data-scraper-agent

You can automate web scraping and data enrichment without managing servers by deploying the pipeline via GitHub Actions. This zero-infrastructure approach uses scheduled runs to collect, enrich, and store data automatically.

An AI-powered data collection pipeline is an automated workflow that scrapes web data, uses LLMs to enrich it, and stores the results. It operates on a three-layer stack: collect, enrich, and store.

Yes, you can use GitHub Actions to run scheduled data scraping tasks. It provides zero-infrastructure deployment for automated pipelines, executing the collect, enrich, and store stack on a schedule.

To store scraped and enriched data in Notion or Supabase, you configure the storage layer of the pipeline. The workflow automatically pushes the AI-enriched results directly into your chosen platform.

The best way to handle LLM batching for large data scraping workflows is to use a pipeline with built-in batching and fallbacks. This optimizes AI calls and ensures reliability under load during the enrichment phase.