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IsKenKenYa
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agent-memory-mcp

Manage persistent, searchable AI agent memories via an MCP server.

35|4|Updated Sep 10, 2025
Search Tags:#knowledge management#ai#search#agent#memory#persistence
One-click install
npx skills add https://github.com/IsKenKenYa/1Panel-Client --skill agent-memory-mcp-iskenkenya
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-memory-mcp
Source: https://github.com/IsKenKenYa/1Panel-Client/tree/main/.cursor/skills/agent-memory-mcp
Command: npx skills add https://github.com/IsKenKenYa/1Panel-Client --skill agent-memory-mcp-iskenkenya

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires npm, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a persistent, searchable memory bank for AI agents, automatically syncing with project documentation to store and retrieve crucial information.

Core Features & Use Cases

  • Persistent Knowledge Management: Stores architecture, patterns, and decisions for long-term recall.
  • Searchable Memory: Allows agents to query memories by keywords, type, or tags.
  • Use Case: An AI agent working on a complex software project can use this skill to recall past design decisions, architectural patterns, and specific implementation details, ensuring consistency and avoiding redundant work.

Quick Start

Use the memory_search tool to find all patterns related to user authentication.

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 knowledge management?▼

Persistent memory for AI agents is provided by a hybrid memory system operating as an MCP server, enabling CRUD and search operations on architecture, patterns, and decisions. It automatically syncs with project documentation to store and retrieve crucial information.

How can an AI agent search past design decisions and architectural patterns?▼

Searching past design decisions is done using the memory_search tool, allowing agents to query memories by keywords, type, or tags. This ensures consistency and avoids redundant work on complex software projects.

Do I need Node.js to use an MCP server for searchable agent memory?▼

Node.js v18+ is required to use this MCP server for searchable agent memory, along with specific npm packages for compilation and execution. The npm dependency is essential for running the server.

What is the best way to maintain AI agent context across complex software projects?▼

The best way to maintain AI agent context is using a persistent knowledge management system that stores architecture and decisions for long-term recall. This operates as an MCP server integrating directly with project documentation.

Can I integrate project documentation with an AI agent memory system?▼

Project documentation integration with an AI agent memory system is supported by automatically syncing files. This creates a searchable memory bank that agents query to recall specific implementation details and past decisions.

What are the limitations of using a hybrid memory system for AI agents?▼

Limitations of this hybrid memory system include the strict dependency on Node.js v18+ and specific npm packages for compilation. Without this environment setup, the MCP server cannot execute CRUD and search operations.

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