# MCP Memory Service

> Store and search persistent semantic memory with a knowledge graph, shared across agents, sessions and AI clients.

- Listing: https://mcp.tc/i/mcp-memory-service
- Connect: this is a local (stdio) server; install it on your machine (see Install). The listing link is a page, not an MCP endpoint.
- Type: local (stdio)
- Auth: no API key needed
- Category: [AI & Machine Learning](https://mcp.tc/c/ai-ml)
- Vendor: doobidoo
- Homepage: <https://github.com/doobidoo/mcp-memory-service>
- Docs: <https://github.com/doobidoo/mcp-memory-service>
- Repository: <https://github.com/doobidoo/mcp-memory-service>
- Package: pypi `mcp-memory-service`

## About

MCP Memory Service is a self-hosted memory backend for AI assistants and agent pipelines. It stores decisions, project context and code patterns, and retrieves them by semantic search. A knowledge graph links memories with typed relationships, and a consolidation step compresses older memories. Embeddings run locally via ONNX.

It runs locally over stdio with uvx (uvx --from mcp-memory-service memory server) or pip install mcp-memory-service. The same service can also run as an HTTP server with a REST API and web dashboard (memory server --http). Storage backends include SQLite, Cloudflare, Hybrid and Milvus. No API key is needed for local use.

## What it can do

- Store memories that persist across sessions and agents
- Search memories by semantic similarity
- Filter memories by tags with AND/OR matching
- Link memories in a knowledge graph with typed edges
- Consolidate older memories automatically
- Use the REST API and web dashboard in HTTP mode
- Tag memories by agent identity with the X-Agent-ID header

## Example prompts

- "Remember that we chose SQLite for this project because of single-user deployment."
- "Search my memory for past decisions about the authentication design."
- "Find memories tagged architecture and database."
- "What did we decide last week about the API versioning?"

## Install

### Claude Code

1. Run this in a terminal, in your project folder:

```bash
claude mcp add --transport stdio mcp-memory-service -- uvx --from mcp-memory-service memory server
```

2. Start Claude Code and type `/mcp`. **mcp-memory-service** should show as connected.

Add `--scope user` to make it available in every project.

### Claude Desktop

1. Open **Settings → Developer → Edit Config**. It opens `claude_desktop_config.json`. Add:

`claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "mcp-memory-service": {
      "command": "uvx",
      "args": [
        "--from",
        "mcp-memory-service",
        "memory",
        "server"
      ]
    }
  }
}
```

2. Save the file and restart Claude Desktop.

Needs uv (Python) on your computer. The file lives in `~/Library/Application Support/Claude/` on macOS and `%APPDATA%\Claude\` on Windows.

### Cursor

[Add to Cursor](<https://cursor.com/install-mcp?name=mcp-memory-service&config=eyJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyItLWZyb20iLCJtY3AtbWVtb3J5LXNlcnZpY2UiLCJtZW1vcnkiLCJzZXJ2ZXIiXX0%3D>) (opens Cursor)

Or add it by hand to `~/.cursor/mcp.json` (all projects) or `.cursor/mcp.json` (this project):

`mcp.json`:

```json
{
  "mcpServers": {
    "mcp-memory-service": {
      "command": "uvx",
      "args": [
        "--from",
        "mcp-memory-service",
        "memory",
        "server"
      ]
    }
  }
}
```

Needs uv (Python) on your computer.

### VS Code

[Install in VS Code](<https://vscode.dev/redirect/mcp/install?name=mcp-memory-service&config=%7B%22type%22%3A%22stdio%22%2C%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22--from%22%2C%22mcp-memory-service%22%2C%22memory%22%2C%22server%22%5D%7D>) (opens VS Code)

Or from a terminal:

```bash
code --add-mcp '{"name":"mcp-memory-service","type":"stdio","command":"uvx","args":["--from","mcp-memory-service","memory","server"]}'
```

Or commit it to the repo in `.vscode/mcp.json`:

`.vscode/mcp.json`:

```json
{
  "servers": {
    "mcp-memory-service": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "mcp-memory-service",
        "memory",
        "server"
      ]
    }
  }
}
```

Needs uv (Python) on your computer.

### Devin Desktop

1. Add it to `~/.config/devin/mcp_config.json` (macOS and Linux) or `%APPDATA%\devin\mcp_config.json` (Windows):

`mcp_config.json`:

```json
{
  "mcpServers": {
    "mcp-memory-service": {
      "command": "uvx",
      "args": [
        "--from",
        "mcp-memory-service",
        "memory",
        "server"
      ]
    }
  }
}
```

2. Refresh the MCP server list in Cascade.

Devin Desktop is the new name for Windsurf.

### Codex

```bash
codex mcp add mcp-memory-service -- uvx --from mcp-memory-service memory server
```

Or edit `~/.codex/config.toml` directly:

`config.toml`:

```toml
[mcp_servers.mcp-memory-service]
command = "uvx"
args = ["--from", "mcp-memory-service", "memory", "server"]
```

Needs uv (Python) on your computer.

### Gemini CLI

```bash
gemini mcp add mcp-memory-service uvx -- --from mcp-memory-service memory server
```

This adds it to the current project. Add `-s user` to use it everywhere.

### Any client

Most clients that start local servers accept this shape:

```json
{
  "mcpServers": {
    "mcp-memory-service": {
      "command": "uvx",
      "args": [
        "--from",
        "mcp-memory-service",
        "memory",
        "server"
      ]
    }
  }
}
```

Zed puts servers under `context_servers` in its settings, with the same `command`, `args` and `env` fields.

Needs uv (Python) on your computer.

## Details

- Server version: 11.15.0
- Last checked: 2026-10-03
- Listed: 2026-10-03
- Updated: 2026-10-03

---
Source: https://mcp.tc/i/mcp-memory-service (mcp.tc is an independent directory, not affiliated with this server's publisher). Corrections: https://mcp.tc/report