MemOS
MemOS is a memory operating system for LLMs and AI agents that provides persistent, multi-modal memory storage with hybrid retrieval, token savings (35.24%), and self-evolving skill management. It offers both cloud-hosted and self-hosted deployment options via TypeScript/Node.js, with integrations for agents like OpenClaw and Hermes.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | MemTensor/MemOS |
| Owner | MemTensor |
| Primary language | TypeScript |
| License | Apache-2.0 — OSI-approved |
| Stars | 10.1k |
| Forks | 920 |
| Open issues | 180 |
| Latest release | v2.0.22 (2026-07-03) |
| Last updated | 2026-07-07 |
| Source | https://github.com/MemTensor/MemOS |
What MemOS is
MemOS unifies memory store/retrieve/manage operations through a graph-structured API, supporting multi-modal inputs (text, images, tool traces), asynchronous ingestion via MemScheduler (Redis Streams), and hybrid search (FTS5 + vector). Core features include Knowledge Base management, memory feedback/correction, and tiered self-evolution (L1 traces, L2 policies, L3 world models, crystallized skills).
Get the MemOS source
Clone the repository and explore it locally.
git clone https://github.com/MemTensor/MemOS.gitcd MemOS# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Docker deployment requires explicit MEMOS_HOME or --home configuration; ensure environment variables or volume mounts are correctly set up before production rollout.
- Redis Streams scheduler (v2.0+) adds infrastructure dependency; plan for Redis cluster redundancy and monitoring if using cloud plugin or scaling to multi-agent setups.
- Multi-modal memory (images, tool traces) requires appropriate embeddings model and storage layer (vector DB); ensure your deployment includes compatible vector indexing infrastructure.
- Memory feedback loop requires LLM calls for correction/refinement; factor in additional token costs and latency for feedback-driven memory evolution in cost models.
- Knowledge base multi-cube isolation is composable but requires governance; design access control and sharing policies upfront to avoid data leakage across users/projects.
When to avoid it — and what to weigh
- Real-time sub-second memory lookups are critical — MemOS is designed for 'millisecond-level latency' on async operations, but synchronous retrieval latency and end-to-end performance under high load are not detailed. Verify benchmarks before ultra-low-latency use.
- Your stack is non-Node.js or requires language-specific SDKs — MemOS is primarily TypeScript. No Go, Rust, Java, or Python SDKs are mentioned. Integration requires API calls or npm modules; tight coupling to Python/Java stacks may be inefficient.
- You need a pure on-device solution with zero external dependencies — While memos-local-plugin offers local-first storage (SQLite), the full v2.0 feature set (KB, feedback, multi-modal) appears to require backend coordination. Self-hosted deployments still need database and scheduler infrastructure.
- Your agents run on proprietary platforms (Slack, Teams, etc.) without custom integrations — MemOS integrates primarily with OpenClaw and Hermes agents. Existing integrations with third-party platforms are not mentioned; custom development would be required.
License & commercial use
MemOS is released under Apache License 2.0 (Apache-2.0), an OSI-approved permissive license permitting commercial use, modification, and distribution with attribution and liability disclaimer.
Apache-2.0 is a permissive OSI license that explicitly permits commercial use, modification, and distribution. However, the data does not clarify whether cloud-hosted services (MemOS Cloud Plugin, MemOS Dashboard) operate under the same open-source terms or separate proprietary SaaS licensing. Self-hosted deployment of the open-source core is commercially viable; cloud service terms require direct review of MemOS service agreement.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Data does not provide explicit security audit results, encryption (TLS/at-rest), or vulnerability disclosure policy. Multi-user isolation via knowledge base cubes is claimed but mechanism is not detailed. Self-hosted deployments inherit OS/database security; ensure Redis and database instances are access-controlled. Cloud plugin data residency, privacy, and compliance certifications are not stated; verify independently before handling sensitive data.
Alternatives to consider
Langchain + ChromaDB / Pinecone
Mature ecosystem for memory/retrieval in LLM apps; broader language support and larger community. MemOS is more specialized for agent self-evolution and multi-modal memory.
Mem0 (or similar agent memory platforms)
Dedicated agent memory systems; may offer tighter integrations with other LLM frameworks. MemOS differentiates on token savings claims and OpenClaw/Hermes plugins.
Custom PostgreSQL + pgvector + embedding service
Full control, mature database, lower lock-in. Requires substantial custom engineering for feedback loops, skill evolution, and multi-agent orchestration that MemOS provides out-of-box.
Build on MemOS with DEV.co software developers
Explore MemOS documentation, try the playground, or deploy locally with memos-local-plugin. Start with the quick deployment guide or review the ArXiv paper for technical deep dive.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
MemOS FAQ
Can I use MemOS with LLMs other than Claude and ChatGPT?
What is the difference between the cloud plugin and local plugin?
How much does the cloud service cost?
Is memory data encrypted in transit and at rest?
Software developers & web developers for hire
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If MemOS is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to add persistent memory to your AI agents?
Explore MemOS documentation, try the playground, or deploy locally with memos-local-plugin. Start with the quick deployment guide or review the ArXiv paper for technical deep dive.