LangSmith is the production platform from LangChain for observing, testing, and improving LLM applications throughout their lifecycle. While LangChain provides the framework for building LLM apps, LangSmith adds the observability and quality assurance layer needed for production deployment.
The tracing system captures every step of LLM chain and agent execution in detail — inputs, outputs, latencies, token usage, and error states. Developers can inspect individual runs, compare traces across versions, and identify performance bottlenecks or quality regressions.
Dataset management enables building test suites from real production data or manually curated examples. Automated evaluation runs these datasets against application versions with custom metrics, LLM-as-judge evaluators, or programmatic checks. This creates a regression testing workflow for LLM applications.
Prompt versioning and management allow teams to iterate on prompts collaboratively, track changes over time, and roll back to previous versions. The annotation queue enables human reviewers to provide feedback on LLM outputs, creating ground truth datasets for evaluation.
LangSmith works with any LLM framework through its Python and JavaScript SDKs, not just LangChain. The free tier includes generous usage limits, with paid plans scaling for teams and enterprises needing higher volumes and additional features.
