OpenAI Agents Instantiation Performance Evaluation

Benchmark 1,000 per-metric OpenAI Agents SDK agent constructions with a function tool using Agno's PerformanceEval.

Demonstrates agent instantiation benchmarking with OpenAI Agents SDK.

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.

openai_agents_instantiation.py
"""
OpenAI Agents Instantiation Performance Evaluation
==================================================

Demonstrates agent instantiation benchmarking with OpenAI Agents SDK.
"""

from typing import Literal

from agno.eval.performance import PerformanceEval

try:
    from agents import Agent, function_tool
except ImportError:
    raise ImportError(
        "OpenAI agents not installed. Please install it using `uv pip install openai-agents`."
    )


# ---------------------------------------------------------------------------
# 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"
    else:
        raise AssertionError("Unknown city")


# ---------------------------------------------------------------------------
# Create Benchmark Function
# ---------------------------------------------------------------------------
def instantiate_agent():
    return Agent(
        name="Haiku agent",
        instructions="Always respond in haiku form",
        model="o3-mini",
        tools=[function_tool(get_weather)],
    )


# ---------------------------------------------------------------------------
# Create Evaluation
# ---------------------------------------------------------------------------
openai_agents_instantiation = PerformanceEval(
    func=instantiate_agent, num_iterations=1000
)

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    openai_agents_instantiation.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-agents

Run the example

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

python openai_agents_instantiation.py

Full source: cookbook/09_evals/performance/comparison/openai_agents_instantiation.py