About LangSmith
LangSmith Observability provides end-to-end visibility into AI agent behavior, helping teams trace, monitor, and analyze agent performance in production. With framework-agnostic support and OpenTelemetry integration, LangSmith delivers actionable insights to debug failures, optimize costs, and improve response quality. Deploy in the cloud, on-premises, or via bring-your-own-cloud for full data control.
FAQ
LangSmith works with any LLM framework, including OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, and custom implementations. It also supports OpenTelemetry for integration with existing pipelines.
Yes, LangSmith offers managed cloud, bring-your-own-cloud (BYOC), and self-hosted options to meet data residency requirements. Contact the team to discuss the best option for your security needs.
No, the LangSmith SDK uses an async callback handler that sends traces to a distributed collector, ensuring your application performance is never impacted. If LangSmith experiences an incident, your agent will continue running normally.
No, Observability and Evaluation are separate but complementary features. You can start with tracing and monitoring, then add evals when ready. Both are available on all plan types, and you only pay for what you use.
You can track token usage, latency (P50, P99), error rates, cost breakdowns, and feedback scores. Alerts can be configured via webhooks or PagerDuty when metrics cross thresholds.
Data is stored in secure infrastructure. For LangSmith hosted at smith.langchain.com, data is stored in GCP us-central-1. Enterprise plan users can run LangSmith on their own Kubernetes cluster in AWS, GCP, or Azure.
LangSmith has a free tier for development and small-scale production. Paid plans scale with trace volume. For detailed pricing, visit the pricing page or contact the team for enterprise pricing.
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