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.

What if OpenAI Deep Research and Dify were one platform? OpenAgent — harness architecture for rapidly building vertical AI agents, with deep reasoning loops, visual workflows, RAG, and A2A delegation.

Weaviate vector database – examples

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

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

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

RAG ReAct Agent - A Retrieval-Augmented Generation system with ReAct (Reasoning+Acting) agent loop for intelligent question answering with multi-hop reasoning

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

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.

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

Official Weaviate TypeScript Client

Website for the Weaviate vector database

Awesome Weaviate

Full Spectrum AI

Weaviate Web UI

Framework for benchmarking fully-managed vector databases