> ## 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.

# Accuracy Evaluation with Custom Evaluator Agent

> Score an agent's step-by-step calculator math against an expected answer using a custom evaluator agent with an AccuracyAgentResponse output schema.

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

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

**1. Install dependencies**

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

**2. 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"
```

**3. Run the example**

Save the code above as `evaluator_agent.py`, then run:

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

Full source: [cookbook/09\_evals/accuracy/evaluator\_agent.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/09_evals/accuracy/evaluator_agent.py)


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