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

# Overview

> Persistent, shared personal memory for your AI agents, so they keep important context across sessions.

## What is Membase?

Membase is a **universal knowledge layer for AI agents**. It gives your agents two persistent, shared stores that survive across sessions, tools, and platforms, so they can keep important context about you.

* **Memory**: Personal context (preferences, decisions, habits, meetings, emails) organized as a knowledge graph
* **Knowledge Wiki**: Factual knowledge as markdown documents linked with `[[wikilinks]]`, organized into Projects, with Notion sync, Obsidian/Markdown import, and hybrid search
* **Cross-agent sharing**: Context stored by one agent is available to other connected agents on your account
* **External integrations**: Connect Gmail, Calendar, Slack, Notion, and other data sources to enrich Memory and Wiki
* **Chat history import**: Bring in past conversations from ChatGPT, Claude, and Gemini to bootstrap your knowledge base
* **Chat with Memory**: Talk directly to your knowledge base from the dashboard, without going through an external agent
* **Smart digesting**: Raw conversations are automatically processed into structured, retrievable memories

## How does it work?

<Frame caption="Membase architecture overview">
  <img src="https://mintcdn.com/aristo_2/SyC5fNZLZj3t6Psb/images/core-concepts/architecture_light.png?fit=max&auto=format&n=SyC5fNZLZj3t6Psb&q=85&s=68a30dccb878c5d708c16b3e4995bc56" alt="Membase architecture diagram" className="block dark:hidden" width="1636" height="1180" data-path="images/core-concepts/architecture_light.png" />

  <img src="https://mintcdn.com/aristo_2/SyC5fNZLZj3t6Psb/images/core-concepts/architecture_dark.png?fit=max&auto=format&n=SyC5fNZLZj3t6Psb&q=85&s=b89fb21728152abd7fc27ac359fd1a36" alt="Membase architecture diagram" className="hidden dark:block" width="1672" height="1214" data-path="images/core-concepts/architecture_dark.png" />
</Frame>

<Steps>
  <Step title="Connect agents and data sources">
    Connect your AI agents (Cursor, Claude, ChatGPT, etc.) via MCP, import past conversations, and link external data sources like Gmail, Google Calendar, Slack, and Notion. Optionally import Notion exports, Obsidian vaults, or Markdown files to bootstrap your Wiki.
  </Step>

  <Step title="Two knowledge stores: Memory and Wiki">
    Incoming context lands in the right place automatically. Personal context (preferences, decisions, meetings) becomes **Memory**, organized as a knowledge graph. Reference material (docs, specs, notes, transcripts) becomes **Wiki**, organized as linked markdown documents in Projects or Basic.
  </Step>

  <Step title="Retrieval when your agent (or Chat) needs it">
    When an agent needs context to respond, it can call `search_memory` for personal context and `search_wiki` for factual knowledge, then combine the results. Chat with Memory in the dashboard can use the same knowledge stores when you ask a question directly.
  </Step>
</Steps>

## Why Membase?

Today's AI agents have three fundamental problems:

<CardGroup cols={3}>
  <Card title="Session Memory Loss" icon="plug-circle-xmark">
    Agents forget everything when a session ends.
  </Card>

  <Card title="Cross-Agent Isolation" icon="circle-nodes">
    Context doesn't carry over between agents.
  </Card>

  <Card title="Context Rot" icon="recycle">
    More context doesn't mean better responses.
  </Card>
</CardGroup>

Every new conversation starts from scratch. You re-explain preferences, past decisions, and project context over and over. Worse, what you told Cursor doesn't exist in Claude, so you end up manually copy-pasting the same information across tools.

Even when you try to fix this by stuffing more context into prompts, it backfires. Without structure, the agent can't tell what's important and what's noise. Signal gets buried under volume.

Membase solves all three. Instead of dumping raw text, Membase builds a **relational knowledge graph** from your conversations and external data. When an agent needs context, it retrieves only the relevant pieces, keeping responses accurate and grounded.

## Get Started

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/getting-started/quickstart">
    Connect your first agent in 3 simple steps.
  </Card>

  <Card title="Bring Your Context" icon="download" href="/getting-started/bring-context">
    Import chat history, connect apps, import Notion/Obsidian/Markdown files, and build your knowledge base.
  </Card>

  <Card title="Use Your Context" icon="comment-dots" href="/getting-started/use-context">
    Chat with Memory, agent retrieval, and dashboard exploration.
  </Card>

  <Card title="Knowledge Wiki" icon="book" href="/features/wiki">
    Store factual knowledge as linked markdown documents that agents can search.
  </Card>
</CardGroup>