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

# Introduction

> MemContext is a memory layer for AI systems that need persistent, evolving, and retrievable context.

## What is MemContext?

MemContext is memory infrastructure for AI agents and applications.

It provides a REST API and MCP server that allow AI systems to:

* **Save** durable user and project knowledge across sessions
* **Isolate** app users and tenants with hard memory containers via `scope`
* **Search** memories with natural-language questions and exact terms
* **Evolve** memories over time with automatic version chains
* **Profile** users with pre-aggregated context from their memory
* **Ingest** workspace documents through Context Vault for RAG-style AI context

## Who is it for?

MemContext serves three primary use cases:

1. **Coding assistants** that need stable preferences, decisions, and project context across sessions
2. **Product applications** that need evolving memory for content generation, support, onboarding, or personalization
3. **Workspace AI tools** that need to retrieve source passages and extracted facts from files, docs, and URLs

## The Three Retrieval Modes

MemContext supports three ways to retrieve context:

| Mode                 | Use it for                                | Returns                           |
| -------------------- | ----------------------------------------- | --------------------------------- |
| **Memories**         | Durable facts, preferences, and decisions | Atomic memories                   |
| **Document chunks**  | RAG over PDFs, Markdown, docs, and URLs   | Source passages with citations    |
| **Hybrid workspace** | Production AI tools that need both        | Document chunks plus memory facts |

This gives your app a clear choice. Use memories when you need concise facts.
Use document chunks when you need source text. Use hybrid when you want the AI
layer to receive both precise passages and clean extracted knowledge.

## Core primitives

| Concept            | Description                                                                  |
| ------------------ | ---------------------------------------------------------------------------- |
| **Scope**          | Hard isolation boundary for app users or tenants (e.g. `user_123`)           |
| **Category**       | Broad memory type: `preference`, `fact`, `decision`, or `context`            |
| **Project**        | Soft grouping/filter inside the selected scope or default memory area        |
| **Version chains** | Memories can supersede old memories without losing history                   |
| **Hybrid search**  | Natural-language search that also handles exact names, tools, and decisions  |
| **MCP tools**      | AI assistants can save and search memory directly via Model Context Protocol |

## Why it exists

Simple retrieval is not enough for memory. Memory needs:

* **Updates** when reality changes
* **History** when strategy evolves
* **Exact-match retrieval** for product names, org names, and decisions
* **Lightweight APIs** other tools can build on

## Next steps

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Make your first API and MCP calls in under 2 minutes.
  </Card>

  <Card title="How It Works" icon="gear" href="/concepts/how-it-works">
    Understand how MemContext saves, updates, and retrieves memory.
  </Card>

  <Card title="API Reference" icon="code" href="/api-reference/overview">
    Full endpoint documentation with interactive examples.
  </Card>

  <Card title="MCP Setup" icon="plug" href="/guides/mcp-setup">
    Connect MemContext to Claude Desktop, Cursor, or any MCP client.
  </Card>
</CardGroup>