Agent With Persistent Memory

Persist user memories in PostgresDb with automatic memory extraction during agent runs.

Set update_memory_on_run=True to extract memories from user input during the run. Memory work starts alongside the main response and is awaited before successful completion. Consume a stream to completion before reading the resulting memories.

Use a disposable database: this example calls db.clear_memories(), which removes the entire memory table’s records, including those for other users.

agent_with_memory.py
"""
Agent With Persistent Memory
============================

This example shows how to use persistent memory with an Agent.
After each run, user memories are created or updated.
"""

import asyncio
from uuid import uuid4

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

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

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    model=OpenAIChat(id="gpt-5.6-luna"),
    db=db,
    update_memory_on_run=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    db.clear_memories()

    session_id = str(uuid4())
    john_doe_id = "john_doe@example.com"

    asyncio.run(
        agent.aprint_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,
        )
    )

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

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

    agent.print_response(
        "Ok i dont like hiking anymore, i like to play soccer instead.",
        stream=True,
        user_id=john_doe_id,
        session_id=session_id,
    )

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

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 agent_with_memory.py, then run:

python agent_with_memory.py

Full source: cookbook/11_memory/01_agent_with_memory.py