Mem0

651

Summary

The Mem0 MCP Server wraps the Mem0 Memory API as a Model Context Protocol server, enabling MCP-compatible clients to manage long-term memories through tools for adding, searching, updating, and deleting memories across users and agents. It provides nine tools including `add_memory`, `search_memories`, `update_memory`, and `delete_memory` that allow LLMs to perform semantic search and structured retrieval of stored conversation history and text data. The server is now archived with an official cloud-hosted replacement available, though it can still be deployed locally via Python package, Docker, or remote hosting services.

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[!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This project has been archived

mem0-mcp-server is no longer actively maintained and this repository is now a public archive.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

Featured

CodeRabbit

AI writes the code. CodeRabbit catches the slop.

Try For Free →

Make your agent a DeFi expert

Agent, run crypto. Access onchain data & trade routes via 1inch.

Install now →

Make money from your Skills

On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.

Start earning →

AppSignal

Monitor with ease. Code with confidence.

Start Free Trial →

Vibe Prospecting MCP

Connect Claude to +800M contacts, +150M companies. Find & Enrich leads in chat.

Try For Free →

Context.dev

Integrate web data into your AI product. One API to scrape website & brand data.

Get API Key Now →

Mem0 MCP Server

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

Mem0

This project has been archived

Mem0 MCP Server

Tools

Usage Options

Quick Start

Installation

Client Configuration

Test with the Python Agent

What You Can Do

Configuration

Environment Variables

Advanced Setup

Docker Deployment

Running with Smithery Remote Server

Development Setup

License

Mem0

This project has been archived

Mem0 MCP Server

Tools

Usage Options

Quick Start

Installation

Client Configuration

Test with the Python Agent

What You Can Do

Configuration

Environment Variables

Advanced Setup

Docker Deployment

Running with Smithery Remote Server

Development Setup

License

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