Content
# Mem0 MCP Server
[](https://pypi.org/project/mem0-mcp-server/) [](LICENSE) [](https://smithery.ai/server/@mem0ai/mem0-memory-mcp)
`mem0-mcp-server` wraps the official [Mem0](https://mem0.ai) 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
```bash
uv pip install mem0-mcp-server
```
Or with pip:
```bash
pip install mem0-mcp-server
```
### Client Configuration
Add this configuration to your MCP client:
```json
{
"mcpServers": {
"mem0": {
"command": "uvx",
"args": ["mem0-mcp-server"],
"env": {
"MEM0_API_KEY": "m0-...",
"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:
```bash
# 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:**
```bash
# 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_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
<details>
<summary><strong>Click to expand: Docker, Smithery, and Development</strong></summary>
### Docker Deployment
To run with Docker:
1. Build the image:
```bash
docker build -t mem0-mcp-server .
```
2. Run the container:
```bash
docker run --rm -d \
--name mem0-mcp \
-e MEM0_API_KEY=m0-... \
-p 8080:8081 \
mem0-mcp-server
```
3. Monitor the container:
```bash
# 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 the MCP server (Smithery dependencies are now bundled):
```bash
pip install mem0-mcp-server
```
2. Configure MCP client with Smithery:
```json
{
"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:
```bash
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
```
</details>
## License
[Apache License 2.0](https://github.com/mem0ai/mem0-mcp/blob/main/LICENSE)
