sentry-setup-ai-monitoring

To set up Sentry for LLM observability, you instrument your Python application by automatically detecting installed AI packages like OpenAI, Anthropic, LangChain, and Google GenAI. This enables detailed tracing of AI operations, including token consumption and latency metrics.

Yes, you can track LangChain agent executions with Sentry by instrumenting your application to monitor the agent lifecycle. This includes tracking tool usage, inter-agent handoffs, and capturing token consumption for better cost control.

Sentry AI monitoring supports both JavaScript and Python environments. It detects installed AI packages in these environments to enable comprehensive tracing of LLM calls, agent executions, and tool usage across your application stack.

AI agent monitoring tracks the lifecycle of AI agents by instrumenting LLM calls, tool usage, and inter-agent handoffs. Tracing works by recording detailed performance metrics including token consumption and latency across various AI SDKs.

Yes, you can record LLM prompts and outputs when monitoring AI agent performance. The instrumentation provides options for prompt and output recording, which requires user confirmation before enabling this detailed data capture.

Sentry agent tracking automatically detects OpenAI, Anthropic, LangChain, and Google GenAI packages. Once detected, it instruments these SDKs to provide detailed tracing of AI operations including token consumption and latency.