Firecrawl vs Steel
Side-by-side comparison of two AI agent tools
Short answer
- Firecrawl is growing faster: +14,035 GitHub stars in the last 30 days vs +156 for Steel.
- Pick Firecrawl for: the Web Data API for AI - Turn entire websites into LLM-ready markdown or structured data. Pick Steel for: open Source Browser API for AI Agents & Apps.
From GitHub data refreshed daily.
Firecrawlfree
π₯ The Web Data API for AI - Turn entire websites into LLM-ready markdown or structured data
Steelopen-source
π₯ Open Source Browser API for AI Agents & Apps. Steel Browser is a batteries-included browser sandbox that lets you automate the web without worrying about infrastructure.
Metrics
| Firecrawl | Steel | |
|---|---|---|
| Stars | 188.1k | 7.7k |
| Star velocity /mo | 14.0k | 155.68421052631578 |
| Commits (90d) | 600 | 10 |
| Releases (6m) | 3 | 2 |
| Downloads (30d, npm + PyPI) | 3.3M | β |
| Overall score | 0.8339842430155152 | 0.5500925879249736 |
Pros
- +Industry-leading reliability with >80% success rate on complex websites including JavaScript-heavy and dynamic content
- +AI-optimized output formats with clean markdown and structured data specifically designed for LLM consumption
- +Comprehensive feature set including media parsing, interactive actions, batch processing, and authentication support
- +Multi-client support allows integration with Puppeteer, Playwright, or Selenium for maximum flexibility
- +Comprehensive session management automatically handles browser state, cookies, and storage persistence
- +Built-in anti-detection capabilities with stealth plugins and fingerprint management help avoid bot blocking
Cons
- -Repository is still in development and not fully ready for self-hosted deployment
- -API-based service likely requires subscription pricing for production use
- -As a relatively new tool, long-term stability and support ecosystem may be uncertain
- -Public beta status indicates the platform is still evolving and may have stability issues
- -Browser automation inherently resource-intensive and can be complex to debug at scale
- -Requires understanding of browser automation concepts and may have learning curve for new users
Use Cases
- β’Building AI agents that need real-time web context and competitor intelligence
- β’Creating training datasets for LLMs by scraping and cleaning large volumes of web content
- β’Automating content monitoring and change detection for business intelligence applications
- β’AI agents that need to interact with dynamic websites, fill forms, or navigate complex user interfaces
- β’Web scraping projects requiring session persistence, proxy rotation, and anti-detection measures
- β’Automated testing scenarios where browser state management and debugging capabilities are essential
FAQ
- Which is more popular, Firecrawl or Steel?
- Firecrawl has more GitHub stars (188,094 vs 7,729).
- Which is more actively developed, Firecrawl or Steel?
- Firecrawl had more commits in the last 90 days (600 vs 10).
- Should I use Firecrawl or Steel?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.