AutoGen Instantiation Performance Evaluation

Measure AutoGen AssistantAgent instantiation over 1,000 iterations per metric with PerformanceEval, using a gpt-5.6-luna OpenAIChatCompletionClient and a weather tool.

Demonstrates agent instantiation benchmarking with AutoGen.

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.

autogen_instantiation.py
"""
AutoGen Instantiation Performance Evaluation
============================================

Demonstrates agent instantiation benchmarking with AutoGen.
"""

from typing import Literal

from agno.eval.performance import PerformanceEval
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient


# ---------------------------------------------------------------------------
# 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")


tools = [get_weather]


# ---------------------------------------------------------------------------
# Create Benchmark Function
# ---------------------------------------------------------------------------
def instantiate_agent():
    return AssistantAgent(
        name="assistant",
        model_client=OpenAIChatCompletionClient(
            model="gpt-5.6-luna",
            model_info={
                "vision": False,
                "function_calling": True,
                "json_output": False,
                "family": "gpt-5.6-luna",
                "structured_output": True,
            },
        ),
        tools=tools,
    )


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

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    autogen_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 "autogen-ext[openai]" autogen-agentchat

Export your API keys

export OPENAI_API_KEY="your_openai_api_key_here"

Run the example

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

python autogen_instantiation.py

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