chore: replace local MCP and Smithery with cloud MCP server (#4532)
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Saket Aryan
+108
-141
@@ -2,28 +2,34 @@
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title: "Mem0 MCP"
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description: "Connect any AI client to Mem0 using Model Context Protocol in minutes"
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icon: "puzzle-piece"
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estimatedTime: "~5 minutes"
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estimatedTime: "~2 minutes"
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---
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<Info>
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**Prerequisites**
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- Mem0 Platform account ([Sign up here](https://app.mem0.ai))
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- API key ([Get one from dashboard](https://app.mem0.ai/settings/api-keys))
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- Python 3.10+, Docker, or Node.js 14+
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- An MCP-compatible client (Claude Desktop, Cursor, or custom agent)
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- Node.js 14+ (for npx)
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- An MCP-compatible client (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode)
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</Info>
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## What is Mem0 MCP?
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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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Mem0 MCP Server exposes Mem0's memory capabilities as MCP tools, letting AI agents decide when to save, search, or update information. The cloud-hosted MCP server requires no local installation — just connect and start using memory.
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## Deployment Options
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## Quick Setup
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Choose from three deployment methods:
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Add Mem0 MCP to your preferred clients with a single command:
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1. **Python Package (Recommended)** - Install locally with `uvx` for instant setup
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2. **Docker Container** - Isolated deployment with HTTP endpoint
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3. **Smithery** - Remote hosted service for managed deployments
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "claude,claude code,cursor,windsurf,vscode,opencode"
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```
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This automatically configures Mem0 MCP for all supported clients at once.
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## Available Tools
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@@ -43,54 +49,105 @@ The MCP server exposes these memory tools to your AI client:
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---
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## Quickstart with Python (UVX)
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## Client-Specific Setup
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<Steps>
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<Step title="Install the MCP Server">
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```bash
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uv pip install mem0-mcp-server
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```
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</Step>
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You can also configure individual clients:
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<Step title="Configure your MCP client">
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Add this to your MCP client (e.g., Claude Desktop):
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<AccordionGroup>
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<Accordion title="Claude Desktop">
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "claude"
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```
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```json
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{
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"mcpServers": {
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"mem0": {
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"command": "uvx",
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"args": ["mem0-mcp-server"],
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"env": {
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"MEM0_API_KEY": "m0-...",
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"MEM0_DEFAULT_USER_ID": "your-handle"
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Or manually add to your Claude Desktop configuration (`claude_desktop_config.json`):
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```json
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{
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"mcpServers": {
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"mem0-mcp": {
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"type": "http",
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"url": "https://mcp.mem0.ai/mcp"
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}
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}
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}
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}
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}
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```
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```
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</Accordion>
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Set your environment variables:
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<Accordion title="Claude Code">
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "claude code"
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```
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</Accordion>
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```bash
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export MEM0_API_KEY="m0-..."
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export MEM0_DEFAULT_USER_ID="your-handle"
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```
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</Step>
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<Accordion title="Cursor">
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "cursor"
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```
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<Step title="Test with the Python agent">
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```bash
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# Clone the mem0-mcp repository
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git clone https://github.com/mem0ai/mem0-mcp.git
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cd mem0-mcp
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Or go to Cursor → Settings → MCP and add:
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```json
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{
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"mcpServers": {
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"mem0-mcp": {
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"type": "http",
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"url": "https://mcp.mem0.ai/mcp"
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}
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}
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}
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```
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</Accordion>
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# Set your API keys
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export MEM0_API_KEY="m0-..."
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export OPENAI_API_KEY="sk-openai-..."
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<Accordion title="Windsurf">
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "windsurf"
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```
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</Accordion>
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# Run the interactive agent
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python example/pydantic_ai_repl.py
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```
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<Accordion title="VS Code">
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "vscode"
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```
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</Accordion>
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<Accordion title="OpenCode">
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "opencode"
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```
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</Accordion>
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</AccordionGroup>
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---
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## Verify Your Setup
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Once configured, your AI client can:
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- Automatically save information with `add_memory`
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- Search memories with `search_memories`
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- Update memories with `update_memory`
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- Delete memories with `delete_memory`
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**Sample Interactions:**
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@@ -104,107 +161,18 @@ Agent: Based on your memories, you love tiramisu.
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User: Update my project: the mobile app is now 80% complete
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Agent: Updated your project status successfully.
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```
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</Step>
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<Step title="Verify the setup">
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Your AI client can now:
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- Automatically save information with `add_memory`
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- Search memories with `search_memories`
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- Update memories with `update_memory`
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- Delete memories with `delete_memory`
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<Info icon="check">
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If you get "Connection failed", ensure your API key is valid and the server is running.
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If you get "Connection failed", ensure you have a valid API key from [Mem0 Dashboard](https://app.mem0.ai/settings/api-keys).
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</Info>
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</Step>
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</Steps>
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---
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## Quickstart with Docker
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<Steps>
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<Step title="Build the Docker image">
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```bash
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docker build -t mem0-mcp-server https://github.com/mem0ai/mem0-mcp.git
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```
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</Step>
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<Step title="Run the container">
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```bash
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docker run --rm -d \
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--name mem0-mcp \
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-e MEM0_API_KEY="m0-..." \
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-p 8080:8081 \
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mem0-mcp-server
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```
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</Step>
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<Step title="Configure your client for HTTP">
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For clients that connect via HTTP (instead of stdio):
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```json
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{
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"mcpServers": {
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"mem0-docker": {
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"command": "curl",
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"args": ["-X", "POST", "http://localhost:8080/mcp", "--data-binary", "@-"],
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"env": {
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"MEM0_API_KEY": "m0-..."
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}
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}
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}
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}
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```
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</Step>
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<Step title="Verify the setup">
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```bash
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# Check container logs
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docker logs mem0-mcp
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# Test HTTP endpoint
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curl http://localhost:8080/health
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```
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<Info icon="check">
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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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</Info>
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</Step>
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</Steps>
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---
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## Quickstart with Smithery (Hosted)
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For the simplest integration, use Smithery's hosted Mem0 MCP server - no installation required.
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**Example: One-click setup in Cursor**
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1. Visit [smithery.ai/server/@mem0ai/mem0-memory-mcp](https://smithery.ai/server/@mem0ai/mem0-memory-mcp) and select Cursor as your client
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2. Open Cursor → Settings → MCP
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3. Click `mem0-mcp` → Initiate authorization
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4. Configure Smithery with your environment:
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- `MEM0_API_KEY`: Your Mem0 API key
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- `MEM0_DEFAULT_USER_ID`: Your user ID
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- `MEM0_ENABLE_GRAPH_DEFAULT`: Optional, set to `true` for graph memories
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5. Return to Cursor settings and wait for tools to load
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6. Start chatting with Cursor and begin storing preferences
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**For other clients:**
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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.
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---
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## Quick Recovery
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- **"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).
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- **"Connection refused"** → Check that the server is running and the correct port is configured
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- **"Connection refused"** → Check your internet connection and ensure the MCP client is correctly configured
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- **"Invalid API key"** → Get a new key from [Mem0 Dashboard](https://app.mem0.ai/settings/api-keys)
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- **"Permission denied"** → Ensure Docker has access to bind ports (try with `sudo` on Linux)
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- **"npx command not found"** → Install Node.js from [nodejs.org](https://nodejs.org)
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---
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@@ -227,6 +195,5 @@ Visit [smithery.ai/server/@mem0ai/mem0-memory-mcp](https://smithery.ai/server/@m
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## Additional Resources
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- **[Mem0 MCP Repository](https://github.com/mem0ai/mem0-mcp)** - Source code and examples
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- **[Platform Quickstart](/platform/quickstart)** - Direct API integration guide
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- **[MCP Specification](https://modelcontextprotocol.io)** - Learn about MCP protocol
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- **[MCP Specification](https://modelcontextprotocol.io)** - Learn about MCP protocol
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