> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Async Accuracy Evaluation

> Example showing how to run accuracy evaluations asynchronously with AccuracyEval.arun().

**1. Create a Python file**

```python accuracy_async.py theme={null}
"""This example shows how to run an Accuracy evaluation asynchronously."""

import asyncio
from typing import Optional

from agno.agent import Agent
from agno.eval.accuracy import AccuracyEval, AccuracyResult
from agno.models.openai import OpenAIResponses
from agno.tools.calculator import CalculatorTools

evaluation = AccuracyEval(
    model=OpenAIResponses(id="gpt-5.2"),
    agent=Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        tools=[CalculatorTools()],
    ),
    input="What is 10*5 then to the power of 2? do it step by step",
    expected_output="2500",
    additional_guidelines="Agent output should include the steps and the final answer.",
    num_iterations=3,
)

# Run the evaluation calling the arun method.
result: Optional[AccuracyResult] = asyncio.run(evaluation.arun(print_results=True))
assert result is not None and result.avg_score >= 8
```

<Snippet file="create-venv-step.mdx" />

**2. Install dependencies**

```bash theme={null}
uv pip install -U openai agno
```

**3. Export your OpenAI API key**

```bash Mac/Linux theme={null}
  export OPENAI_API_KEY="your_openai_api_key_here"
```

```bash Windows theme={null}
  $Env:OPENAI_API_KEY="your_openai_api_key_here"
```

**4. Run Agent**

```bash theme={null}
python accuracy_async.py
```


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