sentry-setup-ai-monitoring

name	sentry-setup-ai-monitoring
description	Setup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, track conversations, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI/Pydantic AI. Detects installed AI SDKs and configures appropriate integrations.
license	Apache-2.0
category	feature-setup
parent	sentry-feature-setup
disable-model-invocation	true

All Skills > Feature 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 send-default-PII (sendDefaultPii: true in JavaScript or send_default_pii=True in Python) or per-integration prompt/output capture (recordInputs/recordOutputs in JS, include_prompts in 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 prompt/output capture without explicit confirmation. 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

Sampling Check

After detecting AI SDKs, check the current sampling configuration:

# JavaScript
grep -E 'tracesSampleRate|tracesSampler' sentry.*.config.* instrument.* src/instrument.* app/instrument.* 2>/dev/null

# Python
grep -E 'traces_sample_rate|traces_sampler' *.py **/*.py 2>/dev/null

If tracesSampleRate / traces_sample_rate is below 1.0 AND no tracesSampler / traces_sampler is configured:

Ask the user:

"Your current sample rate is {rate}. Agent runs are sampled as complete span trees — if the root span is dropped, all child gen_ai spans are lost. For full AI visibility, gen_ai-related transactions should be sampled at 100%. Would you like me to set up a tracesSampler that keeps AI traces at 100% while sampling other traffic at your current rate?"

If user confirms, read ${SKILL_ROOT}/references/sampling.md for implementation patterns.

Supported SDKs

JavaScript

Package	Integration	Min Sentry SDK	Auto?
openai	openAIIntegration()	10.53.0	Yes
@anthropic-ai/sdk	anthropicAIIntegration()	10.53.0	Yes
ai (Vercel)	vercelAIIntegration()	10.53.0	Yes*
@langchain/*	langChainIntegration()	10.53.0	Yes
@langchain/langgraph	langGraphIntegration()	10.53.0	Yes
@google/genai	googleGenAIIntegration()	10.53.0	Yes

*Vercel AI: 10.53.0+ required. Requires experimental_telemetry per-call.

Python

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

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)
  streamGenAiSpans: true, // SDK ≥10.53.0
  // OpenAI, Anthropic, Google GenAI, LangChain integrations auto-enable in Node.js
})

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

Sentry.init({
  dsn: 'YOUR_DSN',
  tracesSampleRate: 1.0,
  streamGenAiSpans: true,
  sendDefaultPii: true,
  integrations: [
    Sentry.openAIIntegration({
      // recordInputs/recordOutputs default to true when sendDefaultPii is true
    }),
  ],
})

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)

Sentry.init({
  dsn: 'YOUR_DSN',
  tracesSampleRate: 1.0,
  streamGenAiSpans: true,
  sendDefaultPii: true,
  integrations: [Sentry.langChainIntegration(), Sentry.langGraphIntegration()],
})

Vercel AI SDK

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

Sentry.init({
  dsn: 'YOUR_DSN',
  tracesSampleRate: 1.0,
  streamGenAiSpans: true,
  sendDefaultPii: true,
  integrations: [Sentry.vercelAIIntegration()],
})

Enable telemetry per-call:

await generateText({
  model: openai('gpt-4o'),
  prompt: 'Hello',
  experimental_telemetry: {
    isEnabled: true,
    recordInputs: true,
    recordOutputs: true,
  },
})

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)
    stream_gen_ai_spans=True,  # SDK ≥2.60.0
    send_default_pii=True,
    # 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. Follow the canonical Sentry Conventions for gen_ai.* attributes — the JS docs may lag behind; do not set attributes marked deprecated in the conventions.

Span Types

op	Span name pattern	Purpose
gen_ai.{operation} (e.g. gen_ai.chat, gen_ai.request)	{operation} {model} (e.g. chat gpt-4o)	Individual LLM call
gen_ai.invoke_agent	invoke_agent {agent_name}	Agent execution lifecycle
gen_ai.execute_tool	execute_tool {tool_name}	Tool/function call
gen_ai.handoff	handoff from {source} to {target}	Agent-to-agent transition

For LLM-call spans, the op follows the pattern gen_ai.{gen_ai.operation.name} — use gen_ai.chat, gen_ai.embeddings, gen_ai.generate_content, or gen_ai.text_completion where the operation is known. Span attributes only accept primitives; arrays/objects must be JSON-stringified.

Example (JavaScript)

const inputMessages = [{ role: 'user', parts: [{ type: 'text', content: 'Tell me a joke' }] }]

await Sentry.startSpan(
  {
    op: 'gen_ai.chat',
    name: 'chat gpt-4o',
    attributes: {
      'gen_ai.request.model': 'gpt-4o',
      'gen_ai.operation.name': 'chat',
      'gen_ai.input.messages': JSON.stringify(inputMessages),
    },
  },
  async (span) => {
    const result = await llmClient.complete(inputMessages)

    const outputMessages = [
      {
        role: 'assistant',
        parts: [{ type: 'text', content: result.text }],
        finish_reason: result.finishReason,
      },
    ]
    span.setAttribute('gen_ai.output.messages', JSON.stringify(outputMessages))
    span.setAttribute('gen_ai.usage.input_tokens', result.inputTokens)
    span.setAttribute('gen_ai.usage.output_tokens', result.outputTokens)
    return result
  },
)

Key Attributes

Common (all AI spans):

Attribute	Required	Description
gen_ai.request.model	Yes	Model identifier (e.g., gpt-4o, claude-sonnet-4-6)
gen_ai.operation.name	No	Operation label (chat, embeddings, invoke_agent, execute_tool, handoff, etc.)
gen_ai.agent.name	No	Agent name (set on agent and tool spans)

Request / response content (PII — enable only after confirming; see Data Capture Warning above):

Attribute	Description
gen_ai.input.messages	JSON-stringified array of input messages. Each item uses {role, parts} where parts is [{type, content}]; role is "user", "assistant", "tool", or "system"
gen_ai.output.messages	JSON-stringified array of response messages (text + tool calls), same shape as inputs
gen_ai.system_instructions	System prompt passed to the model
gen_ai.tool.definitions	JSON-stringified list of tools available to the model

Token usage:

Attribute	Description
gen_ai.usage.input_tokens	Total input tokens — includes cached tokens
gen_ai.usage.input_tokens.cached	Subset of input tokens served from cache
gen_ai.usage.input_tokens.cache_write	Tokens written to cache while processing input
gen_ai.usage.output_tokens	Total output tokens — includes reasoning tokens
gen_ai.usage.output_tokens.reasoning	Subset of output tokens used for reasoning
gen_ai.usage.total_tokens	Sum of input + output tokens

Tool spans (gen_ai.execute_tool):

Attribute	Description
gen_ai.tool.name	Tool identifier
gen_ai.tool.description	Human-readable tool description
gen_ai.tool.call.arguments	JSON-stringified tool arguments
gen_ai.tool.call.result	JSON-stringified tool result

Token Usage and Cost Calculation

Sentry uses token attributes to calculate model costs. Cached and reasoning tokens are subsets, not separate counts — gen_ai.usage.input_tokens already includes gen_ai.usage.input_tokens.cached, and gen_ai.usage.output_tokens already includes gen_ai.usage.output_tokens.reasoning.

Sentry subtracts the cached/reasoning counts from the totals to compute the uncached/non-reasoning portion. Reporting a cached or reasoning count greater than its total produces negative costs in the dashboard.

Example — 100 input tokens total, 90 served from cache:

• Correct: input_tokens = 100, input_tokens.cached = 90
• Wrong: input_tokens = 10, input_tokens.cached = 90 (cached larger than total → negative cost)

The same rule applies to gen_ai.usage.output_tokens vs. gen_ai.usage.output_tokens.reasoning.

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.

Conversations

Conversations gives a readable, chat-style view of past sessions with your AI agent. It groups spans by gen_ai.conversation.id — so whether a user talked across multiple traces or multiple conversations happened inside one trace, you get a timeline of every message, tool call, and response.

Find it at Explore > Conversations in Sentry.

Prerequisites for Conversations

• Tracing enabled with tracesSampleRate > 0
• streamGenAiSpans: true (JS SDK >=10.53.0) / stream_gen_ai_spans=True (Python SDK >=2.60.0) — required so AI spans are sent as standalone items. Without this, spans with large inputs/outputs can hit transaction payload size limits and be dropped.
• Input and output capture enabled — Conversations reconstructs the chat from gen_ai.input.messages and gen_ai.output.messages attributes. Set sendDefaultPii: true (JS) / send_default_pii=True (Python). Without it, conversations appear empty.

Setting a Conversation ID

Some integrations (OpenAI Agents SDK for Python, OpenAI SDK for Node) infer the conversation ID automatically. For all others, set it manually.

JavaScript

import * as Sentry from '@sentry/node' // or @sentry/nextjs, @sentry/nestjs, etc.

// Set at the start of a conversation
Sentry.setConversationId('conv_abc123')

// All subsequent AI calls carry gen_ai.conversation.id: "conv_abc123"
await openai.chat.completions.create({
  model: 'gpt-5.5',
  messages: [{ role: 'user', content: 'Hello' }],
})

Python

import sentry_sdk.ai

# Set at the start of a conversation
sentry_sdk.ai.set_conversation_id("conv_abc123")

# All subsequent AI calls carry gen_ai.conversation.id = "conv_abc123"

Some integrations infer the conversation ID automatically. For example, the Python OpenAI integration picks it up when you use the conversation parameter:

import openai
import sentry_sdk

sentry_sdk.init(...)

conversation = openai.conversations.create()
response = openai.responses.create(
    model="gpt-5.4",
    input=[{"role": "user", "content": "What are the 5 Ds of dodgeball?"}],
    conversation=conversation.id  # automatically sets gen_ai.conversation.id
)

Conversations vs Traces

These are independent concepts:

• A single conversation can span multiple traces (e.g., user refreshes the page mid-conversation — new trace, same conversation ID)
• A single trace can contain spans from different conversations (e.g., user starts a new chat without refreshing)

Troubleshooting

Issue	Solution
AI spans not appearing	Verify tracesSampleRate > 0, check SDK version
Token counts missing	Some providers don't return tokens for streaming
Negative or wrong costs in dashboard	Cached/reasoning tokens are subsets of totals — see Token Usage and Cost Calculation
Prompts not captured	Set sendDefaultPii: true (JS) or send_default_pii=True (Python); use recordInputs/include_prompts only for explicit overrides
Vercel AI not working	Add experimental_telemetry to each call
Conversations view empty	Ensure streamGenAiSpans: true / stream_gen_ai_spans=True, sendDefaultPii: true / send_default_pii=True, and a conversation ID is set