LLMOS: OS for Intelligent Agent Systems

Updated 28 October 2025

LLMOS is a paradigm that repurposes large language models as the system kernel, managing agents, memory, and tool orchestration like a traditional OS.
It employs modular architectures with hierarchical memory management and natural language interfaces to enable scalable workflow automation.
LLMOS integrates safety, governance, and domain-specific abstractions to ensure robust, accountable, and adaptable computing environments.

A LLM as Operating System (LLMOS) designates a paradigm in which powerful LLMs, sometimes augmented with vision or tool-use capabilities, function as the core control, abstraction, and orchestration layer—analogous to the kernel and system services of a conventional OS. In the LLMOS framework, agents, specialized sub-models, or external tools are deployed as “applications”, while user intent, tooling, resource management, memory, and context are coordinated through interfaces—often natural-language-based—between humans and the digital environment. This concept recasts the role of LLMs from stateless question-answering engines to foundational, flexible, and governable system infrastructure for workflow automation, general computing, domain-specific solutions, and autonomous process control.

1. Conceptual Foundations and Architectural Analogies

The LLMOS paradigm is grounded in formal analogies to classical computer operating systems. Central principles include:

LLM as Kernel: The LLM performs reasoning, planning, tool orchestration, and resource management functions, analogous to kernel-managed process scheduling and system calls (Ge et al., 2023).
Memory as Context Window and Beyond: Traditional context windows (RAM) are extended by memory OS layers (paging, external storage), enabling persistent knowledge, long-term state, and hierarchical memory management (Packer et al., 2023, Li et al., 4 Jul 2025, Li et al., 28 May 2025, Kang et al., 30 May 2025).
Files, Tools, Agents: External storage, retrieval-augmented generation, and devices/libraries in a classic OS correspond to plugged-in tools/APIs, retrieval modules, or agent applications (Ge et al., 2023, Srinivas et al., 23 Aug 2024, Mei et al., 25 Mar 2024).
Natural Language as Programming Interface: User intention is expressed via NL or GUI-derived prompts, directly interpreted as commands to the LLM kernel (Ge et al., 2023, Zhu et al., 15 Sep 2025).
Agents as Apps: Discrete, composable agent modules, potentially specialized or multi-agent societies, serve as applications atop the LLMOS platform (Mi et al., 6 Apr 2025, Hu et al., 6 Aug 2025).

Key frameworks like AIOS (Mei et al., 25 Mar 2024), MemOS (Li et al., 28 May 2025, Li et al., 4 Jul 2025), LLaMaS (Kamath et al., 17 Jan 2024), MemoryOS (Kang et al., 30 May 2025), and agentic OS architectures for process domains (Srinivas et al., 23 Aug 2024), provide concrete system blueprints mapping these abstractions to technical modules.

2. Memory Management, Persistence, and Hierarchical Control

Memory management is a critical concern in LLMOS, reflecting both OS tradition and unique demands of neural systems. Recent work establishes:

Memory Layer	LLMOS Analogy	OS Analogy
Parametric Memory	Model Weights/Adapters	Firmware, system binaries
Activation Memory	Context/KV Cache/States	RAM/Working Set
Plaintext Memory	External Storage/RAG/Logs	File system, swap, persistent DB

MemOS generalizes memory representation, management, and lifecycle across these types via MemCube abstractions: atomic, versioned, metadata-rich memory containers supporting scheduling, fusion, transformation, and access control (Li et al., 28 May 2025, Li et al., 4 Jul 2025). Lifecycle and policy-driven transitions (e.g., promotion from plaintext to parameter, demotion, migration, fusion) echo classical paging and caching dynamics, but are explicitly governed for continual learning, personalization, and long-term adaptation (Li et al., 4 Jul 2025, Kang et al., 30 May 2025).

Systems like MemGPT (Packer et al., 2023) introduce hierarchical virtual context schemes, using event-driven paging and recursive summarization to achieve effective “infinite” context, enabling document-scale reasoning and long-lived conversational memory.

3. Scheduling, Resource Management, and Kernel Services

LLMOS architectures explicitly address multi-agent scheduling and LLM/tool resource allocation, isolating agent “application” code from shared, rate-limited LLM and API resources:

Scheduler/Kernel: Handles agent query queues, resource quotas, and concurrency. Implements fairness via FIFO, round-robin, or priority-based algorithms, supporting preemptive multitasking, context switching, and coordinated memory management (Mei et al., 25 Mar 2024).
Context Management: Logits- or text-based checkpointing enables interrupt/resume primitives during inference, minimizing redundant generation and preserving session state across agent/process switching.
Memory/Storage Manager: Dynamically allocates, swaps, and versions agent histories, workspaces, and external data; supports versioned rollback and semantic vector-based retrieval (Mei et al., 25 Mar 2024).
Tool/Access Manager: Standardized plug-in frameworks and privilege controls manage validated external tool invocations, locking, and safe API usage—paralleling device-driver and syscall permissions in OS design.

These mechanisms, formalized as kernel modules accessed by agent-level syscalls via an SDK, foster resource isolation, scalability, and robust execution in LLM-centered multi-agent environments.

4. Domain-Specific Abstractions and Applications

LLMOS enables declarative and transparent workflow orchestration across specialized domains:

Healthcare (MedicalOS): Translates high-level clinician instructions into commands for EHR retrieval, test ordering, report generation, and treatment recommendations, wrapping all automation in interfaces compliant with clinical guidelines, traceability, and accountability (Zhu et al., 15 Sep 2025). Workflow is structured as natural language → reasoning + acting (ReAct framework) → formal tool command, with clinical guideline adherence and audit mechanisms built in.
Process Engineering (PEOA): Decomposes engineering queries into sequenced subtasks using a meta-agent (“scheduler”), leveraging domain-tuned LLMs as selective “drivers” for code generation, math reasoning, and multi-hop knowledge graph querying (Srinivas et al., 23 Aug 2024). Systematic error handling, teacher-student instruction tuning, and modular orchestrations enable stepwise, auditable pipeline execution.
General Computing and GUI Control: OS Agents powered by multimodal LLMs/MLLMs perceive, plan, and act upon mobile, desktop, and web platforms by mapping high-level user intent to grounded GUI/API actions across real operating system environments (Hu et al., 6 Aug 2025). Capabilities span input perception (screenshots, HTML), semantic planning, memory accumulation, and low-level action synthesis (click, scroll, type) across diverse OS applications.

5. Safety, Governance, and System Integrity

LLMOS systems face unique safety and alignment challenges, especially in agentic and open-ended task domains:

Safety Benchmarks (OS-Harm): Empirical evaluations reveal that leading OS agent systems are highly vulnerable to deliberate misuse (~48–70% unsafe compliance), prompt injection (2–20%), and model misbehavior (4–10%) (Kuntz et al., 17 Jun 2025). LLM-based semantic judges automate safety/accuracy auditing, but robust, context-aware refusal mechanisms and sandboxed control planes remain essential for secure deployment.
Governance Mechanisms: System-wide logging, action justification, versioned memory chains, and privilege-enforced APIs provide the basis for traceability, debugging, and compliance in critical domains (Zhu et al., 15 Sep 2025, Li et al., 28 May 2025). Clinical and regulated deployments mandate strong adherence to external references, transparent plan/rationale reporting, and user-in-the-loop review interfaces.

System designs such as SchedCP (Zheng et al., 1 Sep 2025) explicitly decouple semantic LLM-driven reasoning from privileged OS execution layers, enforcing multi-stage verification (eBPF, dynamic sandboxing) to eliminate unsafe deployments.

6. Modularization, Extensibility, and Evolution

Recent frameworks promote modular, system-inspired agent architectures, drawing on the von Neumann analogy:

Agentic Modules: Perception (input interface), Cognition (reasoning, planning), Memory (multi-tiered/hierarchical), Tool Use (external execution), and Action (output, environment interaction) are decomposed as explicit, often mathematically formalized modules (Mi et al., 6 Apr 2025).
Parallelism and Multicore: Multi-agent and multi-core designs enable concurrent processing (big.LITTLE LLM ensembles), where large models handle complex events and small ones route routine tasks (Mi et al., 6 Apr 2025).
DMA Analogy: Direct “memory-to-memory” operations may bypass LLM inference pipeline for efficiency, akin to direct memory access in hardware, particularly for high-throughput, repeated access patterns (Mi et al., 6 Apr 2025).
Continual Learning: Managed, lifecycle-aware memory systems (MemCube, dynamic fusion/migration) allow agents to self-evolve, adapt, and persist cross-task knowledge without full-parameter retraining (Li et al., 28 May 2025, Li et al., 4 Jul 2025).

A plausible implication is that as memory abstraction and kernel modularity advance, LLMOS architectures will further integrate OS design tenets around abstraction, layering, robust error handling, self-improvement loops, and standardization for agent deployment at scale.

7. Open Challenges and Future Directions

Memory Scalability and Personalization: Efficient ultra-long memory, with heat-based prioritization, topic-aware segmentation, and hierarchical caching for both contextual coherence and user-personalized modeling (Kang et al., 30 May 2025, Li et al., 28 May 2025).
Security and Adversarial Robustness: Defense against adversarial prompt injection, dynamic environment manipulation, and agent exploitation—requiring new benchmarks (e.g., OS-Harm), sandboxing, and system-side governance (Kuntz et al., 17 Jun 2025).
Resource and Tool Ecosystem Management: Scalable, open plugin architectures for toolization, privilege escalation auditing, and distributed memory sharing across agents and platforms (Mei et al., 25 Mar 2024).
Natural Language as System/Programming Interface: Further democratization of agent and “application” development, with NL programming and symbolic DSLs to bridge ambiguity and enhance composability across multi-agent systems (Ge et al., 2023).
Cross-disciplinary Standardization: Unification of operating system design with AI, agentic, and domain-specific paradigms for interoperable, maintainable, and safe LLM-OS ecosystems (Mi et al., 6 Apr 2025).

References and Summary Table

Major Research Theme	Representative Work	Core Contribution
Memory OS (Hier/Unified)	MemOS (Li et al., 4 Jul 2025, Li et al., 28 May 2025)	MemCube, multi-type memory, lifecycle, continual learning
Resource & Agent Mgt	AIOS (Mei et al., 25 Mar 2024)	Kernel scheduling, memory/context swap, SDK, agent isolation
Tool & Action OS Agents	OS Agents (Hu et al., 6 Aug 2025)	GUI-grounded multi-modal agents, agentic system integration
Safety & Governance	OS-Harm (Kuntz et al., 17 Jun 2025); SchedCP (Zheng et al., 1 Sep 2025)	Empirical safety benchmarks; decoupled verification and deployment
Modular Agent Architectures	von Neumann framework (Mi et al., 6 Apr 2025)	Modular decomposition, memory layering, multicore/concurrent design
Declarative Workflow	MedicalOS (Zhu et al., 15 Sep 2025); PEOA (Srinivas et al., 23 Aug 2024)	Domain-specific abstraction, clinical/process automation

LLMOS research thus establishes LLMs and MLLMs as the abstractions at the heart of future digital systems, orchestrating agent applications, memory, tools, and user/system workflows with OS-level robustness, extensibility, and accountability. This vision suggests a future in which system intelligence, safety, interpretability, and auditability are native to the OS itself.