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Membase

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Memory layer for AI agents — SDKs, CLI and MCP server for Membase

Membase

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

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Membase is the memory layer for AI agents. It keeps one memory for everything you and your agentsknow, 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. Memories keep projects apart; search them, ask them 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 lineand an MCP server. The memory lives in your Membase account, which theweb app and browser extension also fill, or on your machine withunibaseio-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 fullhistory on LoCoMo and 11× fewer on LongMemEval_S. Method and results: membase.com/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
pip install 'unibaseio-membase[media]'     # local + photos, videos and audio
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.")   # a fact
m.search("what database is the ledger on?")                    # {"items": [...], "partial": [...]}
m.add("Design notes …", custom_id="design-1")                   # a document
m.profile_facts.list()                                         # who the user is (Your profile ticked)
m.capabilities()                                               # what this key may do, and the rules

No call names a Memory: reads cover every Memory the key reaches, and writes land in its writetarget (a key that reaches several Memories and has none is refused with WriteTargetRequiredError).Ids are typed (mem_… a fact, doc_… a document, med_… a photo or video), lists are{items, has_more, partial}, and a removal without confirm=True raises ConfirmationRequiredError.Local memory answers the same routes with the same shapes.

Every method, its REST route and MCP tool, and the access it needs: SDK reference.Upgrading from 0.3: see the changelog.The Python package is unibaseio-membase (import membase); membase on PyPI is unrelated.

Command line

membase --local add "We picked Postgres for the ledger"
membase --local search "what did we pick for the ledger?"
membase --local memories list
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
membase --local media add ~/Movies/trip.mp4                # shots, speech and speakers
membase --local media search "someone skiing"   # any language

Without --local (or --store DIR, both before the command) commands use the hosted API; agent,import and serve need unibaseio-membase[local], and media add and media ask (local only)need unibaseio-membase[media]: see Media memory. A local storewith several Memories writes where MEMBASE_WRITE_TARGET names.

MCP

claude mcp add membase -- membase --local mcp                                   # local
claude mcp add --transport http membase https://api.app.membase.com/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.

Gemini CLI, as an extension (it signs you in on first use; /mcp auth membase signs in again):

gemini extensions install https://github.com/unibaseio/membase

Cline: add the server to cline_mcp_settings.json; llms-install.md has theentry, with and without a developer key.

Docs

  • SDK reference: methods, REST routes, MCP tools, access levels
  • Memory operations: Memories, facts, documents, media, profile, ask
  • Authentication: keys and what each one can reach
  • Connect your AI: per-client MCP setup
  • Agent framework integrations: LangChain, Microsoft Agent Framework, Dify, Hermes Agent, OpenClaw and Strands Agents
  • Local engine: what it stores, membase serve, settings

License

MIT; see LICENSE. unibaseio-membase[local] alsoinstalls unibaseio-membase-core, the compiled engine, under its own Unibase license.

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