cognitive-memory

Manage centralized long-term memory across multi-agent systems.

523|125|Updated Nov 11, 2025

Search Tags:#multi-agent #retrieval #memory #governance #persistence #knowledge-graph

Ready to run

npx skills add https://github.com/anymouschina/TapCanvas --skill cognitive-memory-anymouschina

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cognitive-memory
Source: https://github.com/anymouschina/TapCanvas/tree/main/apps/agents-cli/skills/cognitive-memory
Command: npx skills add https://github.com/anymouschina/TapCanvas --skill cognitive-memory-anymouschina

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Cognitive-memory system that enables Agents-CLI to manage long-term memory stores across core, episodic, semantic, procedural, and vault layers, providing retrievable memory, archiving, and governance for multi-agent workflows.

Core Features & Use Cases

Centralized long-term memory spanning identity, context, knowledge graph, and learned workflows.
Deterministic recall and archiving with governance controls across multiple agents and sessions.
Supports memory routing, recall, forgetting, and reflection workflows to maintain memory hygiene.

Quick Start

Bootstraps the memory system in your workspace and starts the memory routing and reflection cycles.

Frequently Asked Questions about cognitive-memory

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

FAQPage Schema

How do I enable persistent memory for multi-agent systems across multiple sessions?▼

To enable persistent memory for multi-agent systems, you need centralized memory management that stores and indexes long-term knowledge across sessions. This Skill automates recall, archiving, and governance using modular memory stores to maintain reliable agent context.

What is the best way to manage long-term memory routing and retrieval for autonomous agents?▼

The best way to manage long-term memory routing and retrieval is through a routing classifier that directs knowledge to core, episodic, semantic, procedural, or vault stores, enabling deterministic recall and archiving for multi-agent workflows.

Can I use a decay-based forgetting model to maintain memory hygiene in multi-agent workflows?▼

Yes, you can maintain memory hygiene in multi-agent workflows by applying a decay-based forgetting model. This model automates the removal of outdated knowledge, while reflection cycles and an audit trail ensure proper governance of retained memory.

Does this memory governance approach support knowledge graph indexing for procedural and episodic data?▼

Yes, this memory governance approach supports knowledge graph indexing by categorizing data into semantic, procedural, and episodic layers. It provides deterministic retrieval and archiving across multiple agents and sessions.

How do I bootstrap a centralized memory system in my workspace for agent coordination?▼

To bootstrap a centralized memory system in your workspace, you initialize the memory stores and start the routing and reflection cycles. This setup enables agents to coordinate identity, context, and learned workflows across sessions.