Langfuse vs UpTrain
Side-by-side comparison of two AI agent tools
Short answer
- UpTrain has had no commit in 26 months; Langfuse is actively maintained (2,013 commits in the last 90 days).
- Langfuse is growing faster: +1,807 GitHub stars in the last 30 days vs +4 for UpTrain.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick UpTrain for: open-source platform to evaluate and improve generative AI applications with 20+ preconfigured evaluations.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
UpTrainopen-source
Open-source platform to evaluate and improve generative AI applications with 20+ preconfigured evaluations
Metrics
| Langfuse | UpTrain | |
|---|---|---|
| Stars | 35.3k | 2.4k |
| Star velocity /mo | 1.8k | 4.2631578947368425 |
| Commits (90d) | 2.0k | 0 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 22.4M | 667 |
| Overall score | 0.8971312686464765 | 0.17690248302421893 |
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
- +Open-source platform with active community support and transparency
- +Comprehensive evaluation framework with 20+ preconfigured checks covering multiple AI use cases
- +Unified platform approach that handles both evaluation and improvement recommendations
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
- -May require technical expertise to implement and configure effectively
- -Evaluation accuracy depends on the quality and relevance of preconfigured checks
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
- •Evaluating LLM application performance before production deployment
- •Systematic testing of code generation and language processing AI models
- •Quality assurance for embedding-based applications and retrieval systems
FAQ
- Which is more popular, Langfuse or UpTrain?
- Langfuse has more GitHub stars (35,329 vs 2,366).
- Which is more actively developed, Langfuse or UpTrain?
- Langfuse had more commits in the last 90 days (2,013 vs 0).
- Should I use Langfuse or UpTrain?
- 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.