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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.06781 |
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| _version_ | 1866912202023239680 |
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| author | Walters, Shaw Gao, Sam Nerd, Shakker Da, Feng Williams, Warren Meng, Ting-Chien Chow, Amie Han, Hunter He, Frank Zhang, Allen Wu, Ming Shen, Timothy Hu, Maxwell Yan, Jerry |
| author_facet | Walters, Shaw Gao, Sam Nerd, Shakker Da, Feng Williams, Warren Meng, Ting-Chien Chow, Amie Han, Hunter He, Frank Zhang, Allen Wu, Ming Shen, Timothy Hu, Maxwell Yan, Jerry |
| contents | AI Agent, powered by large language models (LLMs) as its cognitive core, is an intelligent agentic system capable of autonomously controlling and determining the execution paths under user's instructions. With the burst of capabilities of LLMs and various plugins, such as RAG, text-to-image/video/3D, etc., the potential of AI Agents has been vastly expanded, with their capabilities growing stronger by the day. However, at the intersection between AI and web3, there is currently no ideal agentic framework that can seamlessly integrate web3 applications into AI agent functionalities. In this paper, we propose Eliza, the first open-source web3-friendly Agentic framework that makes the deployment of web3 applications effortless. We emphasize that every aspect of Eliza is a regular Typescript program under the full control of its user, and it seamlessly integrates with web3 (i.e., reading and writing blockchain data, interacting with smart contracts, etc.). Furthermore, we show how stable performance is achieved through the pragmatic implementation of the key components of Eliza's runtime. Our code is publicly available at https://github.com/ai16z/eliza. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Eliza: A Web3 friendly AI Agent Operating System Walters, Shaw Gao, Sam Nerd, Shakker Da, Feng Williams, Warren Meng, Ting-Chien Chow, Amie Han, Hunter He, Frank Zhang, Allen Wu, Ming Shen, Timothy Hu, Maxwell Yan, Jerry Artificial Intelligence AI Agent, powered by large language models (LLMs) as its cognitive core, is an intelligent agentic system capable of autonomously controlling and determining the execution paths under user's instructions. With the burst of capabilities of LLMs and various plugins, such as RAG, text-to-image/video/3D, etc., the potential of AI Agents has been vastly expanded, with their capabilities growing stronger by the day. However, at the intersection between AI and web3, there is currently no ideal agentic framework that can seamlessly integrate web3 applications into AI agent functionalities. In this paper, we propose Eliza, the first open-source web3-friendly Agentic framework that makes the deployment of web3 applications effortless. We emphasize that every aspect of Eliza is a regular Typescript program under the full control of its user, and it seamlessly integrates with web3 (i.e., reading and writing blockchain data, interacting with smart contracts, etc.). Furthermore, we show how stable performance is achieved through the pragmatic implementation of the key components of Eliza's runtime. Our code is publicly available at https://github.com/ai16z/eliza. |
| title | Eliza: A Web3 friendly AI Agent Operating System |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2501.06781 |