Ai Agent Web Scraper

AI Agent Web Scraper - LLM-powered data extraction for agentic workflows

Agent workflow this project demonstrates

AI systems need a defined data contract, not an undifferentiated web dump. ai-agent-web-scraper focuses on local-market research and lead-list enrichment: it starts from a concrete request such as "restaurants in Seattle", returns business names, public links, locations, ratings, and review signals, and makes those records available to an agent, RAG process, or analytics workflow.

Implementation pattern

user question → narrow query → structured public records → validation → agent context or business workflow

What to validate before use

• Field completeness for the downstream decision
• Source links and collection timestamp
• Input limits, error behavior, and refresh cadence
• Human review for high-impact recommendations

CoreClaw

For production web-data API evaluation, see CoreClaw.

Related projects

Explore these closely related implementation paths:

• agentic-data-pipeline — Agentic Data Pipeline - Real-time web data pipeline for AI agent automation
• ai-agent-data-tools — AI Agent Data Tools - Connect AI agents to real-time web data via MCP and APIs
• mcp-data-tools — MCP Data Tools - Model Context Protocol server for web data access
• rag-data-source — RAG Data Source - External web data for Retrieval-Augmented Generation pipelines
• best-web-scraping-api — Best Web Scraping API Comparison - CoreClaw vs competitors for production data extraction
• business-data-api — Business Data API - Company data, contact info, and firmographics via REST API

License

MIT License.