TY - JOUR
T1 - DeCoAgent
T2 - Large Language Model Empowered Decentralized Autonomous Collaboration Agents Based on Smart Contracts
AU - Jin, Anan
AU - Ye, Yuhang
AU - Lee, Brian
AU - Qiao, Yuansong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Large Language Models (LLMs) empowered agents are effective across various tasks and demonstrate outstanding performance, which can be further enhanced through collaboration with multiple LLM agents. The current approaches for collaboration with multiple LLM agents are static approaches, which adopt a fixed set of agents to interact with each other. However, these approaches suffer from a significant limitation: multi-agent collaboration depends on the assumption that all participants know each other in a local closed environment, can find each other and direct communication, and will act with integrity. To address these challenges, this paper proposes DeCoAgent, a novel framework for decentralized autonomous collaboration between LLMs empowered agents based on smart contracts. This framework enables decentralized autonomous collaboration between LLM agents, allowing them to register themselves, discover the capabilities of other agents, and assign tasks on the platform. LLMs can convert natural language descriptions from human and LLM agent users into smart contract calls, enabling agents to interact with humans, the blockchain, and other agents to achieve automation. This paper implements the platform based on OpenAI and Ethereum, demonstrating the practical feasibility of this approach. The proposed framework has broader applications, including supply chain management, manufacturing, crowdsourcing, and complementing other existing multi-agent collaborations. This framework is open source on GitHub. Please visit the repository at https://github.com/AnanKing/DeCoAgent.
AB - Large Language Models (LLMs) empowered agents are effective across various tasks and demonstrate outstanding performance, which can be further enhanced through collaboration with multiple LLM agents. The current approaches for collaboration with multiple LLM agents are static approaches, which adopt a fixed set of agents to interact with each other. However, these approaches suffer from a significant limitation: multi-agent collaboration depends on the assumption that all participants know each other in a local closed environment, can find each other and direct communication, and will act with integrity. To address these challenges, this paper proposes DeCoAgent, a novel framework for decentralized autonomous collaboration between LLMs empowered agents based on smart contracts. This framework enables decentralized autonomous collaboration between LLM agents, allowing them to register themselves, discover the capabilities of other agents, and assign tasks on the platform. LLMs can convert natural language descriptions from human and LLM agent users into smart contract calls, enabling agents to interact with humans, the blockchain, and other agents to achieve automation. This paper implements the platform based on OpenAI and Ethereum, demonstrating the practical feasibility of this approach. The proposed framework has broader applications, including supply chain management, manufacturing, crowdsourcing, and complementing other existing multi-agent collaborations. This framework is open source on GitHub. Please visit the repository at https://github.com/AnanKing/DeCoAgent.
KW - Large language models
KW - agent collaboration
KW - blockchain
KW - decentralized autonomous
KW - smart contract
UR - http://www.scopus.com/inward/record.url?scp=85207708966&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3481641
DO - 10.1109/ACCESS.2024.3481641
M3 - Article
AN - SCOPUS:85207708966
SN - 2169-3536
VL - 12
SP - 155234
EP - 155245
JO - IEEE Access
JF - IEEE Access
ER -