Langfuse vs OpenLLMetry
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 +80 for OpenLLMetry.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick OpenLLMetry for: open-source observability for your GenAI or LLM application, based on OpenTelemetry.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
OpenLLMetryopen-source
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
Metrics
| Langfuse | OpenLLMetry | |
|---|---|---|
| Stars | 35.3k | 7.5k |
| Star velocity /mo | 1.8k | 80.36842105263159 |
| Commits (90d) | 2.0k | 12 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8971312686464765 | 0.5912367252217405 |
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
- +Built on OpenTelemetry standard with official semantic conventions integration, ensuring compatibility with existing observability infrastructure
- +Open-source with strong community support (6,900+ GitHub stars) and active development backed by Y Combinator
- +Multi-language support covering both Python and JavaScript/TypeScript ecosystems for broad developer adoption
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
- -Requires familiarity with OpenTelemetry concepts and infrastructure setup, which may have a learning curve for teams new to observability
- -As a specialized tool for LLM observability, it may be overkill for simple AI applications or proof-of-concepts
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
- •Production LLM application monitoring to track performance metrics, token usage, and error rates across different models and providers
- •Debugging complex GenAI workflows by tracing requests through multiple AI services and identifying bottlenecks or failures
- •Cost optimization and performance analysis of AI applications to understand usage patterns and optimize model selection
FAQ
- Which is more popular, Langfuse or OpenLLMetry?
- Langfuse has more GitHub stars (35,329 vs 7,467).
- Which is more actively developed, Langfuse or OpenLLMetry?
- Langfuse had more commits in the last 90 days (2,013 vs 12).
- Should I use Langfuse or OpenLLMetry?
- 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.