Langfuse vs MLflow
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 +480 for MLflow.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick MLflow for: open-source AI engineering platform for agents, LLMs, and ML models.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
M
MLflowopen-source
Open-source AI engineering platform for agents, LLMs, and ML models
Metrics
| Langfuse | MLflow | |
|---|---|---|
| Stars | 35.3k | 28.2k |
| Star velocity /mo | 1.8k | 480 |
| Commits (90d) | 2.0k | 1.1k |
| Releases (6m) | 10 | 10 |
| Overall score | 0.9067292616632036 | 0.8404951044062294 |
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
FAQ
- Which is more popular, Langfuse or MLflow?
- Langfuse has more GitHub stars (35,301 vs 28,232).
- Which is more actively developed, Langfuse or MLflow?
- Langfuse had more commits in the last 90 days (2,007 vs 1,072).
- Should I use Langfuse or MLflow?
- 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.