user_memory_agentic.py
"""
User Memory: Agentic Mode
=========================
User Memory captures unstructured observations about users:
- Work context and role
- Communication style preferences
- Patterns and interests
- Any memorable facts
AGENTIC mode gives the agent explicit tools to save and update memories.
The agent decides when to store information - you can see the tool calls.
Compare with: 2a_user_memory_always.py for automatic extraction.
See also: 1b_user_profile_agentic.py for structured profile fields.
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import LearningMachine, LearningMode, UserMemoryConfig
from agno.models.openai import OpenAIResponses
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
# AGENTIC mode: Agent gets memory tools and decides when to use them.
# You'll see tool calls like "update_user_memory" in responses.
agent = Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
learning=LearningMachine(
user_memory=UserMemoryConfig(
mode=LearningMode.AGENTIC,
),
),
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "bob@example.com"
# Session 1: Agent explicitly saves memories
print("\n" + "=" * 60)
print("SESSION 1: Share information (watch for tool calls)")
print("=" * 60 + "\n")
agent.print_response(
"I'm a backend engineer at Stripe. "
"I specialize in distributed systems and prefer Rust over Go.",
user_id=user_id,
session_id="session_1",
stream=True,
)
agent.learning_machine.user_memory_store.print(user_id=user_id)
# Session 2: Agent uses stored memories
print("\n" + "=" * 60)
print("SESSION 2: Memories recalled in new session")
print("=" * 60 + "\n")
agent.print_response(
"What programming language would you recommend for my next project?",
user_id=user_id,
session_id="session_2",
stream=True,
)
agent.learning_machine.user_memory_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
user_memory_agentic.py, then run:python user_memory_agentic.py