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

# Accuracy Evaluation

> Score agent output quality using the LLM-as-a-judge pattern

The `AccuracyEvaluator` uses an LLM judge to compare an agent's generated output against an expected answer. Each evaluation produces a score from 1 to 10 along with detailed reasoning and constructive critique.

## How It Works

1. The **agent under test** receives a query and produces output.
2. A separate **judge agent** evaluates the output against the expected answer and guidelines.
3. The judge returns a structured `EvaluationScore` containing a numeric score, reasoning, pass/fail flag, and critique.
4. If `num_iterations > 1`, the process repeats and scores are averaged.

## Parameters

| Parameter               | Type                     | Required | Description                              |
| ----------------------- | ------------------------ | -------- | ---------------------------------------- |
| `judge_agent`           | `Agent`                  | Yes      | Agent used to evaluate outputs           |
| `agent_under_test`      | `Agent \| Graph \| Team` | Yes      | Entity to evaluate                       |
| `query`                 | `str`                    | Yes      | Input query sent to the entity           |
| `expected_output`       | `str`                    | Yes      | Ground-truth answer for comparison       |
| `additional_guidelines` | `str`                    | No       | Extra criteria for the judge             |
| `num_iterations`        | `int`                    | No       | Number of evaluation rounds (default: 1) |

## Result Structure

`AccuracyEvaluationResult` contains:

* **`average_score`** — Mean score across all iterations (1–10)
* **`evaluation_scores`** — List of `EvaluationScore` objects, one per iteration
* **`generated_output`** — The output produced by the entity
* **`user_query`** / **`expected_output`** — The original inputs

Each `EvaluationScore` includes:

* **`score`** — Numeric score (1–10)
* **`reasoning`** — Step-by-step explanation from the judge
* **`is_met`** — Boolean indicating whether core requirements are met
* **`critique`** — Actionable feedback on how to improve

## Methods

| Method                                              | Description                              |
| --------------------------------------------------- | ---------------------------------------- |
| `await run(print_results=True)`                     | Execute the entity, then evaluate output |
| `await run_with_output(output, print_results=True)` | Evaluate a pre-existing output string    |

## Usage Examples

<CardGroup cols={3}>
  <Card title="Agent" icon="robot" href="/concepts/evals/usage/accuracy/agent">
    Evaluate a single agent
  </Card>

  <Card title="Team" icon="users" href="/concepts/evals/usage/accuracy/team">
    Evaluate a multi-agent team
  </Card>

  <Card title="Graph" icon="diagram-project" href="/concepts/evals/usage/accuracy/graph">
    Evaluate a graph workflow
  </Card>
</CardGroup>