Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexity rises, the workflow involving LLMs can be complicated and thus challenging to implement and maintain. To address this challenge, we propose APPL, A Prompt Programming Language that acts as a bridge between computer programs and LLMs, allowing seamless embedding of prompts into Python functions, and vice versa. APPL provides an intuitive and Python-native syntax, an efficient parallelized runtime with asynchronous semantics, and a tracing module supporting effective failure diagnosis and replaying without extra costs. We demonstrate that APPL programs are intuitive, concise, and efficient through three representative scenarios: Chain-of-Thought with self-consistency (CoT-SC), ReAct tool use agent, and multi-agent chat. Experiments on three parallelizable workflows further show that APPL can effectively parallelize independent LLM calls, with a significant speedup ratio that almost matches the estimation.

APPL是一种桥梁编程语言，能够实现计算机程序与大语言模型之间的无缝嵌入，通过Python函数和LLM之间互相调用，提供直观的语法、高效的并行运行时、支持故障诊断和重播的追踪模块，以及在几个代表性场景和多代理人对话中的实验证明了APPL程序的直观性、简洁性和高效性。并且在三个可并行化的工作流程实验中显示，APPL能够有效地并行化独立的LLM调用，并具有显著的加速比。

APPL: 一个可谐和集成程序和大型语言模型提示的提示编程语言