Reranking Python Packages

mteb

MTEB: Massive Text Embedding Benchmark

flashrank

Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.

qwen3-embed

Lightweight ONNX inference for Qwen3 embedding and reranking models

mxbai-rerank

Crispy reranking models by Mixedbread

knowledge-rag

Drop docs, search instantly from Claude Code — 12 MCP tools, 20 format parsers, hybrid search + reranking. Zero servers, zero API keys, 100% local.

vera-ai

Local code search combining BM25, vector similarity, and cross-encoder reranking. Parses 60+ languages with tree-sitter, runs entirely offline, and returns structured results with file paths, line ranges, and symbol metadata. Built in Rust.

byaldi

Use late-interaction multi-modal models such as ColPali in just a few lines of code.

mixedbread-ai

mixedbread ai python sdk

rankops

Rank fusion and reranking

chunkrank

Model-aware text chunking and answer re-ranking for LLM pipelines. Python 3.10+, async-first, 54 models.

langformers

🚀 Unified NLP Pipelines for Language Models

raglite

🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL

symrank

🐍📦 High-performance cosine similarity ranking for Retrieval-Augmented Generation (RAG) pipelines.

pyllmsearch

Querying local documents, powered by LLM

ferrex

Local-first MCP memory server for AI agents

langchain-localai

Langchain integration package with LocalAI. It provides embeddings and reranker support

rankify

🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥. Our toolkit integrates 40 pre-retrieved benchmark datasets and supports 7+ retrieval techniques, 24+ state-of-the-art Reranking models, and multiple RAG methods.

rankflow

Library for plotting multiple ranks evolved over processing steps - draw a rankflow/bump chart

useb

Heterogenous, Task- and Domain-Specific Benchmark for Unsupervised Sentence Embeddings used in the TSDAE paper.