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.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.", 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.
The Python package is unibaseio-membase (import membase); membase on PyPI is unrelated.
Command line
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
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 (local only, with its own search
and ask) needs unibaseio-membase[media]: see Media memory.
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.
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
- 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] also
installs unibaseio-membase-core, the compiled engine, under its own Unibase license.
