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elite-longterm-memory

Persist AI agent memory using WAL protocol, vector search, and Git notes.

Updated Feb 17, 2026
Search Tags:#memory#ai-agent#vector-search#long-term-memory#wal#persistent-context#git-notes
One-click install
npx skills add https://github.com/Qcasares/saas-app --skill elite-longterm-memory-qcasares
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: elite-longterm-memory
Source: https://github.com/Qcasares/saas-app/tree/main/skills/elite-longterm-memory
Command: npx skills add https://github.com/Qcasares/saas-app --skill elite-longterm-memory-qcasares

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mem0ai, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive memory system for AI agents, ensuring they never lose context, forget decisions, or repeat mistakes.

Core Features & Use Cases

  • Bulletproof WAL Protocol: Ensures memory durability and survival of compaction.
  • LanceDB Vector Search: Facilitates semantic recall of relevant memories.
  • Git-Notes Knowledge Graph: Stores structured decisions and context.
  • File-Based Archives: Provides human-readable logs and daily archives.
  • Cloud Backup: Offers optional SuperMemory sync for cross-device sync.
  • Memory Hygiene: Maintains lean vectors and prevents token waste.
  • Mem0 Auto-Extraction: Automatically extracts facts from conversations.
  • Use Case: Ideal for AI agents that require long-term memory, such as Claude, Cursor, GPT, and OpenClaw agents, ensuring they never forget context or decisions.

Quick Start

Initialize the memory system with npx elite-longterm-memory init.

Frequently Asked Questions about elite-longterm-memory

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

FAQPage Schema
How do I add persistent context to an AI agent to prevent it from forgetting past decisions?▼

To add persistent context to an AI agent, you need a long-term memory system using a WAL protocol and vector search. This ensures memory durability and prevents context loss during compaction.

How does vector search work for retrieving AI agent memory?▼

Vector search for AI agent memory uses a database like LanceDB to facilitate semantic recall. It matches query embeddings with stored memory vectors to retrieve relevant past context and decisions.

Can I use this long-term memory system with Claude and Cursor agents?▼

Yes, this long-term memory system is suitable for Claude, Cursor, GPT, OpenClaw, and Moltbot agents. It ensures they never forget context, decisions, or repeat previous mistakes.

Do I need mem0ai to extract facts from AI conversations automatically?▼

Yes, you need the mem0ai dependency to enable auto-extraction of facts from conversations. This feature automatically captures and stores context into the memory system.

What is the best way to store structured AI agent decisions as a knowledge graph?▼

The best way to store structured AI agent decisions is by using a Git-notes knowledge graph. This approach stores context structured within Git, providing human-readable logs and file-based archives.

Why does my AI agent lose memory during context compaction and how do I prevent it?▼

AI agents lose memory during context compaction due to a lack of durable storage. Implementing a bulletproof WAL protocol ensures memory durability and survival of compaction.

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