AgentKit: Flow Engineering with Graphs, not Coding
Yue Wu Yewen Fan So Yeon Min Shrimai Prabhumoye Stephen McAleer Yonatan Bisk Ruslan Salakhutdinov Yuanzhi Li Tom Mitchell
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