observability-check

You can correlate Langfuse traces with Sentry issues by aggregating data from both APIs to create a unified AI observability view. This process links execution traces directly to errors, accelerating incident diagnosis and performance tuning for workloads.

To set up Langfuse and Sentry correlation, you need to configure LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, and SENTRY_AUTH_TOKEN in your .env.local file. These credentials allow the analyzer to validate access and fetch data from the respective APIs.

Yes, you can diagnose AI agent latency spikes by generating a combined observability report. This report links Langfuse traces to Sentry errors, allowing you to rapidly triage the incident, surface root causes, and identify failing components.

The best way to monitor AI health is to fetch recent traces and issues from Langfuse and Sentry, then run an analyzer to correlate findings. This surfaces bottlenecks and outputs actionable recommendations for ongoing AI workloads.

Yes, unified observability works for ongoing AI incidents by applying correlation across active workloads and performance investigations. It validates credentials, fetches real-time data, and outputs actionable recommendations for rapid remediation.

Correlating AI execution traces with error monitoring tools surfaces root causes by linking performance bottlenecks directly to application errors. This unified observability view accelerates incident diagnosis and enables rapid triage during performance investigations.