Mem0 is a long-term memory service for AI agents that enables persistent storage and retrieval of information across conversations, users, and agent runs. It provides semantic memory capabilities that help LLMs remember context and user preferences over time. Key capabilities of Mem0: - Add and store memories from conversation history or explicit messages for specific users and agents - Semantic search across memories with filtering and pagination support - Retrieve, update, and delete individual memories by ID - Bulk memory management including deleting all memories within a scope - Entity management for organizing memories by user, agent, app, or run - Cloud-hosted MCP server available at mcp.mem0.ai for easy integration with Claude, Cursor, and other MCP-compatible clients

> [!CAUTION] > ## This project has been archived > > mem0-mcp-server is no longer actively maintained and this repository is now a public archive. > > Thank you to the 640+ stargazers, 140+ forkers, and every contributor who helped shape this project. Your support and feedback meant the world to us. > > Looking for Mem0 MCP? We now offer an official cloud-hosted MCP server. Check out the docs to get started. > > Quick install across all major clients: >

> npx mcp-add \
>   --name mem0-mcp \
>   --type http \
>   --url "https://mcp.mem0.ai/mcp" \
>   --clients "claude,claude code,cursor,windsurf,vscode,opencode"
> 

Mem0 MCP Server

[](https://pypi.org/project/mem0-mcp-server/) [](LICENSE) [](https://smithery.ai/server/@mem0ai/mem0-memory-mcp)

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:

Configuration

Environment Variables