumvention and token management built in.
Extract stars, forks, languages, topics, and complete README files at scale.
Capture bio, company affiliations, location, public emails, and follower graphs.
Monitor bug reports, feature requests, patch submissions, and discussion threads.
Extract granular commit data including diff stats, author details, and timestamps.
Map company structures, verified domains, and affiliated developer networks.
Track software version releases, changelogs, and binary asset metadata.
Identify upstream dependencies and downstream dependents across repositories.
Scrape daily, weekly, and monthly trending lists across all programming languages.
Run one-off bulk exports or configure continuous pipelines at hourly cadences.
Brief in. Clean data out.
Provide repository lists, organisation names, or target languages. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and token management for github.com.
Schema validation, null-rate checks, and sample repository extraction before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Github limits API access and monitors scraping patterns. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Github rate limits heavily. We distribute requests across residential proxies and manage complex API token rotation to maintain high throughput without triggering blocks.
We hit undocumented internal endpoints and GraphQL schemas to extract data not available in standard HTML, ensuring complete data capture.
Repositories with millions of commits require distributed crawling strategies to paginate without timeouts or memory exhaustion.
For large organisations, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and storage bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and schema drift, responding before you notice.
Recruiters identify developers based on commit frequency, language expertise, and open-source contributions.
Security teams monitor repositories for leaked credentials, exposed API keys, and vulnerable dependencies.
DevTools companies identify target accounts by analysing organisation tech stacks and repository topics.
Maintainers track project adoption, contributor retention, and issue resolution velocities.
VC firms evaluate startup momentum by measuring repository growth, star velocity, and community engagement.
Machine learning teams build extensive datasets of structured code, commit messages, and issue discussions.
"Github holds the world's most comprehensive graph of developer behaviour, software dependencies, and technical talent. Extracting it requires scale."
Most teams underestimate the investment required: reliable Github scraping requires distributed pagination, GraphQL token management, residential proxies, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our github.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and retry logic. Playwright handles JavaScript rendering and interaction flows for complex UI elements.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to avoid rate limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About github.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Github is generally permissible. DataFlirt targets only public repositories, profiles, and issue trackers. We do not extract private code or circumvent authentication walls.
We use distributed residential proxy pools and manage API token rotation to ensure high throughput without triggering IP bans or rate limit blocks.
We extract email addresses only if they are publicly exposed in commit histories or explicitly listed on public developer profiles.
Pipelines can be configured for daily, hourly, or near real-time cadences depending on the specific repositories or organisations being tracked.
No. We strictly target public data and do not process authenticated sessions for private codebases or internal organisation discussions.
Yes. We provide a sample run of up to 50 repositories as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a specific organisation dump or continuous monitoring across thousands of repositories, we scope, build, and operate the pipeline.
