llm-app

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

58.9k

Stars

+12.3k

Gained

26.5%

Growth

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View on GitHub → ↑-0.1% this week

💡 Why It Matters

The llm-app repository addresses the need for a production-ready solution that simplifies the integration of AI pipelines and real-time data sources. It is particularly beneficial for ML/AI teams looking to implement chatbots and enhance enterprise search capabilities. With a remarkable growth trend of 26.5% over 296 days, this tool is gaining traction as a reliable open source tool for engineering teams. However, it may not be the right choice for projects requiring extensive customisation or those with limited resources for cloud infrastructure.

🎯 When to Use

This repository is a strong choice when teams need a ready-to-run cloud template for AI applications that require seamless integration with various data sources. Teams should consider alternatives if they require a high degree of customisation or are working in environments with strict compliance requirements.

👥 Team Fit & Use Cases

Roles such as machine learning engineers, data scientists, and DevOps teams can effectively utilise this tool. It is commonly integrated into products and systems that involve AI-driven applications, real-time data processing, and enterprise search functionalities.

🎭 Best For

Machine Learning and AI Engineer

⚖️ Compare With

llm-app vs anything-llm llm-app vs meilisearch llm-app vs llama_index

🏷️ Topics & Ecosystem

chatbot hugging-face llm llm-local llm-prompting llm-security llmops machine-learning open-ai pathway rag real-time retrieval-augmented-generation vector-database vector-index

📊 Activity

Latest commit: 2026-07-05. Over the past 295 days, this repository gained 12.3k stars (+26.5% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.