User Profile: Always Mode

ALWAYS mode extracts profile information automatically in parallel while the agent responds - no explicit tool calls needed.

user_profile_always.py
"""
User Profile: Always Mode
=========================
User Profile captures structured profile fields about users:
- Name and preferred name
- Custom profile fields (when using extended schemas)

ALWAYS mode extracts profile information automatically in parallel
while the agent responds - no explicit tool calls needed.

Compare with: 1b_user_profile_agentic.py for explicit tool-based updates.
See also: 2a_user_memory_always.py for unstructured observations.
"""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import LearningMachine, LearningMode, UserProfileConfig
from agno.models.openai import OpenAIResponses

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

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

# ALWAYS mode: Extraction happens automatically after each response.
# The agent doesn't see or call any profile tools - it's invisible.
# UserProfile stores structured fields (name, preferred_name, custom fields)
agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    learning=LearningMachine(
        user_profile=UserProfileConfig(
            mode=LearningMode.ALWAYS,
        ),
    ),
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    user_id = "alice@example.com"

    # Session 1: Share information naturally
    print("\n" + "=" * 60)
    print("SESSION 1: Share information (extraction happens automatically)")
    print("=" * 60 + "\n")

    agent.print_response(
        "Hi! I'm Alice Chen, but please call me Ali.",
        user_id=user_id,
        session_id="session_1",
        stream=True,
    )
    agent.learning_machine.user_profile_store.print(user_id=user_id)

    # Session 2: New session - profile is recalled automatically
    print("\n" + "=" * 60)
    print("SESSION 2: Profile recalled in new session")
    print("=" * 60 + "\n")

    agent.print_response(
        "What's my name again?",
        user_id=user_id,
        session_id="session_2",
        stream=True,
    )
    agent.learning_machine.user_profile_store.print(user_id=user_id)

With learning= enabled, ALWAYS extraction starts in parallel before the current model response. The retained source comments that say “after each response” do not describe that timing. Extraction uses the messages available at the start of the run; include conversation history if it needs earlier assistant replies.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno "psycopg[binary]" openai sqlalchemy

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18

Run the example

Save the code above as user_profile_always.py, then run:

python user_profile_always.py

Full source: cookbook/08_learning/01_basics/1a_user_profile_always.py