Which integrations are available for this server?

Manages environment variables to securely store API keys, including mem0 API key configuration. Provides demo capabilities through GitHub assets, demonstrating the coding preferences functionality in action.

How do I use Mem0 MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Mem0 MCP Server search for my Python coding style preferences" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Mem0 MCP Server

PyPI version License: Apache 2.0

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.

Related MCP server: Mem0 MCP Server

Usage Options

There are three ways to use the Mem0 MCP Server:

Python Package - Install and run locally using uvx with any MCP client
Docker - Containerized deployment that creates an /mcp HTTP endpoint
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

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

Docker Deployment

To run with Docker:

Build the image:
docker build -t mem0-mcp-server .
Run the container:
docker run --rm -d \ --name mem0-mcp \ -e MEM0_API_KEY=m0-... \ -p 8080:8081 \ mem0-mcp-server
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:

Install the MCP server (Smithery dependencies are now bundled):
pip install mem0-mcp-server
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