unibaseio/membase

One memory. Every AI. Only yours.
Teach it once. Every AI remembers.
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Docs ·
Web app ·
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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 LongMemEval_S and 92.2% on DMR, using about 3× fewer tokens than the full
history on LoCoMo and 11× fewer on LongMemEval_S. Method and results: membase.ai/research
Quickstart
Every method, its REST route and MCP tool, and the access it needs: SDK reference.
The Python package is unibaseio-membase (import membase); membase on PyPI is unrelated.
Command line
Without --local (or --store DIR, both before the command) commands use the hosted API; agent,
import and serve need unibaseio-membase[local].
MCP
Hosted without the header, the connection signs you in and is read-only. Setup for Claude, ChatGPT,
Cursor, Codex and other clients: Connect your AI.
Docs
- SDK reference: methods, REST routes, MCP tools, access levels
- Memory operations: containers, documents, memories, profile, ask
- Authentication: keys and what each one can reach
- Connect your AI: per-client MCP setup
- Local engine: what it stores, membase serve, settings
License