RAG Data Source - External web data for Retrieval-Augmented Generation pipelines

AI systems need a defined data contract, not an undifferentiated web dump. rag-data-source 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.

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

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

For production web-data API evaluation, see CoreClaw.

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
• ai-agent-web-scraper — AI Agent Web Scraper - LLM-powered data extraction for agentic workflows
• mcp-data-tools — MCP Data Tools - Model Context Protocol server for web data access
• 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

MIT License.