Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
Abstract
The booming success of LLMs initiates rapid development in LLM agents. Though the foundation of an LLM agent is the generative model, it is critical to devise the optimal reasoning strategies and agent architectures. Accordingly, LLM agent research advances from the simple chain-of-thought prompting to more complex ReAct and Reflection reasoning strategy; agent architecture also evolves from single agent generation to multi-agent conversation, as well as multi-LLM multi-agent group chat. However, with the existing intricate frameworks and libraries, creating and evaluating new reasoning strategies and agent architectures has become a complex challenge, which hinders research investigation into LLM agents. Thus, we open-source a new AI agent library, AgentLite, which simplifies this process by offering a lightweight, user-friendly platform for innovating LLM agent reasoning, architectures, and applications with ease. AgentLite is a task-oriented framework designed to enhance the ability of agents to break down tasks and facilitate the development of multi-agent systems. Furthermore, we introduce multiple practical applications developed with AgentLite to demonstrate its convenience and flexibility. Get started now at: this URL.
Framework
BibTeX
@article{salesforce_causalai23,
title={Salesforce CausalAI Library: A Fast and Scalable framework for Causal Analysis of Time Series and Tabular Data},
author={Arpit, Devansh and Fernandez, Matthew, and Feigenbaum, Itai and Yao, Weiran and Liu, Chenghao and Yang, Wenzhuo and Josel, Paul and Heinecke, Shelby and Hu, Eric and Wang, Huan and Hoi, Stephen and Xiong, Caiming and Zhang, Kun and Niebles, Juan Carlos},
year={2023},
eprint={arXiv preprint arXiv:2301.10859},
archivePrefix={arXiv},
primaryClass={cs.LG}
}