[!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This project has been archived

mem0-mcp-server is no longer actively maintained and this repository is now a public archive.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

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:

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

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:

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

License

Apache License 2.0

Mem0Mem0

Установка

This project has been archived