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

# Accuracy Evaluation

> Measure how accurately your agent answers questions

## Overview

`AccuracyEval` uses an LLM judge to score agent responses against expected answers on a 0.0–1.0 scale.

## Quick Start

```typescript theme={null}
import { AccuracyEval } from "@agentium/eval";
import { Agent, openai } from "@agentium/core";

const agent = new Agent({ name: "qa-bot", model: openai("gpt-4o") });

const eval = new AccuracyEval({
  name: "qa-accuracy",
  agent,
  judge: openai("gpt-4o-mini"),
  cases: [
    { name: "capital", input: "What is the capital of France?", expected: "Paris" },
    { name: "math", input: "What is 2+2?", expected: "4" },
  ],
  threshold: 0.8,
});

const result = await eval.run();
console.log(`Passed: ${result.passed}/${result.total}, Avg: ${result.averageScore}`);
```

## Configuration

| Option      | Type            | Default  | Description                    |
| ----------- | --------------- | -------- | ------------------------------ |
| `name`      | `string`        | required | Name of the evaluation         |
| `agent`     | `Agent`         | required | Agent to evaluate              |
| `judge`     | `ModelProvider` | required | Model used for scoring         |
| `cases`     | `EvalCase[]`    | required | Test cases with input/expected |
| `threshold` | `number`        | `0.7`    | Minimum score to pass          |
| `timeoutMs` | `number`        | `30000`  | Timeout per case               |