Responses Memory

Store user memories and session summaries in Postgres while chatting through the Responses API.

The source docstring contains an older cookbook command. Use the database setup and local memory.py command in the run steps below.

memory.py
"""
This recipe shows how to use personalized memories and summaries in an agent.
Steps:
1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
2. Run: `uv pip install openai sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
3. Run: `python cookbook/agents/personalized_memories_and_summaries.py` to run the agent
"""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses
from rich.pretty import pprint

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

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

agent = Agent(
    model=OpenAIResponses(id="gpt-5.6-luna"),
    user_id="test_user",
    session_id="test_session",
    # Pass the database to the Agent
    db=db,
    # Enable user memories
    update_memory_on_run=True,
    # Enable session summaries
    enable_session_summaries=True,
    # Show debug logs so, you can see the memory being created
)

# -*- Share personal information
agent.print_response("My name is john billings?", stream=True)
# -*- Print memories and summary
if agent.db:
    pprint(agent.get_user_memories(user_id="test_user"))
    pprint(
        agent.get_session(session_id="test_session").summary  # type: ignore
    )

# -*- Share personal information
agent.print_response("I live in nyc?", stream=True)
# -*- Print memories
if agent.db:
    pprint(agent.get_user_memories(user_id="test_user"))
    pprint(
        agent.get_session(session_id="test_session").summary  # type: ignore
    )

# -*- Share personal information
agent.print_response("I'm going to a concert tomorrow?", stream=True)
# -*- Print memories
if agent.db:
    pprint(agent.get_user_memories(user_id="test_user"))
    pprint(
        agent.get_session(session_id="test_session").summary  # type: ignore
    )

# Ask about the conversation
agent.print_response(
    "What have we been talking about, do you know my name?", stream=True
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

Run the Example

Set up your virtual environment

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

Install dependencies

uv pip install -U agno "psycopg[binary]" openai sqlalchemy

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18

Run the example

Save the code above as memory.py, then run:

python memory.py

Full source: cookbook/90_models/openai/responses/memory.py