A side-by-side of two observability & evals (managed) for building AI agents — live GitHub data, languages, and what each is best at.
Short answer: LangSmith leads LangSmith vs Langtrace by community traction (★ 0 vs ★ 0). Pick LangSmith for its strengths; pick Langtrace for its strengths.
| LangSmith | Langtrace | |
|---|---|---|
| GitHub stars | ★ 0 | ★ 0 |
| Language | — | — |
| Category | Observability & evals (managed) | Observability & evals (managed) |
| Best for | ||
| Repository | / | / |
LangSmith and Langtrace are both credible choices. By community traction, LangSmith leads (★ 0). Pick LangSmith for its strengths; pick Langtrace for its strengths.
Both are credible observability & evals (managed). By community traction LangSmith leads (★ 0). Pick LangSmith for its strengths; pick Langtrace for its strengths.
LangSmith is LangChain's managed tracing, evaluation and prompt-engineering platform for LLM and agent apps.. Langtrace is OpenTelemetry-native, open-source tracing and metrics for LLM, vector-DB and framework calls, with a hosted cloud..
LangSmith has more — ★ 0 vs ★ 0 (live counts).
Often yes — many teams combine observability & evals (managed). Check each tool's docs for interop; they solve overlapping but not identical problems.
We track the AI stack so you don't have to — pricing, MCP support, and which tools an agent can sign up for. Free.