Which is more popular, Firecrawl or LaVague?

Firecrawl has more GitHub stars (187,756 vs 6,394).

Which is more actively developed, Firecrawl or LaVague?

Firecrawl had more commits in the last 90 days (578 vs 0).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

LaVague has had no commit in 20 months; Firecrawl is actively maintained (578 commits in the last 90 days).
Firecrawl is growing faster: +14,055 GitHub stars in the last 30 days vs +12 for LaVague.
Pick Firecrawl for: the Web Data API for AI - Turn entire websites into LLM-ready markdown or structured data. Pick LaVague for: large Action Model framework to develop AI Web Agents.

From GitHub data refreshed daily.

🔥 The Web Data API for AI - Turn entire websites into LLM-ready markdown or structured data

LaVagueopen-source

Large Action Model framework to develop AI Web Agents

+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

+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

-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

-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

•Building AI agents that need real-time web context and competitor intelligence
•Creating training datasets for LLMs by scraping and cleaning large volumes of web content
•Automating content monitoring and change detection for business intelligence applications

•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

Which is more popular, Firecrawl or LaVague?: Firecrawl has more GitHub stars (187,756 vs 6,394).
Which is more actively developed, Firecrawl or LaVague?: Firecrawl had more commits in the last 90 days (578 vs 0).
Should I use Firecrawl or LaVague?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.