AIOS Architecture Framework

• AIOS Architecture is a standardized framework that structures autonomous LLM agents using modular, layered abstractions for efficient development and deployment.
• It comprises four layers—LLM, memory, storage, and tool—each providing configurable interfaces and dedicated functionality.
• Integration with an Agent Hub and web interface facilitates comprehensive lifecycle management, reproducible research, and collaborative sharing.

An AIOS architecture is a system framework for the development, deployment, lifecycle management, and discovery of autonomous LLM-based agents, standardizing their construction through modular abstractions. The architecture centralizes agent logic into composable layers—typically encompassing LLM interfacing, working memory, durable storage, and structured tool integration—combined with managed packaging, version control, and registry-driven discovery and distribution workflows. Recent work on Cerebrum (AIOS SDK) details a four-layer modular agent architecture and an ecosystem—Agent Hub and web interface—for reproducible agent research and distributed production deployment (Rama et al., 14 Mar 2025).

1. Modular Layered Architecture

The core of AIOS agent design is a four-layer modular architecture. Each layer abstracts a fundamental aspect of agent capability and provides independently configurable, extendable interfaces:

Layer	Purpose/Function	Core Mechanisms
LLM Layer	Handles all interactions with LLM backbones; model invocation and switching	API abstraction, resource allocation, defaults
Memory Layer	Manages agent’s working memory for context/state tracking	LRU-k eviction, custom policies, limits
Storage Layer	Manages persistent, long-term storage and retrieval (files, vector DB)	Hierarchical FS, vector DBs, custom indexing
Tool Layer	Manages external tool/API invocation and integration	Registration, validation, protocol, I/O

The functional stack ensures that agent logic is strictly modular and that each capability—reasoning, context retention, tool use—is versioned and swappable (see system diagram in (Rama et al., 14 Mar 2025)).

2. Agent Lifecycle and Manager Modules

The architecture governs the full agent lifecycle:

• Development: Agents are composed from plug-and-play modules at each layer. Rapid prototyping and architecture extension are facilitated by layer isolation.
• Deployment: Layer modules interface natively with the AIOS kernel for managed, concurrent agent execution. A manager module coordinates packaging/caching/versioning.
• Distribution: Agents are bundled with dependencies and layer versions, encrypted/compressed for integrity and portability.
• Discovery: Centralized registry (Agent Hub) indexes agents by {author, name, version}, including documentation, dependencies, licensing, and API endpoints for reproducible sharing.

Agent and tool managers support lifecycle orchestration: packaging, caching, dependency resolution, upload/download. A unified client interface abstracts kernel communication for higher layers.

3. Integration with Agent Hub and Web Interface

The AIOS ecosystem comprises both registry (Agent Hub) and interface layers:

• Agent Hub: Inspired by Hugging Face Hub, supports agent package upload/download, dependency management, version control, encryption/compression. Per-agent registry pages surface agent documentation, API access, history, licensing, and code for transparency.
• Web Interface: A browser-based AgentChat system for evaluation and interaction. Features persistent conversations, multi-agent management, user mentions, and rate limiting. Runs agents loaded from packages on a hosted AIOS kernel. Enables reproducible research and practical deployment demonstration.

The full integration flow is: layered agent development → registry distribution via Agent Hub → real-time discovery and invocation via web interface.

4. Supported Agent Paradigms and Formal Modeling

Cerebrum supports a variety of agent architectures:

• Direct I/O Chatbot: $P(y|x) = \text{LLM}(x)$
• Chain of Thought (CoT): Stepwise reasoning: $$P(y|x) = \sum_{s_1,\dots,s_n} P(y|s_n)P(s_n|s_{n-1})\cdots P(s_1|x)$$, explicit prompt progression.
• ReAct Agent: Interleaved reasoning and acting as an MDP (states, actions, transitions).
• Tool-Augmented Agent: Hierarchical workflow: tool selection ($P(\text{tool}|x)$), parameterization, tool execution, response generation.

Each paradigm is formalized via abstracted probabilistic or procedural modeling, composable in layered Cerebrum code.

5. Standardization, Flexibility, and Reproducibility

The architecture addresses persistent challenges in agent development:

• Standardization: All agents are specified using clear, layered descriptors enabling declarative composition; agent packages are versioned for reproducibility.
• Flexibility: Modular layers can be mixed/swapped, supporting custom memory logic, new tool protocols, or novel LLM invocation strategies.
• Community and Sharing: Registry tools (Agent Hub) and the web interface lower onboarding barriers; agents are discoverable, testable, and reusable.
• Reproducibility: Versioned specification and explicit dependency management ensure agents run identically across contexts and environments.

6. Implementation Example and Performance

Agents are defined in Cerebrum via modular layer composition, wrapped with manager and client abstractions for kernel and registry interaction. A practical example is creating a CoT agent:

from cerebrum.agent import AutoAgent, LLMConfig, MemoryConfig, StorageConfig, ToolConfig agent = AutoAgent( LLM=LLMConfig(model="openai/gpt-4", temperature=0.2), memory=MemoryConfig(policy="LRU-k", k=5, limit=10240), storage=StorageConfig(vector_db="faiss", embedding_model="bge"), tools=[ToolConfig(name="wikipedia", protocol="v1")] ) agent.run("Let's solve this problem step by step ...")

Performance benchmarking demonstrates support for diverse architectures (CoT, ReAct, tool-use agents), with standardized packaging and registry evaluation.

7. Significance and Broader Impact

Cerebrum’s AIOS architecture represents a systematic approach to democratizing, standardizing, and scaling LLM-based agent development. It implements layered abstractions to facilitate reproducible research, rapid prototyping, and industrial deployment—with sharing, evaluation, and reuse made practical via registry and web interfaces. The modular design constrains complexity, increases robustness, and allows the agent ecosystem to scale and adapt to concurrent and distributed operation in both research and production contexts (Rama et al., 14 Mar 2025).

Further exploration: Cerebrum live website, GitHub repo, Video demo