weaviate

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

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

Full Spectrum AI

Awesome Weaviate

Framework for benchmarking fully-managed vector databases

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.

🦜🔗 LangChain interface to Weaviate

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.