> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-codex-docs-audit-20260719-0149.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Simple Team Memory Performance Evaluation

> Benchmark memory growth across 5 async runs of a weather team with persistent memory and history in Postgres.

Demonstrates team response performance with memory enabled.

<Warning>
  The source creates a streaming `Team.arun()` async generator and returns without consuming it. The team never runs, so the memory-growth result is invalid. Update the saved file before running.
</Warning>

```python team_response_with_memory_simple.py theme={null}
"""
Simple Team Memory Performance Evaluation
=========================================

Demonstrates team response performance with memory enabled.
"""

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.team import Team

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

# ---------------------------------------------------------------------------
# Create Database
# ---------------------------------------------------------------------------
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)


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


# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
weather_agent = Agent(
    id="weather_agent",
    model=OpenAIChat(id="gpt-5.2"),
    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,
    update_memory_on_run=True,
    add_history_to_context=True,
)

team = Team(
    members=[weather_agent],
    model=OpenAIChat(id="gpt-5.2"),
    instructions="Be concise, reply with one sentence.",
    db=db,
    markdown=True,
    update_memory_on_run=True,
    add_history_to_context=True,
)


# ---------------------------------------------------------------------------
# Create Benchmark Function
# ---------------------------------------------------------------------------
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"


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

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    asyncio.run(
        team_response_with_memory_impact.arun(print_results=True, print_summary=True)
    )
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno "psycopg[binary]" memory-profiler openai sqlalchemy
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

  <Step title="Consume the team stream">
    Replace the `_ = team.arun(...)` block with `async for _ in team.arun(...): pass`. Preserve the existing arguments and indent `pass` inside the loop.
  </Step>

  <Step title="Run the example">
    Save the code above as `team_response_with_memory_simple.py`, then run:

    ```bash theme={null}
    python team_response_with_memory_simple.py
    ```
  </Step>
</Steps>

Full source: [cookbook/09\_evals/performance/team\_response\_with\_memory\_simple.py](https://github.com/agno-agi/agno/blob/v2.7.4/cookbook/09_evals/performance/team_response_with_memory_simple.py)