How do I run the Memory Context Manager_v2 UI?
Clone the repository, run `yarn install`, then `yarn rw dev`, and open `http://localhost:8910` in your browser.
M
Memory Context Manager_v2 MCP Server
@Big0290
Basic information
About Memory Context Manager_v2 MCP Server
🧠 Deep Project Analysis: Memory Context Manager v2
Memory Context Manager_v2 MCP Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"memory-context-manager-v2": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"memory-context-manager-v2:latest",
"python",
"main.py"
],
"env": {
"MCP_LOG_LEVEL": "INFO",
"OLLAMA_BASE_URL": "http://host.docker.internal:11434"
}
}
}
}Memory Context Manager_v2 MCP Server tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is Memory Context Manager_v2?
Memory Context Manager_v2 is an MCP server featuring a Continuous Self-Evolution System that enables autonomous learning, performance improvement, and intelligent adaptation. It includes an Evolution Engine for background optimization and is monitored via a dedicated web UI built with RedwoodJS and Chakra UI.
How to use Memory Context Manager_v2?
To use Memory Context Manager_v2, run its MCP server instance and then access its web UI by cloning the UI repository, installing dependencies with yarn install, starting the development server with yarn rw dev, and navigating to http://localhost:8910. The UI provides real-time monitoring, evolution tracking, and system control.
Key features of Memory Context Manager_v2
- Continuous Self-Evolution System for autonomous improvement
- Real-time system health monitoring with progress bars
- Evolution Engine with scheduled tasks and learning sources
- Dashboard with quick actions and activity feed
- RESTful API endpoints for backend communication
Use cases of Memory Context Manager_v2
- Monitoring autonomous AI system health and performance
- Tracking continuous self-improvement and learning progress
- Managing scheduled evolution tasks and documentation ingestion
Frequently asked questions
What are the prerequisites for using Memory Context Manager_v2?
Node.js 18+, Yarn package manager, and a running instance of the Memory Context Manager v2 MCP server.
Where does system data live?
The README does not specify data storage details; it only describes real-time monitoring and RESTful communication with the MCP server.
What transport or communication does the UI use with the server?
The UI communicates with the MCP server via RESTful API endpoints, as stated in the architecture section.
Does Memory Context Manager_v2 require authentication?
The README does not mention authentication; it only describes a dashboard interface without user management features.
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