Mem0 MCP Server

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": "sk_mem0_...",
        "MEM0_DEFAULT_USER_ID": "your-handle"
      }
    }
  }
}

Test with the Python Agent

<details> <summary><strong>Click to expand: Test with the Python Agent</strong></summary>

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="sk_mem0_..."
export OPENAI_API_KEY="sk-openai-..."

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

</details>

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_MCP_AGENT_MODEL (optional) – default LLM for the bundled agent example (defaults to openai:gpt-4o-mini).

Advanced Setup

<details> <summary><strong>Click to expand: Docker, Smithery, and Development</strong></summary>

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=sk_mem0_... \
     -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 with Smithery support:

   pip install "mem0-mcp-server[smithery]"
   

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": "sk_mem0_..."
         }
       }
     }
   }
   

Development Setup

Clone and run from source:

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

# Run locally
mem0-mcp-server

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

</details>

License

Apache License 2.0