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The User Memory Store captures unstructured observations about users: preferences, behaviors, and context that don’t fit into structured profile fields.
AspectValue
ScopePer user
PersistenceLong-term (with optional curation)
Default modeAlways
Supported modesAlways, Agentic

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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(user_memory=True),
)

# Session 1: Share preferences
agent.print_response(
    "I prefer code examples over explanations. Also, I'm working on a machine learning project.",
    user_id="alice@example.com",
    session_id="session_1",
)

# Session 2: Memory is recalled
agent.print_response(
    "Explain async/await in Python",
    user_id="alice@example.com",
    session_id="session_2",
)
The agent knows to include code examples and may relate to ML context.

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Always Mode

Memories are extracted automatically after each response. 
from agno.learn import LearningMachine, LearningMode, UserMemoryConfig

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        user_memory=UserMemoryConfig(mode=LearningMode.ALWAYS),
    ),
)
Tradeoff: extra LLM call per interaction.

​
Agentic Mode

The agent receives tools to manage memories explicitly. 
from agno.learn import LearningMachine, LearningMode, UserMemoryConfig

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

agent.print_response(
    "Remember that I always want to see error handling in code examples.",
    user_id="alice@example.com",
)
Available tools: save_user_memory, delete_user_memory Tradeoff: agent may miss implicit observations.

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What Gets Captured

Good for User MemoryBetter for User Profile
”Prefers detailed explanations”Name: “Alice Chen"
"Working on ML project”Company: “Acme Corp"
"Struggles with async code”Role: “Data Scientist"
"Uses VS Code”Timezone: “PST”

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Memory Data Model

FieldDescription
memory_idUnique identifier
memoryThe observation text
topicsExtracted topics for categorization
user_idUser this memory belongs to
created_atWhen created
updated_atLast update

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Accessing Memories

lm = agent.get_learning_machine()

# Get all memories
memories = lm.user_memory_store.get_memories(user_id="alice@example.com")
for memory in memories:
    print(f"- {memory.memory}")

# Debug output
lm.user_memory_store.print(user_id="alice@example.com")

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Context Injection

Relevant memories are injected into the system prompt: 
<user_memories>
- Prefers code examples over explanations
- Working on a machine learning project
- Uses Python 3.11
- Prefers concise responses
</user_memories>

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Curation

Over time, memories accumulate. Use the Curator to maintain them: 
lm = agent.get_learning_machine()

# Remove memories older than 90 days
lm.curator.prune(user_id="alice@example.com", max_age_days=90)

# Remove duplicates
lm.curator.deduplicate(user_id="alice@example.com")

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Combining with User Profile

Use both stores for comprehensive user understanding: 
from agno.learn import LearningMachine

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        user_profile=True,  # Structured: name, company
        user_memory=True,   # Unstructured: preferences, context
    ),
)