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Memory lets your agent remember facts about users across conversations. Unlike storage (which persists conversation history), memory stores user-level information like preferences and context.
1

Create a Python file

agent_with_memory.py
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.memory import MemoryManager
from agno.models.openai import OpenAIResponses
from agno.tools.yfinance import YFinanceTools
from rich.pretty import pprint

db = SqliteDb(db_file="tmp/agents.db")

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

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[YFinanceTools()],
    db=db,
    memory_manager=memory_manager,
    enable_agentic_memory=True,
    markdown=True,
)

user_id = "[email protected]"

# Tell the agent about yourself
agent.print_response(
    "I'm interested in AI and semiconductor stocks. My risk tolerance is moderate.",
    user_id=user_id,
    stream=True,
)

# The agent now knows your preferences
agent.print_response(
    "What stocks would you recommend for me?",
    user_id=user_id,
    stream=True,
)

# View stored memories
memories = agent.get_user_memories(user_id=user_id)
print("\nStored Memories:")
pprint(memories)
2

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
3

Install dependencies

uv pip install -U agno openai yfinance sqlalchemy rich
4

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"
5

Run Agent

python agent_with_memory.py

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Memory vs Storage

FeatureStorageMemory
What it storesConversation historyUser preferences and facts
ScopePer sessionPer user (across all sessions)
Use case”What did we discuss?""What do you know about me?”

​
Enabling Memory

  1. enable_agentic_memory=True (used above): Agent decides when to store/recall via tool calls. More efficient.
  2. update_memory_on_run=True: Memory manager runs after every response. Guaranteed capture, higher latency.