LangSmith leans observability. Braintrust leans evaluation. Both stop short of shipping the fix. Here's how they compare — plus a third option for teams that want the failures resolved automatically.
Built around tracing. You see every step, tool call, and token in a run, with the deepest integration if you're on LangChain. Evals are dataset-driven and run on top of traces. The work of turning a bad trace into a better prompt is yours.
Built around evals. You author datasets and custom scorers, wire them into CI, and watch scores over time. Strong eval discipline — but it scores the test sets you write, and deciding and shipping the change is still a human job.
Built around the improvement loop. It diagnoses the failure to a step and root cause, generates a prompt variant, tests it head-to-head in simulation, proposes a governed deployment, and verifies the result on real traffic — the part both tools leave to you.
Tracing and evals answer what happened. Converra answers what to change and whether it worked. That's the gap a third option fills — and it composes on top of whichever observability or eval tool you pick.
LangSmith is stronger for production tracing and debugging — especially if you use LangChain. Braintrust is stronger for evaluation pipelines and custom scorers. Pick LangSmith for observability depth, Braintrust for eval discipline.
Yes. LangSmith and Braintrust give visibility and scoring. Converra closes the loop by generating, simulation-testing, and shipping fixes. They are complementary — pick the tracing or eval tool that fits, then add Converra for autonomous improvement.
Visibility and scoring tell you what failed. Converra tells you why, generates a prompt variant that fixes it, tests it head-to-head in simulation, and proposes deployment with rollback. The diagnose-to-fix loop closes without engineering cycles.
If you're comparing LangSmith and Braintrust, you've already decided you need better visibility or evaluation. The next question is what to do with the failures you surface. Converra answers that — it turns those results into shipped, tested fixes.
It's the same two tools, framed from either side. The decision doesn't change: LangSmith leans production tracing and LangChain depth; Braintrust leans eval datasets and custom scorers. Both stop at telling you what happened — neither generates, tests, or verifies the fix.
If you're shopping for a Braintrust alternative because scoring isn't reducing production failures, the alternative isn't another eval tool — it's closing the loop. Converra diagnoses the failure, ships a simulation-tested fix, and verifies it on real traffic. See the dedicated Braintrust alternative comparison for a side-by-side.
Individual comparisons: vs LangSmith · vs Braintrust · Braintrust alternative · Braintrust vs Galileo · all comparisons
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