Master Sentry AI Monitoring with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up for Sentry and create a project appropriate for the application stack you are instrumenting. Install the relevant Sentry SDK and confirm that your AI framework has supported instrumentation. Add AI monitoring instrumentation using Sentry's documented integration for the framework, such as OpenAI Agents or Vercel AI SDK where applicable. Deploy the instrumented AI application and verify that errors, spans, traces, and performance data appear in the Sentry dashboard. Configure alerts for latency, error rates, usage, or operational thresholds based on production requirements.
💡 Quick Start: Follow these 1 steps in order to get up and running with Sentry AI Monitoring quickly.
Explore the key features that make Sentry AI Monitoring powerful for analytics & monitoring workflows.
Sentry can represent LLM calls and AI workflow steps as spans inside distributed traces, helping teams inspect the path from a user request through model calls, tools, retrieval, and application code when instrumentation is configured.
A multi-step RAG pipeline with slow p95 latency: the trace view helps identify whether latency is coming from retrieval, model response time, custom application code, or a downstream service.
Where supported by SDK instrumentation and model metadata, Sentry can surface token counts and cost-related usage signals so teams can investigate expensive operations and model usage trends.
Engineering team catches a token spike after a prompt template change deployed to production, then compares traces to identify the workflow responsible for the increase.
When AI pipeline errors occur, they can appear in Sentry's issue and trace workflows alongside application errors, making it easier to connect AI failures with releases, user sessions, and backend behavior.
Investigating user reports of broken AI responses: searching Sentry for the relevant user session surfaces the related trace, error, and AI workflow context.
Sentry documents AI monitoring setup examples for OpenAI Agents in Python and the Vercel AI SDK in JavaScript, while broader Sentry SDKs cover many common application environments.
An engineering team adds Sentry AI monitoring to a supported AI application by following the documented integration path and then validating spans in the Sentry dashboard.
Sentry's production monitoring workflow can be used to alert on relevant operational signals such as errors and latency, while AI-specific alerting depends on captured telemetry and configured metrics.
Setting a latency or error-rate alert for a customer-facing AI assistant so the on-call engineer can investigate slow or failing requests with trace context.
It tracks AI and LLM observability signals such as agent runs, LLM calls, error rates, token usage, tool executions, traffic patterns, duration, and trace context when the application is instrumented.
No. Sentry presents it as observability for agents, LLMs, vector stores, tools, and custom application logic, with emphasis on production debugging across the whole AI workflow.
The public AI observability page shows setup examples for Python OpenAI Agents through OpenAIAgentsIntegration and JavaScript Vercel AI SDK instrumentation.
Sentry describes prompt and response context as part of deep trace analysis, but teams should confirm SDK behavior, defaults, and privacy configuration before collecting prompt or response content.
No. Sentry AI Monitoring refers to AI and LLM observability for production behavior. Seer is Sentry's AI debugging agent for root cause analysis, fix generation, and related debugging assistance.
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Tutorial updated March 2026