LaVague vs Steel
Side-by-side comparison of two AI agent tools
Short answer
- LaVague has had no commit in 20 months; Steel is actively maintained (10 commits in the last 90 days).
- Steel is growing faster: +156 GitHub stars in the last 30 days vs +12 for LaVague.
- Pick LaVague for: large Action Model framework to develop AI Web Agents. Pick Steel for: open Source Browser API for AI Agents & Apps.
From GitHub data refreshed daily.
LaVagueopen-source
Large Action Model framework to develop AI Web Agents
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
| LaVague | Steel | |
|---|---|---|
| Stars | 6.4k | 7.7k |
| Star velocity /mo | 12 | 155.68421052631578 |
| Commits (90d) | 0 | 10 |
| Releases (6m) | 0 | 2 |
| Downloads (30d, npm + PyPI) | 142 | β |
| Overall score | 0.1964127072064264 | 0.5500925879249736 |
Pros
- +Well-architected framework with clear separation between World Model (planning) and Action Engine (execution) components
- +Includes specialized LaVague QA tooling that converts Gherkin specs into automated tests for QA engineers
- +Strong open-source community adoption with 6,318 GitHub stars and active development
- +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
- -Framework complexity may require significant learning curve for developers new to web automation
- -Depends on external automation tools like Selenium or Playwright, adding infrastructure dependencies
- -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’Automating multi-step web research tasks like gathering installation instructions or documentation
- β’QA test automation by converting business requirements in Gherkin format into executable test suites
- β’Building user-facing automation tools that can navigate websites and perform complex workflows autonomously
- 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, LaVague or Steel?
- Steel has more GitHub stars (7,729 vs 6,394).
- Which is more actively developed, LaVague or Steel?
- Steel had more commits in the last 90 days (10 vs 0).
- Should I use LaVague 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.