Topics / #reranking

Reranking projects on GitHub

A second-pass relevance model that reorders search or RAG results.

As of , the most-starred Reranking projects on GitHub are WeKnora (34.4K stars), docker-agent (4.5K stars) and memory-lancedb-pro (4.5K stars). GitHub.Locker tracks 7 Reranking repositories with 45.8K stars combined. The fastest-growing on October 16 was WeKnora, with +169 stars in a day.

7Projects
45.8KStars in total
+189Stars in a day

Hot right now

Most stars gained in a day · Oct 16

The all-time top

7 projects · by stars

  1. #1 · Tencent/WeKnora

    WeKnora

    WeKnora is an open-source, self-hosted knowledge platform that turns raw documents into a queryable RAG knowledge base, an autonomous ReAct agent, and a self-maintaining Wiki with an interactive knowledge graph.

    34.4K
  2. #2 · docker/docker-agent

    docker-agent

    Docker Agent is a tool for building, running, and sharing AI agents using a declarative YAML config, with built-in tools and multi-agent orchestration — no code required.

    4.5K
  3. #3 · CortexReach/memory-lancedb-pro

    memory-lancedb-pro

    It is a LanceDB-backed memory plugin for OpenClaw agents that stores preferences, decisions, and project context, then auto-recalls them in future sessions instead of letting the agent forget everything between chats.

    4.5K
  1. 4

    fastembed-rs

    1Kanush008/fastembed-rs · Rust

    A Rust library that generates vector embeddings and reranks results entirely locally — no external API calls. It covers dense and sparse text embeddings, image embeddings, multimodal models, and rerankers.

  2. 5

    SkillCorpus

    686EverMind-AI/SkillCorpus · Python

    SkillCorpus is EverMind's open-source pipeline that aggregates scattered SKILL.md files from public repos, applies safety/license/quality gates, and exports a curated, retrieval-ready corpus plus two fine-tuned retrieval models so agents can fetch the right skill at answer time.

  3. 6

    GLiClass

    563Knowledgator/GLiClass · Python

    GLiClass is an efficient zero-shot model for sequence classification, inspired by GLiNER: give it a text and a list of candidate labels, and it classifies the text with accuracy comparable to cross-encoders.

  4. 7

    LLMSearchRecommender

    124alopatenko/LLMSearchRecommender

    A curated compendium of research papers, surveys, and industry engineering practices on using Generative AI and LLMs to build search engines, recommender systems, personalization engines, and question-answering systems.

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