membase-ai

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

Website · Docs · Web app · Discord · X

Membase 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 membase-ai[local].

Write once, read from any AI

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

Accuracy

On three public long-term memory benchmarks, with a few thousand tokens of context per question.

Quickstart

pip install membase-ai              # hosted · Python 3.10+ · needs MEMBASE_API_KEY
pip install 'membase-ai[local]'     # local  · Python 3.12 or 3.13 · needs OPENAI_API_KEY
npm install membase-ai              # TypeScript · Node 18+

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 package is membase-ai; 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

Without --local (or --store DIR, both before the command) commands use the hosted API; agent, import and serve need membase-ai[local].

MCP

claude mcp add membase -- membase --local mcp                                   # local
claude mcp add --transport http membase https://api.app.membase.io/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
• Local engine: what it stores, membase serve, settings

License

MIT. membase-ai[local] also installs membase-core, the compiled engine, under Unibase's proprietary license.