membase

<p align="center"> <img src="https://raw.githubusercontent.com/unibaseio/membase-ai/main/assets/membase-logo.png" width="72" alt="Membase"> </p> <h1 align="center">Membase</h1> <p align="center"> <b>One memory. Every AI. Only yours.</b><br> Teach it once. Every AI remembers. </p> <p align="center"> <a href="https://pypi.org/project/unibaseio-membase/"><img src="https://img.shields.io/pypi/v/unibaseio-membase?label=pypi" alt="PyPI"></a> <a href="https://www.npmjs.com/package/@unibaseio/membase"><img src="https://img.shields.io/npm/v/@unibaseio/membase?label=npm" alt="npm"></a> <a href="https://github.com/unibaseio/membase-ai/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-proprietary-blue" alt="Proprietary"></a> </p> <p align="center"> <a href="https://membase.ai">Website</a> · <a href="https://docs.membase.ai">Docs</a> · <a href="https://www.app.membase.ai">Web app</a> · <a href="https://discord.gg/nB9EfPGsSf">Discord</a> · <a href="https://x.com/Unibase_AI">X</a> </p> <p align="center"> <img src="https://raw.githubusercontent.com/unibaseio/membase-ai/main/assets/hero.svg" width="840" alt="Add sources, manage one memory, use it from every AI and app"> </p> 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](https://www.app.membase.ai) and browser extension also fill, or on your machine with `unibaseio-membase[local]`. ## Write once, read from any AI <p align="center"> <img src="https://raw.githubusercontent.com/unibaseio/membase-ai/main/assets/in-action.svg" width="840" alt="Your app writes a memory; any AI reads it, here Claude Code"> </p> Recordings: [Python SDK](https://github.com/unibaseio/membase-ai/blob/main/assets/videos/sdk.mp4) (57 s) · [Claude Code](https://github.com/unibaseio/membase-ai/blob/main/assets/videos/switch-ai.mp4) (1 min 33 s). The answer above is illustrative. ## Accuracy <p align="center"> <img src="https://raw.githubusercontent.com/unibaseio/membase-ai/main/assets/results.svg" width="840" alt="LoCoMo 93.1%, LongMemEval_S 92.6%, DMR 92.2%"> </p> 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](https://membase.ai/research) ## Quickstart <p align="center"> <img src="https://raw.githubusercontent.com/unibaseio/membase-ai/main/assets/architecture.svg" width="840" alt="Hosted, local in process, or local over HTTP"> </p> ```bash 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 ``` ```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?") ``` Every method, its REST route and MCP tool, and the access it needs: [SDK reference](https://docs.membase.ai/build/reference/sdk-quickstart/). The Python package is `unibaseio-membase` (import `membase`); `membase` on PyPI is unrelated. ## Command line ```bash membase --local add "We picked Postgres for the ledger" --container Engineering membase --local search "what did we pick for the ledger?" membase --local ask "Which database is the ledger on?" membase --local import ~/Downloads/claude-export.json # Claude, ChatGPT, markdown or JSON chats membase --local agent ingest trace.json --agent coder membase --local agent search "fix flaky deploy" --agent coder ``` Without `--local` (or `--store DIR`, both before the command) commands use the hosted API; `agent`, `import` and `serve` need `unibaseio-membase[local]`. ## MCP <p align="center"> <img src="https://raw.githubusercontent.com/unibaseio/membase-ai/main/assets/mcp.svg" width="840" alt="MCP clients, hosted or local server, same tools"> </p> ```bash claude mcp add membase -- membase --local mcp # local claude mcp add --transport http membase https://api.app.membase.ai/mcp-http \ --header "Authorization: Bearer $MEMBASE_API_KEY" # hosted ``` 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](https://docs.membase.ai/connect/). ## Docs - [SDK reference](https://docs.membase.ai/build/reference/sdk-quickstart/): methods, REST routes, MCP tools, access levels - [Memory operations](https://docs.membase.ai/build/guides/memory-operations/): containers, documents, memories, profile, ask - [Authentication](https://docs.membase.ai/build/reference/authentication/): keys and what each one can reach - [Connect your AI](https://docs.membase.ai/connect/): per-client MCP setup - [Local engine](https://github.com/unibaseio/membase-ai/blob/main/docs/reference.md): what it stores, `membase serve`, settings ## License Copyright © 2026 Unibase. All rights reserved. Proprietary; see [LICENSE](https://github.com/unibaseio/membase-ai/blob/main/LICENSE). `unibaseio-membase[local]` also installs unibaseio-membase-core, the compiled engine, under its own Unibase license.