Entity Memory

Facts about companies, projects, and people.

The Entity Memory Store captures structured knowledge about external entities: companies, people, projects, and systems. Think of it as your agent’s professional rolodex that accumulates knowledge over time.

Aspect	Value
Scope	Configurable (global, user, or custom namespace)
Persistence	Long-term
Default mode	Always
Supported modes	Always, Agentic

Basic Usage

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import LearningMachine
from agno.models.openai import OpenAIResponses

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"),
    learning=LearningMachine(entity_memory=True),
)

# Entities are extracted automatically
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="[e-mail]",
    session_id="session_1",
)

# Later, entity knowledge is recalled
agent.print_response(
    "What do we know about Acme Corp?",
    user_id="[e-mail]",
    session_id="session_2",
)

Three Types of Knowledge

Facts

Timeless truths: “Uses PostgreSQL”, “Headquarters in San Francisco”, “50 employees”

Events

Time-bound occurrences: “Launched v2.0 on January 15”, “Closed $50M Series B”, “Had 4-hour outage”

Relationships

Entity connections: Jane Smith → CEO → Acme Corp, Acme Corp → competitor_of → Beta Inc

Always Mode

Entities are extracted automatically from conversations.

from agno.learn import LearningMachine, LearningMode, EntityMemoryConfig

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        entity_memory=EntityMemoryConfig(mode=LearningMode.ALWAYS),
    ),
)

Tradeoff: extra LLM call per interaction.

Agentic Mode

The agent receives tools to manage entities explicitly.

from agno.learn import LearningMachine, LearningMode, EntityMemoryConfig

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        entity_memory=EntityMemoryConfig(mode=LearningMode.AGENTIC),
    ),
)

agent.print_response(
    "Create an entry for Acme Corp - they're a fintech startup with 50 employees.",
    user_id="[e-mail]",
)

Available tools: search_entities, create_entity, update_entity, add_fact, update_fact, delete_fact, add_event, add_relationship

Data Model

Field	Description
entity_id	Unique identifier (e.g., “acme_corp”)
entity_type	Category: “company”, “person”, “project”
name	Display name
description	Brief description
properties	Key-value metadata
facts	Timeless truths
events	Time-bound occurrences
relationships	Connections to other entities

Accessing Entity Memory

lm = agent.get_learning_machine()

# Search for entities
entities = lm.entity_memory_store.search(
    query="acme",
    entity_type="company",
    limit=10
)

for entity in entities:
    print(f"{entity.name}: {entity.facts}")

# Debug output
lm.entity_memory_store.print(entity_id="acme_corp", entity_type="company")

Context Injection

Relevant entities are injected into the system prompt:

<entity_memory>
**Acme Corp** (company)
Properties: industry: fintech, size: 50 employees

Facts:
  - Uses PostgreSQL and Redis for their data layer
  - Headquarters in San Francisco

Events:
  - Launched v2.0 with new ML features (2025-01-15)
  - Closed $50M Series B led by Sequoia (2024-Q3)

Relationships:
  - CEO: jane_smith
  - competitor_of: beta_inc
</entity_memory>

Namespaces

Control who can access entity data:

from agno.learn import EntityMemoryConfig

# Global: shared with everyone (default)
entity_memory=EntityMemoryConfig(namespace="global")

# User: private per user
entity_memory=EntityMemoryConfig(namespace="user")

# Custom: explicit grouping
entity_memory=EntityMemoryConfig(namespace="sales_team")

Facts vs Events

Use facts for	Use events for
Tech stack	Product launches
Headquarters location	Funding rounds
Employee count	Outages or incidents
Industry/domain	Partnerships announced
Pricing model	Key meetings

Relationship Types

Common patterns for linking entities:

• People: CEO, CTO, engineer_at, founder, reports_to
• Companies: competitor_of, partner_of, acquired_by, subsidiary_of
• Projects: uses, depends_on, integrates_with, owned_by