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elite-longterm-memory

Manage AI agent long-term memory with WAL, vector search, and Git knowledge graphs.

Updated Aug 23, 2026
Search Tags:#memory#ai-agent#vector-search#long-term-memory#wal#persistent-context#ai-coding#git-notes
One-click install
npx skills add https://github.com/DoggyHU/pipipax_claw_backup --skill elite-longterm-memory-doggyhu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: elite-longterm-memory
Source: https://github.com/DoggyHU/pipipax_claw_backup/tree/main/skills/elite-longterm-memory
Command: npx skills add https://github.com/DoggyHU/pipipax_claw_backup --skill elite-longterm-memory-doggyhu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill ensures AI agents never lose context or forget decisions, by maintaining a robust memory system that spans active, warm, and cold storage layers.

Core Features & Use Cases

  • Write-Ahead Log (WAL) Protocol: Ensures context durability, preventing loss during interruptions.
  • LanceDB Vector Search: Facilitates semantic recall of relevant memories.
  • Git-Notes Knowledge Graph: Stores structured decisions in a branch-aware manner.
  • File-Based Archives: Provides human-readable memory summaries and daily logs.
  • Cloud Backup: Optional SuperMemory sync for cross-device access.
  • Memory Hygiene: Optimizes vector storage to prevent token waste.
  • Auto-Extraction: Extracts facts automatically from conversations.
  • Use Case: For an AI agent managing a large project, this Skill would keep track of decisions, preferences, and learnings, ensuring the agent can provide contextually relevant information and avoid repeating mistakes.

Quick Start

Run the 'elite-memory init' command to initialize the memory system in your workspace.

Frequently Asked Questions about elite-longterm-memory

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

FAQPage Schema
How do I maintain long-term memory for an AI agent across different sessions?▼

To maintain long-term memory for an AI agent, you need a memory system that stores active, warm, and cold layers. This Skill uses a WAL protocol and vector search to ensure context durability and semantic recall across sessions.

Does the Write-Ahead Log protocol prevent context loss during AI coding interruptions?▼

Yes, the Write-Ahead Log (WAL) protocol ensures context durability for AI agents. It prevents the loss of decisions and context during unexpected interruptions by logging memory operations before applying them.

Can I use vector search to retrieve past decisions and preferences for my AI agent?▼

Yes, you can use LanceDB vector search to facilitate semantic recall of relevant memories. This allows your AI agent to retrieve past decisions, preferences, and learnings efficiently.

How do I store structured AI agent decisions in a branch-aware manner?▼

You can store structured decisions using a Git-Notes knowledge graph. This approach integrates with your existing version control to manage and retrieve AI agent memory in a branch-aware manner.

Do I need mem0ai to enable persistent context and auto-extraction for my agent?▼

Yes, mem0ai is a required dependency for this Skill. It supports persistent context, memory hygiene to prevent token waste, and auto-extraction of facts directly from your conversations.

What is the best way to back up AI agent memory summaries for cross-device access?▼

The best way to back up AI agent memory is using optional SuperMemory cloud sync. This provides cross-device access to your human-readable memory summaries and daily file-based archives.

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