The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
MCP Memory Keeper
MCP server for persistent context management in AI coding assistants
Install
claude mcp add memory-keeper npx mcp-memory-keeperInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →MCP Memory Keeper - Claude Code Context Management
A Model Context Protocol (MCP) server that provides persistent context management for Claude AI coding assistants. Never lose context during compaction again! This MCP server helps Claude Code maintain context across sessions, preserving your work history, decisions, and progress.
🚀 Quick Start
Get started in under 30 seconds:
# Add memory-keeper to Claude
claude mcp add memory-keeper npx mcp-memory-keeper
# Start a new Claude session and use it!
# Try: Analyze the current repo and save your analysis in memory-keeper
That's it! Memory Keeper is now available in all your Claude sessions. Your context is stored in ~/mcp-data/memory-keeper/ and persists across sessions.
🚀 Practical Memory Keeper Workflow Example
Custom Command + CLAUDE.md = Automatic Context Management
CLAUDE.md (condensed example)
# Project Configuration
## Development Rules
- Always use memory-keeper to track progress
- Save architectural decisions and test results
- Create checkpoints before context limits
## Quality Standards
- All tests must pass before marking complete
- Document actual vs claimed results
Custom Command Example: /my-dev-workflow
# My Development Workflow
When working on the provided project:
- Use memory-keeper with channel:
- Save progress at every major milestone
- Document all decisions with category: "decision"
- Track implementation status with category: "progress"
- Before claiming anything is complete, save test results
## Workflow Steps
1. Initialize session with project name as channel
2. Save findings during investigation
3. Create checkpoint before major changes
4. Document what actually works vs what should work
Usage Example
User: /my-dev-workflow authentication-service
AI: Setting up workflow for authentication-service.
[Uses memory-keeper with channel "authentication-service"]
[... AI works, automatically saving context ...]
User: "Getting close to context limit. Create checkpoint and give me a key"
AI: "Checkpoint created: authentication-service-checkpoint-20250126-143026"
[Continue working until context reset or compact manually]
User: "Restore from key: authentication-service-checkpoint-20250126-143026"
AI: "Restored! Continuing OAuth implementation. We completed the token validation, working on refresh logic..."
The Pattern:
- Custom command includes instructions to use memory-keeper
- AI follows those instructions automatically
- When you notice the conversation getting long, YOU ask Claude to save a checkpoint (like saving your game before a boss fight!)
- When Claude runs out of space and starts fresh, YOU tell it to restore using the checkpoint key
🎯 Key Feature: Memory Keeper is a shared board! You can:
- Continue in the same session after reset
- Start a completely new session and restore
- Have multiple Claude sessions running in parallel, all sharing the same memory
- One session can save context that another session retrieves
This enables powerful workflows like having one Claude session doing research while another implements code, both sharing discoveries through Memory Keeper!
Why MCP Memory Keeper?
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