OpenMemory MCP (Model Context Protocol) is officially launched, providing a unified local memory sharing solution for AI tools. This open-source tool allows users to store AI interaction content locally and share it via the MCP protocol to supported clients, such as Claude, Cursor, and Windsurf. You only need to maintain one copy of the memory content to achieve cross-tool context synchronization.

Core Features: Local Storage, Cross-Tool Sharing

OpenMemory MCP provides persistent, context-aware memory management for MCP-compatible clients through a local memory layer. Its main features include:

Unified Local Storage: All AI interaction content (such as project requirements and code style preferences) is stored on the user's device to ensure data privacy and control.

Cross-Tool Memory Sharing: Supports seamless access to the same memory library by MCP clients such as Claude, Cursor, and Windsurf, eliminating the need to repeatedly enter context.

Metadata Enhancement: Memory content is accompanied by metadata such as topics, emotions, and timestamps for easy search and management.

Visual Dashboard: The built-in OpenMemory dashboard provides a centralized management interface, supporting the addition, browsing, deletion of memories, and control of client access permissions.

OpenMemory MCP uses the Qdrant vector database and Server-Sent Events (SSE) to achieve efficient memory storage and real-time communication. Social media feedback shows that developers have extremely high evaluations of the tool's local operation and cross-tool consistency, especially suitable for multi-tool collaboration scenarios (https://github.com/mem0ai/mem0/tree/main/openmemory).

Installation and Configuration

Prerequisites

Before you start, make sure the following software is installed on your system:

• Docker and Docker Compose
• Python 3.9+
• Node.js
• OpenAI API key
• GNU Make

Detailed Installation Steps

1. Clone the code repository and set the OpenAI API key

Copy# Clone the repositorygit clone https://github.com/mem0ai/mem0.gitcd openmemory# Set the OpenAI API key as an environment variableexport OPENAI_API_KEY=your_api_key_here

1. Set up the backend

Copy# Copy the environment file and update OPENAI_API_KEY and other keysmake env# Build all Docker imagesmake build# Start the Postgres, Qdrant, and FastAPI/MCP serversmake up

The environment file .env.local will contain the following content:

OPENAI_API_KEY=your_api_key

1. Set up the frontend

Copy# Install dependencies using pnpm and run the Next.js development servermake ui

After the installation is complete, you can visit https://localhost:3000 to view the OpenMemory dashboard, which will guide you to install the MCP server in the MCP client.

Practical Use Cases

Case 1: Cross-Tool Project Development Process

Scenario: You are developing a complex software project involving multiple tools and environments.

Implementation Steps:

1. Connect multiple MCP clients

First, we need to install OpenMemory MCP in each tool. Get the installation command from the dashboard:

Copy# Install in Cursornpx install-mcp i https://localhost:8765/mcp/cursor/sse/your_username --client cursor# Install in Claude Desktopnpx install-mcp i http://localhost:8765/mcp/claude/sse/your_username --client claude

1. Define project requirements in Claude

Open Claude Desktop, discuss and define your project requirements with the AI:

I'm developing a data synchronization tool called "DataSync" that needs to implement the following functions:1. Support real-time data synchronization from MySQL and PostgreSQL databases2. Provide a REST API interface for monitoring the synchronization status3. Data changes need to be recorded in the event log4. Provide a simple web interface to view the synchronization status

Claude will give suggestions for the system architecture. At this time, it uses the add_memories() function to store this conversation memory in OpenMemory MCP.

1. Develop in Cursor

Now switch to the Cursor editor. You can directly reference the previous requirements and architecture design:

Please write a connector module for the MySQL data source based on the DataSync project architecture we discussed before.

Cursor will retrieve the project architecture defined in Claude before through the search_memory() function and then generate connector code that meets the previous discussion.

1. Debug in Windsurf

When you encounter a problem, switch to Windsurf for debugging:

The MySQL connector of the DataSync project has performance issues when processing a large number of transactions. Do you have any optimization suggestions?

Windsurf can obtain the previous context through OpenMemory MCP, including project requirements and the code written in Cursor, so as to provide more accurate debugging suggestions.

1. View project memories through the dashboard

In the OpenMemory dashboard, you can see all stored memories, including:

• Project requirement memories (from Claude)
• Code implementation memories (from Cursor)
• Debugging problem memories (from Windsurf)

You can filter these memories by category, creation date, or application.

Case 2: Personalized Coding Assistant

Scenario: You want to create an assistant that can remember your coding style, common problems, and their solutions.

Implementation Steps:

1. Store coding preferences

In Cursor, tell the AI your coding preferences:

My Python coding style preferences:1. Use type hints2. Each function needs to have a clear docstring3. Use the Black formatting tool4. Use snake_case for variable naming

1. Create basic project settings

In a new project, let the AI help you generate the configuration:

Please generate the following files for a Python project based on my coding preferences:1. .flake8 configuration2. pyproject.toml (including Black and mypy configuration)3. Example directory structure

1. Get help when encountering errors

When you encounter a problem while writing code:

I encountered the following error when implementing a SQLAlchemy query:

The AI will solve this problem and store the error and solution using the add_memories() function. The next time you encounter a similar problem, it can directly retrieve this memory.

1. Track learning progress

View the "Memories" section on the dashboard, and you can see:

• All stored coding problems and solutions
• Memories automatically classified by technology stack (such as "Python", "SQLAlchemy", "API design", etc.)
• The most frequently accessed memories to help you identify recurring problems

Advanced Features and Usage Tips

1. Memory Management

OpenMemory MCP provides various ways to manage your memories:

• Pause/Enable Memory: You can pause the access permission of specific memories in the dashboard, which is very useful when you need to temporarily disable certain information
• Archive Memory: Archive memories that are no longer needed but may be useful in the future
• Delete Memory: Permanently remove unwanted memories
• Batch Operations: Select multiple memories for batch pause, archive, or deletion

2. Manually Create Memories

In addition to letting the AI automatically store memories, you can also manually create memories:

1. Click the "Create Memory" button in the dashboard
2. Enter the memory content and select the category
3. Set the access permission (which applications can read this memory)

This is particularly useful for storing important project information, team specifications, or commonly used code snippets.

3. Access Control

Through fine-grained access control, you can:

• Control which applications can access specific memories
• Pause the write permission of an entire application
• Set different access levels for different types of memories

For example, you may want personal preferences to be visible only to Cursor, while the project architecture should be visible to all tools.

Frequently Asked Questions and Solutions

Q1: The OpenMemory MCP server fails to start or the connection fails.

Solution:

1. Make sure Docker Desktop is running
2. Check if ports 8765 and 3000 are occupied by other applications
3. View the logs: make logs
4. Rebuild and start: make down && make build && make up

Q2: Memories are not retrieved correctly.

Solution:

1. Confirm that the memory status is "Active" instead of "Paused" or "Archived"
2. Check if the application access permissions are set correctly
3. Try adjusting the query keywords to make them closer to the original memory content
4. Manually search for the memory in the dashboard to confirm its existence

Q3: How can I share memories across multiple devices?

Solution: Currently, OpenMemory MCP is completely local and does not support native cross-device synchronization. However, you can:

1. Back up and migrate the database file
2. Wait for the upcoming OpenMemory Cloud version, which will provide cloud synchronization functionality

Best Practices

1. Organize memories effectively: Use a clear structure for the AI to store memories, such as "Project: DataSync/Requirements", "Code: SQLAlchemy/Errors"
2. Review and clean up regularly: Regularly check the dashboard, archive or delete memories that are no longer needed to maintain the quality of the memory library
3. Use both automatic and manual memories: Let the AI automatically store conversation memories while manually creating memories for key information
4. Utilize the classification function: Use the classification filtering function of the dashboard to manage and retrieve memories more efficiently

Conclusion

OpenMemory MCP solves one of the most critical limitations of modern AI assistants: context loss and memory disruption. By providing a persistent memory layer for AI, it achieves a truly personalized AI interaction experience, improves the efficiency of multi-tool workflows, and maintains complete local storage of data and user control.

Whether you are developing a complex project, conducting research, or creating a personalized assistant, OpenMemory MCP can significantly enhance your AI workflow. Start trying this powerful tool and let your AI truly understand and "remember" your way of working.