memclaw

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• Recall-before-action workflow, write-after-action logging, and superseding outdated facts.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.