memclaw

Store and recall long-term cross-session memories for multi-agent fleets.

420|51|Updated Apr 27, 2026
One-click install
npx skills add https://github.com/caura-ai/caura-memclaw --skill memclaw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memclaw
Source: https://github.com/caura-ai/caura-memclaw/tree/main/plugin/skills/memclaw
Command: npx skills add https://github.com/caura-ai/caura-memclaw --skill memclaw

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

MemClaw provides long-term, cross-session memory for multi-agent fleets, capturing decisions, findings, and outcomes so agents can recall context and avoid repeating mistakes.

Core Features & Use Cases

  • Centralized memory for multiple agents and fleets with governance and visibility controls.
  • Recall-before-action workflow, write-after-action logging, and superseding outdated facts.
  • Supports memory containers (Memory, Doc, Entity) and lifecycle management, plus a session-loop for recall, work, write, and evolve.
  • Use cases include cross-agent decision recall, knowledge sharing across a fleet, and retroactive analysis of learned rules.

Quick Start

Initialize MemClaw in your agent runtime, then begin a recall of known memories before executing a task, and write the outcomes after completion.

Frequently Asked Questions about memclaw

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

FAQPage Schema
How do I enable persistent cross-session memory for a multi-agent AI fleet?▼

Persistent cross-session memory for a multi-agent AI fleet is enabled by initializing a memory toolset in your agent runtime, allowing agents to store and recall decisions across sessions. You configure it via runtime plugins to manage memory containers.

How does cross-agent memory consolidation work during a session?▼

Cross-agent memory consolidation works through a session loop of recall, work, write, and evolve. Agents recall known memories before executing a task, then write outcomes after completion and evolve facts to supersede outdated information.

Do I need runtime plugins to configure cross-session memory for AI agents?▼

Yes, you need runtime plugins to configure cross-session memory for AI agents. These plugins expose the toolset required for memory management and establish the session loop necessary for agents to recall, write, and evolve long-term context.

What is the best way to share knowledge and prevent repeated mistakes across multiple AI agents?▼

The best way to share knowledge and prevent repeated mistakes across multiple AI agents is using a centralized memory system with governance controls. It captures decisions and outcomes so agents can retroactively analyze learned rules and avoid duplicating errors.

How do I supersede outdated facts in an AI agent's long-term memory?▼

To supersede outdated facts in an AI agent's long-term memory, you use the evolve mechanism within the session loop. This lifecycle management feature updates stored memories, ensuring agents rely on the most current decisions and findings during recall.