Store persistent project memory for AI agents and MCP clients so they can resume work with context, decisions, and tasks intact
memory-mcp helps you keep project memory in one place. It works with OpenCode, Antigravity, Claude Code, and Codex through the Model Context Protocol, or MCP.
Use it to store notes, project details, and past decisions in Supabase. That lets your AI tools remember what matters across sessions.
This guide is for Windows users who want to get started fast.
You will need:
Visit this page to download and set up memory-mcp:
If the page has a release file, download it. If it has setup files or source code, save the files to your PC so you can run the server from that folder.
memory-mcp stores project memory in Supabase. You need a Supabase project before you start.
You also need a table for memory data. Use the schema included in the repository if it is provided. If not, create a table for notes, project IDs, timestamps, and message text.
memory-mcp uses Python on Windows.
To confirm it works, open Command Prompt and run:
python --version
After you download the files, put them in a folder such as:
C:\memory-mcp
Open that folder in File Explorer.
If the repository contains a requirements file, use it to install the needed packages.
Open Command Prompt in the memory-mcp folder and run:
pip install -r requirements.txt
If the project uses another package file, use that file instead.
memory-mcp needs your Supabase details to connect to your memory store.
Create a file named .env in the project folder and add your values:
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key
SUPABASE_TABLE=your_table_name
If the project includes sample settings, copy that format and fill in your own values.
In Command Prompt, stay in the project folder and run:
python main.py
If the repository uses a different start file, run the main file that starts the MCP server.
When the server starts, it will stay open and listen for requests from your AI tool.
Add memory-mcp to the MCP setup for your tool.
Use the server command or file path from the project files, then point it to the Python file that starts the service.
A common setup looks like this:
Then restart your AI tool.
Once connected, your AI tool can store and read project memory.
You can use it for:
This helps the tool keep track of work across chats and sessions.
Try a simple test:
If the setup is correct, the tool will pull the same memory from Supabase.
If the server does not start:
.env file has the correct valuesIf your AI tool cannot see the server:
If memory does not save:
A simple project layout may look like this:
main.py — starts the serverrequirements.txt — lists Python packages.env — stores your Supabase valuesREADME.md — setup notessrc/ — app codeschemas/ — database setup filesmemory-mcp stores project memory in your own Supabase project. That gives you control over where the data lives and who can access it.
Use a private Supabase project and keep your keys safe.
memory-mcp works well for:
Download and setup page:
User75621/memory-mcp
April 4, 2026
April 13, 2026
Python