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

# Team Learning: Configured Stores

> Configure specific learning stores on a Team using LearningMachine.

```python team_configured_learning.py theme={null}
"""
Team Learning: Configured Stores
=================================
Configure specific learning stores on a Team using LearningMachine.

This example enables:
- UserProfile (ALWAYS mode): Captures structured user fields
- UserMemory (AGENTIC mode): Team uses tools to save observations
- SessionContext (ALWAYS mode): Tracks session goals and progress

Each store can be independently configured with its own mode.
"""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import (
    LearningMachine,
    LearningMode,
    SessionContextConfig,
    UserMemoryConfig,
    UserProfileConfig,
)
from agno.models.openai import OpenAIResponses
from agno.team import Team

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")


# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
analyst = Agent(
    name="Data Analyst",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Analyze data and provide insights.",
)

advisor = Agent(
    name="Strategy Advisor",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Provide strategic recommendations based on analysis.",
)


# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
team = Team(
    name="Advisory Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[analyst, advisor],
    db=db,
    learning=LearningMachine(
        user_profile=UserProfileConfig(
            mode=LearningMode.ALWAYS,
        ),
        user_memory=UserMemoryConfig(
            mode=LearningMode.AGENTIC,
        ),
        session_context=SessionContextConfig(
            mode=LearningMode.ALWAYS,
        ),
    ),
    markdown=True,
    show_members_responses=True,
)


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

    # Session 1: Introduction and first task
    print("\n" + "=" * 60)
    print("SESSION 1: Introduction and analysis request")
    print("=" * 60 + "\n")

    team.print_response(
        "I'm Bob, VP of Engineering at a Series B startup. "
        "We have 50 engineers and are scaling to 100. "
        "What should I focus on for our engineering org?",
        user_id=user_id,
        session_id="session_1",
        stream=True,
    )

    lm = team.learning_machine
    print("\n--- User Profile ---")
    lm.user_profile_store.print(user_id=user_id)
    print("\n--- User Memories ---")
    lm.user_memory_store.print(user_id=user_id)
    print("\n--- Session Context ---")
    lm.session_context_store.print(session_id="session_1")

    # Session 2: Follow-up - team knows context
    print("\n" + "=" * 60)
    print("SESSION 2: Follow-up with retained context")
    print("=" * 60 + "\n")

    team.print_response(
        "Given what you know about my situation, "
        "what hiring strategy would you recommend?",
        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 "psycopg[binary]" 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>

  <Snippet file="run-pgvector-step.mdx" />

  <Step title="Run the example">
    Save the code above as `team_configured_learning.py`, then run:

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

Full source: [cookbook/03\_teams/12\_learning/02\_team\_configured\_learning.py](https://github.com/agno-agi/agno/blob/main/cookbook/03_teams/12_learning/02_team_configured_learning.py)