What It Is
ScrapeOps is a web scraping operations platform that helps developers and data teams run production crawlers without assembling every piece themselves. It combines proxy aggregation (residential, mobile, datacenter, JS rendering, anti-bot bypass), structured parsing for major sites, Scrapy job monitoring, and AI-assisted scraper building plus marketing tools, docs, a Chrome extension, and an MCP server for AI agents.
Users manage accounts, jobs, spiders, alerts, and billing from a central dashboard while high-throughput proxy and parser traffic is served by dedicated Go services.
The Problem We Solved
Production scraping teams routinely hit the same walls:
- Proxies from many vendors need rotation, geo targeting, and fallback logic in custom code
- Anti-bot and JS-heavy sites break naive HTTP clients
- Maintaining site-specific parsers is slow and brittle when layouts change
- Scrapy fleets lack unified monitoring, scheduling, and alerting
- New scrapers take too long to prototype, test, and ship
ScrapeOps packages proxies, parsers, monitoring, and AI tooling behind consistent APIs and a single product surface.
What We Work On
Proxy platform
Go proxy gateways (proxy.scrapeops.io) with provider fallbacks, sticky sessions, rendering levels, residential/mobile routes, and pay-per-GB billing stats in Redis.
Parser platform
Go parser service with hardcoded parsers (Amazon, Walmart, eBay, Target, Indeed, Zillow, and more) and AI self-healing parsers schema-driven extraction, LLM validation, and multi-language code export.
Monitoring & jobs
Django and Express backends for Scrapy jobs, spiders, servers, schedules, alerts (email/Slack), and legacy backend.scrapeops.io APIs.
Customer-facing apps
Angular dashboard (jobs, proxy, parser, AI assistant, admin), Next.js marketing site (proxy comparisons, testers, playbook content), and Docusaurus documentation.
AI & integrations
AI scraper generator, URL analyzer, fix sessions, ScrapeOps MCP (maps_web, extract_data), n8n community node, and Chrome extension for on-page analysis.
Data & operations
Prisma/PostgreSQL schema, proxy test pipelines (frontend → queue → proxy-worker → internal Flask executor), Stripe billing, S3/Spaces storage, Docker/PM2/nginx deploys, and CI workflows.
How It Works (In Simple Terms)
- Connect: Developers add API keys and configure spiders, proxies, or parser schemas in the dashboard.
- Route traffic: Requests go through ScrapeOps proxy endpoints with provider selection and anti-bot options.
- Extract: Parser APIs return structured data from known sites or AI-generated parsers.
- Monitor: Jobs and spiders report into ScrapeOps for history, alerts, and server management.
- Iterate: AI assistant, MCP, and testing tools shorten the loop from URL to working scraper.
Proxy testing flows queue work through Redis and workers so performance metrics inform optimal provider sequences.
Key Outcomes
- One API for many proxies: Rotation, geo, rendering, and bypass without vendor-specific glue code.
- Faster structured data: Pre-built and AI-maintained parsers for common domains.
- Operational visibility: Jobs, spiders, alerts, and team roles in a shared dashboard.
- Shorter build cycles: AI scraper builder, analyzers, and agent integrations (MCP, n8n).
- Production-grade footprint: Multi-service architecture with Go data plane and mature billing/storage.
Technologies & Approaches We Used
| Area | What we used | Why it matters |
|---|---|---|
| Proxy / parser APIs | Go 1.22+, Gin | High-throughput gateway services |
| App APIs | Express, Django, Flask | Auth, billing, jobs, internal test execution |
| Frontends | Angular 15, Next.js 15, Docusaurus | Dashboard, marketing, and docs |
| Data | PostgreSQL, Prisma, Redis | Accounts, jobs, proxy stats, queues |
| Scraping | Scrapy ecosystem, Python test tooling | Native fit for crawler customers |
| Payments & storage | Stripe, AWS S3, DigitalOcean Spaces | Subscriptions and artifact storage |
| Integrations | MCP server, n8n node, Chrome extension | AI agents and workflow automation |
| Infra | Docker, PM2, nginx, GitHub Actions | Multi-repo deploy and CI |
Approach in practice: ScrapeOps is intentionally multi-service, Go handles hot proxy/parser paths, Node and Python own product APIs and Scrapy integration, and frontends split dashboard vs. growth content. Shared database and queue patterns let proxy testing and billing stay consistent while each service can scale on its own.
Who It's For
- Web scraping and data engineering teams running Scrapy or custom crawlers
- Developers who need reliable proxies plus structured extraction APIs
- Platform admins managing teams, plans, and proxy/parser configuration
- AI and automation users consuming ScrapeOps via MCP or n8n