Implements a three-layer memory system with reinforcement, forgetting, and recall for AI agents.
npx skills add https://github.com/AIPMAndy/dna-memory --skill dna-memory
Please help me install this Agent Skill. Skill: dna-memory Source: https://github.com/AIPMAndy/dna-memory/tree/main Command: npx skills add https://github.com/AIPMAndy/dna-memory --skill dna-memory
DNA Memory enables AI agents to learn, reinforce, forget, and generalize instead of merely storing data, turning experiences into evolving capabilities.
Clone this skill into your OpenClaw skills directory and start by remembering a user preference with the evolve.py remember command.
High-intent search queries and answers about installing and using this skill.
AI agent memory management uses a three-layer architecture of working, short-term, and long-term memory. This enables agents to reinforce important information, forget irrelevant data, and reflect on experiences to promote memories autonomously.
A self-improving AI assistant requires a memory system with reinforcement and automatic reflection. Instead of merely storing data, the system generalizes experiences into evolving capabilities using recall-augmented search and daemon-based maintenance.
A three-layer memory system in Python handles recall by using augmented search across stored experiences. Forgetting is managed through reinforcement rules and daemon maintenance, automatically promoting or discarding memories based on relevance.
Yes, SQLite and Python support long-running agent memory daemons. This Skill provides a self-contained backend using these technologies to maintain memory, run reflection processes, and manage forgetting for continuous agent workflows.
Yes, recall-augmented search is a core feature for knowledge-centric products. It retrieves relevant memories to inform current tasks, working alongside the daemon maintenance to ensure long-running agents access accurate, reinforced knowledge.
Use a reinforcement and reflection memory architecture instead of simple storage when your agent needs to evolve. If your workflow requires learning from experiences, forgetting outdated data, and generalizing capabilities, simple storage is insufficient.
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