sentry-setup-ai-monitoring

Automatically detect and configure Sentry monitoring for LLM calls, agents, and AI SDKs. Auto-detects installed AI packages (OpenAI, Anthropic, LangChain, Google GenAI, Vercel AI, Pydantic AI, and others) and enables appropriate integrations with zero manual registration in Python Requires tracing enabled ( tracesSampleRate > 0 ) and supports manual span instrumentation via gen_ai.* operation types for unsupported SDKs Captures model, token counts, and latency by default; prompt and output...

npx skills add https://github.com/getsentry/sentry-agent-skills --skill sentry-setup-ai-monitoring

Setup Sentry AI Agent Monitoring

Configure Sentry to track LLM calls, agent executions, tool usage, and token consumption.

Invoke This Skill When

  • User asks to "monitor AI/LLM calls" or "track OpenAI/Anthropic usage"
  • User wants "AI observability" or "agent monitoring"
  • User asks about token usage, model latency, or AI costs

Important: The SDK versions, API names, and code samples below are examples. Always verify against docs.sentry.io before implementing, as APIs and minimum versions may have changed.

Prerequisites

AI monitoring requires tracing enabled (tracesSampleRate > 0).

Data Capture Warning

Prompt and output recording captures user content that is likely PII. Before enabling recordInputs/recordOutputs (JS) or include_prompts/send_default_pii (Python), confirm:

  • The application's privacy policy permits capturing user prompts and model responses
  • Captured data complies with applicable regulations (GDPR, CCPA, etc.)
  • Sentry data retention settings are appropriate for the sensitivity of the data

Ask the user whether they want prompt/output capture enabled. Do not enable it by default — configure it only when explicitly requested or confirmed. Use tracesSampleRate: 1.0 only in development; in production, use a lower value or a tracesSampler function.

Detection First

Always detect installed AI SDKs before configuring:

# JavaScript
grep -E '"(openai|@anthropic-ai/sdk|ai|@langchain|@google/genai)"' package.json

# Python
grep -E '(openai|anthropic|langchain|huggingface)' requirements.txt pyproject.toml 2>/dev/null

Supported SDKs

JavaScript

PackageIntegrationMin Sentry SDKAuto?
openaiopenAIIntegration()10.28.0Yes
@anthropic-ai/sdkanthropicAIIntegration()10.28.0Yes
ai (Vercel)vercelAIIntegration()10.6.0Yes*
@langchain/*langChainIntegration()10.28.0Yes
@langchain/langgraphlangGraphIntegration()10.28.0Yes
@google/genaigoogleGenAIIntegration()10.28.0Yes

*Vercel AI: 10.6.0+ for Node.js, Cloudflare Workers, Vercel Edge Functions, Bun. 10.12.0+ for Deno. Requires experimental_telemetry per-call.

Python

Integrations auto-enable when the AI package is installed — no explicit registration needed:

PackageAuto?Notes
openaiYesIncludes OpenAI Agents SDK
anthropicYes
langchain / langgraphYes
huggingface_hubYes
google-genaiYes
pydantic-aiYes
litellmNoRequires explicit integration
mcp (Model Context Protocol)Yes

JavaScript Configuration

Node.js — auto-enabled integrations

Just ensure tracing is enabled. Integrations auto-enable when the AI package is installed:

Sentry.init({
  dsn: "YOUR_DSN",
  tracesSampleRate: 1.0, // Lower in production (e.g., 0.1)
  // OpenAI, Anthropic, Google GenAI, LangChain integrations auto-enable in Node.js
});

To customize (e.g., enable prompt capture — see Data Capture Warning):

integrations: [
  Sentry.openAIIntegration({
    // recordInputs: true,  // Opt-in: captures prompt content (PII)
    // recordOutputs: true, // Opt-in: captures response content (PII)
  }),
],

Browser / Next.js OpenAI (manual wrapping required)

In browser-side code or Next.js meta-framework apps, auto-instrumentation is not available. Wrap the client manually:

import OpenAI from "openai";
import * as Sentry from "@sentry/nextjs"; // or @sentry/react, @sentry/browser

const openai = Sentry.instrumentOpenAiClient(new OpenAI());
// Use 'openai' client as normal

LangChain / LangGraph (auto-enabled)

integrations: [
  Sentry.langChainIntegration({
    // recordInputs: true,  // Opt-in: captures prompt content (PII)
    // recordOutputs: true, // Opt-in: captures response content (PII)
  }),
  Sentry.langGraphIntegration({
    // recordInputs: true,
    // recordOutputs: true,
  }),
],

Vercel AI SDK

Add to sentry.edge.config.ts for Edge runtime:

integrations: [Sentry.vercelAIIntegration()],

Enable telemetry per-call:

await generateText({
  model: openai("gpt-4o"),
  prompt: "Hello",
  experimental_telemetry: {
    isEnabled: true,
    // recordInputs: true,  // Opt-in: captures prompt content (PII)
    // recordOutputs: true, // Opt-in: captures response content (PII)
  },
});

Python Configuration

Integrations auto-enable — just init with tracing. Only add explicit imports to customize options:

import sentry_sdk

sentry_sdk.init(
    dsn="YOUR_DSN",
    traces_sample_rate=1.0,  # Lower in production (e.g., 0.1)
    # send_default_pii=True,  # Opt-in: required for prompt capture (sends user PII)
    # Integrations auto-enable when the AI package is installed.
    # Only specify explicitly to customize (e.g., include_prompts):
    # integrations=[OpenAIIntegration(include_prompts=True)],
)

Manual Instrumentation

Use when no supported SDK is detected.

Span Types

op ValuePurpose
gen_ai.requestIndividual LLM calls
gen_ai.invoke_agentAgent execution lifecycle
gen_ai.execute_toolTool/function calls
gen_ai.handoffAgent-to-agent transitions

Example (JavaScript)

await Sentry.startSpan({
  op: "gen_ai.request",
  name: "LLM request gpt-4o",
  attributes: { "gen_ai.request.model": "gpt-4o" },
}, async (span) => {
  span.setAttribute("gen_ai.request.messages", JSON.stringify(messages));
  const result = await llmClient.complete(prompt);
  span.setAttribute("gen_ai.usage.input_tokens", result.inputTokens);
  span.setAttribute("gen_ai.usage.output_tokens", result.outputTokens);
  return result;
});

Key Attributes

AttributeDescription
gen_ai.request.modelModel identifier
gen_ai.request.messagesJSON input messages
gen_ai.usage.input_tokensInput token count
gen_ai.usage.output_tokensOutput token count
gen_ai.agent.nameAgent identifier
gen_ai.tool.nameTool identifier

Enable prompt/output capture only after confirming with the user (see Data Capture Warning above).

Verification

After configuring, make an LLM call and check the Sentry Traces dashboard. AI spans appear with gen_ai.* operations showing model, token counts, and latency.

Troubleshooting

IssueSolution
AI spans not appearingVerify tracesSampleRate > 0, check SDK version
Token counts missingSome providers don't return tokens for streaming
Prompts not capturedEnable recordInputs/include_prompts
Vercel AI not workingAdd experimental_telemetry to each call

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