# Source: https://www.traceloop.com/docs/openllmetry/integrations/scorecard.md

> ## Documentation Index
> Fetch the complete documentation index at: https://www.traceloop.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM Observability with Scorecard and OpenLLMetry

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  theme={null}
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 theme={null}
  pip install traceloop-sdk openai
  ```

  ```sh JavaScript theme={null}
  npm install @traceloop/node-server-sdk openai
  ```
</CodeGroup>

Then initialize OpenLLMetry and structure your application using workflows and tasks:

<CodeGroup>
  ```py Python theme={null}
  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 theme={null}
  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).