Langfuse vs Onyx
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 +240 for Onyx.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick Onyx for: open Source AI Platform - AI Chat with advanced features that works with every LLM.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
O
Onyxopen-source
Open Source AI Platform - AI Chat with advanced features that works with every LLM
Metrics
| Langfuse | Onyx | |
|---|---|---|
| Stars | 35.3k | 32.3k |
| Star velocity /mo | 1.8k | 240 |
| Commits (90d) | 2.0k | 1.6k |
| Releases (6m) | 10 | 10 |
| Overall score | 0.9067292616632036 | 0.8102610712797703 |
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 Onyx?
- Langfuse has more GitHub stars (35,329 vs 32,316).
- Which is more actively developed, Langfuse or Onyx?
- Langfuse had more commits in the last 90 days (2,013 vs 1,557).
- Should I use Langfuse or Onyx?
- 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.