llm-app vs weaviate
A neutral, constraint-first comparison - live GitHub stats and typed relationships, not marketing.
| llm-app | weaviate | |
|---|---|---|
| Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | Open-source vector database for semantic search |
| Stars | 59k | 17k |
| Forks | 1.4k | 1.3k |
| Open issues | 10 | 578 |
| Language | Jupyter Notebook | Go |
| License | MIT | BSD-3-Clause |
| Last pushed | Jul 5, 2026 | Jul 7, 2026 |
| Categories | Data & Retrieval, LLM Frameworks | Vector Databases, Data & Retrieval |
llm-app
Pathway Live Data Framework AI Pipelines provides LLM App Templates for deploying high-accuracy retrieval-augmented generation (RAG) and enterprise search applications. The templates sync with various data sources including file systems, Google Drive, Sharepoint, S3, Kafka, PostgreSQL, real-time APIs.
Jupyter Notebook
weaviate
Weaviate is an open-source, cloud-native vector database designed to store both objects and vectors. It supports approximate nearest neighbor search, hybrid search techniques, recommender systems, and more.
Go