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Documentation Index

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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 collections, with Obsidian vault 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, and other data sources to enrich your memory
  • 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

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How does it work?

Membase architecture diagram
1

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, and Slack. Optionally import an Obsidian vault to bootstrap your wiki.
2

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) becomes Wiki, organized as linked markdown documents in collections.
3

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.

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Why Membase?

Today’s AI agents have three fundamental problems:

Session Memory Loss

Agents forget everything when a session ends.

Cross-Agent Isolation

Context doesn’t carry over between agents.

Context Rot

More context doesn’t mean better responses.
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.

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Get Started

Quickstart

Connect your first agent in 3 simple steps.

Bring Your Context

Import chat history, connect apps, import an Obsidian vault, and build your knowledge base.

Use Your Context

Chat with Memory, agent retrieval, and dashboard exploration.

Knowledge Wiki

Store factual knowledge as linked markdown documents that agents can search.