docs: add scorecard integration (#94)

Author: schiehll

mint.json

@@ -128,6 +128,7 @@
         "openllmetry/integrations/newrelic",
         "openllmetry/integrations/otel-collector",
         "openllmetry/integrations/oraclecloud",
+        "openllmetry/integrations/scorecard",
         "openllmetry/integrations/service-now",
         "openllmetry/integrations/signoz",
         "openllmetry/integrations/sentry",
@@ -183,9 +184,7 @@
     },
     {
       "group": "Costs",
-      "pages": [
-        "api-reference/costs/property_costs"
-      ]
+      "pages": ["api-reference/costs/property_costs"]
     }
   ],
   "redirects": [

openllmetry/integrations/introduction.mdx

@@ -40,6 +40,7 @@ in any observability platform that supports OpenTelemetry.
     title="Oracle Cloud"
     href="/openllmetry/integrations/oraclecloud"
   ></Card>
+  <Card title="Scorecard" href="/openllmetry/integrations/scorecard"></Card>
   <Card
     title="Service Now Cloud Observability"
     href="/openllmetry/integrations/service-now"

openllmetry/integrations/scorecard.mdx

@@ -0,0 +1,105 @@
+---
+title: "LLM Observability with Scorecard and OpenLLMetry"
+sidebarTitle: "Scorecard"
+---
+
+Scorecard is an [AI evaluation and optimization platform](https://www.scorecard.io/) that helps teams build reliable AI systems with comprehensive testing, evaluation, and continuous monitoring capabilities.
+
+## Setup
+
+To integrate OpenLLMetry with Scorecard, you'll need to configure your tracing endpoint and authentication:
+
+### 1. Get your Scorecard API Key
+
+1. Visit your [Settings Page](https://app.scorecard.io/settings)
+2. Copy your API Key
+
+### 2. Configure Environment Variables
+
+```bash
+TRACELOOP_BASE_URL="https://tracing.scorecard.io/otel"
+TRACELOOP_HEADERS="Authorization=Bearer <YOUR_SCORECARD_API_KEY>"
+```
+
+### 3. Instrument your code
+
+First, install OpenLLMetry and your LLM library:
+
+<CodeGroup>
+```sh Python
+pip install traceloop-sdk openai
+```
+
+```sh JavaScript
+npm install @traceloop/node-server-sdk openai
+```
+</CodeGroup>
+
+Then initialize OpenLLMetry and structure your application using workflows and tasks:
+
+<CodeGroup>
+```py Python
+from traceloop.sdk import Traceloop
+from traceloop.sdk.decorators import workflow, task
+from traceloop.sdk.instruments import Instruments
+from openai import OpenAI
+
+# Initialize OpenAI client
+openai_client = OpenAI()
+
+# Initialize OpenLLMetry (reads config from environment variables)
+Traceloop.init(disable_batch=True, instruments={Instruments.OPENAI})
+
+@workflow(name="simple_chat")
+def simple_workflow():
+    completion = openai_client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": "Tell me a joke"}]
+    )
+    return completion.choices[0].message.content
+
+# Run the workflow - all LLM calls will be automatically traced
+simple_workflow()
+print("Check Scorecard for traces!")
+```
+
+```js JavaScript
+import * as traceloop from "@traceloop/node-server-sdk";
+import OpenAI from "openai";
+
+// Initialize OpenAI client
+const openai = new OpenAI();
+
+// Initialize OpenLLMetry with automatic instrumentation
+traceloop.initialize({ 
+    disableBatch: true,  // Ensures immediate trace sending
+    instrumentModules: { openAI: OpenAI },
+});
+
+async function simpleWorkflow() {
+return await traceloop.withWorkflow({ name: "simple_chat" }, async () => {
+    const completion = await openai.chat.completions.create({
+    model: "gpt-4o-mini",
+    messages: [{ role: "user", content: "Tell me a joke" }],
+    });
+    return completion.choices[0].message.content;
+});
+}
+
+# Run the workflow - all LLM calls will be automatically traced
+simpleWorkflow();
+console.log("Check Scorecard for traces!");
+```
+</CodeGroup>
+
+## Features
+
+Once configured, you'll have access to Scorecard's comprehensive observability features:
+
+- **Automatic LLM instrumentation** for popular libraries (OpenAI, Anthropic, etc.)
+- **Structured tracing** with workflows and tasks using `@workflow` and `@task` decorators
+- **Performance monitoring** including latency, token usage, and cost tracking
+- **Real-time evaluation** with continuous monitoring of AI system performance
+- **Production debugging** with detailed trace analysis
+
+For more detailed setup instructions and examples, check out the [Scorecard Tracing Quickstart](https://docs.scorecard.io/intro/tracing-quickstart).