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Memory is a part of the Agent’s context that helps it provide the best, most personalized response.
If the user tells the Agent they like to ski, then future responses can reference this information to provide a more personalized experience.

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User Memories

Here’s a simple example of using Memory in an Agent.
memory_demo.py
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.db.postgres import PostgresDb
from rich.pretty import pprint

user_id = "ava"

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

db = PostgresDb(
  db_url=db_url,
  memory_table="user_memories",  # Optionally specify a table name for the memories
)


# Initialize Agent
memory_agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    # Give the Agent the ability to update memories
    enable_agentic_memory=True,
    # OR - Run the MemoryManager automatically after each response
    update_memory_on_run=True,
    markdown=True,
)

db.clear_memories()

memory_agent.print_response(
    "My name is Ava and I like to ski.",
    user_id=user_id,
    stream=True,
)
print("Memories about Ava:")
pprint(memory_agent.get_user_memories(user_id=user_id))

memory_agent.print_response(
    "I live in san francisco, where should i move within a 4 hour drive?",
    user_id=user_id,
    stream=True,
)
print("Memories about Ava:")
pprint(memory_agent.get_user_memories(user_id=user_id))
enable_agentic_memory=True gives the Agent a tool to manage memories of the user, this tool passes the task to the MemoryManager class. You may also set update_memory_on_run=True which always runs the MemoryManager after each user message.
Read more about Memory in the Memory Overview page.

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