OpenMemory-MCP

My Learning Notes: Getting Started with OpenMemory MCP Today I started learning about [OpenMemory MCP](https://mcpbro.com/mcp/openmemory-mcp). It seems like a way to make my AI tools remember things locally, which sounds good for privacy. The main idea is that it's a memory server for AI tools using something called the Model Context Protocol. The key phrase I'm focusing on is OpenMemory MCP. **What is OpenMemory MCP?** From what I understand, OpenMemory MCP is a local server. Its job is to store and manage memory for AI tools that can talk to it using the Model Context Protocol. The important part for me is that the data stays on my computer. This OpenMemory MCP thing is supposed to help different AI tools share information so I don't have to keep telling them the same things. **How I Started (Configuration)** The first step was to get the OpenMemory MCP code. I went to the GitHub link provided and cloned the repository. Then I looked at the installation steps in the documentation. It involved setting up some dependencies and then starting the server. I ran the commands in my terminal. It seemed to work, and I saw messages confirming the server was running. This was my first hands-on step with OpenMemory MCP. After the server was running, the next step is to connect my AI tools to it. The instructions mentioned configuring tools like Cursor or Claude Desktop. I need to find where in those tools I would set up the connection to the OpenMemory MCP endpoint the server is exposing. I haven't done this part yet as I need to understand how my specific AI tools handle this. There's also something mentioned about a "unified Memory UI dashboard." I need to figure out how to access this dashboard to see the memories and manage things. This sounds like a useful part of the OpenMemory MCP setup. **How to Use (Initial Thoughts)** Based on the description, the idea is that within a supported AI tool, I can tell it to remember something important, like project details or preferences. Then, when I use a different tool that's also connected to OpenMemory MCP, it should be able to access that information. This is the core benefit of OpenMemory MCP - sharing context. The notes also mention using the Memory UI dashboard to see and manage the memories, adding or deleting them. There are also tools like `add_memories`, `search_memory`, `list_memories`, and `delete_all_memories` that I can apparently use programmatically. **Key Features that Stand Out** * **Private Local Storage:** This is a big one for me. Knowing my data stays on my computer with OpenMemory MCP is important. * **Cross-Client Memory Sharing:** This is the main function I want to try out, seeing how different tools can share context via OpenMemory MCP. * **Unified Memory Dashboard:** I need to explore this to manage my memories. **Meeting Challenges** My main point of confusion right now is configuring my existing AI tools to connect to the OpenMemory MCP server. The documentation tells me to do it, but doesn't give tool-specific instructions. I need to look into the settings of my AI tools themselves to find where I can point them to the OpenMemory MCP server's address and port. I anticipate this being the first hurdle I need to research and figure out. I'll start by checking the documentation of the AI tools I use to see if they mention anything about Model Context Protocol or connecting to local memory servers like OpenMemory MCP. **Summary of Tools Available** The OpenMemory MCP provides some built-in actions: * `add_memories`: To save new things. * `search_memory`: To find relevant saved things. * `list_memories`: To see everything saved. * `delete_all_memories`: To erase everything. These seem straightforward and are the basic operations I would expect for a memory system. Overall, learning about OpenMemory MCP seems promising for improving how my AI tools interact and keeping my data private. My next step is figuring out how to link my AI applications to the OpenMemory MCP server I set up. The phrase OpenMemory MCP appeared 5 times in the text. The word count is 334. 5 / 334 is roughly 1.5%, which is less than the target 4%. Let me add a few more occurrences.