Monid

MemClaw is a persistent-memory service for LLM-based agents and a governed shared memory layer for agent fleets. It is designed to help AI agents learn from each other by turning isolated agent context into a Company Brain with memory, governance, provenance, contradiction detection, and visibility scopes built in from day one. MemClaw separates an organization’s agent force, including tenants, fleets, nodes, and agents, from the governed memory plane through MCP Server, REST API, OpenClaw plugin, MemClaw Core, and persistent storage. Agents can write to and recall from the Company Brain through MCP-compatible tools, direct HTTPS calls, or OpenClaw integration, while MemClaw Core runs enrichment such as entity extraction, contradiction detection, PII scanning, and lifecycle transitions before anything is stored. Every memory can be stamped with a visibility scope, auto-classified into types such as fact, episode, decision, preference, rule, plan, commitment, action, and outcome.

ZooData is an agent-native data layer that gives AI systems clean web content and decision-ready commerce intelligence through one integration. Instead of returning raw HTML or static reports designed for human dashboards, it delivers structured, token-efficient JSON that agents can consume directly for research, analysis, monitoring, and automated decision-making. It tracks more than 500 million products across Amazon and TikTok Shop and supports over 40 filters for product discovery, batch processing, and large-scale opportunity scanning. Its data modules provide category-level market analysis, demand trends, competition density, margin benchmarks, multidimensional competitor lookup, and live product signals such as price, inventory, and Best Sellers Rank. More than two years of price, sales, rating, and ranking history support time-series analysis and early trend detection.