> ## 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.

# 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

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

**1. Install dependencies**

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

**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 `response_with_storage.py`, then run:

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

Full source: [cookbook/09\_evals/performance/response\_with\_storage.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/09_evals/performance/response_with_storage.py)


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