LaVague vs Playwright MCP server

Side-by-side comparison of two AI agent tools

Short answer

  • LaVague has had no commit in 20 months; Playwright MCP server is actively maintained (35 commits in the last 90 days).
  • Playwright MCP server is growing faster: +630 GitHub stars in the last 30 days vs +12 for LaVague.
  • Pick LaVague for: large Action Model framework to develop AI Web Agents. Pick Playwright MCP server for: playwright MCP server.

From GitHub data refreshed daily.

LaVagueopen-source

Large Action Model framework to develop AI Web Agents

Playwright MCP server

Metrics

LaVaguePlaywright MCP server
Stars6.4k37.8k
Star velocity /mo12.063492063492063630
Commits (90d)035
Releases (6m)010
Overall score0.211314429022619630.7399772511574503

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

    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

      Use Cases

      • •Automating multi-step web research tasks like gathering installation instructions or documentation
      • •QA test automation by converting business requirements in Gherkin format into executable test suites
      • •Building user-facing automation tools that can navigate websites and perform complex workflows autonomously

        FAQ

        Which is more popular, LaVague or Playwright MCP server?
        Playwright MCP server has more GitHub stars (37,761 vs 6,394).
        Which is more actively developed, LaVague or Playwright MCP server?
        Playwright MCP server had more commits in the last 90 days (35 vs 0).
        Should I use LaVague or Playwright MCP server?
        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.