> ## 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.

# Personal Assistant

> A personal assistant that learns about the user over time without being asked.

## Overview

A personal assistant that becomes increasingly personalized through the Learning Machine. Combines user profile tracking, session context, and entity memory to remember preferences, people, and events.

## Code

```python personal_assistant.py theme={null}
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import (
    EntityMemoryConfig,
    LearningMachine,
    LearningMode,
    SessionContextConfig,
    UserProfileConfig,
)
from agno.models.openai import OpenAIResponses

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


def create_personal_assistant(user_id: str, session_id: str) -> Agent:
    """Create a personal assistant for a specific user."""
    return Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        db=db,
        instructions=(
            "You are a helpful personal assistant. "
            "Remember user preferences without being asked. "
            "Keep track of important people and events in their life."
        ),
        learning=LearningMachine(
            user_profile=UserProfileConfig(mode=LearningMode.ALWAYS),
            session_context=SessionContextConfig(enable_planning=True),
            entity_memory=EntityMemoryConfig(
                mode=LearningMode.ALWAYS,
                namespace=f"user:{user_id}:personal",
            ),
        ),
        user_id=user_id,
        session_id=session_id,
        markdown=True,
    )


if __name__ == "__main__":
    user_id = "alex@example.com"

    # Conversation 1: Introduction
    agent = create_personal_assistant(user_id, "conv_1")
    agent.print_response(
        "Hi! I'm Alex Chen. I work as a product manager at Stripe. "
        "I prefer concise responses. My sister Sarah is visiting next month.",
        stream=True,
    )

    # Conversation 2: New session - agent remembers
    agent = create_personal_assistant(user_id, "conv_2")
    agent.print_response(
        "What do you remember about me and my sister?",
        stream=True,
    )

    # Conversation 3: Planning with context
    agent = create_personal_assistant(user_id, "conv_3")
    agent.print_response(
        "Help me plan activities for Sarah's visit. She likes hiking.",
        stream=True,
    )
```

## How It Works

* **User Profile** (ALWAYS mode): Automatically extracts name, workplace, and communication preferences
* **Session Context** (with planning): Tracks goals and progress across multi-turn conversations
* **Entity Memory** (ALWAYS mode): Records people (Sarah), places, and events with relationships

The namespace `user:{user_id}:personal` isolates each user's entity memory.

## Run This Example

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

# Setup (requires Docker for Postgres)
./setup_venv.sh
./cookbook/scripts/run_pgvector.sh

# Run
python 07_patterns/personal_assistant.py
```

Full source: [agno/cookbook/08\_learning/07\_patterns/personal\_assistant.py](https://github.com/agno-agi/agno/blob/main/cookbook/08_learning/07_patterns/personal_assistant.py)

## Related

* [Learning Overview](/cookbook/learning/overview) - All learning stores explained
* [Support Agent](/cookbook/learning/support-agent) - Multi-tenant learning pattern
* [Agent with Memory](/agents/usage/agent-with-memory) - Simpler memory patterns