> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-agui.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# User Memory

> Unstructured observations about users.

The User Memory Store captures unstructured observations about users: preferences, behaviors, and context that don't fit into structured profile fields.

| Aspect              | Value                              |
| ------------------- | ---------------------------------- |
| **Scope**           | Per user                           |
| **Persistence**     | Long-term (with optional curation) |
| **Default mode**    | Always                             |
| **Supported modes** | Always, Agentic                    |

## Basic Usage

```python theme={null}
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.

## Always Mode

Memories are extracted automatically after each response.

```python theme={null}
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.

```python theme={null}
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 tool: `update_user_memory` (supports add, update, delete, and clear operations)

Tradeoff: agent may miss implicit observations.

## What Gets Captured

| 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"         |

## Memory Data Model

| 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                                                 |

## Accessing Memories

```python theme={null}
lm = agent.get_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")
```

## Context Injection

Relevant memories are injected into the system prompt:

```
<user_memory>
- Prefers code examples over explanations
- Working on a machine learning project
- Uses Python 3.11
- Prefers concise responses
</user_memory>
```

## Curation

Over time, memories accumulate. Use the Curator to maintain them:

```python theme={null}
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")
```

## Combining with User Profile

Use both stores for comprehensive user understanding:

```python theme={null}
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
    ),
)
```