GitHub radar
Firecrawl: Clean Web Data for Your AI Agents
Firecrawl's open-source service turns any web page into clean text or structured data an AI agent can use — I review where it saves a founder time and tokens.
Firecrawl is an open-source web data service from the Firecrawl team, also available as a hosted API. It searches the web, opens pages and returns them as clean Markdown, structured JSON or screenshots instead of raw HTML. The team says it covers 96% of the web, including pages that only render with JavaScript, and handles proxies and rate limits itself. It can also click, scroll and type on a page before extracting content, parse PDFs and DOCX files, and connect to AI agents as an MCP tool.
Why a vibe-coder should care
I see two kinds of savings here. Clean page text instead of raw markup means fewer tokens in every agent request, which the team itself highlights. And the jobs founders usually hand to a freelancer — collecting competitor prices, building a list of companies, gathering market data for a pitch — become a task you describe to your agent in one sentence. The same approach works for a small internal tool: an agent that checks a set of pages every morning and sends you what changed.
How to install
Copy this and send it to your agent — Claude Code, Codex, any of them:
Connect Firecrawl to my agent as an MCP tool: https://github.com/firecrawl/firecrawl — follow the README, ask me for a key or set up the open-source version, then collect pricing plans from three competitor websites into one table.
Облачная версия работает без установки по ключу; открытую версию агент развернёт у вас сам, мощный компьютер не нужен.
Open on GitHub▌ More finds