llm-app vs vearch
A neutral, constraint-first comparison - live GitHub stats and typed relationships, not marketing.
| llm-app | vearch | |
|---|---|---|
| Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | Distributed vector search for AI-native applications |
| Stars | 59k | 2.3k |
| Forks | 1.4k | 362 |
| Open issues | 10 | 170 |
| Language | Jupyter Notebook | Go |
| License | MIT | Apache-2.0 |
| Last pushed | Jul 5, 2026 | Jul 7, 2026 |
| Categories | Data & Retrieval, LLM Frameworks | Vector Databases |
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
vearch
Vearch is a cloud-native distributed vector database designed to efficiently handle similarity searches of embedding vectors in AI applications. It supports hybrid search, high performance, scalability, and reliability.
Go