LLM Integration in OS

Updated 16 November 2025

LLM Integration in OS is a technology embedding transformer-based models into OS layers for natural language-driven automation and adaptive resource management.
It encompasses paradigms such as OS augmentation, AI refactoring, and agentic OS constructions, enhancing scheduling, memory, and file management.
Research highlights challenges including latency, security risks, and model drift, guiding ongoing developments for robust and efficient OS designs.

LLM integration in operating systems refers to the embedding of transformer-based LLMs into one or more layers of the OS stack or OS-managed runtime, enabling natural language-driven automation, decision-making, and resource management across traditional and next-generation computing domains. Unlike classical rule-based or heuristic-augmented OS enhancements, LLM integration leverages pretrained deep networks, prompt engineering, and agent-oriented abstractions to adaptively mediate user input, system events, and heterogeneous hardware resources. Recent literature covers architectural blueprints, end-to-end agentic operating environments, kernel-space model invocation APIs, semantic subsystem overlays (e.g., file systems), and domain-specific agent OSs. This entry synthesizes the state of the art in LLM-in-OS design, implementation, performance, and open challenges as reflected in recent foundational research.

1. Architectural Models of LLM Integration

Recent advances were catalyzed by three major integration paradigms: OS augmentation, refactoring for AI-native interfaces, and agentic/autonomous OS constructions. These are distinguished by module placement (user space vs. kernel), interfaces, and degree of control over core OS logic (Zhang et al., 19 Jul 2024, Hou et al., 6 Mar 2025, Ge et al., 2023).

Integration Paradigm	LLM Role / Placement	Example Systems
AI-Powered (Augmentation)	User-space agent or optional kernel module	LSFS, OS-Copilot, AutoOS
AI-Refactored	In-kernel inference engine, privileged module	AIOS kernel, LLMOS, MedicalOS
AI-Driven (Agentic)	LLM (or agent graph) as primary policy engine	SchedCP, LLM-as-OS, MemOS

AI-powered OSs use LLMs for natural language shells, copilot services, and semantic resource queries without altering the OS core. AI-refactored OSs embed quantized or distilled model runtimes as kernel modules, exposing LLM-native syscalls and inference APIs. The agentic paradigm elevates the LLM to the center of decision logic: scheduler, resource manager, and orchestrator, effectively recasting the kernel as an adaptive, language-driven policy machine (Zheng et al., 1 Sep 2025, Li et al., 4 Jul 2025).

A canonical blueprint involves three layers (Hou et al., 6 Mar 2025):

Application Logic (A): User- or agent-facing LLM orchestration, prompt engineering, agent plugins.
Protocol Layer (P): Session, authentication, transport; mapping model calls to OS-native system services.
Hardware Execution Layer (H): Kernel module/driver for accelerator-aware inference, secure memory pools.

This model cleanly separates rich user- or agent-level language tasks from secure, sandboxed, hardware-efficient execution.

2. Key OS Subsystems Enhanced or Recast by LLMs

LLMs have been integrated or proposed as automation engines for several core OS subsystems.

Scheduling and Resource Management

LLM-driven Schedulers: Systems such as SchedCP decouple "what to optimize" (semantic reasoning by LLM) from "how to observe and act" (execution via eBPF and sched_ext), providing up to 1.79× performance improvement and 13× cost reduction in scheduler optimization (Zheng et al., 1 Sep 2025).
Preemptive scheduling for LLM-agent calls: AIOS uses centralized syscall queues and round-robin or FIFO policies to prevent resource monopolization and enable up to 2.1× agent throughput compared to naïve frameworks (Mei et al., 25 Mar 2024).

Memory and Persistent Context

Memory as Resource: MemOS formalizes LLM memory into three tiers—parameter memory (weights, adapters), activation memory (KV-cache, hidden states), and retrieval memory (external plaintext). The kernel schedules MemCubes between these tiers based on utility scores, supporting lifecycle control, plasticity, and continual adaptation (Li et al., 4 Jul 2025).
Context Snapshotting: Systems maintain resumable context for LLM inference, enabling safe preemption, scaling, and isolation (Mei et al., 25 Mar 2024, Li et al., 4 Jul 2025).

File and Data Management

Semantic File Systems: LSFS overlays a conventional file system, adding vector-indexed, LLM-parsed APIs for natural-language file retrieval, summarization, rollback, version control, and group operations. Empirical results show >20% accuracy improvement and >75% latency reduction over baseline search (Shi et al., 23 Sep 2024).
Command-to-Prompt Translation: Macro-level APIs expose file-system operations as natural-language interfaces, mapped to a micro-level of LLM-augmented syscalls with semantic safety checks (Shi et al., 23 Sep 2024).

Application-Oriented and Domain-Specific OSs

Healthcare Agentic OS: MedicalOS encapsulates clinical workflows in a fixed action schema (e.g., retrieve_history, prescribe_medication) and routes natural-language instructions through an LLM agent, interacting via audited FUSE modules and external DBs. Diagnostic accuracy attains 90.24% (with test requests), and medication adherence reaches 94.4% (Zhu et al., 15 Sep 2025).

3. Implementation Patterns, APIs, and Performance Results

Most systems deploy LLMs in user space, with communication to kernel or resource manager modules via RPC, REST, UNIX sockets, or device files (Kamath et al., 17 Jan 2024, Hou et al., 6 Mar 2025). Kernel-resident modules use quantized, resource-bounded model runtimes invoked via inference APIs, e.g.:

1 2	int llm_load(const char path, struct llm_model m_out); int llm_infer(int model_id, const char prompt, size_t prompt_len, char response, size_t *resp_len, unsigned timeout_us);

(Zhang et al., 19 Jul 2024)

Interfaces and SDKs encapsulate LLM and storage functionality in concise, application-facing function calls:

Layer	API Example
LLM	llm_chat(), llm_infer(), llm_chat_with_tool_call()
Memory	create_memory(), search_memories(), delete_memory()
Storage	mount(), retrieve_file(), rollback_file()
Tool	call_tool(tool_name, parameters)

Performance metrics span OS-level (throughput, latency under concurrency), agent-level (diagnostic accuracy, specialty referral precision), and kernel-level (wall-clock reduction for builds, eBPF scheduler validation success rate). Empirical results demonstrate substantial wins—MedicalOS (diagnosis: 90.24%, report consistency: 2.51/patient), SchedCP (makespan: 1.79× speedup), AIOS (agent throughput: 2.1× increase, p90 latency: –40–60%)—always using documented platforms and testbeds (Zhu et al., 15 Sep 2025, Zheng et al., 1 Sep 2025, Mei et al., 25 Mar 2024).

4. Security, Access Control, and Reliability Mechanisms

LLM integration introduces distinct risks: prompt hallucination (fabricated device properties or commands), API latency, unsafe tool execution, privilege escalation, prompt/trace leakage, and model drift (Kamath et al., 17 Jan 2024, Zhang et al., 19 Jul 2024).

Access Control: AIOS and MemOS enforce agent/domain access strictly via per-agent/group privilege mappings and memory/file ACLs. Destructive operations require explicit confirmation dialogs (Mei et al., 25 Mar 2024, Li et al., 4 Jul 2025).
Sandboxing: MedicalOS employs FUSE and process containers to ensure that LLM agents cannot escape or perform unauthorized system modifications, critical for compliance (HIPAA, GDPR) (Zhu et al., 15 Sep 2025).
Verification Pipelines: SchedCP validates all LLM-produced eBPF code using kernel-level verifiers, static analysis for fairness, micro-VM dynamic tests, and canary deployments with circuit breakers reverting on regression (Zheng et al., 1 Sep 2025).
Guardrails and Rule-AI Hybridization: Systems like the AIOS-Agent ecosystem enforce safe fallbacks: if the gap between LLM and rule-based decision exceeds Δmax, deterministic logic is invoked (Zhang et al., 19 Jul 2024).
Audit Trails: All privileged actions (file ops, tool invocations, scheduling changes) are logged for post-mortem analysis and forensics (Zhu et al., 15 Sep 2025, Li et al., 4 Jul 2025).

5. Methodological Pipelines, Evaluation, and Best Practices

LLM-in-OS systems follow a unified development methodology (Zhang et al., 19 Jul 2024, Hou et al., 6 Mar 2025):

Data Collection: OS, workload, and user traces.
Preprocessing/Feature Extraction: Contextual feature engineering.
Offline Training/Fine-Tuning: Joint or per-module; quantize/distill for kernel deployability.
Deployment: User-space/daemon or in-kernel (e.g., /dev/llm0, SCHED_LLM).
Monitoring/Drift Detection: Divergence testing (e.g., KL divergence), automatic rollback.
Online Update: Canary, batch, wide rollouts.
Evaluation: Benchmarks for latency (P50–P99), throughput, resource overhead, predictive accuracy (AUC, MAE), and robustness under workload shift.

Best practices documented across studies include:

Caching prompts, using session pinning per window or agent, and maintaining per-document session history (Teufelberger et al., 31 Jul 2024, Shi et al., 23 Sep 2024).
Unified, high-level SDKs that shield agents or applications from kernel complexity and prevent direct hardware access (Mei et al., 25 Mar 2024).
Versioned storage and semantic rollback to support efficient and safe context and file management (Shi et al., 23 Sep 2024, Li et al., 4 Jul 2025).
Preemptive resource allocation and fair scheduling of LLM inference slots, including time-slicing and snapshot/restore for generative calls (Mei et al., 25 Mar 2024).

6. Limitations, Open Challenges, and Research Directions

Documented limitations and pitfalls shape the current research agenda:

Latency and Resource Overheads: User-space or API-call LLMs introduce 200–400 ms call latencies, which are prohibitive for high-frequency OS events; kernel-side quantized models and hardware acceleration (NPU/TPU) are actively investigated (Kamath et al., 17 Jan 2024, Zhang et al., 19 Jul 2024).
Precision and Hallucinations: Prompt-based feature extraction can invent device or resource attributes. Closed command schemas and user confirmation steps serve as partial mitigation (Zhu et al., 15 Sep 2025, Kamath et al., 17 Jan 2024).
Kernel Security: Embedded LLM models in kernel-space require strong isolation (eBPF verifier, Rust, TEE) and memory quota limits to prevent OOM or privilege escalation (Hou et al., 6 Mar 2025, Zhang et al., 19 Jul 2024).
Explainability and Model Drift: Automated, neural decision logic introduces explainability deficits; audit trails and hybrid rule-AI policies serve as fallback mechanisms (Zhang et al., 19 Jul 2024).
Consistency: Coordinating state across semantic overlays and native OS subsystems (e.g., LSFS vs TFS) can be a source of inconsistency; transactional journaling is recommended (Shi et al., 23 Sep 2024).

Future work targets on-prem LLM distillation (microsecond latency), kernel-level policy push, domain adaptation, and extension to richer device and agent domains (FPGAs, disaggregated NICs, secure enclaves, financial/industrial systems) (Kamath et al., 17 Jan 2024, Hou et al., 6 Mar 2025).

7. Implications and Broader Context

LLM integration in OSs signals a shift towards natural-language mediation of computer systems, agentic reasoning over resource and application orchestration, and a blurring of the boundaries between system software and intelligent automation (Ge et al., 2023, Mei et al., 25 Mar 2024). With the unification of memory, file, scheduler, and API interfaces as prompt-addressable primitives, OS architectures are transitioning to a model in which LLMs serve as substrate for both user interaction and internal optimization.

The implications include fundamentally new modes of usability (e.g., semantic file queries, agentic shells), dynamic, context-dependent policy synthesis (e.g., customized schedulers, adaptive memory/resource usage), and new challenges in verification, compliance, and multi-agent coordination. Research emphasizes modularity, strong security boundaries, hybrid deterministic-AI policy mechanisms, and unified toolchains as enabling technologies for scalable, reliable, and safe deployment of LLM-augmented operating systems (Zhang et al., 19 Jul 2024, Hou et al., 6 Mar 2025, Shi et al., 23 Sep 2024).