A side-by-side of two observability for building AI agents — live GitHub data, languages, and what each is best at.
Short answer: Langfuse leads Phoenix vs Langfuse by community traction (★ 33k vs ★ 11k). Pick Phoenix for OTel tracing; pick Langfuse for LLM tracing.
✓ Live data verified
| Phoenix | Langfuse | |
|---|---|---|
| GitHub stars | ★ 11k | ★ 33k |
| Language | Jupyter Notebook | TypeScript |
| Category | Observability | Observability |
| Best for | OTel tracing | LLM tracing |
| Repository | Arize-ai/phoenix | langfuse/langfuse |
Phoenix and Langfuse are both credible choices. By community traction, Langfuse leads (★ 33k). Pick Phoenix for OTel tracing; pick Langfuse for LLM tracing.
Both are credible observability. By community traction Langfuse leads (★ 33k). Pick Phoenix for OTel tracing; pick Langfuse for LLM tracing.
Phoenix is Arize's open-source observability for LLM apps — OpenTelemetry-based tracing and evaluation.. Langfuse is Open-source LLM engineering platform — tracing, evals, prompt management, and metrics for agent apps..
Langfuse has more — ★ 33k vs ★ 11k (live counts).
Often yes — many teams combine observability. Check each tool's docs for interop; they solve overlapping but not identical problems.
Phoenix is primarily Jupyter Notebook; Langfuse is primarily TypeScript.
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