AgentKit: Structured LLM Reasoning with Dynamic Graphs

• URL: http://arxiv.org/abs/2404.11483v2
• Date: Wed, 24 Jul 2024 20:53:10 GMT
• Title: AgentKit: Structured LLM Reasoning with Dynamic Graphs
• Authors: Yue Wu, Yewen Fan, So Yeon Min, Shrimai Prabhumoye, Stephen McAleer, Yonatan Bisk, Ruslan Salakhutdinov, Yuanzhi Li, Tom Mitchell,
• Abstract summary: We propose an intuitive LLM prompting framework (AgentKit) for multifunctional agents. AgentKit offers a unified framework for explicitly constructing a complex "thought process" from simple natural language prompts.
• Score: 91.09525140733987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose an intuitive LLM prompting framework (AgentKit) for multifunctional agents. AgentKit offers a unified framework for explicitly constructing a complex "thought process" from simple natural language prompts. The basic building block in AgentKit is a node, containing a natural language prompt for a specific subtask. The user then puts together chains of nodes, like stacking LEGO pieces. The chains of nodes can be designed to explicitly enforce a naturally structured "thought process". For example, for the task of writing a paper, one may start with the thought process of 1) identify a core message, 2) identify prior research gaps, etc. The nodes in AgentKit can be designed and combined in different ways to implement multiple advanced capabilities including on-the-fly hierarchical planning, reflection, and learning from interactions. In addition, due to the modular nature and the intuitive design to simulate explicit human thought process, a basic agent could be implemented as simple as a list of prompts for the subtasks and therefore could be designed and tuned by someone without any programming experience. Quantitatively, we show that agents designed through AgentKit achieve SOTA performance on WebShop and Crafter. These advances underscore AgentKit's potential in making LLM agents effective and accessible for a wider range of applications. https://github.com/holmeswww/AgentKit

Related papers

• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [117.94654815220404]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine. G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems [31.113305753414913]
  AUTOGEN STUDIO is a no-code developer tool for rapidly prototyping multi-agent systems. It provides an intuitive drag-and-drop UI for agent specification, interactive evaluation, and a gallery of reusable agent components.
  arXiv  Detail & Related papers  (2024-08-09T03:27:37Z)
• CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only [21.054681757006385]
  We propose an agent that perceives its environment solely through screenshot images. By leveraging the reasoning capability of the Large Language Models, we eliminate the need for large-scale human demonstration data. Agent achieves an average success rate of 94.5% on MiniWoB++ and an average task score of 62.3 on WebShop.
  arXiv  Detail & Related papers  (2024-06-11T05:21:20Z)
• AgentGym: Evolving Large Language Model-based Agents across Diverse Environments [116.97648507802926]
  Large language models (LLMs) are considered a promising foundation to build such agents. We take the first step towards building generally-capable LLM-based agents with self-evolution ability. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration.
  arXiv  Detail & Related papers  (2024-06-06T15:15:41Z)
• AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System [91.41155892086252]
  We open-source a new AI agent library, AgentLite, which simplifies research investigation into LLM agents. AgentLite is a task-oriented framework designed to enhance the ability of agents to break down tasks. We introduce multiple practical applications developed with AgentLite to demonstrate its convenience and flexibility.
  arXiv  Detail & Related papers  (2024-02-23T06:25:20Z)
• Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
  We consider language agents as a promising direction towards artificial general intelligence. We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
  arXiv  Detail & Related papers  (2023-09-14T17:18:25Z)
• SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks [81.9962823875981]
  We introduce SwiftSage, a novel agent framework inspired by the dual-process theory of human cognition. The framework comprises two primary modules: the Swift module, representing fast and intuitive thinking, and the Sage module, emulating deliberate thought processes. In 30 tasks from the ScienceWorld benchmark, SwiftSage significantly outperforms other methods such as SayCan, ReAct, and Reflex.
  arXiv  Detail & Related papers  (2023-05-27T07:04:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.