@@ -0,0 +1,91 @@
+import * as Sentry from '@sentry/node';
+import { generateText, stepCountIs, streamText, tool } from 'ai';
+import { MockLanguageModelV3, simulateReadableStream } from 'ai/test';
+import { z } from 'zod';
+
+const usage = {
+  inputTokens: { total: 10, noCache: 10, cached: 0 },
+  outputTokens: { total: 20, noCache: 20, cached: 0 },
+  totalTokens: { total: 30, noCache: 30, cached: 0 },
+};
+
+const tools = {
+  getWeather: tool({
+    description: 'Get the current weather for a location',
+    inputSchema: z.object({ location: z.string() }),
+    contextSchema: z.object({ units: z.string(), apiKey: z.string() }),
+    execute: async ({ location }) => `Weather in ${location}: Sunny`,
+  }),
+};
+
+async function run() {
+  await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
+    let callCount = 0;
+    await generateText({
+      model: new MockLanguageModelV3({
+        doGenerate: async () =>
+          callCount++ === 0
+            ? {
+                finishReason: { unified: 'tool-calls', raw: 'tool_calls' },
+                usage,
+                content: [
+                  {
+                    type: 'tool-call',
+                    toolCallId: 'call-1',
+                    toolName: 'getWeather',
+                    input: JSON.stringify({ location: 'Vienna' }),
+                  },
+                ],
+                warnings: [],
+              }
+            : {
+                finishReason: { unified: 'stop', raw: 'stop' },
+                usage,
+                content: [{ type: 'text', text: 'Sunny' }],
+                warnings: [],
+              },
+      }),
+      tools,
+      stopWhen: stepCountIs(3),
+      prompt: 'Weather in Vienna?',
+      runtimeContext: { requestId: 'req_generate', tenantId: 'acme', apiToken: 'secret-runtime' },
+      toolsContext: { getWeather: { units: 'metric', apiKey: 'secret-tool' } },
+      telemetry: {
+        isEnabled: true,
+        functionId: 'support-chat',
+        includeRuntimeContext: { requestId: true, tenantId: true },
+        includeToolsContext: { getWeather: { units: true } },
+      },
+    });
+
+    const result = streamText({
+      model: new MockLanguageModelV3({
+        doStream: async () => ({
+          stream: simulateReadableStream({
+            chunks: [
+              { type: 'stream-start', warnings: [] },
+              { type: 'text-start', id: '0' },
+              { type: 'text-delta', id: '0', delta: 'Hello!' },
+              { type: 'text-end', id: '0' },
+              { type: 'finish', finishReason: { unified: 'stop', raw: 'stop' }, usage },
+            ],
+          }),
+        }),
+      }),
+      prompt: 'Hi',
+      runtimeContext: { requestId: 'req_stream', apiToken: 'secret-runtime' },
+      telemetry: {
+        isEnabled: true,
+        functionId: 'support-stream',
+        includeRuntimeContext: { requestId: true },
+      },
+    });
+    for await (const _part of result.fullStream) {
+      void _part;
+    }
+  });
+
+  await Sentry.flush(2000);
+}
+
+run();
@@ -26,7 +26,12 @@ import {
   SENTRY_OP,
   SENTRY_ORIGIN,
 } from '@sentry/conventions/attributes';
-import { GEN_AI_DECIDE } from '@sentry/conventions/op';
+import {
+  GEN_AI_DECIDE,
+  GEN_AI_EXECUTE_TOOL,
+  GEN_AI_GENERATE_CONTENT,
+  GEN_AI_INVOKE_AGENT,
+} from '@sentry/conventions/op';
 import { GEN_AI_TOOL_CALL_ID_ATTRIBUTE } from '../../../../../../packages/server-utils/src/ai/core/gen-ai-attributes';
 import { cleanupChildProcesses, createEsmAndCjsTests, createEsmTests } from '../../../../utils/runner';
 
@@ -1429,3 +1434,62 @@ describe('Vercel AI telemetry metadata (v6)', () => {
     { additionalDependencies: { ai: '^6.0.0' } },
   );
 });
+
+describe('Vercel AI runtime and tool context (v7)', () => {
+  afterAll(() => {
+    cleanupChildProcesses();
+  });
+
+  createEsmTests(
+    __dirname,
+    'scenario-runtime-context.mjs',
+    'instrument.mjs',
+    (createRunner, test) => {
+      test('records the context keys included in telemetry as span attributes', async () => {
+        await createRunner()
+          .expect({
+            span: container => {
+              const spans = container.items.filter(
+                span => span.attributes[SENTRY_ORIGIN]?.value === 'auto.ai.vercelai',
+              );
+              // Keys left out of `includeRuntimeContext`/`includeToolsContext` must not reach any span.
+              expect(JSON.stringify(spans)).not.toMatch(/secret-runtime|secret-tool|apiToken|apiKey/);
+
+              const generateSpans = spans.filter(
+                span => span.attributes['vercel.ai.settings.context.requestId']?.value === 'req_generate',
+              );
+              expect(generateSpans.map(span => span.attributes[SENTRY_OP]?.value).sort()).toEqual([
+                GEN_AI_EXECUTE_TOOL,
+                GEN_AI_GENERATE_CONTENT,
+                GEN_AI_GENERATE_CONTENT,
+                GEN_AI_INVOKE_AGENT,
+              ]);
+              for (const span of generateSpans) {
+                expect(span.attributes['vercel.ai.settings.context.tenantId']).toEqual({
+                  type: 'string',
+                  value: 'acme',
+                });
+              }
+
+              const toolSpan = generateSpans.find(span => span.attributes[SENTRY_OP]?.value === GEN_AI_EXECUTE_TOOL);
+              expect(toolSpan?.attributes['vercel.ai.tool_call.context.units']).toEqual({
+                type: 'string',
+                value: 'metric',
+              });
+
+              const streamSpans = spans.filter(
+                span => span.attributes['vercel.ai.settings.context.requestId']?.value === 'req_stream',
+              );
+              expect(streamSpans.map(span => span.attributes[SENTRY_OP]?.value).sort()).toEqual([
+                GEN_AI_GENERATE_CONTENT,
+                GEN_AI_INVOKE_AGENT,
+              ]);
+            },
+          })
+          .start()
+          .completed();
+      });
+    },
+    { additionalDependencies: { ai: '^7.0.154' } },
+  );
+});
@@ -86,6 +86,8 @@ const VERCEL_AI_OPERATION_ID_ATTRIBUTE = 'vercel.ai.operationId';
 const VERCEL_AI_MODEL_PROVIDER_ATTRIBUTE = 'vercel.ai.model.provider';
 const VERCEL_AI_SETTINGS_MAX_RETRIES_ATTRIBUTE = 'vercel.ai.settings.maxRetries';
 const VERCEL_AI_TELEMETRY_METADATA_ATTRIBUTE_PREFIX = 'vercel.ai.telemetry.metadata.';
+const VERCEL_AI_SETTINGS_CONTEXT_ATTRIBUTE_PREFIX = 'vercel.ai.settings.context.';
+const VERCEL_AI_TOOL_CALL_CONTEXT_ATTRIBUTE_PREFIX = 'vercel.ai.tool_call.context.';
 
 // Tracks the top-level operationId (and whether it streams) per `callId` so a model-call span can
 // name its `doGenerate`/`doStream` operation the same way the OTel integration does. `isStream` is
@@ -108,6 +110,10 @@ const toolDescriptionsByCallId = new Map<string, Map<string, string>>();
 // before the operation's own span ends. The running token sum lives on the span's own attributes.
 const invokeAgentSpanByCallId = new Map<string, Span>();
 
+// Only an operation's start event carries `runtimeContext`; its model-call and tool events copy the
+// resulting attributes from here. Cleared with the operation.
+const runtimeContextAttributesByCallId = new Map<string, SpanAttributes>();
+
 // Only top-level operations own the `callId` → operationId mapping; `step`/`languageModelCall`/
 // `executeTool` share the parent's `callId`, so they must not clear it.
 const ROOT_OPERATION_TYPES = new Set<ChannelEventType>([
@@ -140,6 +146,7 @@ export function clearOperationCallId(callId: string): void {
   operationIdByCallId.delete(callId);
   toolDescriptionsByCallId.delete(callId);
   invokeAgentSpanByCallId.delete(callId);
+  runtimeContextAttributesByCallId.delete(callId);
 }
 
 /**
@@ -450,9 +457,17 @@ export function createSpanFromMessage(
     getOpenAiConversationId(event.providerOptions) ??
     (ROOT_OPERATION_TYPES.has(type) ? undefined : getActiveSpanConversationId());
 
+  const runtimeContextAttributes =
+    (callId ? runtimeContextAttributesByCallId.get(callId) : undefined) ??
+    contextAttributes(event, event.runtimeContext, VERCEL_AI_SETTINGS_CONTEXT_ATTRIBUTE_PREFIX);
+  if (callId && ROOT_OPERATION_TYPES.has(type)) {
+    runtimeContextAttributesByCallId.set(callId, runtimeContextAttributes);
+  }
+
   const baseAttributes: SpanAttributes = {
     [SENTRY_ORIGIN]: ORIGIN,
     ...telemetryMetadataAttributes(event.telemetryMetadata),
+    ...runtimeContextAttributes,
     [GEN_AI_PROVIDER_NAME]: provider,
     [VERCEL_AI_MODEL_PROVIDER_ATTRIBUTE]: provider,
     [GEN_AI_REQUEST_MODEL]: modelId,
@@ -473,7 +488,7 @@ export function createSpanFromMessage(
 
       return buildModelCallSpan(event, baseAttributes, recordInputs, callId, modelId);
     case 'executeTool':
-      return buildToolSpan(event, recordInputs, conversationId);
+      return buildToolSpan(event, recordInputs, conversationId, runtimeContextAttributes);
     case 'embed':
     case 'embedMany': {
       // `embed` carries a single `value`; `embedMany` a `values` array — both map to the embeddings input.
@@ -520,6 +535,26 @@ function telemetryMetadataAttributes(metadata: unknown): Record<string, string>
   return attributes;
 }
 
+/**
+ * One string attribute per context key, as `<prefix><key>`. `ai` < 7.0.114 published the full context to the
+ * channel, ignoring `telemetry.includeRuntimeContext`/`includeToolsContext`, and a filtered context
+ * looks the same as an unfiltered one. `aiVersion` (added in 7.0.154) is the only reliable sign that
+ * the AI SDK filtered it, so without it we record nothing.
+ */
+function contextAttributes(event: Record<string, unknown>, context: unknown, prefix: string): SpanAttributes {
+  const attributes: SpanAttributes = {};
+  if (typeof event.aiVersion !== 'string' || !isObjectLike(context)) {
+    return attributes;
+  }
+  for (const [key, value] of Object.entries(context)) {
+    const attributeValue = stringify(value);
+    if (value !== null && attributeValue !== undefined) {
+      attributes[`${prefix}${key}`] = attributeValue;
+    }
+  }
+  return attributes;
+}
+
 /** Start a `gen_ai.<operation>` span named `<operation> <suffix>` (or just `<operation>` when no suffix). */
 function startGenAiSpan(operation: GenAiOperation, suffix: string | undefined, attributes: SpanAttributes): Span {
   return startInactiveSpan({
@@ -581,6 +616,7 @@ function buildToolSpan(
   event: Record<string, unknown>,
   recordInputs: boolean,
   conversationId: string | undefined,
+  runtimeContextAttributes: SpanAttributes,
 ): Span {
   const toolCall = isObjectLike(event.toolCall) ? event.toolCall : {};
   const toolName = asString(toolCall.toolName);
@@ -592,6 +628,8 @@ function buildToolSpan(
     recordInputs && toolName ? resolveToolDescription(asString(event.callId), toolName, event.tools) : undefined;
   return startGenAiSpan('execute_tool', toolName, {
     [SENTRY_ORIGIN]: ORIGIN,
+    ...runtimeContextAttributes,
+    ...contextAttributes(event, event.toolContext, VERCEL_AI_TOOL_CALL_CONTEXT_ATTRIBUTE_PREFIX),
     [GEN_AI_TOOL_NAME]: toolName,
     [GEN_AI_TOOL_CALL_ID_ATTRIBUTE]: toolCallId,
     [GEN_AI_TOOL_DESCRIPTION]: description,