LangSmith vs Tracely-ai
Trace, test and monitor LLM apps in production. — versus — Trace-native CI/CD for agents: OTLP traces are graded on arrival, failures cluster into issues, and one click freezes a failing run into a hermetic regression case that blocks the PR.
The same trace-to-test loop, different ownership. LangSmith is the hosted incumbent for tracing, testing and monitoring; Tracely is MIT and self-hosted end to end, and pushes past the dashboard — a regression does not move a chart, it blocks the pull request.
| LangSmith | Tracely-ai | |
|---|---|---|
| Stars | 0 | 1.1k |
| Forks | — | 86 |
| Language | TypeScript | Python |
| License | Proprietary | MIT |
| Last activity | 1 week ago | today |
| Topics | evals | evals, agents |
| Curated connections | 4 | 4 |
LangSmith — the curator's take
Trace, test and monitor LLM apps in production.
Tracely-ai — the curator's take
The claim that separates it from every other eval tool: no hand-authored dataset. The recorded production trace is the test — promoted with its tool and LLM responses as fixtures, so CI replays it for $0 with zero live model calls, under a fail-to-pass contract that refuses the promotion unless the case fails on the old code and passes on the fix. Failure clustering is the other real feature: 31 broken runs collapse into one issue instead of 31 rows. Use it when agents are already in production emitting OTLP and your problem is regressions. NOT a lightweight library — self-hosting means Postgres, ClickHouse, Redis and MinIO — and with no traffic there is nothing to grade, so pre-production teams should start with a dataset-first tool.