# 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

Sentry.init({
  dsn: "YOUR_DSN",
  tracesSampleRate: 1.0,
  integrations: [
    Sentry.openAIIntegration({ recordInputs: true, recordOutputs: true }),
  ],
});

import OpenAI from "openai";
import * as Sentry from "@sentry/nextjs";

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

integrations: [
  Sentry.langChainIntegration({ recordInputs: true, recordOutputs: true }),
  Sentry.langGraphIntegration({ recordInputs: true, recordOutputs: true }),
],

integrations: [Sentry.vercelAIIntegration()],

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

import sentry_sdk
from sentry_sdk.integrations.openai import OpenAIIntegration  # or anthropic, langchain

sentry_sdk.init(
    dsn="YOUR_DSN",
    traces_sample_rate=1.0,
    send_default_pii=True,  # Required for prompt capture
    integrations=[OpenAIIntegration(include_prompts=True)],
)

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;
});