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

# Storage-Backed Response Performance Evaluation

> PerformanceEval invokes the benchmark function separately for runtime and memory measurement, producing four model requests total.

`run_agent()` sends two model requests. `PerformanceEval` invokes it once for runtime and once for memory, so the evaluation sends four requests total.

```python response_with_storage.py theme={null}
"""
Storage-Backed Response Performance Evaluation
==============================================

Demonstrates measuring performance when storage-backed history is enabled.
"""

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.eval.performance import PerformanceEval
from agno.models.openai import OpenAIChat

# ---------------------------------------------------------------------------
# Create Database
# ---------------------------------------------------------------------------
db = SqliteDb(db_file="tmp/storage.db")


# ---------------------------------------------------------------------------
# Create Benchmark Function
# ---------------------------------------------------------------------------
def run_agent():
    agent = Agent(
        model=OpenAIChat(id="gpt-5.2"),
        system_message="Be concise, reply with one sentence.",
        db=db,
        add_history_to_context=True,
    )
    response_1 = agent.run("What is the capital of France?")
    print(response_1.content)

    response_2 = agent.run("How many people live there?")
    print(response_2.content)

    return response_2.content


# ---------------------------------------------------------------------------
# Create Evaluation
# ---------------------------------------------------------------------------
response_with_storage_perf = PerformanceEval(
    name="Storage Performance",
    func=run_agent,
    num_iterations=1,
    warmup_runs=0,
)

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    response_with_storage_perf.run(print_results=True, print_summary=True)
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai sqlalchemy
    ```
  </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 `response_with_storage.py`, then run:

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

Full source: [cookbook/09\_evals/performance/response\_with\_storage.py](https://github.com/agno-agi/agno/blob/main/cookbook/09_evals/performance/response_with_storage.py)