How I Built Smart Scraper M2M: A Fast ~30ms Scraper API for AI Agents

A developer built Smart Scraper M2M, a lightweight web scraper API designed for machine-to-machine communication, achieving response times of approximately 30 milliseconds. The tool aims to address the bottleneck of heavy, slow web scraping in AI agent frameworks like CrewAI and LangChain, reducing context bloat and LLM token costs. The source code is available on GitHub.

Building AI Agents with frameworks like CrewAI or LangChain often hits a bottleneck: heavy, slow web scraping that bloats context windows and increases LLM token costs. To solve this, I built Smart Scraper M2M — a lightweight, high-performance web scraper API designed specifically for machine-to-machine M2M communication. ~30ms .You can test the API or check the full source code directly on GitHub: 🔗 GitHub Repository: https://github.com/MRIGL/smart-scraper-m2m https://github.com/MRIGL/smart-scraper-m2m I’m actively improving the API and would love to hear your thoughts, feature requests, or contributions Feel free to star the repo or leave a comment below.