
MemU
Officially listedAn AI memory framework from NevaMind that gives agents persistent, self-evolving memory for 24/7 assistants.
MemU
memU is a next-generation AI memory framework developed by NevaMind; its product, memu.bot, is a downloadable 24/7 personalized intelligent assistant. Designed for AI agents that need persistent memory, it continuously remembers user information, predicts intent, and takes proactive action without explicit instructions.
Core Capabilities
- Memory as a file system: Organizes memory into a three-layer structure of categories → memory items → raw resources, with multimodal input across conversations, documents, images, and audio/video
- Autonomous memory management: A dedicated memory agent automatically decides what to record, modify, or archive — no manually designed schema required
- User intent prediction: Continuously analyzes behavior and predicts intent in real time, enabling proactive action
- Self-evolving memory: Automatically merges redundancy, generates summaries, and infers implicit relationships in the background
- Smart forgetting: Achieves human-like memory decay based on usage patterns, gradually reducing access to unimportant memories
Use Cases Individuals who need AI companions with persistent memory, developers building 24/7 customer service/sales/education agents, application builders seeking low-cost, high-accuracy memory solutions, and privacy-minded users who want local deployment (Ollama supported).
Unique Advantages Unlike RAG-based solutions such as Mem0, memU positions itself as a pure memory layer — no document chunking, no RAG pipeline — making retrieval faster and lighter; it achieves 92%+ accuracy on Locomo while saving about 90% in token costs, and tech media have named it a Best Memory option.
Editor's Review Its position in the AI memory space is clear: an open-source framework (9K+ GitHub stars) coexists with cloud services, suited to building agents that need long-term memory. Community feedback confirms it solves the "forgets everything on restart" pain point, with memory that is readable, debuggable, and editable. Note that the 10-parallel-task cap may limit high concurrency, and public user reviews remain relatively limited.
Pricing
### 💰 定价模式:免费增值 + 按使用量付费 **起步价**:免费(桌面应用可免费下载) #### 主要方案 - **memu.bot 桌面版**:macOS/Windows 免费下载 - **Service Credits**:预付费模式,购买后一年内有效 - **Startup Plan**:早期团队可申请免费计划扩展额度,48 小时内审批 - **Memory API**:按 token 计费,gpt-4.1-mini 输入 $0.00040/1K tokens,输出 $0.00160/1K tokens #### 试用/其他信息 每条存储信息计为 1 个 memory。Service Credits 不可退款。 — Visit website
FAQ
Is memU free?
The memU.bot desktop app is free to download on both macOS and Windows. The API and advanced features use prepaid Service Credits or per-token billing, and early-stage teams can apply for a Startup Plan with extended free allowances.
What are memU's main features?
Core features include file-system-style hierarchical memory management, cross-modal input (conversations/documents/images/audio-video), an autonomous memory agent, user intent prediction and proactive action, self-evolving memory and smart forgetting, plus Telegram/Discord/Feishu/Slack integrations.
How does memU differ from Mem0?
Mem0 is a RAG system (chunking + vectors + retrieval) suited to document search; memU is a pure memory layer with no RAG pipeline, making retrieval faster, lighter, and more accurate — suited to agent scenarios like companions and customer service that need true memory.
What kinds of applications is memU suited for building?
Scenarios that need persistent memory: AI companions, 24/7 customer service/sales/education agents, emotional support, and the like. If your main need is document retrieval, a RAG solution such as Mem0 is a better fit.
Which platforms does memU support?
Desktop supports macOS and Windows; it integrates with Telegram, Discord, Feishu, and Slack; and it is available as a cloud version (app.memu.so) or local deployment (Ollama mode can run without cloud APIs).
How does memU's memory work?
It uses a three-layer architecture: Resource Layer → Memory Item Layer → Memory Category Layer, with bidirectional traceability. A dedicated agent automatically decides whether to record, modify, or archive, while background processes continuously analyze, merge redundancy, generate summaries, and implement smart forgetting based on usage patterns.