Memory AGI
Memory AGI is a runtime memory layer for AI agents, built around the idea of giving agents real muscle memory. Hand over a slice of company data, and Memory AGI builds the organization’s knowledge and runtime memory layer, grounds agents in the business, and keeps that context current automatically. Your AI is only as good as the context you give it; without it, agents stay stuck at an intern-level, guessing at how the company runs. Memory AGI turns processes into knowledge agents that can actually execute, so they run reliably, show their work, and can be trusted with what they ship. It is built on three layers of muscle memory. Dynamic Ingestion captures and structures the company’s unique knowledge from voice notes, internal documents, or the tools where data already lives. The Runtime Memory Layer gives agents access to a live, de-duplicated context layer; a company knowledge base that humans, agents, and automations can all draw on to perform tasks like the best employees.
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SenseLab
SenseLab is a cloud-based memory and learning layer for AI agents and agent-driven applications. It provides shared knowledge spaces (“Rooms”) where agents and teams can record, retrieve, and share context across sessions, models, and tools. Agents can store findings, decision context, and outcomes; the service maintains versioned records and uses outcome feedback to adjust confidence in knowledge. SenseLab provides an MCP server and Python and TypeScript SDKs for connecting agent clients and applications through API-key access. Teams can use it to carry context between agents, coordinate work, review decision histories, and retain structured traces for evaluation or model-training workflows. It is intended for developers and organizations building systems with multiple AI agents. The vendor also documents a self-hosting option through its open-source codebase.
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claude-mem
claude-mem is an offline-first cloud memory for AI agents, built around an open source engine and a cloud sync layer that links agent memory everywhere through one private MCP link. It is designed so coding agents and AI assistants do not start from zero every session, every machine, or every editor. claude-mem takes notes while an agent works, capturing decisions, fixes, dead ends, environment notes, architecture choices, and other structured observations in a temporal database. CMEM Cloud then mirrors that local memory behind a private Model Context Protocol endpoint, allowing any compatible agent or IDE to read and write the same memory across tools such as Claude Code, Cursor, Windsurf, OpenCode, Codex CLI, Gemini CLI, and VS Code. It works locally first, with or without a network, while keeping memory synchronized when cloud access is available.
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MemMachine
An open-source memory layer for advanced AI agents. It enables AI-powered applications to learn, store, and recall data and preferences from past sessions to enrich future interactions. MemMachine’s memory layer persists across multiple sessions, agents, and large language models, building a sophisticated, evolving user profile. It transforms AI chatbots into personalized, context-aware AI assistants designed to understand and respond with better precision and depth.
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