LangSmith
LangChain’s eval and trace platform for LLM apps—datasets, scorers, live monitoring, and human review with the deepest LangChain/LangGraph integration.
Best for
Teams already deep on LangChain / LangGraph that want traces, scoring, datasets, and replay in one loop—especially to ship a change and run 200 regressions in one click.
Less ideal when
Minimal stacks that call APIs directly, strict OSS/air-gapped requirements, or teams that don’t use the LangChain ecosystem.
When comparing
Compare with Langfuse / Braintrust / Arize Phoenix on custom scorer depth, dataset management, and whether offline/online share one store.
Quick checklist
- Verify project-level permissions and PII redaction
- Model trace sampling vs cost at your volume
- Build a 50+ example regression set before deciding
- Review self-hosting/enterprise plan requirements
Search-driven Q&A
LangSmith vs Langfuse—how to choose?
LangSmith is deepest if you already build with LangChain/LangGraph; Langfuse is open-source and self-hostable, which wins when OSS/data-locality matters. Features overlap—wire real traffic into both for a week before committing.
What metrics should an LLM eval cover?
Business Q&A needs groundedness + hallucination sampling + human scores; structured extraction needs field-level F1; agentic tasks add success rate and step count. Always pair these with P95 latency and per-call cost.
When to use it
The summary should help you decide if this tool fits your needs. When many options look similar, consider how often you’ll use it, budget, and data privacy before choosing one.