> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-service-account.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Learning Machines: Agentic Mode

> In AGENTIC mode, the agent receives tools to explicitly manage learning.

In AGENTIC mode, the agent receives tools to explicitly manage learning. It decides when to save profiles and memories based on conversation context.

```python agentic_learn.py theme={null}
"""
Learning Machines: Agentic Mode
===============================
In AGENTIC mode, the agent receives tools to explicitly manage learning.
It decides when to save profiles and memories based on conversation context.

Compare with learning=True (ALWAYS mode) where extraction happens automatically.
"""

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.learn import (
    LearningMachine,
    LearningMode,
    UserMemoryConfig,
    UserProfileConfig,
)
from agno.models.openai import OpenAIResponses

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db = SqliteDb(db_file="tmp/agents.db")

agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    learning=LearningMachine(
        user_profile=UserProfileConfig(mode=LearningMode.AGENTIC),
        user_memory=UserMemoryConfig(mode=LearningMode.AGENTIC),
    ),
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    user_id = "alice2@example.com"

    # Session 1: Agent decides what to save via tool calls
    print("\n--- Session 1: Agent uses tools to save profile and memories ---\n")
    agent.print_response(
        "Hi! I'm Alice. I work at Anthropic as a research scientist. "
        "I prefer concise responses without too much explanation.",
        user_id=user_id,
        session_id="session_1",
        stream=True,
    )
    lm = agent.learning_machine
    lm.user_profile_store.print(user_id=user_id)
    lm.user_memory_store.print(user_id=user_id)

    # Session 2: New session - agent remembers
    print("\n--- Session 2: Agent remembers across sessions ---\n")
    agent.print_response(
        "What do you know about me?",
        user_id=user_id,
        session_id="session_2",
        stream=True,
    )
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai sqlalchemy
    ```
  </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 the example">
    Save the code above as `agentic_learn.py`, then run:

    ```bash theme={null}
    python agentic_learn.py
    ```
  </Step>
</Steps>

Full source: [cookbook/08\_learning/00\_quickstart/02\_agentic\_learn.py](https://github.com/agno-agi/agno/blob/main/cookbook/08_learning/00_quickstart/02_agentic_learn.py)