Langfuse vs Kortix

Side-by-side comparison of two AI agent tools

Short answer

  • Langfuse is growing faster: +1,812 GitHub stars in the last 30 days vs +30 for Kortix.
  • Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick Kortix for: the open-source AI Management System.

From GitHub data refreshed daily.

Langfuseopen-source

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

K
Kortixopen-source

The open-source AI Management System

Metrics

LangfuseKortix
Stars35.3k20.2k
Star velocity /mo1.8k30
Commits (90d)2.0k8.3k
Releases (6m)1010
Overall score0.90672926166320360.7431479549686046

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 Kortix Details

        FAQ

        Which is more popular, Langfuse or Kortix?
        Langfuse has more GitHub stars (35,301 vs 20,240).
        Which is more actively developed, Langfuse or Kortix?
        Kortix had more commits in the last 90 days (8,338 vs 2,007).
        Should I use Langfuse or Kortix?
        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.