data-scraper-agent

Automating web scraping and storing results in Notion or Sheets requires a configurable pipeline that fetches public data, enriches it with AI, and exports it directly to your chosen backend. You define sources and storage targets in a configuration file to run the end-to-end workflow.

AI data enrichment processes raw scraped items from websites or APIs using a free LLM to score and transform them into actionable insights. This pipeline handles enrichment automatically after data collection, improving output quality by learning from your feedback over time.

Scheduling batch processing for scraping public APIs and websites is supported through a configurable pipeline. You can automate runs to fetch, enrich, and store data in Supabase or other backends, handling deduplication and model fallback automatically during scheduled execution.

This data scraping pipeline works with Supabase by providing flexible storage backend configurations for enriched datasets. It supports storing processed items directly into Supabase alongside alternatives like Notion and Google Sheets, allowing seamless integration into your existing data stack.

Configuring workflow automation for monitoring job boards or price trackers involves editing a configuration file to specify public sources and AI preferences. The pipeline then automatically fetches updates, enriches the data, and stores it for review.

Limitations of using a free LLM for data enrichment include potential processing constraints or model availability issues, mitigated by built-in model fallback support. The pipeline relies on this free AI to score raw items, so complex reasoning tasks may require tuning via user feedback to improve accuracy.