feat: add optional AgentPond tracing

.env.example

@@ -1,2 +1,3 @@
 OPENROUTER_API_KEY='add your openrouter api key here'
-EXA_SEARCH_API_KEY='add your exa search api key here'
+EXA_SEARCH_API_KEY='add your exa search api key here'
+AGENTPOND_ENABLED=false

README.md

@@ -89,6 +89,31 @@ bun run dev
 
 Visit `http://localhost:3000` to see your app.
 
+### Optional AgentPond tracing
+
+To export OpenInference AI traces directly to a private Vercel Blob store,
+initialize AgentPond 0.6 from the project root:
+
+```bash
+npx agentpond@0.6.0 init --platform vercel
+```
+
+Follow the generated prompt, connect a private Blob store to the Vercel
+project, enable Vercel System Environment Variables, and set
+`AGENTPOND_ENABLED=true`. After running a research request:
+
+The AI SDK telemetry settings explicitly enable `recordInputs` and
+`recordOutputs`. This records input and output values for each instrumented
+call, which may include prompts, messages, tool data, and generated content. A
+private Blob store restricts access but does not redact these values. Review
+the [Vercel AI SDK telemetry documentation](https://ai-sdk.dev/docs/ai-sdk-core/telemetry)
+before enabling tracing.
+
+```bash
+npx agentpond@0.6.0 sync
+npx agentpond@0.6.0 traces list --limit 10
+```
+
 ## 🌟 Show Your Support
 
 Give a ⭐️ if this project helped you!

instrumentation.ts

@@ -0,0 +1,9 @@
+export async function register() {
+  if (
+    process.env.NEXT_RUNTIME === "nodejs" &&
+    process.env.AGENTPOND_ENABLED === "true"
+  ) {
+    const { registerAgentPond } = await import("./src/lib/agentpond");
+    registerAgentPond();
+  }
+}

package.json

@@ -9,10 +9,18 @@
     "lint": "eslint ."
   },
   "dependencies": {
+    "@agentpond/vercel": "0.5.0",
     "@ai-sdk/react": "^3.0.0",
+    "@arizeai/openinference-vercel": "2.8.1",
     "@hookform/resolvers": "^5.0.0",
     "@openrouter/ai-sdk-provider": "^2.8.1",
     "@opentelemetry/api": "^1.9.0",
+    "@opentelemetry/api-logs": "0.220.0",
+    "@opentelemetry/instrumentation": "0.220.0",
+    "@opentelemetry/resources": "2.9.0",
+    "@opentelemetry/sdk-logs": "0.220.0",
+    "@opentelemetry/sdk-metrics": "2.9.0",
+    "@opentelemetry/sdk-trace-base": "2.9.0",
     "@radix-ui/react-accordion": "^1.2.12",
     "@radix-ui/react-collapsible": "^1.1.12",
     "@radix-ui/react-label": "^2.1.8",
@@ -22,6 +30,7 @@
     "@radix-ui/react-slot": "^1.2.4",
     "@radix-ui/react-tabs": "^1.1.13",
     "@radix-ui/react-tooltip": "^1.2.8",
+    "@vercel/otel": "2.1.3",
     "ai": "^6.0.0",
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",

src/app/api/deep-research/model-caller.ts

@@ -18,6 +18,11 @@ researchState: ResearchState,activityTracker: ActivityTracker ): Promise<T | str
       if(schema){
         // AI SDK v6: Use generateText with Output.object() instead of generateObject
         const { output, usage } = await generateText({
+            experimental_telemetry: {
+                isEnabled: process.env.AGENTPOND_ENABLED === "true",
+                recordInputs: true,
+                recordOutputs: true,
+            },
             model: openrouter(model),
             prompt,
             system,
@@ -33,6 +38,11 @@ researchState: ResearchState,activityTracker: ActivityTracker ): Promise<T | str
         }else{
 
             const { text, usage } = await generateText({
+                experimental_telemetry: {
+                    isEnabled: process.env.AGENTPOND_ENABLED === "true",
+                    recordInputs: true,
+                    recordOutputs: true,
+                },
                 model: openrouter(model),
                 prompt,
                 system,
@@ -55,4 +65,4 @@ researchState: ResearchState,activityTracker: ActivityTracker ): Promise<T | str
   }
 
   throw lastError || new Error(`Failed after ${MAX_RETRY_ATTEMPTS} attempst!`)
-}
+}

src/app/api/generate-questions/route.ts

@@ -20,6 +20,11 @@ const clarifyResearchGoals = async (topic: string) => {
     try{
         // AI SDK v6: Use generateText with Output.object() instead of generateObject
         const { output } = await generateText({
+            experimental_telemetry: {
+                isEnabled: process.env.AGENTPOND_ENABLED === "true",
+                recordInputs: true,
+                recordOutputs: true,
+            },
             model: openrouter("meta-llama/llama-3.3-70b-instruct"),
             prompt,
             output: Output.object({
@@ -58,4 +63,4 @@ export async function POST(req: Request){
 
 
 
-}
+}

src/lib/agentpond.ts

@@ -0,0 +1,21 @@
+import "server-only";
+
+import { createVercelSpanExporter } from "@agentpond/vercel";
+import {
+  isOpenInferenceSpan,
+  OpenInferenceSimpleSpanProcessor,
+} from "@arizeai/openinference-vercel";
+import { registerOTel } from "@vercel/otel";
+
+export function registerAgentPond() {
+  registerOTel({
+    serviceName: "deep-research-ai-agent",
+    spanProcessors: [
+      new OpenInferenceSimpleSpanProcessor({
+        exporter: createVercelSpanExporter(),
+        spanFilter: isOpenInferenceSpan,
+        reparentOrphanedSpans: true,
+      }),
+    ],
+  });
+}