Memmy
Memmy is a local-first AI memory layer that lets every AI tool remember the same version of you. Built for people who work with several assistants every day, it automatically reads authorized collaboration history from tools such as Cursor, Claude, and Codex, then turns scattered conversations, preferences, project context, technical decisions, progress, and recurring pitfalls into structured memory. Its workflow has three stages: Scan reviews selected histories on the user’s device; Organize distills, deduplicates, categorizes, and indexes useful information; and Inject gives the active AI only the most relevant memories through precise, on-demand matching instead of sending a complete data dump. This allows users to switch tools without rebuilding context, merge plans discussed across agents, document recent decisions, preserve writing preferences, and continue unfinished work.
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PlatformPilot
PlatformPilot is a company brain for AI-first teams. It captures how your company actually works, your decisions, playbooks, and tribal knowledge, and turns it into a living memory your team and your AI agents can use to answer questions and take action across all your tools.
Unlike search tools that only retrieve, PlatformPilot reasons across your systems, shows the why behind every answer, and acts on your own playbooks, in your own cloud, getting sharper every time it is used.
It connects to your stack through the Model Context Protocol (MCP), so it works as a shared memory layer inside the tools your team already uses, including Claude Code, Claude Desktop, and OpenAI-based agents. Memory evolves as you work.
- Living memory that learns from outcomes, not just stores notes
- Reasoning across all your tools. We support +200 tools.
- Plain-language search over your team's decisions, playbooks, and history
- Self-organizing knowledge
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MemClaw
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.
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Membase
Membase is a unified AI memory layer platform designed to help AI agents and tools share and persist context so they “understand you” across sessions without forced repetition or isolated memory silos, enabling consistent conversational experiences and shared knowledge across AI assistants. It provides a secure, centralized memory layer that captures, stores, and syncs context, conversation history, and relevant knowledge across multiple AI agents and integrations with tools such as ChatGPT, Claude, Cursor, and others, so all connected agents can access a common context and avoid repeating user intents. Designed as a foundational memory service, it aims to maintain consistent context across your AI ecosystem, reducing friction and improving continuity in multi-tool workflows by keeping long-term context available and shared rather than locked within individual models or sessions, and letting users focus on outcomes instead of re-entering context for each agent request.
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