skills.rest
AlbertLin821
AlbertLin821COMMUNITY
Explore all skills by this author →
GitHub

mem0-mcp

Store and retrieve persistent agent memory via MCP tools.

2|Updated Apr 16, 2026
Search Tags:#context#agent#mcp#memory#persistence#mem0
One-click install
npx skills add https://github.com/AlbertLin821/AIYO_new --skill mem0-mcp-albertlin821
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mem0-mcp
Source: https://github.com/AlbertLin821/AIYO_new/tree/main/archive/legacy/20260605-005529/vendor/mem0/mem0-plugin/skills/mem0-mcp
Command: npx skills add https://github.com/AlbertLin821/AIYO_new --skill mem0-mcp-albertlin821

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of context loss across AI sessions by providing a structured protocol for storing, searching, and retrieving persistent user preferences, project decisions, and task learnings.

Core Features & Use Cases

  • Contextual Recall: Enables agents to remember past architectural decisions, user preferences, and project conventions.
  • Memory Hygiene: Provides clear guidelines on when to search for context versus when to store new learnings, preventing memory clutter.
  • Use Case: When starting a new coding task, the agent can automatically search for previous decisions regarding the project's authentication module or preferred coding style, ensuring consistency without manual reminders.

Quick Start

Use the mem0-mcp skill to search for previous architectural decisions regarding the current project module.

Frequently Asked Questions about mem0-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 so it remembers past decisions across sessions?▼

Persistent memory is added to an AI agent by implementing a standardized memory protocol that enables the retrieval of project-specific context, user preferences, and historical decisions across multiple sessions via MCP tools.

What is the best way to prevent context loss when starting new coding tasks with an AI agent?▼

Preventing context loss requires a structured protocol for storing, searching, and retrieving persistent user preferences and project decisions. The agent can automatically search for previous architectural decisions regarding the current project module to ensure consistency.

How does metadata-based filtering work for persistent memory recall in AI agents?▼

Metadata-based filtering for persistent memory recall ensures high-relevance memory retrieval by structuring stored data and applying specific filters. This allows agents to accurately fetch project conventions and task learnings without manual reminders.

When should an agent search for context versus store new learnings to maintain memory hygiene?▼

Memory hygiene is maintained by following clear guidelines on when to search for context versus when to store new learnings. This structured approach prevents memory clutter while ensuring agents effectively retrieve historical decisions and user preferences.

Can I use MCP tools with persistent storage to retrieve user preferences for my coding agent?▼

MCP tools can be used with persistent storage to facilitate the retrieval of user preferences and project-specific context. This allows intelligent agents to interact with stored data and maintain consistency across different sessions.

Related Skills

Explore More Software Engineering Skills→
matthewdeavesmatthewdeaves

setup-machine

Register a new Classic Mac in the MCP server and verify FTP connectivity.

Community
Intermediate
treejhtreejh

context-loader

Loads project rules and docs from .claude to align Gemini CLI outputs.

Community
Intermediate
Sir-chawakornSir-chawakorn
9

build-vector-search

Configure embedding models, ANN indices, and hybrid search pipelines for vector retrieval.

Community
Advanced
ehfazrezwanehfazrezwan
5

recall

Load stored user and project context from the Neuralscape memory system.

Community
Intermediate