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Prerequisites
  • Mem0 Platform account (Sign up here)
  • API key (Get one from dashboard)
  • Python 3.10+, Docker, or Node.js 14+
  • An MCP-compatible client (Claude Desktop, Cursor, or custom agent)

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What is Mem0 MCP?

Mem0 MCP Server exposes Mem0’s memory capabilities as MCP tools, letting AI agents decide when to save, search, or update information.

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Deployment Options

Choose from three deployment methods:
  1. Python Package (Recommended) - Install locally with uvx for instant setup
  2. Docker Container - Isolated deployment with HTTP endpoint
  3. Smithery - Remote hosted service for managed deployments

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Available Tools

The MCP server exposes these memory tools to your AI client:
ToolDescription
add_memorySave text or conversation history for a user/agent
search_memoriesSemantic search across existing memories with filters
get_memoriesList memories with structured filters and pagination
get_memoryRetrieve one memory by its memory_id
update_memoryOverwrite a memory’s text after confirming the ID
delete_memoryDelete a single memory by memory_id
delete_all_memoriesBulk delete all memories in scope
delete_entitiesDelete a user/agent/app/run entity and its memories
list_entitiesEnumerate users/agents/apps/runs stored in Mem0

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Quickstart with Python (UVX)

1

Install the MCP Server

uv pip install mem0-mcp-server
2

Configure your MCP client

Add this to your MCP client (e.g., Claude Desktop):
{
  "mcpServers": {
    "mem0": {
      "command": "uvx",
      "args": ["mem0-mcp-server"],
      "env": {
        "MEM0_API_KEY": "m0-...",
        "MEM0_DEFAULT_USER_ID": "your-handle"
      }
    }
  }
}
Set your environment variables:
export MEM0_API_KEY="m0-..."
export MEM0_DEFAULT_USER_ID="your-handle"
3

Test with the Python agent

# Clone the mem0-mcp repository
git clone https://github.com/mem0ai/mem0-mcp.git
cd mem0-mcp

# Set your API keys
export MEM0_API_KEY="m0-..."
export OPENAI_API_KEY="sk-openai-..."

# Run the interactive agent
python example/pydantic_ai_repl.py
Sample Interactions:
User: Remember that I love tiramisu
Agent: Got it! I've saved that you love tiramisu.

User: What do you know about my food preferences?
Agent: Based on your memories, you love tiramisu.

User: Update my project: the mobile app is now 80% complete
Agent: Updated your project status successfully.
4

Verify the setup

Your AI client can now:
  • Automatically save information with add_memory
  • Search memories with search_memories
  • Update memories with update_memory
  • Delete memories with delete_memory
If you get “Connection failed”, ensure your API key is valid and the server is running.

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Quickstart with Docker

1

Build the Docker image

docker build -t mem0-mcp-server https://github.com/mem0ai/mem0-mcp.git
2

Run the container

docker run --rm -d \
  --name mem0-mcp \
  -e MEM0_API_KEY="m0-..." \
  -p 8080:8081 \
  mem0-mcp-server
3

Configure your client for HTTP

For clients that connect via HTTP (instead of stdio):
{
  "mcpServers": {
    "mem0-docker": {
      "command": "curl",
      "args": ["-X", "POST", "http://localhost:8080/mcp", "--data-binary", "@-"],
      "env": {
        "MEM0_API_KEY": "m0-..."
      }
    }
  }
}
4

Verify the setup

# Check container logs
docker logs mem0-mcp

# Test HTTP endpoint
curl http://localhost:8080/health
The container should start successfully and respond to HTTP requests. If port 8080 is occupied, change it with -p 8081:8081.

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Quickstart with Smithery (Hosted)

For the simplest integration, use Smithery’s hosted Mem0 MCP server - no installation required. Example: One-click setup in Cursor
  1. Visit smithery.ai/server/@mem0ai/mem0-memory-mcp and select Cursor as your client
Smithery Mem0 MCP Configuration
  1. Open Cursor → Settings → MCP
  2. Click mem0-mcp → Initiate authorization
  3. Configure Smithery with your environment:
    • MEM0_API_KEY: Your Mem0 API key
    • MEM0_DEFAULT_USER_ID: Your user ID
    • MEM0_ENABLE_GRAPH_DEFAULT: Optional, set to true for graph memories
  4. Return to Cursor settings and wait for tools to load
  5. Start chatting with Cursor and begin storing preferences
For other clients: Visit smithery.ai/server/@mem0ai/mem0-memory-mcp to connect any MCP-compatible client with your Mem0 credentials.

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Quick Recovery

  • “uvx command not found” → Install with pip install uv or use pip install mem0-mcp-server instead. Make sure your Python environment has uv installed (or system-wide).
  • “Connection refused” → Check that the server is running and the correct port is configured
  • “Invalid API key” → Get a new key from Mem0 Dashboard
  • “Permission denied” → Ensure Docker has access to bind ports (try with sudo on Linux)

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Next Steps

MCP Integration Feature

Gemini 3 with Mem0 MCP

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Additional Resources