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 Langfuse vs Phoenix by community traction (★ 33k vs ★ 11k). Pick Langfuse for LLM tracing; pick Phoenix for OTel tracing.
✓ Live data verified
| Langfuse | Phoenix | |
|---|---|---|
| GitHub stars | ★ 33k | ★ 11k |
| Language | TypeScript | Jupyter Notebook |
| Category | Observability | Observability |
| Best for | LLM tracing | OTel tracing |
| Repository | langfuse/langfuse | Arize-ai/phoenix |
Langfuse and Phoenix are both credible choices. By community traction, Langfuse leads (★ 33k). Pick Langfuse for LLM tracing; pick Phoenix for OTel tracing.
Both are credible observability. By community traction Langfuse leads (★ 33k). Pick Langfuse for LLM tracing; pick Phoenix for OTel tracing.
Langfuse is Open-source LLM engineering platform — tracing, evals, prompt management, and metrics for agent apps.. Phoenix is Arize's open-source observability for LLM apps — OpenTelemetry-based tracing and evaluation..
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.
Langfuse is primarily TypeScript; Phoenix is primarily Jupyter Notebook.
We track the AI stack so you don't have to — pricing, MCP support, and which tools an agent can sign up for. Free.