Agno vs Langfuse

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 +1,185 for Agno.
  • Pick Agno for: build, run, and manage agent platforms. Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management.

From GitHub data refreshed daily.

A
Agnoopen-source

Build, run, and manage agent platforms.

Langfuseopen-source

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

Metrics

AgnoLangfuse
Stars42.5k35.3k
Star velocity /mo1.2k1.8k
Commits (90d)3502.0k
Releases (6m)1010
Overall score0.84609818181785990.9067292616632036

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 Agno DetailsView Langfuse Details

        FAQ

        Which is more popular, Agno or Langfuse?
        Agno has more GitHub stars (42,495 vs 35,301).
        Which is more actively developed, Agno or Langfuse?
        Langfuse had more commits in the last 90 days (2,007 vs 350).
        Should I use Agno or Langfuse?
        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.