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What is Membase?

Membase is a universal memory layer for AI agents. It gives your agents persistent, shared memory that survives across sessions, tools, and platforms, so they never forget important context about you.
  • Persistent memory: Your agents remember preferences, decisions, and context across every conversation
  • Cross-agent sharing: Memory created in one agent is instantly available in all your other agents
  • External integrations: Connect Gmail, Calendar, Notion, and other data sources to enrich your agents’ context
  • Smart digesting: Raw conversations are automatically processed into structured, retrievable memories
  • Knowledge graph: Entities, relationships, and facts are organized into a relational graph for precise retrieval

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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, and link external data sources like Gmail or Google Calendar to enrich your memory.
2

Automatic indexing into a knowledge graph

Membase automatically extracts important context from conversations and external data, then structures it into a relational knowledge graph of entities, relationships, and facts.
3

Context retrieval when your agent needs it

When an agent needs context to respond, Membase searches the knowledge graph and returns only the most relevant information, so every response is grounded in what actually matters.

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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.

Single-Agent

Set up persistent memory for one agent.

Multi-Agent

Share memory across multiple agents.