Fragments by E2B vs Steel
Side-by-side comparison of two AI agent tools
Short answer
- Steel is growing faster: +156 GitHub stars in the last 30 days vs +25 for Fragments by E2B.
- Pick Fragments by E2B for: open-source Next.js template for building apps that are fully generated by AI. Pick Steel for: open Source Browser API for AI Agents & Apps.
From GitHub data refreshed daily.
Fragments by E2Bopen-source
Open-source Next.js template for building apps that are fully generated by AI. By E2B.
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
| Fragments by E2B | Steel | |
|---|---|---|
| Stars | 6.4k | 7.7k |
| Star velocity /mo | 25.07936507936508 | 156.03174603174602 |
| Commits (90d) | 10 | 10 |
| Releases (6m) | 0 | 2 |
| Overall score | 0.4501392846261406 | 0.5745364089228213 |
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
- β’Building AI coding assistants that can generate, execute, and iterate on full applications in real-time
- β’Creating educational platforms where students can experiment with AI-generated code safely
- β’Developing rapid prototyping tools for businesses to quickly generate and test application concepts
- 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, Fragments by E2B or Steel?
- Steel has more GitHub stars (7,726 vs 6,382).
- Which is more actively developed, Fragments by E2B or Steel?
- Fragments by E2B had more commits in the last 90 days (10 vs 10).
- Should I use Fragments by E2B or Steel?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.