> ## 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.

# Performance Evaluation with Database Logging

> Demonstrates storing performance evaluation results in PostgreSQL.

```python db_logging.py theme={null}
"""
Performance Evaluation with Database Logging
============================================

Demonstrates storing performance evaluation results in PostgreSQL.
"""

from agno.agent import Agent
from agno.db.postgres.postgres import PostgresDb
from agno.eval.performance import PerformanceEval
from agno.models.openai import OpenAIChat


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


# ---------------------------------------------------------------------------
# Create Database
# ---------------------------------------------------------------------------
db_url = "postgresql+psycopg://ai:ai@localhost:5432/ai"
db = PostgresDb(db_url=db_url, eval_table="eval_runs_cookbook")

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

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    simple_response_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 memory-profiler openai psycopg-binary 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>

  <Snippet file="run-pgvector-step.mdx" />

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

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

Full source: [cookbook/09\_evals/performance/db\_logging.py](https://github.com/agno-agi/agno/blob/main/cookbook/09_evals/performance/db_logging.py)