memU
NevaMind-AI
Memory framework for proactive AI agents - typed memory graph from chats, docs and media.
What is memU?
memU is a Python-first memory framework that converts conversations, documents, images, video, audio and local files into a typed memory graph (Resources, MemoryItems, Categories, Relations). It supports SQLite and PostgreSQL backends, configurable LLM routing for chat/embedding/vision/transcription, and offers a managed API alongside self-hosting.
Pros & Cons
Pros
- Typed memory categories (profile, event, knowledge, behavior, skill, tool) for structured retrieval
- Pluggable storage (in-memory, SQLite, PostgreSQL) with pgvector examples
- Active multi-contributor development
Cons
- GitHub shows NOASSERTION (Apache-2.0 confirmed only via README badge)
- Recent commits are mostly docs and bug fixes
- Smaller ecosystem than Mem0 or MemOS
License
Apache-2.0 (OSI-open)
When it is interesting
Python agent projects needing strongly-typed, searchable memory with flexible storage and minimal infrastructure.
When it is too early
Projects needing mature SDK support beyond Python or real-time multimodal memory at scale.
Commercial alternative & related
- Commercial counterpart: Mem0
This repo featured in the 2026-07 edition of the Open-Source AI Radar.
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MemOS
MemTensor
Self-evolving memory OS for LLMs and AI agents with tiered L1-L3 memory.