agent-memory-mcp

Provides persistent searchable memory storage for AI agents via an MCP server.

2|Updated Jan 10, 2026
One-click install
npx skills add https://github.com/Shubh2310-developer/ENGUNITYCORE --skill agent-memory-mcp-shubh2310-developer
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-memory-mcp
Source: https://github.com/Shubh2310-developer/ENGUNITYCORE/tree/main/.claude/skills/agent-memory-mcp
Command: npx skills add https://github.com/Shubh2310-developer/ENGUNITYCORE --skill agent-memory-mcp-shubh2310-developer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? AI agents lose context between sessions, forcing teams to repeatedly re-explain architecture decisions, patterns, and project knowledge. This Skill provides a persistent, searchable memory bank that syncs with project documentation so knowledge survives across conversations. ## Core Features & Use Cases - Memory Search: Query stored knowledge by text, type, or tags using the memory_search MCP tool. - Memory Write & Read: Record architecture decisions, patterns, and notes with memory_write, then retrieve them by key with memory_read. - Usage Analytics & Dashboard: Inspect memory statistics via memory_stats and visualize usage in a standalone dashboard at localhost:3333. - Use Case: After deciding on an authentication approach, save it with memory_write as a decision entry; weeks later, any agent session can retrieve it instantly with memory_search instead of re-deriving the rationale. ## Quick Start Clone the agentMemory repository, run npm install and npm run compile, then start the MCP server with your project ID and workspace path to begin storing and searching memories.

Frequently Asked Questions about agent-memory-mcp

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

FAQPage Schema
How do I give an AI agent persistent memory across sessions?

Run the agent-memory MCP server against your workspace, then use memory_write to store decisions and patterns with keys and tags. Later sessions retrieve them with memory_search or memory_read, so knowledge persists beyond a single conversation.

How do I search stored agent memories by type or tags?

Use the memory_search tool with a query string plus optional type and tags filters. For example, searching with type set to pattern returns only pattern entries matching your query, such as authentication patterns.

What are the requirements to run the agent memory MCP server?

You need Node.js version 18 or later. Clone the agentMemory repository, run npm install and npm run compile, then start the server with npm run start-server passing a project ID and the absolute path to your target workspace.

Can I visualize memory usage and statistics?

Yes. The memory_stats tool returns analytics on memory usage, and a standalone dashboard can be launched with npm run start-dashboard pointing at your workspace. The dashboard is accessible at http://localhost:3333.

What types of knowledge can be stored in agent memory?

The memory bank stores typed entries such as architecture notes, design patterns, and decisions, each with a key, content, and optional tags. Entries are written with memory_write and retrieved individually by key using memory_read.