How to trace LLM applications in TypeScript (2026)

LLM observability in TypeScript requires a comprehensive tracing setup that provides a TypeScript SDK, supports various runtimes, and offers detailed insights into request paths and failures. Effective tracing should capture inputs, outputs, latency, and errors at each step, allowing teams to isolate issues in model calls, tool invocations, and runtime operations. Auto-instrumentation can simplify the integration of tracing into TypeScript applications, while manual instrumentation offers more control. The Vercel AI SDK, enhanced by Braintrust, enables detailed tracing and telemetry, facilitating debugging and evaluation of production traces. This setup supports not only model and tool call tracing but also the transformation of production traces into reusable evaluation datasets. By capturing comprehensive request data, teams can utilize these traces for testing and improving future releases, ensuring that any production failures identified are addressed before they affect users again. For optimal results, teams should select tracing tools that align with their app's runtime, framework, and quality assurance processes.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.