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memories-mcp

Integrate AI agents with memories.sh persistent memory via MCP.

35|4|Updated Jan 30, 2026
Search Tags:#context retrieval#ai memory#mcp#llm integration#persistent storage#agent tools
One-click install
npx skills add https://github.com/webrenew/memories --skill memories-mcp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memories-mcp
Source: https://github.com/webrenew/memories/tree/main/skills/memories-mcp
Command: npx skills add https://github.com/webrenew/memories --skill memories-mcp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a standardized way for AI agents to access and manage persistent memories, overcoming the fragmentation of different AI tools having their own instruction and memory formats.

Core Features & Use Cases

  • Unified Memory Access: Connects various AI clients (Claude Code, Cursor, v0, etc.) to a central memory store.
  • Real-time Context: Provides dynamic rules and relevant memories for AI decision-making via the MCP protocol.
  • Use Case: When an AI agent needs to recall a past decision or adhere to a specific coding standard, it uses get_context to retrieve this information seamlessly.

Quick Start

Use the memories-mcp skill to get context for the current task by running memories serve.

Frequently Asked Questions about memories-mcp

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

FAQPage Schema
How do I share persistent memory across multiple AI agents like Cursor and Claude Code?▼

You can share persistent memory across AI clients like Cursor and Claude Code by connecting them to a unified memory layer via the Model Context Protocol (MCP). This provides standardized access to store and retrieve context.

What is the best way to retrieve past context for an AI agent using MCP?▼

The best way to retrieve past context for an AI agent using MCP is to use the get_context function. It fetches dynamic rules and relevant memories from a persistent storage layer for real-time decision-making.

Can I use HTTP/SSE transport for real-time context retrieval in AI agents?▼

Yes, you can use HTTP/SSE transport for real-time context retrieval in AI agents. The memory layer supports both local stdio and HTTP/SSE transports to manage sessions and serve dynamic context.

How do I store coding standards so my AI agent remembers them?▼

You store coding standards by integrating your AI agent with a persistent memory layer using MCP. The agent then retrieves these specific standards seamlessly via the get_context function during tasks.

Does memories-mcp work with web-based agents like v0?▼

Yes, memories-mcp works with web-based agents like v0. It integrates various AI clients, including v0, Cursor, and Claude Code, with a central memory store to handle lifecycle sessions and reminders.

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