# Agent with Memory (/agents/usage/agent-with-memory)



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 title="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(all=True)],
        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,
    )

    # Ask a follow-up using any preferences the agent chose to save
    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>

    <Step title="Set up your virtual environment">
      <CodeBlockTabs defaultValue="Mac">
        <CodeBlockTabsList>
          <CodeBlockTabsTrigger value="Mac">
            Mac
          </CodeBlockTabsTrigger>

          <CodeBlockTabsTrigger value="Windows">
            Windows
          </CodeBlockTabsTrigger>
        </CodeBlockTabsList>

        <CodeBlockTab value="Mac">
          ```bash
          uv venv --python 3.12
          source .venv/bin/activate
          ```
        </CodeBlockTab>

        <CodeBlockTab value="Windows">
          ```bash
          uv venv --python 3.12
          .venv\Scripts\activate
          ```
        </CodeBlockTab>
      </CodeBlockTabs>
    </Step>

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

  <Step title="Export your OpenAI API key">
    <CodeBlockTabs defaultValue="Mac/Linux">
      <CodeBlockTabsList>
        <CodeBlockTabsTrigger value="Mac/Linux">
          Mac/Linux
        </CodeBlockTabsTrigger>

        <CodeBlockTabsTrigger value="Windows">
          Windows
        </CodeBlockTabsTrigger>
      </CodeBlockTabsList>

      <CodeBlockTab value="Mac/Linux">
        ```bash
        export OPENAI_API_KEY="your_openai_api_key_here"
        ```
      </CodeBlockTab>

      <CodeBlockTab value="Windows">
        ```powershell
        $Env:OPENAI_API_KEY="your_openai_api_key_here"
        ```
      </CodeBlockTab>
    </CodeBlockTabs>
  </Step>

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

## Memory vs Storage [#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 [#enabling-memory]

1. **`enable_agentic_memory=True`** (used above): Adds an `update_user_memory` tool. The model decides whether to call it. Existing memories are added to context.
2. **`update_memory_on_run=True`**: Runs the memory manager for each non-empty user input. This adds a model call and does not guarantee that every detail becomes a memory.