  In recent years, the integration of Large Language Models (LLMs) into
recommender systems has garnered interest among both practitioners and
researchers. Despite this interest, the field is still emerging, and the lack
of open-source R&D platforms may impede the exploration of LLM-based
recommendations. This paper introduces OpenP5, an open-source platform designed
as a resource to facilitate the development, training, and evaluation of
LLM-based generative recommender systems for research purposes. The platform is
implemented using encoder-decoder LLMs (e.g., T5) and decoder-only LLMs (e.g.,
Llama-2) across 10 widely recognized public datasets, catering to two
fundamental recommendation tasks: sequential and straightforward
recommendations. Recognizing the crucial role of item IDs in LLM-based
recommendations, we have also incorporated three item indexing methods within
the OpenP5 platform: random indexing, sequential indexing and collaborative
indexing. Built on the Transformers library, the platform facilitates easy
customization of LLM-based recommendations for users. OpenP5 boasts a range of
features including extensible data processing, task-centric optimization,
comprehensive datasets and checkpoints, efficient acceleration, and
standardized evaluations, making it a valuable tool for the implementation and
evaluation of LLM-based recommender systems. The open-source code and
pre-trained checkpoints for the OpenP5 library are publicly available at
https://github.com/agiresearch/OpenP5.
