Entity Memory

The Entity Memory Store captures structured knowledge about external entities: companies, people, projects, and systems. It accumulates facts about each entity 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. CEO is Jane Smith. They use Python and Postgres.",
    user_id="sales@example.com",
    session_id="session_1",
)

# Later, entity knowledge is recalled
agent.print_response(
    "What do we know about Acme Corp?",
    user_id="sales@example.com",
    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),
    ),
)

The tradeoff is an 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="sales@example.com",
)

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.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")

During a normal agent run, the Agent doesn’t forward a namespace when it calls the Learning Machine, so this falls back to LearningMachine.namespace ("global" by default) instead of the value set on EntityMemoryConfig. Set the same namespace on LearningMachine for it to take effect:

learning=LearningMachine(
    entity_memory=EntityMemoryConfig(namespace="user"),
    namespace="user",
)

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

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Additional Resources

Entity Memory

Basic Usage

Three Types of Knowledge

Facts

Events

Relationships

Always Mode

Agentic Mode

Data Model

Accessing Entity Memory

Context Injection

Namespaces

Facts vs Events

Relationship Types

​Basic Usage

​Three Types of Knowledge

​Facts

​Events

​Relationships

​Always Mode

​Agentic Mode

​Data Model

​Accessing Entity Memory

​Context Injection

​Namespaces

​Facts vs Events

​Relationship Types

Basic Usage

Three Types of Knowledge

Facts

Events

Relationships

Always Mode

Agentic Mode

Data Model

Accessing Entity Memory

Context Injection

Namespaces

Facts vs Events

Relationship Types