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  • Recall-before-action workflow, write-after-action logging, and superseding outdated facts.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recall known memories before executing a task, then write outcomes after completion and evolve facts to supersede outdated information.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.