LangSmith has introduced an alert system designed to enhance the monitoring of LLM (Large Language Model) applications by allowing users to set alerts based on metrics such as error rate, run latency, and feedback scores. This proactive monitoring is crucial given the unique challenges posed by LLM applications, including their dependence on external services and the unpredictability of LLM outputs. The alert system allows users to filter metrics and set thresholds for specific subsets of runs, integrating notifications into existing workflows through services like PagerDuty or custom webhooks. Future enhancements will include additional alert types, such as run count and LLM token usage, and the ability to set change alerts for relative values and alerts over custom time windows. Users can provide feedback or request features via the LangChain Slack Community.