Team with Memory Manager

Attach a custom MemoryManager to a team so user memories are created and updated after each run.

Give a team persistent memory. After each run, user memories are created and updated, so the team remembers information about users across sessions.

Code

team_with_memory_manager.py

from uuid import uuid4

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.memory import MemoryManager
from agno.models.openai import OpenAIResponses
from agno.team import Team
from rich.pretty import pprint

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

session_id = str(uuid4())
john_doe_id = "[e-mail]"

memory_manager = MemoryManager(model=OpenAIResponses(id="gpt-5.2"))

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

team = Team(
    model=OpenAIResponses(id="gpt-5.2"),
    memory_manager=memory_manager,
    members=[agent],
    db=db,
    update_memory_on_run=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,
    session_id=session_id,
)

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

memories = team.get_user_memories(user_id=john_doe_id)
print("John Doe's memories:")
pprint(memories)

Usage

1. Set up your virtual environment

   Mac

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

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Install dependencies

   uv pip install -U agno openai sqlalchemy 'psycopg[binary]'
   
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_memory_manager.py