RankLLM: A Python Package for Reranking with LLMs

Sahel Sharifymoghaddam; Ronak Pradeep; Andre Slavescu; Ryan Nguyen; Andrew Xu; Zijian Chen; Yilin Zhang; Yidi Chen; Jasper Xian; Jimmy Lin

arXiv · 2505.19284

RankLLM: A Python Package for Reranking with LLMs

Sahel Sharifymoghaddam·Ronak Pradeep·Andre Slavescu·Ryan Nguyen·Andrew Xu·Zijian Chen·Yilin Zhang·Yidi Chen·Jasper Xian·Jimmy Lin

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Abstract

The adoption of large language models (LLMs) as rerankers in multi-stage retrieval systems has gained significant traction in academia and industry. These models refine a candidate list of retrieved documents, often through carefully designed prompts, and are typically used in applications built on retrieval-augmented generation (RAG). This paper introduces RankLLM, an open-source Python package for reranking that is modular, highly configurable, and supports both proprietary and open-source LLMs in customized reranking workflows. To improve usability, RankLLM features optional integration with Pyserini for retrieval and provides integrated evaluation for multi-stage pipelines. Additionally, RankLLM includes a module for detailed analysis of input prompts and LLM responses, addressing reliability concerns with LLM APIs and non-deterministic behavior in Mixture-of-Experts (MoE) models. This paper presents the architecture of RankLLM, along with a detailed step-by-step guide and sample code. We reproduce results from RankGPT, LRL, RankVicuna, RankZephyr, and other recent models. RankLLM integrates with common inference frameworks and a wide range of LLMs. This compatibility allows for quick reproduction of reported results, helping to speed up both research and real-world applications. The complete repository is available at rankllm.ai, and the package can be installed via PyPI.

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Sahel Sharifymoghaddam, Ronak Pradeep, Andre Slavescu, Ryan Nguyen, Andrew Xu, Zijian Chen, Yilin Zhang, Yidi Chen, Jasper Xian, Jimmy Lin. 2025-05-25. RankLLM: A Python Package for Reranking with LLMs. https://arxiv.org/abs/2505.19284

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