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This example demonstrates how to use agentic memory with a team. Unlike simple memory storage, agentic memory allows the AI to actively create, update, and delete user memories during each run based on the conversation context, providing intelligent memory management.

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Code

team_with_agentic_memory.py
"""
This example shows you how to use persistent memory with an Agent.

During each run the Agent can create/update/delete user memories.

To enable this, set `enable_agentic_memory=True` in the Agent config.
"""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.memory import MemoryManager  # noqa: F401
from agno.models.openai import OpenAIResponses
from agno.team import Team

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

john_doe_id = "[email protected]"

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
)

team = Team(
    model=OpenAIResponses(id="gpt-5.2"),
    members=[agent],
    db=db,
    enable_agentic_memory=True,
)

team.print_response(
    "My name is John Doe and I like to hike in the mountains on weekends.",
    stream=True,
    user_id=john_doe_id,
)

team.print_response("What are my hobbies?", stream=True, user_id=john_doe_id)

# More examples:
# agent.print_response(
#     "Remove all existing memories of me.",
#     stream=True,
#     user_id=john_doe_id,
# )

# agent.print_response(
#     "My name is John Doe and I like to paint.", stream=True, user_id=john_doe_id
# )

# agent.print_response(
#     "I don't pain anymore, i draw instead.", stream=True, user_id=john_doe_id
# )

​
Usage

1

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
2

Install dependencies

uv pip install -U agno openai psycopg sqlalchemy
3

Set up PostgreSQL database

Start PostgreSQL with pgvector and update the connection string in the code as needed.
4

Set environment variables

export OPENAI_API_KEY=your_openai_api_key_here
5

Run the example

python team_with_agentic_memory.py