langsmith-observability

Debug LLM applications by tracing execution flow to capture detailed logs of LLM calls, chain executions, and tool usage. You can identify faulty prompts or tool calls by reviewing inputs, outputs, latency, and costs captured during the trace.

Monitor production LLM systems to track key metrics, errors, and performance trends. This observability provides visibility into your AI applications running in production, ensuring system reliability and highlighting operational issues.

LangSmith observability works with LangChain and LlamaIndex for automatic tracing of LLM calls and chains. You can set the required environment variables and apply the traceable decorator to your Python functions to log executions.

Evaluate LLM outputs against datasets by using built-in or custom evaluators for systematic testing. This enables performance assessment of your AI applications, allowing you to measure model responses against a benchmark dataset.

Observability for AI applications is needed to understand, debug, and improve Large Language Model systems. It addresses the challenge of comprehending complex LLM behaviors by providing tracing, evaluation, and monitoring capabilities.