PydanticAI Instantiation Performance Evaluation

Benchmark 1,000 per-metric PydanticAI agent constructions, including an inline @agent.tool_plain weather tool, via Agno's PerformanceEval.

Demonstrates agent instantiation benchmarking with PydanticAI.

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.

pydantic_ai_instantiation.py
"""
PydanticAI Instantiation Performance Evaluation
===============================================

Demonstrates agent instantiation benchmarking with PydanticAI.
"""

from typing import Literal

from agno.eval.performance import PerformanceEval
from pydantic_ai import Agent


# ---------------------------------------------------------------------------
# Create Benchmark Function
# ---------------------------------------------------------------------------
def instantiate_agent():
    agent = Agent(
        "openai:gpt-5.6-luna", system_prompt="Be concise, reply with one sentence."
    )

    # Tool definition remains scoped to agent construction by design.
    @agent.tool_plain
    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")

    return agent


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

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    pydantic_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 pydantic-ai

Export your API keys

export OPENAI_API_KEY="your_openai_api_key_here"

Run the example

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

python pydantic_ai_instantiation.py

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