Automatically detect and configure Sentry monitoring for LLM calls, agents, and AI SDKs.
Works with
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
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsentry-setup-ai-monitoringExecute the skills CLI command in your project's root directory to begin installation:
Fetches sentry-setup-ai-monitoring from getsentry/sentry-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate sentry-setup-ai-monitoring. Access via /sentry-setup-ai-monitoring in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Configure Sentry to track LLM calls, agent executions, tool usage, and token consumption.
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.
AI monitoring requires tracing enabled (tracesSampleRate > 0).
Prompt and output recording captures user content that is likely PII. Before enabling recordInputs/recordOutputs (JS) or include_prompts/send_default_pii (Python), confirm:
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.
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
| Package | Integration | Min Sentry SDK | Auto? |
|---|---|---|---|
openai |
openAIIntegration() |
10.28.0 | Yes |
@anthropic-ai/sdk |
anthropicAIIntegration() |
10.28.0 | Yes |
ai (Vercel) |
vercelAIIntegration() |
10.6.0 | Yes* |
@langchain/* |
langChainIntegration() |
10.28.0 | Yes |
@langchain/langgraph |
langGraphIntegration() |
10.28.0 | Yes |
@google/genai |
googleGenAIIntegration() |
10.28.0 | Yes |
*Vercel AI: 10.6.0+ for Node.js, Cloudflare Workers, Vercel Edge Functions, Bun. 10.12.0+ for Deno. Requires experimental_telemetry per-call.
Integrations auto-enable when the AI package is installed — no explicit registration needed:
| Package | Auto? | Notes |
|---|---|---|
openai |
Yes | Includes OpenAI Agents SDK |
anthropic |
Yes | |
langchain / langgraph |
Yes | |
huggingface_hub |
Yes | |
google-genai |
Yes | |
pydantic-ai |
Yes | |
litellm |
No | Requires explicit integration |
mcp (Model Context Protocol) |
Yes |
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)
}),
],
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
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,
}),
],
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)
},
});
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)],
)
Use when no supported SDK is detected.
op Value |
Purpose |
|---|---|
gen_ai.request |
Individual LLM calls |
gen_ai.invoke_agent |
Agent execution lifecycle |
gen_ai.execute_tool |
Tool/function calls |
gen_ai.handoff |
Agent-to-agent transitions |
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;
});
| Attribute | Description |
|---|---|
gen_ai.request.model |
Model identifier |
gen_ai.request.messages |
JSON input messages |
gen_ai.usage.input_tokens |
Input token count |
gen_ai.usage.output_tokens |
Output token count |
gen_ai.agent.name |
Agent identifier |
gen_ai.tool.name |
Tool identifier |
Enable prompt/output capture only after confirming with the user (see Data Capture Warning above).
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.
| Issue | Solution |
|---|---|
| AI spans not appearing | Verify tracesSampleRate > 0, check SDK version |
| Token counts missing | Some providers don't return tokens for streaming |
| Prompts not captured | Enable recordInputs/include_prompts |
| Vercel AI not working | Add experimental_telemetry to each call |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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sentry-setup-ai-monitoring is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: sentry-setup-ai-monitoring is focused, and the summary matches what you get after install.
sentry-setup-ai-monitoring reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in sentry-setup-ai-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: sentry-setup-ai-monitoring is focused, and the summary matches what you get after install.
sentry-setup-ai-monitoring is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in sentry-setup-ai-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added sentry-setup-ai-monitoring from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
sentry-setup-ai-monitoring fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: sentry-setup-ai-monitoring is the kind of skill you can hand to a new teammate without a long onboarding doc.
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