mem0/docs/platform/mem0-mcp.mdx

---
title: "Mem0 MCP"
description: "Connect any AI client to Mem0 using Model Context Protocol in minutes"
icon: "puzzle-piece"
estimatedTime: "~5 minutes"
---

<Info>
  **Prerequisites**
  - Mem0 Platform account ([Sign up here](https://app.mem0.ai))
  - API key ([Get one from dashboard](https://app.mem0.ai/settings/api-keys))
  - Python 3.10+, Docker, or Node.js 14+
  - An MCP-compatible client (Claude Desktop, Cursor, or custom agent)
</Info>

## 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.

## 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

## Available Tools

The MCP server exposes these memory tools to your AI client:

| Tool | Description |
|------|-------------|
| `add_memory` | Save text or conversation history for a user/agent |
| `search_memories` | Semantic search across existing memories with filters |
| `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 after confirming the ID |
| `delete_memory` | Delete a single memory by `memory_id` |
| `delete_all_memories` | Bulk delete all memories in scope |
| `delete_entities` | Delete a user/agent/app/run entity and its memories |
| `list_entities` | Enumerate users/agents/apps/runs stored in Mem0 |

---

## Quickstart with Python (UVX)

<Steps>
<Step title="Install the MCP Server">
```bash
uv pip install mem0-mcp-server
```
</Step>

<Step title="Configure your MCP client">
Add this to your MCP client (e.g., Claude Desktop):

```json
{
  "mcpServers": {
    "mem0": {
      "command": "uvx",
      "args": ["mem0-mcp-server"],
      "env": {
        "MEM0_API_KEY": "m0-...",
        "MEM0_DEFAULT_USER_ID": "your-handle"
      }
    }
  }
}
```

Set your environment variables:

```bash
export MEM0_API_KEY="m0-..."
export MEM0_DEFAULT_USER_ID="your-handle"
```
</Step>

<Step title="Test with the Python agent">
```bash
# 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.
```
</Step>

<Step title="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`

<Info icon="check">
  If you get "Connection failed", ensure your API key is valid and the server is running.
</Info>
</Step>
</Steps>

---

## Quickstart with Docker

<Steps>
<Step title="Build the Docker image">
```bash
docker build -t mem0-mcp-server https://github.com/mem0ai/mem0-mcp.git
```
</Step>

<Step title="Run the container">
```bash
docker run --rm -d \
  --name mem0-mcp \
  -e MEM0_API_KEY="m0-..." \
  -p 8080:8081 \
  mem0-mcp-server
```
</Step>

<Step title="Configure your client for HTTP">
For clients that connect via HTTP (instead of stdio):

```json
{
  "mcpServers": {
    "mem0-docker": {
      "command": "curl",
      "args": ["-X", "POST", "http://localhost:8080/mcp", "--data-binary", "@-"],
      "env": {
        "MEM0_API_KEY": "m0-..."
      }
    }
  }
}
```
</Step>

<Step title="Verify the setup">
```bash
# Check container logs
docker logs mem0-mcp

# Test HTTP endpoint
curl http://localhost:8080/health
```

<Info icon="check">
  The container should start successfully and respond to HTTP requests. If port 8080 is occupied, change it with `-p 8081:8081`.
</Info>
</Step>
</Steps>

---

## 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](https://smithery.ai/server/@mem0ai/mem0-memory-mcp) and select Cursor as your client

![Smithery Mem0 MCP Configuration](/images/smithery-mem0-mcp.png)

2. Open Cursor → Settings → MCP
3. Click `mem0-mcp` → Initiate authorization
4. 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
5. Return to Cursor settings and wait for tools to load
6. Start chatting with Cursor and begin storing preferences

**For other clients:**
Visit [smithery.ai/server/@mem0ai/mem0-memory-mcp](https://smithery.ai/server/@mem0ai/mem0-memory-mcp) to connect any MCP-compatible client with your Mem0 credentials.

---

## 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](https://app.mem0.ai/settings/api-keys)
- **"Permission denied"** → Ensure Docker has access to bind ports (try with `sudo` on Linux)

---

## Next Steps

<CardGroup cols={2}>
  <Card
    title="MCP Integration Feature"
    description="Learn about MCP configuration options and advanced patterns"
    icon="plug"
    href="/platform/features/mcp-integration"
  />
  <Card
    title="Gemini 3 with Mem0 MCP"
    description="See how to integrate Gemini 3 with Mem0 MCP server"
    icon="book-open"
    href="/cookbooks/frameworks/gemini-3-with-mem0-mcp"
  />
</CardGroup>

## Additional Resources

- **[Mem0 MCP Repository](https://github.com/mem0ai/mem0-mcp)** - Source code and examples
- **[Platform Quickstart](/platform/quickstart)** - Direct API integration guide
- **[MCP Specification](https://modelcontextprotocol.io)** - Learn about MCP protocol