> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 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.

<Steps>
  <Step title="Create a Python file">
    ```python agent_with_memory.py theme={null}
    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 = "investor@example.com"

    # 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)
    ```
  </Step>

  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai yfinance sqlalchemy rich
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run Agent">
    ```bash theme={null}
    python agent_with_memory.py
    ```
  </Step>
</Steps>

## 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.