mlsanityData to 14 Oct 2026

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

arXiv 2503.11444 ↗First posted 14 Mar 2025cs.MA cs.AI cs.CLcited by 6

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Autonomous LLM-based agents have emerged as a powerful paradigm for complex task execution, yet the field lacks standardized tools for development, deployment, distribution and discovery of agents. We present Cerebrum, an Agent SDK for AIOS 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; (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-use 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 agents. The live website is at https://app.aios.foundation, the code is at https://github.com/agiresearch/Cerebrum, and video is at https://app.aios.foundation/video-demo.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 16 of its 25 references that are in the map. First posted on arXiv 14 Mar 2025.

Builds on

16 of 25 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024From Commands to Prompts: LLM-based Semantic File System for AIOS23 Sep
  2. 2024AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System23 Feb
  3. 2024AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning23 Feb
  4. 2023A Survey on Large Language Model based Autonomous Agents22 Aug
  5. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar

Other references · 11

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  4. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
  5. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
  6. 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agentscited by 369
  7. 2023OpenAGI: When LLM Meets Domain Expertscited by 197
  8. 2024Game-theoretic LLM: Agent Workflow for Negotiation Gamescited by 56
  9. 2023LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystemcited by 38
  10. 2025OmniRouter: Budget and Performance Controllable Multi-LLM Routingcited by 35
  11. 2025iAgent: LLM Agent as a Shield between User and Recommender Systemscited by 23

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025AgentHub: A Registry for Discoverable, Verifiable, and Reproducible AI Agents
  2. 2026ADK Arena: Evaluating Agent Development Kits via LLM-as-a-Developer
  3. 2026CocoaBench: Evaluating Unified Digital Agents in the Wild
  4. 2025An Empirical Study of Agent Developer Practices in AI Agent Frameworks
  5. 2025Benchmarking and Studying the LLM-based Agent System in End-to-End Software Development
  6. 2025Survey on Evaluation of LLM-based Agents
  7. 2026Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering
  8. 2026Orchard: An Open-Source Agentic Modeling Framework