weaviate and awesome-weaviate

The main project provides a production vector database system, while the curated collection serves as a community resource for discovering integrations, examples, and extensions—making them ecosystem siblings where one is the core technology and the other documents its surrounding tools and use cases.

weaviate
81
Verified
awesome-weaviate
41
Emerging
Maintenance 22/25
Adoption 15/25
Maturity 25/25
Community 19/25
Maintenance 0/25
Adoption 9/25
Maturity 16/25
Community 16/25
Stars: 15,793
Forks: 1,216
Downloads:
Commits (30d): 448
Language: Go
License: BSD-3-Clause
Stars: 85
Forks: 14
Downloads:
Commits (30d): 0
Language:
License: MIT
No risk flags
Stale 6m No Package No Dependents

About weaviate

weaviate/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​.

Weaviate helps developers build powerful AI applications by storing and searching data based on its meaning, not just keywords. You feed it text, images, or other data, and it helps you find related information, power chatbots, or make recommendations. It's used by software engineers and data scientists creating smart applications that understand context.

semantic-search recommendation-systems AI-chatbots content-classification retrieval-augmented-generation

About awesome-weaviate

weaviate/awesome-weaviate

Awesome Weaviate

This is a collection of resources for developers using Weaviate, a vector search engine and vector database. It provides examples, tutorials, blog posts, and conference presentations to help you vectorize and store data, and find answers to natural language queries. Developers who want to build applications with semantic search capabilities or bring custom machine learning models into production at scale will find this useful.

vector-search semantic-search machine-learning-engineering data-storage developer-tools

Scores updated daily from GitHub, PyPI, and npm data. How scores work