Core Concepts
Overview
MOS (Memory Operating System)
What it is: The orchestration layer that coordinates multiple MemCubes and memory operations. It connects your LLMs with structured, explainable memory for reasoning and planning.
When to use: Use MOS whenever you need to bridge users, sessions, or agents with consistent, auditable memory workflows.
MemCube
What it is: A MemCube is like a flexible, swappable memory cartridge. Each user, session, or task can have its own MemCube, which can hold one or more memory types.
When to use: Use different MemCubes to isolate, reuse, or scale your memory as your system grows.
Memory Types
MemOS treats memory like a living system — not just static data but evolving knowledge. Here's how the three core memory types work together:
| Memory Type | Description | When to Use |
|---|---|---|
| Parametric | Knowledge distilled into model weights | Evergreen skills, stable domain expertise |
| Activation | Short-term KV cache and hidden states | Fast reuse in dialogue, multi-turn sessions |
| Plaintext | Text, docs, graph nodes, or vector chunks | Searchable, inspectable, evolving knowledge |
Parametric Memory
What: Knowledge embedded directly into the model's weights — think of this as the model's "cortex". It's always on, providing zero-latency reasoning.
When to use: Perfect for stable domain knowledge, distilled FAQs, or skills that rarely change.
Activation Memory
What: Activation Memory is your model's reusable "working memory" — it includes precomputed key-value caches and hidden states that can be directly injected into the model's attention mechanism. Think of it as pre-cooked context that saves your LLM from repeatedly re-encoding static or frequently used information.
Why it matters: By storing stable background content (like FAQs or known facts) in a KV-cache, your model can skip redundant computation during the prefill phase. This dramatically reduces Time To First Token (TTFT) and improves throughput for multi-turn conversations or retrieval-augmented generation.
When to use:
- Reuse background knowledge across many user queries.
- Speed up chatbots that rely on the same domain context each turn.
- Combine with MemScheduler to auto-promote stable plaintext memory to KV format.
Explicit Memory
What: Structured or unstructured knowledge units — user-visible, explainable. These can be documents, chat logs, graph nodes, or vector embeddings.
When to use: Best for semantic search, user preferences, or traceable facts that evolve over time. Supports tags, provenance, and lifecycle states.
How They Work Together
MemOS lets you orchestrate all three memory types in a living loop:
- Hot plaintext memories can be distilled into parametric weights.
- High-frequency activation paths become reusable KV templates.
- Stale parametric or activation units can be downgraded to plaintext nodes for traceability.
With MemOS, your AI doesn't just store facts — it remembers, understands, and grows.
Over time, frequently used plaintext memories can be distilled into parametric form. Rarely used weights or caches can be demoted to plaintext storage for auditing and retraining.
Cross-Cutting Concepts
Hybrid Retrieval
Combines vector similarity and graph traversal for robust, context-aware search.
Governance & Lifecycle
Every memory unit supports states (active, merged, archived), provenance tracking, and fine-grained access control — essential for auditing and compliance.
Always track provenance and state changes for each memory unit. This helps meet audit and data governance requirements.
Key Takeaway
With MemOS, your LLM applications gain structured, evolving memory — empowering agents to plan, reason, and adapt like never before.
What Is MemOS?
MemOS is a Memory Operating System for large language models (LLMs) and autonomous agents. It treats memory as a first-class, orchestrated, and explainable resource, rather than an opaque layer hidden inside model weights.
Architecture
MemOS is made up of core modules that work together to turn your LLM into a truly memory-augmented system — from orchestration to storage to retrieval.