Team with Agentic Memory

This example shows how to use agentic memory with a team. The team can create and update user memories during each run based on the conversation.

Code

team_with_agentic_memory.py

"""
This example shows you how to use persistent memory with a Team.

During each run the Team can create and update user memories.

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

from agno.agent import Agent
from agno.db.postgres import PostgresDb
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 = "[e-mail]"

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)

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 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