Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

Represent, send, store and search multimodal data

This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.

The universal tool suite for vector database management. Manage Pinecone, Chroma, Qdrant, Weaviate and more vector databases with ease.

AI Agent Development Platform - Supports multiple models (OpenAI/DeepSeek/Wenxin/Tongyi), knowledge base management, workflow automation, and enterprise-grade security. Built with Flask + Vue3 + LangChain, featuring one-click Docker deployment.

Weaviate vector database – examples

A python native client for easy interaction with a Weaviate instance.

一个99%由OpenAI ChatGPT开发的项目。A project that is 99% developed by OpenAI ChatGPT.

A modern desktop application for exploring, managing, and analyzing vector databases

Knowledge work automation with AI agents

Production-ready RAG Framework (Python/FastAPI). 1-line config swaps: 6 Vector DBs (Weaviate, Pinecone, Qdrant, ChromaDB, pgvector, MongoDB), 5 LLMs (Gemini, OpenAI, Claude, Ollama, OpenRouter). OpenAI-compatible API. 2100+ tests.

Telegram LLM bot backed by OpenAI, Whisper, Beam, LLaMA, Weaviate, MinIO and MongoDB

Build super simple end-to-end data & ETL pipelines for your vector databases and Generative AI applications

Home Assistant LLM integration for local OpenAI-compatible services (llamacpp, vllm, etc)

Official Weaviate TypeScript Client

Website for the Weaviate vector database

Full Spectrum AI

Awesome Weaviate

Framework for benchmarking fully-managed vector databases

Weaviate Web UI

🦜🔗 LangChain interface to Weaviate

Ruby wrapper for the Weaviate vector search database API

AI-powered platform for OSINT intelligence analysis. Features archive discovery with hypothesis-driven investigation, GLiNER entity extraction, Mapbox geospatial visualization, network analysis, and document processing. Built with FastAPI, Next.js, Weaviate, and DSPy.

This template demonstrates how to create a collaborative team of AI agents that work together to process, analyze, and generate insights from documents.

Designed for offline use, this RAG application template offers a starting point for building your own local RAG pipeline, independent of online APIs and cloud-based LLM services like OpenAI.

🎩 Magic in Pocket / 🪄 口袋里的“魔法”.

Claude Skills for connecting Claude.ai to local Weaviate vector databases - manage collections, ingest data, and query with RAG

Async bulk data ingestion and querying in various document, graph and vector databases via their Python clients

Weaviate Cluster WebApp is built to manage and interact with Weaviate Vector Database

Piazza-Updater automates updates to a Weaviate database with real-time vectorial data. By continuously searching the internet and integrating with Verba repositories, it enhances retrieval-augmented generation (RAG) capabilities, keeping your applications informed and responsive.