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

# Introduction: What is Mem[v]?

> Context and memory layer for multimodal AI agents

**Mem\[v]** (memory + video) gives your AI agents long-term memory across all content types. We assemble personalized context from text, documents, conversations, videos, audio, and voice notes - delivering structured memories that reduce hallucinations and improve accuracy.

## Video is the superset of all multimedia

If you solve memory for video, you've solved it for everything else.

Video contains:

* Spoken language (audio/transcripts)
* Visual information (images, scenes, motion)
* Text (captions, on-screen text, slides)
* Temporal dynamics (how information unfolds over time)

When you master the form, function, and dynamics of video memory, every other format - documents, images, conversations, audio - becomes simpler.

## How does it work?

<Steps>
  <Step title="Connect your data sources">
    Link Google Drive, Gmail, Notion, S3, Box, OneDrive, or custom sources. Mem\[v] automatically handles extraction.

    <Tip>
      Works with both structured data (databases, spreadsheets) and unstructured content (documents, emails, videos).
    </Tip>
  </Step>

  <Step title="Automatic memory creation">
    Mem\[v] extracts structured memories - facts, preferences, entities - and builds knowledge graphs that show how everything connects.

    * Extract entities, relationships, and facts from all content types
    * Build graphs linking related information
    * Create user profiles with preferences and behavioral patterns
    * Update memories in real-time as new information arrives
  </Step>

  <Step title="Intelligent retrieval">
    When your AI needs information, Mem\[v] retrieves relevant memories, ranks by relevance and recency, and includes connected information for complete context.

    <Check>
      Grounded in verified facts to reduce hallucinations.
    </Check>
  </Step>
</Steps>

## Why it matters?

Your users interact with information everywhere - emails, documents, Slack threads, video calls, tutorials, presentations. But AI agents can't remember any of it beyond the current session.

Without long-term memory:

* Every conversation starts from zero
* Users repeat themselves constantly
* Context from last week is lost
* Information across different formats stays disconnected
* AI hallucinates due to lack of grounded facts

**Mem\[v] creates a persistent memory layer** that connects information across all your data sources, assembling the right context at the right time.

## What you can do with Mem\[v]?

<CardGroup cols={2}>
  <Card title="Connect any data source" icon="plug">
    Integrate Google Drive, Gmail, Notion, S3, Box, OneDrive. Automatic extraction for all formats.
  </Card>

  <Card title="Persistent memories" icon="brain">
    Real-time, evolving memories that update as users interact with new content.
  </Card>

  <Card title="Multimodal understanding" icon="layer-group">
    Unified memory connecting text, conversations, files, images, videos, and audio.
  </Card>

  <Card title="Knowledge graphs" icon="diagram-project">
    Build semantic graphs showing how people, topics, and events relate across all content.
  </Card>
</CardGroup>

## Next steps

<Card title="How it works" icon="gears" href="/core-concepts/how-it-works">
  Understand how Mem\[v] handles context engineering and creates memories for your apps and multimodal agents.
</Card>

***

<Tip>
  Explore more at [https://docs.memv.ai](https://docs.memv.ai)
</Tip>