always_extraction.py
"""
User Profile: Always Extraction (Deep Dive)
============================================
Automatic profile extraction from natural conversation.
ALWAYS mode extracts profile information in the background after each response.
The user doesn't see tools - extraction happens invisibly.
This example shows gradual profile building across multiple conversations.
Compare with: 02_agentic_mode.py for explicit tool-based updates.
See also: 01_basics/1a_user_profile_always.py for the basics.
"""
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")
agent = Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
learning=LearningMachine(
user_profile=UserProfileConfig(
mode=LearningMode.ALWAYS,
),
),
markdown=True,
)
# ---------------------------------------------------------------------------
# Run: Gradual Profile Building
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "marcus@example.com"
# Conversation 1: Basic introduction
print("\n" + "=" * 60)
print("CONVERSATION 1: Basic introduction")
print("=" * 60 + "\n")
agent.print_response(
"Hi! I'm Marcus, nice to meet you.",
user_id=user_id,
session_id="conv_1",
stream=True,
)
agent.learning_machine.user_profile_store.print(user_id=user_id)
# Conversation 2: Share work context
print("\n" + "=" * 60)
print("CONVERSATION 2: Work context")
print("=" * 60 + "\n")
agent.print_response(
"I'm a senior engineer at Stripe, focusing on payment systems.",
user_id=user_id,
session_id="conv_2",
stream=True,
)
agent.learning_machine.user_profile_store.print(user_id=user_id)
# Conversation 3: Preferences
print("\n" + "=" * 60)
print("CONVERSATION 3: Preferences (implicit extraction)")
print("=" * 60 + "\n")
agent.print_response(
"I prefer code examples over long explanations. "
"I'm very familiar with Python and Go.",
user_id=user_id,
session_id="conv_3",
stream=True,
)
agent.learning_machine.user_profile_store.print(user_id=user_id)
# Conversation 4: Nickname
print("\n" + "=" * 60)
print("CONVERSATION 4: Preferred name update")
print("=" * 60 + "\n")
agent.print_response(
"By the way, most people call me Marc.",
user_id=user_id,
session_id="conv_4",
stream=True,
)
agent.learning_machine.user_profile_store.print(user_id=user_id)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai psycopg-binary sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
5
Run the example
Save the code above as
always_extraction.py, then run:python always_extraction.py