Membase MCP server

Membase

One memory. Every AI. Only yours. Teach it once. Every AI remembers.

Website · Docs · Web app · Discord · X

Membase is the memory layer for AI agents. It keeps one memory for everything you and your agents know, and every AI you use reads it. You stop re-explaining yourself each time you switch models or tools.

• Add. Facts, documents, web pages and chat exports from Claude or ChatGPT go into one memory.
• Manage. Containers keep projects apart; search it, ask it questions, forget what should go.
• Use. Your code, Claude Code, Cursor, Codex and ChatGPT read the same memory through the SDK or MCP.

This repository is the developer side: Python and TypeScript clients, the membase command line and an MCP server. The memory lives in your Membase account, which the web app and browser extension also fill, or on your machine with unibaseio-membase[local].

Write once, read from any AI

Recordings: Python SDK (57 s) · Claude Code (1 min 33 s). The answer above is illustrative.

Accuracy

93.1% on LoCoMo, 92.6% on LongMemEvalS and 92.2% on DMR, using about 3× fewer tokens than the full history on LoCoMo and 11× fewer on LongMemEvalS. Method and results: membase.ai/research

Quickstart

pip install unibaseio-membase              # hosted · Python 3.10+ · needs MEMBASE_API_KEY
pip install 'unibaseio-membase[local]'     # local  · Python 3.12 or 3.13 · needs OPENAI_API_KEY
npm install @unibaseio/membase              # TypeScript · Node 18+
npm install -g @unibaseio/membase-cli       # the `membase` command (hosted) without Python

from membase import Membase

m = Membase()                    # hosted
# m = Membase(local=True)        # local, in ~/.membase

m.memories.add("We picked Postgres for the ledger service.", container="Engineering")
m.search("what database is the ledger on?")
m.add("Design notes …", container="Engineering", custom_id="design-1")   # a document
m.ask("Which database did we choose for the ledger?")

Shortened. The full README is on GitHub.