> ## 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 Evaluation with Database Logging

> Run a single-iteration agent run benchmark and persist its PerformanceEval results to the eval_runs_cookbook table in PostgresDb.

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

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

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno "psycopg[binary]" memory-profiler 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. Start Postgres**

Start Postgres on the port used by this example:

```bash theme={null}
docker run -d --name postgres -e POSTGRES_USER=ai -e POSTGRES_PASSWORD=ai -e POSTGRES_DB=ai -p 5432:5432 postgres:17
```

**4. Run the example**

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

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

Full source: [cookbook/09\_evals/performance/db\_logging.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/09_evals/performance/db_logging.py)


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