firecrawl/firecrawl
The API to search, scrape, and interact with the web at scale. 🔥
What it solves
Firecrawl is an API designed to turn the web into a clean, structured data source for AI agents and LLMs. It solves the difficulty of web scraping at scale by handling complex issues like JavaScript-heavy pages, rotating proxies, rate limits, and JS-blocked content without requiring manual configuration.
How it works
Firecrawl provides a set of endpoints that allow users to search, scrape, and interact with the web. It converts web content into LLM-ready formats such as clean Markdown or structured JSON. Key capabilities include:
- Search & Scrape: Finding sources and converting URLs into clean data.
- Crawl & Map: Discovering all URLs on a website and scraping them in bulk.
- Interact: Using AI prompts or code to click, scroll, and navigate pages before extracting content.
- Agent: An autonomous data gathering tool that searches and retrieves information based on a natural language prompt, optionally using a schema for structured output.
- Media Parsing: Extracting content from web-hosted PDFs and DOCX files.
Who it’s for
Developers building AI agents, RAG (Retrieval-Augmented Generation) systems, and dynamic applications that require real-time, high-reliability web data.
Highlights
- LLM-Ready Output: Produces clean Markdown and structured JSON to reduce token usage.
- High Reliability: Covers 96% of the web, including JS-heavy sites.
- Agentic Capabilities: Includes an autonomous agent for data gathering and an interaction API for navigating pages.
- Broad SDK Support: Available in Python, Node.js, Go, Java, Elixir, Rust, and Ruby.
- MCP Integration: Connects to MCP-compatible clients with a single command.