mem0-local-mcp

A fully local, self-hosted mem0 memory server for MCP clients (Claude Code, Claude Desktop, Cursor, …).

• Local embeddings via fastembed (ONNX, no PyTorch, no GPU) — BAAI/bge-small-en-v1.5, 384-dim. Can be used with different LLM via API key.
• Local vector store via Qdrant on localhost.
• Switchable extraction LLM — Anthropic Claude (default) or Google Gemini, chosen by one env var.
• No API key needed for normal use. Reads and verbatim writes (infer=False) run entirely on your machine. A provider key is used only when you opt into LLM fact-extraction (infer=True).

Tools

Memories are scoped by any of user_id / agent_id / run_id.

Instructions for your agent

Copy the block below and paste it to your coding agent (Claude Code, Cursor, …) — it will install and verify everything for you:

Set up the mem0-local-mcp memory server on this machine for me:

1. Ensure a local Qdrant is running on port 6333: run `curl -s http://localhost:6333/`.
   If it is not running, start it with Docker:
   docker run -d --name qdrant --restart always -p 6333:6333 -v ~/.qdrant_storage:/qdrant/storage qdrant/qdrant
   If Docker is unavailable, download the Qdrant binary for my OS from
   https://github.com/qdrant/qdrant/releases and run it.
2. Ensure `uv` is installed (`which uv`); if not, install it from https://docs.astral.sh/uv/.
3. Register the MCP server at user scope:
   claude mcp add mem0 --scope user -- uvx --from git+https://github.com/sanchezvivi/mem0-local-mcp.git mem0-local-mcp
4. Verify with `claude mcp list` that `mem0` shows Connected. The first run downloads the
   embedding model (~130 MB), so allow up to a minute.
5. Reads and infer=False writes need no API key. Only if I want infer=True (LLM) extraction,
   confirm ANTHROPIC_API_KEY (or GOOGLE_API_KEY with MEM0_LLM_PROVIDER=gemini) is set in my
   environment before launching the client. Then tell me it is ready.

Prerequisites

1. A running Qdrant (pick one):

# Docker
docker run -d --name qdrant -p 6333:6333 -v ~/.qdrant_storage:/qdrant/storage qdrant/qdrant

# or the binary (macOS/Linux release from https://github.com/qdrant/qdrant/releases)
./qdrant

2. [uv](https://docs.astral.sh/uv/) to run the server (uvx), or pip install mem0-local-mcp.

The embedding model (~130 MB) downloads automatically on first use.

Install in Claude Code

claude mcp add mem0 --scope user -- \
  uvx --from git+https://github.com/sanchezvivi/mem0-local-mcp.git mem0-local-mcp

For Gemini extraction, install the extra and select the provider:

claude mcp add mem0 --scope user \
  --env MEM0_LLM_PROVIDER=gemini \
  -- uvx --from "git+https://github.com/sanchezvivi/mem0-local-mcp.git#egg=mem0-local-mcp[gemini]" mem0-local-mcp

Other MCP clients

Any stdio MCP client can launch it. Example config entry:

{
  "mcpServers": {
    "mem0": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/sanchezvivi/mem0-local-mcp.git", "mem0-local-mcp"]
    }
  }
}

Configuration

All optional, with defaults:

Keys are read from the process environment; nothing is written to disk by this server.

Local development

git clone https://github.com/sanchezvivi/mem0-local-mcp.git
cd mem0-local-mcp
uv venv && uv pip install -e ".[gemini]"
.venv/bin/mem0-local-mcp        # stdio server

License

MIT — see LICENSE.