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

# Accuracy Evaluation with Custom Evaluator Agent

> Demonstrates accuracy evaluation using a custom evaluator agent.

```python evaluator_agent.py theme={null}
"""
Accuracy Evaluation with Custom Evaluator Agent
================================================

Demonstrates accuracy evaluation using a custom evaluator agent.
"""

from typing import Optional

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

# ---------------------------------------------------------------------------
# Create Evaluator Agent
# ---------------------------------------------------------------------------
evaluator_agent = Agent(
    model=OpenAIChat(id="gpt-5"),
    output_schema=AccuracyAgentResponse,
)

# ---------------------------------------------------------------------------
# Create Evaluation
# ---------------------------------------------------------------------------
evaluation = AccuracyEval(
    model=OpenAIChat(id="o4-mini"),
    agent=Agent(model=OpenAIChat(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",
    evaluator_agent=evaluator_agent,
    additional_guidelines="Agent output should include the steps and the final answer.",
)

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    result: Optional[AccuracyResult] = evaluation.run(print_results=True)
    assert result is not None and result.avg_score >= 8
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```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"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    Save the code above as `evaluator_agent.py`, then run:

    ```bash theme={null}
    python evaluator_agent.py
    ```
  </Step>
</Steps>

Full source: [cookbook/09\_evals/accuracy/evaluator\_agent.py](https://github.com/agno-agi/agno/blob/main/cookbook/09_evals/accuracy/evaluator_agent.py)