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mem0-mcp

@mem0ai532

This demonstrates a structured approach for using an MCP server with mem0 to manage coding preferences efficiently. The server can be used with Cursor and provides essential tools for storing, retrieving, and searching coding preferences.

MCP
Mem0
Coding Preferences
Context Management
AI

Mem0 MCP Server

PyPI version License: Apache 2.0 smithery badge

mem0-mcp-server wraps the official Mem0 Memory API as a Model Context Protocol (MCP) server so any MCP-compatible client (Claude Desktop, Cursor, custom agents) can add, search, update, and delete long-term memories.

Tools

The server exposes the following tools to your LLM:

Tool Description
add_memory Save text or conversation history (or explicit message objects) for a user/agent.
search_memories Semantic search across existing memories (filters + limit supported).
get_memories List memories with structured filters and pagination.
get_memory Retrieve one memory by its memory_id.
update_memory Overwrite a memory's text once the user confirms the memory_id.
delete_memory Delete a single memory by memory_id.
delete_all_memories Bulk delete all memories in the confirmed scope (user/agent/app/run).
delete_entities Delete a user/agent/app/run entity (and its memories).
list_entities Enumerate users/agents/apps/runs stored in Mem0.

All responses are JSON strings returned directly from the Mem0 API.

Usage Options

There are three ways to use the Mem0 MCP Server:

  1. Python Package - Install and run locally using uvx with any MCP client
  2. Docker - Containerized deployment that creates an /mcp HTTP endpoint
  3. Smithery - Remote hosted service for managed deployments

Quick Start

Installation

uv pip install mem0-mcp-server

Or with pip:

pip install mem0-mcp-server

Client Configuration

Add this configuration to your MCP client:

{
  "mcpServers": {
    "mem0": {
      "command": "uvx",
      "args": ["mem0-mcp-server"],
      "env": {
        "MEM0_API_KEY": "m0-...",
        "MEM0_DEFAULT_USER_ID": "your-handle"
      }
    }
  }
}

Test with the Python Agent

Click to expand: Test with the Python Agent

To test the server immediately, use the included Pydantic AI agent:

# Install the package
pip install mem0-mcp-server
# Or with uv
uv pip install mem0-mcp-server

# Set your API keys
export MEM0_API_KEY="m0-..."
export OPENAI_API_KEY="sk-openai-..."

# Clone and test with the agent
git clone https://github.com/mem0ai/mem0-mcp.git
cd mem0-mcp-server
python example/pydantic_ai_repl.py

Using different server configurations:

# Use with Docker container
export MEM0_MCP_CONFIG_PATH=example/docker-config.json
export MEM0_MCP_CONFIG_SERVER=mem0-docker
python example/pydantic_ai_repl.py

# Use with Smithery remote server
export MEM0_MCP_CONFIG_PATH=example/config-smithery.json
export MEM0_MCP_CONFIG_SERVER=mem0-memory-mcp
python example/pydantic_ai_repl.py

What You Can Do

The Mem0 MCP server enables powerful memory capabilities for your AI applications:

  • Remember that I'm allergic to peanuts and shellfish - Add new health information to memory
  • Store these trial parameters: 200 participants, double-blind, placebo-controlled study - Save research data
  • What do you know about my dietary preferences? - Search and retrieve all food-related memories
  • Update my project status: the mobile app is now 80% complete - Modify existing memory with new info
  • Delete all memories from 2023, I need a fresh start - Bulk remove outdated memories
  • Show me everything I've saved about the Phoenix project - List all memories for a specific topic

Configuration

Environment Variables

  • MEM0_API_KEY (required) – Mem0 platform API key.
  • MEM0_DEFAULT_USER_ID (optional) – default user_id injected into filters and write requests (defaults to mem0-mcp).
  • MEM0_ENABLE_GRAPH_DEFAULT (optional) – Enable graph memories by default (defaults to false).
  • MEM0_MCP_AGENT_MODEL (optional) – default LLM for the bundled agent example (defaults to openai:gpt-4o-mini).

Advanced Setup

Click to expand: Docker, Smithery, and Development

Docker Deployment

To run with Docker:

  1. Build the image:

    docker build -t mem0-mcp-server .

  2. Run the container:

    docker run --rm -d \\
  --name mem0-mcp \\
  -e MEM0_API_KEY=m0-... \\
  -p 8080:8081 \\
  mem0-mcp-server

  3. Monitor the container:

    # View logs
docker logs -f mem0-mcp

# Check status
docker ps

Running with Smithery Remote Server

To connect to a Smithery-hosted server:

  1. Install the MCP server (Smithery dependencies are now bundled):

    pip install mem0-mcp-server

  2. Configure MCP client with Smithery:

    {
  "mcpServers": {
    "mem0-memory-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "@mem0ai/mem0-memory-mcp",
        "--key",
        "your-smithery-key",
        "--profile",
        "your-profile-name"
      ],
      "env": {
        "MEM0_API_KEY": "m0-..."
      }
    }
  }
}

Development Setup

Clone and run from source:

git clone https://github.com/mem0ai/mem0-mcp.git
cd mem0-mcp-server
pip install -e ".[dev]"

# Run locally
mem0-mcp-server

# Or with uv
uv sync
uv run mem0-mcp-server

License

Apache License 2.0

# mcpServer Config

No mcpServer Config instructions provided.

# sseURL 

http://0.0.0.0:8080/sse
Transport:
sse
Language:
Python
Created: 2/18/2025
Updated: 2/4/2026
Homepage:
https://github.com/mem0ai/mem0-mcp

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