Performance Evaluation with Database Logging

Demonstrates storing performance evaluation results in PostgreSQL.

With one iteration per metric and zero warm-ups, the evaluator calls run_agent() twice.

PerformanceEval runs the callable separately for each enabled metric: warm-ups first, then runtime measurements, then memory measurements. num_iterations applies to each metric, and the default is 10 additional warm-up calls. Model retries, tools, delegation, and memory extraction can add provider requests beyond the callable count.

Memory measurements use Python’s tracemalloc; they do not measure process RSS, GPU memory, database-server memory, or remote model memory. Record dependency versions, database state, and model settings when comparing results.

db_logging.py

"""
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

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno "psycopg[binary]" openai sqlalchemy

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Start Postgres

Start Postgres on the port used by this example:

docker run -d --name postgres -e POSTGRES_USER=ai -e POSTGRES_PASSWORD=ai -e POSTGRES_DB=ai -p 5432:5432 postgres:17

Run the example

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

python db_logging.py

Full source: cookbook/09_evals/performance/db_logging.py