Agent-with-Tool Instantiation Performance Evaluation

Measure runtime and memory of 1,000 per-metric Agno Agent constructions using OpenAIChat gpt-5.6-luna plus a weather tool.

Demonstrates measuring instantiation performance for a tooled agent.

This evaluation performs 2,010 callable invocations: 10 warm-ups, 1,000 runtime measurements, and 1,000 memory measurements. It measures construction, not model inference.

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.

instantiate_agent_with_tool.py
"""
Agent-with-Tool Instantiation Performance Evaluation
====================================================

Demonstrates measuring instantiation performance for a tooled agent.
"""

from typing import Literal

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


# ---------------------------------------------------------------------------
# Create Benchmark Tool
# ---------------------------------------------------------------------------
def get_weather(city: Literal["nyc", "sf"]):
    """Use this to get weather information."""
    if city == "nyc":
        return "It might be cloudy in nyc"
    elif city == "sf":
        return "It's always sunny in sf"


tools = [get_weather]


# ---------------------------------------------------------------------------
# Create Benchmark Function
# ---------------------------------------------------------------------------
def instantiate_agent():
    return Agent(model=OpenAIChat(id="gpt-5.6-luna"), tools=tools)  # type: ignore


# ---------------------------------------------------------------------------
# Create Evaluation
# ---------------------------------------------------------------------------
instantiation_perf = PerformanceEval(
    name="Agent Instantiation", func=instantiate_agent, num_iterations=1000
)

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    instantiation_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 openai

Run the example

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

python instantiate_agent_with_tool.py

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