fastCRW is a managed cloud web data API for AI agents. It is described as a single API for searching, scraping, crawling, extracting, and monitoring the web, with use cases named as RAG, agents, and scraping workflows. The service also says it requires no infrastructure to run and offers one-command setup for an AI agent.
Its feature set includes search with full page content and multi-engine, structured results; scraping that returns clean markdown, JSON, or HTML with full JavaScript rendering; monitoring that watches a page for changes and sends an AI verdict and a webhook; crawling that returns every page on a site as structured data; domain mapping that discovers URLs without full page loads; structured extraction using a prompt or JSON schema; and PDF parsing that converts web-hosted PDFs to markdown using CPU-only processing and no LLM. The page also mentions smart caching, anti-detection, media parsing, and intelligent waiting for dynamic content.
fastCRW is shown with Python, JavaScript, Rust, Go, Ruby, cURL, and MCP access. It says it works with LangChain, CrewAI, n8n, Vercel, Dify, Botpress, and more, and that it is available on npm and PyPI. The page also says it can be installed with one command and that it supports self-host docs.
Pricing is described as simple usage-based plans with a free tier. The free tier includes 500 one-time credits, and no credit card is required. The page also states that it is open source and lists AGPL-3.0.
fastCRW is an API design, testing & docs project. It focuses on providing scalable, open-source web scraping and crawling for AI agent pipelines. It is built as an open-source project for AI developers. fastCRW is open source under the AGPL-3.0 license. It runs on the web, the command line, and API, and it can be self-hosted.
fastCRW first shipped in 2026. The project is developed in the open on GitHub with 220 stars and 451 commits in the last 90 days. Among its 5 catalogued features are web scraping, web crawling, and data extraction. It exposes integrations via a public API and an MCP server.
Summary written by a language model from the project’s public pages.
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