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Membase MCP

by unibaseio

Shared hosted or local memory for AI agents, exposed through SDKs, CLI commands, and MCP.

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Install Membase MCP

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "unibaseio-membase-mcp": {
      "command": "uvx",
      "args": [
        "unibaseio-membase"
      ],
      "env": {
        "MEMBASE_API_KEY": "YOUR_VALUE_HERE",
        "OPENAI_API_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

unibaseio/membase-mcp connects AI clients to Membase, a shared memory layer for storing and retrieving facts, documents, and conversations. It can use the hosted Membase API or a local engine, with MCP access available through a local command or hosted HTTP endpoint. Reach for it when agents need persistent memory across models, tools, projects, or client applications. Containers help keep memories separated by project.

Use cases

  • Save project decisions for reuse across coding agents
  • Search prior conversations when switching AI clients
  • Ask questions about imported Claude or ChatGPT chats
  • Separate team or project knowledge with containers
  • Ingest agent traces and retrieve debugging context

Key features

  • Hosted and local memory storage
  • MCP access over local stdio or hosted HTTP
  • Natural-language memory search and questions
  • Project containers for separated knowledge
  • Chat export and agent trace ingestion
  • Optional local media memory

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked

Recent health check succeeded.

24/25 checks healthy over the last 95d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 5d ago via OSV.dev · unibaseio-membase (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars142Stargazers on the source repository.
Last commit3d agoMost recent push to the default branch.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How Membase MCP works

What unibaseio/membase-mcp does

unibaseio/membase-mcp gives MCP-compatible AI clients access to Membase memory. Agents can save facts, documents, and other knowledge, then search that memory or ask questions about it later. The same account-level memory can be used from the SDK, command line, web app, browser extension, and MCP clients.

Membase supports two storage modes. Hosted usage keeps memory in a Membase account and is accessed through the hosted API. Local usage stores data on the machine, under ~/.membase by default, and can run through the local engine. Containers provide a way to separate memories for different projects.

How it works

The MCP connection can point to a local server started with the membase command, or to Membase's hosted HTTP endpoint. A local setup uses the membase --local mcp command. Hosted access uses https://api.app.membase.com/mcp-http and can authenticate with a bearer token. A hosted connection without that header signs the user in and provides read-only access.

The underlying Membase package also provides Python and TypeScript clients and a command-line interface. The Python package requires Python 3.10 or newer for hosted use and Python 3.12 or 3.13 for the local extra. The TypeScript package requires Node.js 18 or newer. Local operation uses an OpenAI API key, while hosted operation uses a Membase API key.

Setup and configuration

Install the Python package with:

Terminal
pip install unibaseio-membase

For the local engine, install the local extra instead:

Terminal
pip install 'unibaseio-membase[local]'

The TypeScript package and standalone CLI are available with npm:

Terminal
npm install @unibaseio/membase
npm install -g @unibaseio/membase-cli

Add a local MCP connection with:

Terminal
claude mcp add membase -- membase --local mcp

For hosted MCP access, configure the HTTP endpoint and pass MEMBASE_API_KEY as a bearer token. The local installation requires OPENAI_API_KEY. The optional media extra adds local processing for photos, videos, and audio.

Tools and capabilities

  • Save individual memories and longer documents.
  • Search stored memory with natural-language queries.
  • Ask questions that use the stored memory as context.
  • Organize information into project-specific containers.
  • Import Claude, ChatGPT, Markdown, or JSON chat exports through the CLI.
  • Ingest and search agent traces with agent-specific commands.
  • Process local media for shots, speech, speakers, and semantic search when the media extra is installed.
  • Share memory across supported SDK, CLI, web, and MCP access paths.

Limitations and notes

Hosted and local deployments have different requirements and behavior. Hosted access needs a Membase account and API key for writable authenticated use; a hosted connection without the authorization header is read-only. Local mode requires the local package extra and an OpenAI API key.

Media processing is local-only and requires the separate media extra. The Python distribution is named unibaseio-membase, while the unrelated PyPI package named membase should not be substituted. The main package is MIT licensed; the compiled local engine installed by the local extra has its own Unibase license.

Read the full README View source on GitHub

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Frequently Asked Questions about Membase MCP

unibaseio/membase-mcp is an MCP server that connects AI clients to Membase, a hosted or local memory layer. Its main capabilities let agents save memories and documents, search stored knowledge, ask questions about it, and organize information in containers. The same memory can be accessed through MCP, SDKs, the CLI, and other Membase applications.

Install `unibaseio-membase` with pip for hosted use, or install `unibaseio-membase[local]` for the local engine. You can also install `@unibaseio/membase` with npm or the standalone `@unibaseio/membase-cli`. Local MCP connections use `membase --local mcp`.

Connect these clients through their MCP configuration using either the local `membase --local mcp` command or the hosted HTTP endpoint `https://api.app.membase.com/mcp-http`. Hosted writable access uses an `Authorization: Bearer $MEMBASE_API_KEY` header. Client-specific setup is available through Membase's connect instructions.

Yes, local memory is supported through the `unibaseio-membase[local]` installation and the `--local` option. Data is stored on the machine, in `~/.membase` by default, and local operation requires `OPENAI_API_KEY`. Hosted mode instead stores memory in a Membase account.

Hosted use requires `MEMBASE_API_KEY`, while local use requires `OPENAI_API_KEY`. The hosted MCP endpoint can receive the Membase key as a bearer token. Read-only hosted access can work without that header through sign-in.

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