AIOS: AI-Integrated Operating System

Updated 5 April 2026

AIOS is an LLM-centric operating system that orchestrates agents, memory, and tool access via natural language system calls.
It integrates traditional OS primitives with AI-specific functions, achieving performance improvements in scheduling and context management.
The framework supports decentralized, peer-to-peer agent collaboration, enabling scalable, multi-agent application development and execution.

AIOS (Artificial Intelligent Operating System) refers to a class of LLM-centric operating systems re-architected to natively host, orchestrate, and manage LLM–based agents, integrating LLM reasoning, resource control, tool invocation, semantic memory, and communication protocols into a unified kernel. AIOS is explored both as a system-level runtime (with agents and tools as first-class objects) and as a programming and execution model enabling globally distributed, peer-to-peer agent collaboration, semantic middleware, and natural-language–driven application construction (Ge et al., 2023, Mei et al., 2024, Zhang et al., 19 Apr 2025).

1. Conceptual Foundation and Operating System Analogy

AIOS is formalized as an “operating system with soul,” in which the LLM takes the place of the OS kernel, mediating memory, tool/devices, scheduling, storage, and application execution (Ge et al., 2023). The core correspondences, presented in Table 1 below, recapitulate the mapping to traditional OS primitives:

OS Component	AIOS Counterpart	Description
Kernel	LLM	Central reasoning, scheduling, orchestration
Memory	Context Window	Prompt/context management; short-term state
File, File System	External Storage, Retrieval	Long-term knowledge; semantic and versioned access
Device/Library	Hardware/Software Tools	API- and prompt-defined modules; tool drivers
OS SDK, UI	AIOS SDK, NL Prompts	Programmatic/natural-language interfaces
App	Agent Application (AAP)	LLM-powered workflows, multi-agent systems

In this model, user instructions in natural language act as system calls, tool APIs are invoked as peripherals, and the LLM kernel governs resource allocation and process control. Agents (AAPs) act as applications, each combining profiles, toolsets, and memory abstractions.

2. Kernel and Runtime Architecture

Core AIOS implementations consolidate LLM cores and resource management modules into an explicit AIOS kernel. This kernel exposes fundamental services required for agent operation (Mei et al., 2024):

Scheduling: Central queue for all syscalls (LLM, tool use, memory, storage) with FIFO and round-robin policies. Empirically, AIOS achieves up to 2.1× throughput increase and 60–70% reduction in resource contention latency compared to unmanaged baselines.
Context/Memory Management: LLM invocations can be interrupted and resumed, with text- or logits-based context snapshotting.
Memory Management: Per-agent runtime memory blocks support LRU-k-eviction (RAM→disk). CRUD and semantic retrieval interfaces are exposed.
Storage: Persistent storage includes both filesystem (with versioning, default V=20) and vector database backends for semantic search.
Access Control: Privilege groups and explicit inter-agent permission checks are enforced prior to any cross-agent or destructive operation.
Toolkit Interface: Tool invocations and API calls are managed via a dedicated module that formalizes external tool registration and usage patterns.

All agent apps interact with the kernel exclusively via a standardized SDK over POST/IPC methods, ensuring isolation and accounting.

3. Programming and Execution Models

AIOS compilers such as CoRE (Xu et al., 2024) provide a unified syntax for constructing agent workflows in (structured) natural language, pseudo-code, and flow-programming style. The system’s grammar in EBNF is:

$\langle \text{program} \rangle ::= \langle \text{step} \rangle^+ \ \langle \text{step} \rangle ::= \langle \text{name} \rangle ::: \langle \text{type} \rangle ::: \langle \text{instruction} \rangle ::: \langle \text{connections} \rangle$

Step types: Process (sequential), Decision (branch), Terminal (exit). The LLM serves as an interpreter, executing each natural language instruction, retrieving contextually relevant memory content, invoking external tools where necessary, and dynamically deciding branching based on response evaluation.

A high-level interpreter loop pseudocode is:

current_step = "Start"
Memory = {}
while True:
    O = retrieve(Memory, current_step)
    P = build_prompt(TaskDesc, Progress, O, program[current_step].instruction)
    R = LLM(P)
    if is_tool_call(R):
        out = call_tool(parse_tool_request(R))
        Memory[current_step] = out
        response = out
    else:
        response = R
    next_step = decide_next_step(response, program[current_step])
    if next_step is None:
        break
    current_step = next_step

Memory retrieval uses embedding similarity; tool registry enables LLMs to extend abilities (e.g., real-time search, vision modules).

4. Semantic Storage, Context Management, and Virtualization

AIOS incorporates semantic file systems (LSFS (Shi et al., 2024)) and context virtualization processors (CMV (Santoni, 25 Feb 2026)) as kernel-integrated middleware layers:

LSFS: Sits atop conventional filesystems intercepting syscalls (open, read, write), maintains file chunk embeddings in a vector store indexed for semantic retrieval (e.g., via FAISS), and exposes high-level APIs (retrieve, change-summary, rollback, link) triggered via NL prompts. Semantic operations enable ∼22.5 pp improvement in retrieval accuracy and order-of-magnitude reduction in operation time vs. baseline LLM code. Version control and rollback scale sub-linearly with version count.
CMV: Implements conversation/session state as a versioned DAG; primitives: snapshot, branch, trim. Structural, lossless trimming algorithm reduces session token budgets by mean 20% (up to 86%) by excising tool output, images, and metadata while preserving all user/assistant turns. Enables context recycling across sessions and minimizes context rebuild costs.

These layers are essential for dynamic, multi-session, and multi-agent environments, where memory scalability and efficient history management are key to agent performance and economic viability.

5. Distributed Operation, Networking, and Agent Ecosystem

AIOS servers (Zhang et al., 19 Apr 2025) are designed for global-scale decentralized operation, forming the substrate for Internet of AgentSites (IoA):

Layered Architecture: Protocol Layer (handles Model Context Protocol and JSON-RPC), Agent Layer (manages agent lifecycles), Service Layer (task processing, node monitoring, DHT connector).
Communication: Standardized RPC messaging, multi-turn dialogue, tool/function execution via MCP.
Discovery: Agents and nodes discovered and registered via Kademlia DHT (O(log N) lookups) and near-real-time Gossip protocol. Registry nodes serve as hubs (AgentHub, AgentChat) for browsing, discovery, and live interaction.
Decentralized Execution: Any node can serve as agent host or client; task delegation proceeds via O(log N) routing; empirical latency remains sub-200 ms at scale.
Agent Distribution: Cerebrum SDK (Rama et al., 14 Mar 2025) provides a four-layer modular agent architecture (LLM, memory, storage, tools), versioned agent/tool repository, dependency management, and web interfaces for live diagnostics, testing, and monitoring.

This peer-to-peer model eliminates central orchestrators and supports scalable, robust, and collaborative multi-agent application ecosystems.

6. Application Domains and Use Cases

AIOS supports a wide spectrum of agent types and application modalities:

Computer-Use Agents (CUA): Via Model Context Protocol, the OS state (UI, controls, window metadata) is exposed to agents in structured JSON, enabling LLMs to operate digital environments by reasoning over semantically abstracted system states rather than pixel or DOM layouts. Reference agents (e.g., LiteCUA (Mei et al., 24 May 2025)) achieve state-of-the-art or competitive OSWorld benchmark performance.
Multi-Agent and Distributed Systems: Applications span information retrieval, automated software development, autonomous robotics, negotiation, and simulation-driven reasoning. Decentralized agent collaboration is natively supported with robust task, memory, and tool sharing.
Flow Programming, No-Code Development, and Semantic Interfaces: AIOS compilers allow end-user agent specification and pipeline construction via natural language instructions. These features democratize agent creation and orchestration for non-specialists—a plausible implication is the reduction of software engineering barriers.

7. Research Roadmap, Challenges, and Future Directions

AIOS research is evolving along major fronts (Ge et al., 2023, Mei et al., 2024):

Memory and Scheduling: Work is ongoing on semantic scheduling (task dependency-aware), context compression, delta-based snapshotting, and hierarchical memory for multi-agent workflows.
Agent Orchestration: Community-driven agent hubs and modular SDKs lower friction for collaborative research and deployment; multi-agent orchestration and dynamic dependency resolution are active topics.
Governance and Security: Enforcing robust access control, performing static/dynamic analysis of agent behaviors, and developing red-teaming techniques for tool and prompt interfaces are highlighted as open challenges.
Scalability: Distributed architectures, DHT-based registry, and sublinear communication protocols permit scalable operation from local to Internet scale.
Integration: Future AIOS kernels will increasingly merge OS-level resource management with LLM-intrinsic functions, exposing kernel-level primitives (e.g., snapshot/branch/trim) and integrating semantic middleware directly as kernel services.

This framework signals a paradigm shift in digital infrastructure: from command- or API-driven software and classical OS/APP dichotomy to contextually aware, tool-integrated, multi-agent, and prompt-programmable AI-native environments. The field is rapidly progressing toward the vision of a “planetary brain” comprising interconnected, autonomous AgentSites (Zhang et al., 19 Apr 2025).