A production-ready boilerplate to build, test, and ship an Instagram scraping pipeline from a GitHub repository. It focuses on resiliency against UI/API changes, proxy hygiene, and safe scaling.
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This repository is a robust template for building an Instagram scraper that you can deploy from GitHub to containers or serverless runners. It handles login, pagination, data extraction, retries, and storage pipelines with proxy rotation and anti-detect best practices. Ideal for growth teams, data engineers, and researchers.
- Saves time and automates setup.
- Scalable for multiple use cases.
- Safer with anti-detect and proxy logic.
| Feature | What it does |
|---|---|
| Headless browser layer | Playwright/Puppeteer/Selenium adapters with stealth plugin |
| Resilient selectors | CSS/XPath fallback + semantic locators to withstand UI shifts |
| Proxy & session pool | Rotating residential/mobile proxies, per-session cookies/fingerprints |
| Rate-limit guard | Token bucket throttling, jittered delays, backoff & circuit breaker |
| Pluggable storage | Write to JSON/CSV, SQLite/Postgres, S3/GCS, or Webhooks |
| Config via .env | Centralized runtime toggles, credentials, and feature flags |
| Structured logs | JSON logs + request/response tracing for observability |
| Dockerized runner | One-command local runs and reproducible CI builds |
- Competitor monitoring (hashtags, mentions, profiles)
- UGC/review collection for sentiment analysis
- Influencer discovery and campaign tracking
- Academic research & trend analysis
Q: What happens if GitHub scraper breaks (due to Instagram changes)?
A: The boilerplate includes selector fallbacks, semantic locators, and a rules-based parser. When a DOM change happens, the retry layer captures failures, snapshots the HTML, and opens a “break report” in logs. You can then adjust locators in one place (/scraper/selectors.*) without touching business logic. CI smoke tests validate critical paths so breaks are caught early.
Q: Can I deploy scraper in production / scale it?
A: Yes. Use the included Dockerfile and docker-compose.yml for horizontal workers. Scale with a queue (Redis/RQ, BullMQ, or Celery) and run N workers per proxy pool. Add a scheduler (GitHub Actions, Cron, or Argo Workflows) and centralize storage (Postgres/S3). The rate-limit guard and session pools keep concurrency safe.
Q: What tools or libraries are commonly used for Instagram scraping?
A: Headless browsers (Playwright, Puppeteer, Selenium), stealth plugins, proxy managers (residential/mobile), HTML parsers (Cheerio/BeautifulSoup), request tooling (Axios/Requests), queues (BullMQ/Celery), and datastores (SQLite/Postgres/S3). This repo shows reference adapters so you can swap stacks easily.
10x faster posting schedules
80% engagement increase on group campaigns
Fully automated lead response system
Average Performance Benchmarks:
- Speed: 2x faster than manual posting
- Stability: 99.2% uptime
- Ban Rate: <0.5% with safe automation mode
- Throughput: 100+ posts/hour per session
##Do you have a customize project for us ? Contact Us
- Node.js or Python
- Git
- Docker (optional)
# Clone the repo
git clone https://github.com/yourusername/instagram-scraper-github.git
cd instagram-scraper-github
# Install dependencies
npm install
# or
pip install -r requirements.txt
# Setup environment
cp .env.example .env
# Run
npm start
# or
python main.py$ npm start -- --hashtag "fitness" --limit 50 --out data/fitness.json
# => scrapes recent posts for #fitness with safe delays and saves JSON
$ python main.py --profile zeeshanahmad --out data/profile.csv
# => collects profile metadata, posts, and basic engagement statsMIT License