MemClaw

MemClaw is a governed shared memory platform designed for fleets of AI agents, providing a persistent and secure layer for knowledge sharing across agents and teams. The platform addresses the challenge of enabling AI agents to learn from each other's discoveries without compromising on data governance or security. It facilitates cross-agent and cross-fleet recall, allowing information discovered by one agent to be accessed by the right teams, while ensuring that sensitive data is not improperly shared. Key features of MemClaw include built-in permissions management, audit trails, tenant isolation, visibility scopes, agent trust tiers, row-level isolation, and comprehensive audit logging for every operation. These governance mechanisms are integrated from the outset rather than added as afterthoughts, supporting compliance and secure collaboration within enterprise environments. The platform also supports per-agent retrieval tuning, a feature called "LLM crystallizer," and lifecycle automation, enabling the memory system to improve itself as agents interact with it. This self-improving memory is designed to make AI agent fleets smarter over time as every interaction contributes to collective learning. MemClaw offers integration via two primary paths: MCP, which is suitable for any AI client, and the OpenClaw plugin for fleet deployments. The platform is delivered as an online database and supports rapid connection, with setup times advertised as brief. It is open source under the Apache 2.0 license and highlights its use in production environments, such as at eToro. The platform also emphasizes security and compliance, referencing SOC 2 standards. MemClaw is positioned as a shared cognition layer for enterprise AI agents, targeting organizations that deploy multiple AI agents and require persistent, governed memory to facilitate collaboration and learning while maintaining strict access controls and auditability.