sentry-setup-ai-monitoring

To monitor LLM token usage and latency, you can configure Sentry observability to automatically detect installed AI SDKs and apply tracing configurations that capture performance data. This allows you to track agent execution lifecycles and identify slow calls.

Setting up Sentry tracing for OpenAI and LangChain involves detecting the installed AI SDKs and applying appropriate tracing configurations to capture gen_ai span attributes. This automated instrumentation tracks token consumption and agent execution lifecycles.

Full-stack observability for AI agents is achieved by implementing custom Sentry sampling logic that ensures 100% visibility into AI-related transactions while managing resource usage. This captures token usage, latency, and execution lifecycle data across JavaScript and Python.

Yes, you can use Sentry to trace AI agent execution lifecycles across both JavaScript and Python environments. The configuration detects installed SDKs and applies tracing to capture gen_ai span attributes for comprehensive monitoring.

Custom sampling rates for AI-specific transactions are needed to maintain efficient resource usage while ensuring 100% visibility into expensive LLM calls. This advanced sampling logic captures necessary performance data without overwhelming your system.

Yes, Sentry AI monitoring automatically detects installed AI SDKs like OpenAI, Anthropic, and LangChain to configure tracing automatically. This instrumentation ensures token usage, latency, and agent execution lifecycles are captured without manual setup.