# RankLLM: A Python Package for Reranking with LLMs

arXiv: https://arxiv.org/abs/2505.19284 · HTML: https://mlsanity.com/paper/2505.19284/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in MoE large language models, one of 12 directions in Mixture-of-experts.
- First posted on arXiv 25 May 2025.

## Placed in

- Mixture-of-experts / [MoE large language models](https://mlsanity.com/moe/moe-large-language-models/)

## Built on by (most cited first, 16 of 16)

- [Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking](https://mlsanity.com/paper/2510.14824/) (2025)
- [Evaluating Large Language Models for Cross-Lingual Retrieval](https://mlsanity.com/paper/2509.14749/) (2025)
- [Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track](https://mlsanity.com/paper/2603.09891/) (2026)
- [100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models](https://mlsanity.com/paper/2603.15970/) (2026)
- [On the Effectiveness of Proposed Techniques to Reduce Energy Consumption in RAG Systems: A Controlled Experiment](https://mlsanity.com/paper/2601.02522/) (2026)
- [Very Efficient Listwise Multimodal Reranking for Long Documents](https://mlsanity.com/paper/2605.11864/) (2026)
- [Lighting the Way for BRIGHT: Reproducible Baselines with Anserini, Pyserini, and RankLLM](https://mlsanity.com/paper/2509.02558/) (2025)
- [SEEK: Skill-Routed Evaluation with Evolvable Knowledge for Industrial Search](https://mlsanity.com/paper/2609.29803/) (2026)
- [Embedding-Based Context-Aware Reranker](https://mlsanity.com/paper/2510.13329/) (2025)
- [Selective LLM-Guided Regularization for Enhancing Recommendation Models](https://mlsanity.com/paper/2512.21526/) (2025)
- [Diagnosing LLM Reranker Behavior Under Fixed Evidence Pools](https://mlsanity.com/paper/2602.18613/) (2026)
- [A Reproducibility Study of Metacognitive Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.19899/) (2026)
- [Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models](https://mlsanity.com/paper/2605.01591/) (2026)
- [CompRank: Efficient LLM Reranking via Token-Level Compression and Decoding-Free Scoring](https://mlsanity.com/paper/2606.11700/) (2026)
- [A Reranker for Orchestrating Heterogeneous Speech and Text Retrievers](https://mlsanity.com/paper/2608.26194/) (2026)
- [JevGPT: Thinking Fast and Slow in Autonomous Research](https://mlsanity.com/paper/2610.18272/) (2026)

## Abstract (arXiv)

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.