membase-ai

One memory. Every AI.
Drop-in memory infrastructure for AI agents and apps — the Membase SDK.

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The Membase SDK: a Python client, the membase command line, an MCP server and a TypeScript client. The memory lives in your Membase account or on your machine, behind the same API.

See it in action

From your code · recording, 57 s

From Claude Code · recording, 1 min 33 s

Outputs in the cards are illustrative.

Benchmarks

Install

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+

The package is membase-ai; membase on PyPI is unrelated.

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.profile()
m.ask("Which database did we choose for the ledger?")

• Deleting a document or forgetting a memory needs confirm=True; without it you get what would be removed.
• Hosted, ask needs an agent-endpoint key, and each key is limited to its containers and access level.

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 import ~/chats/ --dry-run                 # a directory; --dry-run only reports
membase --local documents add notes.md --container Engineering
membase --local agent ingest trace.json --agent coder
membase --local agent search "fix flaky deploy" --agent coder

• --local and --store DIR go before the command. Without them, commands use the hosted API.
• agent and serve are local only. import needs membase-ai[local] to read the exports.
• MEMBASE_LOCAL=1 makes local the default when MEMBASE_API_KEY is not set.

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.

Local memory over HTTP

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

The hosted API's agent-protocol routes, answered by the local engine: point any client's base URL at it. Set MEMBASE_LOCAL_TOKEN to require it as a bearer token.

Local settings

Stores: ~/.membase/memory.db (default container), ~/.membase/containers/<id>/ (others), ~/.membase/profile/ (static facts). Settings come from the environment, or from a .env file in the current directory or a parent.

Repository

membase/ is the Python package (client.py, cli.py, local/ for local memory and membase serve, mcp/ for membase mcp); typescript/ is the npm package. membase-sdk (PyPI and npm) is the earlier name, kept as an alias.

License

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