Set Up AI Agent Monitoring

import * as Sentry from "___SDK_PACKAGE___";
import { openAIIntegration } from "___SDK_PACKAGE___";

Sentry.init({
  dsn: "___PUBLIC_DSN___",
  tracesSampleRate: 1.0,
  integrations: [openAIIntegration()],
});

import * as Sentry from "___SDK_PACKAGE___";
import { openAIIntegration } from "___SDK_PACKAGE___";

Sentry.init({
  dsn: "___PUBLIC_DSN___",
  tracesSampleRate: 1.0,
  integrations: [
    openAIIntegration({
      recordInputs: false, // Don't capture prompts
      recordOutputs: false, // Don't capture responses
    }),
  ],
});

import * as Sentry from "___SDK_PACKAGE___";

// Set conversation ID at the start of a conversation
Sentry.setConversationId("conv_abc123");

// All subsequent AI calls will be linked to this conversation
await openai.chat.completions.create({
  model: "gpt-4",
  messages: [{ role: "user", content: "Hello" }],
});

// Later in the conversation
await openai.chat.completions.create({
  model: "gpt-4",
  messages: [
    { role: "user", content: "Hello" },
    { role: "assistant", content: "Hi there!" },
    { role: "user", content: "What's the weather?" },
  ],
});

// Both calls will have gen_ai.conversation.id: "conv_abc123"

// To unset it
Sentry.setConversationId(null);

const messages = [{ role: "user", content: "Tell me a joke" }];

await Sentry.startSpan(
  {
    op: "gen_ai.request",
    name: "request o3-mini",
    attributes: {
      "gen_ai.request.model": "o3-mini",
      "gen_ai.request.messages": JSON.stringify(messages),
    },
  },
  async (span) => {
    // Call your LLM here
    const result = await client.chat.completions.create({
      model: "o3-mini",
      messages,
    });

    span.setAttribute(
      "gen_ai.response.text",
      JSON.stringify([result.choices[0].message.content]),
    );
    // Set token usage
    span.setAttribute(
      "gen_ai.usage.input_tokens",
      result.usage.prompt_tokens,
    );
    span.setAttribute(
      "gen_ai.usage.output_tokens",
      result.usage.completion_tokens,
    );
  },
);

await Sentry.startSpan(
  {
    op: "gen_ai.invoke_agent",
    name: "invoke_agent Weather Agent",
    attributes: {
      "gen_ai.request.model": "o3-mini",
      "gen_ai.agent.name": "Weather Agent",
    },
  },
  async (span) => {
    // Run the agent
    const result = await myAgent.run();

    span.setAttribute(
      "gen_ai.response.text",
      JSON.stringify([result.output]),
    );
    // Set token usage
    span.setAttribute(
      "gen_ai.usage.input_tokens",
      result.usage.inputTokens,
    );
    span.setAttribute(
      "gen_ai.usage.output_tokens",
      result.usage.outputTokens,
    );
  },
);

await Sentry.startSpan(
  {
    op: "gen_ai.execute_tool",
    name: "execute_tool get_weather",
    attributes: {
      "gen_ai.tool.name": "get_weather",
      "gen_ai.tool.input": JSON.stringify({ location: "Paris" }),
    },
  },
  async (span) => {
    // Call the tool
    const result = await getWeather({ location: "Paris" });

    span.setAttribute("gen_ai.tool.output", JSON.stringify(result));
  },
);

await Sentry.startSpan(
  {
    op: "gen_ai.handoff",
    name: "handoff from Weather Agent to Travel Agent",
  },
  () => {}, // Handoff span just marks the transition
);

await Sentry.startSpan(
  { op: "gen_ai.invoke_agent", name: "invoke_agent Travel Agent" },
  async () => {
    // Run the target agent here
  },
);