> ## 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.

# Performance on Agent Response

> Example showing how to analyze the runtime and memory usage of an Agent's run, given its response.

**1. Create a Python file**

```python performance_simple_response.py theme={null}
"""Run `uv pip install openai agno` to install dependencies."""

from agno.agent import Agent
from agno.eval.performance import PerformanceEval
from agno.models.openai import OpenAIResponses


def run_agent():
    agent = Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        system_message="Be concise, reply with one sentence.",
    )

    response = agent.run("What is the capital of France?")
    print(f"Agent response: {response.content}")

    return response


simple_response_perf = PerformanceEval(
    name="Simple Performance Evaluation",
    func=run_agent,
    num_iterations=1,
    warmup_runs=0,
)

if __name__ == "__main__":
    simple_response_perf.run(print_results=True, print_summary=True)
```

<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 performance_simple_response.py
```


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