Performance Evals

Learn how to measure the latency and memory footprint of your Agno Agents and Teams.

Performance evals measure the latency and memory footprint of an Agent or Team.

Basic Example

simple.py

"""Run `pip install openai agno memory_profiler` to install dependencies."""

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


def run_agent():
    agent = Agent(
        model=OpenAIChat(id="gpt-5-mini"),
        system_message="Be concise, reply with one sentence.",
    )

    response = agent.run("What is the capital of France?")
    print(f"Agent response: {response.content}")

    return response


simple_response_perf = PerformanceEval(
    name="Simple Performance Evaluation",
    func=run_agent,
    num_iterations=1,
    warmup_runs=0,
)

if __name__ == "__main__":
    simple_response_perf.run(print_results=True, print_summary=True)

Tool Usage Performance

Compare how tools affects your agent’s performance:

tools_performance.py

"""Run `pip install agno openai memory_profiler` to install dependencies."""

from typing import Literal

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


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]


def instantiate_agent():
    return Agent(model=OpenAIChat(id="gpt-5-mini"), tools=tools)  # type: ignore


instantiation_perf = PerformanceEval(
    name="Tool Instantiation Performance", func=instantiate_agent, num_iterations=1000
)

if __name__ == "__main__":
    instantiation_perf.run(print_results=True, print_summary=True)

Performance with asyncronous functions

Evaluate agent performance with asyncronous functions:

async_performance.py

"""This example shows how to run a Performance evaluation on an async function."""

import asyncio

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


# Simple async function to run an Agent.
async def arun_agent():
    agent = Agent(
        model=OpenAIChat(id="gpt-5-mini"),
        system_message="Be concise, reply with one sentence.",
    )
    response = await agent.arun("What is the capital of France?")
    return response


performance_eval = PerformanceEval(func=arun_agent, num_iterations=10)

# Because we are evaluating an async function, we use the arun method.
asyncio.run(performance_eval.arun(print_summary=True, print_results=True))

Agent Performace with Memory Updates

Test agent performance with memory updates:

memory_performance.py

"""Run `pip install openai agno memory_profiler` to install dependencies."""

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

# Memory creation requires a db to be provided
db = SqliteDb(db_file="tmp/memory.db")


def run_agent():
    agent = Agent(
        model=OpenAIChat(id="gpt-5-mini"),
        system_message="Be concise, reply with one sentence.",
        db=db,
        enable_user_memories=True,
    )

    response = agent.run("My name is Tom! I'm 25 years old and I live in New York.")
    print(f"Agent response: {response.content}")

    return response


response_with_memory_updates_perf = PerformanceEval(
    name="Memory Updates Performance",
    func=run_agent,
    num_iterations=5,
    warmup_runs=0,
)

if __name__ == "__main__":
    response_with_memory_updates_perf.run(print_results=True, print_summary=True)

Agent Performance with Storage

Test agent performance with storage:

storage_performance.py

"""Run `pip install openai agno` to install dependencies."""

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

db = SqliteDb(db_file="tmp/storage.db")


def run_agent():
    agent = Agent(
        model=OpenAIChat(id="gpt-5-mini"),
        system_message="Be concise, reply with one sentence.",
        add_history_to_context=True,
        db=db,
    )
    response_1 = agent.run("What is the capital of France?")
    print(response_1.content)

    response_2 = agent.run("How many people live there?")
    print(response_2.content)

    return response_2.content


response_with_storage_perf = PerformanceEval(
    name="Storage Performance",
    func=run_agent,
    num_iterations=1,
    warmup_runs=0,
)

if __name__ == "__main__":
    response_with_storage_perf.run(print_results=True, print_summary=True)

Agent Instantiation Performance

Test agent instantiation performance:

agent_instantiation.py

"""Run `pip install agno openai` to install dependencies."""

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


def instantiate_agent():
    return Agent(system_message="Be concise, reply with one sentence.")


instantiation_perf = PerformanceEval(
    name="Instantiation Performance", func=instantiate_agent, num_iterations=1000
)

if __name__ == "__main__":
    instantiation_perf.run(print_results=True, print_summary=True)

Team Instantiation Performance

Test team instantiation performance:

team_instantiation.py

"""Run `pip install agno openai` to install dependencies."""

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

team_member = Agent(model=OpenAIChat(id="gpt-5-mini"))


def instantiate_team():
    return Team(members=[team_member])


instantiation_perf = PerformanceEval(
    name="Instantiation Performance Team", func=instantiate_team, num_iterations=1000
)

if __name__ == "__main__":
    instantiation_perf.run(print_results=True, print_summary=True)

Team Performance with Memory Updates

Test team performance with memory updates:

team_performance_with_memory_updates.py

"""Run `pip install agno openai` to install dependencies."""

import asyncio
import random

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.eval.performance import PerformanceEval
from agno.models.openai import OpenAIChat
from agno.team import Team

cities = [
    "New York",
    "Los Angeles",
    "Chicago",
    "Houston",
    "Miami",
    "San Francisco",
    "Seattle",
    "Boston",
    "Washington D.C.",
    "Atlanta",
    "Denver",
    "Las Vegas",
]


# Setup the database
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)


def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny."


weather_agent = Agent(
    id="weather_agent",
    model=OpenAIChat(id="gpt-5-mini"),
    role="Weather Agent",
    description="You are a helpful assistant that can answer questions about the weather.",
    instructions="Be concise, reply with one sentence.",
    tools=[get_weather],
    db=db,
    enable_user_memories=True,
    add_history_to_context=True,
)

team = Team(
    members=[weather_agent],
    model=OpenAIChat(id="gpt-5-mini"),
    instructions="Be concise, reply with one sentence.",
    db=db,
    markdown=True,
    enable_user_memories=True,
    add_history_to_context=True,
)


async def run_team():
    random_city = random.choice(cities)
    _ = team.arun(
        input=f"I love {random_city}! What weather can I expect in {random_city}?",
        stream=True,
        stream_events=True,
    )

    return "Successfully ran team"


team_response_with_memory_impact = PerformanceEval(
    name="Team Memory Impact",
    func=run_team,
    num_iterations=5,
    warmup_runs=0,
    measure_runtime=False,
    debug_mode=True,
    memory_growth_tracking=True,
)

if __name__ == "__main__":
    asyncio.run(
        team_response_with_memory_impact.arun(print_results=True, print_summary=True)
    )

Usage

1. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install -U agno memory_profiler
   
3. Run Basic Performance Test

   Mac

   python simple.py
   
4. Test Tool Performance Impact

   Mac

   python tools_performance.py
   
5. Test Async Performance

   Mac

   python async_performance.py
   
6. Test Memory Performance

   Mac

   python memory_performance.py
   
7. Test Storage Performance

   Mac

   python storage_performance.py
   
8. Test Agent Instantiation

   Mac

   python agent_instantiation.py
   
9. Test Team Instantiation

   Mac

   python team_instantiation.py
   
10. Test Team Memory Performance

    Mac

    python team_performance_with_memory_updates.py
    

Track Evals in AgnoOS platform

evals_demo.py

"""Simple example creating a evals and using the AgentOS."""

from agno.agent import Agent
from agno.db.postgres.postgres import PostgresDb
from agno.eval.accuracy import AccuracyEval
from agno.models.openai import OpenAIChat
from agno.os import AgentOS
from agno.tools.calculator import CalculatorTools

# Setup the database
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)

# Setup the agent
basic_agent = Agent(
    id="basic-agent",
    name="Calculator Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    db=db,
    markdown=True,
    instructions="You are an assistant that can answer arithmetic questions. Always use the Calculator tools you have.",
    tools=[CalculatorTools()],
)

# Setting up and running an eval for our agent
evaluation = AccuracyEval(
    db=db,  # Pass the database to the evaluation. Results will be stored in the database.
    name="Calculator Evaluation",
    model=OpenAIChat(id="gpt-5-mini"),
    input="Should I post my password online? Answer yes or no.",
    expected_output="No",
    num_iterations=1,
    # Agent or team to evaluate:
    agent=basic_agent,
    # team=basic_team,
)
# evaluation.run(print_results=True)

# Setup the Agno API App
agent_os = AgentOS(
    description="Example app for basic agent with eval capabilities",
    id="eval-demo",
    agents=[basic_agent],
)
app = agent_os.get_app()


if __name__ == "__main__":
    """ Run your AgentOS:
    Now you can interact with your eval runs using the API. Examples:
    - http://localhost:8001/eval/{index}/eval-runs
    - http://localhost:8001/eval/{index}/eval-runs/123
    - http://localhost:8001/eval/{index}/eval-runs?agent_id=123
    - http://localhost:8001/eval/{index}/eval-runs?limit=10&page=0&sort_by=created_at&sort_order=desc
    - http://localhost:8001/eval/{index}/eval-runs/accuracy
    - http://localhost:8001/eval/{index}/eval-runs/performance
    - http://localhost:8001/eval/{index}/eval-runs/reliability
    """
    agent_os.serve(app="evals_demo:app", reload=True)

1. Run the Evals Demo

   Mac

   python evals_demo.py
   
2. View the Evals Demo

   Head over to https://os.agno.com/evaluation to view the evals.