aimemory
aimemory is an extension for AI workflows in Cursor and VS Code that provides persistent, context-aware memory. It allows the AI assistant to remember project context, decisions, and progress across sessions, reducing repetitive tasks and enabling the AI to learn and grow with the project.
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AI Memory Extension for Cursor ๐น
Persistent, context-aware memory for AI workflows in Cursor and VS Code
Give your AI assistant a memory bank that remembers your project context, decisions, and progress across sessions. No more repeating yourselfโyour AI learns and grows with your project.
๐ Quick Start
Installation
For Cursor Users:
- Install the extension from Cursor marketplace (coming soon)
- Open Command Palette (
Cmd/Ctrl+Shift+P
) - Run:
AI Memory: Open Dashboard
- Click "Initialize Memory Bank"
For Developers:
git clone https://github.com/sm-moshi/aimemory.git
cd aimemory
pnpm install && pnpm build
# Press F5 in Cursor/VS Code to launch extension
Basic Usage
# Chat commands in Cursor
/memory status # Check memory bank health
/memory list # See all stored memories
/memory read projectbrief # Read specific memory file
โจ What It Does
- ๐ง Persistent Memory: Your AI remembers project context between sessions
- ๐ Organized Structure: Auto-creates folders for project briefs, technical context, and progress
- ๐ Self-Healing: Automatically repairs missing or corrupted files
- ๐ฌ Chat Integration: Use
/memory
commands directly in Cursor chat - ๐ก๏ธ Secure: Input validation, path protection, no data sent to external services
- โ๏ธ Set Log Level: Configurable logging from trace to error levels
๐ Memory Bank Structure
memory-bank/
โโโ core/ # Essential project information
โ โโโ projectBrief.md # Project overview and goals
โ โโโ productContext.md # Product requirements and context
โ โโโ activeContext.md # Current focus and priorities
โโโ progress/ # Project tracking and history
โ โโโ index.md # Progress overview
โ โโโ current.md # Current tasks and status
โ โโโ history.md # Completed work history
โโโ systemPatterns/ # Architecture and design patterns
โ โโโ index.md # Pattern overview
โ โโโ architecture.md # System architecture
โ โโโ patterns.md # Design patterns used
โ โโโ scanning.md # Code analysis patterns
โโโ techContext/ # Technical stack and environment
โโโ index.md # Tech stack overview
โโโ stack.md # Technology choices
โโโ dependencies.md # Key dependencies
โโโ environment.md # Development environment
๐ ๏ธ Key Features
- Zero Configuration - Works out of the box
- STDIO MCP Server - Optimized for Cursor compatibility
- React Dashboard - Modern webview for memory management
- Efficient File Operations - Optimized for typical memory bank file sizes
- British English - Consistent language throughout
๐ Documentation
- Phase 4 Restructuring Plan - Current development progress
- Troubleshooting Guide - Common issues and solutions
- Contributing Guidelines - How to contribute
๐ค Contributing
We welcome contributions! Please:
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature
- Make your changes following our coding standards
- Run tests:
pnpm test
- Submit a pull request
Requirements:
- British English throughout codebase
- Comprehensive test coverage
- Follow existing code patterns
๐ License & Support
License: Apache 2.0
Need Help?
- ๐ Report Issues
- ๐ฌ Ask Questions
- ๐ Read Documentation
Built with โค๏ธ for the Cursor and VS Code communities
Taskade's MCP (Model Context Protocol) initiatives provide tools and APIs related to the integration of AI workflows. It features the ability to generate MCP tools from OpenAPI specifications, facilitating easy integration with AI clients. Notably, it offers a server to connect Taskade's API to any MCP-compatible client.