
Open-source agentic memory framework for 24/7 proactive AI agents with file-system memory, intention prediction, and lower token costs.
memU is an open-source memory infrastructure for LLM applications and AI agents, designed for long-running, always-on assistants that need persistent, evolving memory. It organizes memories like a file system (categories as folders, memory items as files, cross-links as symlinks) and supports proactive behavior such as capturing user intent, predicting next steps, and injecting relevant memory into the agent’s context. memU aims to reduce token spend for continuous agents by caching insights and avoiding redundant LLM calls, and it includes companion components like a backend service (memU-server) and a web UI (memU-ui). In addition to the open-source framework, memU offers hosted APIs (Memory API / Response API) with usage-based pricing and a “start free” path.
75/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
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For ai agent infrastructure teams
Exploring memU? For AI agent infrastructure teams, your own AI agent can investigate execution failures, improve shared agent services, and verify platform releases. Start with a setup tailored to this work.
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