from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import (
EntityMemoryConfig,
LearningMachine,
LearningMode,
SessionContextConfig,
UserProfileConfig,
)
from agno.models.openai import OpenAIResponses
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
def create_personal_assistant(user_id: str, session_id: str) -> Agent:
"""Create a personal assistant for a specific user."""
return Agent(
model=OpenAIResponses(id="gpt-5.2"),
db=db,
instructions=(
"You are a helpful personal assistant. "
"Remember user preferences without being asked. "
"Keep track of important people and events in their life."
),
learning=LearningMachine(
user_profile=UserProfileConfig(mode=LearningMode.ALWAYS),
session_context=SessionContextConfig(enable_planning=True),
entity_memory=EntityMemoryConfig(
mode=LearningMode.ALWAYS,
namespace=f"user:{user_id}:personal",
),
),
user_id=user_id,
session_id=session_id,
markdown=True,
)
if __name__ == "__main__":
user_id = "alex@example.com"
# Conversation 1: Introduction
agent = create_personal_assistant(user_id, "conv_1")
agent.print_response(
"Hi! I'm Alex Chen. I work as a product manager at Stripe. "
"I prefer concise responses. My sister Sarah is visiting next month.",
stream=True,
)
# Conversation 2: New session - agent remembers
agent = create_personal_assistant(user_id, "conv_2")
agent.print_response(
"What do you remember about me and my sister?",
stream=True,
)
# Conversation 3: Planning with context
agent = create_personal_assistant(user_id, "conv_3")
agent.print_response(
"Help me plan activities for Sarah's visit. She likes hiking.",
stream=True,
)
