> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Learning

> Agents that learn, adapt, and improve over time using the Learning Machine.

The Learning Machine enables agents to learn from every interaction. Instead of building memory, knowledge, and feedback systems separately, configure one system that handles all learning.

**The goal:** An agent on interaction 1000 is fundamentally better than it was on interaction 1.

```python theme={null}
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=True,  # Enable all learning stores
)

# Session 1
agent.run("I'm Alex, I prefer concise answers.", user_id="alex@example.com")

# Session 2 - agent remembers
agent.run("What do you know about me?", user_id="alex@example.com")
# -> "You're Alex, you prefer concise answers"
```

## Learning Stores

The Learning Machine coordinates five specialized stores:

| Store                 | What It Captures                      | Scope        |
| --------------------- | ------------------------------------- | ------------ |
| **User Profile**      | Structured fields (name, preferences) | Per user     |
| **User Memory**       | Unstructured observations             | Per user     |
| **Session Context**   | Goal, plan, progress, summary         | Per session  |
| **Entity Memory**     | Facts, events, relationships          | Configurable |
| **Learned Knowledge** | Insights, patterns, best practices    | Global       |

## Configuration Levels

```python theme={null}
from agno.learn import LearningMachine, UserProfileConfig, SessionContextConfig, LearningMode

# Level 1: Dead Simple
agent = Agent(model=model, db=db, learning=True)

# Level 2: Pick What You Want
agent = Agent(
    model=model,
    db=db,
    learning=LearningMachine(
        user_profile=True,
        session_context=True,
        entity_memory=False,
        learned_knowledge=False,
    ),
)

# Level 3: Full Control
agent = Agent(
    model=model,
    db=db,
    learning=LearningMachine(
        user_profile=UserProfileConfig(mode=LearningMode.AGENTIC),
        session_context=SessionContextConfig(enable_planning=True),
    ),
)
```

## Learning Modes

Each store can run in different modes:

| Mode        | Behavior                             | Best For                       |
| ----------- | ------------------------------------ | ------------------------------ |
| **ALWAYS**  | Automatic extraction after each turn | User profiles, session context |
| **AGENTIC** | Agent decides when to save           | Learned knowledge              |
| **PROPOSE** | Agent proposes, user confirms        | High-stakes knowledge          |

## Featured Patterns

<CardGroup cols={2}>
  <Card title="Personal Assistant" icon="user" iconType="duotone" href="/cookbook/learning/personal-assistant">
    Remembers preferences, tracks context, learns from feedback.
  </Card>

  <Card title="Support Agent" icon="headset" iconType="duotone" href="/cookbook/learning/support-agent">
    Multi-tenant learning with namespace isolation. Tracks customer context and accumulates support knowledge.
  </Card>
</CardGroup>

## Run the Examples

```bash theme={null}
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/08_learning

# Setup
./setup_venv.sh

# Run examples
python 01_basics/1a_user_profile_always.py
python 07_patterns/personal_assistant.py
```