LangSmith vs Braintrust
If you're building an LLM app and need tracing plus a way to score outputs, these two come up a lot. LangSmith is the observability and eval platform from the LangChain team. It wires tightly into that ecosystem but works on its own. Braintrust is a dev-first eval platform built around fast experiment loops and comparing outputs side by side. Both do tracing and both do evals. The real difference is which workflow you live in.
| Dimension | LangSmith | Braintrust |
|---|---|---|
| What it is | Tracing, evals, and prompt tooling | Eval-first platform with tracing |
| License | Commercial, hosted (self-host available) | Commercial, hosted (self-host available) |
| Origin | From the LangChain team | Independent, dev-first |
| Ecosystem fit | Deep with LangChain and LangGraph, plus SDKs for other stacks | Framework-agnostic SDKs |
| Center of gravity | Trace and debug your app | Run experiments, compare versions |
| Scoring | Code and LLM-as-judge evaluators | Code and LLM-as-judge, strong compare view |
| Prompt work | Prompt management and a playground | Playground with the eval loop built in |
| Best for | Teams already on LangChain | Teams who want tight eval iteration |
You're already building on LangChain or LangGraph. Tracing lights up with almost no wiring, the whole call tree just shows up, and prompt management lives in the same place. If your stack is LangChain, this is the low-friction default.
Your work is eval-heavy and you want fast loops. Change a prompt, rerun the dataset, compare versions side by side. Braintrust is framework-agnostic, so it fits when you're off LangChain and want a tool built around scoring and iteration.
If you're on LangChain, use LangSmith. The integration is real, and fighting it to run a different tracer is wasted effort. If you're off LangChain, or evals are the main event and you want the tightest compare-and-iterate loop, I'd reach for Braintrust. The common mistake is picking LangSmith by reflex because you use LangChain elsewhere, then finding the eval workflow feels bolted on. The other mistake is letting \"we'll add evals later\" turn into never. Both are commercial and both are good. Decide by your framework and by whether tracing or eval iteration is your real pain. Then wire up ten real test cases this week.