Langfuse vs Midscene.js

Side-by-side comparison of two AI agent tools

Short answer

  • Langfuse is growing faster: +1,807 GitHub stars in the last 30 days vs +180 for Midscene.js.
  • Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick Midscene.js for: gUI Agent for E2E Testing.

From GitHub data refreshed daily.

Langfuseopen-source

Open-source LLM engineering platform for observability, evaluation, prompt and dataset management

M
Midscene.jsopen-source

GUI Agent for E2E Testing

Metrics

LangfuseMidscene.js
Stars35.3k15.1k
Star velocity /mo1.8k180
Commits (90d)2.0k290
Releases (6m)1010
Overall score0.89713126864647650.7106927766699894

Pros

  • +Open source with MIT license allowing full customization and transparency, plus active community support
  • +Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
  • +Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK

    Cons

    • -May require significant setup and configuration for self-hosted deployments
    • -Could be overwhelming for simple use cases that only need basic LLM monitoring
    • -Self-hosting requires technical expertise and infrastructure resources

      Use Cases

      • •Production LLM application monitoring to track performance, costs, and identify issues in real-time
      • •Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
      • •LLM evaluation and testing to measure model performance across different datasets and use cases
        View Langfuse DetailsView Midscene.js Details

        FAQ

        Which is more popular, Langfuse or Midscene.js?
        Langfuse has more GitHub stars (35,329 vs 15,064).
        Which is more actively developed, Langfuse or Midscene.js?
        Langfuse had more commits in the last 90 days (2,013 vs 290).
        Should I use Langfuse or Midscene.js?
        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.