Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/â€‹OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationDebuggingDeploymentLangSmithLangSmith WalkthroughModel ComparisonEcosystemAdditional resourcesGuidesLangSmithLangSmith WalkthroughOn this pageLangSmith WalkthroughLangChain makes it easy to prototype LLM applications and Agents. However, delivering LLM applications to production can be deceptively difficult. You will likely have to heavily customize and iterate on your prompts, chains, and other components to create a high-quality product.To aid in this process, we've launched LangSmith, a unified platform for debugging, testing, and monitoring your LLM applications.When might this come in handy? You may find it useful when you want to:Quickly debug a new chain, agent, or set of toolsVisualize how components (chains, llms, retrievers, etc.) relate and are usedEvaluate different prompts and LLMs for a single componentRun a given chain several times over a dataset to ensure it consistently meets a quality barCapture usage traces and using LLMs or analytics pipelines to generate insightsPrerequisitesâ€‹Create a LangSmith account and create an API key (see bottom left corner). Familiarize yourself with the platform by looking through the docsNote LangSmith is in closed beta; we're in the process of rolling it out to more users. However, you can fill out the form on the website for expedited access.Now, let's get started!Log runs to LangSmithâ€‹First, configure your environment variables to tell LangChain to log traces. This is done by setting the LANGCHAIN_TRACING_V2 environment variable to true.