"""
Entity Memory: Always Mode
==========================
Entity Memory stores knowledge about external things:
- Companies, people, projects
- Facts, events, relationships
- Shared context across users
ALWAYS mode automatically extracts entity information from conversations.
No explicit tool calls - entities are discovered and saved behind the scenes.
Compare with: 5b_entity_memory_agentic.py for explicit tool-based management.
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import EntityMemoryConfig, LearningMachine, LearningMode
from agno.models.openai import OpenAIResponses
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
# ALWAYS mode: Entities are extracted automatically after responses.
# The agent doesn't see memory tools - extraction happens invisibly.
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
db=db,
instructions="You're a sales assistant. Acknowledge notes briefly.",
learning=LearningMachine(
entity_memory=EntityMemoryConfig(
mode=LearningMode.ALWAYS,
),
),
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
from rich.pretty import pprint
user_id = "sales@example.com"
# Session 1: Mention entities naturally
print("\n" + "=" * 60)
print("SESSION 1: Discuss entities (extraction happens automatically)")
print("=" * 60 + "\n")
agent.print_response(
"Just met with Acme Corp. They're a fintech startup in SF, "
"50 employees. CTO is Jane Smith. They use Python and Postgres.",
user_id=user_id,
session_id="session_1",
stream=True,
)
print("\n--- Extracted Entities ---")
entities = agent.learning_machine.entity_memory_store.search(query="acme", limit=10)
pprint(entities)
# Session 2: Add more info about same entity
print("\n" + "=" * 60)
print("SESSION 2: Update same entity")
print("=" * 60 + "\n")
agent.print_response(
"Update on Acme Corp: they just raised $50M Series B from Sequoia. "
"Jane Smith mentioned they're hiring 20 engineers.",
user_id=user_id,
session_id="session_2",
stream=True,
)
print("\n--- Updated Entities ---")
entities = agent.learning_machine.entity_memory_store.search(query="acme", limit=10)
pprint(entities)
