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](/mcp/servers/integrations/github) assets, demonstrating the coding preferences functionality in action.

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