feat(server-utils): Implement orchestrion-based instrumentation for vercel-ai v6

.github/workflows/build.yml

@@ -781,7 +781,7 @@ jobs:
     needs: [job_get_metadata, job_build]
     if: needs.job_build.outputs.changed_node_integration == 'true' || github.event_name != 'pull_request'
     runs-on: ubuntu-24.04
-    timeout-minutes: 15
+    timeout-minutes: 20
     strategy:
       fail-fast: false
       matrix:

dev-packages/node-integration-tests/suites/tracing/vercelai/span-streaming-v4/test.ts

@@ -19,6 +19,7 @@ import {
   GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,
 } from '../../../../../../packages/core/src/tracing/ai/gen-ai-attributes';
 import { cleanupChildProcesses, createEsmAndCjsTests } from '../../../../utils/runner';
+import { isOrchestrionEnabled } from '../../../../utils';
 
 /**
  * Helper to match a typed attribute value in a SerializedStreamedSpan.
@@ -29,7 +30,7 @@ function attr(value: unknown) {
   return expect.objectContaining({ value });
 }
 
-describe('Vercel AI integration (streaming)', () => {
+describe.skipIf(isOrchestrionEnabled())('Vercel AI integration (streaming v4)', () => {
   afterAll(() => {
     cleanupChildProcesses();
   });

dev-packages/node-integration-tests/suites/tracing/vercelai/span-streaming-v6/test.ts

@@ -19,6 +19,7 @@ import {
   GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,
 } from '../../../../../../packages/core/src/tracing/ai/gen-ai-attributes';
 import { cleanupChildProcesses, createEsmAndCjsTests } from '../../../../utils/runner';
+import { isOrchestrionEnabled } from '../../../../utils';
 
 /**
  * Helper to match a typed attribute value in a SerializedStreamedSpan.
@@ -29,11 +30,13 @@ function attr(value: unknown) {
   return expect.objectContaining({ value });
 }
 
-describe('Vercel AI integration (streaming, V6)', () => {
+describe('Vercel AI integration (streaming, v6)', () => {
   afterAll(() => {
     cleanupChildProcesses();
   });
 
+  const origin = isOrchestrionEnabled() ? 'auto.vercelai.channel' : 'auto.vercelai.otel';
+
   const EXPECTED_SPANS_DEFAULT_PII_FALSE = {
     items: expect.arrayContaining([
       // First span - invoke_agent for simple generateText
@@ -48,10 +51,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(30),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('invoke_agent'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.invoke_agent'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
-          'vercel.ai.pipeline.name': attr('generateText'),
-          'vercel.ai.streaming': attr(false),
-          'vercel.ai.request.headers.user-agent': expect.objectContaining({ value: expect.any(String) }),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Second span - generate_content for simple generateText
@@ -66,9 +66,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(30),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('generate_content'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.generate_content'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
-          'vercel.ai.pipeline.name': attr('generateText.doGenerate'),
-          'vercel.ai.streaming': attr(false),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Third span - invoke_agent for explicit telemetry generateText
@@ -82,7 +80,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(30),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('invoke_agent'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.invoke_agent'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Fourth span - tool call invoke_agent
@@ -96,7 +94,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(40),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('invoke_agent'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.invoke_agent'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Fifth span - tool call generate_content
@@ -110,7 +108,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(40),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('generate_content'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.generate_content'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Sixth span - execute_tool
@@ -124,7 +122,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_TOOL_TYPE_ATTRIBUTE]: attr('function'),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('execute_tool'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.execute_tool'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
     ]),
@@ -149,9 +147,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(30),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('invoke_agent'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.invoke_agent'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
-          'vercel.ai.pipeline.name': attr('generateText'),
-          'vercel.ai.streaming': attr(false),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Second span - generate_content with input/output messages
@@ -170,7 +166,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(30),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('generate_content'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.generate_content'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Third span - explicit telemetry invoke_agent with messages
@@ -188,7 +184,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(30),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('invoke_agent'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.invoke_agent'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Fourth span - tool call invoke_agent with messages (V6: no text part, only tool_call)
@@ -208,7 +204,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(40),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('invoke_agent'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.invoke_agent'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Fifth span - tool call generate_content with available_tools
@@ -225,7 +221,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(40),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('generate_content'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.generate_content'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       // Sixth span - execute_tool with description and input/output
@@ -241,7 +237,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_TOOL_TYPE_ATTRIBUTE]: attr('function'),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('execute_tool'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.execute_tool'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
     ]),
@@ -254,7 +250,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
         attributes: expect.objectContaining({
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('invoke_agent'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.invoke_agent'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       expect.objectContaining({
@@ -267,7 +263,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: attr(40),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('generate_content'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.generate_content'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
       expect.objectContaining({
@@ -279,7 +275,7 @@ describe('Vercel AI integration (streaming, V6)', () => {
           [GEN_AI_TOOL_TYPE_ATTRIBUTE]: attr('function'),
           [GEN_AI_OPERATION_NAME_ATTRIBUTE]: attr('execute_tool'),
           [SEMANTIC_ATTRIBUTE_SENTRY_OP]: attr('gen_ai.execute_tool'),
-          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr('auto.vercelai.otel'),
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: attr(origin),
         }),
       }),
     ]),

dev-packages/node-integration-tests/suites/tracing/vercelai/test.ts

@@ -22,8 +22,9 @@ import {
   GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,
 } from '../../../../../packages/core/src/tracing/ai/gen-ai-attributes';
 import { cleanupChildProcesses, createEsmAndCjsTests } from '../../../utils/runner';
+import { isOrchestrionEnabled } from '../../../utils';
 
-describe('Vercel AI integration', () => {
+describe.skipIf(isOrchestrionEnabled())('Vercel AI integration (v4)', () => {
   afterAll(() => {
     cleanupChildProcesses();
   });

dev-packages/node-integration-tests/suites/tracing/vercelai/v5/test.ts

@@ -18,8 +18,9 @@ import {
   GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,
 } from '../../../../../../packages/core/src/tracing/ai/gen-ai-attributes';
 import { cleanupChildProcesses, createEsmAndCjsTests } from '../../../../utils/runner';
+import { isOrchestrionEnabled } from '../../../../utils';
 
-describe('Vercel AI integration (V5)', () => {
+describe.skipIf(isOrchestrionEnabled())('Vercel AI integration (v5)', () => {
   afterAll(() => {
     cleanupChildProcesses();
   });

dev-packages/node-integration-tests/suites/tracing/vercelai/v6_v7/scenario-concurrent-stream.mjs

@@ -0,0 +1,61 @@
+import * as Sentry from '@sentry/node';
+import { streamText } from 'ai';
+import { MockLanguageModelV3, simulateReadableStream } from 'ai/test';
+
+function makeStreamModel(text) {
+  return new MockLanguageModelV3({
+    doStream: async () => ({
+      stream: simulateReadableStream({
+        chunks: [
+          { type: 'stream-start', warnings: [] },
+          { type: 'text-start', id: '0' },
+          { type: 'text-delta', id: '0', delta: text },
+          { type: 'text-end', id: '0' },
+          {
+            type: 'finish',
+            finishReason: { unified: 'stop', raw: 'stop' },
+            usage: {
+              inputTokens: { total: 10, noCache: 10, cached: 0 },
+              outputTokens: { total: 20, noCache: 20, cached: 0 },
+              totalTokens: { total: 30, noCache: 30, cached: 0 },
+            },
+          },
+        ],
+      }),
+    }),
+  });
+}
+
+async function consume(result) {
+  for await (const _part of result.fullStream) {
+    void _part;
+  }
+}
+
+async function run() {
+  // A single model instance shared by two *concurrent* streamText calls. The shared model carries
+  // only a single captured-parent slot, so naive parent tracking could attribute a model call to
+  // whichever operation resolved the model last. Each `generate_content` (doStream) must land under
+  // its own `invoke_agent`, and both invoke_agents under `main`.
+  const sharedModel = makeStreamModel('shared!');
+
+  await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
+    // Start both operations before consuming either, so both resolve the shared model first.
+    const streams = [
+      streamText({
+        experimental_telemetry: { isEnabled: true },
+        model: sharedModel,
+        prompt: 'Concurrent stream A?',
+      }),
+      streamText({
+        experimental_telemetry: { isEnabled: true },
+        model: sharedModel,
+        prompt: 'Concurrent stream B?',
+      }),
+    ];
+
+    await Promise.all(streams.map(consume));
+  });
+}
+
+run();

dev-packages/node-integration-tests/suites/tracing/vercelai/v6_v7/scenario-embeddings.mjs

@@ -0,0 +1,35 @@
+import * as Sentry from '@sentry/node';
+import { embed, embedMany } from 'ai';
+import { MockEmbeddingModelV3 } from 'ai/test';
+
+async function run() {
+  await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
+    // Single embedding
+    await embed({
+      model: new MockEmbeddingModelV3({
+        doEmbed: async () => ({
+          embeddings: [[0.1, 0.2, 0.3]],
+          usage: { tokens: 10 },
+        }),
+      }),
+      value: 'Embedding test!',
+    });
+
+    // Multiple embeddings
+    await embedMany({
+      model: new MockEmbeddingModelV3({
+        maxEmbeddingsPerCall: 5,
+        doEmbed: async () => ({
+          embeddings: [
+            [0.1, 0.2, 0.3],
+            [0.4, 0.5, 0.6],
+          ],
+          usage: { tokens: 20 },
+        }),
+      }),
+      values: ['First input', 'Second input'],
+    });
+  });
+}
+
+run();

dev-packages/node-integration-tests/suites/tracing/vercelai/v6_v7/scenario.mjs

@@ -21,11 +21,9 @@ async function run() {
       prompt: 'Where is the first span?',
     });
 
-    // This span should have input and output prompts attached because telemetry is explicitly enabled.
-    // Vercel AI treats this as recording inputs and outputs by default
-    // but in v7, we do not have access to this, so we do not override data collection settings for this
+    // Recording and inputs explicitly enabled
     await generateText({
-      experimental_telemetry: { isEnabled: true },
+      experimental_telemetry: { isEnabled: true, recordInputs: true, recordOutputs: true },
       model: new MockLanguageModelV3({
         doGenerate: async () => ({
           finishReason: { unified: 'stop', raw: 'stop' },