fragments vs steel-browser
Side-by-side comparison of two AI agent tools
fragmentsopen-source
Open-source Next.js template for building apps that are fully generated by AI. By E2B.
steel-browseropen-source
🔥 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
| fragments | steel-browser | |
|---|---|---|
| Stars | 6.2k | 6.7k |
| Star velocity /mo | 518.6666666666666 | 561.9166666666666 |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 6 |
| Overall score | 0.5036016433624478 | 0.6216412748992806 |
Pros
- +Comprehensive multi-stack support with 5 different development environments (Python, Next.js, Vue.js, Streamlit, Gradio)
- +Secure code execution through E2B SDK isolation, allowing safe running of AI-generated code
- +Extensive LLM provider compatibility supporting 8+ providers including OpenAI, Anthropic, and local models via Ollama
- +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
- -Requires multiple API keys (E2B + LLM provider) which adds setup complexity and ongoing costs
- -Dependency on E2B's cloud infrastructure for code execution may introduce latency or availability concerns
- -Limited to predefined stack templates, requiring custom development to add new frameworks or languages
- -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
- •Building AI coding assistants that can generate, execute, and iterate on full applications in real-time
- •Creating educational platforms where students can experiment with AI-generated code safely
- •Developing rapid prototyping tools for businesses to quickly generate and test application concepts
- •AI agents that need to interact with dynamic websites, fill forms, or navigate complex user interfaces
- •Web scraping projects requiring session persistence, proxy rotation, and anti-detection measures
- •Automated testing scenarios where browser state management and debugging capabilities are essential