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agent-memory-mcp

Store and retrieve persistent, searchable memories via an MCP server.

Updated Apr 9, 2026
Search Tags:#ai#search#mcp#dashboard#memory#persistence#knowledge
One-click install
npx skills add https://github.com/freedomdp/LightRAG-Management --skill agent-memory-mcp-freedomdp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-memory-mcp
Source: https://github.com/freedomdp/LightRAG-Management/tree/main/.agents/skills/agent-memory-mcp
Command: npx skills add https://github.com/freedomdp/LightRAG-Management --skill agent-memory-mcp-freedomdp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a persistent, searchable memory bank that automatically syncs with project documentation. It runs as an MCP server to allow reading/writing/searching of long-term memories.

Core Features & Use Cases

  • Memory_search, memory_write, memory_read, memory_stats tools for storing and retrieving knowledge.
  • Dashboard support for visualizing memory usage and analytics.
  • Use case: capture architecture decisions, patterns, and project knowledge for long-term reference.

Quick Start

Start the MCP server for your project using the start-server helper script and begin recording memories with memory_write.

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 memory to an AI agent for long-term project knowledge?▼

Persistent memory for AI agents is created by running an MCP server that stores and retrieves searchable knowledge. This allows agents to recall architecture decisions and project documentation across ongoing workflows.

How do I store and search architecture decisions using an MCP server?▼

You can store and search architecture decisions by calling the memory_write tool to save knowledge and the memory_search tool to query it. This MCP server manages long-term reference data for your agents.

Can I visualize AI agent memory usage and analytics in a dashboard?▼

Yes, you can visualize AI agent memory usage through an optional dashboard. It provides analytics for the memories stored by the MCP server, helping you track knowledge persistence and retrieval stats.

Does this MCP memory server support searching across long-term project documentation?▼

Yes, the MCP memory server supports searching across long-term project documentation. It automatically syncs with your project files to allow reading, writing, and searching of persistent memories.

What is the best way to retain knowledge across ongoing AI agent workflows?▼

The best way to retain knowledge across ongoing AI agent workflows is using a persistent memory bank. This MCP server captures patterns and project knowledge, ensuring data is not lost between sessions.

What are the limitations of using an MCP server for AI agent memory persistence?▼

When using an MCP server for AI agent memory persistence, you must manually start the server with a helper script before recording memories. Memory operations are limited to search, write, read, and stats functions.

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