mem0/docs/overview.mdx

---
title: 📚 Overview
description: 'Welcome to the Mem0 docs!'
---

> Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.

## Core features

- **User, Session, and AI Agent Memory**: Retains information across user sessions, interactions, and AI agents, ensuring continuity and context.
- **Adaptive Personalization**: Continuously improves personalization based on user interactions and feedback.
- **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
- **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
- **Managed Service**: Provides a hosted solution for easy deployment and maintenance.

If you are looking to quick start, jump to one of the following links:

<CardGroup cols={2}>
  <Card title="Quickstart" icon="square-1" href="/quickstart/">
    Jump to quickstart section to get started
  </Card>
  <Card title="Examples" icon="square-2" href="/examples/overview/">
    Checkout curated examples
  </Card>
</CardGroup>

## Common Use Cases

- **Personalized Learning Assistants**: Long-term memory allows learning assistants to remember user preferences, past interactions, and progress, providing a more tailored and effective learning experience.

- **Customer Support AI Agents**: By retaining information from previous interactions, customer support bots can offer more accurate and context-aware assistance, improving customer satisfaction and reducing resolution times.

- **Healthcare Assistants**: Long-term memory enables healthcare assistants to keep track of patient history, medication schedules, and treatment plans, ensuring personalized and consistent care.

- **Virtual Companions**: Virtual companions can use long-term memory to build deeper relationships with users by remembering personal details, preferences, and past conversations, making interactions more meaningful.

- **Productivity Tools**: Long-term memory helps productivity tools remember user habits, frequently used documents, and task history, streamlining workflows and enhancing efficiency.

- **Gaming AI**: In gaming, AI with long-term memory can create more immersive experiences by remembering player choices, strategies, and progress, adapting the game environment accordingly.

## How is Mem0 different from RAG?

Mem0's memory implementation for Large Language Models (LLMs) offers several advantages over Retrieval-Augmented Generation (RAG):

- **Entity Relationships**: Mem0 can understand and relate entities across different interactions, unlike RAG which retrieves information from static documents. This leads to a deeper understanding of context and relationships.

- **Recency, Relevancy, and Decay**: Mem0 prioritizes recent interactions and gradually forgets outdated information, ensuring the memory remains relevant and up-to-date for more accurate responses.

- **Contextual Continuity**: Mem0 retains information across sessions, maintaining continuity in conversations and interactions, which is essential for long-term engagement applications like virtual companions or personalized learning assistants.

- **Adaptive Learning**: Mem0 improves its personalization based on user interactions and feedback, making the memory more accurate and tailored to individual users over time.

- **Dynamic Updates**: Mem0 can dynamically update its memory with new information and interactions, unlike RAG which relies on static data. This allows for real-time adjustments and improvements, enhancing the user experience.

These advanced memory capabilities make Mem0 a powerful tool for developers aiming to create personalized and context-aware AI applications.

If you have any questions, please feel free to reach out to us using one of the following methods:

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