Cerebrum ( AIOS SDK ): A Platform for Agent Development, Deployment, Distribution, and Discovery (2025)

Abstract: Autonomous LLM-based agents have emerged as a powerful paradigm for complex task execution, yet the field lacks standardized tools for development, deployment, and distribution. We present Cerebrum, an open-source platform that addresses this gap through three key components: (1) a comprehensive SDK featuring a modular four-layer architecture for agent development, encompassing LLM, memory, storage, and tool management; (2) a community-driven Agent Hub for sharing and discovering agents, complete with version control and dependency management; and (3) an interactive web interface for testing and evaluating agents. The platform’s effectiveness is demonstrated through implementations of various agent architectures, including Chain of Thought (CoT), ReAct, and tool-augmented agents. Cerebrum advances the field by providing a unified framework that standardizes agent development while maintaining flexibility for researchers and developers to innovate and distribute their work. Live url for demo can be found at https://app.aios.foundation. Code can be found at https://github.com/agiresearch/Cerebrum. Video demo can be found at https://app.aios.foundation/video-demo.

## introduction autonomous llm-based agents (agents for short) have emerged as a transformative paradigm in applying and advancing the capabilities of large language models (llms) beyond text prediction to executing complex tasks through planning, reasoning, tool using, and goal-directed actions @xcite @xcite @xcite . the paradigm scales to real-world issues such as web browsing @xcite , social simulation @xcite , and decisionmaking @xcite . although with the fast advancement of llmbased agent research in the recent year, there still lacks a unified platform for researchers and developers to develop, deploy, and distribute their agents, and for users to discover and use the agents. this demonstration paper introduces cerebrum agen-thub, which not only provides an sdk for agent development and deployment, but also provides a web-based platform for agent developers to share their agents, and for agent users to easily use the agents both through interactive web-based ui interface and through code-based calling of pre-loaded agents through one line of code. more specifically, cerebrum is a library accompanied with a live demo dedicated to supporting a standardized way to build, run, deploy, and distribute agents and agent components. at the core of the library is a unified framework for constructing diverse agents, containing optimized implementations of popular agent methodologies such as chain of thought (cot) @xcite and react @xcite , with the goal of supporting implementations of agent variants that are easy to read, extend, and deploy. furthermore, the library supports the distribution and usage of user-created agents in a centralized agent hub. the emergence of llms has drastically expanded the potential for advancing agent technology, as demonstrated by recent breakthroughs @xcite . traditional prompt-based interactions are typically static, functioning as direct input-output exchanges with limited adaptability. in contrast, llm-driven agents are designed to enable dynamic decision-making processes, granting them the ability to interpret context, generate flexible responses, and act independently @xcite . this evolution allows agents to transition from handling simple, singlestep tasks to becoming versatile, general-purpose problem solvers @xcite . ## cerebrum library design cerebrum is designed to provide a standardized framework for developing llm-based agents, addressing the growing need for systematic agent architectures in the artificial intelligence community. the library implements a modular approach that facilitates both research and production deployments while maintaining flexibility for various use cases. the library's architecture consists of two primary components: (1) a layered system for agent composition and (2) a client interface for kernel communication. this dual architecture enables both fine-grained control over agent behavior and rapid development through high-level abstractions. ## agent standard the library provides a comprehensive agent building framework that emphasizes composition over inheritance. unlike traditional agent frameworks that often require deep understanding of implementation details and rigid structual foundations, cerebrum's building system enables rapid development through high-level abstractions while maintaining access to low-level controls when needed. the framework introduces the concept of agent specifications -declarative definitions that describe an agent's capabilities, resource requirements, and behavioral patterns. these specifications can be composed, extended, and modified dynamically, enabling flexible agent architectures that can adapt to different deployment scenarios. its implemented resource management strategies include: ① automatic tool resolution and dependency management. ② dynamic resource allocation based on agent specifications. a key innovation in the building framework is its handler system, which provides extension points for customizing agent behavior without modifying core components. these handlers can intercept and modify agent operations at various stages, enabling behavior patterns while maintaining the benefits of the standard agent lifecycle. ## community agent hub cerebrum implements an open-source distribution platform for ai agents, following a model similar to hugging face's hub architecture. the community agent hub serves as a central repository where researchers can freely share, discover, and utilize agents that conform to the cerebrum agent specification. the hub itself is a hosted, publicly accessible server featuring both an overall listing of all agents and agent-specific pages. agents and tools are stored in an encrypted, hashed, and compressed format, containing references to their individual component files. individual agent landing pages provide comprehensive information including: ① version control and release history. ② direct access to the agent's inference api endpoints via agent chat. ③ licensing information and readme documentation. ④ source code accessibility for transparency and reproducibility. ⑤ usage instructions. a current limitation of the hub is the absence of a formal vetting process for uploaded agents. future work may explore implementing security scanning, performance validation, and compliance checking mechanisms. ## community agent chat to facilitate direct interaction with and evaluation of agents, we provide a public chat interface that enables real-time communication with agents hosted in the community hub. this interface serves as both a research tool for analyzing agent behavior and a demonstration platform for agent capabilities. the chat system implements a mention-based inusers can interact with any agents stored in the agent hub, which are served on a remote aios instance. the agent chat functions as a wrapper around a cerebrum client that communicates with this remote instance. the system supports persistent, on-device chat and conversation memories, allowing users to maintain multiple different chats, which can also be deleted as needed. ## applications to demonstrate the real-world applications of cerebrum, we implemented four distinct agents that showcase different prompting techniques and capabilities: chain of thought (cot), react, a baseline chatbot, and a tool-augmented agent. these implementations serve to validate the flexibility and expressiveness of our agent specification framework. ## conclusion we presented cerebrum, a platform for developing, deploying, and distributing llm-based agents. the platform addresses fundamental challenges in the agent development ecosystem through three key innovations: (1) a modular four-layer architecture that standardizes agent development while maintaining flexibility, (2) a community-driven agent hub that facilitates agent sharing and discovery, and (3) an interactive chat interface for direct agent evaluation and testing. our implementations of various agent architectures, including cot, react, and tool-augmented agents, demonstrate the platform's versatility and effectiveness. looking forward, we envision several directions for future work. first, enhancing the agent hub with formal security and performance validation mechanisms would increase trust and reliability in shared agents. second, expanding the tool layer to support more complex multi-agent interactions and collaborative scenarios could enable more sophisticated agent behaviors. finally, developing standardized benchmarks and evaluation frameworks specifically for testing agents built with cerebrum would help quantify and improve agent performance across different architectures and use cases. through its open-source nature and emphasis on standardization, cerebrum aims to accelerate research and development in the rapidly evolving field of llm-based agents, while fostering a collaborative ecosystem for sharing and building upon existing agent implementations.