entity_memory.py
"""
Entity Memory: The Four Tools
=============================
Entity memory is the agent's knowledge about the WORLD - the people,
projects, companies and systems around the user - as opposed to user
memory, which is about the user themselves.
It is AGENTIC-only: the agent records through four tools (remember_about,
link_entities, search_entities, forget), and the store does the librarian
work - ids are slugified from names, "Sarah Chen" and "sarah chen" resolve
to one person, and a correcting fact retires the stale one (supersession).
Deep dives: cookbook/08_learning/04_entity_memory/
Run:
.venvs/demo/bin/python cookbook/08_learning/01_basics/5_entity_memory.py
"""
from uuid import uuid4
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import EntityMemoryConfig, LearningMachine
from agno.models.openai import OpenAIResponses
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
# Fresh per-run namespace so the demo starts clean on every execution.
NAMESPACE = f"basics_{uuid4().hex[:6]}"
agent = Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
instructions="You are a sales assistant. Acknowledge notes briefly.",
learning=LearningMachine(
entity_memory=EntityMemoryConfig(namespace=NAMESPACE),
),
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
agent.print_response(
"Note on Acme Corp: fintech startup in SF, about 50 people. "
"Jane Smith is their CTO.",
session_id="s1",
stream=True,
)
# A fresh session: the entity directory plus relevance recall carry the
# context - no tool call needed to answer.
agent.print_response(
"What do we know about Acme?",
session_id="s2",
stream=True,
)
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 "psycopg[binary]" openai 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 \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
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
5
Run the example
Save the code above as
entity_memory.py, then run:python entity_memory.py