Documentation Index
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Unstructured observations about users.
| Aspect | Value |
|---|---|
| Scope | Per user |
| Persistence | Long-term |
| Default mode | Always |
| Supported modes | Always, Agentic |
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",
)
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),
),
)
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",
)
update_user_memory (supports add, update, and delete operations). Clearing all memories requires enable_clear_memories=True.
The tradeoff is that the agent may miss implicit observations.
| Good for User Memory | Better 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” |
| Field | Description |
|---|---|
user_id | User this memory belongs to |
memories | List of memory entries (id, content, optional metadata) |
agent_id | Agent context for audit trail |
team_id | Team context for audit trail |
created_at | When created |
updated_at | Last update |
lm = agent.learning_machine
# Get all memories
memories = lm.user_memory_store.get(user_id="alice@example.com")
if memories:
for memory in memories.memories:
print(f"- {memory.get('content')}")
# Debug output
lm.user_memory_store.print(user_id="alice@example.com")
<user_memory>
- Prefers code examples over explanations
- Working on a machine learning project
- Uses Python 3.11
- Prefers concise responses
</user_memory>
lm.curator.prune() and lm.curator.deduplicate() read the User Profile store, which has no memories field, so both calls return 0 without removing any user memories. To remove memories, use the agentic update_user_memory tool, which supports delete operations.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
),
)
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