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CenredJun
CenredJunCOMMUNITY
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agent-memory-mcp

Store and search persistent agent memory via a local Node.js MCP server.

Updated Mar 27, 2026
Search Tags:#search#agent#mcp#dashboard#memory#nodejs#knowledge-management
One-click install
npx skills add https://github.com/CenredJun/openclaw-claudecode-setup-kit --skill agent-memory-mcp-cenredjun
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-memory-mcp
Source: https://github.com/CenredJun/openclaw-claudecode-setup-kit/tree/main/skills/agent-memory-mcp
Command: npx skills add https://github.com/CenredJun/openclaw-claudecode-setup-kit --skill agent-memory-mcp-cenredjun

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a persistent, searchable long-term memory and knowledge management layer so agents can retain architecture notes, design patterns, and decisions across sessions and projects for reliable retrieval.

Core Features & Use Cases

  • Persistent searchable memory: Indexes and stores architecture, patterns, and decision records for fast retrieval.
  • MCP server interface: Exposes memory_search, memory_write, memory_read, and memory_stats endpoints for programmatic agent access.
  • Project sync & dashboard: Automatically syncs with project documentation and includes a local dashboard to visualize memory usage and analytics.
  • Use Case: Capture architecture decisions during design meetings, then query historical patterns to guide future implementations or onboarding.

Quick Start

Clone the agentMemory repo into .agent/skills/agent-memory, install dependencies, compile, and start the MCP server for your project with the npm run start-server command providing a project_id and the absolute path to your workspace.

Frequently Asked Questions about agent-memory-mcp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add persistent searchable memory to an AI agent for retrieving architecture decisions?▼

You can achieve persistent searchable memory by running a local Node.js MCP server with memory_search, memory_write, memory_read, and memory_stats endpoints, allowing agents to index and retrieve architecture decisions and project documentation across sessions.

How do I sync project documentation to an agent knowledge base using MCP?▼

Syncing project documentation uses a local MCP server that automatically indexes workspace files into agent memory, exposing endpoints for programmatic read and write operations to keep architecture patterns updated.

Can I use an MCP memory server with Node.js to track design patterns across sessions?▼

Yes, the MCP memory server runs on Node.js v18+ and provides persistent storage to track design patterns across sessions, requiring you to clone the repository, install dependencies, and start the server with a project_id and workspace path.

What is the best way to provide long-term knowledge management for local development agents?▼

Long-term knowledge management for local development agents is best handled by a local MCP server that indexes decision records and architecture notes, including a dashboard to visualize memory usage and analytics within your development workspace.

Do I need Node.js v18 to run a local agent memory MCP server?▼

Yes, Node.js v18 or higher is required to run the local MCP server, which must be cloned into your workspace, compiled, and started using npm run start-server with a specific project_id and absolute path.

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