A self-hostable 'memory operating system' that packages long-term memory into MemCube units and manages their lifecycle (store / retrieve / update / schedule) outside the model.
- Storage
- Pluggable backends; the local plugin uses persistent SQLite. Supports text, images, tool traces, and personas as memory.
- Retrieval
- Hybrid retrieval combining full-text search (FTS5) and vector similarity, with task summarization and cross-task skill reuse.
- Self-host
- Self-host: moderate
- License
- Apache-2.0
- Pricing
- Open source (Apache-2.0), free to self-host · Free / OSS
- GitHub stars
- 10,018
- Last release
- 2026-06-18
- Last commit
- 2026-06-18
- First catalogued
- 2026-06-28
Strengths
- Active, high-traction project (10k+ stars)
- Hybrid full-text + vector retrieval
- Multi-modal memory (text, images, tool traces, personas)
- Vendor reports ~35% token savings
Watch out
- 'Memory OS' framing overlaps confusingly with the separate BAI-LAB MemoryOS project — they are different systems
- A hosted/cloud offering (OpenMem) may exist; pricing for any managed tier not confirmed
Best for
- Teams wanting a self-hosted memory layer with hybrid retrieval and skill reuse
Benchmark results
| Benchmark | Value | Backbone | Trust | Source |
|---|---|---|---|---|
| locomo | 75.8 accuracy | GPT-4o-mini | Self-reported | MemOS (MemTensor et al.) ↗ |
| longmemeval | 77.8 accuracy | GPT-4o-mini | Self-reported | MemOS (MemTensor et al.) ↗ |
Sources
- https://github.com/MemTensor/MemOS (vendor)
- MemOS: A Memory OS for AI System (paper)
Last verified 2026-06-28 · updated by manual-refresh