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MCP AutoMem

Adds persistent graph-vector memory to MCP-compatible AI clients through an AutoMem service.

Install MCP AutoMem

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Overview

MCP AutoMem connects AI clients to the AutoMem graph-vector memory service, using FalkorDB and Qdrant to retain and retrieve context across sessions. Its memory capabilities cover recalling information, storing durable memories, and linking memories through relationship types. Use it when assistants need shared context across projects, devices, or MCP platforms rather than starting each conversation from scratch. The server can connect to a local, hosted, or existing AutoMem endpoint.

Use cases

  • Recall coding preferences across sessions
  • Store durable project decisions
  • Associate related memories in a knowledge graph
  • Share assistant context across devices
  • Connect remote MCP clients to hosted memory

Key features

  • Graph-vector memory with FalkorDB and Qdrant
  • Persistent recall and durable memory storage
  • Memory associations with 11 public relationship types
  • Guided installation with configuration previews and backups
  • Local, hosted, and existing-endpoint support
  • Optional Streamable HTTP and SSE sidecar

Security & health

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8/8 checks healthy over the last 45d

Supply-chain signal

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Scanned 6d ago via OSV.dev

Adoption & maintenance

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Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How MCP AutoMem works

What MCP AutoMem does

MCP AutoMem gives MCP-compatible AI clients access to persistent memory through the AutoMem service. It is intended for information that should remain available beyond one conversation, including decisions, working patterns, project context, and personal preferences.

The memory backend uses a graph-vector design built around FalkorDB and Qdrant. Vector retrieval helps locate relevant memories, while graph relationships connect related memories. The service supports 11 public relationship types for authoring associations; retrieved results can also contain read-only system or internal relationships that are not valid inputs when creating associations.

Memory is available across supported clients and devices when they use the same AutoMem service. Local deployments suit single-machine use, while a hosted endpoint can provide shared access across Mac, Windows, and Linux systems.

How it works

The MCP server acts as the client-facing connection to an AutoMem endpoint. An AI client invokes memory operations to recall relevant context, save information judged to be durable, and associate related memories. Claude Code can also use bundled hooks for silent recall and store tracking, while Cursor setup can add project rules and a session-start recall hook.

The backend must be running separately from the MCP server. A local development AutoMem service is available at http://localhost:8001 when started with the AutoMem project’s development command. Hosted deployments, including Railway-based deployments, can be used by supplying their endpoint and authentication details.

Setup and configuration

The guided installer is available through npm:

Terminal
npx @verygoodplugins/mcp-automem install

It helps select where AutoMem runs, checks the endpoint, writes environment configuration, and configures supported agents. It presents a review plan before changing files and creates .bak backups for modified files. Use --dry-run to preview changes or --yes for non-interactive installation. setup is an alias for install, and config prints configuration snippets again.

Claude Desktop can use the downloadable .mcpb extension. During setup, provide the AutoMem API URL and, when required by the deployment, an API key. The extension contains a fixed MCP server version and must be reinstalled from a newer bundle to update it.

Claude Code also provides a plugin installation path. Cursor can be configured by the guided installer or with an existing endpoint using the cursor command. Windows users running Claude Code hooks need a POSIX shell such as Git Bash, MSYS2, or WSL.

Tools and capabilities

  • Recall stored context for a current conversation or task.
  • Store durable memories through the MCP memory interface.
  • Associate memories using supported relationship types.
  • Reuse memory across sessions, clients, projects, and devices.
  • Connect to local, hosted, or existing AutoMem endpoints.
  • Support remote MCP access through an optional HTTP sidecar for compatible platforms.

The optional remote sidecar supports Streamable HTTP or SSE connections for platforms such as ChatGPT Developer Mode connectors, Claude.ai web and mobile, and ElevenLabs Agents.

Reviews

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Frequently Asked Questions about MCP AutoMem

MCP AutoMem is an MCP server that connects AI clients to an AutoMem graph-vector memory service backed by FalkorDB and Qdrant. Its main capabilities are recalling stored context, saving durable memories, and associating memories through relationship types, so assistants can reuse information across conversations, projects, devices, and supported MCP platforms.

Run `npx @verygoodplugins/mcp-automem install` to start the guided installer. It helps configure the AutoMem endpoint, writes environment settings, and sets up supported agents. Claude Desktop can instead use the downloadable `.mcpb` extension, while Claude Code and Cursor have dedicated setup paths.

Yes, MCP AutoMem works with Cursor. Run the guided installer from the project and select Cursor, or use the `cursor` command with an existing endpoint. The setup can add project memory rules and register a session-start recall hook.

Yes, you can run it with a local AutoMem service. Start the AutoMem backend for local development, then configure the MCP server with the local endpoint, typically `http://localhost:8001` or `http://127.0.0.1:8001`. An API key can be omitted for local use.

Deploy the optional HTTP sidecar and connect the remote client through Streamable HTTP or SSE. The remote setup supports ChatGPT Developer Mode connectors, Claude.ai web and mobile, and ElevenLabs Agents.

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