membase-ai

Membase — long-term memory for AI. One API for the hosted Membase service and for memory that runs on your own machine, with a command line and an MCP server.

pip install membase-ai                 # hosted (just httpx)
pip install 'membase-ai[local]'        # + the local engine, membase-core (Python 3.12+; OpenAI,
                                       #   Anthropic or Ollama models; images, PDFs, web pages)
pip install 'membase-ai[local,mcp]'    # + the MCP server
npm install membase-ai                 # TypeScript client (see typescript/)

from membase import Membase

m = Membase()                    # hosted: MEMBASE_API_KEY (Connect › Developer keys)
m = Membase(local=True)          # local: ~/.membase, same methods, same answers

m.memories.add("We picked Postgres for the ledger service.", container="Engineering")
m.search("what database is the ledger on?")
m.ask("Which database did we choose for the ledger?")
m.add("Design notes …", container="Engineering", custom_id="design-1")   # a document
m.profile()

Every method is one operation of the Membase agent protocol (list_containers, search_memories, get_profile, list_documents, memory_rules, add_memory, add_document, delete_document, forget_memory, ask_agent). Hosted, the service enforces each key's reach and access level. Local, the same routes are answered by the membase-core engine: a memory becomes dated episodes, a document becomes a topic tree, and search is the engine's multi-round retrieval. Removing a document or forgetting a memory needs confirm=True in both.

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 / JSON chats
membase --local documents add notes.md --container Engineering
membase --local profile

Without --local the same commands use the hosted API (MEMBASE_API_KEY). MEMBASE_LOCAL=1 (or a directory) makes local the default.

MCP

claude mcp add membase -- membase --local mcp           # local memory
claude mcp add membase -e MEMBASE_API_KEY=mbk_… -- membase mcp   # hosted memory

The server offers the same tools as the hosted endpoint (https://api.app.membase.io/mcp-http), so a client sees one tool set either way. With membase-protocol installed and MEMBASE_PRIVATE_KEY it also offers automem_save / automem_list / automem_fetch / automem_delete: a client's auto-memory notes, signed and encrypted by the wallet and kept on the Membase Hub, restorable on any device with the same key.

Local memory over HTTP

membase --local serve            # http://127.0.0.1:8787/v1

membase serve answers the /v1 routes of the hosted API from the local engine, so the TypeScript client — or anything else that speaks the API — can use local memory by pointing its base URL at it. Set MEMBASE_LOCAL_TOKEN to require a bearer token.

Where things live

Local stores: the default container is ~/.membase/memory.db; others are ~/.membase/containers/<id>/. Settings for the engine (models, providers) are the MEMBASE_* variables documented in membase-core.

membase-sdk (PyPI and npm) is the earlier name of this package and is kept as an alias.

License

MIT.