Agent with Memory

Store user preferences that persist across conversations.

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 = "[e-mail]"
   
   # 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

   Mac

   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

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   
5. Run Agent

   python agent_with_memory.py
   

Memory vs Storage

Feature	Storage	Memory
What it stores	Conversation history	User preferences and facts
Scope	Per session	Per 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.